Mohamed Maouche

Brij Mohan

Lal Srivastava

Nathalie Vauquier

Aurélien Bellet

Marc Tommasi

Emmanuel Vincent

Enhancing Speech Privacy with Slicing

Keywords: anonymization, speaker verification, automatic speech recognition, segmentation

Privacy preservation calls for anonymization methods which hide the speaker's identity in speech signals while minimizing the impact on downstream tasks such as automatic speech recognition (ASR) training or decoding. In the VoicePrivacy 2020 Challenge, voice anonymization methods have been proposed to transform speech utterances in a way that preserves their verbal and prosodic contents while reducing the accuracy of a speaker verification system. In this paper, we propose to further increase the privacy achieved by such methods by segmenting the utterances into shorter slices. We show that our approach has two major impacts on privacy. First, it reduces the accuracy of speaker verification with respect to unsegmented utterances. Second, it also reduces the amount of personal information that can be extracted from the verbal content, in a way that cannot easily be reversed by an attacker. We also show that it is possible to train an ASR system from anonymized speech slices with negligible impact on the word error rate.

Introduction

With the increasing popularity of smart devices, more users have access to voice-based interfaces. The underlying technologies, especially automatic speech recognition (ASR), are often trained on speech data collected from the users to improve performance and adapt to new domains. The collection and exploitation of this data raises privacy threats. Indeed, speech carries personal or sensitive information about the speaker (e.g., gender, age, emotion) [START_REF] Ribaric | De-identification for privacy protection in multimedia content: A survey[END_REF][START_REF] Nautsch | Preserving privacy in speaker and speech characterisation[END_REF] and it is a biometric characteristic that can be used to recognize the speaker through, e.g., ivector [START_REF] Dehak | Front-end factor analysis for speaker verification[END_REF] or x-vector [START_REF] Snyder | X-vectors: Robust DNN embeddings for speaker recognition[END_REF] based speaker verification. To address this privacy issue, various voice anonymization 1 methods have been proposed in the literature. These methods, which rely on simple feature transformation [START_REF] Magariños | Reversible speaker de-identification using pre-trained transformation functions[END_REF][START_REF] Qian | Voicemask: Anonymize and sanitize voice input on mobile devices[END_REF][START_REF] Patino | Speaker anonymisation using the McAdams coefficient[END_REF], feature perburbation [START_REF] Han | Voiceindistinguishability: Protecting voiceprint in privacy-preserving speech data release[END_REF], Gaussian mixture model based voice conversion [START_REF] Jin | Speaker deidentification via voice transformation[END_REF][START_REF] Pobar | Online speaker de-identification using voice transformation[END_REF], or neural network based voice conversion [START_REF] Bahmaninezhad | Convolutional neural network based speaker de-identification[END_REF][START_REF] Fang | Speaker anonymization using x-vector and neural waveform models[END_REF][START_REF] Prajapati | Voice privacy through x-vector and CycleGAN-based anonymization[END_REF], aim to transform speech signals in a way that preserves all content except features related to the speaker identity, thereby making it hard for an attacker to re-identify the speaker.

Besides speech signals, ASR system training also requires the corresponding text transcripts, irrespective of whether the speech signals have been subject to voice anonymization or not. These transcripts can contain personal information about the speaker too. Text sanitization methods, which redact or replace sensitive words in the text, can mitigate this issue for textonly data [START_REF] Tang | Preserving privacy in spoken language databases[END_REF][START_REF] Uzuner | Evaluating the state-of-theart in automatic de-identification[END_REF][START_REF] Adelani | Privacy guarantees for de-identifying text transformations[END_REF][START_REF] García-Pablos | Sensitive data detection and classification in Spanish clinical text: Experiments with BERT[END_REF]. Unfortunately, word replacement is unusable 1 In the legal community, the term "anonymization" means that this goal has been achieved. Following the VoicePrivacy 2020 Challenge [START_REF] Tomashenko | Introducing the VoicePrivacy initiative[END_REF], we use it to refer to the task, even when the method has failed.

for ASR system training since it breaks the correspondence between the transcripts and the verbal contents of speech, while word redaction often fails to detect and remove some sensitive words. As a result, the transcripts (or the verbal content of speech) could be exploited by an attacker to break the protection offered by voice anonymization.

The method we introduce in this paper follows a different path. We propose to segment every speech utterance into shorter slices after it has been anonymized. In this way, we reduce the amount of speech available to the attacker in each slice, which is expected to lower the risk of speaker re-identification with respect to unsegmented utterances. On top of that, the amount of personal information that can be extracted from the transcript of each slice is also reduced, since it becomes isolated from its context. We quantify the risk of speaker re-identification, and the risk that an attacker could reverse the slicing procedure by reassembling successive speech signals or text transcripts together. Most importantly, we also evaluate the impact of slicing on the utility of the data for ASR acoustic model training. Our experiments are conducted on LibriSpeech [START_REF] Panayotov | Librispeech: An ASR corpus based on public domain audio books[END_REF] and follow the VoicePrivacy 2020 Challenge setup [START_REF] Tomashenko | Introducing the VoicePrivacy initiative[END_REF][START_REF] Tomashenko | The VoicePrivacy 2020 Challenge: Results and findings[END_REF] with a stronger attacker. In particular, we use the x-vector based voice conversion baseline [START_REF] Srivastava | Design choices for x-vector based speaker anonymization[END_REF][START_REF] Srivastava | Privacy and utility of x-vector based speaker anonymization[END_REF] of the Challenge for anonymization, not only for reproducibility purposes but also because it is representative of modern neural network based voice anonymization methods and it still offers one of the best privacy/utility trade-offs today.

While slicing may be seen as a simple method, it has not been studied in the context of privacy so far. Previous studies on the impact of utterance duration on speaker verification performance focused on clear (non-anonymized) utterances and did not evaluate the utility for ASR [START_REF] Poddar | Speaker verification with short utterances: a review of challenges, trends and opportunities[END_REF][START_REF]Quality measures for speaker verification with short utterances[END_REF][START_REF] Viñals | An analysis of the short utterance problem for speaker characterization[END_REF]. Our study is the first one that tackles privacy, utility and reassembling together. Our results highlight a sweet spot in the choice of slice length for which our method provides a large increase in privacy (larger than the one provided by voice anonymization itself) with no loss of utility. In addition, we show the difficulty for an attacker to reassemble the utterance from short slices of speech or text.

