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Stroke rate and arm coordination management in swimming in a double Paralympic triathlete champion
No full textInternational audienceThe 2024 Paris Paralympic triathlon required swimming with and against the current which requested to adapt stroke mechanics. To understand how a Paralympic triathlete champion might adapt his stroke mechanics under varying current conditions, this study aimed to 1) determine the range and optimal stroke rate (SR) and index of coordination (IdC); 2) examine the flexibility of SR, IdC and associated total energy expenditure. The para triathlete performed two front crawl tests: 10 times 25m incremented in swimming speed (S), from which S-SR and S-IdC relationships have been modelled to detect two regimes of functioning and the most effective SR; then, 6 times 50 m at the speed of the 800 m freestyle using 6 different SR conditions: spontaneous SR (SRs), SRs imposed by tempo trainer, SRs+3, SRs+6, SRs-3 and SRs-6 cycles. Total energy expenditure was computed from post-exercise oxygen uptake and blood lactate measurements. In test 1, the highest effective SR equals 44 cycle.min-1, which corresponds to the preferred SR in 800 m freestyle competition. In test 2, the para triathlete struggled to perform the high SR conditions, which was associated to higher total energy expenditure; conversely, the para triathlete naturally decreased SR. It is advised to modulate SR around the preferred SR to optimise efficiency under varying current conditions
An entropy penalized approach for stochastic control problems. Complete version
No full textInternational audienceIn this paper, we propose an original approach to stochastic control problems. We consider a weak formulation that is written as an optimization (minimization) problem on the space of probability measures. We then introduce a penalized version of this problem obtained by splitting the minimization variables and penalizing the discrepancy between the two variables via an entropy term. We show that the penalized problem provides a good approximation of the original problem when the weight of the entropy penalization term is large enough. Moreover, the penalized problem has the advantage of giving rise to two optimization subproblems that are easy to solve in each of the two optimization variables when the other is fixed. We take advantage of this property to propose an alternating optimization procedure that converges to the infimum of the penalized problem with a rate , where is the number of iterations. The relevance of this approach is illustrated by solving a high-dimensional stochastic control problem aimed at controlling consumption in electrical systems
Online Markov Decision Processes with Terminal Law Constraints
No full textTraditional reinforcement learning usually assumes either episodic interactions with resets or continuous operation to minimize average or cumulative loss. While episodic settings have many theoretical results, resets are often unrealistic in practice. The infinite-horizon setting avoids this issue but lacks non-asymptotic guarantees in online scenarios with unknown dynamics. In this work, we move towards closing this gap by introducing a reset-free framework called the periodic framework, where the goal is to find periodic policies: policies that not only minimize cumulative loss but also return the agents to their initial state distribution after a fixed number of steps. We formalize the problem of finding optimal periodic policies and identify sufficient conditions under which it is well-defined for tabular Markov decision processes. To evaluate algorithms in this framework, we introduce the periodic regret, a measure that balances cumulative loss with the terminal law constraint. We then propose the first algorithms for computing periodic policies in two multi-agent settings and show they achieve sublinear periodic regret of order Õ(T 3/4 ). This provides the first non-asymptotic guarantees for reset-free learning in the setting of M homogeneous agents, for M > 1
Uncertainty sources in a large ensemble of hydrological projections: Regional Climate Models and Internal Variability matter
No full textInternational audienceMulti-scenario, multi-model ensembles of hydrological projections are widely used to describe possible futures of regional hydrology and inform adaptation strategies. The Explore2 dataset is such an ensemble of river flow projections in Metropolitan France. It provides future simulations for 1735 catchments with modeling chains composed of different hydrological models forced by 36 regional climate projections based on bias-adjusted EUROCORDEX simulations. This study assesses the uncertainties of this ensemble with QUALYPSO, a method specifically designed to deal with incomplete ensembles and to disentangle and quantify all uncertainty sources, including that due to internal variability. Focusing on results obtained at the end of the century, this study shows a strong agreement between modeling chains towards decreases in low flows in a large southern part of France for a high-emission scenario, and very uncertain changes for the annual mean and high flows. Emission scenario uncertainty is the dominant source of uncertainty for low flows over the whole of France, and for mean annual flows in southeastern France. The contribution of the global and regional climate models is important for mean and high flows, especially in rainfall-dominated areas. Regional climate models contribute considerable uncertainty to low flows, much more than global models. The contribution of hydrological model uncertainty is large for low flows, moderate for mean annual flows, and small for high flows. For all climate and hydrological indicators, internal variability is often large and cannot be overlooked. It is often of the same order and sometimes larger than the uncertainty on the climate change response
