Modeling of Critical Heat Flux Using Lattice Boltzmann Methods: Application to the Experimental Devices of the RJH
LBM (Lattice Boltzmann Methods) are numerical techniques used to simulate transport phenomena in complex systems. They allow modeling fluid behavior in terms of particles moving on a discrete grid (a "lattice"). Unlike classical methods, which solve the differential equations of fluids directly, LBM simulate the evolution of the fluid particle distribution functions in a discrete space using propagation and collision rules.
The choice of lattice in LBM is a crucial step, as it directly affects the accuracy, efficiency, and stability of the simulations. The lattice determines how fluid particles interact and move through space, as well as how the discretization of space and time is performed.
LBM methods exhibit a natural parallelism because the computations at each grid point are relatively independent. Compared to classical CFD methods, LBM can better capture certain complex phenomena (such as multiphase, turbulent, or porous media flows) because they rely on a mesoscopic modeling of the fluid, directly derived from particle kinetics, rather than on a macroscopic resolution of the Navier–Stokes equations. This approach allows for a finer representation of interfaces, nonlinear effects, and local interactions, which are often difficult to model accurately using classical CFD methods. LBM therefore enables the capture of complex phenomena at a lower computational cost. Recent studies have notably shown that LBM can reproduce the Nukiyama boiling curve (pool boiling) and, consequently, accurately calculate the critical heat flux. This flux corresponds to a bulk boiling, known as a boiling crisis, which results in a sudden degradation of heat transfer.
The critical heat flux is a crucial issue for the experimental devices (DEX) of the Jules Horowitz Reactor, as they are cooled by water either via natural convection (fuel capsule-type devices) or forced convection (loop-type devices). Thus, to ensure the proper cooling of the DEX and reactor safety, it is essential to verify that the critical heat flux is not reached within the studied parameter range. It must therefore be determined with precision. Previous studies conducted on a fuel-capsule-type DEX using the NEPTUNE-CFD code (classical CFD methods) have shown that modeling is limited to regions far from the critical heat flux. In general, flows with high void fractions (greater than 10%) cannot be easily resolved using classical CFD approaches.
The student will first define a lattice to apply LBM to a RJH device under natural convection. They will consolidate the results obtained for the critical heat flux on this configuration by comparing them with available data. Finally, exploratory calculations under forced convection (laminar to turbulent regime) will be conducted.
The student will be hosted at the IRESNE institute.
Electrical impédanceTomography for the Study of Two-Phase Liquid Metal/Gas Flows
As part of the sustainable use of nuclear energy within a carbon-free energy mix in combination with renewable energies, fourth-generation fast neutron reactors are crucial for closing the fuel cycle and controlling uranium resources. Ensuring the safety of such a sodium-cooled reactor relies for a significant part on the early detection of gas voids in their circuits. In these opaque and metallic environments, optical imaging methods are ineffective, making it necessary to develop innovative techniques.
This PhD project is part of the development of Electrical Impedance Tomography (EIT) applied to liquid metals, a non-intrusive approach enabling the imaging of local conductivity distributions within a flow.
The work will focus on the study of electromagnetic phenomena in two-phase metal/gas systems, in particular the skin effect and eddy currents generated by oscillating fields.
Artificial-intelligence approaches, such as Physics-Informed Neural Networks (PINNs), will be explored to combine numerical learning with physical constraints and will be compared with purely numerical simulations.
The objective is to establish refined physical models adapted to metallic environments and to design inversion methods robust against measurement noise.
Experiments on Galinstan will be conducted to validate the models and demonstrate the feasibility of detecting gas inclusions in a liquid metal.
This research, carried out at IRESNE Institute of CEA Cadarache, will open new perspectives in electromagnetic imaging for opaque, highly conductive media.
Towards a new iterative approach for the efficient modeling of mechanical contact
As part of the modeling and simulation of nuclear fuel behavior across different reactor types, the Institute for Research on Nuclear Energy Systems for Low-Carbon Energy Production (IRESNE) at CEA Cadarache, in partnership with various industrial and academic stakeholders, is developing the PLEIADES software platform for fuel behavior simulation. In this context, the interaction between the fuel and its cladding, the first containment barrier, is a key phenomenon for understanding and predicting the behavior of fuel elements.
The modeling and numerical simulation of mechanical contact phenomena represent a major scientific and technological challenge in solid mechanics, due to the intrinsic complexity of the problem, characterized by its highly nonlinear and non-smooth nature.
To overcome the limitations of classical approaches, such as the penalty or Lagrange multiplier methods, new contact resolution strategies based on iterative fixed-point schemes are currently being explored at the CEA. These approaches offer several advantages: they avoid the direct solution of complex and ill-conditioned systems, significantly improve numerical efficiency, and exhibit very low sensitivity to algorithmic parameters, making them particularly well suited for high-performance computing (HPC) environments.
