Design of thermal meta-structures made of functionally graded materials using isogeometric density-based topology optimization

The thermal conductivity of Functionally Graded Materials (FGMs) can be efficiently designed through topology optimization to obtain thermal meta-structures that actively steer the heat flow. Compared to conventional analytical design methods, topology optimization allows handling arbitrary geometries, boundary conditions and design requirements and producing alternate designs for non-unique problems. Additionally, as far as the design of meta-structures is concerned, topology optimization does not need intuition-based coordinate transformation or the form invariance of governing equations, as in the case of transformation thermotics. We explore isogeometric density-based topology optimization in the continuous setting, which perfectly aligns with FGMs. In this formulation, the density field, geometry and solution of the governing equations are parameterized using non-uniform rational basis spline entities. Accordingly, the heat conduction problem is solved using Isogeometric Analysis. We design various 2D & 3D thermal meta-structures under different design scenarios to showcase the effectiveness and versatility of our approach. We also design thermal meta-structures based on architected cellular materials, a special class of FGMs, using their empirical material laws calculated via numerical homogenization

Enhancing Asynchronous Learning in immersive Environments: Exploring Baseline Modalities for Avatar-Based AR Guidance

This study investigates baseline modalities for evaluating Augmented Reality (AR) avatar guidance in asynchronous collaboration on spatially complex tasks. A formative study with three participants compared smartphone video, HoloLens video, and AR avatars across usability, collaboration, learning, and spatial awareness. Results suggest smartphone video as a reliable baseline due to usability and familiarity. Avatars showed potential for enhancing spatial awareness, task engagement, and learning outcomes but require interface improvements. Despite the small sample size, this study offers insights into immersive technologies for industrial training and collaboration

Experimental fatigue characterization and modeling of a bi-component structural acrylic adhesive: Application to single-lap joints

The aim of this paper is to predict the fatigue behavior of bonded joints made of a bi-component structural acrylic adhesive. The approach considered is based on a characterization of the fatigue properties of the bulk adhesive combined with a finite element modeling of the bonded joint to provide the heterogeneous stress field within the adhesive. The identification of the fatigue model is conducted with experimental bulk adhesive tests under two loadings (tensile–compression and tensile–tensile) in order to account for the effect of the mean stress. A modified Crossland criterion, in the limited life time domain, is used to predict the fatigue life of the bonded joint assembly. The numerical fatigue life determined with this approach is compared to the experimental fatigue life of the assembly. A good correlation is found between the numerical model and the experimental results

Screen Printed Piezoelectric Transducers for Structural Health Monitoring of Curved Thick Composite Panels

This research focuses on the development and experimental validation of a novel printed piezoelectric transducers network employed on a foreign object damage panel substructure of an aircraft engine fan blade. The main goal of the work is to leverage the screen printing technology to fabricate arrays of piezoelectric transducers and ultimately employ these transducers for operations, enabling the development of structural health monitoring methods for the panel. The printed transducer is made up of a piezoelectric layer sandwiched between two silver electrodes, each printed in a controlled manner. Upon printing and drying of the layers, the transducers undergo polarization. The electromechanical behaviour of the printed transducers, characterized using impedance measurements, exhibits high repeatability, thus indicating its potential for large scale industrial deployment. Following this, it is demonstrated that the transducers are capable of accurately sensing impact, which is one the most common yet critical sources of damage to an engine fan blade. It is also shown that the printed transducers are able to detect acoustic emission events. The ability of the printed transducers to actuate and sense guided wave signals over a range of ultrasonic frequencies is also demonstrated. Furthermore, apart from the noticeable advantages of the non-intrusive nature, and negligible weight as compared to their traditional ceramic counterparts, the printed piezoelectric transducers can potentially be integrated into the manufacturing process in the future, and the presence of transducer arrays ensures the availability of other transducers in case of an individual failure during service. This innovative printing technology for PZT transducer networks thus holds significant promise in bridging the gap between research advancements and the industrial implementation of SHM technology

Experimental and numerical analysis of heat transfer and thermal deformation in small-dimension liquid mechanical seals

This paper presents an experimental and numerical analysis of heat transfer and thermal deformation in small-dimension (1.4 mm) liquid mechanical seals operating in an unstable dynamic tracking mode. The studied non-contacting mechanical seal is used in a liquid pump for turbojets. The study aims to estimate the values of pressure, temperature, and thermal deformations that can prevent excessive wear of the sealing rings and control the increase in leakage rate or power loss during operation. Experimental investigations were conducted under a nominal inner pressure of 0.7 MPa, across a wide range of rotational speeds (from 1000 to 6000 rpm), and at low Reynolds numbers (Re < 70). Two high-viscosity fluids, glycerol and engine oil, were used as sealing fluids. Rotational speeds and inner pressure were set as boundary conditions in the simulations. Temperatures measured by thermocouples during the experiments were used to compare with the simulation results. Simulations were performed using the computational fluid dynamics (CFD) software COMSOL. The two-dimensional numerical models accounted for thermal transfers and face seal deformations, coupled with the pressure field in the lubrication fluid. The effects of various sealing fluids and rotational speeds on the time-dependent behavior of temperature, displacement, and pressure within the thin liquid lubricant film were investigated. Subsequent comparisons between experimental and numerical results, particularly for temperature data measured by thermocouples under various operating conditions, demonstrated strong consistency. The greatest discrepancy observed was less than 1.2 °C. © 2025 The Author

Performance of new cutting tool multilayer coatings for machining Ti-6Al-4V titanium alloy under cryogenic cooling conditions

