RSS export of vacancies - Seulement les offres à la une : No / Profil : Défis technologiques

SL-DRT-24-0016 - Advanced modeling of Gas Diffusion Layers for Fuel Cells: ink impregnation and drying, 3D phase distribu

Wed, 11 Oct 2023 02:15:11 Z

Category : Technological challenges
Contract : Thèse
Thesis topic details :
In the frame of advanced H2 solutions for the energy transition, the Proton Exchange Membrane Fuel Cell (PEMFC) is a relevant solution for the production of low-carbon electrical energy. The European Project DECODE proposes to develop a fully digital chain of design tools, including raw material properties, manufacturing and assembly of the different components, to predict the performance of such ‘virtual’ stack. This will help reducing the development cost and time of improved materials/components suitable for different applications in the future. The component considered in this thesis is the Gas Diffusion Layer (GDL), which is a combination of a fibrous microporous substrate and of a micro/nano porous layer (MPL for microporous layer). The work will be split into different steps: a) based on (real or virtual) 3D images of the substrate, simulation of the hydrophobic and MPL coating and drying to derive the 3D distribution of the components (fibers, hydrophobicity and MPL); b) simulation of single and two-phase transport properties of the GDL to supply inputs to upper scale performance models; c) sensitivity analysis of the main manufacturing processes (ink properties, drying parameters…)

Advanced modeling of Gas Diffusion Layers for Fuel Cells: ink impregnation and drying, 3D phase distribution, and effective properties

SL-DRT-24-0005 - Learning Fine-Grained Dexterous Manipulation through Vision and Kinesthetic Observations

Wed, 11 Oct 2023 02:15:11 Z

Category : Technological challenges
Contract : Thèse
Thesis topic details :
Fine-grained dexterous manipulation presents significant challenges for robots due to the need for precise object handling, coordination of contact forces, and utilization of visual observations. This research aims to address these challenges by investigating the integration of vision and kinesthetic sensors, sim2real techniques, and generalization through embodiment. The objective is to develop end-to-end algorithms and models that enable robots to manipulate objects with exceptional precision and adaptability. The research will focus on learning from large-scale data, transferring knowledge from simulations to real-world scenarios, and efficiently generalizing through low-shot fine-tuning.

Learning Fine-Grained Dexterous Manipulation through Vision and Kinesthetic Observations

SL-DRT-24-0030 - Scenario-Based Testing for Automated Systems: Enhancing Safety and Reliability in Compliance with Regula

Wed, 11 Oct 2023 02:15:11 Z

Category : Technological challenges
Contract : Thèse
Thesis topic details :
This research aims to investigate the effectiveness of scenario-based testing as a comprehensive and robust approach for evaluating ASs' performance while enhancing their safety and reliability with respect to regulations and standards. The primary objective of this thesis will be to investigate the benefits of scenario-based testing for automated systems and its compliance with regulations and standards. The following sub-objectives will be pursued: - Conduct an in-depth review of relevant literature and industry practices on ASs testing methodologies, with a focus on the unique challenges posed by ASs' complex decision-making algorithms and interaction with the dynamic environment. - Develop a scenario-based testing framework for systematic identification, generation, selection, and execution of realistic and diverse scenarios, for automated systems - Analyze gaps or areas of non-compliance about the key concepts and requirements outlined in regulations and standards and examine their applicability, implications and improvments for scenario-based testing. - Conduct extensive validation of the proposed scenario-based testing framework through different real-world and near-realistic transportation applications (various types, varying levels of automation, and diverse real-world scenarios) to evaluate the practical applicability and benefits of the methodology. The findings and recommendations from this research will ultimately guide AV manufacturers, regulators, and stakeholders in developing and validating ASs that comply with the regulatory framework, fostering the safe and responsible deployment of automated systems in the future.

Scenario-Based Testing for Automated Systems: Enhancing Safety and Reliability in Compliance with Regulations and Standards

SL-DRT-24-0026 - Deep Neural Network Uncertainty Estimation on Embedded Targets

Wed, 11 Oct 2023 02:15:11 Z

Category : Technological challenges
Contract : Thèse
Thesis topic details :
Over the last decade, Deep NeuralNetworks (DNNs) have become a popular choice to implement Learning-Enabled Components LECs in automated systems thanks to their effectiveness in processing complex sensory inputs, and their powerful representation learning that surpasses the performance of traditional methods. Despite the remarkable progress in representation learning, DNNs should also represent the confidence in their predictions to deploy them in safety-critical systems. Bayesian Neural Networks (BNNs) offer a principled framework to model and capture uncertainty in LECs. However, exact inference in BNNs is difficult to compute. Thus, we rely on sampling techniques to approximate the true posterior of the weights for computing the posterior predictive distribution (inference). In this regard, relatively simple though computationally and memory expensive sample-based methods have been pro posed for approximate Bayesian inference to quantify uncertainty in DNNs, e.g., Monte-Carlo dropout or Deep Ensembles. Efficient DNN uncertainty estimation in resource-constrained hardware platforms remains an open problem, limiting the adoption within applications from highly automated systems that possess strict computation and memory budgets, tight time constraints, and safety requirements. This thesis aims to develop novel methods and hardware optimizations for efficient and reliable uncertainty estimation in modern DNN architectures deployed in hardware platforms with limited computation resources.

