Spatio-temporal physics-informed neural networks to solve boundary value problems for classical and gradient-enhanced continua

Recent advances have prominently highlighted physics informed neural networks (PINNs) as an efficient methodology for solving partial differential equations (PDEs). The present paper proposes a proof of concept exploring the use of PINNs as an alternative to finite element (FE) solvers in both classical and gradient-enhanced solid mechanics. To this end, spatio-temporal PINNs are designed to represent continuous solutions of boundary value problems within spatio-temporal space. These PINNs directly incorporate the equilibrium and constitutive equations in their differential and rate forms, bypassing the requirement for incremental implementation. This simplifies application of PINNs to solve complex mechanical problems, particularly those involved in the context of gradient-enhanced continua. Moreover, traditional meshing is no longer required as it is replaced by a point cloud, making it possible to overcome meshing drawbacks. The results of this investigation prove the effectiveness of the proposed methodology, especially with regards to non-monotonic loading conditions and irreversible plastic deformation. Compared to classical FE approaches, the proposed spatio-temporal PINNs are more readily applied to complex problems, which are tackled in their raw form. This is especially true for gradient-enhanced continuum problems, where there is no need to introduce additional degrees of freedom as in classical FE approaches. However, PINNs training generally requires more computation time, a challenge that can be mitigated by employing the concept of transfer learning as shown in this paper. This concept, which is very useful when performing parametric studies, involves applying knowledge grained from solving one problem to another different but related one. The use of PINNs as mechanical solvers is shown to be highly promising in the forthcoming era, where advancements in GPU technology can further enhance their performance in terms of computation time

Evidence of Dislocation Mixed Climb in Quartz From the Main Central and Moine Thrusts: An Electron Tomography Study

In this study we apply electron tomography to characterize 3D dislocation microstructures in two quartz mylonite specimens from the Moine and Main Central Thrusts, both of which accommodated displacements by dislocation creep in the presence of water. Both specimens show dislocation activity with dislocation densities of the order of 3–4 × 1012 m−2 and evidence of recovery from the presence of subgrain boundaries. ⟨a⟩ slip occurs predominantly on pyramidal and prismatic planes (⟨a⟩ basal glide is not active). [c] Glide is not significant. On the other hand, we observe a very high level of activation of ⟨c + a⟩ glide on the , , (n = 1,2) and even planes. Approximately 60% of all dislocations show evidence of climb with a predominance of mixed climb, a deformation mechanism characterized by dislocations moving in a plane intermediate between the glide and the climb planes. This atypical mode of deformation demonstrates comparable glide and climb efficiency under natural deformation conditions. It promotes dislocation glide in planes not expected for the quartz structure, probably by inhibiting lattice friction. Our quantitative characterization of the microstructure enables us to assess the strain that dislocations can generate. We show that glide systems indicated by the observed dislocations are sufficient to accommodate any arbitrary 3D strain by themselves. Although historically dislocation glide has been regarded as being primarily responsible for producing strain, activation of climb can also directly contribute to the finite strain. On the basis of this characterization, we propose a numerical modeling approach for attempting to characterize the local stress state that gave rise to the observed microstructure

Neurophysiological basis of respiratory discomfort improvement by mandibular advancement in awake OSA patients

Patients with obstructive sleep apneas (OSA) do not complain from dyspnea during resting breathing. Placement of a mandibular advancement device (MAD) can lead to a sense of improved respiratory comfort (“pseudo‐relief”) ascribed to a habituation phenomenon. To substantiate this conjecture, we hypothesized that, in non‐dyspneic awake OSA patients, respiratory‐related electroencephalographic figures, abnormally present during awake resting breathing, would disappear or change in parallel with MAD‐associated pseudo‐relief. In 20 patients, we compared natural breathing and breathing with MAD on: breathing discomfort (transitional visual analog scale, VAS‐2); upper airway mechanics, assessed in terms of pressure peak/time to peak (TTP) ratio respiratory‐related electroencephalography (EEG) signatures, including slow event‐related preinspiratory potentials; and a between‐state discrimination based on continuous connectivity evaluation. MAD improved breathing and upper airway mechanics. The 8 patients in whom the EEG between‐state discrimination was considered effective exhibited higher Peak/TTP improvement and transitional VAS ratings while wearing MAD than the 12 patients where it was not. These results support the notion of habituation to abnormal respiratory‐related afferents in OSA patients and fuel the causative nature of the relationship between dyspnea, respiratory‐related motor cortical activity and impaired upper airway mechanics in this setting

A location-based model using GIS with machine learning, and a human-based approach for demining a post-war region

