Simulation Methods for Compressible Flows Using Staggered Discretization Methods
Thermal-hydraulic codes require robust and accurate numerical methods capable of handling multiphase flows over a wide range of Mach numbers. In this context, staggered-grid methods provide a particularly attractive framework. This PhD thesis builds upon recent work carried out as part of a previous PhD project, which led to the development of a new method with promising theoretical and numerical properties. The objective is to pursue this work and extend its range of applications. The first part will focus on the theoretical analysis of the explicit version of the scheme applied to the drift-flux model, in order to establish its conservation properties, its behaviour in the low-Mach-number limit, and the convergence of the numerical solutions. The method will then be extended to the Baer-Nunziato two-phase flow model, in which the two phases have distinct velocities and pressures. Finally, high-order reconstruction techniques will be investigated to improve the accuracy of the scheme by reducing numerical dissipation. The expected outcomes are a better mathematical understanding of these schemes and the development of robust and accurate numerical methods suitable for industrial applications. These methods will first be validated in a C++ and Python research code, and then implemented and assessed within the open-source TRUST platform dedicated to high-performance thermal-hydraulic simulations.
Incremental Generation of Polycubes Driven by Geometric Quality
The proposed work consists of designing and developing a new algorithm for generating block-structured hexahedral meshes using a “Polycube”-typed strategy. Usually, these methods deform a geometric domain G to be discretized into a polycube P, i.e. a polyhedron whose all the faces are orthogonal to one of the X, Y, or Z axes. This polyhedron can be easily discretized using a hexahedral mesh, which is then transformed via the inverse deformation to pave G.
Unlike traditional approaches, which are based on constructing the deformation function, we focus on the inverse function by treating the pair (geometry G, polycube P) as input. Our goal is to compute the inverse function F that transforms P into G, and, depending on some local properties of F, modify P to provide a “better” mesh of G from a geometric point of view.
Generation of Curved Hexahedral-Dominant Block Structures Using Median Objects
The automatic generation of block-structured hexahedral meshes is a challenging problem, solved in practice through the use of dedicated interactive software, such as Magix3D, which is developed and maintained at CEA DAM. Using such software, a specialist engineer may spend several days creating the expected mesh.
The objective of this thesis is to help such an engineer to quickly sketch initial block structures with a hexahedral-dominant structure by relying on the medial object of the 3D geometric domain to be discretized. When considering CAD-type geometric domains, represented by their boundary, the medial object provides volumetric information intrinsic to the domains, which will guide the engineer in creating individual hexahedral blocks.
The objective of this thesis is twofold: first, to propose an efficient method for generating 3D medial objects; and second, to develop an algorithm and an associated interactive tool for creating predominantly hexahedral block structures.
New Reliable Strategies for Optimizing Predictive Thermodynamics Models
Predictive thermodynamic models, developed by the Calphad method, are essential for designing new materials by anticipating their behavior without resorting to costly and time-consuming experiments. These models allow for the extrapolation of the properties of complex materials, predicting their behavior in extreme environments, and linking energy properties to in-service performance. However, current methods for developing these models are complex, and uncertainties are not quantified in existing software. Scientists still rely on their expertise to adjust and validate these models, which is time-consuming and poorly suited to the era of automation.
To address this, it is proposed to develop a reliable, autonomous, and fast digital tool capable of optimizing thermodynamic models based solely on experimental data provided by users. The goal is to provide simple, reliable, validated, and modular models, enabling users to make strategic decisions with confidence, such as evaluating new process conditions or optimizing manufacturing without risking uncertain extrapolations. This project aims to bridge the gap between specific experimental data and modern nonlinear programming methods, using advanced optimization approaches.
Statistical optimization and calibration of lithography model
This thesis provides an opportunity to develop statistical methods to optimize and calibrate lithography models used to generate optimal photomask designs by mean of optical proximity correction (OPC).
