Juan Antonio Lossio-Ventura

Clement Jonquet

Mathieu Roche

Maguelonne Teisseire

Combining C-value and Keyword Extraction Methods for Biomedical Terms Extraction

The objective of this work is to extract and to rank biomedical terms from free text.

We present new extraction methods that use linguistic patterns specialized for the biomedical field, and use term extraction measures, such as C-value, and keyword extraction measures, such as Okapi BM25, and TFIDF. We propose several combinations of these measures to improve the extraction and ranking process. Our experiments show that an appropriate harmonic mean of C-value used with keyword extraction measures offers better precision results than used alone, either for the extraction of single-word and multi-words terms. We illustrate our results on the extraction of English and French biomedical terms from a corpus of laboratory tests. The results are validated by using UMLS (in English) and only MeSH (in French) as reference dictionary.

Introduction

Language evolves faster than our ability to formalize and catalog concepts or possible alternative terms of these concepts. This is even more true for French in which the number of terms formalized in terminologies is significantly less important than in English. That is why our motivation is to improve the precision of automatic terms extraction process. Automatic Term Recognition (ATR) is a field in language technology that involves the extraction of technical terms from domain-specific language corpora [START_REF] Zhang | A Comparative Evaluation of Term Recognition Algorithms[END_REF]. Similarly, Automatic Keyword Extraction (AKE) is the process of extracting the most relevant words or phrases in a document with the propose of automatic indexing. Keywords, which we define as a sequence of one or more words, provide a compact representation of a document's content; two popular AKE measures are Okapi BM25 [START_REF] Robertson | Okapi at TREC-7: automatic ad hoc, filtering, VLC and interactive track[END_REF] and TFIDF (also called weighting measures). These two fields are summarized in Table 1. In our work, we adopt as baselines an ATR method, C-value [START_REF] Frantzi | Automatic Recognition of Multi-Word Terms: the C-value/NC-value Method[END_REF], and the best two AKE methods [START_REF] Hussey | Automatic keyphrase extraction: a comparison of methods[END_REF], previously mentioned and considered state-of-the-art. Indeed, the C-value, compared to other ATR methods, often gets best precision results and specially in biomedical studies [START_REF] Knoth | Towards a Framework for Comparing Automatic Term Recognition Methods[END_REF], [START_REF] Zhang | A Comparative Evaluation of Term Recognition Algorithms[END_REF], [START_REF] Zhang | A Comparison of Keyword-and Keyterm-Based Methods for Automatic Web Site Summarization[END_REF]. Moreover, Cvalue is defined for multi-word term extraction but can be easily adapted for single-word term and it has never been applied to French biomedical text, which is appealing in our case.

Our experiments present a great improvement of the precision with these new combined methods. We give priority to precision in order to focus on extraction of new valid terms (i.e., for a candidate term to be a valid biomedical term or not) rather than on missed terms (recall).

The rest of the paper is organized as follows: Section 2 describes the related work in the field of ATR, and specially the uses of the C-value; Section 3 presents our combination of measures for ranking candidate terms; Section 4 shows and discusses our experiment results; and Section 5 concludes the paper.

Related work

ATR studies can be divided into four main categories: (i) rule-based approaches, (ii) dictionarybased approaches, (iii) statistical approaches, and (iv) hybrid approaches. Rule-based approaches for instance [START_REF] Gaizauskas | Term Recognition and Classification in Biological Science Journal Articles[END_REF], attempt to recover terms thanks to the formation patterns, the main idea is to build rules in order to describe naming structures for different classes using orthographic, lexical, or morphosyntactic characteristics. Dictionary-based approaches use existing terminology resources in order to locate term occurrences in texts [START_REF] Krauthammer | Term identification in the biomedical literature[END_REF]. Statistical approaches are often built for extracting general terms [START_REF] Eck | Automatic term identification for bibliometric mapping[END_REF]; the most basic measure is frequency. C/NC-value [START_REF] Frantzi | Automatic Recognition of Multi-Word Terms: the C-value/NC-value Method[END_REF], is another statistical method well known in the literature that combines statistical and linguistic information for the extraction of multi-word and nested terms. While most studies address specific types of entities, C/NC-value is a domain-independent method. It was also used for recognizing terms from biomedical literature [START_REF] Hliaoutakis | The AMTEx approach in the medical document indexing and retrieval application[END_REF]. The C/NC-value method was also applied to many different languages besides English [START_REF] Frantzi | Automatic Recognition of Multi-Word Terms: the C-value/NC-value Method[END_REF] such as Japanese [START_REF] Mima | An application and evaluation of the C/NC-value approach for the automatic term recognition of multi-word units in Japanese. Japanese Term Extraction[END_REF], Serbian [START_REF] Nenadić | Morpho-syntactic clues for terminological processing in Serbian[END_REF], Slovenian [START_REF] Vintar | Comparative Evaluation of C-Value in the Treatment of Nested Terms[END_REF], Polish [START_REF] Kupsc | Extraction automatique de termes à partir de textes polonais[END_REF], Chinese [START_REF] Ji | Chinese Terminology Extraction Using Window-Based Contextual Information[END_REF], Spanish (Barrón et al., 2009), andArabic (Khatib et al., 2010), however to the best of our knowledge not to French. An objective of this work is to combine this method with AKE methods and to apply the combined measures to English and French. We believe that the combination of biomedical term extraction and the extraction of keywords describing a document, could be beneficial since keywords techniques give greater importance to the actual terms of this domain. This combination has never been proposed and experimented in the literature.

Proposed Methodology for Automatic Biomedical Term Extraction

This section describes the baselines measures and their customizations as well as the new combinations of these measures that we propose for automatic biomedical terms extraction and ranking.

Our method for automatic term extraction has four main steps: (1) Part-of-Speech tagging, (2) Candidate terms extraction,(3) Ranking of candidate terms, (4) Computing of new combined measures. Note, C-value is a method that deals with an unique corpus as input whereas AKE methods deal with several documents (cf. Table 1) then we need to do the union of documents for C-value to consider the whole corpus as an unique document. A preliminary step is the creation of patterns for French and English, as described hereafter.

Part-of-Speech tagging

Part-of-speech (POS) tagging is the process of assigning each word in a text to its grammatical category (e.g., noun, adjective). This process is performed based on the definition of the word or on the context which it appears in.

We apply part-of-speech to the whole corpus. We evaluated three tools (TreeTagger, Stanford Tagger and Brill's rules), and finally choose Tree-Tagger which gave best results and is usable both for French and English.

Candidate terms extraction

As previously cited work, we supposed that biomedical terms have similar syntactic structure. Therefore, we build a list of the most common lexical patterns according the syntactic structure of biomedical terms present in the UMLS1 (for English) and the French version of MeSH2 (for French). We also do a part-of-speech tagging of the biomedical terms using TreeTagger3 , then compute the frequency of syntactic structures. We finally choose the 200 highest frequencies to build the list of patterns for each language. The number of terms used to build these lists of patterns was 2 300 000 for English and 65 000 for French.

Before applying measures we filter out the content of our input corpus using patterns previously computed. We select only the candidate terms which syntactic structure is in the patterns list.

Ranking of candidate terms 3.3.1 Using C-value

The C-value method combines linguistic and statistical information [START_REF] Frantzi | Automatic Recognition of Multi-Word Terms: the C-value/NC-value Method[END_REF]; the linguistic information is the use of a general regular expression as linguistic patterns, and the statistical information is the value assigned with the C-value measure based on frequency of terms to compute the termhood (i.e., the association strength of a term to domain concepts). The aim of the C-value method is to improve the extraction of nested terms, it was specially built for extracting multi-word terms.

C-value(a) =            w(a) × f (a) if a / ∈ nested w(a) × f (a) -1 |Sa| × b∈Sa f (b) otherwise (1)
Where a is the candidate term, w(a) = log 2 (|a|), |a| the number of words in a, f (a) the frequency of a in the unique document, S a the set of terms that contain a and |S a | the number of terms in S a . In a nutshell, C-value either uses frequency of the term if the term is not include in other terms (first line), or decrease this frequency if the term appears in other terms, by using the frequency of those other terms (second line).

We modified the measure in order to extract all terms (single-word + multi-words terms), as suggested in [START_REF] Barrón-Cedeño | An Improved Automatic Term Recognition Method for Spanish[END_REF] in different manners: in the formula w(a) = log 2 (|a|), we use w(a) = log 2 (|a| + 1) in order to avoid null values (for single-word terms). Note that we do not use a stop word list nor a threshold for frequency as it was originally proposed.

Using Okapi -TFIDF

Those measures are used to associate each term of a document with a weight that represents its relevance to the meaning of the document it appears relatively to the corpus it is included in. The output is a ranked list of terms for each document, which is often used in information retrieval, to order documents by their importance given a query [START_REF] Robertson | Okapi at TREC-7: automatic ad hoc, filtering, VLC and interactive track[END_REF]. Okapi can be seen as an improvement of TFIDF measure, taking into account the document length.

The outputs of Okapi and TFIDF are calculated with a variable number of data so their values are heterogeneous. To manipulate these lists, the weights obtained from each document must be normalized. Once values normalized we have to merge the terms into a single list unique for the whole corpus to compare the results. Clearly the precision will depend on the method used to perform such merging. We merged following three functions, which calculate respectively the sum(S), max(M ) and average(A) of the measures values of the term in whole the corpus. At the end of this task we have three lists from Okapi and three lists from TFIDF. The notation for these lists are Okapi X (a) and T F IDF X (a), where a is the term, X the factor ∈ {M, S, A}. For example, Okapi M (a) is the value obtained by taking the maximum Okapi value for a term a in the whole corpus.

Computing the New Combined Measures

With the goal of improving the precision of terms extraction we have conceived two new combined measures schemes, described hereafter, taking into account the values obtained in the above steps.

F-OCapi and F-TFIDF-C

Considered as the harmonic mean of the two used values, this method has the advantage of using all the values of the distribution.

