Roger Botton

Dominique Cosserat

Souhila Poncin

Gabriel Wild

  A SIMPLE GAS-LIQUID MASS TRANSFER JET SYSTEM, 

Keywords: Gas-liquid reactors, impinging jet, energy efficiency, bubble columns, stirred tank. 

An original gas-liquid contacting system is proposed, consisting of a pump, an orifice, a vertical tube coaxial to the orifice and an impinging plate. The pump generates a downward vertical liquid jet through the orifice situated above the gas-liquid dispersion level. The two phase jet is directed towards an impinging plate near the bottom of the tank and dispersed in the volume of the liquid. Liquid is withdrawn below the impinging plate and recycled. This reactor may be used for gas-liquid reactions (ie hydrogenations) and also to mix liquids, to disperse particles, to oxygenate waste water etc…. Performances and design rules of this equipment are proposed. Then, the results are compared to performances of bubble columns, stirred tanks, and other academic and industrial jet systems. It is shown that, at a given energy dissipation, this system yields much higher mass transfer densities than a classical stirred tank provided with a Rushton turbine. Finally some suggestions about mass transfer mechanisms and efficiency of dissipated power are given.

INTRODUCTION

 Chemical and biochemical industries need simple high performance gas-liquid systems to achieve high productivity and selectivity reactors. The purpose of this article is to propose a very simple reactor achieving this goal. It consists only of an orifice, a tube, a plate and a pump. In the present system, mechanical energy is provided by the liquid pump exclusively, while in bubble columns it is provided by the gas compressor, and in stirred tank reactors by the stirrer engine and the gas compressor. The aim of this article is to provide simple and reliable design rules for the jet device presented here, and to compare the energy efficiency in terms of k L a values with that of other devices. 

EXPERIMENTAL SET-UP AND OPERATING CONDITIONS

2.1 Description of the impinging jet device used here.  Fig. 1 shows an example of an experimental set-up. It consists of a tank, a pump, a nozzle with an orifice diameter d o , a perforated tube D (coaxial to the orifice) above and below the dispersion surface, and of an impinging plate P. The gas is introduced in the tank by the top. It is entrained downwards in tube D by the liquid jet provided by the nozzle; further gas and gas-liquid mixture is sucked in through the holes in the wall of D and is entrained downwards. The gas-liquid jet is then carried through tube D and impinges on plate P; the impact causes the gas to be dispersed in the whole the tank. Above the plate, the dispersion of bubbles has a uniform aspect, while below the plate, with an appropriate design, no bubbles are observed. The liquid is recycled from below the plate through the pump. The dimensions indicated in Fig. 1 correspond to optimal results according to experiments made in industrial scale equipment. If necessary, several tanks can be used to obtain the gas and liquid flow rates needed by the reaction. If, for safety reasons (e.g. hydrogenations), the exit of liquid at the bottom of tank is not wanted, it is possible to place it at the top, using a tube parallel or coaxial to tube D and the nozzle d 0 . The advantages, compared with a stirred tank are the absence of moving parts (eliminating sealing problems) and the possibility, if a heat exchanger is required, to install it outside of the tank on the liquid recycling line.

 

Operating conditions and measuring techniques using the impinging jet device.



The gas used in the experiments was air at room temperature and pressure, the liquid was either tap water or a water-sulfite solution in case of mass transfer or interfacial area measurements. Several jet systems were tested with air and water using a nozzle diameter of d o =0.010m and tanks of diameters T=0.25 and 0.48m where dispersion levels were adjusted by overflow. Tests have consisted of measuring the total bubble volume and mass transfer rate generated by a given energy input. In the case of a coalescing gasliquid system, under the conditions investigated, no gas entrainment below the plate was observed, so that an effective recycle of the liquid was possible. The effect of the orifice diameter, (always with air and water) was investigated using the following two plants. The first one, with a nozzle orifice diameter d o =0.012m, was called "optimized system N°1" and the second, with d o =0.024m, "optimized system N°2". The other dimensions are those indicated in Fig. 1. During all these studies, the following variables were generally tested: jet velocity V o (4 to 12 m.s -1 ), nozzle orifice diameter d o (0.009, 0.010, 0.012, 0.024 m), tube diameter D (0.025, 0.033, 0.045, 0,057, 0.067 m with or without immersed holes to recycle dispersion), tank diameter T (0.25, 0.48 m), dispersion height H E fixed by t overflow (0.5, 0.85, 1.85, 2.35 m). Some experiments were made with a sodium sulfite solution to investigate the effect of a coalescence inhibiting system and to determine the volumetric mass transfer coefficient k L a or the gas-liquid interfacial area by the chemical technique.

The measuring techniques are quite classical: the gas volume in the tank was determined by stopping the liquid flow and by measuring the liquid level before and after gassing. The liquid flow rate was determined using a rotameter, the induced gas flow rate by a Pitot tube at the gas entrance and the pressure in the nozzle by a manometer. The global liquid side mass transfer capacity Q T (Q T =k L a E V E, =k L a L V L ) (m 3 .s -1 ) was determined by using the classical dynamic reoxygenation technique or by the slow oxidation of sufite reaction technique. The interfacial area by using the pseudo-mth order rapid reaction technique (oxidation of sulfite catalyzed by cobalt sulfate).

Experimental results

Contrarily to the classical gas-liquid mixed tanks and bubble columns, the induced gas flowrate depends on the liquid velocity at the outlet of the nozzle (ie the liquid flowrate and orifice diameter d o of the nozzle). Only some hydrodynamics and mass transfer results obtained with the optimized reactors will be presented here. The effect of operating conditions on hydrodynamic (ie gas induced flow rate and gas holdup) and mass transfer parameters (interfacial area and volumetric liquid side mass transfer k L a) has been studied in order to establish correlations between design parameters and operating conditions for design purpose.

Hydrodynamic parameters :

The effect of the liquid velocity at the nozzle outlet on the induced gas flowrate Q G obtained with both (N°1 and N°2) optimized jet impinging reactors is represented on figure 2. One can observe the logical increase of Q G with V o due to the decrease of pressure as V o increases at the outlet of the nozzle. Furthermore, no effect of reactor geometry is obtained, which is probably due to the fact that the optimized reactors are almost geometrically similar. The evolution of the gas holdup with V o (figure not shown) is similar to that of Q G .

Mass transfer parameters:

Results of the volumetric gas-liquid mass transfer coefficient k L a =Q T /V L obtained from the dynamic method using air/water system are reported on figure 3 as a function of the gas to the liquid volume ratio. As expected, considering the increase of the gas induced flowrate and the gas holdup with the liquid velocity V o at the nozzle outlet, the values of k L a also increase with V B /V L regardless of the geometry of the optimized impinging jet reactor as in the case of the gas induced flow rate results shown before.

Other results of k L a have also been obtained for different emulsion heights and from the slow sulfite oxidation reaction technique. The volumetric mass transfer coefficient k L a generally increases with a decrease of the emulsion height due to a the increase of the gas induced flowrate. In presence of coalescence inhibiting system which is the case of the air/sulfite system, k L a values are much higher than in presence of air/water system due to higher gas suction rates and gas holdups (not represented here). This result is commonly reported in literature for other types of gas-liquid reactors (stirred tanks, bubble columns etc…). [START_REF] Van 't Riet | Mass Transfer in Fermentation[END_REF], [START_REF] Midoux | Réacteurs gaz liquide à cuves agitées[END_REF], [START_REF] Botton | Operating zone and scale up of mechanically stirred gasliquid reactors[END_REF]; (5) Impingement of two aerated jets. [START_REF] Gaddis | Hydrodynamics and mass transfer in a impinging-stream loop reactor aerated from the top[END_REF]; (6) Plume dispersion confined into a tube. [START_REF] Evans | Performance of confined plunging liquid jet bubble column as a gas-liquid reactor[END_REF] The volumetric gas-liquid mass transfer coefficients (k L a = Q T /V L ) obtained with the impinging jet reactor was finally compared to those determined in the studied bubble columns and stirred tanks and also to those published for other reactor geometries. For this purpose, k L a values have been represented in figure 4 as a function of the specific power input P W /V L . Figure 4 shows that the impinging jet system is more efficient than bubble columns and stirred tanks for coalescence inhibiting liquids. For coalescent liquids, it is more efficient than the classical gas-liquid stirred tanks provided with a Rushton turbine and avoids the sealing problems commonly encountered in stirred tanks; it is almost as efficient as bubble columns but easier to scale up and to simulate than bubble columns. Also indicated are values obtained with different other contacting devices: higher volumetric mass transfer coefficients can be achieved (e.g. by the impingement of two aerated jets, [START_REF] Gaddis | Hydrodynamics and mass transfer in a impinging-stream loop reactor aerated from the top[END_REF], but at the expense of higher mechanical energy dissipation.

Fig. 1 .

 1 Fig. 1. Description of an optimized gas-liquid impinging jet system

Fig. 2 .

 2 Fig.2. Gas rate induced by the liquid jet for both optimized reactors :N°1 (d o =12 mm): open symbols; N°2 (d o =24 mm) black symbols. Results obtained with Air/water system.

Fig. 3 .

 3 Fig. 3. Evolution of k L a (= Q T /V L ) with the gas to liquid volume ratio for both optimized reactors: N°1 (d o =12 mm): open symbols; N°2 (d o =24 mm): black symbols. Air/ water system.Comparison of the impinging jet reactor with other classical reactors:Hydrodynamic and mass transfer experiments have also been conducted in bubble columns and in stirred tanks provided with a Rushton turbine in order to compare k L a values at a given specific dissipated power. The chosen reactors had quite similar heights and diameters as those of the impinging jet reactors.

