In this research, we will investigate and develop applications of multistable composite panels and shells as morphing surfaces capable of large shape changes under external loading and embedded actuation. This research topic implies the study of complex non linear phenomena that take place during large displacements of slender structures (beams, arches, plates or shells) in order to develop models and tools for the prediction, design and control of such phenomena. Large non linearities are the fundamental keys to understand stability or loss of stability of slender structures, both in the static and dynamic domains. Our aim is to tackle two relevant subjects in the very large domain of non linear structural behaviour, which will represent the two main axes of the present research project: i. a first goal is to develop an integrated theoretical and numerical approach to design composite laminates which may hold several equilibrium configurations, exploiting the effects of geometric non-linearities, anisotropy, and pre-stresses induced by the through-the-thickness variation of the material properties. The idea is to start from reasonably simple analytical models for the study of complex non linearities, in order to capture the fundamental phenomena involved, and to validate the captured trends through numerical and experimental tools. These results will open the way to the modelling, design and optimisation of more complex multistable structures; ii. a second aspect is the theoretical study and experimental test of the use of embedded active materials, such as Shape Memory Alloys (SMA) wires/stripes, to control the shape of the structure without the need for joints or conventional actuators. Synthetically, our topic is the modelling, optimal design and shape control of slender multistable structures, i.e. structures that can hold several equilibrium positions without the application of external forces. Domains of application are quite wide and they stand at the border of very innovative and recent research topics: such structures are present in Nature, but also are constitutive parts of modern devices, such as morphing or deployable structures for aeronautical and aerospace applications, mirrors, thin screens, and so on. The developments proposed in our project embrace several aspects: modelling, numerical and experimental validation, optimal design of non linear slender structures. Considering the field of application and the envisaged research, the present project can be evaluated in the framework of the FRAE-ANR protocol (strong links to themes I, VI, VIII, IX; also possible link to theme III). This project aims at gathering a group of young scientists who are experts in different fields of structural mechanics (modelling, optimisation and design, elasticity, smart materials, singularity formation, stability, damage,…) in order to build up and consolidate a research team based at Institut Jean Le Rond d’Alembert (IJLRdA), Université Pierre et Marie Curie (UPMC) with external collaborations from Ecole Nationale Supérieure de Cachan and Università Roma 1 La Sapienza. This project will also enhance exchanges among the different fields of structural mechanics which are the background of the team members. Indeed, this project will be also an opportunity to give a contribution to the nascent experimental activity at IJLRdA by the development of simple experimental setups. This experimental activity was initiated few years ago (in the field of fluid mechanics) and it is highly encouraged by the direction of the institute IJLRdA and CNRS.

Machine-learning (ML) holds significant promise in revolutionizing a wide range of applications, in particular in the domain of multi-scale and multi-physics problems. Success in realizing the promise of ML is predicated on the availability of training data, which are often obtained from scientific computations. Conventional approaches to solving the equations of physics require difficult and specialized software development, grid generation and adaptation, and the use of specialized data and software pipelines that differ from those adopted in ML. A disruptive new approach that was recently proposed by the US team is Evolutional Deep Neural Networks (EDNN, pronounced ``Eden") which leverages the software and hardware infrastructure used in ML to replace conventional computational methods, and to tackle their shortcomings. EDNN is unique because it does not rely on training to express known solutions, but rather the network parameters evolve using the governing physical laws such that the network can predict the evolution of the physical system. In the proposed effort, the EDNN framework will be extended to solve high-dimensional partial differential equations, used to model a vast range of phenomena in economics, finance, operational research, and multi-phase fluid dynamics, where population balance equations govern phenomena as diverse as aerosol transmission of airborne pathogens or mixing enhancement in energy conversion devices. The simulation of such flows is an open issue of particular interest to the US and French teams, a strong motivation for the proposed collaboration. We will demonstrate the ease of software development using automatic differentiation tools and the capacity of EDNN to eliminate the curse of dimensionality and the tyranny of moment closure. Success stands to disrupt and transform the decades-old computational approach to solving nonlinear differential equations and to remove the barriers to generation of training data required for ML.

The PLASINTER project main objective is to expand the knowledge on catalysts behavior, both in structure and chemical nature, under non-thermal plasma CO2 hydrogenation. There is consensus, in the plasma community, that upgrading of this process can be achieved by untangling plasma-chemical processes. The design of catalysts specifically tailored for activation by plasma combined to in-situ / operando characterization with the support of modelling / simulation would yield in-depth information on the underlying processes and allow the development of optimum catalytic phases and supports. This fundamental project aims at understanding how the micro-meso porosity of monolithic channels modifies the plasma discharge propagation, thus activation barrier on the catalyst and subsequent CO2 activation. The analysis of the catalyst surface and gas phase by in-situ infrared probing techniques will be performed in order to establish the properties of atmospheric pressure plasma developing within the monoliths and identify the plasma chemistry induced on surfaces. The design of well-defined catalysts with controlled-structure will help to link the effects of the monolith’s micro-channels on the hydrogenation reaction. Ni or Cu supported mesoporous alumina and silica will be synthesized and effects of promoters on catalyst physico-chemical properties evaluated. The investigation by in-situ combined to ex-situ experimentations will provide key knowledge on catalyst design and reaction mechanisms under plasma conditions.

