Investigation of the Thermomechanical Response of Cyclically Loaded NiTi Alloys by Means of Temperature Frequency Domain Analyses

Nickel–Titanium (NiTi) shape memory alloys subjected to cyclic loading exhibit reversible temperature changes whose modulation is correlated with the applied load. This reveals the presence of reversible thermomechanical heat sources activated by the applied stresses. One such source is the elastocaloric effect, accounting for the latent heat of Austenite–Martensite phase transformation. It is, however, observed that when the amplitude of cyclic loads is not sufficient to activate or further propagate this phase transformation, the material still exhibits a strong cyclic temperature modulation. The present work investigates the thermomechanical behaviour of NiTi under such low-amplitude cyclic loading. This is carried out by analysing the frequency domain content of temperature sampled over a time window. The amplitude and phase of the most significant harmonics are obtained and compared with the theoretical predictions from the first and second-order theories of the Thermoelastic Effect, this being the typical reversible thermomechanical coupling prevailing under elastic straining. A thin strip of NiTi, exhibiting a fully superelastic behaviour at room temperature, was investigated under low-stress amplitude tensile fatigue cycling. Full-field strain and temperature distributions were obtained by means of Digital Image Correlation and IR Thermography. The work shows that the full field maps of amplitude and phase of the first three significant temperature harmonics carry out many qualitative information about the stress and structural state of the material. It is, though, found that the second-order theory of the Thermoelastic Effect is not fully capable of justifying some of the features of the harmonic response, and further work on the specific nature of thermomechanical heat sources is required for a more quantitative interpretation

Investigation of the thermomechanical response of cyclically loaded niti alloys by means of temperature frequency domain analyses

Nickel\u2013Titanium (NiTi) shape memory alloys subjected to cyclic loading exhibit reversible temperature changes whose modulation is correlated with the applied load. This reveals the pres-ence of reversible thermomechanical heat sources activated by the applied stresses. One such source is the elastocaloric effect, accounting for the latent heat of Austenite\u2013Martensite phase transfor-mation. It is, however, observed that when the amplitude of cyclic loads is not sufficient to activate or further propagate this phase transformation, the material still exhibits a strong cyclic temperature modulation. The present work investigates the thermomechanical behaviour of NiTi under such low-amplitude cyclic loading. This is carried out by analysing the frequency domain content of temperature sampled over a time window. The amplitude and phase of the most significant harmonics are obtained and compared with the theoretical predictions from the first and second-order theories of the Thermoelastic Effect, this being the typical reversible thermomechanical coupling prevailing under elastic straining. A thin strip of NiTi, exhibiting a fully superelastic behaviour at room temperature, was investigated under low-stress amplitude tensile fatigue cycling. Full-field strain and temperature distributions were obtained by means of Digital Image Correlation and IR Thermography. The work shows that the full field maps of amplitude and phase of the first three significant temperature harmonics carry out many qualitative information about the stress and structural state of the material. It is, though, found that the second-order theory of the Thermoelastic Effect is not fully capable of justifying some of the features of the harmonic response, and further work on the specific nature of thermomechanical heat sources is required for a more quantitative interpretation

Scalable Reed-Solomon-based Reliable Local Storage for HPC Applications on IaaS Clouds

International audienceWith increasing interest among mainstream users to run HPC applications, Infrastructure-as-a-Service (IaaS) cloud computing platforms represent a viable alternative to the acquisition and maintenance of expensive hardware, often out of the financial capabilities of such users. Also, one of the critical needs of HPC applications is an efficient, scalable and persistent storage. Unfortunately, storage options proposed by cloud providers are not standardized and typically use a different access model. In this context, the local disks on the compute nodes can be used to save large data sets such as the data generated by Checkpoint-Restart (CR). This local storage offers high throughput and scalability but it needs to be combined with persistency techniques, such as block replication or erasure codes. One of the main challenges that such techniques face is to minimize the overhead of performance and I/O resource utilization (i.e., storage space and bandwidth), while at the same time guaranteeing high reliability of the saved data. This paper introduces a novel persistency technique that leverages Reed-Solomon (RS) encoding to save data in a reliable fashion. Compared to traditional approaches that rely on block replication, we demonstrate about 50% higher throughput while reducing network bandwidth and storage utilization by a factor of 2 for the same targeted reliability level. This is achieved both by modeling and real life experimentation on hundreds of nodes