Fig. 4 .

 4 Fig. 4. Liquid-side mass transfer versus specific dissipated power. Continuous lines: air/water system; doted lines: air/sodium sulphite solution; (1A, 1B) .Studied impinging reactors for power at entrance and exit of orifice; (2) Bubble columns (This work); (3h, 3b) Plume dispersions with down and up aerated jets. Tojo et al (1982); (4) Mechanically stirred tanks[START_REF] Van 't Riet | Mass Transfer in Fermentation[END_REF],[START_REF] Midoux | Réacteurs gaz liquide à cuves agitées[END_REF],[START_REF] Botton | Operating zone and scale up of mechanically stirred gasliquid reactors[END_REF]; (5) Impingement of two aerated jets.[START_REF] Gaddis | Hydrodynamics and mass transfer in a impinging-stream loop reactor aerated from the top[END_REF]; (6) Plume dispersion confined into a tube.[START_REF] Evans | Performance of confined plunging liquid jet bubble column as a gas-liquid reactor[END_REF]


Yiqing Yan
email: yan@eurecom.fr

Nimisha Chaturvedi
email: nimisha.chaturvedi@accelom.com

Raja Appuswamy
email: raja.appuswamy@eurecom.fr

Optimizing the Accuracy of Randomized Embedding for Sequence Alignment 1

Keywords: Terms-alignment, mapping, embedding

Gapped alignment of sequenced data to a reference genome has traditionally been a computationally-intensive task due to the use of edit distance for dealing with indels and mismatches introduced by sequencing. In prior work, we developed Accel-Align [1], a Seed-Embed-Extend (SEE) sequence aligner that uses randomized embedding algorithms to quickly identify optimal candidate locations using Hamming distance rather than edit distance. While Accel-Align provides up to an order of magnitude improvement over state-of-the-art aligners, the randomized nature of embedding can lead to alignment errors resulting in lower precision and recall with downstream variant callers. In this work, we propose several techniques for improving the accuracy of randomized embedding-based sequence alignment. We provide an efficient implementation of these techniques in Accel-Align, and use it to present a comparative evaluation that demonstrates that the accuracy improvements can be achieved without sacrificing performance. Code is accessible in github.com/raja-appuswamy/accel-align-release.

I. INTRODUCTION

Often the first step, and the most time consuming one, in analyzing genomic datasets is sequence alignment--the process of determining the location in the reference genome of each sequencing read. Despite the fact that modern gapped read aligners like BWA-MEM [START_REF] Li | Aligning sequence reads, clone sequences and assembly contigs with bwa-mem[END_REF], Bowtie2 [START_REF] Langmead | Fast gapped-read alignment with bowtie 2[END_REF], Minimap2 [START_REF] Li | Minimap2: pairwise alignment for nucleotide sequences[END_REF], SNAP [START_REF] Zaharia | Faster and more accurate sequence alignment with snap[END_REF] and HISAT2 [START_REF] Kim | Graph-based genome alignment and genotyping with hisat2 and hisatgenotype[END_REF] can map thousands of reads to a reference genome per second, the sheer size of modern shortread sequencing datasets often makes sequence alignment one of the most time consuming steps in genomic data analysis.

The key bottleneck that makes sequence alignment slow is the need to use edit distance for comparing sequenced reads with multiple candidate locations in the the reference genome, combined with the use of affine-gap penalty scoring and soft clipping to identify an alignment with the best score. The high computation complexity of such an edit-distance-based alignment makes exhaustive search of all possible candidate locations infeasible. Instead, modern aligners typically use a Seed-Filter-Extend (SFE) strategy for performing alignment. In SFE, seeds, the subsequences of strings extracted from a read, are used to look up candidate locations in an indexed reference. The candidates are then filtered to eliminate unlikely matches, and the surviving candidates are passed along for edit-distance-based extension.

The filtering step in SFE plays a crucial role in balancing the accuracy and performance of a sequence aligner. Most se-quence aligners use elimination-based filtering techniques, like count filtering [START_REF] Liao | The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote[END_REF], adjacency filtering [START_REF] Xin | Accelerating read mapping with fasthash[END_REF], or shifted hamming distance [START_REF] Xin | Shifted Hamming distance: a fast and accurate SIMDfriendly filter to accelerate alignment verification in read mapping[END_REF], that conservatively eliminate candidates without significantly increasing the probability of misalignment due to accidental elimination of a true match. In contrast, in prior work [START_REF] Yan | Accel-align: a fast sequence mapper and aligner based on the seed-embed-extend method[END_REF], we introduced a new selection-based filtering scheme based on randomized embedding algorithms [START_REF] Chakraborty | Streaming algorithms for embedding and computing edit distance in the low distance regime[END_REF] that can embed strings such that edit distance between original strings can be approximated by the Hamming distance between embedded strings. Using such embedding algorithms, we developed Accel-Align [START_REF] Yan | Accel-align: a fast sequence mapper and aligner based on the seed-embed-extend method[END_REF], a Seed-Embed-Extend (SEE) sequence aligner that uses the Hamming distance between embedded reference and embedded read to rank and select, rather than eliminate, candidates with a high rank.

While we showed that Accel-Align provides 3-10× improvement over state-of-the-art aligners at comparable accuracy for short reads, we observed that the randomized nature of the embedding algorithm can result in Accel-Align mapping reads to the wrong location in some cases. This, in turn, leads to a drop in accuracy of downstream variant calling using Accel-Align mapped reads. In this paper, we propose several techniques for improving the accuracy of the embedding-based filtering step in SEE-based sequence alignment. We implement these techniques in Accel-Align, and using a comparative evaluation demonstrate that the accuracy improvements can be realized while maintaining the performance advantage over contemporary aligners.

II. BACKGROUND

In this section, we first provide a brief overview of Accel-Align. Accel-Align processes reads in three stages, namely, seeding, embedding, and extension. During seeding, Accel-Align extracts non-overlapping k-mers from each read and converts to hash values based on a simple modulo-based hash function. By default, Accel-Align sets the k-mer size to 32 to enable a k-mer to fit in a single 64-bit integer, and the hash value to fit in a 32-bit integer. The generated hash value is used to lookup a hash table to identify candidate locations. Once Accel-Align gets the candidate locations by seeding, it identifies optimal candidates based on the theory of low distortion embedding. The goal of embedding is to transform both the reference strings at candidate locations and the read string to embedded forms such that edit distance between the original strings can be approximated using the Hamming distance of embedded strings. Accel-Align extracts strings of length equal to the read length from the reference genome at each candidate location, transforms these reference strings and the query string (which is the read) into embedded strings, and calculates the Hamming distance between embedded strings. Finally, Accel-Align picks the top two candidate locations with the least Hamming distance as the best candidates and passes them to the extension stage, where a full dynamicprogramming-based edit computation is performed to determine the alignment score and CIGAR.

As the focus of this work is on improving the accuracy of embedding, we refer the reader to our prior publication [START_REF] Yan | Accel-align: a fast sequence mapper and aligner based on the seed-embed-extend method[END_REF] for further details about indexing and seeding which remain unchanged for this work. In the rest of this section, we will provide a detailed description of the randomized embedding algorithm used by Accel-Align, and illustrate how the randomized nature of embedding could potentially contribute to a loss in accuracy. In particular, we have implemented two randomized embedding algorithms in Accel-Align, namely, 3N-Embedding (3NE) that was proposed by Chakraborthy et al. [START_REF] Chakraborty | Streaming algorithms for embedding and computing edit distance in the low distance regime[END_REF], and 2N-Embedding (2NE) which was a modified version of 3NE that we proposed concurrently with Zhang et al. [START_REF] Zhang | Neural embeddings for nearest neighbor search under edit distance[END_REF]. As Zhang et al. [START_REF] Zhang | Neural embeddings for nearest neighbor search under edit distance[END_REF] have shown that 2NE and 3NE have similar worst case bounds, and as we have already demonstrated that 2NE, which is the default embedding algorithm in Accel-Align, is faster than 3NE and works well for sequence alignment [START_REF] Yan | Accel-align: a fast sequence mapper and aligner based on the seed-embed-extend method[END_REF], we only provide a description of 2NE. The input string is a string of length N consisting of four possible characters (A,C,G,T). The output is a string of length 2N with the same alphabets, and potential multiple repeats of a pad character (P). In each iteration, the algorithm appends a character from the input string once or twice to the output string depending on a random binary bit string associated with that character. The net effect of this is that some input characters appear uniquely in the output string, while others are repeated. Chakraborthy et al. showed that given two strings x, y of length N such that d E (x, y), the edit distance between x and y, is less than K, there exists an embedding function f , such that the distortion D(x, y) = d H (f (x), f (y))/d E (x, y) lies in [1, O(K)] with at least 0.99 probability, where d H (x, y) is the Hamming distance between the embedded strings. In other words, the Hamming distance of embedded strings produced by their 3NE algorithm is at most square of the edit distance between original strings for small values of K. Zhang et al. showed that the worst case behavior of 2NE is similar to 3NE.