Long Term Safety studies of the deep geological repository Cigéo project with code_saturne
No full textThis article presents a methodology for the Long-Term Safety Assessment (LTSA) of the French Deep Geological Repository (DGR) Cigéo. A key feature of the methodology is a fully three-dimensional representation of the DGR, designed to avoid some of the approximations typically introduced in hierarchically nested multi-scale hydrogeological simulations when applying boundary conditions and interpolating solution fields. This choice places strong demands on both the numerical methods and the meshing strategy. The groundwater flow equation and the radionuclide transport equations must be simulated over a kilometer-scale domain while resolving fine-scale geometric details such as waste containers. Furthermore, the simulation must cover time spans up to one million years. To meet these requirements, the proposed methodology combines Compatible Discrete Operator (CDO) schemes, highperformance computing and a semi-automated meshing strategy based on a limited number of elementary meshes that are duplicated and transformed on the fly. The resulting polyhedral mesh helps control the total number of mesh cells. The proposed methodology is first assessed on a simplified DGR test case, and then on a representative Cigéo configuration involving up to one billion unknowns. This demonstrates the robustness, scalability, and practical relevance of the proposed methodology for LTSA. Overall, the proposed framework provides a consistent basis for constructing reliable reference solutions for LTSA in deep geological repositories
Path-following methods for phase-field simulation of quasi-static crack propagation
No full textInternational audienceThe variational approach to fracture, particularly through its regularization as a phase-field model, has become a widely used tool for simulating the quasi-static propagation of cracks in structures. However, classic incremental loading can induce unstable crack growth, violating the quasi-static assumption, and in some cases, leads to a loss of force balance, preventing self-consistency and the estimation of dissipated energy during snapback instabilities. To address this challenge, path-following methods are investigated. Their aim is to adjust the applied load so that it stays at the propagation threshold, thereby preserving the quasi-static assumption and ensuring equilibrium solutions. In this work, we apply and evaluate multiple path-following methods within the framework of variational phase-field fracture models, which are developed to regularize linear elastic variational sharp crack evolution problems. Our study pursues two objectives. First, we review several existing path-following methods, with a focus on partitioned strategies based on the displacement field, which decouple the path-following control equation from the rest of the system, facilitating easier integration with staggered solvers. In addition, we introduce a new path-following method whose particularity is to limit the maximum strain increment outside the cracked regions. Second, we use the Γ-convergence to the sharp crack model to evaluate these methods across three crack propagation problems of increasing complexity. The comparison demonstrates that the proposed path-following method offers a simple yet highly effective approach to capture the equilibrium path in phase-field fracture simulations. This method robustly maintains the quasi-static assumption, ensuring physically meaningful results. By enabling accurate estimation of the energy dissipated during snapback instabilities, it paves the way for the rational design of more resistant heterogeneous materials
A gradient-enhanced GTN model with Lode-dependent nucleation for ductile fracture in ferritic steels : from specimens to structural components
No full textInternational audienceDuctile fracture behavior in ferritic steels is investigated using two complementary experimental databases. The first database involves a wide range of cracked and uncracked specimen geometries tested at various temperatures for a A533 (18MND5) steel, enabling a detailed analysis of the effect of stress states, particularly stress triaxiality and the Lode parameter, on damage nucleation and growth. The second database, for a WB36 (15NiCuMoNb5) steel, includes both laboratory-scale specimens and full-scale structural tests on precracked pipes at various temperatures. A gradientenhanced energy GTN (Gurson-Tvergaard-Needleman) model incorporating a Lode-parameterdependent nucleation function is employed to simulate ductile damage across different stress states.The model is first calibrated and validated on the A533 dataset. It is then applied to the WB36 dataset to assess its transferability from specimens to structural components. The results confirm the model's ability to accurately capture damage evolution and crack propagation, demonstrating its robustness and relevance for structural integrity assessments.</div