The objective of this PhD work is to extend these strategies to more complex and realistic situations, by taking into account nonlinear material behaviors and incorporating more sophisticated contact laws, such as friction. Depending on the progress of the work, the final phase will focus on transferring the developments to a high-performance computing (HPC) environment, using a parallel finite element solver.
The project will benefit from internationally recognized expertise in mechanics, applied mathematics, and nuclear fuel simulation, with supervision from CEA researchers and additional academic collaborations (CNRS).
[1] P. Wriggers, "Computational Contact Mechanics", Springer, 2006. doi:10.1007/978-3-540-32609-0.
[2] V. Yastrebov, "Numerical Methods in Contact Mechanics", ISTE Ltd and John Wiley & Sons, 2013. doi: 10.1002/9781118647974
[3] I. Ramière and T. Helfer, “Iterative residual-based vector methods to accelerate fixed point iterations”, Computers & Mathematics with Applications, vol. 70, no. 9, pp. 2210–2226, 2015. doi: 10.1016/j.camwa.2015.08.025.
Multi-physics modelling of a light water nuclear reactor operating under natural convection: study of innovative solutions for startup and power control
Among the most recent designs of water-moderated Small Modular Reactors (SMR), several concepts are characterized by natural convection in the primary circuit during normal and abnormal operation, with the aim of increasing the inherent safety of the design. The absence of primary pumps in this type of SMRs significantly complicates the start-up and power increase ramps. This requires the development of specific start-up procedures to heat up the primary water circuit and enable the reactor to reach its nominal conditions, in accordance with safety requirements. These kinds of procedures rely on simulations using validated models to understand the reactor behavior during these phases and define the accessible parameters domain.
The goal of this PhD project is to develop a numerical model capable of simulating the startup of an SMR operating in natural convection, and to contribute to the validation of this model. The PhD study also aims at developing a methodology for reactor control systems optimization, to attain a fast startup while remaining within the prescribed safety criteria.
The analysis of the reactor startup procedure entails two disciplines: thermal-hydraulics and neutronics, which requires the development of multi-physics coupled simulation tools. Three scientific calculation tools in particular will be coupled in the framework of the PhD study: CATHARE3 (reactor system thermal-hydraulics), FLICA5 (core thermal-hydraulics) and APOLLO3 (neutronics).
The PhD student will work in a team of neutron physicists and thermohydraulic engineers at the IRESNE Institute (CEA Cadarache). He/she will develop skills in nuclear reactor physics and modeling.
Simulation of crack initiation and propagation in random heterogeneous materials
This PhD thesis is concerned with cracking in nuclear fuels at the microstructure level, a phenomenon that is essential to understand in order to model the behavior of materials under irradiation. Indeed, crack initiation and propagation can lead to the release of fission gases and the formation of fragments inducing fissile matter displacement. Current industrials models are based on simplified representations of the porous microstructure and empirical fracture criteria, which limits their physical accuracy and validation by separate effects.
To overcome these limitations, the proposed thesis work consists of using multi-scale approaches and high-performance computing (HPC) finite element simulations. The main objectives are to define a Representative Volume Element (RVE) for crack initiation in materials with random porosity, improve the failure criteria used in legacy codes and define their uncertainties, and finally establish the domain of validity for analyzing crack propagation in the RVE.
The first line of research consists of rigorously defining the size of the RVE based on local physical variables such as the maximum principal stress. Variance reduction methods will be used to optimize the number of calculations required and estimate the associated errors.
In a second step, simulations performed to determine the RVE size will be used to improve industrial models. The approach will seek to separate the mechanical effects of an isolated bubble from those resulting from interactions between neighboring bubbles. Machine learning techniques may be used to develop this new model. Validation will be based on indirect measurements of cracking, such as gas release observed during thermal annealing, particularly for high burn-up structure (HBS) fuels, where legacy models fail to predict the kinetics of cracking.
Finally, crack propagation within the RVE will be studied using 3D phase field simulations, which allow for detailed representation of the various stages after the crack initiation. The influence of boundary conditions on the RVE will be examined by comparison with simulations on larger domains.
The thesis will be carried out at the Institute for Research on Nuclear Systems for Low-Carbon Energy Production (IRESNE) of the CEA Cadarache, within the PLEIADES platform development team, which is specialized in fuel behavior simulation and multiscale numerical methods. It will be conducted in collaboration with the CNRS/LMA as part of the MISTRAL joint laboratory, notably on aspects relating to the analysis of random medium representativeness and micromechanical simulation of crack propagation.
Numerical Simulation of Fluid–Structure Interactions with Contact under Flow Using a Penalized Direct Forcing Method
This PhD work is part of the study of the dynamics of fuel assemblies subjected to axial flow and external mechanical excitation, particularly of seismic type. The objective is to develop an innovative numerical approach capable of accurately predicting the three-dimensional dynamic response of one or several assemblies, while accounting for the coupled effects between the fluid flow and mechanical loads. This problem is particularly complex due to the need to consider large displacements, potential contacts between structures, and strong interactions with the surrounding fluid.