Accel-Align relies on the Hamming distances between embedded candidates and the embedded read generated by 2NE to accurately approximate their corresponding edit distances. However, in practice, a "bad" random bit sequence could, in some cases, lead to a large distortion. To illustrate this, let us consider an example with two strings, "CTGACTGA" (#1) and "CTCACTGA" (#2). The two strings have an edit distance of 1. Given below are three different random sequences, and the embedded versions of these two strings for each random sequence. Although the edit distance of the original strings is 1, the Hamming distance between embedded strings can vary dramatically and even be inflated to 11 as shown in example 3. Similarly, mismatches or indels at the beginning of strings will also lead to higher distortion than those at the end of a string. For instance, let us consider the string "CTGACTGC". Compared to #1, it differs only in its last character. Thus, the edit distance between them is 1. When the two strings are embedded, the embedded strings will be be identical for the initial set of characters except the last one. When the embedding algorithm reaches the last character, depending on whether the random bit is 0 or 1, the embedded strings will differ by 1 or at most 2. However, if we consider "ATGACTGA", which also has an edit distance of 1, but differs from #1 in the first character, the Hamming distance of their embedded strings will depend entirely on the random string. For instance, it will be embedded to "AATTGACCTTGGAAPP" using the random string in Example 1, with a Hamming distance of 10, or "ATTGAACTTGGAPPPP" using the random string in Example 2, with a distance of 1.

Accel-Align embeds each candidate reference only once. In the general case, the difference in edit distance between a good candidate location and a bad one is very large. As a result, despite this randomness, the embedded string obtained from a good candidate will typically have a lower Hamming distance than a bad candidate. However, in cases where the difference in edit distance is not large, or in cases with an unfavorable random bit string, this randomness can inflate the Hamming distance of a true match and result in Accel-Align picking a wrong candidate.

III. METHODS

In this section, we describe the changes we made to Accel-Align to minimize the accuracy impact of randomization in 2NE without adversely affecting performance.

A. Multiple embedding

A simple strategy for dealing with distortion caused by embedding is to perform embedding multiple times with the goal that a high distortion produced by a "bad" random string will be overridden by a low distortion outcome from another random string. In the context of Accel-Align, this translates into the following per read operations: (i) C × R embedding operations for embedding C candidate locations R times, (ii) embed the query read itself R times, and (iii) C × R Hamming distance computations to identify the best candidate. A naive implementation of multiple embedding will also require ((C + 1) × 2N × R) bytes of memory per read, where N is the length of read (each of the C candidates and the read itself have to be embedded R times, with each embedding producing a string of length 2N).

During experimentation, we found that the computational and memory requirements of multiple rounds of embedding were high. Thus, we implemented a pipelined version of multiple embedding which works as follows. First, we embed the read R times. Then, we process each candidate one at a time by embedding it using a random string and computing the edit distance from the embedded read based on the same random string. After computing R Hamming distances, we only keep the minimum Hamming distance per candidate, and use this to identify the candidate with the lowest overall minimum. We adopted this approach as it integrates seamlessly with two lower-level optimizations already performed by Accel-Align.

First, the embedding algorithm in Accel-Align does not generate the entire embedded string for each candidate. Rather, given a candidate location and random string, it generates Algorithm 2 Embedding end if 17: end for 18: return d min one embedded character at a time, compares it with the corresponding character in the embedded read, updates the Hamming distance, and discards the character. This results in a CPU-cache-efficient embedding implementation. Second, the embedding algorithm is parameterized with a threshold so that it stops embedding as soon the threshold is exceeded. Instead of storing the embedded distance of all candidates, Accel-Align already dynamically tracks the lowest and secondlowest distances, and uses the latter as the threshold parameter. Our pipelined implementation of multiple embedding exploits both these optimizations to efficiently track the minimum embedding distance for each candidate.

Input: A reference string R ∈ {A, C, G, T } m , a querying read string Q ∈ {A, C, G, T } n ,
d min = M AX 2: for l = 0 ← N -1 do 3: j = 0, string Q, R 4: for k = 0 → n -1 do 5: if k / ∈ [s qi , e qi ),
We further optimize embedding by doing an early-stop for a candidate as soon as we find a random string under which the Hamming distance is computed to be less than or equal to 1. If the embedded Hamming distance is 0, the two embedded strings must be the same, so the original strings are same and edit distance is 0. If the embedded Hamming distance is 1, there is 1 bit different in the embedded strings, same for the original strings, and the edit distance is 1. In either case, there is no need to do an additional round of embedding with a different random string as we have already found the minimum distance.

B. Chain embedding

Let us consider the string x to represent a read, and string y to represent a candidate in the reference genome. Originally, Accel-Align embedded the entire read and an entire candidate string of length equal to the read. However, any candidate y identified by Accel-Align must have at least one k-mer that produced an exact match between the reference and the read which led to this candidate being identified as a potential match during seeding. If two strings are identical, their edit distance, and hence their embedded Hamming distance, will be zero. Thus, the embedded Hamming distance of all exact matching k-mers would already be zero. This implies that we only need to embed the non-matching parts of the read and the reference. We refer to such an approach as chain embedding, as it is reminiscent of the way aligners like Minimap2 use chaining to align gaps between exact matching regions.

Chain embedding improves both performance and accuracy. It improves performance as it reduces the length of the string that needs to be embedded. On the accuracy front, as mentioned earlier, the distortion of the randomized embedding algorithm depends on the edit distance value K. For any read x and a reference candidate y, let x i represents the i-th nonmatching substring in the read, y i represents the corresponding non-matching part in the reference. These substrings are the parts that are found outside or between exact matching kmers. The edit distance between them d E (x i , y i ) is K i , and

d E (x i , y i ) = d E (x, y) = K.
Original Accel-Align embeds x and y as a whole. Thus, the overall distortion is bounded by [K, O(K 2 )]. Our modified Accel-Align with chain embedding, in contrast, embeds each substring separately. As each K i is smaller than K, this should lower the distortion for each chain embedding, thereby improving accuracy. Putting together multiple and chain embedding techniques, Algorithm 2 shows the pseudo-code for the improved embedding algorithm.

C. K-mer Selection

Originally, Accel-Align used non-overlapping k-mers from a read as seeds. All candidates identified by seeding would be forwarded to the embedding stage. While this approach provided competitive performance when we performed embedding only once, preliminary evaluation revealed that multiple embedding emerged as a computational overhead in the updated Accel-Align, particularly for single-end reads. On further examination, we found that a small fraction of reads that contain k-mers that mapped to several thousands of candidates disproportionately affected the performance in the single-end case but not in the paired-end case.

For single-end reads with such k-mers, Accel-Align did not apply any filtering and thus ends up embedding each candidate location multiple times. For paired-end reads, however, Accel-Align already does pair-wise filtering by identifying candidates from one read that have a matching pair within the specified distance in the other read. Unlike other aligners, Accel-Align does this pair-wise filtering in a very efficient fashion before the embedding stage. Typically, at the end of seeding, we get two sorted list of candidates, one per read in the pair. Thus, Accel-Align scans the two lists looking for candidates that lie within a configurable distance threshold of each other. Due to the sorted nature of the lists, this operation is done by a quick search for lower and upper bounds as shown in Algorithm 3. This pair-wise filtering eliminated several needless embedding steps resulting in multiple embedding not being an overhead Algorithm 3 Paired-end filtering return {pos ij } 19: end if for paired-end reads. Interestingly, this approach, also referred to as fuzzy set matching, has also been implemented recently as an effective filter by SNAP [START_REF] Bolosky | Fuzzy set intersection based paired-end short-read alignment[END_REF] and URMAP [START_REF] Edgar | Urmap, an ultra-fast read mapper[END_REF].

Input: A reference string R ∈ {A, C, G, T } m ,
In order to optimize the performance for single-end reads also, we modify Accel-Align to adopt judicious k-mer selection similar to FastHASH [START_REF] Xin | Accelerating read mapping with fasthash[END_REF], BWA-MEM and Minimap2. During seeding, Accel-Align uses the number of candidates generated per seed to classify a read. In particular, if more than half of seeds in the read have more than a threshold count of candidates (default 1000 similar to Minimap2), we flag it as potentially belonging to a highly repetitive region. For such reads, we do not apply any filtering, and forward all the candidates to the embedding stage. However, for reads that are not flagged, we apply k-mer selection by only taking into account k-mers whose candidate count falls below the threshold. The overall algorithm in given in Algorithm 4.

D. Extension and MAPQ

Accel-Align can be configured to run in alignment-free mapping mode where only the identified candidate location is reported, or full-alignment mode where base-by-base extension is performed and the CIGAR string is reported. For the mapping mode, we pick the best candidate, which is the one with the least embedding distance, as the target. Then, we embed the first seed of the read and the k-mers in reference genome at multiple positions around the final candidate position, and pick the position with the least embedding distance. This is done to take into account indels in the first few characters of a read.

For the full-alignment mode, originally Accel-Align did a global alignment between the read and the substring of same length in the reference's candidate position. But we found the lack of soft clipping to adversely affect accuracy of downstream variant calling. So the updated Accel-Align now extracts a substring of length longer than read length and performs "glocal" alignment using lib-ksw [START_REF] Suzuki | Introducing difference recurrence relations for faster semi-global alignment of long sequences[END_REF] on either In addition to the CIGAR, Accel-Align also reports a mapping quality (MAPQ) that represents the degree of confidence in the alignment for each read. Previously, Accel-Align used Bowtie2's MAPQ estimation procedure [START_REF] Langmead | Fast gapped-read alignment with bowtie 2[END_REF] adjusted to use the embedded Hamming distance of the top two candidates in order to produce a MAPQ value between 0 and 42. However, using embedded Hamming distance for MAPQ makes the aligner more conservative in estimating MAPQ which led slightly lower accuracy during downstream variant calling. Further, with the addition of chain embedding to Accel-Align, we no longer compute the end-to-end Hamming distance. Thus, we have simplified and updated the MAPQ estimation procedure. Accel-Align now uses the cumulative Hamming distance obtained from chain embedding for identifying the top two candidates. If d 1 is the least embedded Hamming distance and d 2 is the second least, the MAPQ is computed as

M AP Q = 60 * (1 -d 1 /d 2 ) 2 .