F -OCapiX (a) = 2 × OkapiX (a) × C-value(a) OkapiX (a) + C-value(a) (2) F -T F IDF -CX (a) = 2 × T F IDFX (a) × C-value(a) T F IDFX (a) + C-value(a) (3)

C-Okapi and C-TFIDF

Our assumption is that C-value can be more representative if the frequency, in Equation ( 1), of the terms is replaced with a more significant value, in this case the Okapi's or TFIDF's values of the terms (over the whole corpus).

C-mX (a) =            w(a) × mX (a) if a / ∈ nested w(a) × mX (a) -1 |Sa| × b∈Sa mX (b)
otherwise

Where m X (a) = {Okapi X |T F IDF X }, and X ∈ {M, S, A}.

Experiments and Results

Data and Experimental Protocol

We used biological laboratory tests, Labtestonline.org, as corpus. This site provides information in several languages to patient or family caregiver on clinical lab tests. Each test which forms a document in our corpus, includes the formal lab test name, some synonyms and possible alternate names as well as a description of the test. Our extracted corpus contains 235 clinical tests (about 400 000 words) for English and 137 (about 210 000 words) for French.

To automatically validate our candidate terms we compute a validation dictionary that include the official name, the synonyms and alternate names of the labtestonline tests plus all UMLS terms for English and the MeSH terms for French. These terminologies are references in the domain therefore each extracted term found in those is validated as a true term. Note that as a consequence we obtain 100% Recall with the whole list of extracted terms.

Experiments and results

Results are evaluated in terms of Precision obtained over the top k terms at different steps of our work presented in previous section. Okapi and T F IDF provided three lists of ranked candidate terms (M, S, A). For each combined measure using Okapi or T F IDF , the experiments are done with the three lists. Therefore, the number of ranked list to compare is C-value(1)

+ Okapi(3) + T F IDF (3) + F -OCapi(3) + F - T F IDF -C(3)+C-Okapi(3)+C-T F IDF (3) = 19.
In addition we experimented either for all (single and multi) or multi terms which finally give 38 ranked lists. Then, we select all terms (single and multi) or only muli-terms (19 × 2 = 38 experiments for each language).

The following lines show part of the experiment results done all or multi terms, only and considering the top 60, 300 and 900 extracted terms, because it is appropriate and easier for an expert to evaluate the first best extracted terms. Table 2 and Table 3 compare the precision between the best baselines measures and the best combined measures. Best results were obtained in general with F -T F IDF -C M for English and F -OCapi M for French. These tables prove that the combined measures based on the harmonic mean are better than the baselines measures, and specially for multi word terms, for which the gain in precision reaches 16%. This result is particularly positive because in the biomedical domain it is often more interesting to extract multi-word terms than single-word terms. However, one can notice that results obtained to extract all terms with C-Okapi S and C-T F IDF S are not better than Okapi X or T F IDF X used directly. The reason is because the performance of those new combined measures is affected when single word terms are extracted. Definitively, the new combined measures are really performing for multi word term.

Results of AKE methods for English show that TFIDF X obtains better results than Okapi X . The main reason for this, is because the size of the English corpus is larger than the French one, and Okapi is known to perform better when the corpus size is smaller [START_REF] Lv | When documents are very long, BM25 fails[END_REF].

In addition, Table 3 shows that C-value can be used to extract French biomedical terms with a better precision than what has been obtained in previous cited works with different languages. The precision of C-value for the previous work was between 26% and 31%. We also have done experiments with two more corpus: (i) the Drugs data from MedlinePlus4 in English and, (ii) PubMed5 citations' titles in English and French, we have verified that the new combined measures are performing better, particularly these based on the harmonic mean, F -T F IDF -C M and F -OCapi M .

Conclusions and Perspectives

This work present a methodology for term extraction and ranking for two languages, French and English. We have adapted C-value to extract French biomedical terms, which was not proposed in the literature before. We presented and evaluated two new measures thanks to the combination of three existing methods. The best results were obtained by combining C-value with the best results from AKE methods, i.e., F -T F IDF -C M for English and F -OCapi M for French.

For our future evaluations, we will enrich our dictionaries with BioPortal's6 terms for English and CISMeF's7 terms for French. Our next task will be the extraction of relations between these new terms and already known terms, to help in ontology population. In addition, we are currently implementing a web application that implements these measures for the community.

Table 1 :

 1 Differences between ATR and AKE.

		ATR	AKE
	Input	one large corpus	single document
	Output	terms of a domain	keywords of a doc
	Domain very specific	none
	Exemples C-value	TFIDF, Okapi

Table 2 :

 2 Extract of precision comparison for term extraction for English.

			All Terms		Multi Terms
		60	300	900	60	300	900
	OkapiM	0.96 0.95 0.82	0.68 0.62 0.54
	OkapiS	0.83 0.89 0.85	0.58 0.57 0.55
	OkapiA	0.72 0.31 0.27	0.48 0.39 0.26
	T F IDFM	0.97 0.96 0.84	0.71 0.63 0.54
	T F IDFS	0.96 0.95 0.93	0.82 0.71 0.61
	T F IDFA	0.78 0.74 0.63	0.50 0.40 0.37
	C-value	0.88 0.92 0.89	0.72 0.71 0.62
	F -OCapiM	0.73 0.87 0.84	0.79 0.69 0.58
	F -T F IDF -CM	0.98 0.97 0.86	0.98 0.73 0.65
	C-OkapiS	0.88 0.86 0.80	0.61 0.58 0.53
	C-T F IDFS	0.96 0.95 0.86	0.85 0.71 0.61
			All Terms			Multi Terms
		60	300	900	60	300	900
	OkapiM	0.90 0.61 0.37	0.53 0.31 0.18
	OkapiS	0.30 0.31 0.37	0.23 0.30 0.37
	OkapiA	0.52 0.31 0.16	0.30 0.17 0.16
	T F IDFM	0.75 0.51 0.37	0.45 0.28 0.18
	T F IDFS	0.68 0.48 0.42	0.53 0.33 0.22
	T F IDFA	0.12 0.39 0.29	0.17 0.16 0.11
	C-value	0.43 0.42 0.43	0.35 0.34 0.26
	F -OCapiM	0.73 0.62 0.43	0.65 0.35 0.22
	F -T F IDF -CM	0.85 0.57 0.39	0.62 0.31 0.19
	C-OkapiS	0.28 0.32 0.34	0.23 0.28 0.20
	C-T F IDFS	0.65 0.55 0.38	0.50 0.32 0.19

Table 3 :

 3 Extract of precision comparison for term extraction for French.

http://www.nlm.nih.gov/research/umls

http://mesh.inserm.fr/mesh/

www.cis.uni-muenchen.de/ ˜schmid/tools/TreeTagger

http://www.nlm.nih.gov/medlineplus/

http://www.ncbi.nlm.nih.gov/pubmed

http://bioportal.bioontology.org/

http://www.chu-rouen.fr/cismef/

Acknowledgments

This work was supported in part by the French National Research Agency under JCJC program, grant ANR-12-JS02-01001, as well as by University Montpellier 2 and CNRS.


Ruding Lou
email: ruding.lou@ensam.fr

Franca Giannini
email: franca.giannini@ge.imati.cnr.it

Bianca Falcidieno
email: bianca.falcidieno@ge.imati.cnr.it

Jean-Philippe Pernot
email: jean-philippe.pernot@ensam.fr

Philippe V Éron
email: philippe.veron@ensam.fr

Alexei Mikchevitch
email: alexei.mikchevitch@edf.fr

Rapha Ël

DIRECT MODIFICATION OF SEMANTICALLY-ENRICHED FINITE ELEMENT MESHES

Keywords: Finite Element mesh, Groups, Semantics, Mesh deformation, Crack, Hole

Behaviour analysis loop is largely performed on virtual product model before its physical manufacturing. The last avoids high expenses in terms of money and time spent on intermediate manufacturing. It is gainful from the reality to the virtuality but the process could be further optimized especially during the product behaviour optimization phase. This process involves repetition of four main processing steps: CAD design and modification, mesh creation, Finite Element (FE) model generation with the association of physical and geometric data, FE Analysis. The product behaviour analysis loop is performed on the first design solution as well as on the numerous successive product optimization loops. Each design solution evaluation necessitates the same time as required for the first product design that is particularly crucial in the context of maintenance.

In this paper we propose a new framework for CAD-less product optimisation through FE analysis which reduces the model preparation activities traditionally required for FE model creation. More concretely, the idea is to directly operate on the 1 firstly created FE mesh, enriched with physical/geometric semantics, to perform the product modifications required to achieve its optimised version.

In order to accomplish the proposed CAD-less FE analysis framework, modification operators acting on both the mesh geometry and the associated semantics need to be devised. In this paper we discuss the underlying concepts and present possible components for the development of such operators. A high-level operator specification is proposed according to a modular structure that allows an easy realisation of different mesh modification operators. Here, two instances of this high-level operator are described: the planar cracking and the drilling. The realised prototypes validated on industrial FE models show clearly the feasibility of this approach.

Introduction

Numerical behaviour simulation is fundamental in various engineering activities for avoiding physical validation tests. Behaviour simulation is generally used during the product development, maintenance and lifecycle problem analysis of machinery, new solution assessment in order to improve the mechanical behaviour of the machinery. Such activities are frequently subjected to various constraints. For example, in the maintenance/lifecycle problem analysis context, the main constraints are due to the time and cost of the production process stops. Therefore, it is important to be able to provide fast solutions improving production equipment characteristics as well as satisfying the concerned safety criteria. For example, in the field of power production, it is critical to identify the problem source and to provide the appropriate solution in the shortest time. Thus, numerical tools helping experts to rapidly evaluate various alternative solutions should be made available.

