Adel Taweel
email: ataweel@birzeit.edu

Elyes Lamine
email: elyes.lamine@univ-jfc.fr

Richard Bache
email: richard.bache@kcl.ac.uk

A Model for Computing Temporal Eligibility Criteria on Large and Diverse Data Repositories

Keywords: Eligibility Criteria, Query Generator, Clinical Trial, Temporal Reasoning

There have been numerous attempts to build query generators that compute eligibility criteria (EC) for a clinical trial automatically on repositories of patient data. However, one of the challenging key features of EC is the ability to express and compute complex temporal aspects. Existing EC generators has limited temporal capability and those do rely on underlying database technology to perform temporal reasoning. We propose a model that incorporates temporal features of existing generators. However, it separates the computation of the criteria, and in particular the temporal semantics, from the extraction of clinical data from the database to increase the efficiency of execution. We explain the implementation of this model and in particular its temporal algorithm, which runs in O(n log(n)) time where n is the number of clinical facts stored making it more efficient than existing reported generators, where performance, at best, has been reported to be O(n 2 ). We perform an empirical validation to demonstrate the results.

I. INTRODUCTION

Clinical sites such as GP practices and hospitals routinely store clinical data about patients, increasingly in a structured (as opposed to free text) format in electronic health record (EHR) systems. Clinical trials need to recruit patients according to a set of eligibility criteria (EC), which will often relate to the clinical facts stored in EHR systems. Naturally, there is growing interest in automating the identification of patients for clinical trials using EHR systems. To enable computerised identification of suitable patients first requires a formal and machine-readable expression of a set of EC for the trial in question. Where eligibility criteria express temporal conditions, as Ross et al. [START_REF] Ross | Analysis of Eligibility Criteria Complexity in Clinical Trials[END_REF] have identified, in 40% of cases a means of formalising time and performing temporal reasoning is also required. Most existing approaches, in computing temporal queries, depend on using the query language of the underlying database or use combined query to reason over complex temporal ECs. We argue here that by formalising the expression of EC independently of the representation of the clinical data allows the temporal reasoning to be performed separately from the retrieval of the clinical data. This offers substantial improvements in the time taken to compute EC on large repositories of data. We described such a system and perform an empirical study to show that it does offer significant performance advantage over previously implemented systems.

There have been various attempts to formalise ECs with the ultimate aim of computing them automatically against data from EHR systems. This problem has been approached from two directions: formalising natural language ECs in general and computing a subset of computable ECs from actual clinical data. In the first, approaches such as EligWriter [START_REF] Gennari | Cross-tool communication: From protocol authoring to eligibility determination[END_REF], ERGO [START_REF] Tu | A Practical Method for Transforming Free-Text Eligibility Criteria into Computable Criteria[END_REF], Gello [START_REF] Sordo | Description and status update on GELLO: a proposed standardized object-oriented expression language for clinical decision support[END_REF] and Arden Syntax [START_REF]Health Level Seven International, Arden Syntax[END_REF] seek to represent EC in a formal or semi-formal notation. These enable varying degrees of formal reasoning that is not possible with natural language representations. The EliXR [START_REF] Weng | EliXR: an approach to eligibility criteria extraction and representation[END_REF] tool seeks to analyse natural language text to create a formal representation. However, although such notations are useful for communicating EC in an unambiguous way, they are currently not sufficient to compute patients' eligibility from repositories of clinical facts. This is partly due to the fact that they are not restricted to information that is likely to be recorded by EHR systems in a structured format. Furthermore, just because a concept can be formalised and rendered into a machinereadable notation does not necessarily mean that there exists an algorithm for computing it against structured data. This paper defines an approach for expressing and computing EC from databases of clinical data with both relative and absolute temporal semantics comprising three components:

1. A data-source independent model of EC based on the capabilities of existing query generators. 2. A mechanism for extracting the relevant data from the data source and rendering it into a form that can be subsequently processed against complex temporal criteria. 3. An algorithm that takes the modelled EC and extracted data and calculates the set of eligible patients in O(n log(n)) time where n is the number of clinical facts. Our motivation for constructing this model was to develop a platform that enables a non-technical user to create a query at a remote workbench and then distribute this query to a number of remote sites to be computed over diverse patient data repositories and receive counts of eligible patients. For reasons of governance, any processing requiring access to sensitive patient data should be performed locally within the clinical site firewall so that only counts are returned to the researcher working outside the institution. It will also require secure communication technologies to link the user interface operated remotely by the researcher to components of the system lying within the clinical site's firewall. Although these communication technologies are beyond the scope of this paper, we do address the issue of expressing the query in a serialised form that these technologies would require. A key requirement is to ensure that computation of ECs is done efficiently. Thus, the approach was evaluated against a set of databases of varying sizes to determine its time efficiency.

The rest of the paper is organised as follows, section II reviews related work, section III describes the proposed approach, section IV presents evaluation results, sections V and VI discuss and conclude the paper.

II. RELATED WORK

Researchers have also pragmatically constructed working systems, which we shall term query generators that compute only a subset of all possible EC, specifically those that may be computed from the clinical data available. Such systems often provide a graphical user interface (GUI) e.g., FARSITE [START_REF] Ainsworth | Preserving consent-for-consent with feasibilityassessment and recruitment in clinical studies: FARSITE architecture[END_REF], VISAGE [START_REF] Zhang | VISAGE : A query interface clinical research[END_REF], PatternFinder [START_REF] Lam | PatternFinder in Microsoft Amalga: Temporal query formulation and result visualization in action[END_REF], i2b2 [START_REF] Murphy | Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2)[END_REF], STRIDE [START_REF] Lowe | STRIDE-An integrated standards-based translational research informatics platform[END_REF], ePCRN [START_REF] Epcrn | The electronic patient care research network[END_REF], TRANFoRm [START_REF] Zhao | Query Formulation Workbench[END_REF][START_REF] Ethier | A unified structural/terminological interoperability framework based on LexEVS: application to TRANSFoRm[END_REF][START_REF] Zhao | A loosely coupled framework for terminology controlled distributed EHR search for patient cohort identification in clinical research[END_REF] and Trial DB [START_REF] Deshpande | Temporal query of attributevalue patient data: utilizing the constraints of clinical studies[END_REF]. However, the DXtractor [START_REF] Nigrin | Temporal expressiveness in querying a timestamp-based clinical database[END_REF] system is purely textual. Two further technologies are also relevant here: Chronus II [START_REF] O'connor | The Chronus II temporal database mediator[END_REF] and CLEF [START_REF] Austin | Implementation of a query interface for a generic record server[END_REF][START_REF] Taweel | CLEF-joining up healthcare with clinical and post-genomic research[END_REF][START_REF] Taweel | CLEFjoining up healthcare with clinical and post-genomic research[END_REF][START_REF] Rector | Models and inference methods for clinical systems: A principled approach[END_REF]. Chronus II is an extension to SQL to allow temporal queries but is proposed for use in encoding EC. CLEF is a programming interface that allows the construction of queries in Java code. Table 1 shows a summary of the key features of each system. The ECs supported by these generators and related technologies are constrained in two ways:

• They must relate to information that is actually captured in the EHR systems in a structured format.

• Any criterion must be capable of being computed against that information without human intervention.

We note that these technologies may also be used to answer research questions other than how many patients are eligible for a trial. We argue that the ultimate aim of such work is to construct a system for automatically computing EC against patient data in general. However, there is an important limitation to this aspiration. Natural language criteria as expressed in clinical trials are often subjective, require some degree of clinical judgment or require information not generally held in patient records, at least as structured data. At best, for automatic EC computation to work with complete accuracy, it can only apply objective criteria to structured patient data. Therefore, there is a theoretical limitation to which EC can be computed. Nevertheless, such a system is still useful in that it can automatically create a shortlist of patients to whom the incomputable criteria can be applied manually.