IV. RESULTS

In this section, we compare the accuracy and performance of the updated Accel-Align (v2), with both the older version of Accel-Align (v1), and three other state-of-the-art short-read aligners, namely, BWA-MEM (v0.7.17), Bowtie2 (v2.3.5), Minimap2 (v2.17). All experiments are performed on a server equipped with a quad-core Intel(R) Core(TM) i5-7500 CPU clocked at 3.40GHz, 32GB RAM, and a 256GB SATA SSD.

We run each aligner five times, ignored the first "cold" run and report the average time of last four "warm" runs. We also tried to evaluate SNAP (v2.0) and BWA-MEM2 (v2.2.1) [START_REF] Vasimuddin | Efficient architectureaware acceleration of bwa-mem for multicore systems[END_REF], but we do not report the results here as they failed due to their peak memory usage exceeding our server capacity.

A. Simulated data.

We used Mason2 [START_REF] Holtgrewe | Mason: a read simulator for second generation sequencing data[END_REF] to simulate a VCF file with variants with default SNP and indel rates from the hg37 reference genome. We simulated multiple 10M Illumina single-end and paired-end read datasets of length 100bp, 150bp and 200bp.

1) Alignment evaluation: We consider a read to be correctly mapped if the reported alignment and the Mason-provided alignment overlap by at least 90%. From the figures we can make two observations. First, Accel-Align v2 clearly improves accuracy over Accel-Align v1, especially at longer read lengths. Second, Accel-Align v2 provides accuracy better than, or comparable to, Minimap2 under all datasets while lagging behind BWA-MEM or Bowtie2 by less than 0.2%.

We show the throughput, measured as the number of reads processed per second per thread, for all aligners in Fig 3 and We can see that Accel-Align v2 is slightly slower than v1 for 100bp and 150bp single-end datasets. But in the paired-end datasets, Accel-Align v2 provides performance comparable to Accel-Align v1. Analyzing the 10M 100bp pairend dataset further, we found that there are 630M candidate positions before pair filtering, and only 74M positions after filtering. Thus, as the pair filter eliminates nearly 88% of candidates, it helps to minimize the overhead of multiple embedding. This is not the case for single-end dataset leading to a slow down of Accel-Align v2 over v1. More importantly, comparing Accel-Align v2 with other aligners, we see that it is 4∼9× faster than BWA-MEM, 6∼12× faster than Bowtie2 and 2∼3× faster than Minimap2 similar to Accel-Align v1.

2) Alignment-free mapping evaluation: Accel-Align and Minimap2 provide an alignment-free mapping mode which does not report the CIGAR. Such mapping is useful in the applications that only need the position while not requiring a base-by-base alignment. Hence, we compare the performance of two aligners in alignment-free mode. 3) Multiple embedding microbenchmark: As a key technique to improving the accuracy is multiple embedding, we show the relationship between multiple rounds of embedding (x-axis) and execution time/accuracy (y-axis) in Fig 6 for the 10M 200bp single-end read dataset. We use the alignment-free mode to highlight the impact of embedding. As expected, the execution time increases proportionate to the number of rounds Fig. 5: Alignment-free mapping throughput for pair-end reads. Fig. 6: Performance and accuracy with multiple embedding of embedding. However, while accuracy improves 0.25% from embedding once to embedding three times, the improvement for higher rounds of embedding is too low to justify the increase in execution time. This is the reason why Accel-Align v2 performs three rounds of embedding by default.

B. Real data.

To evaluate Accel-Align v2's performance on real data, we use a human whole-exome sequencing dataset that has 150M paired-end reads of length 151bp. We align the reads to the hg37 reference genome and use GATK HaplotypeCaller (v4.1.0) [START_REF] Depristo | A framework for variation discovery and genotyping using nextgeneration dna sequencing data[END_REF] for variant calling. We capture variant locations using the SureSelect Human All Exon v2 target capture kit bed file (ELID:S0293689), and validate with high confidence variant calls (v2. [START_REF] Roberts | Reducing storage requirements for biological sequence comparison[END_REF] Table I presents a comparison of various aligners under several metrics. The wall-clock time to align 150M pairedend reads (300M total reads) is reported in the field Exec. time HH:MM. Similar to the performance with simulated dataset, Accel-Align v2 is the fastest aligner, providing a 7× speedup over BWA-MEM and Bowtie2, and 3× speedup over Minimap2. The second metric is the transition-to-transversion ratio (Ti/Tv), which should fall in the range of 2.6∼3.3 for this dataset. All aligners satisfied this requirement. We found that Accel-Align v2's result is more consistent than Accel-Align v1's compared to the other three aligners according to the Ti/Tv. As it is illustrated in Table I, the Ti/Tv difference between Accel-Align v2 and other 3 aligners is 0∼0.1, while Accel-Align v1's difference is 0.22∼0.24.

Looking at the overall F-score across SNP and Indel variants, we see that all aligners are comparable. To classify the variants found by each aligner, we list true positive (TP) variants found in both GiaB and by the variant calling pipeline, false positive (FP) variants determined by pipeline but not validated by GiaB, false negative (FN) variants which are validated by GiaB but not determined by pipeline, for SNP and InDels separately. The F-score is computed as 2*TP/(2*TP+FP+TN).

Comparing Accel-Align v1 and v2, we can see that the new version with accuracy improvements is able to capture substantially more SNP and Indel variants. Comparing all aligners, we see that Accel-Align v2 detects the same number of TP InDels as BWA-MEM and Minimap2. In terms of TP SNP variants, Accel-Align v2 detects only 13 fewer variants than BWA-MEM and Minimap2, while several more than Bowtie2. To better understand the difference between detected variants, we show a Venn diagram of variants detected by various aligners in Fig 7 and Fig 8. Majority of SNPs and Indels captured by other aligners are also captured by Accel-Align. There are 10 SNPs and 1 Indel that are concordantly detected by other aligners but not Accel-Align v2. Upon further inspection, we found that the missing variants are mainly due to the use of large k-mer length and non-overlapping seeding which led to 4.6% of reads being not aligned as shown by the % Mapped metric. We plan to optimize the seeding stage as a part of future work.

V. CONCLUSION

In this work, we presented several strategies to improve the accuracy of randomized embedding in the context of sequence alignment. We implemented these strategies in Accel-Align and showed that it is up to 3× faster than Minimap2, 9× times faster than BWA-MEM, and 12× times faster than Bowie2, while providing comparable accuracy. An area of future work that we are investigating is the utility of Accel-Align for longread alignment with a higher error rate. Another interesting avenue of future work is combining embedding with more accurate, effective strategy based on spaced seeds [START_REF] Ma | Patternhunter: faster and more sensitive homology search[END_REF], minimizers [START_REF] Roberts | Reducing storage requirements for biological sequence comparison[END_REF], or strobemer [START_REF] Sahlin | Effective sequence similarity detection with strobemers[END_REF], to be able to map more reads and thus, be able to identify the missing SNP variants.

Algorithm 1 1 : j ← 0 2 : 10 :′

 11210 2N -embedding Input: A string S ∈ {A, C, G, T } N , and 4 random strings r A , r C , r G , r T ∈ {0, 1} 2N Output: The embedded string S ′ ∈ {A, C, G, T } 2N for i = 0 → Nfor j = j + 1 → 2N -Algorithm 1 presents the pseudocode for the 2NE algorithm.

Fig. 1 :

 1 Fig. 1: Accuracy of single-end simulated datasets.

Fig. 2 :

 2 Fig. 2: Accuracy of pair-end simulated datasets.

Fig. 3 :

 3 Fig. 3: Throughput for single-end simulated reads.

Fig. 4 :

 4 Fig. 4: Throughput for pair-end simulated reads. end to support soft clipping. The matching score is set to 2, mismatching, gap-open and gap-extension penalty are set to 8, 12, 2, to compute the alignment score and CIGAR.In addition to the CIGAR, Accel-Align also reports a mapping quality (MAPQ) that represents the degree of confidence in the alignment for each read. Previously, Accel-Align used Bowtie2's MAPQ estimation procedure[START_REF] Langmead | Fast gapped-read alignment with bowtie 2[END_REF] adjusted to use the embedded Hamming distance of the top two candidates in order to produce a MAPQ value between 0 and 42. However, using embedded Hamming distance for MAPQ makes the aligner more conservative in estimating MAPQ which led slightly lower accuracy during downstream variant calling. Further, with the addition of chain embedding to Accel-Align, we no longer compute the end-to-end Hamming distance. Thus, we have simplified and updated the MAPQ estimation procedure. Accel-Align now uses the cumulative Hamming distance obtained from chain embedding for identifying the top two candidates. If d 1 is the least embedded Hamming distance and d 2 is the second least, the MAPQ is computed as M AP Q = 60 * (1 -d 1 /d 2 ) 2 .

