National Engineering School of Saint-Étienne

REBCO, the leading candidate conductor for ultra high field magnets, is typically produced in the form of thin tapes, consisting of multiple layers of diverse materials with very different natures and properties. Knowledge of the mechanical properties of these different layers is crucial for magnet design. In this paper, we propose a methodology to measure the elasticity, plasticity and fracture toughness of conductor layer materials, at the scale of its constituents, based on nanoindentation, micropillar compression and micropillar splitting techniques. Measurements with these techniques are carried out at room temperature on a commercial conductor, and the results obtained are compared to the values found in the literature and to those specified by the manufacturers.

This work presents a new approach for the fabrication of 316L/Al2O3 composites, based on a combination of spray granulation, radio frequency (RF) plasma spheroidization and spark plasma sintering (SPS). Initially, a suspension containing 316L and alumina powders is formulated by precisely adjusting the pH and selecting an appropriate dispersant, thereby ensuring homogeneous dispersion of the constituents. The spray granulation process then produces granules with controlled size and morphology. RF plasma spheroidization, carried out using a TekSphero-40 system, is investigated by varying parameters such as the power, gas flow rates, injection position and feed rate, in order to optimize the formation of spherical and dense particles. The analysis reveals a marked sensitivity to heat transfer from the plasma to the particles, with a tendency for fine particles to segregate, which underscores the necessity for precise control of the processing conditions. Finally, SPS densification, performed under a constant pressure and a rigorously controlled thermal cycle, yields composites with excellent density and hardness characteristics. This study thus demonstrates that the proposed hybrid process offers an optimal synergy between a uniform distribution of alumina and a controlled microstructure, opening up promising avenues for the design of high-performance composite materials for demanding applications.

During a machining operation, the tool tip is subjected to elevated interface temperatures and contact pressures. A considerable improvement can be achieved through an appropriate selection and application of a cutting fluid. Although many technologies attempt to reduce their use to move to a cleaner production, they are still widely employed in industry. Under such severe conditions, it is necessary to understand their exact contribution from a tribological point of view in order to optimize their use. The aim of this study is to evaluate the ability of a fluid to penetrate and remain at the tool-material interface.

Natural skin tension plays an important role during surgical procedures and during the healing process. Existing studies performed ex vivo give only a qualitative map of skin tension. In this study, we propose a quantitative characterization of skin tension in vivo using a new model. This model consists in calculating the tension indices based on the equilibrium equation, and uses the Fourier transform. The study was carried out on 42 volunteers. Tension indices are calculated primarily from skin topology images performed on seven body areas: forearm, thigh, cheek, belly, upper chest, and arm (front and back face). A feasibility study of applying the model to LC-OCT and confocal microscopy images was then carried out. The results show that the skin tension is higher in the family of tension lines, and lower in the family of lines perpendicular to the tension lines. The tension indices quantify the state of the skin tension forces and allow to classify body areas according to their state of tension. With age, the skin loses its tension, leading to an imbalance of tension forces between the two families of lines. The results also show that the model can be used on deep skin images to study fiber tension.

The objective of this study is to explore the physical and mechanical behaviour of concretes comprising four different ratios of recycled fine (RF), namely (5%, 10%, 15% and 20%) along with that of a reference concrete (Cref-0%), under three different heating–cooling cycles (200 °C, 400 °C and 600 °C). The thermal properties of concrete during heating and cooling (20 °C – 600 °C – 20 °C) were also investigated. It was determined that the physical properties (mass loss and ethanol porosity) of recycled concrete (RC) with 5% of recycled fine (RC-5%) were similar to those of Cref-0%. At ambient temperatures, the higher the ratio of recycled fines, the lower the residual compressive strength and residual elastic modulus of the recycled concrete. After thermal loading at 600 °C, the residual mechanical properties of all types of concrete were equivalent, regardless of the content recycled fine.