The structure of the paper is as follows. We describe the threat model in Section 2 and introduce the slicing method in Section 3. Section 4 reports the evaluation on real data in terms of both privacy and utility. We present our study on the reversibility of the slicing in Section 5. We conclude in Section 6.

Threat Model

The attack scenario is depicted in Fig. 1. Speakers process their voice through an anonymization method. This anonymization step takes as input one or more private speech utterances along with some configuration parameters, and outputs a new speech signal. The transformed utterances from one or more speakers form a public speech dataset that is processed by a third-party Given unprocessed or anonymized utterances from a known speaker, an attacker attempts to find which anonymized utterances in the public dataset are spoken by this speaker [START_REF] Qian | Towards privacy-preserving speech data publishing[END_REF][START_REF] Srivastava | Evaluating voice conversion-based privacy protection against informed attackers[END_REF]. Formally, an attacker has access to two sets of utterances: A (enrollment/found data) and B (trial/public speech), but knows the corresponding speakers in A only. The attacker designs a linkage function LF (a, b) that outputs a score for any a ∈ A and b ∈ B. Typically, this score is a similarity obtained through a speaker verification system. The attacker then makes a binary decision (same vs. different speaker) based on this score. We also consider that the attacker knows the anonymization method used and can leverage it to enhance the attack.

Anonymization techniques must achieve a suitable privacy/utility trade-off. On the one hand, privacy is measured by the attacker's ability to re-identify the speaker using metrics such as equal error rate (EER) or linkability [START_REF] Maouche | A comparative study of speech anonymization metrics[END_REF]. On the other hand, utility is measured by the performance of the desired downstream task(s), e.g., the word error rate (WER) of an ASR system or the intelligibility for a human listener. In the following, we are interested in the utility of the data for training an ASR acoustic model, assuming that the other components of the ASR system (lexicon, language model) are available or have been trained on text-only data (see Fig. 2).

Word-Level Slicing of Speech

To increase privacy beyond the level achieved by the voice anonymization methods mentioned in Section 1, we propose to cut the anonymized utterances into multiple, shorter slices. To ensure that the sliced transcripts match the sliced spoken content, we constrain these cuts to happen between successive words rather than in the middle of a word. At the same time, we wish the duration of the slices to be close to a target duration δ in order to control the trade-off between privacy and utility. This is achieved by force-aligning each original (unsegmented) transcript with the corresponding utterance. For a given utterance u and transcript w = w1 . . . wn, alignment yields two series of timestamps (t s k ) 1≤k≤n and (t e k ) 1≤k≤n where [t s k , t e k ] is the time interval when word w k has been uttered in u. To create the first slice, we start from the first word and include the following words one by one until we reach a number k such that the duration becomes at least δ. Besides the words, we keep the silence between them, as well as the silence before the first word and after the last word. We then start again from the (k + 1)-th word to create the second slice, and so on. The final segment (if any) whose duration is shorter than δ is discarded. See Algorithm 1 for details.

Algorithm 1 Word-level slicing method. A ← Align(u, w)

4:
tprv ← 0 5:

kprv ← 0 6:
for k in range(|w|) do 7: return slices 20: end function

t s k , t e k ← A[k] # w k is uttered in [t s k ,

Utility and Privacy Evaluation

In this section, we evaluate how the duration of the slices impacts the privacy/utility trade-off.

Experimental Setup

Voice anonymization: We use the first baseline of the VoicePrivacy 2020 Challenge [START_REF] Tomashenko | Introducing the VoicePrivacy initiative[END_REF] as the voice anonymization method. This method extracts pitch, bottleneck, and (source) x-vector features from the input speech. It then re-synthesizes a speech signal using the original pitch and bottleneck features and a new target x-vector generated from a public pool of x-vectors using one of several possible strategies. In the following, we do not use the default strategy reported in [START_REF] Tomashenko | Introducing the VoicePrivacy initiative[END_REF]. Instead, we choose the so-called dense strategy with random gender, which was reported to be the most successful in [START_REF] Srivastava | Privacy and utility of x-vector based speaker anonymization[END_REF]. Data which have not been anonymized are referred to as clear data.

Slicing: Slicing is performed using forced-alignments obtained using the pretrained model Gentle (https:// lowerquality.com/gentle/).

Privacy metric: The attacker assesses the speaker similarity between an enrollment and a trial utterance using the probabilistic linear discriminant analysis (PLDA) score between their x-vectors. Privacy is evaluated via the linkability D sys ↔ , which measures the non-overlap between the distributions of sameand different-speaker scores [START_REF] Maouche | A comparative study of speech anonymization metrics[END_REF]. Lower linkability means higher privacy. We assume a semi-informed attacker who knows the anonymization method (but not the mapping from source to target x-vectors) and uses that knowledge to anonymize the enrollment data and the training data for the x-vector and PLDA models [START_REF] Srivastava | Evaluating voice conversion-based privacy protection against informed attackers[END_REF]. In contrast to the VoicePrivacy 2020 Challenge where all training utterances of a given speaker are mapped to the same target x-vector, our attacker maps each training utterance to a different target. 2 This greatly increases the attacker's strength and highlights the limited privacy offered by voice anonymization alone, with linkability jumping from 0.18 in [22, Fig. 11 right] to 0.63 here. The x-vector and PLDA models are trained and tested using the Kaldi [START_REF] Povey | The Kaldi speech recognition toolkit[END_REF] recipe in [START_REF] Tomashenko | Introducing the VoicePrivacy initiative[END_REF], except that the enrollment and trial data are sliced.

ASR system and utility metric: To evaluate the utility of sliced utterances for ASR acoustic model training, we use the state-of-the-art Kaldi [START_REF] Povey | The Kaldi speech recognition toolkit[END_REF] ASR recipe for LibriSpeech involving a factorized time delay neural network (TDNN-F) acoustic model and a 3-gram language model. The recipe is identical to [START_REF] Tomashenko | Introducing the VoicePrivacy initiative[END_REF], except that we train it on sliced utterances and test it on unsegmented utterances. We report the resulting WER.