Fig 1 and

 1 Fig 2 report the accuracy of various aligners in terms of fraction of reads correctly mapped for various single-end and paired-end datasets.

Fig 4 .

 4

  Fig 5 shows the throughput per second per thread for 100bp, 150bp and 200bp pair-end read. Comparing Fig 5 and Fig 4, we see that the alignment-free mode provides a further 1.15∼1.36× improvement in throughput over full alignment mode. Second, Accel-Align is 2.1∼3.2× faster than Minimap2 at all read lengths (Fig 5) with alignment-free mapping.

Fig. 7 :

 7 Fig. 7: Venn diagram of SNPs detected by various aligners.

Fig. 8 :

 8 Fig. 8: Venn diagram of InDels detected by various aligners. VI. ACKNOWLEDGEMENTS

  a normalized candidate start position s r with |M | matches between the reference and read whose corresponding start and end indexes are s ri , e ri and s qi , e qi , and N times to embed Output: The candidate's embedded Hamming distance d 1:

  ∀i ∈ [0, |M |) then

	6:		Qj = Qk
	7:		Rj = Rsr+k
	8:		+ + j
	9:	end if
	10:	end for
	11:	string	Q′ ,	R′ ← the embedded string of Q,	R
	12:	d ← the Hamming distance between	Q′	and	R′
	13:

d min = min(d, d min ) 14: if d min == 0 or d min == 1 then 15: return d min 16:

  a querying pair-end read with mates Q 1 , Q 2 ∈ {A, C, G, T } n , and the maximum allowed distance between mates dist Output: A list of normalized candidate position 1: {pos 1i } ← the candidate positions of Q 1 gets by singleend seeding 2: {pos 2i } ← the candidate positions of Q 2 gets by singleend seeding 3: list l 1 , l 2 4: for pos 1i in {pos 1i } do {pos ij } ← all the positions mapped by S 1 , S 2 , ..., S k 12: else 13: {pos ij } ← the positions mapped by the seeds whose C i < 1000 14: end if 15: if exist a position occurs more than once in {pos ij } then

	5:	start ← lower bound of pos 1i that is not less than
		pos 1i -dist
	6:	end ← upper bound of pos 1i that is greater than
		pos 1i -dist
	7:	if start ̸ = end then
	8:	add pos 1i into list l 1
	9:	add positions between start and end into list l 2
	10:	end if
	11: end for
	12: return l 1 , l 2
	Algorithm 4 Single-end filtering
	4:	C i ← count the number of normalized candidate
		position of seed S i
	5:	pos i1 , pos i2 , ..., pos iCi ← get normalized candidate
		position of seed S i
	6:	if C i > 1000 then
	7:	+ + cnt
	8:	end if
	9: end for
	10: if cnt > k/2 then
	11:
	16:	return positions occur more than once in {pos ij }
	17: else
	18:

Input: A reference string R ∈ {A, C, G, T } m , a querying read string Q ∈ {A, C, G, T } n Output: A list of normalized candidate positions 1: S 1 , S 2 , ..., S k ← get non-overlapping seeds from Q 2: cnt = 0 3: for i = 0 ← k -1 do

  from Genome in a Bottle (GiaB) containing 23,686 SNVs and 1,258 INDELs.

TABLE I :

 I Evaluation on real data

		BWA-MEM Bowtie2 Minimap2 AA-v1 AA-v2
	Exec. time HH:MM	7:43	7:31	4:43	1:21	1:21
	Ti/Tv	2.84	2.86	2.85	2.62	2.85
	Overall F-score	0.990	0.992	0.992	0.991	0.992
	TP(SNP)	23521	23457	23521	23447	23508
	FP(SNP)	235	79	163	872	146
	FN(SNP)	165	229	165	239	178
	F-score(SNP)	0.991	0.993	0.993	0.993	0.993
	TP(InDels)	1223	1213	1223	1194	1223
	FP(InDels)	49	30	27	105	30
	FN(InDels)	35	45	35	64	35
	F-score(InDels)	0.966	0.970	0.975	0.959	0.974
	% Mapped	99.4%	97.1%	99.3%	95.5%	95.4%

We are grateful to Matthew Holt for for providing valuable feedback and data for comparative evaluation. This work was partially funded by the European Union's Horizon 2020 research and innovation programme, project OligoArchive, under Grant agreement No. 863320.


domain: spi phys

text:
Arti Kalra

Laurent Binet

HELICOPTER AUGMENTED CONTROL LAWS FOR SHIP DECK LANDING: HACLAS ONERA/DLR JOINT TEAM

Keywords: ∆φ, ∆θ, ∆ψ Attitude error

Different types of control laws are implemented, tested and compared for maritime operations particularly ship deck landing maneuvers at the flight simulation facilities at both DLR (German Aerospace Center) and ONERA (The French Aerospace Lab). At DLR, "classical" cyclic and collective stick flight controls were used during the piloted simulator trials while active side-sticks were operated at ONERA. A joint maritime scenario for ship deck landing in the simulation environments of both institutes is presented. Test methodologies and assessment techniques to evaluate the ship deck landings are harmonized based on different criteria such as quantitative measures and handling qualities (HQ) ratings in order to analyze the developed control laws. Simulation results based on pilot studies for an EC135 in the DLR simulator and an EC225 at ONERA are presented.

INTRODUCTION

Maritime helicopter operations often create high stress levels and increased pilot workload because of not only the complexity of the maritime manoeuvres themselves but also the intricate aerodynamic environment, high sea states and high ship motions. Such conditions, turbulent ship air-wake, poor visibility and atmospheric disturbances can lead to the limitation of operational capabilities of the rotorcraft. In the framework of the DLR (German Aerospace Center)/ONERA (French Aerospace Lab) cooperation, one of the objectives of the Smart Rotorcraft Research Field 3 (RF3) is to develop and evaluate the enabling technologies for "easy flying" helicopters. This objective can be reached using augmented control laws. Such control laws are particularly valuable for rotorcraft ship deck operations, which are some of the most demanding of all rotorcraft piloting tasks [START_REF] Mehling | Piloted simulation of helicopter shipboard recovery with visual and control augmentation[END_REF]. Assessing different levels of augmentation and determining flight control law requirements for such complex maneuvers can help to improve safety, reduce pilot effort and workload and increase the helicopter-ship operational envelope [START_REF] Fang | Helicopter-ship qualification testing the dutch clearance process[END_REF].

There also exist control methodologies in literature in order to improve the overall performance of the ship deck landing missions. The implementation and pilot evaluation studies of different augmented response types such as Attitude Command Attitude Hold (ACAH), Acceleration Command velocity Hold (ACVH), Translational Rate Command Position Hold (TRC/PH) and amplitude dependent ACVH/TRC hybrid response types based on diverse control methodologies like Nonlinear Dynamic Inversion or model-following architecture were presented in [START_REF] Perrins | Development of a pilot assisted landing system for helicopter/ship recoveries[END_REF] and [START_REF] Soneson | Simulation testing of advanced response types for ship-based rotorcraft[END_REF]. The current collaboration allows evaluating and comparing ship deck landing control laws for different helicopter types (e.g. EC135 at the DLR vs EC225 at the ONERA) and leading further investigation in this field.

The structure of the paper is the following: Section 1 provides an overall introduction of the challenges faced during maritime maneuvers and the state of the art of the development of control laws. Section 2 describes the objectives of project HACLAS. Section 3 provides the details of the simulation facilities and the maritime simulation environment used for the development of helicopter augmented control laws for the shipboard operations at both DLR and ONERA. The recent developments on the helicopter ship deck control laws at both the institutes are presented in Section 4. Section 5 describes the overall scenario for the pilot studies that have been conducted in both the simulation facilities at DLR and ONERA to evaluate and compare implemented basic and advanced control modes for shipboard operations. In Section 6, the simulation results are discussed demonstrating the investigation and the comparison of advanced control laws implemented at DLR and ONERA based on piloted simulation studies. The paper also discusses additional benefits of using adapted stick dynamics and haptic cueing for better efficiency and reduced workload.

PROJECT HACLAS

DLR and ONERA are working together to bundle their experience in flight controls and maritime operations. This is currently done through a common and cooperative project called HACLAS.

The goal of the joint team is to implement, test and compare different types of control laws for maritime operations especially ship deck landing maneuvers and to deliver requirements regarding the control law characteristics. The main objectives of this project are:

1. Prepare a joint scenario for ship deck landing in the simulation environments of DLR and ON-ERA (including ship motions, ship air wake etc.). 2. Harmonize test methodologies and assessment techniques. Different criteria could be defined and used such as handling qualities (HQ) ratings, safety criteria, impact on Ship-Helicopter Operational Limits (SHOL), etc. [START_REF] Mitchell | Rotorcraft handling qualities for shipboard operations[END_REF].

Analyze different flight control laws such as

Rate Command Attitude Hold (RCAH), Attitude Command Attitude Hold (ACAH), Translational Rate Command (TRC). Optionally implement and analyze advanced control laws such as relative TRC, transition between control laws etc.

SIMULATION ENVIRONMENT

The following section describes in detail the simulation environments used for the current pilot studies to investigate the augmented control laws implemented specifically for ship landings at both institutes: DLR and ONERA.