Electrical power generation is one of the major issues in the context of economic developments and eco-responsible policy. By 2020 for solar, wind and gas turbines (GTs) fuel types by the Californian Independent System Operator (CalISO). The load ramps highlighted by the black arrows in the early morning and in the evening are expected to be of the order of +8.103MW, -6.3.103MW and +13.5.103MW, respectively. These loads would need to be generated or absorbed (GT shut down) within a range of two hours each. According to the US Department of Energy, by 2040, natural gas (for GTs) and renewables would be the two first fuel types for electricity generation. Both natural gas and renewables would see their contribution in electrical power generation increase by 15 and 30%, respectively. The other actual resources (nuclear, coal, liquids) would see their contribution decrease. Therefore, in order to fulfill the electrical power demand, especially when it comes to face with the energy supply fluctuations inherent to renewable energy sources, gas turbines provide a reliable solution. However, a large variability in their operating conditions leads to a high level of pollutant emissions and high risks of damage. Diluted regime combustion (e.g. MILD - Moderate or Intense Low oxygen Dilution) appears to be an auspicious process for gas turbines to ensure low emission levels over a wide range of operating conditions. In this regime, fuel is mixed with a highly diluted and heated air to create a spatially distributed reaction zone with a reduced peak temperature. COnfined COunterflow Reactors (CO²Res) were design with the aim to provide the most suitable flow features favorable to the MILD combustion. Therefore, they can be viewed as the ideal benchmark to research studies devoted to bring the technological breakthrough needed to: i) improve the efficiency and; ii) reduce pollutants emissions in MILD combustion applications. To optimize MILD combustion, fast and efficient mixing of reactants with exhaust gases is mandatory. The latter is not only a technical issue, but the lack of theoretical knowledge on turbulent mixing in such flow configurations is clearly pointed out (Kruse et al. 2015). This combustion regime is a priori characterized by a competition between the mixing and chemistry time-scales with a strong influence of the differential diffusion effects (Christo and Dally, 2005, Cavaliere and de Joannon 2004). It thus drastically differs from the conventional jet flames for which mathematical models generally account for fast chemistry and negligible differential diffusion effects. This project aims at bringing concrete elements of fundamental research to understand, model and predict the turbulent active-scalar mixing, in the context of MILD combustion. Such mixing does couple back on the flow dynamics. As a consequence, a correct accounting of mixing is even required to describe the flow dynamics. The multi-disciplinary consortium composed of 4 young researchers, 1 post-doc and 1 senior Professor has proven its experimental, numerical and theoretical expertise in the analysis of turbulent mixing. We therefore aim at performing analytical developments as well as experimental approaches and numerical simulations of a simplified academic based MILD combustor. The project will be divided in three interconnected blocs: i) Turbulent mixing; ii) Turbulent/Non-Turbulent interface and iii) Turbulent modelling. A full description of the phenomenology of the turbulent active-scalar mixing in CO²Res will be provided. Finally, models for CO²Res based on joint experimental, numerical and analytical developments will be proposed in order to perform reliable predictive simulations.

The propagation of respiratory diseases involves the expectoration of small droplets, in various manners such as speach, sneeze or cough. A single sneeeze may send droplets up to six meters. Droplets as small as 100nm may contain the SARS Cov 2 virus which has a diameter of approximately 60-140 nm. The natural history and quantitative analysis of these tiny droplets is very poorly known. While droplets of sizes larger than a few microns can in principle be observed experimentally by optical methods, the observation is not easy and the number and physical fate of droplets of small sizes is thus unknown. This motivates the study which is organized on three axes - how many droplets of each size are created in a cough or sneeze in connection with an extensive experience and an intense ongoing effort on the topic of droplet size distribution; - how do small particles diffuse/disperse in the environment (This may seem a well studied topic, but the interaction with turbulence, rheology, multicompositional systems and evaporation dynamics make it much more complex); - how do small particles of mucus liquid dry in the environment both when suspended in air and when deposited on a surface (This topic is related to that of heat and mass transfer from droplets) . The local environment of a micron size droplet in even very moderately turbulent air may be fluctuating so the rate of drying may be a characteristic of air turbulence (just as a hot wire signal) or of local fluctuations in the humidity and temperature. Starting on this basis, the project will be performed in collaboration between the Paris-SU group specialising on theoretical and numerical aspects of atomization and the Cambridge-MIT group. The groups are already involved in collaborative work on the physics of drops, and the MIT group on mathematical-statistical studies of covid transmission. The most typical mechanisms for the production of the droplet size distribution are the hole formation and the ligament breakup mechanism. Both will be reviewed together with an analysis of the ligament size distribution and a re-analysis of the experiments. For a given expectorated mass, the likely ranges of minimum and maximum droplet sizes and the corresponding numbers will be predicted from the literature and previous studies. We stress that the objective of the project is not to perform new experiments or computer simulations of the processes, but organize what is already known in a manner that allows useful quantitative predictions for epidemiologists. In particular, attention will be focused on the aerosol (diameter less than 100 microns) droplet formation mechanisms. These are - the satellite droplet mechanism which leads to the formation of approximately ten times smaller droplets than the main droplets in an atomization process. - the thin sheet/hole perforation mechanism for saliva or mucus, currently unexplored. The diffusion and dispersion of droplets in the environment is a critical process. Micron-size droplets settle to the floor in thirty minutes to eight hours in a perfectly quiet atmosphere, but the situation is much more complex whenever air turbulence, always present to some degree, is taken into account. Turbulent dispersion will considerably increase the range of droplet sizes that remain suspended in air for a long time. The humidity of the air influences the survival of the virus, by affecting its bilipidic layer envelope, thus the dynamics of vapor exchange on small droplets have relevance. As the research will considerably improve our knowledge of the aerosol and large droplet properties, it will have enormous impact on recommendations for transmission reduction. In particular, it is extremely important to exclude or confirm the existence of long distance disease transmission by areosol particles.