The most general framework for temporal reasoning is provided by Allen's interval algebra [START_REF] Allen | Maintaining knowledge about temporal interval[END_REF]. This assumes that any clinical fact to be represented as an event associated with a (contiguous) time interval. Allen's algebra provides 13 different relations, which include before, meets, overlaps, starts, finishing, equals and the inverse of these relations. Arguably this provides far more expressive power than would be necessary for the EC routinely drafted by researchers or for the clinical data that would typically be found in EHR systems. The implementation of temporal semantics in the query generators cited above varies considerably, as shown in TABLE I. We distinguish here between absolute and relative temporal semantics. Absolute temporal semantics relate the date of a patient event, such as diagnoses, procedures, medications and lab test results to a specified time point, which may be an explicit date or implicitly the time at which the query submitted. Relative temporal semantics relate two or more events that apply to a specific patient such as a particular procedure performed at most one year after the first diagnosis of a (related) condition. FARSITE, i2b2, VISAGE and ePCRN express only limited temporal semantics. In FARSITE only the time interval of a clinical event compared to the date on which the query is executed can be specified. ePCRN also supports only absolute time defined against the current time. Since the XML-based repository is a snapshot of a particular patient's details, it will contain only the most recent measurements of vital signs and lab tests. In i2b2 and VISAGE the only temporal constraint is that two clinical facts apply to the same encounter/visit. This is a limited example of relative temporal semantics since events in different encounters cannot be related.

In CLEF, constraints for clinical characteristics are defined first (any, first, most recent, all). Temporal relations can then be defined via temporal anchors. Two types of anchors are supported, an age-anchor and an event anchor. The age-anchor specifies a clinical event as basis for the age calculation, e.g., age at the time of diagnosis. Clinical characteristics may be defined relative to an anchor event described by the time interval, e.g., start of treatment with drug Y within a month from diagnosis. It should be noted that the implementation of the temporal semantics results in issues of time efficiency. As the authors concede "performance was a problem" [START_REF] Austin | Implementation of a query interface for a generic record server[END_REF].

In PatternFinder temporal queries are constructed via sentinel events (index event). The sentinel event is linked to an event in the past (baseline) and/or in the future (follow-on) using the relations: after, before, within x prior, within Y following and equal. A user interface to define sentinel, baseline and follow-on events and temporal relations has been implemented. Not clear whether this approach is restricted to link a maximum of three events via temporal relations or linked as a sub query in another query (e.g. Boolean link to other characteristics). However, Lam [START_REF] Lam | PatternFinder in Microsoft Amalga: Temporal query formulation and result visualization in action[END_REF] does evaluate its time efficiency and point out that when using a temporal condition of one event after (or before) another, the query can take hours to process. TRANSFoRm supports a similar approach, although no results of its query generator have been reported.

Trial DB (or strictly speaking an extension to Trial DB) specifies temporal relations via temporal operators linking two clinical characteristics. The temporal operators cover a subset of Allen's relations, including before, after and during. In addition, events can be characterized by start, end and duration. To support users, a graphical tool allows users to select the appropriate Allen operator when expressing temporal conditions. The restriction to temporal relations between two characteristics is a limitation, which may inhibit the intuitive creation of more complicated queries.

In STRIDE, filtering with temporal constraints can be performed for pairs of events. In a first step, operations are performed on the time-stamps of an event to select from three options: any, earliest, most recent (similar to CLEF). In the next step two events are set in relative order with three options: follows, precedes or precedes or follows. Then the range for comparison sets the time between two events and has three options: less than, greater than and between two values. For each criterion an age-anchor can be set. Again, the restriction to temporal relations between two characteristics is a limitation similar to Trial DB.

The DXtractor approach uses rules to define sets of patients satisfying each rule and then imposes further temporal constraints on each set of patients. Boolean and temporal operations can be performed by defining new sets operating on the existing sets. Similar to other approaches the earliest or latest event can be specified in case an event has several instances. Nigrin and Kohane [START_REF] Nigrin | Temporal expressiveness in querying a timestamp-based clinical database[END_REF] argue convincingly that by modelling all clinical facts as zeroduration events gives sufficient richness to express the temporal queries that would be in the clinical domain. Where events are given a zero-duration time stamp, the [START_REF] Zhao | Query Formulation Workbench[END_REF] Allen operators collapse to just five: before and after (with optionally some specified interval of time), equals (simultaneous with), first and last. This approach is both elegant and generic since it is not restricted to relations between pairs of events (STRIDE) or triples of events (PatternFinder). However, no recent implementation of this approach could be identified.

Chronus II is a superset of SQL that allows temporal constraints using a 'WHEN' clause. Thus, it is not restricted to clinical data but was designed with this purpose in mind. Chronus II adopts a valid-time temporal model and supports states (interval time-stamps), events (instant time-stamps) and non-temporal tables. It implements all of Allen's 13 relations. Chronus II adapts TSQL2 [19] temporal query language to extend the standard relational model and the SQL query language to support temporal queries that include temporal projection, joins, granularity conversion and coalescing. It was not possible to derive the status of implementation and practical use of Chronus II from the literature or indeed its time efficiency.

Lam [START_REF] Lam | PatternFinder in Microsoft Amalga: Temporal query formulation and result visualization in action[END_REF] identifies the fundamental problem with implementation of relative temporal semantics. If applied to a database of clinical facts it requires joins, and often self joins, across very large tables. Yet the problem of computing time is not inherently O(n 2 ) but the problem of time efficiency arises when attempting to compute the criteria using a general-purpose query language such as SQL. To this end we propose the creation of a data-source independent Eligibility Criteria Model (ECM) that separates the computation of the ECs with temporal semantics from the extraction of data from the data source.

III. PROPOSED ELIGIBILITY MODEL

The Eligibility Criteria Model (ECM) was designed to be used on two different warehouses of clinical data where, except for demographic data (age, gender and death), each row represents an event with a simple time stamp. The two data warehouses were constructed with distinct schemata: the i2b2 schema [START_REF] Murphy | Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2)[END_REF] and a purpose-built native schema based on the HL7 Reference Information Model (RIM) [START_REF] Bache | An Adaptable Architecture for Patient Cohort Identification from Diverse Data Sources[END_REF]. However, we note the wide applicability of the single-timestamp approach since Deshpande et al. [START_REF] Deshpande | Temporal query of attributevalue patient data: utilizing the constraints of clinical studies[END_REF] and Nigrin and Kohane [START_REF] Nigrin | Temporal expressiveness in querying a timestamp-based clinical database[END_REF] observe that most clinical data is stored in this way. Furthermore, where an event has time stored as an interval (a two-timestamp approach) it can be represented as two onetimestamp events, namely the beginning and end of the activity.

A. Definition of Non-temporal Capabilities

The non-temporal capabilities of the query generators reviewed above all combine a set of rules where each rule applies to one patient attribute. These rules are linked with Boolean operators, logical operators, such as conjunction (AND) or disjunction (OR) to construct a set of EC. Each rule addresses a particular patient attribute e.g., age, gender, HBA1c results or diagnosis of a particular cancer. Therefore, each rule would be computed against a particular type of clinical fact e.g., dates of birth, recorded gender, lab tests and diagnoses respectively. Although natural language EC may make reference to several patient attributes in one criterion e.g., "Male patients over 18 with lung cancer," this would count as three rules for gender, age and diagnosis respectively. The terms 'atomic queries' [START_REF] Ross | Analysis of Eligibility Criteria Complexity in Clinical Trials[END_REF] and 'simple criterion' [START_REF] Nigrin | Temporal expressiveness in querying a timestamp-based clinical database[END_REF] are also used in the literature as synonyms for rule. The proposed ECM supports three types of predicate types: existential, numeric and categorical. There are certain ordinal scales such as the ECOG scale of ambulatory status that may express its value in either a numerical or coded value. For these data elements both categorical and numerical predicates were permitted.

B. Definition of Temporal Capabilities

The ECM assumes zero-duration events and each rule specified whether the event used in the predicate is the first or last for that patient except for certain unique events such as birth where this qualifier is not needed. Any rule may have at most one temporal constraint relating the event in the predicate to one of three anchors. The ECM supports three temporal anchors: now (the time of the query), a rule-based anchor using the time of the event used in a previous rule and a birth anchor using the patient's date of birth. This means that age is not explicitly queried but expressed as a temporal constraint on the birth (being x years before the now anchor). Gender is determined by a gender identification event assumed to occur at birth. Thus, the ECM treats all clinical facts as zero-duration events making no distinction between demographic and other data. Fig. 1 shows the high-level object representation of the ECM

C. Interoperability

The ECM was defined to be independent of both the structure (database schema) and semantic representation (clinical coding systems used). Furthermore, where a rule applies to physical quantity and a unit of measurement is specified, it does not require that the repository use the same unit as the query. This is particularly important for lab tests when measuring the concentration of some substance in a urine or blood sample (e.g., liver function tests) where either mass or molarity per unit volume are routinely used.

