Publications

Boundary layers of adiabatic and isothermal curved walls are investigated for a supersonic turbine cascade, including the effects of shock-boundary layer interactions (SBLIs). Wall-resolved large eddy simulations (LES) are performed for a linear cascade of blades with an inlet Mach number of $M_\infty = 2.0$ and Reynolds number based on the axial chord $Re_\infty = 200\,000$. The wall to inlet temperature ratio of the isothermal case is $T_w/T_{\infty}=0.75$, representing a cooled wall. An assessment of the effects of pressure gradient, thermal boundary conditions and SBLIs is presented in terms of the downstream variation of mean flow quantities such as density, temperature, and momentum profiles. The different thermal boundary conditions affect the density and temperature profiles along the boundary layer, where cooling increases the density of the gas near the wall, and reduces its temperature and viscosity. Both of these effects make the momentum profiles fuller and, hence, the boundary layer of the isothermal case is less prone to separate than that of the adiabatic wall. The mean density profiles are also affected by pressure gradients induced by the convex and concave curvatures of the blade, which lead to expansion and compression of the flow, respectively. The analysis of separate terms from the momentum balance equation explains the behavior of various physical mechanisms in the inner and outer regions of the supersonic boundary layers. The importance of mean flow advection, compressibility, and Reynolds stresses is presented in terms of flow acceleration and deceleration. The impact of the SBLIs in the momentum balance mechanisms is also investigated, showing that a combination of compressions and expansions impact the boundary layers by redirecting the flow toward the wall due to the shock formations.

The study performs large-eddy simulations of supersonic free jet flows using the Discontinuous Galerkin Spectral Element Method (DGSEM). The main objective of the present work is to assess the resolution requirements for adequate simulation of such flows with the DGSEM approach. The study looked at the influence of the mesh and the spatial discretization accuracy on the simulation results. The present analysis involves four simulations, incorporating three different numerical meshes and two different orders of spatial discretization accuracy. The numerical meshes are generated with distinct mesh topologies and refinement levels. Detailed descriptions of the grid generation and refinement procedures are presented. The study compares flow property profiles and power spectral densities of velocity components with experimental data. The results show a consistent improvement in the computed data as the simulation resolution increases. This investigation revealed a trade-off between mesh and polynomial refinement, striking a balance between computational cost and the accuracy of large-eddy simulation results for turbulent flow analyses.

This review provides a comprehensive examination of artificial intelligence methods applied to the design, optimization, and performance prediction of composite-based hydrogen storage vessels, with a focus on composite overwrapped pressure vessels. Targeted at researchers, engineers, and industrial stakeholders in materials science, mechanical engineering, and renewable energy sectors, the paper aims to bridge traditional mechanical modeling with evolving AI tools, while emphasizing alignment with standardization and certification re­quirements to enhance safety, efficiency, and lifecycle integration in hydrogen infrastructure. The review begins by introducing HSV types, their material compositions, and key design challenges, including high-pressure durability, weight reduction, hydrogen embrittlement, leakage prevention, and environmental sustainability. It then analyzes conventional approaches, such as finite element analysis, multiscale modeling, and experimental testing, which effectively address aspects like failure modes, fracture strength, liner damage, dome thickness, winding angle effects, crash behavior, crack propagation, charging/discharging dynamics, burst pressure, durability, reliability, and fatigue life. On the other hand, it has been shown that to optimize and predict the characteristics of hydrogen storage vessels, it is necessary to combine the conventional methods with artificial intelligence methods, as conventional methods often fall short in multi-objective optimization and rapid predictive analytics due to computational intensity and limitations in handling uncertainty or complex datasets. To overcome these gaps, the paper evaluates hybrid frameworks that integrate traditional techniques with AI, including machine learning, deep learning, artificial neural networks, evolutionary algorithms, and fuzzy logic. Recent studies demonstrate AI’s efficacy in failure prediction, design optimization to mitigate structural risks, structural health monitoring, material property evaluation, burst pressure forecasting, crack detection, com­posite lay-up arrangement, weight minimization, material distribution enhancement, metal foam ratio optimi­zation, and optimal material selection. By synthesizing these advancements, this work underscores AI’s potential to accelerate development, reduce costs, and improve HSV performance, while advocating for physics-informed models, robust datasets, and regulatory alignment to facilitate industrial adoption.