Datasets: The experiments are conducted on LibriSpeech [START_REF] Panayotov | Librispeech: An ASR corpus based on public domain audio books[END_REF]. The x-vector and PLDA models and the ASR system are trained on the train-clean-360 set (∼1k speakers, ∼100k utterances and 360 h of speech). Part of the test-clean set (40 speakers, 1,496 utterances) forms the trial/public data. The remaining part (29 speakers, 438 utterances) forms the enrollment/found data. This is the established VoicePrivacy 2020 setup [START_REF] Tomashenko | Introducing the VoicePrivacy initiative[END_REF].

Effect of Slicing on Utility

We first explore which utterance durations are suitable for training an ASR acoustic model. Table 1 reports the WERs achieved in four cases, depending on whether the training data has been anonymized or not before slicing and whether the test data has been anonymized or not. In each case, we report the results achieved with the original training utterances and with different slicing durations. We notice an increase in the WER when decoding anonymized data with an ASR acoustic model trained on clear data and vice-versa, which can be attributed to a training/test mismatch. Nevertheless, the WER obtained when training and testing on unsegmented anonymized data (4.86%) is similar to training and testing on unsegmented clear data (4.26%). As for the effect of slicing itself, we notice that, for clear training data, 1.5 s is the shortest possible duration below which the WER degrades a lot. With anonymized training data, the duration can be shortened to 1 s only when decoding anonymized speech. The resulting WER (4.92%) is statistically equivalent to training on unsegmented anonymized data.

Effect of Slicing on Privacy

In terms of privacy, we present in Fig. 3 the linkability achieved with utterances of different durations. We consider the setting where the attacker aims to re-identify speakers in the trial/public data, hence the focus is now on test data (instead of training data). The purple and orange curves are obtained by shortening the utterances to a fixed duration (irrespective of word boundaries). The results on clear data illustrate the positive impact of shorter utterances on linkability, especially for durations shorter than 1 s. Unfortunately, our utility experiment demonstrated that utterances shorter than 1 s are too short to train an ASR system. Also, for durations longer than 1 s the level of privacy offered by shortening alone is insufficient. For this reason, shortening must be used together with anonymization: this combination yields a high level of privacy for δ = 1 s.

In addition, we show the results obtained with word-level slicing (Algorithm 1) and with unsegmented anonymized utterances. Word-level slicing achieves consistent results with shortening to a fixed duration of 1 s or 1.5 s. We observe that the word-level constraint, which is desirable for ASR training, does not come at the cost of a privacy loss. The linkability achieved when slicing anonymized utterances with δ = 1 s decreases to 0.14, compared to 0.63 before slicing. 3To sum up, the results of Sections 4.2-4.3 show that slicing anonymized data with δ = 1 s greatly decreases the linkability while maintaining the utility for ASR training.

Reversibility of the Slicing

We assess the risk that an attacker manages to reverse the slicing and reassemble speech slices or text transcripts together. We focus on the task of linking two successive slices, since an attacker who performs poorly on this task is unlikely to reassemble entire utterances. Due to the novelty of slicing for privacy, there exists no reassembling method which we can compare to.

Text Successiveness

Regarding text, we design an attacker that leverages a language model (LM) to construct a "text successiveness score" to be used as linkage function, similarly to the speaker verification attack described in Section 2. The LM estimates the probability P (w) of any sentence w. The attacker uses P to compute a score function SF (w, v) between two transcripts w and v. The higher the score, the higher the chance that the transcripts are successive. In our experiments, we used a 3-gram LM P (w) where wi:j = wiwi+1 . . . wj and wpwq denotes concatenation between words/sentences. To restrict our attention to the terms involving both w and v, we define the score function as SF (w, v) = P (wn-1wnv1v2).

(

) 2
To retrieve the successor of a given slice w in the public dataset, the attacker computes the scores SF (w, v) for all other slices v in the dataset and sorts them in decreasing order. The success of the attack can be quantified via the rank r(w) of the correct successor. We consider the following ranking metrics: (1) Average normalized rank: mean of r(w) over all w, divided by the maximum possible rank (that is the number of slices minus one); (2) Median normalized rank: median of r(w) divided by the maximum possible rank; (3) Precision at top-1: how often the slice with top score is the successor; (4) Precision at top-10%: how often the successor belongs to the top-10% scores. In addition to the LibriSpeech test set, which may be more easily attacked due to speakers reading text from distinct books including specific words like character names, we also consider the Mozilla Common Voice test set. In the latter case, we slice the transcripts into 3-word slices (the average number of words per second is 2.7) and we retrain the LM.

Table 2 presents the results. We notice that the correct successive slice usually has large rank (27%-32% normalized rank in average and 16%-23% median rank) meaning that thousands of wrong successive slices have a better score. We also see that the correct slice almost never ranks first (less than 3% of the cases), and rarely in the top-10% (only one third of the cases). non-successive different-speaker pairs. For δ = 1.5, we obtain on average 232 successive pairs, 5, 378 non-successive samespeaker pairs, and 125, 796 non-successive different-speaker pairs. We observe that, even though the overall accuracy of the classifiers is 80%, the vast majority of correct classifications are for the easier, non-successive different-speaker class. We consider the same ranking metrics as in Section 5.1. The results given in Table 3 show that for δ = 1, the average rank of the correct slice is 268 (top-43%), with a median of 158 (top-25%). Furthermore, the top-1 precision is again lower than 2%. Overall, these results show that it is very difficult for an attacker to consistently find the correct successive slice, and thus it is even harder to reassemble entire utterances.

Conclusion

We provided the first study on slicing speech utterances into shorter segments in the context of speech anonymization. Combining our slicing approach with state-of-the-art x-vector based anonymization methods, we showed that slices of 1 s of speech can be used to train an ASR system with 4.92% WER (compared to 4.86% without slicing) while reducing speaker verification performance to 0.14 linkability (compared to 0.63 without slicing). In addition, our approach naturally helps to obfuscate sensitive information contained in the verbal content as each slice contains few words that become isolated from their context. Finally, we showed that reversing the slicing to reconstruct the original utterances is a very difficult task.

