A theoretical framework for the task-based optimal design of Modular and Reconfigurable Serial Robots for rapid deployment
The innovations that gave rise to industrial robots date back to the sixties and seventies. They have enabled a massive deployment of industrial robots that transformed factory floors, at least in industrial sectors such as car manufacturing and other mass production lines.
However, such robots do not fit the requirements of other interesting applications that appeared and developed in fields such as in laboratory research, space robotics, medical robotics, automation in inspection and maintenance, agricultural robotics, service robotics and, of course, humanoids. A small number of these sectors have seen large-scale deployment and commercialization of robotic systems, with most others advancing slowly and incrementally to that goal.
This begs the following question: is it due to unsuitable hardware (insufficient physical capabilities to generate the required motions and forces); software capabilities (control systems, perception, decision support, learning, etc.); or a lack of new design paradigms capable to meet the needs of these applications (agile and scalable custom-design approaches)?
The unprecedented explosion of data science, machine learning and AI in all areas of science, technology and society may be seen as a compelling solution, and a radical transformation is taking shape (or is anticipated), with the promise of empowering the next generations of robots with AI (both predictive and generative). Therefore, research can tend to pay increasing attention to the software aspects (learning, decision support, coding etc.); perhaps to the detriment of more advanced physical capabilities (hardware) and new concepts (design paradigms). It is however clear that the cognitive aspects of robotics, including learning, control and decision support, are useful if and only if suitable physical embodiments are available to meet the needs of the various tasks that can be robotized, hence requiring adapted design methodologies and hardware.
The aim of this thesis is thus to focus on design paradigms and hardware, and in particular on the optimal design of rapidly-produced serial robots based on given families of standardized « modules » whose layout will be optimized according to the requirements of the tasks that cannot be performed by the industrial robots available on the market. The ambition is to answer the question of whether and how a paradigm shift may be possible for the design of robots, from being fixed-catalogue to rapidly available bespoke type.
The successful candidate will enrol at the « Ecole Doctorale Mathématiques, STIC » of Nantes Université (ED-MASTIC), and he or she will be hosted for three years in the CEA-LIST Interactive Robotics Unit under supervision of Dr Farzam Ranjbaran. Professors Yannick Aoustin (Nantes) and Clément Gosselin (Laval) will provide academic guidance and joint supervision for a successful completion of the thesis.
A follow-up to this thesis is strongly considered in the form of a one-year Post-Doctoral fellowship to which the candidate will be able to apply, upon successful completion of all the requirements of the PhD Degree. This Post-Doctoral fellowship will be hosted at the « Centre de recherche en robotique, vision et intelligence machine (CeRVIM) », Université Laval, Québec, Canada.
Artificial Intelligence for the Modeling and Topographic Analysis of Electronic Chips
The inspection of wafer surfaces is critical in microelectronics to detect defects affecting chip quality. Traditional methods, based on physical models, are limited in accuracy and computational efficiency. This thesis proposes using artificial intelligence (AI) to characterize and model wafer topography, leveraging optical interferometry techniques and advanced AI models.
The goal is to develop AI algorithms capable of predicting topographical defects (erosion, dishing) with high precision, using architectures such as convolutional neural networks (CNN), generative models, or hybrid approaches. The work will include optimizing models for fast inference and robust generalization while reducing manufacturing costs.
This project aligns with efforts to improve microfabrication processes, with potential applications in the semiconductor industry. The expected results will contribute to a better understanding of surface defects and the optimization of production processes.
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.
Innovative techniques for evaluating critical steps and limiting factors for batteries formation
The battery manufacturing sector in Europe is currently experiencing strong growth. The electrical formation step that follows battery assembly and precedes delivery has received little academic attention, despite being crucial for battery performance (lifespan, internal resistance, defects, etc.). It is an essential time-consuming and costly step in the process (>30% of the cell manufacturing cost, and 25% of the equipment cost in a Gigafactory) that would greatly benefit from optimization.
