The durability of materials exposed to corrosive conditions is a major stake as it affects process and plant safety and implies large costs. In real applications and in future “zero emission technologies”, metallic alloys are and will be subjected to oxidizing and water-rich environments at high temperature. Under such conditions, the volatilization of the chromia scale takes place, speeding up the material end of life. While the chromium loss due to volatilization has been estimated many times to assess the material lifetime in past and recent studies, the gas phase evolution and its influence on the volatilization rate are rarely considered although they affect the alloy end of life. To respond to such problem, the DYNAMIC project, which associate 3 academic labs with 2 industries, proposes to evaluate the high temperature oxidation of refractory metallic alloys and the volatilization of their protective oxide layer by an original approach combining high temperature oxidation tests and simulations of the gas phase. Oxidation tests will be carried out between 600 and 1100 °C, under intermediate to high gas velocities (from few tens of cm.s-1 to few m.s-1) and over the complete water vapour content range, i.e. from few ppm to nearly 100 %. Also, characterizations of the samples, before and after oxidation, will be performed. In parallel, the gas phase within the oxidation rigs and the volatilization reaction will be simulated by computational fluid dynamics (CFD). This methodology will be conducted to better understand the influence of dynamic flows on oxidation and volatilization kinetics, and therefore the degradation mechanisms at work in such environments. It shall make it possible the determination of laws capable of predicting lifetime and the evaluation of the effects of geometry to propose solutions to delay the end of life of alloys.

The market introduction of high temperature wide bandgap power semiconductor devices with junction temperature exceeding 200°C significantly accelerates the trend towards high power density and severe ambient temperature electronics applications. Such evolution may have a great impact in aeronautics applications, especially with the development of More Electric Aircraft (MEA), since it can allow to reduce the mass and volume of power electronics systems. As a consequence, the aircraft operating cost can decrease. However, for electronics used under such harsh conditions, the package reliability and the heat evacuation are very critical issues. The goal of this project is to design and fabricate high performance double sided cooled power electronics modules with optimized thermomechanical properties. The assembly is based on copper joints and a copper heat sink and integrates several technological breakthroughs. Three main technological bricks will be deeply addressed in order to reach the target: 1) Synthesis of nanoporous copper films, either freestanding or directly deposited on metallized substrates with controlled microstructure: In order to limit the risks, three independent strategies will be investigated during the project: the synthesis of nanoporous copper free standing films using melt-spinning and chemical dealloying techniques, the direct on-substrate electroforming of copper-alloy followed by anodic dealloying, and the direct growth of nanoporous structures without any additional treatment by tuning electrolyte formulation and plating parameters. 2) Thermocompression of the nanoporous copper films for die attach: Conventional heating will be achieved at low pressure and in inert/reductive atmosphere. An alternative method based on laser induced fast heating will also be evaluated to thermocompress the nanoporous copper in air. Both solutions allow to limit the oxidation copper issues. The underlying physical mechanisms taking place during the thermocompression of the various morphologies and microstructures of nanoporous copper films will be in-depth investigated. The joint stability under electro-thermo-mechanical aging conditions will be evaluated. 3) Deposition of thick copper layers for substrate/heatsink assembly using electroforming process: A thick dense metal layer will be deposited on a designed sacrificial polymer preform allowing to create a wide range of complex shapes directly on the metallized substrate with low residual stresses. This technology combined to virtual prototyping will allow us to fabricate high performance heat sink patterns (liquid forced convection without phase change) in terms of high local heat transfer coefficient and low pressure drop. The thermal-hydraulic performances of the heat sinks will be analyzed with an experimental setup. The robustness of the assembly (substrate/heat-sink) under repetitive temperature variations will be also evaluated. Silicon Carbide (SiC) devices based power modules (inverter phase leg) using the aforementioned technological bricks will be realized and evaluated in the project. Electrical, thermal and robustness tests are planned to estimate the module performances. The COPPERPACK project will contribute to validate and push our concept from Technology Readiness Level (TRL) 2 up to a TRL 3-4 with a functional technological demonstrator.