The ECM contains an evaluation algorithm, which operates independently of the syntactic and semantic representation of the data repository. This approach requires a mechanism to extract the clinical facts needed to compute a particular rule and render them into a standard representation. Thus, to connect with each data repository, a set of adaptors was constructed [START_REF] Bache | An Eligibility Criteria Query Language for Heterogeneous Data Warehouses[END_REF] for each different repository type. This setup is shown in Fig. 2. The advantage of this approach is that the adaptors are lightweight when compared to the ECM and its evaluation algorithm and can rapidly be assembled for new types of repositories.

D. Eligibility Criteria Evaluation Algorithm

Each rule has a predicate that refers to clinical facts of some type e.g., a diagnosis for type II diabetes or administration of the drug metformin. The evaluation algorithm has three distinct steps:

1. For each rule we determine the set of patients that meet this criterion alone ignoring for the moment any NOT operators. This is achieved by running through each patient in turn and using the clinical facts related to that rule to determine if the patient is included in the set. Note that an absence of facts about a patient leads to exclusion.

2. We compute the Boolean operators linking the rules, AND, OR and NOT, by applying the respective operations intersection, union and set subtraction on the subsets of patients yielded by each rule. This is how the NOT operator is able to convert an inclusion criterion into an exclusion criterion.

3.

Where counts rather than a list of patients are required, we determine the cardinality of the resultant subset.

In the first step, patients are computed for inclusion in the resultant set using the following steps: a. The facts related to a single patient for a given rule are identified. So, for a rule including all patients with most recent BMI reading within 3 months is greater than 23, all BMI readings for that patient are considered b. The temporal constraint is used to filter the facts, removing those that fall outside the specified time window. If there is no temporal constraint no facts are filtered out. In the example, all BMI readings more than 3 months old are excluded.

c. The first or last fact is selected, according to what is specified in the rule. In our example we select the last.

d. This single fact is used to compute the truth-value of the predicate to determine if the patient is in the resultant subset or not. The actual BMI reading is compared to the value 23. If it exceeds this then predicate is true and the patient is included. If the value is less than or equal to 23 or no reading exists then the predicate is false.

Note that for existential predicates, the mere presence of the fact makes the predicate true.

E. ECLECTIC Notation

A human-readable notation called ECLECTIC (Eligibility Criteria Language for European Clinical Trial Investigation and Construction) [START_REF] Chen | An SOA-based Platform for Automating Clinical Trial Feasibility Study[END_REF] was devised to give users of the system a clear and unambiguous description of the query. A set of ECs, which together form the query, is expressed as a list of consecutively numbered rules. Each rule defines the clinical event used to express the predicate. This may be existential or else value-based in which case there is an 'in' clause to specify the reference range. Except for demographic events, such as born, gender and deceased, which are, by definition, unique events, there is also a first or last qualifier prefixing each event. Each rule may have a temporal constraint in which one of the temporal operators before, after and contemp is followed by one of the three anchors: now, birth or a rule specified by its number. The notation is intended to be readable by clinicians and is logged when any user queries a particular data repository, for governance reasons. Fig. 3 shows an example.

IV. EVALUATION AND RESULTS

The model was validated by showing that an implementation was possible that was independent of either the GUI and the repository used to hold clinical facts. Such an implementation was shown to be compatible with a GUI created subsequently and was also shown to be able to access diverse clinical data repositories. Benchmarking was performed by running a query with temporal constraints on a series of databases of increasing size to show that the observed relationship of time with database size is O(n log(n)).

A. Implementation of EHR4CR Platform

An implementation of the ECM has been constructed as part of the EHR4CR platform for computing eligibility criteria. This platform enables a user to compose a set of eligibility criteria at the workbench and then launch a federated query to obtain patient counts from nominated clinical sites. The query is accepted by the orchestrator and then sent on the nominated clinical sites, in this case hospitals. Each hospital contains an endpoint, which executes the query on the respective data warehouse that has been populated from the hospitals' EHR systems. It derives counts, which are sent back to the workbench, from which the query originated, via the orchestrator. The ECM represent both within the workbench and the endpoint. In the workbench it is used to express the composed query. It is then serialised and sent to the endpoint where it is de-serialised. Here the query is used to generate the SQL queries and the results are processed by the evaluation algorithm. Under construction is a variant that enables users at a single site to produce a list of eligible patients.

The EHR4CR currently uses two database designs: one developed in the project (the native database) and the i2b2 database (without the i2b2 query generator). Four i2b2 data warehouses and three data warehouses using the native database schema were populated using an ETL (extracttransform-load) process from hospital EHR (electronic healthcare systems). Multiple local terminologies were used. They have been queried by researchers working at eight Pharma sites.

A query expressed in the implementation of the ECM is a hierarchy of Java objects. This can be readily serialised and communicated via the Internet to remote data sources. Since the query is de-serialised within the firewall of the clinical site the evaluation algorithm is executed within the firewall. Thus for feasibility only these counts are returned to the workbench and the user at the workbench has no direct access to sensitive clinical data.

B. Benchmarking of Evaluation Algorithm

To demonstrate that the evaluation algorithm was (at worst) O(n log(n)), a query with temporal constraints was run on a series of databases each an order of magnitude larger than the last. Times for the extraction of data from the database and the evaluation of the algorithm were measured to show that these times did not increase by more than an order of magnitude. Real clinical data from 100 diabetic patients was obtained and this was used to populate a database. This data was then replicated 10, 100, times etc. A query was constructed so that it would make use of all three types of temporal anchor and would also yield a non-zero number of eligible patients. Fig. 4 shows the ECLECTIC for the benchmarking. The first rule uses a now anchor to include patients over 50 years old. The second uses a birth anchor to include patients who were first diagnosed with type II diabetes after their fortieth birthday. The third rule tests on the most recent BMI reading. The fourth rule tests on a BMI reading at least 6 months prior to the event used in rule 3. The experiment was run on a standalone version of the software. Instead of using the workbench, the query was hard coded. No remote communication across the Internet was used. It should be noted that the size of the query and results of counts would not be affected by the size of the database used. The databases were held in Microsoft SQL Server running on a virtual server with 4 virtual CPUs and GB of RAM, on a Zen Server. Database tables were indexed to improve efficiency. The SQL queries were launched from a Mac Book with a 2.4 Ghz processor and 4GB of RAM, which was also used to perform the evaluation algorithm. The two were connected by a local network. For each database the query was run 10 times and the mean time was calculated. II. shows the number of seconds taken to perform the database queries and also to perform the evaluation algorithm as well as the total time for both. The factor increases in the times as the database increase with size is also shown. Fig. 5 represents the data in graphical form. To be consistent with an O(n log(n)) algorithm, the factor increase in time for an order of magnitude increase in the database size should, at worst, barely exceed 10. In reality it never exceeded 10. Since the experiment shows an increase of 4 orders of magnitude, any part of the algorithm that was O(n 2 ) or higher would manifest itself with factor increases greater than 10.

V. DISCUSSION

The only query generator that mentions the algorithmic complexity of temporal reasoning explicitly is PatternFinder [START_REF] Lam | PatternFinder in Microsoft Amalga: Temporal query formulation and result visualization in action[END_REF], which is O(n 2 ) time for one event before or after another event. Profiling of PatternFinder showed that queries could take hours to perform. This is due to a self-join on a (typically large) database table holding clinical facts. It is reasonable to assume that if other implementations, addressed the temporal semantics at the SQL level, they would also suffer a similar problem. To express four cycles of chemotherapy, that is four events each after the other, the complexity becomes O(n 4 ) making the approach not scalable to typical data warehouses holding >10 8 clinical facts. The algorithm used in the ECM avoids this problem by transferring the problem to an application programming language, where unlike SQL -a declarative language, there is explicit control of the algorithm.