Figure 1 :

 1 Figure 1: Anonymization procedure and attack model.

Figure 2 :

 2 Figure 2: ASR system architecture.

1 :

 1 function SLICE(u: speech signal, w: transcript, δ: target (minimum) duration)

Figure 3 :

 3 Figure 3: Linkability achieved by shortening utterances to a fixed duration (purple and yellow curves), by word-level slicing (short horizontal bars) and without slicing (long horizontal bar). The horizontal bar of a given setup is positioned by the set's overall linkability and spans the mean and standard deviation of the utterance durations.

≈

  P (w1)P (w2 | w1) len(w) k=3 P (w k | w k-2:k-1 ),(1)

Table 1 :

 1 WER (%) achieved on (unsegmented) test data when training the ASR acoustic model on sliced data.

	Training Data	Slicing	Test Data Clear Anonymized
		None	4.26	7.56
	Clear	δ = 3 s δ = 2 s	4.31 4.44	7.36 7.58
		δ = 1.5 s	4.66	8.00
		δ = 1 s	6.11	11.4
		δ = 0.5 s 26.59	35.67
		None	10.93	4.86
		δ = 3 s	13.38	4.90
	Anonymized	δ = 2 s δ = 1.5 s 21.46 15.94	4.95 4.90
		δ = 1 s	30.13	4.92
		δ = 0.5 s 71.28	8.77

Table 2 :

 2 Text-based successor identification performance.

	Test set		LibriSpeech		Common Voice
	Target slice duration δ (s)	1	1.5	3	4	/
	Number of slices	14,931 11,330 5,407 5,487	5,292
	Average normalized rank (%)	27.25	28.31 29.37 30.24	32.38
	Median normalized rank (%)	16.11	17.87 18.81 20.92	23.14
	Precision at top-1 (%)	1.39	1.41	2.18	2.56	0.75
	Precision at top-10% (%)	40.48	37.8 38.36 37.84	33.89
	5.2. Speech Successiveness

We now consider the problem of linking two successive speech signals. Our approach is to concatenate the two signals and score them by the softmax score of a binary classifier trained

Table 3 :

 3 Speech-based successor identification performance. We report the average results of 5 repeated experiments (the standard deviation is not reported as it is lower than 0.01) To prevent the model from learning to classify most examples as non-successive, we train it on a balanced dataset: half of the training examples are successive, one fourth are non-successive from the same speakers and one fourth are non-successive from different speakers. The training data are taken from LibriSpeech train-clean-360 sliced with δ = 1 s or (resp., δ = 1.5 s) and anonymized. To evaluate the attacker's performance, we sample five times 100 utterances from Librispeech test-clean sliced with δ = 1 s (resp., δ = 1.5 s) and anonymized. This allows us to construct on average for δ = 1 s, 528 successive pairs, 15, 142 non-successive same-speaker pairs, and 378, 821

	Anonymized Librispeech Test Set	Number of slices	Average normalized rank (%)	Median normalized rank (%)	Precision at top-1 (%)	Precision at top-10% (%)
	Sliced δ = 1.5 s	364	43.48	19.83	2.48	38.29
	Sliced δ = 1 s	627	42.77	25.28	1.34	29.52
	to distinguish successive vs. non-successive pairs. We use the
	TDNN architecture proposed in [4] for speaker classification.

This newly proposed attacker has recently been selected for evaluation in the VoicePrivacy 2022 Challenge[START_REF] Tomashenko | The VoicePrivacy 2022 Challenge evaluation plan[END_REF].

Surprisingly, the linkability achieved on unsegmented utterances (0.63) is lower than on utterances shortened to 7 or 10 s (up to 0.79). We attribute this to the wide range of utterance durations (8.6 s average with ±5.2 s standard deviation), which increases x-vector variability.

Acknowledgements

This work was supported in part by the European Union's Horizon 2020 Research and Innovation Program under Grant Agreements No. 825081 COMPRISE and No. 952215 TAILOR and by the French National Research Agency under project DEEP-PRIVACY (ANR-18-CE23-0018). Experiments presented in this paper were carried out using the Grid'5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr).


Hung Dinh

Abderahmane Truong

Agathe Marouf
email: amarouf@unistra.fr

Yannick Chouippe
email: chouippe@unistra.fr

Jan B Hoarau
email: hoarau@unistra.fr

Dominique Vos

Alain Charbonnier
email: dominique.charbonnier@cfse.ch

Gehri

H Truong
email: dh.truong@unistra.fr

A Chouippe

Y Hoarau

J B Vos

D Charbonnier

A Gehri

Flow analysis around a VTOL aircraft near stall conditions and application of Active Flow Control to enhance the aerodynamic performances at real flight conditions

d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

I. Nomenclature

II. Introduction

I n the 21st century, reducing the negative impacts of traveling by aircraft on the environment is one of the main concerns of the aviation industry. These include the reduction in carbon dioxide emission, aircraft noise and fossil fuel burn. Active Flow Control might be used to overcome or reduce flow separations, thereby increasing the aerodynamic efficiency (resulting in less fuel burn) and reducing noise. A large number of AFC systems exist, and might be classified according their input energy, orientation with respect to the external flow, frequency response and bandwith, [START_REF] Cattafesta | Actuators for Active Flow Control[END_REF]. This paper is concerned with the use of so called ZNMF actuators, which belong to the category of fluidic devices. The ZNMF actuators produce an oscillating jet either by an sinusoidal oscillating membrane or a piston to alternating force fluid through an orifice into the external flow field and entrain fluid back. During the blowing stroke a vortex will be produced at the orifice and is entrained with the external flow. The efficiency of ZNMF actuators depends on various parameters of which two key, ones are the dimensionless frequency of the jet 𝐹 + = 𝑓 𝑗𝑒𝑡 𝑐/𝑈 ∞ and the ratio of the actuator velocity to the free-stream velocity 𝑉 𝑗𝑒𝑡 /𝑈 ∞ (or the blowing momentum coefficient