Deep Neural Network Uncertainty Estimation on Embedded Targets

SL-DRT-24-0017 - Exploring the Future of Satellite Communications: Dual-Band Electronically Reconfigurable Flat Lens Ante

Wed, 11 Oct 2023 02:15:11 Z

Category : Technological challenges
Contract : Thèse
Thesis topic details :
CEA Leti offers a PhD topic to develop new electronically scanning antennas for efficient data transmission in satellite communications (Satcom). Novel efficient electronically scanning antennas are essential for future satellite communications (Satcom). Electronically reconfigurable flat lens antennas, also known as transmitarrays, are a promising architecture to achieve high scanning performance. Each element of the flat lens introduces an optimized phase shift on the impinging wave emitted by a primary source, to steer and shape the radiation pattern. The phase profile over the lens can be dynamically modified by adding reconfigurable devices in the cells, such as switches (e.g. pin diodes) or varactors. Compared to phased arrays, these antennas attain high-gain beam-steering with a significantly lower power consumption and architectural complexity. The Ph.D. work aims to propose and experimentally demonstrate novel concepts and design methods for wideband/multi-band electronically beam-steering flat lens antennas. The main research goals are: . Study of new approaches for designing unit cells with broad radiation patterns, stable performance under oblique incidence and wideband/multiband operation. . Electrically thin subwavelength cells and Huygens’ radiating elements will be investigated to tailor the angular and frequency response of the cell. . Novel design solutions to enable a fine electronic control of the phase shift introduced by the cells. Multilayer cells comprising either pin diodes or varactors, or a combination of both, will be analyzed. The trade-offs between phase resolution, bandwidth, power consumption, number of reconfigurable devices and bias lines, will be studied. . Development of dedicated synthesis procedures to enable the independent control and shaping of the radiation pattern at two or multiple frequencies. . Experimental demonstration of high-gain dual-band fixed-beam and electronically 2-D beam-steering prototypes achieving extremely wide scan ranges (±60° or greater). The demonstratators will be optimized to work in typical Satcom bands (e.g. around 20 GHz and 30 GHz).

Exploring the Future of Satellite Communications: Dual-Band Electronically Reconfigurable Flat Lens Antennas with Ultra-Wide Scan Range

SL-DRT-24-0055 - New sustainable electrode materials for High Temperature Electrolysis

Wed, 11 Oct 2023 02:15:11 Z

Category : Technological challenges
Contract : Thèse
Thesis topic details :
High temperature electrolysis is considered as the high efficiency technology for hydrogen production with low carbon emissions. The electrolysis reaction occurs in a solid oxide cell (SOC) composed of a dense electrolyte of yttria stabilized zirconia (YSZ), sandwiched between two porous electrodes. The most common hydrogen electrode material is a cermet of Ni and YSZ, and the oxygen electrode is a perovskite La0.6Sr0.4Co0.2Fe0.8O3 (LSCF). To make the high temperature electrolysis more sustainable to better support the European eco-system towards the achievement of the Sustainable Development Goals and the objectives of the Paris Agreement, there is a critical need to reduce reliance on critical raw materials (CRM). The objective of the thesis is therefore to limit the use of CRM in the oxygen electrode material. Critical elements such as cobalt will be substituted by new cations on the A and/or B site of the crystal lattice, while maintaining equivalent performance and long-term stability. At the same time, in order to limit losses during synthesis, a part of the work will be carried out on the synthesis process efficiency and on the increase in capacity of the synthesis method. After a bibliographic study on oxygen electrode materials for high temperature electrolysers, the proposed work will initially be focused on the synthesis by chemical routes as well as on fine characterization of the perovskites. The thermal and chemical compatibility with the other materials constituting the cell will be studied, then this work will lead to the shaping of the materials with the most interesting properties in order to test them electrically and electrochemically. The electrochemical behaviour of the electrodes will be analysed in order to understand the influence of substitutions and to determine the electrochemical performance of the different compositions studied.

New sustainable electrode materials for High Temperature Electrolysis

SL-DRT-24-0004 - Study of inversion methods based on simulation and machine learning for defect characterisation in ultra

Wed, 11 Oct 2023 02:15:11 Z

Category : Technological challenges
Contract : Thèse
Thesis topic details :
The thesis work is part of the activities of CEA-List department dedicated to Non-Destructive Testing (NDT), and aims to study simulation-based inversion methods to characterise defects from ultrasonic images, such as TFM (Total Focusing Method) or PWI (Plane Wave Imaging) images. The inversion methodology will rely on machine learning algorithms and numerical training databases generated with the CIVA software platform. A first part will study the ability of such an inversion method to characterise a defect (location, size, orientation...) without any a priori information, by exploiting the noise and reconstruction artefacts due to the use of unsuitable propagation modes. In a second part, the simulation-based inversion will be evaluated in more realistic situations where images are of poor quality due to uncertainties on the properties of the component and/or on the experimental setup. In order to reduce the generation time of the training database, and to gain in robustness and accuracy, the feasibility of inverting fast imaging (e.g.: combining PWI and fast reconstruction algorithms in the Fourier domain) will be studied, as well as the feasibility of directly inverting signals or spectra without the need to compute images. The inversion method will be experimentally evaluated with different mock-ups representative of industrial components and, at the end of the thesis, a real-time proof of concept will be demonstrated by implementing the imaging and inversion algorithms in a laboratory prototype system.

Study of inversion methods based on simulation and machine learning for defect characterisation in ultrasonic array imaging