In this thesis, we propose studying battery formation using innovative, complementary, operando non-intrusive techniques. The goal is to identify the limiting mechanisms of the electrolyte impregnation step (filling electrode pores) and of the initial charge. The candidate will implement experimental methods to monitor and analyze these mechanisms. He will also establish a methodology and protocols for studying these steps, combining electrochemical measurements with non-intrusive physical characterizations under operating conditions. The research will focus on optimizing formation time and quality control during this stage.
From angstroms to microns: a nuclear fuel microstructure evolution model whose parameters are calculated at the atomic scale
Controlling the behavior of fission gases in nuclear fuel (uranium oxide) is an important industrial issue, as fission gas release or precipitation limit the use of fuels at extended burn-ups. The gas behavior is strongly influenced by the material’s microstructure evolution due to the aggregation of irradiation-induced defects (gas bubbles, dislocation loops and lines). Cluster dynamics (CD) (a kind of rate theory model) is relevant for modelling the nucleation/growth of the defect clusters, there gas content and the gas release. The current model has been parameterized following a multiscale approach, based on atomistic calculations (ab initio or empirical potentials). This model has been successfully applied to annealing experiments of UO2 samples implanted with rare gas atoms and has emphasized the impact of the irradiation damage on gas release. The aim of this PhD thesis is now to improve the model, particularly the damage parameterization, and to extend its validation domain through in depth comparison of simulation with a large set of recently obtained experimental results, such as gas release measurement by annealing of sample implanted in ion beam accelerator, bubble and loop observation by transmission electrons microscopy of implanted or in-pile irradiated samples. This global analysis will finally yield an improved parameterization of the CD model.
The research subject combines a “theoretical” dimension (improving the model) with an “experimental” one (interpreting existing experiments or designing some new ones). The variety of techniques will introduce you into the experimental world and thus broaden your scientific skills. You will be welcomed at the Fuel Behavior Modeling Laboratory (part of the Institute for Research on Nuclear Systems for Low-Carbon Energy Production, IRESNE, CEA Cadarache), where you will benefit from an open environment rich in academic collaborations. You also have to manage collaborations for the experiments analysis, for the model development and for the specification of additional atomistic calculations. You will be at the interface of atomistic techniques, large-scale simulation and various experimental techniques. Therefore, You will develop a broad view of irradiation effects in materials and of multi-scale modelling in solids in general.
This project is an opportunity to contribute to the overall development of numerical physics applied to multi-scale modeling of materials, occupying a pivotal position and adopting a global viewpoint. This will allow experiencing yourself the way computed fundamental microscopic data finally helps solving complex practical issues.
Further readings:
Skorek et al. (2012). Modelling Fission Gas Bubble Distribution in UO2. Defect and Diffusion Forum, 323–325, 209.
Bertolus et al. (2015). Linking atomic and mesoscopic scales for the modelling of the transport properties of uranium dioxide under irradiation. Journal of Nuclear Materials, 462, 475–495.
In situ and real-time characterization of nanomaterials by plasma spectroscopy
The objective of this Phd is to develop an experimental device to perform in situ and real time elemental analysis of nanoparticles during their synthesis (by laser pyrolysis or flame spray pyrolysis). Laser-Induced Breakdown Spectroscopy (LIBS) will be used to identify the different elements present and their stoichiometry.
Preliminary experiments conducted at LEDNA have shown the feasibility of such a project and in particular the acquisition of a LIBS spectrum of a single nanoparticle. Nevertheless, the experimental device must be developed and improved in order to obtain a better signal to noise ratio, to increase the detection limit, to take into account the different effects on the spectrum (effect of nanoparticle size, complex composition or structure), to automatically identify and quantify the elements present.
In parallel, other information can be sought (via other optical techniques) such as the density of nanoparticles, the size or shape distribution.