Today, most of the product behaviour analyses rely on the following steps: conceptual solution proposal and its modelling using Computer-Aided Design (CAD) tools; CAD data meshing; mesh adaptation and enrichment for specific behaviour studies; Finite Element (FE) simulation and result evaluation. Depending on the obtained results, geometric modifications can be required, which are generally performed on the CAD models, thus requiring a re-generation of the FE mesh models corresponding to the new solution. Therefore, all the above listed preparation steps necessary for FE Analysis (FEA) need to be repeated, as long as the optimal solution is not achieved, every time changes are done on the product shape. In figure 1 all these steps are shown on a real industrial case provided by the Électricite de France Group (EDF).

Up to now no real automatic FE model generation process from CAD data is available, and a specific mesh treatment as definition of physical/geometric parameters required for FEA needs a strong user involvement. Examples of physical data, in the following referred as mechanical semantics, are Boundary Conditions or BCs, material laws, geometric/mechanical characteristics. To be used in the FE model they are associated to the shape of the model using groups of mesh elements. Mesh groups can be created in two different ways: either semi-manually by selecting a set of mesh entities and or more automatically by using the corre- sponding sets of geometric entities of different dimensions preliminarily defined in the CAD model. In both cases, the semantic enrichment of meshes (i.e. mesh group creation and physical data association) requires great skills and is generally quite time-consuming, also considering the possible high number of groups and involved elements. For instance, in the case of the caisson model depicted in figure 1, 30 groups of mesh entities were created to support the needed mechanical semantic data (see fig. 2). Other industrial models of the EDF Group can contain up to 500 mesh groups dedicated to the FEA (e.g. the definition of BCs, link relations, different behaviour laws, geometric parameters, mechanical modelling of specific phenomena) as well as particular post-processing data.

It is then clear that the existing classical methodology for product behaviour analysis is not appropriate in the case of fast industrial studies subjected to important time constraints. It is even less appropriate in all the cases where the numerical behaviour analysis has to be performed on structures which are physically existing but do not have a CAD model counterpart. This is typical of the maintenance context of real equipments or buildings, whose 3D digital model was never created or is not anymore corresponding to the actual object configuration. In maintenance, the real mechanical/geometrica state of the machinery has to be taken into account for more realistic FE simulation, and the CAD model created during the product development phase is no longer usable for maintenance studies during the product exploitation stage.

Sometimes, data necessary for realistic FE simulation can be obtained though scanning techniques. Thus, the creation of the corresponding CAD models starting from scratch or through Reverse Engineering techniques slows down the process and should then be avoided as much as possible. Additionally, a tuning/validation of the FE model through the comparison of experimental and simulation results is usually necessary. Tuned FE model including mesh and mechanical semantics becomes "frozen" and cannot be modified during the numerical study. Any change of the geometry necessitates a new optimization loop consisting in updating of all geometric/mechanical parameters required for realistic FEA.

Unfortunately, current commercial CAD systems do not make it possible to automate the process of direct complex mesh creation and modification while preserving mechanical/geometric semantic data tuned and required for realistic FEA. Therefore specific attention has to be paid to guarantee that the FE model created after each object shape modification is reflecting all the characteristics of the physically tuned model with a consequent slow down in the FE model preparation.

In order to overcome these limits, we propose a fast prototyping framework working directly at the level of FE mesh enriched by mechanical simulation semantics of geometrical nature (mesh groups). This corresponds to a CAD-less approach allowing the engineer to directly operate on the meshes containing an arbitrary number of semantic data already validated with respect to the real behaviour of the structure. The figure 3 shows the comparison between the current classical workflow for FEA model preparation (on the left side) and the proposed workflow (on the right side). With our proposal, engineers can directly modify locally the tuned FE mesh model while preserving existing mesh groups and transferring the already associated semantics required for FEA. We think that this approach is much more suitable than current one both when the perfect CAD model is not available but also in simultaneous engineering approach when an almost complete product definition is achieved and different experts are working in parallel to evaluate and optimise the provided solutions.

The proposed CAD-less approach requires the implementation of shape modification operators able to simultaneously act on the mesh geometry and on the associated semantics. In this paper, we present deformation-based operations applied to FE mesh able to take into account the presence of mesh groups corresponding to geometric semantics. Different constraints are created during the mesh deformation to preserve the shape of the model as well as the shape of the groups of different dimension. The paper is organized as follows. After a discussion of related works (section 2), section 3 describes the modular framework and presents main components of operators allowing manipulating the enriched FE models. section 4 describes two instances of operators: mesh cracking and drilling. section 5 presents some results obtained on industrial models. section 6 draws some conclusions and perspectives of future research.

Related works

Mesh or grid modification has been subject to various researches since many years [START_REF] Campen | Kobbelt Exact and Robust (Self-)Intersections for Polygonal Meshes[END_REF] and it has become even more topic of interest recently due the improved computation capabilities and the diffusion of specific applications, such as animation movies, gaming and Virtual Reality. Recently several modelers (IDEAS R , NX R , LMS Virtual.Lab R , SALOME R , ...) working directly on meshes came out. They generally offer functionalities to create shapes and modify mesh models. Because of their context of use demands, they generally do not care neither about the quality of the obtained mesh elements nor about the preservation of the possible associated semantics.

Bremberg and Dhondt 9 propose an approach for crack insertion into a mesh by creating a blending between the surface mesh of the crack profile and the mesh of the cracked volume. The skin surface mesh is firstly extracted from the initial volume mesh and merged with the crack profile surface mesh. The resulting surface mesh is then tetrahedralized. The main disadvantage of this approach is that for making crack feature the whole model is re-meshed, which make invalid the tuning on the initial mesh as well as the semantics associated to the mesh.

Numerical crack introduction and propagation schemes were augmented in an elegant manner with a X-FEM (eXtended Finite Element) method [START_REF] Mikchevitch | Towards fast numerical studies for maintenance and lifecycle problem analysis: New simulation methods on example of an industrial study case[END_REF][START_REF] Barsoum | Application of quadratic isopa-rametric elements in linear fracture mechanics[END_REF][START_REF] Strouboulis | The design and analysis of the generalized finite element method[END_REF] . The use of special enrichment functions as well as a discontinuous function along the sides of the crack allows one to achieve "virtually" a complete crack analysis (on mechanical computation level only using so-called "level sets") without any geometric mesh modification [START_REF] Mikchevitch | Towards fast numerical studies for maintenance and lifecycle problem analysis: New simulation methods on example of an industrial study case[END_REF] . These works aim at predicting crack behaviour and not to insert a specific crack shape on the mesh without using CAD model. However, the application of this method could be difficult in the case of geometrically complex cracks having no regular shape (because it is difficult to describe such a crack feature).

The insertion of cracks into a mesh model could be performed by splitting of mesh elements [START_REF] Bechet | Improved implementation and robustness study of the X-FEM for stress analysis around cracks[END_REF][START_REF] Schoellmann | Development of a new software for adap-tive crack growth simulations in 3D structures[END_REF] . The proposed direct split of elements is mainly aimed at realtime visualization of cracking process. Whereas, from the FEA point of view, the resulting mesh is not appropriate because the split elements could have bad quality.

N ienhuys and al. 11 describe a cutting algorithm continuously deforming tetrahedra so that the cutting trajectory aligns with the tetrahedron face or edge. This method reduces the need to introduce new nodes but can produce degenerate tetrahedra. Another approach 12 allows multiple consecutive incisions of tetrahedra in the crack zone. In this method, a tetrahedron maintains its state information including the number and position of cuts. Multiple cuts are merged, and the affected tetrahedra are subdivided along the cut plane when a portion of the mesh is completely severed from the rest.

A cutting mesh approach [START_REF] Turkiyyah | Mesh cutting during real-time physical simulation[END_REF][START_REF] Bruyns | A survey of interactive mesh-cutting techniques and a new method for implementing generalized mesh cutting using virtual tools[END_REF][START_REF] Bielser | Interactive cuts through 3-dimensional soft tissue[END_REF] is proposed to directly split the elements in order to follow the cutting trajectory. However, also in this case the mesh quality is not acceptable for FEA. On the contrary, some other cutting operators [START_REF] Ji | Easy Mesh Cutting[END_REF][START_REF] Turini | Simulating Drilling on Tetrahedral Meshes[END_REF] take care about the mesh quality, but the cutting profile on the mesh does not perfectly match the cutting tool. A method for the simulation of cutting a mesh with a mesh model of the tool is presented [START_REF] Dakowicz | Interactive TIN modification with a cutting tool[END_REF] . The intersection points between the two interacting meshes are inserted so that the mesh elements are directly split and removed. The cut tool should be meshed and the quality of the resulting mesh is not enough good for the FE simulation.

The use of Boolean intersection and cut operations between the original model and crack masks have been presented [START_REF] Martinet | Procedural modeling of cracks and fractures[END_REF] . Boolean operations [START_REF]Complex human tissues fem models prepared by boolean operations[END_REF] are performed on volume mesh by doing intersection of boundary meshes and completely re-filling the tetrahedral mesh. This is not admissible when manipulating tuned FE meshes that can be only modified locally.

As shown form the above overview, several results have been obtained for mesh modification, all the described work are limited to change only the mesh topology and geometry while more or less preserving its elements quality, but none is taking care of the associated semantic information, Here we present deformation-based cracking and cutting/drilling operations applied to FE mesh able to take into account the presence of mesh groups corresponding to geometric semantics. Different constraints are created during the mesh deformation to preserve the shape of the model as well as the shape of the groups of different dimension.

A multi-layered CAD-less modification approach

As previously described, FEA requires various data/parameters of different nature (geometric, physical ...). This information can be classified according to several levels (fig. 4). The geometric data corresponding to the mesh model can be considered as the lowest level of information. This type of data describes the shape of the structure to be simulated: the coordinates of the nodes, the links between the nodes, etc. The accuracy of the physical simulation strongly depends on the type of elements (tetrahedra, hexahedra, etc.) that have been chosen, mesh quality as well as on the number of elements (coarse or fine discretization).