The frequent occurrence of major earthquakes highlights the structural challenges posed by long-period ground motions (LPGMs). This study investigates the seismic performance and resilience of five reinforced concrete (RC) columns with different high-strength steel bars under LPGM-induced cyclic loading, both experimentally and numerically. The results show that low-bond and debonded high-strength steel bars significantly enhance self-centering capabilities and reduce residual drift, with lateral force reductions of 7.6% for normal cyclic loading and 19.2% for multiple reversed cyclic loading. The concrete damage in the hinge zone of the columns was increased; however, the significant inside damage of the concrete near the steel bars made it easier to restore the columns for the damage accumulation caused by multiple loading. Based on the experiment, a numerical model was developed for the columns, and a simplified model was proposed to predict energy dissipation capacity, providing practical design methods for resilient RC structures that may be attacked by LPGMs.

Hydroquinones constitute a family of antioxidants, but are also known as electron donor molecules in charge transfer complexes. Raman and IR spectra of 1,4-dihydroquinone (H2Q) and its derivatives, 2,5-dichloro-1,4-dihydroquinone (H2QCl2) and 2-methoxy-1,4-dihydroquinone (H2QOCH3) in solid form have been recorded. Theoretical calculations using DFT (B3LYP) methods have been performed giving optimized structures, energies and harmonic vibrational frequencies. The comparison between the vibrational frequencies calculated and scaled with Raman and FT-IR experimental values shows good agreement. The influence of the substitutions on the electron donor capacity, geometrical parameters and normal modes of vibrations have been discussed. The presence of –Cl group increases the O–H bond length and decreases the electron donor capacity and inversely for the presence of –OCH3. The O–H stretching vibrations shift to higher wave numbers in H2QCl2 and H2QOCH3. The aromatic C–H stretching vibrations are not much affected by the presence of –Cl and –OCH3. The C–C and C=C stretching vibrations shift to higher wave numbers in H2QCl2 and shift to lower wave numbers in H2QOCH3. These studies show the influences of substitutions on molecular properties of hydroquinones.

This paper proposes a new numerical method that allows efficient solving of Differential Linear Matrix Inequality (DLMI) and Time-Dependent Linear Matrix In- equality (TDLMI) within a finite interval. For this purpose, we convert a quadratic time-dependent inequality within a finite time interval into a finite-dimensional linear matrix inequality (LMI). The proposed method is then applied in the context of finite-time stability to obtain new tractable and efficient sufficient conditions guaranteeing the finite-time stability (FTS) of linear time-invariant (LTI) systems.

To conclude this monograph, we want to recall the general philosophy behind the approach developed from chapter to chapter as well as the difficulty we can encounter when solving an engineering design problem. As a matter of fact there are three major concepts that have to be considered: the concept of system, the necessity of formulating an optimization problem, and the problem of solving of finding an acceptable solution.

This chapter introduces some methods aimed at solving difficult optimization problems arising in many engineering fields. By difficult optimization problems, we mean those that are not convex. Recall that for the class of non-convex problems, there is no algorithm capable of guaranteeing, in reasonable a reasonable amount of time (By reasonable time, we mean polynomial in the size of the problem.), that the solution found is the global optimum. Under these conditions, we must be content with finding an acceptable solution. After introducing the notion of acceptable solution, a brief overview of the main stochastic methods which can be used for solving continuous non-convex constrained optimization problems is presented, i.e., Pure Random Search Methods (PRSM), Simulated Annealing (SA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). These methods are designed to produce an acceptable solution. The last part is dedicated to the problem of robust optimization, i.e., optimization in the presence of uncertainties on the problem data.

In this chapter a new optimization method is presented, called the Heuristic Kalman Algorithm (HKA). This new algorithm is proposed as an alternative approach for solving continuous non-convex optimization problems. The principle of HKA is to consider explicitly the optimization problem as a measurement process intended to give an estimate of the optimum. A specific procedure, based on the Kalman estimator, was developed to improve the quality of the estimate obtained through the measurement process. A significant advantage of HKA against other metaheuristics lies mainly in the small number of parameters which have to be set by the user. In addition, it is shown that HKA converges almost surely to a near-optimal solution. The efficiency of HKA is evaluated in detail through several non-convex test problems, both in the unconstrained and constrained cases. The results are then compared to those obtained via other metaheuristics. These various numerical experiments show that the HKA has very interesting potentialities for solving non-convex optimization problems, especially with regard to the computation time and the success ratio.

