Front-surface cooling of infrared thermophotovoltaic cells

International audienceThis paper proposes a front-surface cooling method for thermophotovoltaic (TPV) cells utilizing microfluidic channels for efficient heat dissipation. Unlike conventional back-surface cooling, front-surface cooling minimizes thermal resistance by directly cooling the top surface of the cell. The microfluidic channel layer also functions as an antireflection layer through the gradual change in the refractive index. The proposed cooling method was evaluated using a thermo-fluid analysis, considering factors such as the emitter temperature, cell reflectance, thermal resistance, and fluid optical properties. We examined liquids with ideal absorption characteristics and actual liquids whose absorption coefficients were measured. The results showed that front-surface cooling significantly outperformed back-surface cooling in terms of the net power density. This method is particularly advantageous for high emitter temperatures or in cases where the thermal resistance between the cell and backsurface liquid is high. Moreover, this study highlights the potential application of the cooling method in bifacial TPV cells, which can generate electricity from thermal radiation incident on both sides. Bifacial cells offer higher power generation per unit area but face cooling challenges. The proposed cooling technique addresses these challenges, paving the way for innovative TPV system configurations and improved performance

Orchestrating on-board sensors for global hybrid robust stabilization of unicycles

We consider mobile robots described through unicycle dynamics equipped with on-board range sensors and cameras, one facing forward and one facing backward, providing measurements of the distance and misalignment to a target. We propose a hybrid control law combining the two on-board measurements and discuss stability results for the closed-loop expressed in the on-board camerabased coordinates, using Lyapunov-based arguments. We prove robustness of the stability properties to uncertainties affecting the sensors and external perturbations acting on the robot. The results are illustrated via simulations

Swarm dynamics for global optimisation on finite sets

International audienceConsider the global optimisation of a function $U$ defined on a finite set $V$ endowed with an irreducible and reversible Markov generator.By integration, we extend $U$ to the set $\mathcal{P}(V)$ of probability distributions on $V$ and we penalise it with a time-dependent generalised entropy functional.Endowing $\mathcal{P}(V)$ with a Maas' Wasserstein-type Riemannian structure, enables us to consider an associated time-inhomogeneous gradient descent algorithm.There are several ways to interpret this \cP(V)-valued dynamical system as the time-marginal laws of a time-inhomogeneous non-linear Markov process taking values in $V$, each of them allowing for interacting particle approximations.This procedure extends to the discrete framework the continuous state space swarm algorithm approach of Bolte, Miclo and Villeneuve \cite{Bolte}, but here we go further by considering more general generalised entropy functionals for which functional inequalities can be proven.Thus in the full generality of the above finite framework, we give conditions on the underlying time dependence ensuring the convergence of the algorithm toward laws supported by the set of global minima of $U$.Numerical simulations illustrate that one has to be careful about the choice of the time-inhomogeneous non-linear Markov process interpretation

On L¹ and time-optimal state transitions in piecewise linear models of gene-regulatory networks

International audienceIn this paper, we investigate optimal state transfers for a generic class of piecewise-linear models widely used to qualitatively describe gene-regulatory networks. Motivated by the main practical drawbacks of artificially regulating gene expression through chemical inducers, the optimality of the transitions is defined as the convex combination of the total time and the L¹ cost of the control. Solutions are studied through a Hybrid Pontryagin's Maximum Principle approach, which allows to characterize the optimal trajectories and control for the general formulation of the problem. Then, we focus on two practical examples of two-dimensional regulatory networks: the bistable switch, for which the objective is to induce optimal transitions between its two stable steady states, and the damped genetic oscillator, where the goal is to induce sustained oscillatory behaviors. The resulting optimal control strategies can be expressed in state feedback form, involving both bang arcs and inactive control periods, and are shown to slide over certain separatrices of the uncontrolled system that characterize the boundaries of the admissibility set

Challenges of operating multiple distributed generators with different primary control strategies in microgrids: Interactions and performance assessment

International audienceThis study investigates the effectiveness of hybrid power-sharing control strategies in microgrid systems. It integrates various droop controllers, including conventional droop, universal droop, dVOC, and VSG. The contribution of each controller is evaluated in terms of system stability, efficiency, and adaptability. These assessments consider how different test conditions influence overall system performance. The performance analysis focuses on power sharing during both transient and steady-state conditions. It accounts for DERs connected through complex transmission line impedances and subjected to variable local loads. The study concludes with extensive real-time simulations using the Typhoon HIL 604 platform. These scenarios test different operating conditions to identify the most stable microgrid configuration