VI. CONCLUSION

The ECM expresses queries in the form of a set of ECs with temporal constraints and supports both absolute and relative temporal semantics. The ECM and its associated implementation are able to support federated queries from a workbench to multiple clinical sites with diverse data warehouses differing in both structural and semantic representation. This is made possible by separating the query model and its evaluation algorithm from the data sources holding the clinical data. The use of a Java object model means that the query may be serialised to enable communication between the workbench used by the researcher and the clinical sites to be queried. The evaluation algorithm has been shown to run in O(n log(n)) time and thus addresses the performance issues that have beset other attempts in implementing temporal semantics.

Fig. 1 .

 1 Fig. 1. ECM-Generic Temporal Model

Fig. 2 .

 2 Fig. 2. System Architecture incorporating ECM

Fig. 3 .

 3 Fig. 3. Example of ECLECTIC

Fig. 4 .

 4 Fig. 4. ECELECTIC Used for the Benchmarking TABLEII. shows the number of seconds taken to perform the database queries and also to perform the evaluation algorithm as well as the total time for both. The factor increases in the times as the database increase with size is also shown. Fig.5represents the data in graphical form. To be consistent with an O(n log(n)) algorithm, the factor increase in time for an order of magnitude increase in the database size should, at worst, barely exceed 10. In reality it never exceeded 10. Since the experiment shows an increase of 4 orders of magnitude, any part of the algorithm that was O(n 2 ) or higher would manifest itself with factor increases greater than 10.

Fig. 5 .

 5 Fig. 5. Execution Time Against Database size (number of patients)

TABLE I

 I

	.	KEY FEATURES OF QUERY GENERATORS REVIEWED
					Temporal
	Name	Technology Type		semantics
		Graphical		query	absolute time, age at
	STRIDE	generator			event
					time	interval
		Graphical		query	between actual date
	FARSITE	generator			and event date
		Graphical		query
	(Extended)	generator	with	text	Subset of Allen's
	TrialDB	output			operators
		Graphical		query
	PatternFinder	generator			Allen's operators
		Graphical		query
	i2b2	generator			same visit as
		Graphical		query	same	visit	as,
	Visage	generator			absolute time
		Graphical		query
	e-PCRN	generator			last, absolute time
					first, last, before,
	DXtractor	Textual query generator	after, equals
					before or after 3
					anchors: birth, prior
					event or now, first,
	CLEF	Java Libraries		last
	Chronus II	Query Language		Allen's operators

TABLE II .

 II RESULTS OF BENCHMARKING

	Database	Database	Running	Factor	Evaluation	Factor	Combined Factor
	Size	Size	DB	increase	Algorithm	increase		increase
	(Patients)	(Clinical	Queries		ECM-(sec)
		Facts)	(sec)
	100	8.8K	0.4	-	1.8	-	2.2	-
	1K	88K	0.7	1.8	2.3	1.3	3.0	1.4
	10K	880K	2.3	3.3	3.5	1.5	5.8	1.9
	100K	8.8M	8.8	3.8	11.7	3.3	20.5	3.5
	1M	88M	82.7	9.4	103.2	8.8	185.9	9.1

ACKNOWLEDGMENT

This research is supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at Guy's and St Thomas' NHS Foundation Trust and King's College London.This work was part funded by the IMI-funded collaborative project EHR4CR (Electronic Healthcare Records for Clinical Research), Grant agreement no.: 115189. We would like to acknowledge the work on designing and implementing the EHR4CR platform performed by many members of the project.


Guillaume Fasse

Florent Becker

Frederic Hauville

Jacques-Andre Astolfi

Gregory Germain

An experimental blade-controlled platform for the design of smart cross-flow propeller

Keywords:

As an interesting alternative naval propeller, cycloidal propulsion offers a huge maneuverability to the vessel due to the ability to almost instantly orientate the thrust over 360 • . While this system has been industrialized since the beginning of the 20th century by Voith-Schneider industries to supply low-speed ship (such as tug, buoyage boat, or some ferries), emergence of fast unmanned underwater vehicles renewed interest in this type of propulsion. Most ordinary cycloidal propellers use so far a mechanical system (gears, crankshaft-rod, belt-pulley) to control the blade rotation which restrains its use for a kinematic mode (epicycloidal or trochoidal) depending on the advance parameter. As an academic technology platform, the French Naval Academy Research Institute (IRENav) developed a large-scale experimental cycloidal propeller (800kg, 0.4m radius) with the aim of testing and running diverse blade pitch laws in the quest for higher energy efficiency for both kinematic modes. Blades' pitching is here performed by servo-motors in order to control each blade independently. An embedded micro-controller sends pitch orders to speed controllers connected with each servo-motors. The experimental blade-controlled platform is also instrumented with four fixed load sensors which measure global forces on the frame. An embedded 2D load-cell outfits one of the blade to measure local inflow hydrodynamic forces during the rotation. A torque sensor also measures the torque on the main shaft. This paper presents this experimental platform, capable of reproduce every imaginable kinematic motions, and widely instrumented to evaluate performances of cross-flow propeller. Pitch regulation has been improved to obtain a sufficient quick response of auxiliary engines. However, pitch tracking validation shows a systemic error due the standard PID regulation loop used by speed controllers which depends essentially on the rotational speed and the considered pitch law (more sharp is the law and more the delay is high). First in-water experiments have been performed at the IFREMER wave-current flume tank for a range of sinusoidal pitch laws to confirm the post process procedure and to determine performance curves for a range of advance parameters. Results show that depending on the advance parameter the more efficient law is different: more the advance parameter is high more the sinusoidal amplitude must be low for a better efficiency. These results confirm the requirement of an adaptable pitch control for cycloidal propeller to boost performances through different operating modes.

Introduction

For the last two decades, innovative naval propulsion systems have been investigated thanks to the growing development of unmanned underwater vehicles. Cross-flow propellers are promising alternative concepts from usual screw propellers. These systems take energy from unsteady hydrodynamic forces generated by blades oscillation or heaving like natural marine animal swimmers (bio-mimetism) [START_REF] Barrett | Drag reduction in fish-like locomotion[END_REF][START_REF] Sparenberg | Survey of the mathematical theory of fish locomotion[END_REF][START_REF] Triantafyllou | Review of experimental work in biomimetic foils[END_REF]. Cross-flow propellers are characterized by the rotation of several blades around a main axis which is perpendicular to the freestream, as opposed to conventional screw propeller for which rotation axis is usually aligned with the freestream. The main rotation is associated with the oscillation of each blade around its own axis commonly called pitch rotation. The combination of these two rotations produces an unsteady hydrodynamic load which results on the thrust force (in the ship advance direction) and side force (perpendicular to the ORCID(s): advance direction). Theses forces are varying along the main rotation angle, called orbital position , and highly depend on the running pitch law. The main advantage of these propellers is to generate a 360 • vectorial thrust force that significantly improves the ship's maneuverability. For this kind of propulsion, the advance parameter is defined as the ratio of the ship advance speed to the rotational tip speed = Ω (where is the vertical axis propeller radius) :

= (1)
Depending on its value two modes are distinguished by the blade kinematic which describes a cycloid path (cross-flow propeller is also referred as cycloidal propeller). Epicycloidal mode is defined for < 1 : the rotational tip speed is higher than the ship advance speed. The blade leeding edge is hence mainly parallel to the tangent of the rotational tip speed to ensure that the relative kinematic velocity (vectorial sum of and ) is coming from the leeding edge (and not from the trailing edge). Propellers using this kinematic mode are referred as low-pitch systems. On the other hand, trochoidal mode for > 1 allow the decrease of the rotational speed in comparison with the epicycloidal for a same advance speed. Because the advance speed is predominant in the expression of the kinematic relative velocity, the blade leeding edge is roughly aligned with the advance direction (see Figure 2). Decreasing the rotational speed has the main benefit to delaying the blades' cavitation. These propellers are referred as high-pitch systems. Cross-flow propellers have been studied since 20th century beginning by Kirsten [START_REF] Kirsten | Propeller[END_REF] with the dual scope of propeller and generator purposes with a fixed low-pitch mechanical system. Schneider [START_REF] Schneider | Blade wheel[END_REF] improved on Kirsten's propeller and partnered with Voith in 1928 to industrialize and commercialize their propeller renowned as the Voith-Schneider Propeller [START_REF] Bartels | The voith schneider propeller: Current applications and new developments[END_REF]. Since many experiments have been performed in Japan [START_REF] Taniguchi | Sea trial analysis of the vertical axis propellers[END_REF], in North America [START_REF] Bose | Experimental performance of a trochoidal propeller with high-aspect-ratio blades[END_REF][START_REF] Ficken | Experimental performance and steering characteristics of Cycloidal Propellers[END_REF][START_REF] Haberman | Performance of Vertical Axis (Cycloidal) Propellers Calculated by Taniguchi's Method[END_REF][START_REF] Nakonechny | Design of a 9-inch cycloidal propeller model unit and some experimental results[END_REF][START_REF] Roesler | Experimental performance of a novel trochoidal propeller[END_REF], and in Netherlands [START_REF] Van Manen | Results of systematic tests with vertical axis propellers[END_REF] to investigate this specific type of propulsion.