The Flight Simulation Facility at DLR

The pilot assessments were conducted at the Air Vehicle Simulator (AVES) located at DLR, Braunschweig as shown in Fig. 1. Two interchangable cockpits can be operated on a full-sized six degrees of freedom (DOF) hexapod motion-based platform or a fixedbased platform using a roll-on/roll off (RoRo) system. The cockpit of DLR's research helicopter Active Control Technology/ Flying Helicopter Simulator (ACT/FHS) is represented, which is a highly modified EC135. The cockpit is incorporated with additional research measurement equipment such as side-stick controls and experimental displays with touchscreen capability on the pilot side.

Visual Environment in AVES

The visual projection system contains in total 15 DLP-based LED projectors, each with a native resolution of 1920x1200 pixels. A vertical field of view (FoV) of -55°to 40°and a horizontal FoV of -120°to 120°is displayed in the dome shaped design of the simulator. The software architecture is based on a distributed software design concept where several software modules can run either on a single computer connected via Ethernet, or run together on multiple computers.

For the image generation software, DLR's in-house developed PC-based image generation system that uses OpenGL based render engine, Real Time Image Generator (RTIG) is used. This object-oriented software is adaptable to extend functionalities or integrate new features according to the research requirement. Several visual features can be integrated varying from simple 3D dynamic objects up to special visual features of diverse environmental effects like fog, brownout and wave and water effects.

Ship Dynamics and Weather Simulation in AVES

For the helicopter deck landing study at DLR, the maritime environment comprised of a detailed 3D graphical model of the German Navy F124 'Sachsen class' frigate shown in Fig. 2, a vehicle simulation model to represent the ship dynamics and a wave and water effects and weather simulation.

The in-house enhanced traffic server software Ve-hicleControl is used for simulating the ship dynamics. It can be used to model the dynamics of vehicles using simple motion equations, trajectory following algorithms or by using complex MATLAB/Simulink models. The vehicle dynamics can be computed synchronously to other simulation models such as the helicopter flight model. For the current study, a MATLAB/Simulink model was used, which was created using the Maritime Systems Simulator (MSS) library [START_REF] Perez | An Overview of the Marine Systems Simulator (MSS): A Simulink Toolbox for Marine Control Systems[END_REF]. Currently, it simulates the dynamics of a S175 ship model and uses the Joint North Sea Wave Project (JONSWAP) wave spectrum [START_REF] Hasselmann | Measurements of wind-wave growth and swell decay during the Joint North Sea Wave Project (JONSWAP)[END_REF] for the wave The Sundog Triton Ocean Software Development Kit (SDK) is implemented in the RTIG software in AVES for representing a water and wave visualisation. The waves are generated using the JONSWAP wave program via the SDK. The weather simulation and 3D cloud simulation are generated using the Sundog Silverlining SDK.

Helicopter Flight Model in AVES

DLR's nonlinear real-time helicopter modeling program HeliWorX has been used at AVES to model the ACT/FHS. It is based on the helicopter modeling program SIMH [START_REF] Hamers | Nonlinear Helicopter Model Validation Applied to Realtime Simulations[END_REF] and each helicopter model consists of a set of modular components (fuselage, stabilizers, main rotor, etc.). The main rotor blades are considered as rigid blades and each main rotor blade is divided into 10 blade segments. The Pitt & Peters dynamic inflow model is used [START_REF] Pitt | Theoretical Prediction of Dynamic-Inflow Derivatives[END_REF].

Moreover, the nonlinear helicopter model in Heli-WorX comprises of additional features such as an interface to superimpose additional velocities from turbulent ship airwakes on 44 Airload Computational Points (ACPs) at the helicopter model (Fig. 3). These wind fields have been generated using Highfidelity Computational Fluid Dynamics (CFD) [START_REF] Štrbac | Evaluation of Pilot Assistance Systems for Helicopter Ship Deck Landing[END_REF].

The unsteady airwake data is used as a lookup table within the helicopter simulation, which is looped in time to enable continuous simulation above the ship deck.

The Flight Simulation Facility at ONERA

In parallel, the piloted simulations to evaluate ship deck landings were also performed at the PycsHel The real-time core data-centric structure based on DDS (OpenSpliceDDS Community Version by Vortex) is built as a mutimodel architecture allowing the integration of multiple language models (MAT-LAB/SIMULINK, C++, FORTRAN, etc.).

Visual Environment in PycsHel

ONERA's PycsHel is a prototyping simulation bench dedicated to Vertical Take-Off and Landing Systems. The half cave video projection room offers a 270°h orizontal field of view plus the ground display. Cockpit displays are three tactile screens, not representative of existing on-board systems but all information required to perform elementary missions are present. They are built with OpenSceneGraph and can be customized according to the needs. Of- Visuals are based on the Open-source 3D toolkit OpenSceneGraph, orthophotographic and heightfield based terrains fuelled by real-world geomatic data. Four new laser-based projectors from Barco (F80) have been integrated in the simulation bench in 2021. This projection system has native WQXGA resolution (2560x1600) and up to 4K using pixel shift technics. It also allows stereoscopic or dual pilot point of view capabilities.

Ship Dynamics and Weather Simulation in PycsHel

A 3D model of a Lafayette frigate is integrated in the visual environment. Sea and atmospheric representations are performed through SunDog Triton Silverlining software, enabling real 3D waves for any Beaufort scale and ship wake. Ship movements are generated using a model, identified on the basis of experimental data on the deck movements of a Lafayette-class frigate.

Helicopter Flight Model in PycsHel

The flight mechanic code used in this project is the HOST code from Airbus Helicopters, modelling an EC225 helicopter. HOST code integrates a ship air-wake model developed by ONERA in 1997 and based on wind tunnel measurement campaigns on a Lafayette frigate model as shown in Fig. 5. The frigate air-wake model and its implementation in

TECHNICAL DEVELOPMENTS

The technical developments performed at DLR and ONERA joint team under the project HACLAS are described in the following section.

Frigate Air-wake at DLR

A turbulent air-wake behind a F124 Sachsen Frigate shown in Fig. 6 is generated and implemented in the non-linear model of ACT/FHS, HeliWorX. This model is used for real-time simulations in the AVES. The HeliWorX model features an interface to superimpose velocities from turbulent ship air-wakes. The unsteady air-wake data is used as a lookup table within the helicopter simulation, which is looped in time to enable continuous simulation above the ship deck. In addition, temporal blending is performed at each time loop for smooth transition.

The spatial dimension (120 x 80 x 40 m), the spatial discretization (∆x = 0.3 m) and the temporal discretization (∆t = 0.04 s) of the unsteady air-wake cause a memory consumption of approximately 60GB.

Advanced Flight Control Modes at DLR

Another development from DLR side is the implementation of advanced control modes to make the landing task easier for the pilots. Three basic command types:

• Attitude Command Attitude Hold (ACAH)

• Rate Command Attitude Hold (RCAH)

• Translational Rate Command (TRC) were implemented [START_REF] Kalra | Evaluation of Helicopter Ship Deck Landing Control Laws in Piloted Simulations[END_REF]. The control modes are implemented in a pre-established and flight-tested model following control system which imposes the desired command model dynamics on the controlled helicopter [START_REF] Greiser | Results of the pilot assistance system "assisted low-level flight and landing on unprepared landing sites" obtained with the act/fhs research rotorcraft[END_REF]. This model-following control system has three major components: a command model, a feed-forward controller and a decoupled cascaded feedback controller as shown in Fig. 7.

The command model generates the reference signals for the desired helicopter motion. Various command types combined with various hold functions are implemented in the command model.

The feed-forward controller provides basic response decoupling and improves the response quickness. A 11-DoF helicopter dynamic model which is derived by system identification in time domain is the base for the feed-forward controller [START_REF] Seher-Weiss | Ec135 system identification for model following control and turbulence modeling[END_REF]. The first advanced control mode, Attitude Command Velocity Hold (ACVH) combines a trajectory hold (ground speed and heading) function when the cyclic stick is released with an attitude command mode when the stick is moved as shown in Fig. 9. The pilot can also adjust the heading and speed of the helicopter using the 4-way stick button on the cyclic stick. The key feature of this mode is that no communication is required between the ship and the helicopter. Such a mode could be useful in for example for EMCON (Emission Control) missions where no communication with the ship is allowed. The vertical mode provides a classical rate command / altitude hold. Another advanced control mode, Relative Translational Rate Command (RTRC) has been developed and implemented in the ACT/FHS flight control system at DLR. When the mode is activated, the helicopter velocity is matched to the current ship velocity when the cyclic stick is released and can be used as a standard TRC mode when the stick is moved.

The third advanced mode named ACVsH also involves communication with ship. In this mode, upon activation the helicopter holds the ground speed and heading of the ship when the stick is not moving and is a standard attitude command mode when the stick is moved. Hence, on activation the ship speed and heading is matched and the pilot can also adjust the heading and speed of the helicopter using the 4-way stick button on the cyclic stick.

Simulator Trials Analysis Tools at DLR

For the simulator trials performed in DLR's AVES, an overall evaluation toolchain for assessing the helicopter ship deck landings was implemented [START_REF] Kalra | An overall evaluation toolchain for assessing helicopter ship deck landings[END_REF]. The toolchain evaluates various touchdown conditions like position, velocity and attitude errors between the ship deck and the helicopter along with additional evaluation parameters for the entire approach such as levels of control activity: amplitude and frequency, lateral track: distance about the ship axis, position and track error (x and y) and heading variations during the whole flight trajectory. For a subjective assessment, qualitative pilot ratings using different rating scales namely Cooper-Harper rating scale and DIPES (Deck Interface Pilot Effort Scale) were recorded during the pilot study [START_REF] Cooper | The use of pilot rating in the evaluation of aircraft handling qualities[END_REF] and [START_REF] Advani | Dynamic interface modelling and simulation -a unique challenge[END_REF].

