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Elastoplastic bifurcation analysis for the lateral–torsional buckling of straight beams
No full textInternational audienceThis paper deals with the lateral–torsional buckling of elastoplastic steel beams under pure bending considering various cross-section geometries. Critical buckling moment expressions for elastic beams are well documented. In contrast, less attention has been devoted to the lateral–torsional buckling phenomenon in elastoplastic regime. When considering short beams (with sufficiently low slenderness), plastic zones will develop before buckling occurs. The problem being faced here is however much more complex than the classical problem of an elastoplastic beam subjected to an axial compressive force. Under such loading, plasticity occurs instantaneously and evolves uniformly over the cross-section and along the beam length whereas, in the other case, plasticity will spread gradually and the plastic zones will change with the loading. The main objective of the present paper is to develop original analytical solutions for the elastoplastic lateral–torsional buckling of perfectly straight beams. A general formulation based on a previously developed 3D plastic bifurcation theory is first proposed and then applied to the cases of two specific cross-section geometries, namely rectangular and I profiles. For the sake of simplicity, transverse shear strains are neglected in the analysis. The von Mises yield criterion with a linear isotropic hardening is adopted. In the case of a rectangular cross-section, closed-form solutions are obtained for the elastoplastic critical buckling moment, which is shown to depend on the geometric and material parameters of the beam, including the yield stress. For validation purposes, all the analytical solutions are compared against the results of numerical computations performed with an in-house program based on a shell finite element formulation. Analytical and numerical results are in very good accordance, making the analytical method presented here an efficient and precise tool to analyze the elastoplastic lateral–torsional buckling phenomenon
Differentiability and Regularization of Parametric Convex Value Functions in Stochastic Multistage Optimization
No full textIn multistage decision problems, it is often the case that an initial strategic decision (such as investment) is followed by many operational ones (operating the investment). Such initial strategic decision can be seen as a parameter affecting a multistage decision problem. More generally, we study in this paper a standard multistage stochastic optimization problem depending on a parameter. When the parameter is fixed, Stochastic Dynamic Programming provides a way to compute the optimal value of the problem. Thus, the value function depends both on the state (as usual) and on the parameter. Our aim is to investigate on the possibility to efficiently compute gradients of the value function with respect to the parameter, when these objects exist. When nondifferentiable, we propose a regularization method based on the Moreau-Yosida envelope. We present a numerical test case from day-ahead power scheduling
GPT-3-driven pedagogical agents for training children's curious question-asking skills
No full textInternational audienceStudents' ability to ask curious questions is a crucial skill that improves their learning processes. To train this skill, previous research has used a conversational agent that propose specific cues to prompt children's curiosity during learning. Despite showing pedagogical efficiency, this method is still limited since it relies on generating the said prompts by hand for each educational resource, which can be a very long and costly process. In this context, we leverage the advances in the natural language processing field and explore using a large language model (GPT-3) to automate the generation of this agent's curiosity-prompting cues to help children ask more and deeper questions. We then used this study to investigate a different curiosity-prompting behavior for the agent. The study was conducted with 75 students aged between 9 and 10. They either interacted with a hand-crafted conversational agent that proposes "closed" manually-extracted cues leading to predefined questions, a GPT-3-driven one that proposes the same type of cues, or a GPT-3-driven one that proposes "open" cues that can lead to several possible questions. Results showed a similar question-asking performance between children who had the two "closed" agents, but a significantly better one for participants with the "open" agent. Our first results suggest the validity of using GPT-3 to facilitate the implementation of curiosity-stimulating learning technologies. In a second step, we also show that GPT-3 can be efficient in proposing the relevant open cues that leave children with more autonomy to express their curiosity
Modelling edge effects at the interface in bonded joints using gradient functions in the mechanical properties of the adhesive: Application of the method to the Arcan test loaded in tension and shear
No full textInternational audienceThe design of bonded joints requires studies of stress concentrations due to edge effects. For complex joint configurations, the finite element method can be quite costly. The objective is to develop a fast and reliable numerical design tool for bonded assemblies. Therefore, an approach to design bonded assemblies is presented which aims to meet the needs of a design office, particularly in terms of calculation costs. The latter consists in reproducing the edge effects with a single element through the joint thickness using a modulus function. The concept was tested on Arcan specimen with two loading cases: tension (γ = 0°) and shear (γ = 90°). A 2D model under the elastic assumption is developed to describe the edge effects of the joint using Abaqus subroutines (UMat). The approach is set up to solve this problem in the form of two blocks. First, mesh refinement studies for bonded specimens loaded in tension were performed, within a good level of accuracy, on the free edge with the use of local discretization error estimation. After that, the effects of local geometry and modulus ratio are investigated. Afterwards, the von Mises stress at the interface level of the adhesive joint was used to identify a modulus function to describe the behavior of a joint with straight edge geometry for tension and shear loadings. The proposed approach has improved the performance of the model. Actually, the calculation is practically three times faster than for the conventional model
Carrier-envelope phase controlled dynamics of relativistic electron beams in a laser-wakefield accelerator