Microelectronic devices with high circuit density are in high demand and are extensively researched and pursued by industries. One way to achieve higher circuit density is to decrease pattern dimension or pitch. However as pattern dimension decreases, fabrication challenge increases. Resolution Enhancement Technique (RET) such as OPC has therefore to be used to generate photomask of such circuits.
OPC aims to improve the wafer pattern fidelity by compensating errors arising due to optical or process effects during fabrication steps. To implement this correction, a lithography model has to be generated taking into account the exposure system and photo resist characteristics. These models are calibrated using very large volume of experimental data which includes CD-SEM measurements and contour extracted from SEM images. The data acquisition and image post processing is a bottleneck in model calibration flow, consuming huge amount of time and resources.
During the period of thesis, work will be focused on:
Innovative test patterns to optimize input data for model calibration
Statistical and algorithmic optimization of model calibration flow
Impact of experimental data variability on lithography models
Integrated waste treatment: design and optimisation of a multi-waste treatment scheme for a multi-purpose energy production
At the city scale, multiple waste streams such as household waste, compost, sewage sludge, yard waste, non-recyclable plastics, used oils, metals, glass, and others. All of these feedstocks exhibit variable seasonality and carbon content. Nowadays, the aforementioned streams are managed through recycling, and in some cases incineration or landfilling. Alternative treatment technologies, such as gasification, hydrothermal gasification, and anaerobic digestion, are being explored as potential pathways to improve the overall sustainability of waste management.
Existing scientific studies have largely focused on the conversion of individual waste types or on the application of a single technology to a specific waste stream, without accounting for regional integration, resource variability or systemic assessment. A city-scale analysis of waste streams could enable the identification of synergies between different waste types and the identification of optimal conversion pathways.
In this context, a key scientific challenge lies in the development of an integrated, multi-waste treatment framework capable of modelling, optimizing, and assessing a multi-waste, multi-product energy system at the city scale. The objective of this PhD project is to investigate waste treatment at the city scale, accounting for the seasonality of waste generation, waste stream composition, and local energy demand (heat, electricity, and gas). The work will consider local and European regulations (Waste Framework Directive, AGEC law, and RED III directive) as well as techno-economic and environmental aspects. The study will focus on one to three representative geographic areas and will establish a methodology that can be further applied to a broad range of territorial contexts.
Prediction of elastic wave dispersion effects using a semi-analytical model under high-frequency approximation
Ultrasonic testing (UT) methods are a fundamental component of non-destructive testing (NDT). They are widely used to inspect mechanical components such as welds (in nuclear and petrochemical industries) and composite material structures (in aeronautics). To understand the physical phenomena involved in a given configuration, simulation is a valuable tool and sometimes an essential step in implementing the inspection process.
Modeling approaches fall into two main categories: purely numerical models based on finite elements (FE) and semi-analytical methods derived from high-frequency (HF) approximations, such as paraxial rays. While the latter are often favored for their computational efficiency, they introduce simplifications that can compromise the quantitative accuracy of results, particularly for phenomena like dispersion (variation in wave speed with frequency), which are common in certain industrial contexts.
This thesis project aims to enhance the paraxial ray approach by integrating models of dispersive interfaces (composite interplies, coupling layers), dispersive viscoelastic media, and a modal guided wave model. The goal is to develop a simulation tool capable of faithfully reproducing realistic inspection configurations, thereby improving the representativeness of the results.
Electromagnetic Signature Modeling and AI for Radar Object Recognition
This PhD thesis offers a unique opportunity to work at the crossroads of electromagnetics, numerical simulations, and artificial intelligence, contributing to the development of next-generation intelligent sensing and recognition systems. The intern will join the Antenna & Propagation Laboratory at CEA-LETI, Grenoble (France), a world-class research environment equipped with state-of-the-art tools for propagation channel characterization and modelling. A collaboration with the University of Bologna (Italy) is planned during the PhD.