Meanwhile theoretical models has emerged to handle the complex flow description and predict the performance of these systems. In 1962, Taniguchi [START_REF] Taniguchi | Sea trial analysis of the vertical axis propellers[END_REF] has developed a quasisteady method to numerically estimate the performance of epicycloidal propellers. His method was improved in 1981 by Zhu [START_REF] Zhu | A computational method for cycloidal propellers[END_REF]. Sparenberg [START_REF] Sparenberg | On the optimum one-bladed cycloidal ship propeller[END_REF] proposed a theoretical model for single-blade propeller by minimizing kinematic energy in the wake. Mendenhall and Spangler [START_REF] Mendenhall | Theoretical Analysis of Cycloidal Propellers[END_REF] used a discretewake-vortex method to predict the behavior of a cycloidal propeller. Bose [START_REF] Bose | Rotary foil propellers[END_REF] adapted a multiple-streamtube-model from wind turbine theory to study high-pitch cycloidal propeller. Recent studies [START_REF] Epps | A viscous vortex lattice method for analysis of cross-flow propellers and turbines[END_REF][START_REF] Halder | Hydrodynamic modeling and experimental validation of a cycloidal propeller[END_REF] has implemented more complex models to continue to estimate as well as possible cycloidal propeller performance. Lastly improvements on modern CFD solvers are accurate validation resources and a new interesting area of work for optimization purpose [START_REF] Bakhtiari | Cfd data based neural network functions for predicting hydrodynamic performance of a low-pitch marine cycloidal propeller[END_REF][START_REF] Esmailian | Numerical investigation of the performance of voith schneider propulsion[END_REF][START_REF] Fasse | Numerical study of a sinusoidal transverse propeller[END_REF][START_REF] Hu | Two-dimensional simulation of the hydrodynamic performance of a cycloidal propeller[END_REF].

Cycloidal propellers blades are so far basically actuated by mechanical rotating technology (connecting rods, crank, camshaft, gears...) and then restrained to only one kinematic mode (epicycloidal or trochoidal). While low-pitch systems provide high thrust at very low ship speed (for harbour manoeuvring or dynamic positioning for example) they are lim-ited in maximal ship speed (approximately 15knots for the Voith-Schneider Propeller). High-pitch systems can reach higher ship speed but are less efficient during starting phase. To take advantage of both modes, the IRENav interested in the design of an experimental cross-flow propeller with an electrical blade actuating. Three servo-motors enslaved by the main rotation are imposing independent blade movements. Blade control is no more limited by a mechanical system, all types of pitch law could be played. This allows to play pitch laws from both modes and to switch from one to the other mode during the rotation of the propeller. Another interest is to play non-symmetric laws (from the two half of rotation disk point of view). Indeed, as mentioned by Roesler [START_REF] Roesler | Experimental performance of a novel trochoidal propeller[END_REF], since downstream blades pass through the wakes of upstream blades it is logical that optimal pitch law on the downstream half should be different than that on the upstream half. Physical phenomenon described by Halder [START_REF] Halder | Hydrodynamic modeling and experimental validation of a cycloidal propeller[END_REF] and resumed as dynamic virtual camber effects lead also to view the two half disk domains different for the generation of hydrodynamic loads. This experimental cross-flow propeller is also instrumented with a blade-embedded load-cell, a torque sensor and encoders to measure time dependant hydrodynamic loads and angles during the rotation. The goal of this paper is to present this experimental system from a mechanical aspect and its blade control/command. Several pitch laws from both kinematic modes are tested and pitch track back is compared to the reference. Then experimental setup and post-process are described. Lastly preliminary measurements for sinusoidal pitch law are presented and discussed.

Experimental Propeller Design

The experimental transverse axis propeller presented here is an academic project driven by the French Naval Academy Institute in collaboration with IFREMER. The goal of this experimental blade-controlled platform is testing transverse axis systems pitch law for design or optimization purpose. In this article, the system is described as a propeller but it could also be used to test pitch law for marine energy converters (as it has originally been designed [START_REF] Delafin | Performance improvement of a darrieus tidal turbine with active variable pitch[END_REF][START_REF] Paillard | Uranse simulation of an active variable-pitch cross-flow darrieus tidal turbine: Sinusoidal pitch function investigation[END_REF]).

Mechanical Description

The experimental blade-controlled platform is named SHIVA for Intelligent Hydrodynamic System with Angle Variation. Figure 3 gives a Computer Aided Design (CAD) rendering of SHIVA and the main characteristics in the table (bold values highlight those used for the configuration of the presented results). SHIVA is a large experimental system (800kg, 2.5m high) composed of a steel triangular frame [F] connected to the main vertical driveshaft by a Morse taper and rotating through the fixed outer frame [D]. This outer frame is build from steel I-beams which ensures stability during the experiments. On the triangular frame three vertical shafts are assembled by sliding link to be able to move them far or toward the triangular frame center. These sliding guides are 7°-tilted to allow for an axisymmetric system, and to be able to double the diameter to greatly modify the system solidity (for present experiments smallest diameter is chosen, D=0.8m). Radius of the propeller is defined as the distance between the center of the triangular frame and the center of each vertical secondary shafts. Three blades [I] are hooked up to each shaft and secured by bolts. Blades are made with filled stainless steel on a NACA0018 profile of 0.15m chord and 1m span. Blades can be wrapped by epoxy-fiberglass shell to increase the chord length at 0.35m. These shells also allow to change the blade profile and thus also modify the solidity of SHIVA. The solidity for the present experiments is 2.62. The triangular frame is driven by the 2.5kW HRS115E6-130S (from SEM brand) main motor located at the top of the propeller [A]. A 50-ratio speed reducer [C] is used to increase the nominal torque and fit the rotational speed for our experiment values (∼ 0-50 RPM). Each blade is also independently actuated by a 0.8kW HDM105C10-54S (from SEM brand) auxiliary motor [E] enslaved by the main rotation. A 10-ratio speed reducer is also used to adapt the motor rotational speed to the blade kinematic. Blades are rotating on their chord quarter which is the aerodynamic center for the NACA0018 airfoil. Blades are rotating following the pitch law which is the expression of the angle in function of the orbital angle . is defined as the oriented angle between the ortho-radial line and blade chord. Another representation of the pitch is which is the oriented angle between the -axis and blade chord. The latter definition is used for a simpler representation of the law in trochoidal mode because the blade is oscillating around the -axis which corresponds to the advance direction (whereas it oscillates around the ortho-radial line for epicycloidal mode). These two pitch angles are related by the relation = -(see Figure 4). Pitch is directly related to the incidence which is the angle between the blade chord and the local relative velocity . Indeed, for cross-flow propellers, the aim is to orientate the blade during the rotation with the pitch angle in order to maximize the contribution of lift and drag forces, generated by the incidence , that produces a thrust in the advance direction ( direction in this case). Pitch angle have a major impact on cross-flow propeller design because it also influences back hydrodynamic loads by affecting the flow inci- To carry out experiments, SHIVA is operating at the Boulognesur-Mer IFREMER wave and current flume tank which is 18 meters long by 4 meters wide and 2 meters depth (see Figure 5). Dimensions of SHIVA are suited for the IFREMER water tank to reduce hydrodynamic blockage effect. Indeed the ratio of the propeller frontal area divided by the tank section leads to a blockage of 15%. According previous study [START_REF] Consul | Blockage effects on the hydrodynamic performance of a marine cross-flow turbine[END_REF], the blockage effect overestimates hydrodynamic loads and efficiency for a ratio up to 50%. Here, these effects are thus neglected.