Spatial support vector regression to detect silent errors in the exascale era

As the exascale era approaches, the increasing capacity of high-performance computing (HPC) systems with targeted power and energy budget goals introduces significant challenges in reliability. Silent data corruptions (SDCs) or silent errors are one of the major sources that corrupt the executionresults of HPC applications without being detected. In this work, we explore a low-memory-overhead SDC detector, by leveraging epsilon-insensitive support vector machine regression, to detect SDCs that occur in HPC applications that can be characterized by an impact error bound. The key contributions are three fold. (1) Our design takes spatialfeatures (i.e., neighbouring data values for each data point in a snapshot) into training data, such that little memory overhead (less than 1%) is introduced. (2) We provide an in-depth study on the detection ability and performance with different parameters, and we optimize the detection range carefully. (3) Experiments with eight real-world HPC applications show thatour detector can achieve the detection sensitivity (i.e., recall) up to 99% yet suffer a less than 1% of false positive rate for most cases. Our detector incurs low performance overhead, 5% on average, for all benchmarks studied in the paper. Compared with other state-of-the-art techniques, our detector exhibits the best tradeoff considering the detection ability and overheads.This work was supported by the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research Program, under Contract DE-AC02-06CH11357, by FI-DGR 2013 scholarship, by HiPEAC PhD Collaboration Grant, the European Community’s Seventh Framework Programme [FP7/2007-2013] under the Mont-blanc 2 Project (www.montblanc-project.eu), grant agreement no. 610402, and TIN2015-65316-P.Peer ReviewedPostprint (author's final draft

Preliminary design and control of a soft exosuit for assisting elbow movements and hand grasping in activities of daily living

The development of a portable assistive device to aid patients affected by neuromuscular disorders has been the ultimategoal of assistive robots since the late 1960s. Despite significant advances in recent decades, traditional rigid exoskeletonsare constrained by limited portability, safety, ergonomics, autonomy and, most of all, cost. In this study, we present thedesign and control of a soft, textile-based exosuit for assisting elbow flexion/extension and hand open/close. We describea model-based design, characterisation and testing of two independent actuator modules for the elbow and hand,respectively. Both actuators drive a set of artificial tendons, routed through the exosuit along specific load paths, thatapply torques to the human joints by means of anchor points. Key features in our design are under-actuation and the useof electromagnetic clutches to unload the motors during static posture. These two aspects, along with the use of 3Dprinted components and off-the-shelf fabric materials, contribute to cut down the power requirements, mass and overallcost of the system, making it a more likely candidate for daily use and enlarging its target population. Low-level control isaccomplished by a computationally efficient machine learning algorithm that derives the system’s model from sensorydata, ensuring high tracking accuracy despite the uncertainties deriving from its soft architecture. The resulting system isa low-profile, low-cost and wearable exosuit designed to intuitively assist the wearer in activities of daily living