At the highest level, FE models include the various physical semantics (SEM1, SEM2, etc.). For modelling the mechanical behaviour of the structure there might be material properties, BCs, loading description, interaction with a fluid, etc. For example, in order to characterize the material properties, we use Young's modulus, Poisson's ratio, or particular non-linear material laws. Model idealization needs other parameters, for example, a thickness for a thin structure described as a shell. BCs correspond to physical loads of different nature like pressure, concentrated forces, imposed displacements, relationships to simulate a fracture phenomenon or to handle a contact problem, etc. For enriching shape information of the mesh model there might be shape semantics like plane, sphere, cylinder, etc. These semantics will be used to constrain the mesh modification.

To be able to relate physical semantic data required for FEA with the discretized geometry, an intermediate layer is needed (fig. 4). This so-called low-level semantics of purely geometric nature maintains the groups (sets) of FE entities (0D, 1D, 2D or 3D elements) and allows associating the physical semantic data to the lower geometric level (e.g. nodes, edges, faces and tetrahedra in the case of tetrahedral mesh model). For example, a group of tetrahedra representing a part of a 3D model can be used to specify a particular material law in order to simulate locally a plastic behaviour of the structure. A group of triangles can be used to apply the pressure to the structure. A group of edges may idealize a beam-like-shape part. A group of nodes can be used to fix a part of the structure or to describe displacement relations of different nature. It is worth to mention that the decomposition provided by the group structure is only dependent on the structure configuration in the real counterpart and therefore traditional mesh decomposition methods based on morphological and structural characteristics cannot be applied.

Therefore, during the direct manipulation of FE mesh models containing different semantic data, the mesh modification operators should correctly maintain and update not only the geometry (mesh) but also all the data associated with a given mesh model. Moreover, the associated semantics constrain the FE mesh modification process. Thus, mesh modification operators should be able to take into account the presented triplet level information. Figure 5 shows the general structure of the proposed CAD-less operator which takes as input the semantics enriched FE mesh and modifies locally the mesh while transferring the semantics. This general operator is constituted by five main components: (1) Group treatment, (2) Topology modification, (3) Mesh deformation, (4) Semantics transfer and (5) Mesh quality control. All these components are detailed in the following sub-sections.

Group treatment

To associate physical semantic information to the FE mesh model we exploit the notion of groups of mesh entities (or FE mesh groups) corresponding to the low-level geometric semantic. FE groups can be used by different FE solvers. For example, the Code ASTER R FE solver [START_REF] Aster | [END_REF] , developed and distributed by EDF R&D, uses currently mesh groups in order to simplify advanced FEA. As shown in figure 2, groups, either of same or of different dimension, can be overlapping. Additionally, groups may be constituted by a combination of entities having different dimensions.

Maintaining and propagating semantic information during the mesh modification operation goes through the preservation of FE mesh groups. In particular, this involves an appropriate handling of mesh group content and mesh group geometry, i.e. the shape of the groups' boundaries and, if existing, the geometric curve or surface support. In this work, we adopt the notion of the so-called Virtual Group Boundaries 2 (VGB). In a mesh of dimension dm the VGB for a group is made of two sets of mesh elements:

• Bounding Elements (BE): a set of mesh elements that 1) belong to the group or are connected to elements of the group; 2) have one dimension lower than the mesh dimension dm; 3) form closed cycles enclosing the areas of dimension dm covered by group elements. • Isolated Elements (IE): a set of group elements that are not contributing to cover an area of dimension dm with other group elements. Nodes located on VGB are used to apply additional constraints during the local mesh modification to make these nodes staying on the shape of the group boundary. Once the group boundary is handled the group content is also considered. Therefore if the elements of the group are lying on a specific surface or curve geometry such characteristic should be preserved and thus this information should be used for constraining some mesh elements (those that will form the corresponding group) in the modified mesh. Finally, if in the mesh modification process some mesh areas are re-meshed by removing and adding mesh elements the re-assignment of group definition on the newly added mesh elements is necessary. A possible approach is to make a link between the VGB elements and their concerned group so that the group definition of the bounded area can be found via the closest VGB. In case of overlapping groups the re-assignment of group definition on the re-meshed zone will be fuzzy because each area may be bounded by several sub-sets of VGB from different groups. So we introduce the notion of Elementary Group 2 (EG).

The elementary groups collect all those mesh elements that are belonging to the same group sets. They are computed by intersecting the areas covered by the two operated groups. The process can be divided into three steps: area computation, area splitting by intersection calculation and new groups' generation according to the split areas. The figure 7 shows a simple example of EGs computation from a group of faces and a group of nodes in a 2D mesh. The figure 7.a shows the initial two groups and the figure 7.b shows their covered areas (domains) composed

Topology modification

Topology modification is related to mesh elements removing, addition, cracking, subdivision, etc. This topological modification is an optional operation depending on the different instance of the general operator. The figure 8 shows these operations on a triangular mesh example. The initial mesh is shown in figure 8.a, while figure 8.b presents the mesh resulting from a deletion operation removing some triangles from the initial mesh. In figure 8.c two separated parts of a mesh are glued by mesh element addition operation. In figure 8.c the mesh is cracked into two separated parts by duplicating a set of nodes and edges on the mesh. Finally, the figure 8.e shows the result of mesh refinement applied on the mesh shown in the figure 8.d. Depending on the specific mesh operator none of several of the above topological modifications may be used.

Additionally the topology modification in our CAD-less mesh operator is constrained by the mesh group definition. For example the operator of re-meshing which needs removing and filling mesh elements should take care of the group shape given by the VGB and the containing elements. Another example is mesh refinement operator which needs to insert new nodes and swaps edges/faces in 2D/3D mesh. The refinement without any constraint could modify the shape of the group boundary. Lou and al. [START_REF] Lou | Merging enriched Finite Element triangle meshes for fast prototyping of alternate solutions in the context of industrial maintenance[END_REF] have shown how to preserve the VGB and group characteristics while accomplish a re-meshing process and a refinement for merging two triangle meshes. The VGB is preserved during the deletion before triangulation and the group definition is re-assigned after creation of new mesh elements.

Mesh deformationn

The deformation engine considered in our general mesh operator provides a repositioning of nodes of a mesh during the mesh modification operation. It is applied to:

• Maximize the global mesh quality, • Produce and/or preserve certain shapes.

The deformation engine we adopt is based on the so-called Force Density Method 1 (FDM). It allows specifying linear and non-linear position constraints required for achieving a certain shape. It uses the minimization of a quadratic objective function based on a simple mechanical spring/bar-like model and provides the final solution in term of local deformation. Indeed, a bar network is created and coupled to the edges of the 2D/3D mesh.

The optimization problem to be solved corresponds to equation 1. The unknowns are the positions of the free nodes to relocate. They are gathered together in the unknown vector X. The position of blocked nodes is known in the optimization system. The objective function to minimize is φ and the equality constraints are in the constraint vector G. They are both linking to the unknown vector.

G(X) = 0 min φ(X) (1)
In the Force Density Method adopted each bar applied on each mesh edge can be seen as a spring with a null initial length and a stiffness q i (more precisely a force density). To preserve the static equilibrium state of bars of length i , f i external forces have to be applied to the endpoints of the bar:

f i = q i • . i .
The set of external forces applied to the initial bar network can be obtained through the static equilibrium equations at each node. At the end, we obtain a set of linear equations between the nodes positions X and the external forces applied to them. Let F be the vector containing the components of the external forces applied to the nodes free to move, there exists a linear mapping function g between X and F (equation 2).

X = g(F ) (2)
With the formulation given by equations ( 1) and ( 2) we can specify separately two aspects:

• the geometric constraints G that may be imposed to meshes such as position or specific shape (e.g. plane). These constraints produce a set of possibly nonlinear equations liking directly the position of the free vertices. The resulting constraint vector G can then be expressed as function of the external forces F applied to the free nodes using equation 2.

• the objective function φ to minimize. This is a higher-level parameter enabling the specification of various deformation behaviours through the combination of several geometric and/or mechanical quantities relative to the bar network [START_REF] Pernot | Hybrid models deformation tool for free form shapes manipulation[END_REF] . For example the minimization of external forces tends to minimize the surface area and enable an interesting repositioning of the mesh vertices. At the opposite, the minimization of the external forces variations tends to preserve the shape during the deformation.

Some geometric constraints for specific primitive surface type constraints are shown here. Let P m be a mesh node of coordinates (x m , y m , z m ), P 0 be a 3D point of coordinates (x 0 , y 0 , z 0 ) and n 0 be a unit normal vector of components (n x0 , n y0 , n z0 ), the following constraints can be defined on P m :

• planar constraint so that P m has to stay on a plane defined by the point P 0 and the normal n 0 :

G pm0 (x m , y m , z m ) = (P m -P 0 )n 0 = 0 (3)
• spherical constraint defined with a sphere centered in P0 and with a radius R:

G sm0 (x m , y m , z m ) = P m -P 0 2 -R 2 = 0 (4)
• cylindrical constraint defined by a unit vector n 0 characterizing its axis and a point P 0 that the mesh node P m has to match:

G cm0 (x m , y m , z m ) = (P m -P 0 ) ∧ n 0 2 -R 2 = 0 (5)
• free-form constraint when the identified shape does not correspond to any of the previously introduced constraints. This is done while constraining at iteration k the node P m to move according to the plane defined by the position of P m at iteration k -1 and the normal n m to the mesh at this point:

G [k] f m (x m , y m , z m ) = (P [k] m -P [k-1] m )n [k-1] m = 0 (6)
A very simple example of mesh adjustment using constrained deformation is shown in the figure 9. At the left part different boundary conditions are defined on all nodes of the initial mesh. These nodes are labeled in different ways detailed under the pictures. The "fixed nodes" do not move during the deformation. The "free nodes" can move without any constraint in the space. The "nodes on plane" are free to move only on the plane and the "nodes on cylinder" are free to move only on the cylinder. The right picture gives the result of the deformation by applying external forces minimization under the above shape constraints. The various constraints are satisfied so that some constrained nodes stay either on the plane or on the cylinder or both. The node under two constraints is staying on the intersection zone between the two surfaces. The free node is situating at a place for relaxing the mesh. The two fixed nodes remain at their initial position.