Faced with the rising number of electric vehicles, the recycling of permanent magnet (PM) rotors of electrical machines is a pivotal concern since PM, for this, application are generally made with Critical Raw Materials, i.e. rare earth materials. Therefore, the development of effective End of Life strategies for PM is essential to mitigate the environmental impact associated with their production and meet the rising demand sustainably. This paper presents a method to reconstruct the magnetization state of the PM on site within the rotor based on external field measurements. This information will be really useful to evaluate the PM state of health in order to evaluate the possibility of reuse of the rotor or to recycle the PM. The process of reconstruction is based on an inverse method and it has been fully simulated using A Finite Element (FE) model for the rotor. It is shown on different rotor topologies (surface PM mounted rotor, PM buried rotor…) that it is possible to determine the magnetization state of the PM.

A new physics-informed deep homogenization neural network (DHN) framework is proposed to identify the homogenized and local behaviors in periodic heterogeneous microstructures. To achieve this, the displacement field is decomposed into averaged and fluctuating contributions, with the local unit cell solution obtained via neural networks subject to periodic boundary conditions. The periodic microstructures are divided into sub­domains representing the fiber and matrix phases, respectively. A key contribution of the proposed method is the marriage of elasticity solution and physics-informed neural network to each phase of the composite, namely, the fiber phase as a mesh-free component whose fluctuating displacements are expanded using a discrete Fourier transform, and the matrix phase using material points with fluctuating displacements handled through fully connected neural network layers. The interfacial continuity conditions are enforced by minimizing the traction and displacement differences at separate material points along the interface. Transfer learning is exploited further to facilitate training new microstructures from pre-trained geometry. This hybrid formulation inherently satisfies stress equilibrium equations within the fiber, while efficiently handling the periodic boundary conditions of hexagonal and square unit cells via a series of trainable sinusoidal functions. The innovative use of distinct neural network architectures enables accurate and efficient predictions of displacement and stress when discontinuities are present in the solution fields across the interface. We validate the proposed DHN with the finite-element predictions for unidirectional composites comprised of elastic fiber significantly stiffer than the matrix, under various volume fractions and loading conditions.

Background: Advances in prosthetic technology, especially microprocessor-controlled knees (MPKs), have helped enhance gait symmetry and reduce fall risks for individuals who have undergone transfemoral amputation. However, challenges remain in walking in constrained situations due to the limitations of passive prosthetic feet, lacking ankle mobility. This study investigates the benefits of SYNSYS®, a new microprocessor-controlled knee–ankle–foot system (MPKA_NEW), designed to synergize knee and ankle movements. Methods: A randomized crossover trial was conducted on 12 male participants who had undergone transfemoral amputation who tested both the MPKA_NEW and their usual MPK prosthesis. Biomechanical parameters were evaluated using quantitative gait analysis in various walking conditions. Participants also completed self-reported questionnaires on their quality of life, locomotor abilities, and prosthesis satisfaction. Results: The MPKA_NEW showed a significant reduction in the risk of slipping and tripping compared to standard MPK prostheses, as evidenced by increased flat-foot time and minimum toe clearance during gait analysis. The MPKA_NEW also improved physical component scores in quality-of-life assessments (Short-Form 36 General Health Questionnaire), suggesting enhanced stability and reduced cognitive load during walking. Conclusions: The MPKA_NEW offers significant improvements in gait safety and quality of life for people who have undergone TFA, particularly in challenging conditions. Further studies are needed to assess the long-term benefits and adaptability across diverse amputee populations.