𝐶 𝜇 = 𝑉 2 𝑗𝑒𝑡 𝐷/ 1 2 𝑈 2 ∞ 𝑐)
The study [START_REF] Seifert | Oscillatory Control of Separation at High Reynolds Numbers[END_REF], conducted for the first time experiments to study the effectiveness of synthetic jets at high Reynolds numbers at around 10 7 . The active flow control was applied for a NACA0015 airfoil without flap and equipped with a 30% chord trailing-edge flap deflected 20°. The study has demonstrated the effectiveness of oscillatory blowing for separation control regardless of the Reynolds number. The work [START_REF] Mccormick | Boundary Layer Separation Control with Directed Synthetic Jets[END_REF], investigated experimentally the use of a synthetic jet located at 4% chord from the leading edge for control of airfoil separation at 𝑅𝑒 = 5 × 10 5 . The synthetic jet with an oscillation frequency (𝐹 + = 1.3) and jet momentum coefficient (𝐶 𝜇 = 0.5%) can increase maximum lift coefficient by 25% and mitigate the stall angle by 6°. In [START_REF] Tuck | Active flow control over a naca 0015 airfoil using a znmf jet[END_REF], they conducted experiments of a flow over a NACA0015 airfoil (𝑅𝑒 = 1.54 -3.08 × 10 4 ) with a ZNMF jet located at the leading edge. An increase of 46% in the maximum lift coefficient was obtained under the controlling conditions with 𝐹 + = 1.3 and 𝐶 𝜇 = 0.14%. Moreover, [START_REF] Tang | On the use of synthetic jet actuator arrays for active flow separation control[END_REF] studied the effects of synthetic jet arrays placed at 23% and 43% of the chord on the aerodynamic performance of an LS(1)-0421MOD airfoil at the Reynolds number 𝑅𝑒 = 1.2 × 10 5 . The jet arrays allow to increase the lift by 27% and reduce the drag by 19%.

The influence of synthetic jets is also investigated by many numerical studies using different airfoils. The employment of a RANS approach in the application of unsteady jets to an airfoil NACA0012 at high Reynolds number 𝑅𝑒 = 8.5 × 10 6 was examined in [START_REF] Donovan | Active flow control applied to an airfoil[END_REF]. At a non-dimensional frequency 𝐹 + ≈ 1, a synthetic jet placed at 1.5% chord with an inclination angle of 𝛼 = 22°can improve the lift by 27%. They also found that the lift was inversely proportional to the blowing momentum coefficient 𝐶 𝜇 . By employing an unstructured-grid LES method, [START_REF] You | Active control of flow separation over an airfoil using synthetic jets[END_REF] analyzed the flow separation over a NACA0015 airfoil with synthetic jet control at Reynolds number of 8.96 × 10 5 . The study confirmed the experiments conducted by [START_REF] Gilarranz | A New Class of Synthetic Jet Actuators-Part II: Application to Flow Separation Control[END_REF], where the airfoil was installed in a wind tunnel with a synthetic jet at 12% of the chord. About 70% increase in the lift coefficient was obtained with a jet of which the momentum coefficient was 1.23%. The work [START_REF] Kim | Separation control on NACA23012 using synthetic jet[END_REF] numerically studied the benefits of single and multi synthetic jets on a NACA23012 airfoil by solving URANS equations at 𝑅𝑒 = 2.19 × 10 6 . In the case of multiple synthetic jets, several jets were arranged side by side to overcome the limit of the jet peak velocity. The study showed that the jets operating at a frequency 𝐹 + = 1 should be placed at the separation point to reach the maximum lift and the effectiveness of the jet was proportional to the jet momentum.

An automatic optimization of flow control devices is a sensitive subject due to the large computational time related to high accuracy analysis of unsteady flow and the possible multi-constraints of the objective function. To the best of our knowledge, there are only a few studies acquiring optimal parameters of synthetic jets by employing optimization algorithms. Duvigneau and Visonneau [START_REF] Duvigneau | Optimization of a synthetic jet actuator for aerodynamic stall control[END_REF] examined the effects of a synthetic jet located at 12% chord from the leading edge of a NACA0015 airfoil. They used the URANS modelling to simulate the flow at 𝑅𝑒 = 8.6 × 10 5 over the airfoil with an angle of attack varying from 12°to 24°. The solver was then coupled with an optimization procedure to find the optimal parameters of the jet. The synthetic jet reached its optimal performance under the conditions of a dimensionless velocity 𝑉 𝑗𝑒𝑡 /𝑈 ∞ = 1.72, a frequency 𝐹 + = 0.748 and an angle with respect to the wall 𝛼 = 25°. [START_REF] Montazer | Optimization of a synthetic jet actuator for flow control around an airfoil[END_REF] determined the optimal parameters of a synthetic jet in the control flow around the NACA0015 airfoil at the stall angle 13°and the post stall angle 16°. The Navier-Stokes equations for incompressible two-dimensional flow were solved by a RANS model at 𝑅𝑒 = 8.96 × 10 5 . The Response Surface Methodology (RSM) optimization method was employed to maximize the lift-drag ratio where the optimization parameters were the jet location, the jet frequency and the jet size. According to their study, when the angle of attack increase, the size of the synthetic jet and the frequency should decrease to obtain the optimal performance. Under this circumstance, the optimum synthetic jet location was moving toward the leading edge to delay the flow separation. The synthetic jet was shown to be the most effective at the post-stall angle with a 66% improvement of the lift-drag ratio. More recently, we studied two-dimensional active control using different strategies of steady blowing, transient blowing and blowing-suction method similar as piezo-actuators embedded inside the flap of a high-lift system [START_REF] Marouf | Unsteady CFD simulations for Active Flow Control[END_REF]. The AFC was coupled with a morphing flap [START_REF] Tekap | Design of a Large-Scale High-Lift Morphing A320 Wing Based on Electro-Mechanical Actuators and Shape Memory Alloys[END_REF] using the cambering approach [START_REF] Marouf | Predictive Numerical Study of Cambered Morphing A320 High-Lift Configuration Based on Electro-Mechanical Actuators[END_REF][START_REF] Tekap | Design and Experimental Validation of A320 Large Scale Morphing Flap Based on Electro-mechanical Actuators[END_REF][START_REF] Tekap | Design of a Large-Scale High-Lift Morphing A320 Wing Based on Electro-Mechanical Actuators and Shape Memory Alloys[END_REF]. The concept of cambering lead to large flow separations on the suction side of the flap, resulting in an important increase of the drag and decrease of the aerodynamic efficiency. The study showed that coupling both AFC and morphing could re-attach the boundary layer, enhance the pressure distribution over both the flap and the wing system and decrease the width of the wake. In the present work, the role of AFC will be examined for a three-dimensional Vertical Take-off and Landing (VTOL) aircraft in the context of the Active Flow Control for TiltRotor aicraft AFC4TR a CleanSky2 European projet (2020-2022). High-Fidelity numerical simulations are examined using the chimera patched grid to better implement AFC actuators in the aircraft's wing without any remeshing. Real parameters as the dimension, jet orientation-velocity and location of already available piezo-actuators are chosen.