Decoding of Multiple Wrist and Hand Movements Using a Transient EMG Classifier

: The design of prosthetic controllers by means of neurophysiological signals still poses a crucial challenge to bioengineers. State of the art of electromyographic (EMG) continuous pattern recognition controllers rely on the questionable assumption that repeated muscular contractions produce repeatable patterns of steady-state EMG signals. Conversely, we propose an algorithm that decodes wrist and hand movements by processing the signals that immediately follow the onset of contraction (i.e., the transient EMG). We collected EMG data from the forearms of 14 non-amputee and 5 transradial amputee participants while they performed wrist flexion/extension, pronation/supination, and four hand grasps (power, lateral, bi-digital, open). We firstly identified the combination of wrist and hand movements that yielded the best control performance for the same participant (intra-subject classification). Then, we assessed the ability of our algorithm to classify participant data that were not included in the training set (cross-subject classification). Our controller achieved a median accuracy of ~96% with non-amputees, while it achieved heterogeneous outcomes with amputees, with a median accuracy of ~89%. Importantly, for each amputee, it produced at least one acceptable combination of wrist-hand movements (i.e., with accuracy >85%). Regarding the cross-subject classifier, while our algorithm obtained promising results with non-amputees (accuracy up to ~80%), they were not as good with amputees (accuracy up to ~35%), possibly suggesting further assessments with domain-adaptation strategies. In general, our offline outcomes, together with a preliminary online assessment, support the hypothesis that the transient EMG decoding could represent a viable pattern recognition strategy, encouraging further online assessments

A robot-aided visuomotor wrist training induces gains in proprioceptive and movement accuracy in the contralateral wrist

Proprioceptive training is a neurorehabilitation approach known to improve proprioceptive acuity and motor performance of a joint/limb system. Here, we examined if such learning transfers to the contralateral joints. Using a robotic exoskeleton, 15 healthy, right-handed adults (18-35 years) trained a visuomotor task that required making increasingly small wrist movements challenging proprioceptive function. Wrist position sense just-noticeable-difference thresholds (JND) and spatial movement accuracy error (MAE) in a wrist-pointing task that was not trained were assessed before and immediately as well as 24 h after training. The main results are: first, training reduced JND thresholds (- 27%) and MAE (- 33%) in the trained right wrist. Sensory and motor gains were observable 24 h after training. Second, in the untrained left wrist, mean JND significantly decreased (- 32%) at posttest. However, at retention the effect was no longer significant. Third, motor error at the untrained wrist declined slowly. Gains were not significant at posttest, but MAE was significantly reduced (- 27%) at retention. This study provides first evidence that proprioceptive-focused visuomotor training can induce proprioceptive and motor gains not only in the trained joint but also in the contralateral, homologous joint. We discuss the possible neurophysiological mechanism behind such sensorimotor transfer and its implications for neurorehabilitation

Decoding of Multiple Wrist and Hand Movements Using a Transient EMG Classifier

The design of prosthetic controllers by means of neurophysiological signals still poses a crucial challenge to bioengineers. State of the art of electromyographic (EMG) continuous pattern recognition controllers rely on the questionable assumption that repeated muscular contractions produce repeatable patterns of steady-state EMG signals. Conversely, we propose an algorithm that decodes wrist and hand movements by processing the signals that immediately follow the onset of contraction (i.e., the \textit {transient} EMG). We collected EMG data from the forearms of 14 non-amputee and 5 transradial amputee participants while they performed wrist flexion/extension, pronation/supination, and four hand grasps (power, lateral, bi-digital, open). We firstly identified the combination of wrist and hand movements that yielded the best control performance for the same participant (intra-subject classification). Then, we assessed the ability of our algorithm to classify participant data that were not included in the training set (cross-subject classification). Our controller achieved a median accuracy of 96% with non-amputees, while it achieved heterogeneous outcomes with amputees, with a median accuracy of 89%. Importantly, for each amputee, it produced at least one \textit {acceptable} combination of wrist-hand movements (i.e., with accuracy >85%). Regarding the cross-subject classifier, while our algorithm obtained promising results with non-amputees (accuracy up to 80%), they were not as good with amputees (accuracy up to 35%), possibly suggesting further assessments with domain-adaptation strategies. In general, our offline outcomes, together with a preliminary online assessment, support the hypothesis that the transient EMG decoding could represent a viable pattern recognition strategy, encouraging further online assessments