Locating and removing landmines and other ERW (Explosive Remnants of War) is dangerous, hazardous, and time-consuming. It requires implementing multilevel on-site surveys: general non-technical surveys to mark the areas affected and technical surveys to determine the perimeter of related minefields. This paper introduces a landmine location-based prediction model, combining military experience with machine-learning techniques and spatiotemporal data, by introducing a new approach for area selection and adding military-based features for context modelling and model training. Besides predicting landmine’s location areas, this model classifies the affected regions by priority and difficulty of clearance, in such a way as to minimise the long time needed by surveys and reduce the danger related to that task, thus providing the clearance organisations with a good resource allocation for their operations. We applied several machine learning techniques that combine Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBOOST), taking into consideration the imbalanced data problem and tweaking for the best performance and accuracy. The experimental results show that the model has the potential to provide reliable predictions and valuable services for demining operations on the field

Physics-informed machine learning prediction of the martensitic transformation temperature for the design of “NiTi-like” high entropy shape memory alloys

The present study proposes a physics-informed machine learning (PIML) algorithm-based approach aimed at predicting the martensitic transformation temperature (Ms) for the design of “NiTi-like” high entropy shape memory alloys (HESMAs). A previously established HESMAs database is enriched and extended to include bi­nary, ternary, quaternary, quinary and senary alloys containing the most employed alloying elements for HEAs design such as Ni equivalents (Fe, Cu, Co, Pd, Pt and Au), Ti equivalents (Zr and Hf), Nb and Ta. The Extremely Randomized Trees algorithm, based on the concept of multiple random decision tree predictions, is adopted as the regression method for Ms temperature prediction. Two strategies for the algorithm inputs have been adopted, discussed, and compared in terms of reliable predictions. The first relies on the composition of the alloying el­ements, whereas the second exploits a defined set of intrinsic material descriptors. The latter are based on mixing enthalpy, atomic radius, electronegativity, atomic number and number of elements. A high accuracy of the M S prediction has been reached when considering the material descriptors. In fact, the second strategy induces a mean absolute error that is less than 30C for alloys containing up to 4 elements. For more elements there are more discrepancies due to the homogenization state required for HEAs. The validation of the developed approach has been performed using 6 home-made HESMAs prepared specifically for this study. It demonstrated the pre­dictive capabilities of the developed physics-informed machine learning based approach. Finally, a HESMA design tool has been implemented to virtually design new HESMAs with a targeted Ms temperature above 400C. It is worth noting that this aspect is one of the most challenging engineering issues for such alloys. An illustrative case applied to the (NiCuPd) 50 (TiZr) 50 family of alloys demonstrates the predictive capabilities of the developed approach to design such alloys to achieve a Ms temperature in the range of 300C to 700C

Development and validation of a local thermal non-equilibrium model for high-temperature thermal energy storage in packed beds

High-temperature thermal energy storage (TES) in packed beds is gaining interest for industrial energy recovery. The wide range of temperature distributions causes significant variations in thermophysical properties of the fluid and solid phases, leading to inaccuracies of classical TES models and heat transfer correlations. The objective of this work is to develop and validate a detailed but pragmatic model accounting for high-temperature effects. Based on a literature survey spanning over several communities interested in high- temperature porous media, we propose a generic local thermal non-equilibrium model for granulate porous media accounting for conservation of mass, momentum and energy (two-equation temperature model). The effective parameters needed to inform the model are the effective thermal conductivities of the different phases and the heat transfer coefficient. An experimental-numerical inverse analysis method is employed to determine these parameters. A dedicated experimental facility has been designed and built to study a model granulate made of glass bead of 16 mm diameter. Experiments are conducted using the Transient Single-Blow Technique (TSBT) by passing hot air (ranging from 293 K to 630 K) through cold particles at various mass flow rates, covering a Reynolds number range of 58 to 252. The new model was implemented in the Porous material Analysis Toolbox based on OpenFoam (PATO) used to compute the transient temperature fields. Two optimization algorithms were employed to determine the parameters by minimizing the error between experimental and simulated temperatures: a Latin Hypercube Sampling (LHS) method, and a local optimization method Adaptive nonlinear least-squares algorithm (NL2SOL). The results indicate that the value of heat transfer coefficient ℎ�� in the two-equation model falls in the range of 1.0 × 104 ∼ 1.93× 104 W/(m3 K) under the given conditions. The axial dispersion gas thermal conductivity was found to be around 5.9 and 67.1 times higher than the gas thermal conductivity at Peclet numbers of around 55 and 165, respectively. Furthermore, two improved correlations of Nusselt number (���� = 2+1.54����(�� )0.6�� ��(�� )1∕3) and of axial dispersion gas thermal conductivity (��������,∥ = 0.00053����(�� )2.21�� ��(�� ) ⋅ ���� ) are proposed and validated for a range of Reynolds number from 58 to 252. The overall approach is therefore validated for the model granulate of the study, opening new perspectives towards more precise design and monitoring of high-temperature TES systems