The fixed outer frame leans on two additional horizontal Ibeams from IFREMER which elevate all the system just above the water: only the blades are under the water line of the tank (see Figure 3 and Figure 5). An alignment of the outer frame with the water tank guarantees that the flow direction is parallel to the outer frame. A mechanical adjustment of the 0°position of the main rotation (corresponding to the triangular frame) and 0°-positions of the three secondary motors are also done before every experiments to ensure that the incoming flow is also parallel to each 0°-positions pitch blades (no incidence angle) for theta 0°-position. The flow velocity is adjustable from 0.1 to 2.2 m/s, fixed to 1.2 m/s for this work, with a turbulence intensity of 1.5 %. Further information about IFREMER tank facilities are given by Gaurier [START_REF] Gaurier | Flume tank characterization of marine current turbine blade behaviour under current and wave loading[END_REF][START_REF] Ikhennicheu | Experimental study of coherent flow structures past a wall-mounted square cylinder[END_REF].

Measurement Instrumentation

SHIVA is instrumented with 4 fixed 3-components loadcells [H] located between the outer frame I-beams and IFRE-MER I-beams (see Figure 3). These load-cells provide global resulting solicitations of the propeller acting on the frame in the three directions X, Y and Z (see Figure 5). They measure loads for a range of ±5000 in the X and Y direction and +7000 in the Z direction (compression). Transfer matrix and uncertainty are given by the supplier (Six-axes) and resumed in the table of the Figure 3. One blade is instrumented with a 4-components load-cell [G] which measures local hydrodynamic loads in the direction of blade's chord Tb and normal to the blade Nb (see Figure 5). In-house calibration procedure was undertaken to determine matrix transfer to optimally reproduce the way the blade is stressed during experiments. A torque sensor (4503B 020 L P0 00 KA2 from Kistler brand) is installed between the main motor and the main speed reducer [B] and measures the torque applied on the main driveshaft with a measuring range between 0 and 20 N.m and an uncertainty of 0.5% of measuring range. A CompactDAQ system from National Instruments (embedded NI cDaq-9184 linked with the stationary NI cDaq-9185) collects and synchronizes the data from all these measurement devices (embedded and fixed). During the rotation, pitch angles of each blade and orbital position are also recorded thanks to incremental rotary encoders with a 1024 pulse rate (8.KIS40.1362.1024 from Kübler brand) to provide both the real angle values (for projection of local forces into the global reference frame) and a feedback for the pitch control which is detailed in the next section. These encoders are rotating with pulley-belt systems. According to the ratio of pulley transmission (25 for the principal axis and 4 for secondary axis) the resulted angle precision is 0.014°for and 0.088°for . Due to the belt strain, an approximate error of 0.2°is estimated for angle (for angle, the belt is always tightened because SHIVA is always rotating in the same direction). A 12-channels slip ring is used to transfer measurement signals from the blade load-cell to the triangular frame and an other 32-channels slip ring is used to transfer signals and power of embedded devices from rotating triangular frame to the outer frame and vice versa.

A Labview code allows users to synchronize collected measurements and set the parameters for capture data as sampling time and duration of acquisition. The particularity of SHIVA lies on its electrical bladecontrol system which allows a wide range of pitch law investigation. As mentioned previously, SHIVA is composed of four permanent magnet synchronous machines (PMSM) (Figure 6), one for the main rotation and one for each blade. The main PMSM (MP), which provides the rotational speed, is alimented by a back to back converter powered by the 400V Alternative Current network from the IFREMER. The MP speed is controlled by a servo drive controller, TUBA series from Elmo brand (TUBA12/460R), which regulate the speed Ω of the MP in regards to the reference speed Ω . The speed control is done by two Proportional-Integral (PI) controller strategies (one for the speed Ω and one for the current) which are used by default for the TUBA servo drive controller. This main axis is supervised by a LabVIEW code through a Computer-Human Interface (CHI) which allows users to set the reference speed of SHIVA. This CHI communicates through a controller area network (CAN bus) with the TUBA, which is installed in an electrical cabinet alongside the IFRE-MER tank, and the MP at the top of SHIVA.

Electrical Description 2.3.1. Macro Overview

Another network 120V Direct Current is creating through a transformer and a rectifier also installed inside the electrical cabinet from the 400V AC (Figure 6). Then this network powers the three secondary PMSM (MS1, MS2, MS3) which rotating the blades through an embedded inverter for each secondary axis (the main slip ring is used to transfer power from the electrical cabinet to SHIVA). MSi are controlled in position by digital servo drives, CELLO series from Elmo brand (CEL-A15/200R). The position of the blade is the oriented angle between the main rotation tangent and the blade chord and referred . Three PI controllers for each MSi are used: one for current, one for pitch rate ̇ and one for pitch angle . The pitch reference is tabulated in function of the orbital position of each secondary axes. These tabulated functions are referred as pitch laws. One of the special capacity of SHIVA is that each blade can follow a particular pitch law that procures a wide flexibility and range of investigation. Thanks to a second CHI, these pitch laws can be loaded even during the rotation.

Specific Description of Blade-control

Contrary to the main axis, blade pitch control system is embedded on SHIVA. In-house tests and developments over the past two years have involved the installation of an embedded Micro-Controller Unit (MCU) to dramatically boost the responsiveness of the system in comparison with a external computer. This MCU, a CompactRIO system from National Instruments, is divided into two entities : a Real Time computer module (RT) which executes LabVIEW Real-Time applications and offers multi-rate control, execution tracing and communication with peripherals ; associated with a userprogrammable FPGA module which excels at smaller tasks that require high-speed logic and precise timing.

Figure 7 shows the classic blade-control used for SHIVA. The position loop is executed into the MCU for each secondary axis and then speed and current loops are executed by CELLOs which are chained on SHIVA. The advantage of moving the position control from the industrial CELLO to the MCU is to be able to modify PI parameters in real time to have an accurate control under various kinematics conditions.

Pitch laws are tabulated into the RT which holds a large memory space. For each blade-control iteration, pitch reference is given according to the blade orbital position . Because of the use of embedded system, an incremental rotary encoder is mounted on the triangular frame to measure this orbital position of SHIVA during the rotation and send the information directly to the MCU. Real pitch angle feedback, , is also given by incremental rotary encoders mounted on each blade shaft. All these encoders are actuated by belt pulley system. The use of this PI controller strategy leads to a systematic error of pitch angle. Figure 8 illustrates this blade-control error for the case of a linear theoretical law (for example the case of the circular translation, =-, the blade is always parallel to the incoming flow). For each blade-control itera- Figure 8 illustrates the error in a case of a linear pitch law (such as trochoidal mode, the blade rotates between 0°a nd 360°in order to keep the leeding edge parallel to the incoming flow). For the case of a law in epicycloidal mode, the reference oscillates around =0°. Hence the error fluctuates between delay or advance according to the derivative ∕ sign. Because pitch laws are stored in the RT module, the regulation loop is limited by its sampling time which is around 2ms. The table below gives angular steps Δ in function of rotational speed for Δ =2ms:

Ω (RPM) 10 20 30 40 Δ (°)
0.12 0.24 0.36 0.48

Given the derivative ratio ∕ (which mostly depends on the orbital position) for an arbitrary pitch law and the rotational speed, this systematic error can be then estimated. Another delay of the real pitch control occurs when the pitch acceleration is higher than the of auxiliary engines. In particular, for some pitch laws in epicycloidal mode (where the rotation is higher than trochoidal mode), acceleration limits of engines are reached during some period of the law. Next section illustrates these points by giving results of pitch tracking for various pitch laws under different kinematic conditions.