Machine learning is an area of artificial intelligence that aims to develop systems that can learn and improve from data. The central concept of machine learning is based on the idea of using algorithms to analyze and interpret sets of data, in order to detect patterns, relationships, or trends. There are different types of machine learning algorithms, including supervised learning and unsupervised learning. In supervised learning, models are trained on labeled data, which means data for which the desired response is known. The algorithm learns to associate data features with their corresponding labels, enabling it to make predictions about new, unlabeled data. In contrast, in unsupervised learning, models are used to discover hidden structures or patterns in data without prior labels. These algorithms can group the data based on similarities (e.g., clustering) or reduce the data to smaller dimensions to facilitate understanding (e.g., principal component analysis).

This chapter introduces how a control design problem can be formulated as an optimization problem. To this end, the standard control problem as well as the notion of stabilizing controllers are first briefly reviewed. This chapter focuses on the case of structured controllers, i.e., structural constraints have to be taken into account in the optimization problem. These structural constraints make the resulting optimization problem nonsmooth and non-convex, which result into intractability. This is why the use of stochastic optimization methods are suggested for finding an acceptable solution. The robustness issue is also briefly discussed.

One of the main objectives of this chapter is to introduce some basic concepts related to preventive maintenance. This is a crucial topic, as the ever-increasing complexity of automated systems has been accompanied by a growing demand for the availability and security of industrial installations. It is indeed financially futile to design installations that frequently break down and pose a danger to people, the environment, and property. Increased availability can be achieved through improved reliability of functional units, as well as the implementation of a maintenance strategy tailored to the specific installation. Maintenance refers to all actions intended to maintain or restore an asset to a specified condition, perform a required function, or ensure a determined service.

One of the main objectives of this chapter is to introduce some modeling concepts. Indeed, as seen in the previous chapters, to apply an optimization method we need to have a mathematical model of the physical system that we want to optimize. Thus, given some design specifications, this model can be used to formulate the corresponding optimization problem. To illustrate how a mathematical model of a given physical system can be obtained, two examples are presented: the active magnetic bearing (AMB) and the quadrotor unmanned aerial vehicle. These models will be used in the last part of this chapter to formulate and solve the optimal sizing problem related to these systems for given design specifications.

The study aims to investigate the influence of fraction of coarse undeformed particles on the microstructure evolution and mechanical properties of alloys processed by isothermal multidirectional forging (MDF). For this purpose, Al-Mg-Ni-Sc-Zr-based alloys with different Ni concentrations and a fraction of Al3Ni particles of solidification origin phase were subjected to MDF at 350 °C. Precipitates of the L12-structured Al3(Sc,Zr) phase retained their structure, morphology, and size after MDF and were coherent with the aluminum matrix. The Al3Ni phase particles stimulated the nucleation of recrystallized grains and contributed significantly to the formation of an ultrafine-grained structure. The uniformity of the grain structure increased, and the average grain size decreased with an increase in the fraction of Al3Ni particles. A fine-grained structure with a mean grain size of 2.4–3.4 µm was observed after MDF with a cumulative strain of 12. The results demonstrate that a bimodal particles size distribution with a volume fraction of nanoscale f~0.1% and microscale f~8% particles provided for the formation of a homogenous fine-grained structure after MDF and improved the mechanical properties.

Effects of different drying methods on structural changes of ternary gypsum-based composites were investigated by mercury intrusion porosimetry. The composites were prepared from gypsum, lime, ceramic waste, and sand. The samples were stored for 28 days in a climate chamber (relative humidity 80%, 20 ± 2 °C), and then, they were crushed into pieces smaller than 5 mm, and these pieces were dried at temperatures of 50 °C and 105 °C. The results obtained by mercury intrusion porosimetry confirmed that even a relatively low drying temperature of 50 °C, which is commonly used for drying cement composites, affects the structure of the gypsum-based composites. It was also found that a composition of ternary mixture with an optimized amount of fine ceramic powder can suppress the negative effect of drying.