III. Numerical methods

A. Navier Stokes Multi Block solver

The CFD simulations were carried out using the Navier Stokes Multi Block solver NSMB which is developed in a consortium composed of different universities and industries [START_REF] Vos | Recent advances in aerodynamics inside the NSMB (Navier Stokes Multi Block) consortium[END_REF] and was mainly used for aeronautics and aerospace applications over the past 30 years. NSMB has a parallel and a vectorial structure and it can run on high-performance computers to solve a wide range of industrial aerodynamics design problems. NSMB solves the Reynolds-averaged Navier Stokes equations for compressible flows on multi-block structured grids. NSMB offers all functionalities of a modern CFD code used for aerospace applications, among others, ALE (Arbitrary Lagrangian Eulerian) approach [START_REF] Donea | An arbitrary lagrangian-eulerian finite element method for transient dynamic fluid-structure interactions[END_REF], turbulence models [START_REF] Hoarau | Analyse physique par simulation numérique et modélisation des écoulements décollés instationnaires autour de surfaces portantes[END_REF], chemistry modelling, grid flexibility, FSI (Fluid-Structure Interactions) coupling such as morphing wings modelling ( [START_REF] Marouf | Numerical investigation of frequency-amplitude effects of dynamic morphing for a high-lift configuration at high Reynolds number[END_REF][START_REF] Simiriotis | Morphing of a supercritical wing by means of trailing edge deformation and vibration at high Reynolds numbers: Experimental and numerical investigation[END_REF][START_REF] Tô | Effects of vibrating and deformed trailing edge of a morphing supercritical airfoil in transonic regime by numerical simulation at high Reynolds number[END_REF]), Chimera overlapping technique [START_REF] Hoarau | Recent Developments of the Navier Stokes Multi Block (NSMB) CFD solver[END_REF]. Space discretization schemes include 2nd-and 4th-order central schemes with artificial dissipation and various upwind schemes (Roe, AUSM, Van Leer ...) up to 5th order. Time integration methods are explicit (Runge-Kutta schemes) or implicit (LU-SGS) schemes. Convergence acceleration as multigrid, local time stepping, preconditioning, full multigrid can be used to accelerate the convergence. NSMB includes also a large variety of well tested and validated turbulence models that are standard in the aeronautical industry, like the one-equation Spalart-Allmaras [START_REF] Spalart | A one-equation turbulence model for aerodynamic flows[END_REF], the Organized Eddy Simulation model [START_REF] Hoarau | Analyse physique par simulation numérique et modélisation des écoulements décollés instationnaires autour de surfaces portantes[END_REF][START_REF] Bourguet | Anisotropic Organised Eddy Simulation for the prediction of non-equilibrium turbulent flows around bodies[END_REF], or the two-equation by Menter [START_REF] Menter | Zonal Two Equation k-w Turbulence Models For Aerodynamic Flows[END_REF], that have been applied in the present study. Different morphing concepts are also implemented [START_REF] Tekap | Design of a Large-Scale High-Lift Morphing A320 Wing Based on Electro-Mechanical Actuators and Shape Memory Alloys[END_REF][START_REF] Marouf | Numerical investigation of frequency-amplitude effects of dynamic morphing for a high-lift configuration at high Reynolds number[END_REF][START_REF] Marouf | Numerical Study of Trailing-Edge Dynamics of a Two Element Airfoil-Flap with Morphing Flap at High Reynolds Number[END_REF][START_REF] Tô | Effects of vibrating and deformed trailing edge of a morphing supercritical airfoil in transonic regime by numerical simulation at high Reynolds number[END_REF][START_REF] Tekap | Design of a Large-Scale High-Lift Morphing A320 Wing Based on Electro-Mechanical Actuators and Shape Memory Alloys[END_REF] in the code. Unsteady simulations are made using the dual time stepping approach or using Newton's approach.

B. Mesh generation

The ANSYS ICEM CFD pre-processer tool was used to generate the multi-block structured grids needed by the NSMB flow solver. The structured grids were designed following the gridding guidelines of the AIAA CFD High Lift/Drag Prediction Workshops. The Chimera overlapping technique available in NSMB solver as well as the patch grid connectivity technique were employed in order to simplify the mesh generation. Indeed, the Chimera technique allows to create one structural grid for a single configuration, and obtain the other configurations, or configuration with moving parts during unsteady simulations, applying only rotations and displacements of partial grid elements (groups of structured blocks) around the moving parts like the aileron, the engine and the rotor blades for example as shown in Figure 1. The patch grid connectivity was used to reduce the number of cells in the far-field region and the far-field boundary distance has been set to 100 times the reference chord (cf. Figure 1). To resolve the viscous boundary layer, O-grid topologies with a geometric cell distribution were used near the solid walls (fuselage, wing, aileron, nacelle, engine, rotor blades). The first cell height in the wall normal direction was set to obtain a 𝑦+ value around 1 to ensure the proper use of low-Reynolds turbulence modelling, Δ𝑌 wall cell < 0.006 mm, and the growth ratio of the cells normal to the wall was typically close to 1.2. The chordwise spacing was set below 0.1% of the local chord at the leading edge and the trailing edge of the main wing as well as for the blades. The spanwise spacing was set below 0.1% of the semi-span at the root (fuselage junction) and at the tip (nacelle junction). Due to the aileron located close to the nacelle junction, the mesh density at the tip was extended widely in this region. Figure 2 shows the mesh on the airplane surface as well as the volume mesh around chimera components.

Particular attention was also paid to the refinement of the mesh close to the areas where the flow control devices ZNMF devices will be applied later in the study (cf. Figure 3).