Bourse CSCCr/CrN/AlCrN multilayer coatings were recently developed to meet the high challenges of machining Ti-6Al-4V alloy under cryogenic cooling conditions. The multilayer coatings were optimized by multiple deposition conditions and were characterized by multi-methods. It was proved that they are suitable for tribological applications with this alloy under extreme conditions. This paper addresses the performance of these coatings through tool wear tests and analysis. This performance was compared with that obtained in standard machining conditions used in the aerospace industry, which include flood metalworking fluids and uncoated cemented carbide tools. The results show that the application of a multilayer coating can improve significantly the tool life under cryogenic cooling conditions compared to the flood conditions. 33 % improvement of tool life was found under cryogenic cooling conditions when comparing this coating to the uncoated one. A statistical analysis shows a strong correlation between tool wear and the machining forces. This analysis also permitted to build models for predicting tool wear in function of measured forces

Recurrent Neural Networks model for injury prevention within a professional rugby union club: a proof of concept over one season

Background In professional rugby, injury prevention and player availability are major challenges. Sports analytics use data from trainings and matches to address these issues. This study leveraged comprehensive daily data from a professional rugby club to predict players' readiness for training. Using this metric helped assess its effectiveness in predicting intrinsic injuries and improving injury prevention strategies. Methods Models including logistic regression, decision trees, and Long Short-Term Memory-based neural networks, were evaluated for their predictive accuracy and ability to discern patterns indicative of injury risks or readiness for physical activities. Findings The study demonstrated that long-short term memory and convolutional one-dimension models outperform traditional machine learning methods in analyzing players' physical conditions. This approach may support earlier identification of injury risks and inform workload management. Using model evaluation and interpretability techniques, including Local Interpretable Model-Agnostic Explanations (LIME) module, the study provided a framework for sports scientists, coaches, and medical staff to mitigate injury risks and optimize training sessions. Interpretation As a preliminary exploration, this study paves the way for further research into the integration of machine learning and neural networks in sports science, promising transformative impacts on injury prevention strategies in rugby

Comprehensive review of multi-scale Lithium-ion batteries modeling: From electro-chemical dynamics up to heat transfer in battery thermal management system

The growing development of lithium-ion battery technology goes along with the new energy storage era across various sectors, e.g., mobility (electric vehicles), power generation and dispatching. The need for sophisticated modeling approaches has become a crucial tool to predict and optimize battery behavior given the demand of ever-higher performance, longevity, and safety. This review integrates the state-of-the-art in lithium-ion battery modeling, covering various scales, from particle-level simulations to pack-level thermal management systems, involving particle scale simplifications, microscale electrochemical models, and battery scale electrical models with thermal and heat generation prediction. Beyond that, authors highlight the growing trend in integrating highly accurate physics-based with thermal approaches such as the electrochemical-thermal coupled model to fully answer the multiscale challenges. Through capturing the electrochemical phenomena and thermal dynamics, and developing a comprehensive understanding of battery kinetics, safety risks such as thermal runaway can be thoroughly mitigated. Authors emphasize the trade-offs between computational efficiency and model complexity, explaining the limitations, strengths, and applications of diverse modeling approaches. This review illuminates the integration of battery management systems and cooling strategies

Design of (TiHfZr)(NiCoCu) High-Entropy Shape Memory Alloys: From Firstov's Experiments to Data-Driven Approach

This paper deals with the design of (TiHfZr)(NiCoCu) high-entropy and high-temperature shape memory alloys (HE-HT-SMAs). It explains the chronology and the progress of this design starting from the experimental work of Georgi Firstov initiated in the 2015s until the advent of data-driven alloy approaches. A state-of-the-art (TiHfZr)(NiCoCu) HE-HT-SMA family is presented and enriched by a database used as input for a data-driven approach. The paper then focuses on the comparison of martensitic transformation temperatures provided by: (i) the experimental work of Firstov et al. started in 2015, (ii) other recent experimental studies and, (iii) those predicted by two numerical approaches. The first approach consists of a linear regression model proposed by Peltier et al., while the second one is proposed and enriched by Thiercelin et al. using a data-driven technique (random forest regression). The results from the data-driven approach yield accurate predictions that align with the experimental data from both the literature and previous studies. Thus demonstrating the importance of physics-informed, inspired techniques to optimize the design of future alloys, in particular HE-HT-SMAs

Optimizing CrAlN coatings: Effects of deposition temperature on mechanical, tribological, and wettability properties

With the aim of improving the lifespan of different steel tools by reducing their degradation, CrAlN nitride coatings were investigated. The CrAlN coatings were deposited on X38CrMoV5 steel substrates using the DC reactive magnetron sputtering process under various deposition temperatures between ambient temperature and 300 ◦C. The effects of deposition temperature were systematically explored: XRD, EDS, SEM, optical profilometer, contact angles, nanoindentation, and tribometry were carried out to establish the structural, mechanotribological, and wetting properties’ relationship. Results show that the high deposition temperature promotes the growth of (200) CrN preferentially orientation with the appearance of AlN phase. As the deposition temperature increases, the contact angle of the CrAlN surface films changes to a higher hydropholicity and the hardness of the coatings gradually increases to reach a maximum value of 28.1 GPa. The main wear mechanisms of CrAlN coating deposited at 300 ◦C against Al6061 ball are a combination of abrasive and adhesive features. This coating also has the lowest friction coefficient (0.63) and wear rate (1.72 × 10�� 3 mm3/N.m). Indeed, the preferred deposition temperature of about 300◦C could effectively adjust the microstructure and improve the mechanical and tribological properties of CrAlN coatings, thereby indicating its potential as an effective coating material for the protection of X38CrMoV5 steel in industrial fields