Pitch Tracking validation

The following results of pitch tracking have been computed from in-water measurements. The aim was to test various pitch laws for both kinematic cases, epicycloidal mode ( <1) and trochoidal mode ( >1). The flow velocity is set at 1.2 m/s for trochoidal laws and 0.8 m/s for epicycloidal laws and rotational speed is calculated to reach desired value. Real pitch is recorded from rotary encoder and then phaseaveraged over 30 rotation periods. The mean pitch is compared to the input reference law over one period. For trochoidal mode, pitch angle is shown instead of angle for a simpler viewing. is the oriented angle between the x-axis (flume tank direction) and the blade chord.

Figure 9 shows pitch tracking for three trochoidal laws. The circular translation (Figure 9-a) is defined as the blade leading edge is always parallel to the tank flow direction (i.e. =0 or =-). For this law, the rotational speed is set at 28.6 RPM. A low constant delay is observed just under 0.4 • which is due to the systematic error shown previously. The real pitch is always late compared to the reference. The standard deviation shows a uniform distribution of the real pitch over orbital positions for all the different laps (here the pitch angle is close to 0 compared to other figures, it is why the deviation seems wide). The sinusoidal pitch law with 20 • amplitude (Figure 9-b) is well tracked with a satisfying maximal error under 1 • . This maximal error occurs at the position =120 • and is correlated to the manifestation of the highest measured hydrodynamic load. Indeed hydrodynamic forces involve pressure on blades that makes the reference law more difficult to follow. The standard deviation shows a low dispersion over the measurement laps. The third pitch law (Figure 9-c) refers to a kinematic process of optimization [START_REF] Hauville | Pitch law optimization of a cycloidal propulsor[END_REF] in trochoidal mode used to boost the efficiency of the system for complex law. This law presents some quick transitions which lead to an increasing tracking error (for between 340 • and 20 • and around 180 • ) but globally the blade is closely following the reference even if the rotational speed is higher than for the sinusoidal case.

Figure 10 shows pitch tracking results for epicycloidal mode. For the case of the 0-pitch law (Figure 10-a ), the setpoint is zero for all orbital positions : the blade stays parallel to the main rotor disk (it's the case for no-pitch system as Darrieus turbines in marine energy domain). The blade is well following the reference with a maximal error around 0.6 • . This error oscillates around 0 because of the changeable relative flow direction over the orbital position. Again the standard deviation seems high here because of the closeness of the pitch law to 0, but it shows a uniform dispersion over orbital positions. these laws, blades are reversing at =180 • ( switch from negative to positive value) that induces a high acceleration to servo-motors. The pitch tracking is thus less accurate during this blade reversal. The difference is even higher for the case of =0.9 pitch law because of the quicker reversal than =0.7 pitch law. Fortunately this is not during this reversal blade movement that the blade generates the higher hydrodynamic thrust. In case of =0.7 (Figure 10-b), the rotation speed is higher than in case of =0.9 for the same flow speed (due to the definition of ). This leads to a higher systematic error during the soft motions phases of the blade ( ∈ [30:150] and [210:330]). For both cases, standard deviation shows again a well repeatability over measurement laps.

These results show an accurate blade pitch tracking for trochoidal case whereas a higher tracking error is observed for epicycloidal mode essentially due to the reversal blade phenomenon and higher rotational speed. Indeed the use of servo-motors for blade pitching allow the propeller to run various pitch laws but is also subject to the main following limitations:

• Servo-motors speed and acceleration limits involve restrictions on the pitch curve shape and the derivative ∕

• Blade control strategy which implicates a systematic error that depends on the rotational speed Ω and the sampling time of regulation loop Δ Nevertheless, presence of rotary encoders provides the real pitch feedback for both real time tracking and post-process procedure. The control-CHI also allows users to adjust both PI parameters ( , ) in the position loop in order to adapt the reactivity of blade-control and thus reduce the tracking error.

To decrease the systematic error, ongoing developments are undertaken to reduce the sampling time of regulation loop by saving reference pitch laws into the FPGA module which is sampled at 0.05 ms and hence reduce the orbital step Δ between two regulation iterations for a same rotational speed. However, classical PI controller strategy is not the most suitable method for fluctuating pitch law. Here the industrial speed controllers (CELLO and TUBA) use this method of blade control but a new strategy using non linear controllers could be investigated, even though this area of work is out of scoop for the moment.

Cross-flow propeller prototype efficiency

This last section presents results from in-water measurements for sinusoidal pitch laws with three different amplitudes in the trochoidal mode. Experimental setup and postprocess are first presented then results are discussed.

Experimental setup and Post-process procedure

First, in-air measurements are used to eliminate centrifugal effect from the embedded load-cell measurements and mechanical internal resistance on the main torque sensor. Then measurements in the tank without flow velocity and rotation are carried out to remove buoyancy effects and prestress of fixed load-cell. These static measurements are done for a range of orbital positions ( ∈ [0, 360, 30]) and pitch angles ( ∈ [0, 360, 30]). Because the variation of voltages doesn't exceed 5% as shown in Figure 11, the mean value is calculated and subtracted to the in-flow measurements. This step is repeated everyday, only for orbital positions, to ensure any unexpected behaviour. A mechanical verification of each blade position and the position of the main axis is done before starting the flow motion to ensure that recorded angles correspond to the real positions of motors. In-flow measurements are launched after waiting that the flow is well established at =1.2m/s and the wake is stabilized with the rotational speed of SHIVA and the chosen pitch motion. and parameters are eventually adjusted to guarantee an accurate pitch tracking. Temporal signals are then recorded over 30 rotation periods. Because of the electromagnetic environment mainly caused by Pulse Width Modulation (PWM) of speed controllers, these temporal signals are perturbed by high frequencies. A low-pass filter (cut frequency of 30Hz) is applied on temporal signals.

Temporal voltages are then transformed to loads thanks to the transfer matrix. Pitch and orbital angles are used to convert local hydrodynamic loads from the embedded loadcell to global forces given by equations ( 2) and (3).

= cos( ) -sin( ) -sin( ) -cos( )

= cos( ) sin( ) -sin( ) cos( )

All the load are then phase-averaged over all the periods as shown in Figure [START_REF] Ficken | Experimental performance and steering characteristics of Cycloidal Propellers[END_REF]. Mean and standard deviation are calculated for each of the load signals. Finally, as the embedded load-cell measures the hydrodynamic force on only one blade, mean-averaged loads are shifted of 3 and 2 3 and added to construct the total hydrodynamic loads acting on all the three blades. By making this operation, it is assumed that all blade has the same pitch tracking. The four fixed load-cells signals are also summed up to yield the total forces applied on the fixed frame. Dimensionless force coefficients are given by equations [START_REF] Bartels | The voith schneider propeller: Current applications and new developments[END_REF] where is a hydrodynamic force and a torque:

= 0.5 2 = 0.5 2 (4)
Where is the swept frontal area ( = ) and the flume tank flow velocity. Hydrodynamic efficiency is lastly defined as:

= Ω (5)
Where and are respectively the mean of total Thrust and total Torque over 360°.

Sinusoidal pitch results

Post-process procedure is adopted on a 20°sinusoidal pitch law measurements at =1. (corresponding to =1.2), for which the local Reynolds number, = is around 420 000.

Figure 13 gives single-blade force coefficients on the left and total reconstructed coefficients on the right. As shown on the polar plot, maximal thrust and side force are generated for between 90°and 120°. In the wake, thrust tends toward zero and becomes even negative while side force reverses. The total reconstructed thrust is always positive with some fluctuations while total side force is still negative, the wake is deflected on the tank side. This observation on the wake deflection is explained by the fact that for cycloidal propulsion the hydrodynamic center of thrust is not aligned with the rotor center. For the normal and tangential coefficients, the distribution over orbital positions is quite different. Normal force is almost always spread outwards the rotor with two maximum at =70°and =210°while tangential force is almost always negative with two maximum at =120°and =270°. As the total tangential coefficient is still negative, this proves that generated hydrodynamic force counters the rotation of the main motor with fluctuations which can provoke damages. So the propeller needs to provide a torque to generate the desired thrust.