Semantics transfer

During the mesh modification the semantics should not only be maintained through the preservation of the groups but they should also be transferred. The semantics transfer is mandatory when a topology modification is performed, notably when the characteristics of the surface evolve. To illustrate this aspect, we consider the mesh drilling operator. The figure 10.a shows a cube-like model A on which a fluid pressure is applied on the four faces. We want to make a cylindrical hole going through the model. Figure 10.b shows half of the model A in which we can see the half hole realized. Since the pressure is produced by the fluid and the two sides of the hole (top and bottom) receive the pressure, it is reasonable to suggest the user to automatically propagate the pressure to the interior cylinder wall of the hole thus saving manipulation times (fig. 10.b).

Mesh quality control

The mesh quality control is very important for FEA. The quality could concern different criteria. The first quality criterion would be the mesh elements ratio aspect. For triangle mesh a degenerated triangle is characterized by exactly one or two small angles. To quantify the degeneracy without computing the three angles, we use an indicator introduced to check the quality of FE meshes [START_REF] Pfeifle | Triangular B-Splines for blending and filling of polygonal holes[END_REF] and it is defined as follows:

Q = α S hp (7)
where Q is the aspect ratio of a triangle with α = 2 √ 3 a normalization coefficient so that Q = 1 for an equilateral triangle, h is the longest edge length, S is the area of the triangle and p its half-perimeter. This quality factor belongs to the interval [0, 1]. The limit zero corresponds to flat triangles. It is commonly accepted that a triangulation is a good one, with respect to the FE approximation, if the aspect ratio of the worst triangle is greater than 0.5. Similarly for a tetrahedral mesh the definition of the quality of a tetrahedron 22 could be computed:

Q = α ρ h max (8)
where h max is the diameter of tetrahedron (length of the longest edge in the tetrahedron), ρ is the radius of the inscribed sphere and α = 2 √ 6 is a normalization factor such that the quality of an equilateral tetrahedron is 1. This quality function varies in the interval (0, 1] (a well-shaped tetrahedron has a quality close to 1, while an ill-shaped element has a quality close to 0).

The second mesh quality criterion is about self-intersection problem where a part of a surface mesh collides with another part of itself, i.e., two mesh elements intersect each other. It destroys the integrity of the mesh and makes the mesh unusable for FEA applications. In triangle or tetrahedral meshes the check of the intersection between edges and triangles is necessary.

According to different application interest the mesh quality criteria could be completed.

CAD-less operator instances

In this section, two instances of the general CAD-less operator are detailed. The first one is planar crack that generates a planar separation of the mesh in correspondence of the crack surface. The second operator is the drilling one that gives rise to cylindrical holes. These two high-level operators are presented for showing how the different basic tools presented in the previous sections can be arranged to achieve the wished FE mesh modifications.

Planar crack operator

The purpose of our operators is to directly modify the semantically enriched FE mesh shape and not to define operators that somehow simulate the physical phenomenon: In this perspective, crack operator is aimed at introducing a "geometric" fissure inside the volume of a structure that results in having two coincident internal surfaces corresponding to the crack. Actually, cracks are usually idealized as having no volume. The surfaces representing the two sides are distinct but coincident so that nodes on opposite sides of crack faces should have identical coordinates. So, we can speak about duplicated overlapping mesh entities (double nodes and face elements). Here the planar crack operator is presented. This crack is supposed closed at the initial instant t 0 =0.

It can be mentioned that the duplication of nodes and face elements into a volume corresponds geometrically to the introduction of what we call "contact zone". Thus, the crack operator discussed in this section can be used to handle the contact problem in the case of planar surfaces in contact.

Our approach is mainly based on three steps: crack interface identification (section 4.1.1), mesh deformation on the level of crack interface (section 4.1.2) and duplication of nodes and faces (section 4.1.3). The crack interface is a set of triangles in case of 3D mesh or a set of edges for 2D mesh. It corresponds to the boundary of the tetrahedral/triangles lying completely in one side with respect to the crack plane and that have to be deformed to respect this plane. The nodes relative to the interface elements are forced to move onto the crack plane. To avoid degenerated triangles/tetrahedra, some elements not directly in contact with the crack plane are also moved. The deformation process is based on the one presented in section 3.3. Finally, the crack interface is created by duplicating nodes, edges and/or faces, depending on the mesh dimension, on the two sides of crack.

These steps are detailed in the following subsections. In the example the planar crack operation is delimited by a circle. An example is shown to better understand (fig. 11). It consists of a tetrahedral mesh on which three groups of tetrahedra are defined (fig. 11.a and .b). The figures (11.d and 11.e) show the initial mesh on which different important nodes are identified. The figures (11.f and 11.g) show the deformed mesh for planar cracking.

Crack Interface identification

This first step separates all the mesh nodes into two sets (N1, N2) according to their position with respect to the two half-spaces (P,N) defined by the crack plane.

In the case of 3D mesh, a set T1 gathers together the tetrahedra having their 4 nodes belonging to the half-space P, respectively T2 for tetrahedra having at least one node in the half-space N. Analogously, for 2D mesh, the set T1 gathers together triangles whose 3 nodes belong to the half-space P, and respectively T2 for triangles defined by at least one node belonging to the half-space N. The figure 12.a shows a 3D mesh and the identified sets T1 and T2 when applying a planar crack. Now, the Crack Interface (CI) has to be identified. For 3D mesh, the CI is defined as a set of triangles f which are shared by one tetrahedron t i in T1 and one tetrahedron t j in T2. Analogously, in the case of 2D mesh, the CI is a set of edges e which are shared by one triangle f i in T1 and one triangle f j in T2. Before computing CI, one of the two sets, let us suppose T1, is chosen and processed such that it contains only tetrahedra (resp. triangles) sharing at maximum one triangle (resp. edge) with tetrahedra (resp. triangles) of other set T2. The set of these triangles/edges will constitute the CI. Actually, tetrahedra which have 2 or 3 shared triangles (resp. triangles with 2 shared edges) should not take part in the definition of the CI since they will be flattened after the deformation as all CI triangles (resp. edges) will be moved onto a plane (resp. a intersection line between the crack plane and triangle plane).

The processing of the set is performed as follows:

• in the case of 3D mesh, if a tetrahedron in T1 has two triangles shared with elements in T2, then the edge shared by the other two triangles should be split so that the tetrahedron is subdivided into two sub-tetrahedra which contain one of the two problematic triangles each. Figure 12.a shows the tetrahedron abcd associated with two problematic triangles acb and adc and the dihedral angle θ that would become 180 • after deformation. Here, the edge b -d is split in two by inserting a new node o (fig. 12.b). The tetrahedron is then subdivided into two tetrahedra abco and acdo and each of them has one of the two interface triangles. All neighbor tetrahedra associated to the split edge b -d should be also split. Figure 12.c shows all the neighbor tetrahedra in the original mesh and the figure 12.d shows them split, • in the case of 3D meshes, if a tetrahedron in T1 contains three triangles shared with tetrahedra in T2, this tetrahedron is moved from T1 to T2. In this way the fourth triangle becomes an interface triangle. Analogously, in the case of 2D meshes, if a triangle in T1 has two edges shared with triangles in T2, it should be moved from T1 to T2. In this way the third edge becomes an interface edge.

Crack Interface deformation

Being the elements of the CI defined, the deformation process described in section 3.3 is applied so that the elements of the CI match the shape of the desired crack. It requires the identification of sets of elements to be moved in a different manner, i.e. using different constraints for each set. We define a set CN (Crack Nodes) constituted by all the nodes associated with the elements in CI. Figures (11.d and 11.e) show the identified CN nodes in a 3D mesh.

To enable a smooth transition between the CI elements and the surroundings, a set of TN (Transition Nodes) is defined. It contains the nodes located in the i th neighborhood of the ones in CN. The bandwidth "i" can be user-defined, or it can be computed by dividing the biggest distance between one crack node and the crack plane by the mean edge length. The bigger it is, the smoother the transition will be, and the better the quality of the mesh will be. Figures (11.d and 11.e) show some TN nodes identified with i = 2. For a tetrahedral mesh, all nodes belonging to only triangles shared by exactly two tetrahedra in the set are internal, and all nodes associating with at least one triangle which is shared by exactly one tetrahedron are external. For a triangular mesh, all nodes belonging to only edges shared by exactly two triangles are internal, and all nodes associating to at least one edge which is shared by exactly one triangle are external. The external nodes may form a characteristic shape of the model. For identifying such characteristic shape, we have devised a tool [23] partially based on [24] for the detection of surface primitives such as plane, sphere, cylinder etc in 2D/3D meshes. For the mesh shown in figure 11 different surface primitives (planar and spherical) are identified for the external surface (fig. 11.c). For preserving the shape of the original model we define also a set of MBN (Model Boundary Nodes). This set contains the CN and TN nodes on the mesh boundary. Some MBN nodes are shown in figure 11.d.

If we do not want the crack operation influence the entire model we need to define a delimitation shape. The shape could be a rectangle, circle etc so that the CN nodes are delimited as well as the corresponding TN. The MBN set needs to be updated too. We define the set of CBN (Crack Boundary Nodes) containing the CN nodes associating with delimited nodes and kept nodes on the crack plane. In the example of figure 11.e circular delimitation is applied for the planar crack operation, and some CBN nodes are shown.

For preserving the group information the constraint from the virtual group boundary shape is also imposed during the deformation. A set of GBN (Group Boundary Nodes) is defined for all free nodes on the crack interface and in the transition zone.