Numerical and experimental study of cryogenic refrigeration system for HTS-based nuclear fusion reactors
The challenge of climate change and the promise of CO2-free energy production are driving the development of new nuclear fusion reactor concepts that differ significantly from systems such as ITER or JT60-SA [R1]. These new fusion reactors push the technological boundaries by reducing investment and operating costs through the use of high-temperature magnets (HTS) to confine the plasma [R4]. These HTS promise to achieve high-intensity magnetic fields while operating at higher cooling temperatures, thereby reducing the complexity of cryogenic cooling, which is normally achieved by forced circulation of supercritical helium at approximately 4.5 K (see 1.8 K for WEST/Tore Supra) delivered by a dedicated cryogenic plant.
The pulsed operation of tokamaks induces a temporal variation in the thermal load absorbed by the cooling system. This operating scenario has led to the development of several load smoothing techniques to reduce the amplitude of these thermal load variations, thereby reducing the size and power of the cooling system, with beneficial effects on cost and environmental impact. These techniques use liquid helium baths (at approximately 4 K) to absorb and temporarily store some of the thermal energy released by the plasma pulse before transferring it to the cryogenic installation [R5].
The objective of this thesis is to contribute to the development of innovative concepts for the refrigeration of large HTS systems at temperatures between 5 and 20 K. It will include (1) the modeling of cryogenic system and cryodistribution architectures as a function of the heat transfer fluid temperature, and (2) the exploration of innovative load smoothing techniques in collaboration with the multidisciplinary "Fusion Plant" team of the PEPR SUPRAFUSION project. The first part will involve the development and improvement of 0D/1D numerical tools called Simcryogenics, based on Matlab/Simscape [R6], through the implementation of physical models (closure laws) and the selection of appropriate modeling techniques to analyze and compare suitable architectural solutions. The second part will be experimental and will involve conducting load smoothing experiments using an existing cryogenic loop operating between 8 and 15 K.
This activity will be at the forefront of the nuclear fusion revolution currently underway in Europe [R3, R7] and the United States [R4], addressing a wide range of cryogenic engineering fields such as refrigeration technologies, superfluid helium, thermo-hydraulics, materials properties, system and subsystem design, and the design and execution of cryogenic tests. It will thus be useful for the development of new generations of particle accelerators using HTS magnets.
[R1] Cryogenic requirements for the JT-60SA Tokamak https://doi.org/10.1063/1.4706907]
[R2] Analysis of Cryogenic Cooling of Toroidal Field Magnets for Nuclear Fusion Reactorshttps://hdl.handle.net/1721.1/144277
[R3] https://tokamakenergy.com/our-fusion-energy-and-hts-technology/fusion-energy-technology/
[R4] https://tokamakenergy.com/our-fusion-energy-and-hts-technology/hts-business/
[R5] “Forced flow cryogenic cooling in fusion devices: A review” https://doi.org/10.1016/j.heliyon.2021.e06053
[R6] “Simcryogenics: a Library to Simulate and Optimize Cryoplant and Cryodistribution Dynamics”, 10.1088/1757-899X/755/1/012076
[R7] https://renfusion.eu/
[R8] PEPR Suprafusion https://suprafusion.fr/
Differential phase contrast imaging based on quad-pixel image sensor
Biopharmaceutical production is booming and consists of using cells to produce molecules of interest. To achieve this, monitoring the culture and the state of the cells is essential. Quantitative phase imaging by holography is a label-free optical method that has already demonstrated its ability to measure the concentration and viability of cultured cells. However, implementing this technique in a bioreactor faces several challenges related to the high cell density. It is therefore necessary to develop new quantitative phase imaging methods, such as differential phase contrast imaging.
The objective of the PhD is to develop this technique using a specific image sensor for which a prototype has been designed at CEA-LETI. The PhD candidate will use this new sensor and develop the reconstruction and image-processing algorithms. They will also identify the limitations of the current prototype and define the specifications for a second prototype that will be developed at CEA-LETI. Finally, they will consider the design of an inline probe to be immersed in the bioreactor.