Additive manufacturing revolutionized the production of personalized objects from 3D models across various industries, including medical, aerospace and automotive, primarily through the widely accessible material extrusion process. Despite additive manufacturing advantages, the parts fabricated using additive manufacturing often exhibit lower elastic properties compared to conventionally injection-molded thermoplastic components. Adding fiber-reinforced polymer composites during printing has a significant influence both on tensile and flexural strength, as well as on the overall quality of resulting composite structure. Adding a low volume of reinforcement (<15%) to a sample is advantageous without increasing the stiffness of the sample significantly. This study aims at investigating the impact of low reinforcement volume and placement on the flexural properties of samples while assessing the repeatability of the printing process through 3-point bending tests. A finite element model of the test is also proposed in order to predict the flexural properties of samples. Process induced defects such as voids are modeled in the samples as the results show that the addition of fiber-reinforced polymer led to an increase in the porosity rate. The experimental results of 3-point bending test were compared with those of finite element analysis. Printing rules aimed at minimizing errors and addressing limitations inherent in the printing process are defined in the current study.

Background At equivalent speeds, above-knee amputee subjects have a higher metabolic cost than non-amputees. Following amputation, the ankle propulsion is reduced, and using other joints to compensate is mechanically less efficient. Objective This study investigated the link between mechanical work and metabolic cost in abled-bodied subjects using a prosthesis simulator, and the influence of foot energy restitution by comparing a foot with restitution to one without. Method Six volunteers fitted with an orthosis immobilising their ankle and knee, enabling the use of a prosthesis, carried out a gait analysis and an analysis of metabolic cost. The total lower limb mechanical work and works at the hip, knee and ankle were computed. Results With an almost twofold increase, metabolic cost and hip work were significantly higher in both configurations with prosthesis than without (p < 0.001 for both variables in both configurations), while total lower limb mechanical work showed no significant difference between configurations. No significant difference was observed between the two prosthetic feet in terms of metabolic cost nor mechanical work performed by the subject. Discussion Total lower limb mechanical work alone cannot explain the extra metabolic cost in subjects fitted with a knee-foot prosthesis simulator; internal inefficiencies exist. We also found that metabolic cost and hip work increase and decrease simultaneously, thus studying hip muscles work could be interesting. With no significant difference between the two feet, optimising ankle propulsion seems to be ineffective in improving metabolic cost. These findings should be evaluated in a sample of above-knee amputee subjects.

Introduction Children with X Linked Hypophosphatemia (XLH) suffer from carential ricket, bone deformities and lameness. No previous study demonstrated a morphological distinction in muscles in these patients. The aim of this prospective study was to characterize, using Magnetic Resonance Imaging (MRI), the muscle morphology of pelvis, thigh and leg in children with XLH and to compare it with typically developed (TD) children. Hypothesis We hypothesized that lower limbs muscles in children with XLH are different from TD children and could explain limp walking. Material and methods Three-dimensional reconstructions of the muscles were performed in 11 patients with XLH and 15 TD children. Muscle lengths, sections and volumes were calculated and normalized with height and weight. Mean age was 10. Results Lengths were all smaller in children with XLH except for the Medius/minimus gluteus muscles (p = 0.64). The difference seemed higher in muscles with a long tendinous part as semitendinosus (0.139 vs 0,164; p < 0.01). All volumes were significantly inferior in children with XLH. This preliminary study showed significant differences in muscle structures between patients with XLH and TD children. Discussion Medius/minimus gluteus seemed to be particularly developed in children with XLH. Nevertheless it is not possible to conclude if it is related to XLH or a consequence of bone deformities.

The contact between the tool and the workpiece/chip in metal cutting is complex, resulting in high local temperatures and stresses, which may cause severe tool wear and failure. Developments in cryogenic-assisted machining have shown an ecological alternative to the classical metal working fluids, besides tool wear reduction during machining difficult-to-cut materials due to the good ability to dissipate the heat generated by this process. The objective of this work is to analyze the tribological conditions and performance of new coatings specially developed for cryogenic-assisted machining in terms of friction coefficient, volume of build-up material (adhesion) to the tool, and tool temperature. The results have shown that the sliding speed and cooling/lubrication strategy are two main factors that affect the friction coefficient and adhesion of Ti–6Al–4V alloy to the pins. These tribological tests should allow us to select the best coating(s) to be used in cutting tools for further tool wear analysis. Moreover, the obtained friction coefficients could be further implemented into metal cutting models to predict the machining outcomes, including the surface integrity of the machined parts and tool wear.