MOBI-DEEP addresses the development of technologies for autonomous navigation in unknown environments using low cost vision sensors. The project relies on the assumption that the inference of semantic information (presence of particular structures, identification of objects of interest, obstacles, etc.), the inference of depth maps as well as the one of motion maps describing a scene given by a monocular camera can be sufficient for guiding a person, robot, etc. in an open and unfamiliar environment. This project departs from the current dominant approaches where good prior knowledge of the environment and the ability to reconstruct 3D metric structure of this environment (SLAM, Lidar, etc.) are needed. It allows to deal with situations where the systems should be able to navigate with limited knowledge of their environment and using a perceptual system as light as possible. MOBI-DEEP will address these situations through two use cases: the guidance of the visually impaired and the navigation of mobile robots in open areas. In both cases, the problem studied can be formulated as follows: an on-board camera, roughly localized by GPS, has to move to a specified position given by GPS coordinates. No accurate map is available, and the navigation should be done through a series of local displacements. The image sensor has to extract from the images sufficient information to make the navigation possible. The carrier can be a robot or a person. We further assume that it is possible to reach the destination by simply moving toward this direction. The problem studied is the one of the planning a path in an unknown environment by building over time an egocentric and semantics representation of the navigable space. This raises three main questions which be studied in the project for both use cases: what are the minimum semantic/3D/dynamic information required to allow the navigation? How to extract the information from monocular images? How to dynamically navigate through local representations, in a geometrically and semantically described environment? Special emphasis will be given to experiments within a Living Lab that will have a dual purpose: conducting real scale experiments and allowing to conduct scientific mediation.

Increasing the efficiency of model-based industrial processes requires to improve their uncertainty quantification and numerical optimization. Such issues appear in most of the engineering domains (e.g. energy, transport, agriculture) and scientific fields (e.g. biology, high-energy physics). A major problem comes from the black-box nature of the process/function of interest that is often not directly accessible: in general, the only available information are the outputs of the black-box simulation workflow. In particular, derivative information, which is very valuable in the context of optimization and uncertainty quantification, does not exist or is not available. This is a direct consequence of the increasing complexity and diversity of the industrial problems to be addressed (e.g. coupling of multi-physics or multidisciplinary simulators, creation of economic models, working with more sophisticated machine learning models, handling of uncertainty and non-Euclidean variables). Solving this problem is therefore a major issue with direct and significant industrial benefits. In the two last decades, the field of black-box optimization (BBO) methods – especially, derivative-free optimization and surrogate or metamodel (MM) management frameworks – has experienced major theoretical and practical developments. Nevertheless, despite the growing popularity of these methods, some fundamental limitations remain: in particular, the scale of the problems that can currently be efficiently solved by BBO methods does not exceed a few tens of variables and methods to deal with high-dimensional or categorical variables are limited. In real-world applications, the simulation budget is often very restricted. Moreover, BBO algorithms have become complex tools in themselves, which raises questions about their generality of use (choice of kernels for MM) and the reliability of hyper-parameter learning. The main objectives of the project are, jointly, to develop innovative simulation and surrogate-based optimization methodologies, while pushing back their current limits of performance and applicability, guided by real-world applications. These applications are related to the design and risk assessment of critical and complex systems. Hence, the partners of the project will provide challenging and critical applications in the fields of renewable and low-carbon energies and reduced CO2 air transport domains, in order to demonstrate the relevance and the efficiency of the developed methodologies. More precisely, the partners’ ambition is to solve the four following major challenges: - design MM adapted to large scale problems (typically around 100 input variables) in the context of a limited budget of simulations (around 500); - adapt sequential enrichment strategies to large scale problems for reliability-based design optimization and reliability-based inversion purposes; - design efficient black-box optimization methods capable of handling problems mixing input variables of different types: continuous, ordinal and nominal variables; - increase the performance of the iterative process (optimization and MM building) in case of instabilities, failures or non-physical results of the simulation workflow: the aim will be to learn the hidden constraints and deal with them in the adaptive design procedure. Thus, the project aims at (i) consolidating and extending the existing surrogate-based optimization methods to provide a real improvement of their application to industrial problems, (ii) sharing experiences and methodologies of industrial partners for practical problems, (iii) integrating the resulting methods and methodologies in open-source platforms developed by the partners.

In this project, we study new machine-learning methodologies to improve statistical turbulence models for aeronautical applications. In aerodynamics, currently used turbulence models in industry are based on strong approximations and are tuned on very simple configurations. Such models exhibit strong weaknesses as soon as more complex industrial flows are considered. Machine-learning techniques may leverage the wealth of numerical high-fidelity and (incomplete) experimental data that is currently available to improve such models. ONERA / DLR / SAFRAN TECH / ROLLS-ROYCE intend to explore and improve the so-called field-inversion (FI)-machine learning (ML) methodology introduced recently by Duraisamy. The goal is to explore more sophisticated turbulence models, new AI-based techniques for the regularisation of the FI step in the case of incomplete reference data, new systematic tools for the selection of input flow features, and new learning strategies. These disruptive methodologies will be incorporated within numerical platforms that allow CFD codes to take advantage of data-driven techniques. The capabilities of such tools will finally be evaluated and assessed on industrially relevant configurations, such as an aircraft in high-lift configuration and a compressor cascade.