Once the various sets of nodes are identified, geometric constraints can be assigned to drive the deformation process. In the case of 3D mesh, nodes in:

• CN are constrained to stay on the crack plane;

• CN are constrained to stay on the crack plane;

• CBN are constrained to stay on the delimitation shape;

• MBN are constrained to stay on the external skin of the 3D mesh by preserving its surface shape; • GBN are constrained to stay on the shape of corresponding group boundary; • TN could move; and all the other nodes are fixed. For 2D mesh, nodes in:

• CN are constrained to stay on the crack plane and on the mesh by preserving its surface shape; • CBN are constrained to stay on the delimitation shape;

• MBN are constrained to stay on the mesh boundary by preserving its curve shape;

• GBN are constrained to stay on the shape of corresponding group boundary;

• TN are constrained to stay on the mesh by preserving its surface shape; and all the other nodes are fixed.

In case of complex free form shapes, we use a tangent plane to the node to constrain locally the deformation.

During the deformation of the mesh, the deformation engine solves underconstrained set of equations based on the mechanical model described in section 3.3. Additionally, a mesh quality criterion is also used in order to guarantee the quality of deformed mesh from the mechanical FEA point of view.

Definitive crack insertion

In order to complete the insertion of the crack, the mesh entities belonging to the CI are duplicated to make the required topological modification and the incident relations with edges, triangles and tetrahedra are accordingly updated. Figures (11.f and 11.g) show separately the exterior and the interior of the 3D mesh after insertion of the limited crack and local mesh deformation in the crack zone.

Through Hole drilling operator

The second operator here described is the drilling operator. Similarly to the crack operation, the drilling of a through cylindrical hole is performed in three steps: hole interface creation, interior hole mesh elements removal and hole interface deformation. The Hole Interface is the set HI of triangles for 3D mesh (resp. edges for 2D mesh) between the removed mesh elements and the kept mesh elements after cut operation to create the through hole. The HI is deformed to match the cylindrical hole shape by using previously adopted deformation engine with new constraints as input.

Cylindrical HI identification

Similarly to the crack insertion, the nodes of the mesh are separate into two sets which respectively gathers together the nodes inside (I) and outside (O) of the drilling tool (cylinder).

In the case of 3D mesh (resp. 2D mesh), we define a set RT containing all the removed tetrahedra (resp. triangles) whose four nodes (resp. three nodes) are in the set labeled I and, a set KT gathering together the remaining tetrahedra (resp. triangles). For 3D meshes (resp. 2D), a set HI is defined by all the triangles (resp. edges) which are exactly shared by one tetrahedron (resp. triangle) in RT and one tetrahedron (resp. triangle) in KT.

Hole drilling

Similarly to the crack insertion problem, the geometric constraints specification will not be the same for all the mesh entities, and an identification of specific node sets to be constrained is required.

First, a set HN gathers together all the nodes associated with HI elements. Second, a set TN (Transition Nodes) is defined similarly to the ones used in the crack insertion problem, i.e. using a bandwidth of ith neighborhood.

For preserving the shape of the original model we define also a set of MBN (Model Boundary Nodes). This set contains the HN and TN nodes on the mesh boundary.

For preserving the group information the constraint from the virtual group boundary shape is also imposed during the deformation. One important point is that the VGB should be computed before the deletion of the elements in RT. A set of GBN (Group Boundary Nodes) is defined for all free nodes on the crack interface and in the transition zone.

The elements corresponding to the hole, RT tetrahedra (resp. RT triangles) are then removed from the mesh model.

Then, geometric constraints are assigned to different nodes depending on the sets to which they belong. For 3D meshes nodes in:

• HN are constrained to stay on the cylindrical hole;

• MBN are constrained to stay on the external skin of the model; • GBN are constrained to stay on the shape of corresponding group boundary; • TN could move; and all the other mesh nodes are fixed. In the case of 2D meshes, nodes in:

• HN are constrained to stay on the cylindrical hole and on the mesh by preserving its surface shape; • MBN are constrained to stay on the model boundary by preserving its curve shape ; • GBN are constrained to stay on the shape of corresponding group boundary;

• TN are constrained to stay on the mesh by preserving its surface shape; and all the others mesh nodes are fixed. Once the constraints are defined, the deformation engine detailed in section 3.3 gives a solution in term of local deformation of the drilled hole surface.

Figure 13 illustrates the whole process on a cube-like tetrahedral mesh. Two groups of tetrahedra are shown in figures (13.b and 13.c). The mesh model before deformation is shown in figures (13.d and 13.e). First, the removal tetrahedra (RT) and hole interface nodes are identified. These tetrahedra are then removed, the newly appearing boundary triangles constitute the HI and different node sets used by our technique are identified. Next, the constraints are applied, and the solution in term of local deformation of the drilled hole is computed using the deformation engine (fig. 13.f and g).

Extension to two new operators

Based on the previously described operators, the cylindrical crack and the incision/cutting operators can easily be defined and implemented.

Figures (14.a to 14.c) illustrate the principle of the cylindrical crack operator, that permits the separation of the object in parts having the contact region of cylindrical shape. Figure 14.a shows an initial mesh on which a cylindrical crack is performed. The profile is defined by a cylinder of center o and radius r. In a first step, using an extension of the HI identification process, the CI is detected (fig. 14.b) and handled similarly to the planar cracks (fig. 14.c), i.e. mesh deformation for reaching the cracking surface, duplication of nodes and updating of the incident elements. Figures (14.d and 14.f) present the principle of the cutting operation. Figure 14.d shows an initial mesh on which an incision having a non null width is performed. The incision is defined by two trimming planes and a distance between them. The first step involves the detection of all the mesh elements which are between the two cutting planes (fig. 14.e). This can be made using an extension of the crack model splitting. Next, the in-between elements are removed, as for cylindrical hole. The two incision interfaces corresponding to the trimming planes are then handled similarly to the crack case (fig. 14.f).

Results

This section gives some results relative to the direct modification of industrial 3D FE mesh model using our technique: insertion of different mesh features (crack, hole) following local deformation under constraints taking into account the shape of the group boundaries (low-semantics enriching the FE mesh).

The first example (fig. 15) shows successively the adoption of the developed operators for obtaining the two changes performed by EDF engineers on the caisson model of figure 1. Figure 15.a shows a part of the model in which one stiffener is cracked. The two sub-parts P1 and P2 (depicted in blue and red) of the mesh are defined by using the crack plane. The cracking deformation is limited to the stiffener so that the rest of the sub-parts is kept. Figure 15.c shows the sub-part P1. On the cracked model a cylindrical hole is inserted on the bottom of the stiffener (fig. 15.b). The sub-part P1 on the drilled mesh is shown in figure 15.d. On this example, 84 nodes have been moved onto the crack plane and 341 nodes are moved to relax the transition mesh zone between fixed and free nodes. Therefore, 84 crack plane constraints and 425 tangent plane constraints have been used to accomplish accurately the required deformation.

Another example illustrates the insertion of a cylindrical hole and a crack into a volume mesh on which several groups of tetrahedra are defined (fig. 16.a). Here, not only it is important to obtain the hole and crack feature, but also the boundaries of these groups have to be maintained.

Conclusion and future works

In this paper, a new framework for CAD-less FEA has been introduced for fast prototyping of product design solutions during the product optimization phase. With the proposed approach, the geometric modifications required to achieve the optimal solution can be directly performed on the FE meshes enriched with their physical semantic information through the use of mesh groups. This avoids the need to return to the CAD product model and then to re-perform all the time consuming steps necessary to obtain the complete FE model. This capability is also very useful when the CAD model is not available and a mesh could be obtained easily, e.g. through object scanning. Therefore the different alternative solutions could be realized on the available mesh models. To fully exploit such a capability it is important that not only the shape aspects are considered but also the already present semantics is correctly managed. Depending on the modification on the shape and on the specific semantics' meaning, the attached semantic data may need different treatment.

The proposed framework is designed as modular so that the development of new operators simply requires the substitution and/or addition of new components or constraints. Here, two operators have been proposed to cover the primary needs in terms of direct 2D/3D mesh modification: crack and through hole operators. These operators use a deformation engine applying two types of constraints: those relative to the shape of the tools (e.g. cylinder for holes and plane for cracks) and of the object itself and those relative to the group boundaries associated to physical semantics that have to be preserved. Since our approach is aimed at complying with the maintenance context in which it is important to deal with FE meshes that correctly reflect the behaviour of the equipment physical counterpart already validated, we reduce as much as possible the existing mesh elements on which the modifications are applied. Thus the mesh is modified only in a restricted bandwidth so that the quality of the elements (e.g. aspect ratio) remains good with respect to the FEA requirements. Additionally, the proposed operators reposition nodes without adding new ones. Only in special cases, when the operated mesh does not have enough nodes in the modification area, a mesh pre-refinement step is necessary.

Future work will be related to the extension of our mesh modification toolbox to other shapes (e.g. spheres, torus or free form) as well as to other operators such as material addition, which is often required for fast modifying advanced FE mesh models. Moreover, since at present we treat semantics only at the group level preservation, additional effort will be devoted to investigate more into details the propagation (e.g. group extension and gluing) dependent of the semantics meaning.

We can state that the results of the research here described give a new vision about the need and possibilities to develop modelling tools that are not only dealing with the geometric aspects of objects but fully use the attached application specific semantics in order to provide tools that really improve the product life cycle development.

Fig. 1 .

 1 Fig. 1. Mainstream methodology for product behaviour analysis and optimization: (a) caisson CAD modelling, (b) meshing, (c) definition of physical/geometric parameters (semantics enrichment) required for FE model creation, (d) FEA, (e) analysis phase consisting in a crack-feature insertion into the more loaded component, (f) local modification consisting in drilling a hole on the bottom of the stiffener, (g) another modification solution consisting in stiffener removal. After each local modification realised on the CAD model, similar steps from (a) to (d) have to be repeated to assess the modification's effects (courtesy EDF-R&D).

Fig. 2 .

 2 Fig. 2. Mesh groups and associated mechanical semantics defined on the Caisson model (courtesy EDF R&D).

Fig. 3 .

 3 Fig. 3. Workflow for FE model preparation.

Fig. 4 .

 4 Fig. 4. Three levels of information contained in a FE model.

Fig. 5 .