Figure 14 presents the comparison between reconstructed force coefficients from the embedded load-cell and the measurements from fixed load-cells and the torque sensor. Mean values over 360°are represented by a short line at the left of each figure. Even if fixed sensors and embedded load-cell seem to measure the same global behaviour of the propeller, mean values over the period are slightly different. That implies a strong disparity on the hydrodynamic efficiency if it is expressed by fixed sensors or by embedded load-cell measurements. These differences are mainly explained by the fact that embedded and fixed load-cells have a large measurement range (-5000N to 5000N) in comparison with measured hydrodynamic loads (-100N to 400N). For this reason, the flow velocity has been set at 1.2 m/s and blade chord has been increased with the addition of the epoxy-fiberglass shells to raise hydrodynamic loads. This loads increasing causes a shaking of all the frame during the rotation of the propeller. This shaking is observed by fixed load-cell as shown large fluctuations on frame signals in Figure 14. It is also the case of the torque sensor which measures these oscillations on the main axis. But this shaking seems to be less detected on the instrumented blade. Embedded load-cell underestimates the total hydrodynamic loads. A new embedded load-cell with a smaller measuring range (±900N for normal blade direction and ± 400N for tangential blade direction) is currently manufactured to remedy this problem. For the presented results, hydrodynamic efficiency is thus expressed using the mean of the total frame thrust divided by the mean of the torque sensor instead of the blade embedded load-cell measurements to take care about the true solicitations of the experimental platform which are not detected by the embedded load-cell. Measurements for other rotational speeds and other sinusoidal pitch laws are undertaken to determine the performance curve for sinusoidal pitch propeller. Figure 15 shows the results for sinusoidal pitch laws of 10°, 20°, 30°and 40°o f maximal amplitude. Results reveal that for the presented propeller configuration, the Sinus40 law have the lower hydrodynamic efficiency which falls when the advance parameter exceed 0.8 value. This law isn't adapted to our configuration of solidity for an efficient purpose and for trochoidal mode. Then, depending of the value of the advance parameter, each other pitch law is interesting with a maximal efficiency reached for different values of . These results prove the need of adapting the pitch to the value. It also means that during ac-celeration phase, it is preferable to start with high amplitude pitch law then decrease the pitch amplitude. Concerning the thrust, the Sinus10 law have the higher values for all advance parameters. It shows that for manoeuvre which requires high thrust, the pitch must be decreased, balancing a reduction of the efficiency. Lastly, theses results justify the use of vertical axis propeller with a variable pitch control system. To go further, as perspective for next experiments, SHIVA will be used for experimental optimization purpose. Indeed, the complexity of the flow through cross-flow propeller implies a strong discrepancy between upstream and downstream halves. Hence, there is no reason why the pitch law definition should be symmetrical (as in case of most conventional cycloidal propeller). The optimization procedure consists primarily to parameterize the pitch law with an appropriate method such as the one described by Abbaszadeh [START_REF] Abbaszadeh | Experimental investigation of an optimised pitch control for a vertical-axis turbine[END_REF]. This method employs a spline generated through several control points, the parameters, which can evolve inside fixed ranges. This parameterisation allows asymetrical pitch generation. Parameters are then optimized using a Gaussian Process method developed by Sacher [START_REF] Sacher | Efficient optimization procedure in non-linear fluid-structure interaction problem: Application to mainsail trimming in upwind conditions[END_REF]. A Meta-model is then utilized to represent the objective function in parameters space. This objective function is calculated from in water measurements. Multiple-objective optimization could be done the determine the Pareto efficiency through the dual objectives: maximizing the thrust and the efficiency. By repeating this process for many values, optimal pitch laws will be established for various operation mode (low advance speed, cruising speed or transitional mode).

Conclusion and Perspectives

The experimental blade-controlled propeller designed at the Naval Academy Research Institute is presented in this study. Modular mechanical features allow comparisons for different solidity, Reynolds or aspect ratio numbers. The size of the system makes it ideal to test large-scale prototypes. The technical progress published here is its electrical bladecontrol design which offers a huge range of pitch law investigations. Blade control/command is tested with in-house experiments comparing pitch reference and real pitch tracking. Even if results reveal a suitable tracking, some limitations due to the technology of speed controller are underlined. Indeed the use of regulation loop implies a systematic error which is hard to overcome. Using a more complex speedcontrol strategy could mitigate this error. Speed limitations of electric motors are also faced for high speed kinematics. As blade position angles are registered during acquisition of measuring signals, this position error is acceptable for our investigations. However, long-term upgrade of the control system is expected to improve the pitch tracking and the precision of angle measurements. Post-process has been validated on sinusoidal pitch laws for trochoidal mode. Discrepancies on hydrodynamic measurements occurred between the blade embedded load cell and fixed load cells. These differences are mainly explained by a strong stress of the frame due to the high velocities encountered. Next experiments will be carried on with more precise load sensor measuring ranges, so that flume tank velocity and rotational speed could be reduced to be no more subject to high solicitations of the platform. Results exposed here show promising capacities of the platform in terms of propeller performance measurement. As perspective in the quest of the ideal pitch law and because the electrical blade-control system allows it, an experimental optimization procedure is intended for various advance parameter values.

Figure 1 :

 1 Figure 1: Illustration of the epicycloidal path of one blade

Figure 2 :

 2 Figure 2: Illustration of the trochoidal path of one blade

Figure 4 :

 4 Figure 4: Representation of angles for an arbitrary position and an arbitrary relative velocity W

Figure 5 :

 5 Figure 5: IFREMER wave and current flume tank overview

Figure 8 :

 8 Figure 8: Schematic Error of Blade-control

Figure 7 :

 7 Figure 7: Diagram of Position Loop Control

Figure 9 :Figure 10 :

 910 Figure 9: Pitch tracking in trochoidal mode for a) Circular Translation =1 pitch law; b) Sinusoidal 20deg =1.6 pitch law; c) Trochoidal =1.2 pitch law. Blade pitch angle on the left axis and difference between real and reference on the right axis

Figure 11 :

 11 Figure 11: Representation of P1 Voltage of the embedded load-cell for the static measurements over 360°in and

Figure 12 :

 12 Figure 12: Phase-average operation over 30 periods for the Thrust signal in the case of the 20°sinusoidal pitch law for =1.2 and V=1.2m/s

Figure 13 :

 13 Figure 13: Single-blade Force coefficients at the left and Reconstructed total Force coefficients at the right for the case of the 20°sinusoidal pitch law for =1.2 and V=1.2m/s

Figure 14 :

 14 Figure 14: Comparison of blade reconstructed force coefficients and frame sensor force coefficients for the case of the 20°sinusoidal pitch law for =1.2 and V=1.2m/s

Figure 15 :

 15 Figure 15: Efficiency and force coefficients versus advance parameter for 10°, 20°, 30°, 40°sinusoidal pitch laws at V=1.2m/s

  On the left is a CAD view of SHIVA and on the right is the table of characteristics (for sensors, percents are giving the uncertainty of the measure)

	[ ] Main Engine	Nominal Speed Ω	3000 [	]
		Nominal Power		2500 [ ]
	[ ] Torque Sensor	Measuring Range and	±20 [ . ] ; 0.5%
		Uncertainty (% of range)
	[ ] Speed Reducer	Ratio		50
	[ ] Outer Frame	Total Weight		1000 [ ]
	[ ] Auxiliary Engines	Nominal Speed Ω	1800 [	]
	with Speed Reducer	Nominal Power		790 [ ]
		Ratio		10
	[ ] Triangular Frame	Diameter ( = 2 )	0.8 -1.6 [ ]
		Solidity ( =	∕ )	0.5625 -1.12∕1.3 -2.62
	[ ] Blade load-cell	Measuring Range	± 5000 [ ] ; 0.81%
		and Uncertainty		± 1000 [ ] ; 1.76%
	[ ] Fixed load-cells	Measuring Range	± 5000 [ ] ; 0.8%
		and Uncertainty		± 5000 [ ] ; 0.22%
				7000 [ ] ; 0.54%
	[ ] Blades	Number of blades	3
		Blade chord ( )		0.15∕0.35 [ ]
		Aspect Ratio (	= ∕ )	6.7∕2.8
		Chord ratio ( = ∕ )	0.19 -0.38∕0.44 -0.87
		Blades cross section	NACA 0018
	Figure 3:

Table Pitch Table β2

 Pitchβ2

	β2ref
	β3
	β3ref
	Figure 6: Electrical Diagram of the command architecture