Heat transfer modelling of radiant flux from a halogen lamp for enhancing Infrared thermography simulation

International audienceInfraRed Thermography (IRT) is widely used in Non-Destructive Evaluation (NDE) for its ability to provide real-time, two-dimensional, non-contact measurements of heat distribution. Enhancing the analysis of thermal results requires a comprehensive understanding of the entire measurement chain from the heat source, through propagation, to detection, signal processing and data interpretation, which demands an effective combination of simulation and experimental approaches. This study presents the modelling of heat transfer, with particular emphasis on accurately characterising the radiant heat flux emitted by a halogen lamp. A fluxmeter sensor was employed to measure the radiant heat flux at different distances and spatial locations. Subsequently, 3D heat transfer models incorporating these heat sources were established and applied to an Acrylonitrile Butadiene Styrene plate to investigate thermal behaviour and the influence of factors within the measurement chain. Critical parameters were also considered, including thermal conductivity, convective heat transfer coefficients, fluxmeter sensor sensitivity, heat flux characteristics and measurement methods. Simulation results were validated against experimental data using both an infrared camera and a pyrometer and demonstrated strong agreement. Relative errors were below 4.2 % for pyrometer measurements, whereas slightly higher errors, up to 5.9 % for IRT method, which is mainly attributed the influence of environmental factors on this technique. These findings confirm the accuracy and reliability of the calibrated heat source and modelling parameters. Integrating experimental data into the thermal simulation enhances both accuracy and consistency, thereby establishing a more robust framework for the application of numerical methods throughout the IRT measurement chain in NDE applications

Représentations dérivées des variétés de caractères quantiques

Quantum moduli algebras $\mathcal{L}_{g,n}^{\mathrm{inv}}(H)$ were introduced by Alekseev-Grosse-Schomerus and Buffenoir-Roche in the context of quantization of character varieties of surfaces and exist for any quasitriangular Hopf algebra $H$. In this paper we construct representations of $\mathcal{L}_{g,n}^{\mathrm{inv}}(H)$ on cohomology spaces $\mathrm{Ext}_H^m(X,M)$ for all $m \geq 0$, where $X$ is any $H$-module and $M$ is any $\mathcal{L}_{g,n}(H)$-module endowed with a compatible $H$-module structure. As a corollary and under suitable assumptions on $H$, we obtain projective representations of mapping class groups of surfaces on such Ext spaces. This recovers the projective representations constructed by Lentner-Mierach-Schweigert-Sommerhäuser from Lyubashenko theory, when the category $\mathcal{C} = H\text{-}\mathrm{mod}$ is used in their construction. Other topological applications are matrix-valued invariants of knots in thickened surfaces and representations of skein algebras on Ext spaces

Automated growth of AlAs/GaAs Bragg mirror with real-time feedback reflectometry.

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Critical function placement based on service chains in multi-administrative federated networks

International audienceAlthough the Service Function Chains (SFCs) embedding problem is broadly investigated in the literature, few works address it in a sliced multi-administrative network federation. In this work, we provide several insights into the problem. First, we describe a new federated-level topology abstraction. Second, we introduce a novel optimization model and heuristic (for large scale), which solve SFC embedding. Third, we conduct experiments on various multi-domain topologies and compare the algorithms regarding resource allocation efficiency and runtime. We analyze the trade-off between slice deployment costs and link utilization. Finally, we emulate two security scenarios on Containernet, Docker, and Open vSwitch architecture

On the Private Estimation of Smooth Transport Maps

International audienceEstimating optimal transport maps between two distributions from respective samples is an important element for many machine learning methods. To do so, rather than extending discrete transport maps, it has been shown that estimating the Brenier potential of the transport problem and obtaining a transport map through its gradient is near minimax optimal for smooth problems. In this paper, we investigate the private estimation of such potentials and transport maps with respect to the distribution samples.We propose a differentially private transport map estimator achieving an $L^2$ error of at most $n^{-1} \vee n^{-\frac{2 \alpha}{2 \alpha - 2 + d}} \vee (n\epsilon)^{-\frac{2 \alpha}{2 \alpha + d}}$ up to poly-logarithmic terms where $n$ is the sample size, $\epsilon$ is the desired level of privacy, $\alpha$ is the smoothness of the true transport map, and $d$ is the dimension of the feature space. We also provide a lower bound for the problem