The Symmetric-weight Chain-Precedence Knapsack Polytope
No full textWe study the Symmetric-weight Chain Precedence Knapsack Problem (SCPK), a knapsack variant where items are partitioned into groups, and items in the same group are subject to chain precedence constraints. The precedence chains and the item weights are identical across groups, inducing a rich set of polyhedral symmetries, defined as permutations of the variables that preserves the feasible set, independently of the objective function. The (SCPK) is motivated by hydro unit commitment problems, where operating points are activated cumulatively and associated water flows are identical across time periods. Rather than handling symmetry as a source of redundancy to be eliminated, we exploit the invariance of the (SCPK) polytope under permutations of the groups to guide its polyhedral description. We introduce the notion of patterns, symmetry invariant objects that represent equivalence classes of valid inequalities. These are called pattern inequalities and generalize classical cover-type inequalities to this symmetric setting. We prove that pattern inequalities dominate existing binary inequalities derived from cover and induced cover approaches. Moreover, we show that all facet-defining inequalities of the (SCPK) with 0-1 coefficients are characterized by pattern inequalities. We provide necessary and, in a special case, sufficient conditions for a pattern inequality to be facet-defining. In a second paper, experimental results show the efficiency of pattern inequalities within a symmetry-aware Branch&Cut framework
Steady-State Algorithm with Structural Periodicity: Application to Computation of Railways’ Ballast Plastic Strains
No full textInternational audienceThe geometry of ballasted railway tracks is crucial for ensuring railway safety and efficiency. This paper introduces the use of innovative steady-state algorithms designed to compute plastic strains in linear geotechnical structures like railway ballast layers, within Finite Element Methods (FEMs). Facing the specificities of moving loads, traditional step-by-step algorithms, while simple and adaptable, are computationally expensive and time-consuming. In contrast, the proposed steady-state algorithms leverage an Eulerian approach to describe the movement of loads significantly reducing computational time while maintaining accuracy. This paper proposes these algorithms as a methodological improvement and demonstrates the applicability and efficiency of the method for non-periodic structures, as well as for periodic structures, such as railway tracks with evenly spaced sleepers. This paper demonstrates the applicability and efficiency of theses algorithms through comparative studies with traditional methods on typical railway structures. The results show that the presented algorithm not only matches the accuracy of step-by-step methods but also drastically reduces computation time and data storage requirements. This advancement has practical applications for railway infrastructure managers, enabling more efficient and accurate predictions of track geometry evolution and preventing incidents through improved maintenance strategies
Data-driven discovery of dimensionless numbers and governing laws: A scaling technique for nuclear test facility design
No full textNew designs of water-cooled reactors that include thermal-hydraulics systems undergo safety analysis during the licensing process. For economic reasons and safety concerns, systems are firstly tested on reduced scale test facilities. A proper scaling ensures that dominant thermalhydraulics safety-related phenomena are captured, even though unavoidable scale distortions occur. Some existing scaling methods, based on prior knowledge of the physical phenomena at stake, can quantify such distortions. They are however limited when the phenomena are non-linear and coupled, or even not formalized as an equation. Dimensionless numbers play a central role in scaling techniques as their scale invariant properties help preserve similarities between reactor and test facilities. Building on this principle, this paper proposes a data driven alternative to traditional scaling techniques. From a dataset of physical variables describing the phenomenon of interest, the method identifies a governing law that captures the dominant safety related phenomenon. This governing law is expressed in terms of dimensionless numbers, which are physically meaningful combinations of dimensional variables and are automatically inferred by the algorithm. Based on a clear mathematical formulation, the proposed data driven method combines advanced regression analysis with the constrained optimization of a cross validation based objective function. Two variants are presented: one assuming that the output of the nondimensional governing law is known, and an extension in which this output is estimated. Both variants identify the dominant input dimensionless number as well as the explicit form of the governing law. As a proof of concept, the method is tested on a simulated dataset representative of single-phase natural circulation in a passive heat removal system