IV. Results and discussion

A. Baseline confugrations (without control)

Different unsteady simulations were performed around the Next Generation Civil TiltRotor (NGCTR) aircraft of Leonardo Helicopters to identify critical flow configurations that lead to a considerable detachment of the boundary layer and a loss in aerodynamic performances due to an increase of the drag and a decrease in lift. A subsonic speed range was selected, considering take-off and landing phases. From the large number of simulations executed, the following three configurations (cf. Figure 4) were identified that could benefit from ZNMF devices:

•

Configuration 1: fixed-wing plane

Firstly, we consider the case where the tiltrotor has a configuration of an ordinary fixed-wing plane with a horizontal nacelle. At a low angle of attack, a separation will occurs with time near the trailing-edge of the wing along the spanwise direction, but this separation remains small as can be seen from the blue colour region near the trailing-edge. A small corner vortex is formed near the wing-nacelle junction with a small size. This is because of the blockage effect due to the presence of the rotating rotors. For this case there is no need to introduce an active or passive flow control. When the angle of attack is set higher, a more interesting case is obtained. This can be seen in Figure 5 which shows unsteady snapshots saved in time and highlights the development of corner vortices. At the time 𝑡 = 0.1 s, a small formation of a corner vortex can be seen near the nacelle and the fuselage. When time arrives at 𝑡 = 0.3 s, both vortices become more important, and they lead to a continuous separation near the trailing-edge along the span wise direction of the wing. For 𝑡 = 0.6 s, the trailing-edge separation is more developed due to the non-linear interactions with both corner vortices. This separation becomes more important and travels upstream on the wing, it is affected by the considerable higher angle of attack near stall conditions and the rotation of the blades through their periodic effects. The existence of this trailing-edge separation was also found in a study of [START_REF] Ryu | Large-Eddy Simulation of a Wing-Body Junction Flow[END_REF]. They investigated a typical behaviour of a corner vortex between a classical wing-fuselage and its influence along the span. They found the existence of a trailing-edge separation along the span created by the presence of this corner vortex. This configuration needs to be optimized through the use of flow control to attenuate or suppress the flow separation and to increase the aerodynamic performances near the fuselage and the nacelle.

Configuration 2: nacelle tilting

The nacelle tilting effect has also been investigated and similar numerical parameters are considered as in the previous cases. When the nacelle is oriented in the upward direction the blockage effect of the rotors is reduced and a deviation of flow starts to be formed near the nacelle-wing junction. The flow remains attached at low angle of attack (not shown). The same configuration at a higher angle of attack showed a small vortex formed at 60% of the span (distance from the fuselage) and the flow is deviated from the nacelle. The size of the vortex increases through the interaction with the rotation of the blades and the deviated flow. This separation becomes more important when the simulation time achieves 0.3 s. It interacts with the separation along the trailing-edge to turn into a more important flow detachment. Figure 6 shows the nacelle tilting configuration at a high angle of attack. Largely detached boundary layers can be seen for this high angle of attack. The separation covers approximately half of the wing surface. It shows an important detachment of the flow from the trailing-edge where the separation point is traveling upstream of the wing. It can be concluded that when the nacelle is tilted and the rotation speed of the blades is increased, the flow becomes unstable, and an important separation of the boundary layer occurs even at lower angles of attack. The flow in this configuration is critical and it is very well adapted to the use of passive and active flow control devices.

Configuration 3: aileron deflection

The aileron deflection is analysed and highlighted in Figure 7. The gaps are now open due to the angle of deflection of the aileron. The aileron is deflected in the downward direction. The nacelle tilting is set back to the same position as in configuration 1.

When the aileron is deflected in the downward position (Figure 7), the flow is fully detached on the upper surface of the aileron. Different snapshots are saved in time to follow the unsteady development of the separation. The separation remains the same on the aileron extrados and the flow is fully attached on the wing extrados. A small deviation of the flow is observed near the wing-nacelle junction, but this last is not important. The aileron dimensions are smaller compared to the dimension of the wing, and in particular its thickness. This might lead to difficulties with the implementation of ZNMF actuators inside the aileron. The ZNMF location will be carefully investigated specifically for this configuration with the aileron deflection in the downward direction, to make sure of the

B. Configurations with Active Flow Control)

We present here the unsteady simulations of the flow around the NGCTR aircraft with the active flow control employed by using the ZNMF devices. There are 6 ZNMF devices embedded in different regions near the fuselage-wing junction, the middle of the span and the aileron-nacelle junction. The jets were translated along the chordwise direction in order to investigate the sensitivity of the jet location to the corner vortex. From the initial position (position 3), the jets were moved upstream 20 % (position 1) and 10 % (position 2) then downstream 10 % (position 4) and 20 % (position 5) of the wing chord as shown in Figure 8(1). At position 3, it can be noticed in Figure 9 that the flow is reattached in the location of the aileron-nacelle junction leading to the disappearance of the corner vortex. As the jets were moved upstream and far away from the separation line, the effect of these ZNMF becomes less important. However, when they were moved downstream and closer to the trailing edge where the flow starting to detach, the size of the separation zone was reduced and the vortex at the aileron-nacelle junction almost disappeared. At approximately the middle of the span, and for all cases, the trailing-edge separation is divided into different counter rotating vortices leading to a partial reattachment of the flow. This has been noticed in the 3D simulations of the high lift system with a constant span using the ASPIC actuators and revealed that a separating distance between two jets permits a formation of two-counter rotating vortices [START_REF] Marouf | CFD simulations of active flow control devices applied on a cambered flap[END_REF]. The present corner vortex in the fuselage-wing junction has been shifted along the span for the ZNMF case but not fully suppressed because the positions of the jets are are not well adapted. Therefore, there is a need to optimize the position and/or the number of the jets that are embedded in the wing.

Configuration 2

The ZNMF devices are firstly placed at the same positions as for the case 1. The purpose of the study is to optimize the ZNMF location, and if possible, find a unique position for all the configurations, which is more realistic and feasible for the constructor. When the nacelle is tilted, the flow is deviated leading to a decrease of the blocking effects caused by the blades and a formation of an important periodic separation at the middle of the span. Based on the simulations, it has been found that for this configuration the flow is automatically reattached and the corner jets situated in the aileron-nacelle junction are not effective. The jets located in the middle of the span are more important because of the location of the separation. This last travels upstream towards the wing leading-edge and unfortunately the jets become less effective as it was demonstrated for the 3D simulations (cf. Figure 10). Theses jets should be located upstream of the flow separation to be able to reattach the flow.