ONERA Developments

From ONERA side, the following basic control modes: Attitude Command Attitude Hold (ACAH), Rate Command Attitude Hold (RCAH), Translational Rate Command (TRC), HH (Height Hold = Heave TRC) and AcVH (Acceleration Command, Velocity Hold) were implemented in ONERA'S PycsHel simulator and tuned for an EC225 helicopter model. While not evaluated yet, a relative TRC (based on the DLR approach) has been also developed and implemented in PycsHel.

At DLR, "classical" cyclic and collective stick flight controls were used during these trials while active side-stick units were operated at ONERA. Dur-ing preliminary trials, it was observed that, with TRC, pressing the trim button can induce very slight movements of the stick, and thus a transient speed set point. To avoid this, the nominal effort/stick position law (1 Newton/deg), as shown in Fig. 9a, has been replaced by friction (2 Newtons) on the longitudinal axis (see Fig. 9b) while the classical force/displacement curve was maintained on the lateral axis. This modification allows to place the stick at a given position (i.e. given target speed), and release it without any unintentional move as the trim button has no more to be pressed.

Moreover, detents were implemented on both cyclic and collective sticks to indicate specific speeds to the pilot. A detent simulates a ball and groove feature where a ball and spring are moved over a surface with a groove in it (e.g. an aircraft throttle).

The detent parameters that can be set are the center position, width, height, dead-band (located around the center) and the scale factor (affecting the sharpness of the detent).

One detent was placed at the neutral position of collective, enabling the precise recognition of the null vertical speed by the pilot. In addition, a glide slope guidance function sets a detent at the collective position corresponding to the required vertical speed to follow a predefined glide path angle. On cyclic, different possibilities are offered. Positioning the detent at the neutral stick position enables the recognition of null angular rate commands in RCAH, null attitudes in ACAH, or null velocity in TRC depending on the law engaged (RCAH, ACAH or TRC). In addition, ship speed can also be indicated by a detent positioned at the associated longitudinal cyclic stick displacement. This last feature can be considered as a kind of relative TRC, as the pilot is then able to position its cyclic around this detent, and to control the horizontal speed around the ship speed. The use of haptic cueing showed real benefits and the possibility to increase the efficiency of augmented flight control laws.

PILOTED SIMULATION

During the pilot studies at DLR and ONERA, the pilots performed multiple ship deck landings on a frigate class ship in order to evaluate the implemented control modes. The complete details about the piloted simulation are described in the following section.

Pilots and Pilot Task

A total of five helicopter pilots with different levels of experience as described in Tab. The task performed during the simulator trials at DLR was based on the standard port lateral approach as defined in the Helicopter Operations from Ships other than Aircraft Carriers (HOSTAC) as shown in Fig. 10 [18]. Standard Port Lateral Approaches are usually performed in the direction opposite to the air-wake. However, the chosen maneuver is with an approach from the turbulent air-wake to make the task more challenging and hence more suitable for the evaluation of the control modes.

The pilots begin the task at a height 65 ft with a forward speed 20 knots approximately 600 ft behind the ship and establishes a hover alongside the deck, followed by a lateral transition maintaining the same altitude over the deck [START_REF] Geiger | Advanced modeling and flight control design for gust alleviation on shipbased helicopters[END_REF]. Upon detection of a quiescent period, a vertical descent followed by the landing is performed. The pilots repeated each landing using different control modes 2-3 times.

It has to be noted that, while at DLR the scenarios were started in the vicinity of the ship, simulation runs were initiated much away from the ship at ON-ERA, allowing the study of the entire approach. The maneuver was defined as depicted in Fig. 11. Only straight-in maneuvers were performed at ONERA, defined by:

• At 4000 yards behind the ship, the helicopter is in level flight, at 60 kts, at 500 ft above sea; • At 2550 yards behind the ship, helicopter initiates descent at constant glide (following glide slope indicator of the ship) along the trajectory. • At ship fantail, the helicopter velocity is the ship velocity (10 kts during the trials); • The vertical speed is adapted to maintain a constant glide slope of -3 deg.

Test Scenario and Test Configurations

During the pilot study at DLR, the deck motion simulation modelled after a German Navy F124 'Sachsen class' frigate was used. The frigate was proceeding at 10 knots in sea state 6 with the sea approaching at 30°to the bow. The frigate ship dynamic model also included a simulation of turbulent ship air-wake attached to it. A good visual environment (GVE) as shown in Fig. 2 was used for the evaluation of helicopter ship deck landings.

The major goal of the piloted evaluations was to assess and compare the performance of augmented control modes during ship deck landing maneuvers for different helicopter types at both DLR and ON-ERA. Therefore, the test matrix was created for very particular conditions.

For the pilot study in AVES at DLR, test points were based on five different flight control configurations (except RCAH) as provided in Tab. 2 for each individual axis. Pilots highlighted that the RCAH is clearly not adapted to this procedure as it resembles flying in direct mode. Hence due to time constraints, RCAH was not considered in the final test matrix.

During the simulator trials at ONERA, the Lafayette Frigate was following a North route at a constant speed of 10 kts. As proceeding to straight in approaches, the wind was aligned with the ship (azimuth 0 deg) with a magnitude of 10 kts. Different

Assessment Methods

The evaluation of the implemented control laws was performed using the implemented toolchain by analyzing objective simulation data, subjective pilot ratings and the pilot feedback during the simulation. For the objective evaluation, task performance is evaluated during the approach and at the touchdown point. The implemented toolchain evaluates the touchdown conditions specifically the position error (∆x , ∆y ) , velocity error (∆V x , ∆V y , ∆V z ) and attitude error (∆φ, ∆θ, ∆ψ) between the ship deck and the helicopter.The quantitative mission success is determined depending on if the pilot could achieve the desired or adequate limits during landing. The desired and adequate boundaries are defined for each of the touchdown conditions based on the size of helicopter, size of ship and the pilot feedback [START_REF] Kalra | Evaluation of Helicopter Ship Deck Landing Control Laws in Piloted Simulations[END_REF].

Along with the touchdown evaluation, the objective criteria to evaluate the entire manoeuvre such as levels of control activity: amplitude and frequency and position and track error (x and y ) during the whole flight is also investigated for extra knowledge but not used as a part of evaluation.

For the subjective evaluation, pilots awarded ratings based on different rating scales namely: Cooper-Harper Handling Qualities Rating Scale (HQR) and Deck Interface Pilot Effort Scale (DIPES) after each experiment.

The Cooper-Harper rating scale was used to assess the handling qualities of the helicopter during the final phase of the landing [START_REF] Cooper | The use of pilot rating in the evaluation of aircraft handling qualities[END_REF]. It consists of a decision tree and ranges from ratings 1 to 10 defining the best handling qualities of an aircraft as 1 "excellent, highly desirable" and the worst handling qualities as 10 "major deficiencies". The handling qualities ratings (HQR) in the range of 1-3 represents Level 1, in the range of 4-5 represents Level 2 and the rest Level 3 Handling Qualities. The Cooper-Harper rating scale is a highly complex scale and hence is normally used only by experimental test pilots.

DIPES was used to quantify the workload of an average fleet pilot and is designed in particular for ship deck landings [START_REF] Advani | Dynamic interface modelling and simulation -a unique challenge[END_REF]. Using this 5-point scale, the pilot rates the landing maneuver based on workload, performance, accuracy, and consistency. Ratings between DIPES-1 to DIPES-3 are considered as acceptable and ratings of DIPES-4 to DIPES-5 are considered as unacceptable. Moreover, each rating can be supplemented with letter suffixes explaining the cause of growing workload (e.g. 'D' for deck motion or 'V' for visual cues). The pilots were asked to give at most three additional suffixes, if needed.

SIMULATOR TRIAL RESULTS

In the following section, the results of the evaluation of the ship deck landing scenario and the flight control modes at DLR and ONERA are presented.

Evaluation at DLR

The simulator trials at DLR were focused to evaluate the ship deck landing scenario and to evaluate and compare the flight control modes for their suitability for ship deck landing missions. All aforementioned pilots participated in the simulation trials at the AVES. Each pilot performed each test independent from the other pilots. The simulation results are focused on Pilot 3, Pilot 4 and Pilot 5 as they had experience with maritime missions.

The objective assessment majorly involved the evaluation of the touchdown conditions. In addition, the entire maneuver was also analysed to have a deeper knowledge of the approach phase. Three metrics; position offset, velocity error, and attitude error at the touchdown point were evaluated. Fig. 12 shows the touchdown positions of the helicopter relative to the deck center by the three maritime pilots for different control configurations. Due to time constraints, Pilot 3 was not able to perform a landing using ACVsH command type. The figure demonstrates the pilots could achieve a desired or at least adequate performance with almost all the command types. Fig. 13 illustrates the position of the helicopter relative to the deck center throughout the approach and during the landing phase using ACAH and ACVH control configuration. It can be seen from the landing phase in the trajectory and was also remarked by the pilots that it was much simpler to perform the landing in the ship air wake using ACVH The level of control activity for performing the maneuver is also considered as a significant metric to be studied. Fig. 14 illustrates the pilot control inputs for the approach and landing phase for ACAH and ACVH control modes by Pilot 5. Lesser pilot activity was observed with ACVH mode when compared to ACAH mode. It can be also be observed that the pilots had to use more effort during the landing phase (towards the end of the flight) in comparison to the approach phase. This behaviour was observed for all control modes. This behavior is expected as the pilot has to make adjustments against the drift caused by turbulence.