 5 Fig. 5. General structure of CAD-less FE model modification operator

Fig. 6 .

 6 Fig. 6. Examples of VGB computed from groups of different dimension in a 2D mesh.

Fig. 7 .

 7 Fig. 7. Examples of EGs computed from two groups of different dimension in 2D mesh.

Fig. 8 .

 8 Fig. 8. Different topological modification examples: b) removal, c) addition, d) cracking and e) subdivision.

Fig. 9 .

 9 Fig. 9. Example of deformation using force minimization under plane and cylinder type constraint.

Fig. 10 .

 10 Fig. 10. Fluid pressure propagation in case of mesh drilling.

Fig. 11 .

 11 Fig. 11. Insertion of a planar crack (d-g) into the 3D mesh containing three groups highlighted in different colors (a) (b) and delimited by planar and spherical surfaces (c).

Fig. 12 .

 12 Fig. 12. Splitting schema for the tetrahedron with 2 potential interface triangles.

Fig. 13 .

 13 Fig. 13. Creation of a through hole in a 3D mesh of a cube characterized by two FE groups colored in blue and red.

Fig. 14 .

 14 Fig. 14. Overview of two additional operators: cylindrical crack and cutting/incision.

Figure 16 .

 16 b shows the tetrahedron rough deletion for hole insertion. The deformation result of drilling operation is shown by two pictures (fig. 16.c and .d) presenting the model from outside and inside points of view. On the drilled model a limited planar crack is inserted then (fig. 16.e). The cracking deformation is shown in figure 16.f.

Fig. 15 .

 15 Fig. 15. Insertion of a crack (planar contact zone) (a) (c) and of a cylindrical hole (b) (d) into the 3D FE mesh of a caisson model courtesy EDF-R&D).

Fig. 16 .

 16 Fig. 16. Insertion of a cylindrical hole and planar crack into a 3D mesh containing three overlapping groups.

Acknowledgements

This work is supported by the Research and Development direction of the EDF Group and it is partially carried out within the scope of the FOCUS K3D project supported by the European Commission 25 .


Borjan Tchakaloff
email: b.tchakaloff@estaca.fr

Sébastien Saudrais
email: s.saudrais@estaca.fr

Jean-Philippe Babau
email: babau@univ-brest.fr

Integration of Physical Models in the ORQA Framework for Electric Vehicle Energy Management

Keywords: D.2.11 [Software Engineering]: Domain-Specific Architectures, D.2.13 [Software Engineering]: Reusable Software-domain engineering, reuse models, J.7 [Computers in Other Systems]: Consumer Products-electric vehicle model driven architecture, energy consumption, mechatronic system, Quality

The energy management of electric vehicles is located in the storage system. It considers only the storage physical constituents and adapts the demand of the drive-chain technologies but is blind to other parts of the vehicle. Furthermore it has a very focused approach -to optimise the vehicle movement -and misses the driver's pragmatical needs. ORQA is a framework dedicated to the modelling of the vehicle consuming devices complementing the embedded systems models. It characterises each consuming device by its energetic needs and the quality levels it offers from a driver perspective. ORQA is realised by a system-level energy manager which looks for a driving strategy matching the driver needs and constraints. As the components used in ORQA are generic devices, the consumption of the devices is an approximation of their real consumptions. This paper presents the adaptation of the ORQA framework to have a closer estimation of the consuming devices. We propose the use of physical models issued from mechatronic systems for the main devices, drive-chain and energy storage, and discuss the benefits and constraints.

INTRODUCTION

During the last years, the number of electric vehicles models have increased and their use is present in all the main countries. The main problem against this type of propulsion is the short autonomy provided, hence certain reluc-tance about their usage (commonly known as range anxiety). Current vehicles can handle a few hundred kilometres. One way to deal with the embedded energy issue of the electric vehicles is through software. An energy management system (EMS) is a high level software which monitors and manages an environment through specific-purposed components. EMS are commonly used in (full-and hybrid-) electrical vehicles, though they mainly manage only the engine and ignore the end-user provided Quality of Service (QoS).

To provide an efficient energy management and to take into account the user-related QoS, an electric vehicle EMS has to consider all the devices and the user expectations. Several works allow for complete analysis and validation of the software embedded in vehicles, such as EAST-ADL [START_REF] Debruyne | East-adl -an architecture description language[END_REF]. Though they have a systemic approach, they lack the energy dimension required to provide an EMS.

In [START_REF] Tchakaloff | ORQA: Modeling energy and quality of service within AUTOSAR models[END_REF], the ORQA framework is presented to tackle the global energy management while providing a QoS as good as possible. In ORQA, each embedded device is represented by a component and is characterised at design phase by its energy consumption(s) and its quality(ies), if applicable. The framework offers to realise a components architecture, in AUTOSAR [START_REF] Fürst | Autosar-a worldwide standard is on the road[END_REF] (an automotive component framework), which will elaborate on-line a solution to achieve the driver request (reach a destination) while providing the best possible vehicle QoS. The ORQA principle is to limit by software the devices and engine usage. In the previous works, consumption models of the drive-chain and the battery were simple and generic in order to validate the ORQA framework for any kind of electric vehicles. From an electric vehicle to another one, the characteristics of the different devices change. The use of models from the mechatronic domain offers models closer to the real devices.

In this paper, we show that accurate and specific models offer more precise estimation of the energy consumption. Moreover, the use of physical models offers also the possibility to apply ORQA to hybrid vehicles. The paper presents the physical models that can be introduced in ORQA and the corresponding constraints, and illustrates them on some case studies.

The rest of the paper is structured as follows. Section 2 presents the ORQA framework and its operation. Section 3 details the evolution of the current models to the physical models of the drive-chain and the battery. Section 4 discusses about the modification of the implementation of ORQA. Section 5 assesses the interest of a physical modelling. Finally, Section 6 concludes.

ORQA APPROACH

ORQA is a framework to set up an energy management system for electrical vehicles. It is based on models of the devices energy consumption and models of user-oriented Quality of Service. It provides the energy and quality knowledge to the embedded systems. By the mean of an energy manager, ORQA assures the driver of a solution trip and of an on-line control of the devices based on her preferences.

ORQA methodology

At design stage, the designers create the energy and QoS models of the vehicle devices. The framework offers a library of predefined models to help them, comprising the engine and several devices such as the lighting system, the climate control unit and the entertainment system. An energy manager implementing these models is generated to take place in the embedded systems of the vehicle. It connects to the existing software and provides them with energy and quality knowledge. At run-time, the energy manager handles the driver request to find a solution trip. The inputs and the outputs of ORQA are illustrated in Figure 1. We now describe the framework operation in five steps.

1. The driver defines a destination point and its preferences. Driver preferences cover embedded devices ordering and consumption policy with possible constraints over trip duration and consumption. These definitions are later used to compute and select the best possible solution.

2. The possible routes are retrieved or generated from the system (e.g. from the GPS unit). Each route is refined to provide different driving conditions.

3. Each solution is evaluated, that is its energy consumption, trip duration, and global quality are computed.

A solution is composed of a route and a combination of the devices operating states.

4. The solutions are passed through scoring functions depending on the driver preferences. Driver-defined constraints are used to discard undesirable driving strategies. The driver is then informed of the higher ranked solution as the best possible match.

5. Finally, the embedded energy manager is used to control the devices on-line throughout the trip. It controls the devices behaviour following the proposed strategy by the mean of brokers.

Modelling the devices

The vehicle devices are controlled by and supply information to the embedded systems (ES). The ES models already contain the vehicle devices. ORQA proposes to characterise both energy and quality of these devices in a specific model. It integrates them back in the existing ES models such as to keep the compatibility with the original meta-model.

A device is modelled as a software component, it offers a service. A service can be realised through different states, each with a defined quality. The physical realisation of a service consumes some energy. So we say that a device has one or more operating states realising a service, and each state is characterised by an energy requirement and a quality of service. As the devices are already modelled in the system to offer their services, their operating state are also accessible. ORQA extracts the devices and their operating states in its specific model. It allows the designers to characterise their energy requirements and quality levels. The energy requirement of an operating state is modelled by its required power. By multiplying this power to the usage duration of the device, we obtain the device energy consumption. The quality level of an operating state is a qualitative percentage of the given service -100% is the best QoS possible.

Providing a solution

A request from the driver triggers the solution searching process of the energy manager. It evaluates the possible routes combined with the devices different uses. Each route retrieved from the system is refined into variations to represent different driving conditions. In this way each solution is composed of three elements: i) a route to destination, ii) a combination of devices operating states, and iii) the evaluation results (duration, energy consumption, and quality). The operating states combination of a solution is the optimal state in which the driver uses the devices according to their consumptions and qualities. It directly depends on the driver preferences and on the available energy during the journey. Scoring functions evaluate each solution results so that minimum (duration, consumption) or maximum (quality) results obtain the best score. The results scores are combined into the solution score according to weights defined by the driver preferences. The best solution is the one with the higher score; it becomes the driving strategy proposed by ORQA to the driver.

The driving strategy is computed before the effective vehicle departure. It is a prevision of the journey and by nature a journey has uncertainties such as the exact traffic flow and the driver behaviour.

PHYSICAL MODELS

In order to have a more accurate estimation, the models can be replaced by physical models. These new models will be specific for a given vehicle where the engine and the storage have been designed specifically so the associated models will vary between the different vehicles. The physical models concern the drive-chain, the energy storage and specificities of the hybrid vehicles case.

The drive-chain

The drive-chain of a vehicle is what makes it move, it goes from the engine to the wheels. The engine is the main consuming device of an electric vehicle. The engine of a simulated urban vehicle1 consumes about 57%, 79%, and 93% of the total energy consumption in the Common Artemis Driving Cycles (CADC) [START_REF] André | The ARTEMIS european driving cycles for measuring car pollutant emissions[END_REF] (respectively urban, rural, and motorway). We define the whole drive-chain required power as a function of the vehicle velocity, acceleration, and the road slope. This function is based on the well-known dynamic equations of an electric vehicle [START_REF] Larminie | Electric Vehicle Technology Explained[END_REF] and estimates the vehicle characteristics as constants. The engine required power is strictly negative when it is used for braking (i.e. regenerative braking that allows to charge the battery) and positive otherwise. This model is encapsulated in the component corresponding to the drive-chain in ORQA.