Consequently, we have modified the jet positions as shown in Figure 8 in order to improve their efficiency. The middle jets were moved such that they were placed upstream of the separation zone while two corner jets were shifted closer to the middle in order to reattach the flow more effectively. The skin friction 𝐶𝐹 𝑥 contours of all the cases were shown in Figure 11. Around 𝑡 = 0.16 𝑠, the rotation of the blades induced a big vortex which caused the flow to separate along the wing span. With the application of the jets, the size of the separation zone started to be reduced especially in the case of position 3. The flow at the aileron-nacelle and the fuselage-wing junctions were well reattached while a noticeable zone of separation still existed in the middle of the span. Further investigations should be conducted in this area in order to improve the aerodynamic performance of this configuration.

Configuration 3

In the case of the configuration 3, the flow remains attached along the span of the wing and is less sensitive to the rotation of the blades. However, the flow around the aileron is fully detached as shown in Figure 7. Thus, the interest of using active flow control will be focused on the aileron part where the ZNMF devices will be the most effective. There were in total 6 jets embedded on the aileron (cf. Figure 8) and the skin friction 𝐶𝐹 𝑥 contours on the aileron are shown in Figure 12 for these cases. While the flow on the aileron was partially reattached, especially in the region behind the jets, new vortices were formed in the gap between the jets. The effect of these ZNMFs is better shown by looking at the time history of the lift and drag coefficient of the aileron as presented in Figure 13. Due to the rotation of the blades, the forces fluctuates in a wide range. Nevertheless, the average value of the drag coefficient 𝐶𝑥 seems to be unaffected while the lift coefficient in the controlled cases are improved with respect to the baseline. In addition, the position 1,2 and 3 outperform the other two configurations characterized with a higher lift.

V. Conclusion and outlook

The flow around the Leonardo Next Generation of Civil Tilt Rotor was studied in detail using the NSMB CFD solver employing the chimera grid method to facilitate the modification of the nacelle tilting angle, the pitching of rotors and the aileron deflections. The flow behaviour on the NGCTR was studied in detail for different angles of attack, different angles of nacelle tilting and different deflection angles of the aileron to identify critical configurations in terms of high boundary layer detachment. Three configurations were selected for the use of ZNMF devices for active flow control. Results of these simulations showed that the effectiveness of these devices is highly dependent on their location, and that they should be placed upstream of the flow separation. As perspectives, different parameters of the ZNMF devices will be examined as the location, the number of actuators as well as the jet velocity and its orientation.

  𝐹 + = jet dimensionless frequency 𝐶 𝜇 = blowing momentum coefficient 𝑉 𝑗𝑒𝑡 = jet velocity magnitude 𝜌 = air density 𝑅𝑒 = Reynolds number 𝑈 ∞ = streamflow velocity

Fig. 1 Fig. 2

 12 Fig. 1 Patch grid connectivity between near field around the aircraft (left) and far field (right). Topology of the grid with far-field boundary at 100 × 𝐶 𝑟 𝑒 𝑓 .

Fig. 3

 3 Fig. 3 AFC devices embedded inside the wing.

Fig. 4

 4 Fig. 4 The three studied configurations of the NGCTR aircraft: the fixed-wing plane (1), the nacelle tilting (2) and the aileron deflection (3).

Fig. 5

 5 Fig. 5 Skin friction 𝐶𝐹 𝑥 component contours with streamlines of CF (𝐶𝐹 𝑥 , 𝐶𝐹 𝑦 , 𝐶𝐹 𝑧 ), for the first configuration with blade rotation.

Fig. 6

 6 Fig. 6 Skin friction 𝐶𝐹 𝑥 component contours with streamlines of CF (𝐶𝐹 𝑥 , 𝐶𝐹 𝑦 , 𝐶𝐹 𝑧 ) for the configuration 2 with a tilted nacelle and the rotation of blades.

Fig. 7

 7 Fig. 7 Skin friction 𝐶𝐹 𝑥 component contours with streamlines of CF (𝐶𝐹 𝑥 , 𝐶𝐹 𝑦 , 𝐶𝐹 𝑧 ) for the configuration 3 where the aileron is deflected downward and blades are rotating.

Fig. 8

 8 Fig. 8 The three studied configurations of the NGCTR aircraft and the optimized position of the ZNMF devices

Fig. 9

 9 Fig. 9 Skin friction 𝐶𝐹 𝑥 component contours with streamlines of CF (𝐶𝐹 𝑥 , 𝐶𝐹 𝑦 , 𝐶𝐹 𝑧 ) for the baseline (top left) and the controlled cases of configuration 1 with the rotation of blades.

Fig. 10

 10 Fig. 10 Skin friction 𝐶𝐹 𝑥 component contours on the wing in the case of the configuration 2 with rotation of blades for the baseline case (a) and the flow controlled by ZNMF devices of which locations are the same as in configuration 1.

Fig. 11

 11 Fig. 11 Skin friction 𝐶𝐹 𝑥 component contours for the baseline (top left) and the controlled cases of configuration 2 with a tilting nacelle and rotating blades.

Fig. 12

 12 Fig. 12 Skin friction 𝐶𝐹 𝑥 component contours for the baseline (top left) and the controlled cases of configuration 3 with the aileron deflection and rotation of blades.

Fig. 13

 13 Fig. 13 Time history of the lift coefficient (left) and the drag coefficient (right) acting on the aileron part for different cases of the baseline flow (in black) and the one controlled with ZNMF devices (in color).

Downloaded by Jan Vos on June 27, 2022 | http://arc.aiaa.org | DOI: 10.2514/6.2022-3238

Acknowledgments

The results presented in this paper are carried in the CleanSky2 project AFC4TR (funded by the European Union H2020 program under Grant Agreement 886718) and the H2020 project SMS (under Grant Agreement 723402). This work was granted access to the HPC resources of [CINES/TGCC] under the allocation 2020-2021-2022-[A0102A11355] made by GENCI. The authors would like to acknowledge the High Performance Computing Center of the University of Strasbourg for supporting this work by providing scientific support and access to computing resources. Part of the computing resources were funded by the Equipex Equip@Meso project (Programme Investissements d'Avenir) and the CPER Alsacalcul/Big Data. Leonardo Helicopters is acknowledged for providing the geometry, calculation conditions and for their support in performing the simulations.