For the objective assessment, the pilots went through a set of questionnaires based on different rating scales namely DIPES and HQR after each experiment respectively. For awarding Cooper-Harper Handing Qualities Ratings, the focus remained on the landing phase where as for other scales whole maneuver was addressed.

It should be highlighted that the HQR ratings were only awarded by Pilot 1, Pilot 2 and Pilot 5, because of the fact that the HQR is a relatively complicated scale. Fig. 15 shows that the pilots could achieve Level 2 or Level 1 (for advanced command types) Cooper-Harper HQRs which depicts that they achieved desired or adequate performance with a moderate compensation. It can be observed from the Fig. 15 that there was a improvement in the ratings from Level 2 to Level 1 when using the advanced command types.

Fig. 16 depicts the DIPES ratings awarded by all five pilots for ship deck landings performed using all the command types. The figure demonstrates the DIPES ratings to be 'acceptable' for most of the advanced command types (TRC, RTRC and ACVsH) for almost all the pilots whereas the pilots provided higher ratings for the basic command types (ACAH and ACVH).

The ratings illustrate that using the advanced command types the task became acceptable. The DIPES ratings highlighted most clearly the benefits of the RTRC mode, contrary to the ACAH (also ACVH by Pilot 4) mode, which was deemed 'unacceptable' by some of the pilots. Along with the DIPES ratings, the pilots can also describe the cause of the increased workload by providing one or more suffixes. In this study, nearly all the pilots remarked that the deck motion (D) and air wake turbulence (T) were the major causes of increased workload and made the overall task more challenging. Apart from deck motion (D) and turbulence (T), lateral positioning (L), and height control (H) were some other factors stated by the pilots for increased workload.

It should also be noted that even if some of the landings with basic command types displayed better task performance, it actually took higher pilot workload to achieve such accuracy. Besides that the higher ratings by some pilots could be impacted by not only the task being demanding but can also vary depending on the level and type of experience of the evaluation pilot as described in Tab. 1. It can be noted that the tests were performed by limited number of pilots, although according to [START_REF] Padfield | Helicopter Flight Dynamics: The Theory and Application of Flying Qualities and Simulation Modeling[END_REF] three pilots seems to be bare minimum with four likely to lead to a more reliable result.

Some of the pilots experienced a higher workload with ACVH mode, because they were not habitual using the 4-way test button to adjust the speed and heading instead of classical control inputs. However, some of them experienced a relatively higher overall workload with TRC mode specially during the approach. This is because they had to give large control inputs because usually TRC is generally used for final precision during landing whereas it was also used for the approach phase in the current study.

Evaluation at ONERA

ONERA simulator trials were performed by Pilot 5, and led to a classification of the basic control modes based on the pilot subjective feedback. Thus, it was highlighted that the RCAH and ACAH are clearly not adapted to this procedure. RCAH was a bit better rated but still required a high workload. TRC is the most efficient with a large decrease of the workload. Contrary to the results already experienced in previous studies, RCAH was here better rated than ACAH. This can be explained by less disturbance rejection capability of the ACAH implemented in the simulator compared to the RCAH for the case of PycsHel. Figure 15 HQR results for ship approaches using different control modes at DLR Therefore, the pilot had more difficulties to precisely control the helicopter speed above the deck.

In Fig. 17 and Fig. 18, a comparison of pilot controls and flight parameters during the entire procedure is done, between TRC (blue line) and RCAH (red line).

It is clear that the requested inputs on controls are lower with TRC, and the trajectory is much more stabilized. In this case, the pilot didn't land and fly when using the RCAH.

As previously mentioned, the force displacement curve used on the longitudinal cyclic in TRC was switched to pure friction. A deceleration from 20 kts to 10 kts (blue curve) performed with the classical force displacement curve on the longitudinal cyclic can be seen in Fig. 19. The force gradient being 1N/deg. It can be seen that at around 60s, the pilot is acting on the trim release button (red curve), generating small control variations. In Fig. 19 , the same maneuver is performed but using a 2N friction on the longitudinal, and a detent positioned to the corresponding ship velocity. The difference in terms of required pilot actions on the flight controls is clearly visible on the red curve. The pilot is able to feel the detent and place the stick immediately at the proper position to reach the ship velocity. This force law on the cyclic was considered as very well adapted. If keeping a classical force gradient curve on the lateral is adapted for straight in approaches, where only small lateral velocity corrections are needed, setting friction on the lateral axis could be beneficial for relative wind or cross deck procedures, where maintaining a lateral speed is requested.

Finally, the HH law on collective was very well appreciated, enabling a precise control of the vertical speed during the approach. In addition, the use of detents to indicate specific vertical speeds were also very well appreciated, as also demonstrated in [START_REF] Binet | Development and evaluation of coupling modes and haptic functions enabled by active inceptor in cockpits[END_REF].

Pilot Feedback and Future Developments

Some requirements were expressed by the pilot after simulation trials at ONERA:

• Use of the 4-way button cyclic stick to enter set points (+-1kt) in relation to the reference ground ship speed. Both in longitudinal and lateral.

CONCLUSIONS

A maritime simulation environment has been implemented in both DLR and ONERA simulators, enabling the evaluation of helicopter augmented control laws in ship deck operations. Three basic (ACAH, RCAH, TRC) and three advanced command types (ACVH, RTRC, ACVsH) were developed and tested by DLR, while ONERA focused on three basic control modes (RCAH, ACAH, TRC). Simulator trials results illustrated that even if some of the landings with basic command types displayed better task performance, it actually took higher pilot workload to achieve them. The pilot feedback indicated an extensive reduction in the workload when using the advanced command types. Among these advanced command types, Relative Translational Rate command has shown larger benefits. Finally, it has been shown that the use of active inceptors, with specific haptic cues (detents), can bring benefits in the management of augmented flight control laws.

Figure 1

 1 Figure 1 Air Vehicle Simulator (AVES) at DLR

Figure 2 Figure 3

 23 Figure 2 DLR's simulator -Cockpit point of view during maritime simulation Vertical Stabilizer (1 ACP) Horizontal Stabilizer (1 ACP)

Figure 4

 4 Figure 4 ONERA's simulator -Cockpit point of view during maritime simulation

Figure 5

 5 Figure 5 Ship air-wake measurements at IMFL wind tunnel facilities

Figure 6 F

 6 Figure 6 F-124 class frigate with turbulent air-wake.

Figure 7

 7 Figure 7 Schematic of the model-following control system

( a )Figure 8

 a8 Figure 8 ACVH control mode activation and handling

( a )Figure 9

 a9 Figure 9 Classical Force/displacement curves

  1 participated in the piloted simulation study. Pilot 1 and Pilot 2 are doing research in the area of scientific flight testing and both of them are experienced with subjective pilot rating methods. Pilot 1 is qualified as an experimental test pilot, Pilot 2 and Pilot 5 are doing research and their experience is considered highly valuable for the evaluation. Pilot 3 is a military pilot from the German Navy and is familiar with helicopter ship deck landings of Lynx helicopters on the considered F-124 class frigate. Pilot 4 is a pilot from the German police and Pilot 5 is a French navy pilot. Both Pilot 4 and Pilot 5 are familiar with helicopter deck landings.

Figure 10

 10 Figure 10Deck Landing Mission Task Element[START_REF] Owen | A virtual engineering approach to the ship-helicopter dynamic interface -a decade of modelling and simulation research at the university of liverpool[END_REF]

Figure 12

 12 Figure 12 Landing positions relative to deck using different control configurations (Blue: Pilot 3, Purple: Pilot 4 and Orange: Pilot 5)

Figure 13

 13 Figure 13 Helicopter position relative to landing position performed by Pilot 4 (Blue: ACAH and Yellow: ACVH)

Figure 14

 14 Figure 14 Time Histories of pilot control activity throughout ACAH and ACVH approach by Pilot 5

Figure 16 Figure 17

 1617 Figure 16 DIPES results for ship approaches using different control modes at DLR

Figure 18

 18 Figure 18 Comparison of flight parameters between TRC and RCAH during the procedure

Figure 19

 19 Figure 19 Comparison of a deceleration maneuver with different force displacement laws

Table 1

 1 Overview of pilot experience

	Pilot 1	Pilot 2	Pilot 3	Pilot 4	Pilot 5

Figure 11 ONERA's deck landing scenario -Straight In approach

Table 2

 2 Test configurations used at DLR

	Config. ACAH	TRC	ACVH RTRC
	Pitch	ACAH	TRC	ACVH RTRC
	Roll	ACAH	TRC	ACVH RTRC
	Yaw	RCDH RCDH ACVH RCDH
	Heave	HH	HH	HH	HH
	runs were based on three different flight control
	configurations as provided in Tab. 3

Table 3

 3 Test configurations used at ONERA

	Config. ACAH RCAH	TRC
	Pitch	ACAH RCAH	TRC
	Roll	ACAH RCAH	TRC
	Yaw	RCDH RCDH RCDH
	Heave	HH	HH	HH

ACKNOWLEDGMENTS

The work in this paper was funded through the project HACLAS, which is a cooperation project between DLR and ONERA. The authors would like to express their gratitude and acknowledge the work of all the pilots who participated in the piloted simulation trials and contributed by providing their valuable feedback and comments for the evaluation process.