The vehicle dynamic equations are a simple and fast way to compute the engine required power to drive the vehicle. Yet an electric engine has dynamic characteristics (such as maximal torque and efficiency) that fluctuate along its speed and affect its power requirement. The current ORQA approach is to assume a constant efficiency for each engine mode (motor to drive the vehicle or generator when braking). The use of a physical model relying on an efficiency map offers a better estimation. It interpolates the engine efficiency for a given velocity and torque couple that can be deduced from the vehicle acceleration and velocity on a given road slope. Figure 2 shows an example of an engine efficiency map based on the engine speed and rated torque of a 30 kilowatts engine. For example, when the engine delivers a torque of 10 N•m (no matter its speed) its efficiency is of 82%, meaning that at the end of the drive-chain 18% of the energy is lost. On the contrary, at a constant speed of 3000rpm and torque of 100N•m, only 6% of the consumed energy is lost. The physical model is able to give a more precise consumption based on more parameters and specific to the vehicle: rated torque and engine speed. During the solution selection, the candidate solutions with low efficiencies should be ignored and the high efficiency ones preferred. The actual overall efficiency of a driving strategy can be mixed in the engine QoS so that it is taken into account at the selection phase.

The design phase requires a specific development for each engine and for the corresponding vehicle. The knowledge of the mechatronic behaviour of the engine is needed to model and validate the efficiency map with experimental results. The replacement of the generic model by the physical model is done in ORQA by simply replacing the generic engine component by the specific vehicle's component.

The energy storage

The energy storage system -simplified by "the battery" in the rest of the paper -is ignored by the current version of ORQA apart from the remaining energy level. The component responsible of the battery just provides this level. This modelling does not take into account the physical response of batteries to the need of power. There are many storage technologies -such as batteries and fuel-cells -with different characteristics (maximal stored energy, maximal current, weight, and cost). Several works [START_REF] Gao | Power enhancement of an actively controlled battery/ultracapacitor hybrid[END_REF][START_REF] Dai | Cell-bms validation with a hardware-in-the-loop simulation of lithium-ion battery cells for electric vehicles[END_REF][START_REF] Allegre | Influence of control strategies on battery/supercapacitor hybrid energy storage systems for traction applications[END_REF][START_REF] Mesbahi | A new energy management strategy of a battery / supercapacitor hybrid energy storage system for electric vehicular applications[END_REF] study the possibility of coupling different storage technologies to improve the system operation. Some types of storage are better to respond to instant demands of power while other have better results for long-term demands. The battery management system (BMS) must comply with the underlying storage technology and its characteristics, so it is calibrated specifically for a vehicle. From the ORQA point of view, the knowledge of the different elements of the battery permits to optimize a trip, not only on the vehicle consumption but also on how the energy is consumed.

Hybrid vehicle

An hybrid vehicle has an electric and a internal combustion engine (ICE) with the corresponding energy storage. The electric engine is used during urban phases and so the estimation solution has to only evaluate these phases but has to know the whole route to anticipate it. The model has to include the recharge phase of the battery with the modeling of a specific kinetic energy recovery system, KERS [START_REF] Moreno | Energy-management system for a hybrid electric vehicle, using ultracapacitors and neural networks[END_REF], which differs from a full electric vehicle. The ORQA methodology needs some adaptation to separate the part of the route operated by the ICE and the part operated by the electric engine. During the thermic phase, an estimation of the recharge of the battery has to be modelled to improve the estimation of the level of energy left. Depending on the hybrid type, the recharge is realise through the ICE and/or through regenerative braking.

IMPLEMENTATION

A prototype of the ORQA framework is implemented for Autosar applications. The consumption models are introduced in the Autosar components with model transformations. The integration of the physical models into ORQA does not change the prototype interface but may change its execution time and its memory load.

Current implementation

The first phase of ORQA occurs at conception at the embedded systems modelling. Though ORQA carries general concepts, our prototype is implemented to run on an Autosar compliant system. Autosar is a methodology to model the embedded systems widely spread in the automotive world. It stands on the software components paradigm and allows the reuse and share of functionalities through encapsulation. So our prototype actually relies on Autosar models of the embedded systems. The vehicle devices characterisation is realised in a dedicated model. It breaks down to three phases: 1) the devices are extracted from the available models, 2) they are characterised in a dedicated model, and 3) the initial models are enhanced with the characterisation model. The Figure 3 illustrates the three phases of the AUTOSAR transformation. A first model transformation browses the initial models to extract the different devices. The energetic and qualitative characterisation model fits a specific Ecore meta-model [START_REF] Steinberg | EMF: Eclipse Modeling Framework, 2nd Edition[END_REF]. The integration back in the initial models stays compatible with their interfaces, the existing components are not modified. So a second model transformation creates the new components (the Energy Manager, a broker per controlled device) and assures the good components linkage. Finally, a third model transformation generates the adequate implementation of the new components. In our prototype oriented Autosar, these are ANSI-C files.

Physical models integration

The integration of the physical models into ORQA does not have any impact on the transformation process from the initial Autosar components to the energy-aware components. The models are integrated inside a specific component (the Energy Manager, EM) and communicate with the existing components by their original interfaces. The difference between a generic model and a physical model of an engine for an Autosar application occurs during the second phase of the transformation: the consumption and quality modelling deviates accordingly. This model is then encapsulated inside the EM component.

APPLICATION

We want to assess the interest of a more detailed consumption model for ORQA. We take as illustration two engine models in ORQA, one theoretical and one physical, and compare their consumption to the engine consumption of a simulated electric vehicle. The simulation has the same engine characteristics that the two models. The difference lies in that the simulation follows a route trip based on the vehicle capacities, the simulated vehicle is driven in a closedloop to follow the route trip velocity reference. The ORQA evaluation uses a faster algorithm that assumes a static acceleration with three pre-defined values (negative, null or positive). We assume the simulation to have the more accurate results, so we compare both models to the simulation results. However, it should be noted that the models used in the simulation are not suitable for an on-line EMS as they have an important computational burden.

Theoretical modelling of the engine.

We make use of the engine model currently proposed by ORQA. It relies on the vehicle dynamics equations to represent the theoretical consumption of the engine. The equation is a function of the vehicle velocity and acceleration, and the road slope. The engine is assumed to have a constant efficiency in motor mode and another one in generator mode.

Physical modelling of the engine.

This more detailed model is defined with the engine efficiency map presented earlier (Figure 2). The engine consumption is the same than in the theoretical model except for the engine efficiency. It is not constant any more but is interpolated from the efficiency map. Also, as the presented efficiency map is about the motor, we assume an equivalent map in generator mode. Here, to use the physical modelling, we just replace the engine energy characterisation by the physical one. Then, the energy manager components are re-generated to take into account the model update.

Discussion.

Table 1 lists the engine consumptions of three generated routes for the simulation and the two engine models. These examples illustrates that, compared to the simulation, the physical model brings more accurate results than the theoretical model does. Respectively by 3.9, 0.8 and 4.5 points for the urban, rural and motorway routes.

These preliminary results show the interest for more detailed consumption models. With more accurate evaluations of the possible routes and their variations, ORQA can propose better solutions. Typically, this means that the best solutions use the engine optimal operation to provide a better Quality of Service.

The computation and memory overload are not detailed here. The execution time are not yet monitored as it requires special tooling. We can assume that the physical models require more time to evaluate because they are more complex. However, the memory overload can be statically measured as no dynamic allocations are allowed in the Autosar components. The prototype shows a low static overload (ROM) of a few kilobytes. On the other hand, run-time memory (RAM) suffers an important overload of around ten kilobytes with this example modelling. A typical controller unit on which runs an Autosar application features between 512 and 1024 kilobytes of ROM and between 32 and 64 kilobytes of RAM. So the memory overload of physical models must be closely measured to match the target controller capacities. In the case of an excessive overload (processor or memory), the physical models are simplified with discrete versions to lower their complexity.

CONCLUSION

This paper presented possible extensions of the ORQA framework to take into account other type of vehicles, like hybrid vehicles, and to have a more accurate estimation of the trip solution by using physical models. The framework is ready to incorporate the new models by replacing the generic components by more precise components containing mechatronic information. The validation of the physical models needs more feedback of a real vehicle as these models need to be adapted to each type of vehicle. The inclusion of the physical models in the on-line implementation also need more experimentation as they are more complex than the generic ones. The solution computation should comply with the system constraints over execution time and memory load. Other extensions to the solution search can be done by looking from a higher system point of view. Adding information about smart-home or smart-grid [START_REF] Druilhe | Energy-driven consolidation in digital home[END_REF] will permit to optimise the consumption in both way (discharging the battery or storing the energy).

Simulation

Figure 1 :

 1 Figure 1: Overview of the ORQA Energy Management System.

Figure 2 :

 2 Figure 2: Engine efficiency based on engine speed and rated torque.

Figure 3 :

 3 Figure 3: The AUTOSAR models transformations, from an initial model to an energy-and qualityaware model.

Table 1 :

 1 Engine consumption in a simulated vehicle compared to the evaluated consumptions in ORQA on three generated routes.

			ORQA: theoretical model ORQA: physical model
	Route environment consumption consumption	error consumption	error
	urban	388Wh	371Wh	-4.4%	390Wh	+0.5%
	rural	1814Wh	1762Wh	-2.9%	1852Wh	+2.1%
	motorway	8062Wh	7046Wh	-12.6%	7409Wh	-8.1%

The simulated vehicle is described in[START_REF] Tchakaloff | ORQA: Modeling energy and quality of service within AUTOSAR models[END_REF], we assume a constant accessory load of 1500W.