Design and Optimisation of an innovative process for CO2 capture
A 2023 survey found that two-thirds of the young French adults take into account the climate impact of companies’ emissions when looking for a job. But why stop there when you could actually pick a job whose goal is to reduce such impacts? The Laboratory for Process Simulation and System analysis invites you to pursue a PhD aiming at designing and optimizing a process for CO2 capture from industrial waste gas. One of the key novelties of this project consists in using a set of operating conditions for the process that is different from those commonly used by industries. We believe that under such conditions the process requires less energy to operate. Further, another innovation aspect is the possibility of thermal coupling with an industrial facility.
The research will be carried out in collaboration with CEA Saclay and the Laboratory of Chemical Engineering (LGC) in Toulouse. First, a numerical study via simulations will be conducted, using a process simulation software (ProSIM). Afterwards, the student will explore and propose different options to minimize process energy consumption. Simulation results will be validated experimentally at the LGC, where he will be responsible for devising and running experiments to gather data for the absorption and desorption steps.
If you are passionate about Process Engineering and want to pursue a scientifically stimulating PhD, do apply and join our team!
Topologic optimization of µLED's optical performance
The performance of micro-LEDs (µLEDs) is crucial for micro-displays, a field of expertise at the LITE laboratory within CEA-LETI. However, simulating these components is complex and computationally expensive due to the incoherent nature of light sources and the involved geometries. This limits the ability to effectively explore multi-parameter design spaces.
This thesis proposes to develop an innovative finite element method to accelerate simulations and enable the use of topological optimization. The goal is to produce non-intuitive designs that maximize performance while respecting industrial constraints.
The work is divided into three phases:
- Develop a fast and reliable simulation method by incorporating appropriate physical approximations for incoherent sources and significantly reducing computation times.
- Design a robust topological optimization framework that includes fabrication constraints to generate immediately realizable designs.
- Realize such a metasurface on an existing shortloop in the laboratory. This part is optional and will be tackled only if we manage to seize an Opportunity to finance the prototype, via the inclusion of the thésis inside the "metasurface
topics" of european or IPCEI projets in the lab .
The expected results include optimized designs for micro-displays with enhanced performance and a methodology that can be applied to other photonic devices and used by other laboratories from DOPT.
Modeling and characterization of CFET transistors for enhanced electrical performance
Complementary Field Effect Transistors (CFETs) represent a new generation of vertically stacked CMOS devices, offering a promising path to continue transistor miniaturization and to meet the requirements of high-performance computing.
The objective of this PhD work is to study and optimize the strain engineering of the transistor channel in order to enhance carrier mobility and improve the overall electrical performance of CFET devices. The work will combine numerical modeling of technological processes using finite element methods with experimental characterization of crystalline deformation through transmission electron microscopy coupled with precession electron diffraction (TEM-PED).
The modeling activity will focus on predicting strain distributions and their impact on electrical properties, while accurately accounting for the complexity of the technological stacks and critical fabrication steps such as epitaxy. In parallel, the experimental work will aim to quantify strain fields using TEM-PED and to compare these results with simulation outputs.
This research will contribute to the development of dedicated modeling tools and advanced characterization methodologies adapted to CFET architectures, with the goal of improving spatial resolution, measurement reproducibility, and the overall understanding of strain mechanisms in next-generation transistors.
Development of machine learning algorithms to improve image acquisition and processing in radiological imaging
The Nuclear Measurements Laboratory at the LNPA (Laboratory for the Study of Digital Technologies and Advanced Processes) in Marcoule consists of a team specializing in nuclear measurements in the field. Its activities are divided between developing measurement systems and providing technical expertise to CEA facilities and external partners (ORANO, EDF, IAEA).
The LNPA has been developing and using radiological imagers (gamma and alpha) for several years. Some of the developments have resulted in industrial products, while other imagers are still being developed and improved. Alpha imaging, in particular, is a process that allows alpha contamination zones to be detected remotely. Locating the alpha source is an important step in glove boxes, whether for a cleanup and dismantling project, for maintenance during operation, or for the radiation protection of workers. The alpha camera is the tool that makes alpha mapping accessible remotely and from outside glove boxes.
The objective of the thesis is to develop and implement mathematical prediction and denoising solutions to improve the acquisition and post-processing of radiological images, and in particular alpha camera images.
Two main areas of research will be explored in depth:
- The development of real-time or post-processing image denoising algorithms
- The development of predictive algorithms to generate high-statistics images based on samples of real images.
To do this, an experimental and simulation database will be established to feed the AI algorithms.
These two areas of research will be brought to fruition through the creation of a prototype imager incorporating machine learning capabilities and an image acquisition and processing interface, which will be used in an experimental implementation.
Through this thesis, students will gain solid knowledge of nuclear measurements, radiation/matter interaction, and scientific image processing, and will develop a clear understanding of radiological requirements in the context of remediation/decommissioning projects.