Austenitic-to-austenitic-ferritic stainless steel transformation via PVD powder surface functionalization and spark plasma sintering

The present work investigates a new alloy design approach to elaborate stainless steel grades with an austeniticferritic microstructure. The originality of the study is the use, as starting material, of a 316 L austenitic powder coated by a thin chromium layer deposited by physical vapor deposition (PVD) technique. The coated powder was then consolidated by spark plasma sintering (SPS), a powder metallurgy process allowing the fast elaboration of dense materials with a fine-grained microstructure. The chromium coating, characterized by scanning and transmission electron microscopy, presents a columnar microstructure, formed by nanometric crystallites, well reproduced by the simulation of the film growth. The characterizations performed after sintering show that the initial austenitic powder particles are still visible in the bulk microstructure. On the other hand, a tetragonal σ phase enriched in chromium and molybdenum forms in the interparticular regions. After annealing treatment followed by quenching, the tetragonal phase transforms into the expected ferrite. The results prove that using a coated powder is a promising and innovative way to elaborate new steel grades with a two-phase austeniticferritic microstructure. This original approach can have the advantage of obtaining steels with a controlled microstructure and the desired amount of phases in the final bulk

Advanced Deep Learning Techniques for Industry 4.0: Application to Mechanical Design and Structural Health Monitoring

Nowadays, Deep Learning (DL) techniques are increasingly employed in industrial applications. This paper investigate the development of data-driven models for two use cases: Additive Manufacturing-driven Topology Optimization and Structural Health Monitoring (SHM). We first propose an original data-driven generative method that integrates the mechanical and geometrical constraints concurrently at the same conceptual level and generates a 2D design accordingly. In this way, it adapts the geometry of the design to the manufacturing criteria, allowing the designer better interpretation and avoiding being stuck in a timeconsuming loop of drawing the CAD and testing its performance. On the other hand, SHM technique is dedicated to the continuous and non-invasive monitoring of structures integrity, ensuring safety and optimal performances through on-site real-time measurements. We propose in this work new ways of structuring data that increase the accuracy of data driven SHM algorithms and that are based on the physical knowledge related with the structure to be inspected. We focus our study on the damage classification step within the aeronautic context, where the primary objective is to distinguish between different damage types in composite

Enhancing weight perception in virtual reality: an analysis of kinematic features

This study investigates weight perception in virtual reality without kinesthetic feedback from the real world, by means of an illusory method called pseudo-haptic. This illusory model focuses on the dissociation of visual input and somatosensory feedback and tries to induce the sensation of virtual objects' loads in VR users by manipulating visual input. For that, modifications on the control-display ratio, i.e., between the real and virtual motions of the arm, can be used to produce a visual illusionary effect on the virtual objects' positions as well. Therefore, VR users perceive it as velocity variations in the objects' displacements, helping them achieve a better sensation of virtual weight. A primary contribution of this paper is the development of a novel, holistic assessment methodology that measures the sense of the presence in virtual reality contexts, particularly when participants are lifting virtual objects and experiencing their weight. Our study examined the effect of virtual object weight on the kinematic parameters and velocity profiles of participants' upward arm motions, along with a parallel experiment conducted using real weights. By comparing the lifting of real objects with that of virtual objects, it was possible to gain insight into the variations in kinematic features observed in participants' arm motions. Additionally, subjective measurements, utilizing the Borg CR10 questionnaire, were conducted to assess participants' perceptions of hand fatigue. The analysis of collected data, encompassing both subjective and objective measurements, concluded that participants experienced similar sensations of fatigue and changes in hand kinematics during both virtual object tasks, resulting from pseudo-haptic feedback, and real weight lifting tasks. This consistency in findings underscores the efficacy of pseudo-haptic feedback in simulating realistic weight sensations in virtual environments

From bio-sourced to bio-inspired cellular materials: A review on their mechanical behavior under dynamic loadings

Natural cellular materials can be used directly or as a constituent of bio-sourced composites for industrial applications involving dynamic loadings, usually for the purpose of absorbing mechanical energy. These biological materials can also be used as an inspiration to conceive more efficient heterogeneous structures for impact mitigation. In this review letter, we present two natural materials for which the properties have been studied dynamically: balsa wood and corkbased agglomerates. Both display an important strain-rate dependence but because of their different microstructure, this dependence is not the same. Consequently, a better understanding of the relationship between the hierarchical structure of natural cellular materials and their mechanical behavior, from quasi-static to dynamic, would be beneficial for the conception of new bio-inspired architected structures. We then focus on two types of bio-inspired architected structures: the functionally density graded cellular structures and the multi-layered architected structures. These two types of structures are gaining interest, but it appears that their dynamic behavior still lacks studying and understanding. More research linking the local strain mechanisms to their macroscopic mechanical behavior in quasistatic and dynamic would allow further architected structure optimization for mechanical energy absorption