No full textInternational audienceIn laser-wakefield acceleration, an ultra-intense laser pulse is focused into an underdense plasma to accelerate electrons to relativistic velocities. In most cases, the pulses consist of multiple optical cycles and the interaction is well described in the framework of the ponderomotive force where only the envelope of the laser has to be considered. But when using single-cycle pulses, the ponderomotive approximation breaks down, and the actual waveform of the laser has to be taken into account. In this paper, we use nearsingle-cycle laser pulses to drive a laser-wakefield accelerator. We observe variations of the electron beam pointing on the order of 10 mrad in the polarization direction, as well as 30% variations of the beam charge, locked to the value of the controlled laser carrier-envelope phase, in both nitrogen and helium plasma. Those findings are explained through particle-in-cell simulations indicating that low-emittance, ultrashort electron bunches are periodically injected off-axis by the transversally oscillating bubble associated with the slipping carrier-envelope phase
Towards Truly Accessible MOOCs for Persons with Cognitive Impairments: A Field Study
No full textInternational audienceMassive Online Open Courses (MOOCs) should offer lifelong education opportunities for persons with disabilities. However, most of the current MOOCs are not fully accessible, especially for persons with cognitive impairments and limited learning capacities. To bridge this gap, we have developedan accessible MOOC player following a participatory design process and used it to deliver a course on a mainstream MOOC platform. In this article we describe a field study to evaluate the impact of the player’s accessibility features on the learning experience of persons with disabilities. Following a mixed method approach, we first present the results of a quantitative assessment done with Learning Analytics methods to study the impact of the player on the retention of students with disabilities. Then we present the results of a questionnaire assessment to study the global experience of students in terms of learning performance, usability of the MOOC player, perceived cognitive load and self-determination. Both Learning Analytics and questionnaire -related results confirm our contribution to designing a more inclusive e-learning environment and providing a solid base to improve the accessibility assessment methodology for MOOCs
Shape optimization of peristaltic pumps transporting rigid particles in Stokes flow
No full text24 pages, 8 figuresInternational audienceThis paper presents a computational approach for finding the optimal shapes of peristaltic pumps transporting rigid particles in Stokes flow. In particular, we consider shapes that minimize the rate of energy dissipation while pumping a prescribed volume of fluid, number of particles and/or distance traversed by the particles over a set time period. Our approach relies on a recently developed fast and accurate boundary integral solver for simulating multiphase flows through periodic geometries of arbitrary shapes. In order to fully capitalize on the dimensionality reduction feature of the boundary integral methods, shape sensitivities must ideally involve evaluating the physical variables on the particle or pump boundaries only. We show that this can indeed be accomplished owing to the linearity of Stokes flow. The forward problem solves for the particle motion in a slip-driven pipe flow while the adjoint problems in our construction solve quasi-static Dirichlet boundary value problems backwards in time, retracing the particle evolution. The shape sensitivities simply depend on the solution of one forward and one adjoint (for each shape functional) problems. We validate these analytic shape derivative formulas by comparing against finite-difference based gradients and present several examples showcasing optimal pump shapes under various constraints
Properties and Proximal Point Type Methods for Strongly Quasiconvex Functions in Hilbert Spaces
No full textWe study strongly quasiconvex functions defined on Hilbert spaces and taking extended real values from both theoretical and algorithmic points of view. To this end, we verify the existence of (global) minimizers and the properties of the proximal operator for such functions, previously investigated merely on finitely dimensional spaces. Subsequently, we discuss a relaxed-inertial proximal point-type algorithm for solving optimization problems consisting in minimizing strongly quasiconvex functions over closed convex sets in Hilbert spaces. We obtained a Q-linear convergence rate for the relaxed proximal point-type method and, for the corresponding function values, an asymptotic rate of O(1/k) to the optimal value of the considered optimization problem
MDT3D: Multi-Dataset Training for LiDAR 3D Object Detection Generalization
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Étude sur l'intégration des technologies d'intelligence artificielle dans la défense : analyse du réseau "Défense/IA" et comparaison sectorielle
No full textInternational audienceThis paper uses network analysis tools to study the evolution of the intertwining of defense and artificial intelligence (AI) technologies over the past two decades. AI’s development has brought about a profound change in the research sphere, in some cases altering the development of entire sectors. Many studies have been conducted on the impact of AI mainly on productivity and on the labor market, but there have been few studies on its impact on innovation processes.This disruptive power makes AI of pillar of the fourth industrial revolution. And indeed, AI has become a central component of many countries’ defense policies The technology is also a central element of the unprecedented export control measures of the U.S. administration of October 2022 against China, with the aim of hindering the development of “force-multiplying” technologies for defense capabilities. Focusing our analysis on the evolution of these technologies in defense appears particularly interesting for several reasons: technological edge and adaptability is highly valued for defense material; decision-making often needs to be quick in a complex and evolving environment; high-end and customized product necessitates evolving scientific and technological capacities.After analyzing the evolution of AI technologies in the defense sector, we study the evolution in other sectors of the economy to compare with defense. Network analysis tools are applied to patent data extracted from the software Orbit Intelligence and PATSTAT (European Patent Office) database. Results on the “Defense/AI” network show that the defense sector was one of the first to introduce AI into its innovation process. The combinations number of AI and defense technologies has increased over time, alongside an intensification of the usage of such links to produce new patents. When we compare defense sector to other in terms of integration of AI technologies, we see that defense sector has a wide variety of AI technologies used in its innovation process. The technology corresponding to position in space (G05D 1) has mostly driven the “Defense/AI” sector. Furthermore, after performing a principal components analysis, defense’s innovation process is appearing to be similar to the transport sector in its integration process