This PhD thesis aims to develop advanced electromagnetic models of near-field radar backscattering, tailored to radar and Joint Communication and Sensing (JCAS) systems operating at mmWave and THz frequencies. The research will focus on the physics-based modeling of the radar signatures of extended objects, accounting for near-field effects, multistatic and multi-antenna configurations, as well as the influence of target materials and orientations. These models will be validated through electromagnetic simulations and dedicated measurement campaigns, and subsequently integrated into scene-level and multipath propagation simulation tools based on ray tracing. The resulting radar signatures will be exploited to train artificial intelligence algorithms for object recognition, material property inference, and radar imaging. In parallel, physics-assisted AI approaches will be investigated to accelerate electromagnetic simulations and reduce their computational complexity. The final objective of the thesis is to integrate radar backscattering-based information into a 3D Semantic Radio SLAM framework, in order to improve localization, mapping, and environmental understanding in complex or partially obstructed scenarios.
We are seeking a student at engineering school or Master’s level (MSc/M2), with a strong background in signal processing, electromagnetics, radar, or telecommunications. An interest in artificial intelligence, physics-based modeling, and numerical simulation is expected. Programming skills in Matlab and/or Python are appreciated, as well as the ability to work at the interface between theoretical models, simulations, and experimental validation. Scientific curiosity, autonomy, and strong motivation for research are essential.The application must include a CV, academic transcripts, and a motivation letter.
Hybrid CPU-GPU Preconditioning Strategies for Exascale Finite Element Simulations
Exascale supercomputers are based on heterogeneous architectures that combine CPUs and GPUs, making it necessary to redesign numerical algorithms to fully exploit all available resources. In large-scale finite element simulations, the solution of linear systems using iterative solvers and algebraic multigrid (AMG) preconditioners remains a major performance bottleneck.
The objective of this PhD is to study and develop hybrid preconditioning strategies adapted to such heterogeneous systems. The work will investigate how multilevel and AMG techniques can be structured to efficiently use both CPUs and GPUs, without restricting computations to a single type of processor. Particular attention will be paid to data distribution, task placement, and CPU–GPU interactions within multilevel solvers.
From a numerical point of view, the research will focus on the analysis and construction of multilevel operators, including grid hierarchies, intergrid transfer operators, and smoothing procedures on avalible GPU's and CPU's. The impact of these choices on convergence, spectral properties, and robustness of preconditioned iterative methods will be studied. Mathematical criteria guiding the design of efficient hybrid preconditioners will be investigated and validated on representative finite element problems, e.g., regional-scale earthquake analysis.
These developments will be coupled with domain decomposition and parallelization strategies adapted to heterogeneous architectures. Particular attention will be paid to CPU–GPU data transfers, memory usage, and the balance between compute-bound and memory-bound kernels. The interaction between numerical choices and hardware constraints, such as CPU and GPU memory hierarchies, will be designed and developed to ensure scalable and efficient implementations.
Control & optimization of fuel cell temperature
Proton exchange membrane fuel cells (PEMFC) represent a key technology for the development of clean and sustainable energy systems, particularly for heavy-duty transport applications where their energy density is very attractive. However, in order to represent a viable industrial alternative, a number of obstacles still need to be overcome, including operating costs and, above all, the durability of the systems under real-world conditions. Among the levers for action, optimizing operating conditions is a promising avenue for limiting the degradation phenomena occurring within the cell. The operating temperature is a particularly key parameter because it affects all aspects of the system, from the kinetics of degradation mechanisms to the thermal capacity that the system can dissipate, including the water balance within the fuel cell. Despite the influence of this parameter on durability, it is generally only optimized at the system level to achieve the best performance, the shortest possible response time and to limit the size of the thermal management system.
The aim of this thesis is to work on optimizing the temperature management of a fuel cell within a system, taking into account not only performance but also sustainability criteria. To do this, the impact of operating temperature on degradation mechanisms will be analyzed using various simulation tools already available at LITEN and the teams' fifteen years of experience in studying PEMFC fuel cell degradation. Various thermal architectures will be proposed and evaluated in conjunction with the work on temperature control optimization. The latter will be implemented on a real fuel cell system in order to demonstrate the relevance of the proposed solution using concrete experimental data.