Organic Rankine Cycle (ORC) power systems offer a great potential for waste heat recovery and environmental-friendly power generation but relatively little is known regarding the impact of real-gas effects on loss mechanisms in ORC turbine expanders. A further increase of ORC turbine efficiencies can only be achieved if relevant non-ideal compressible fluid dynamical phenomena are better understood and modeled. Computational fluid dynamics (CFD) tools currently used in ORC design, based on Reynolds-averaged Navier-Stokes (RANS) models, are affected by many notorious flaws and uncertainties, and large-eddy-simulation (LES) or direct-numerical-simulation (DNS) methods are a promising tool for improving our fundamental understanding of these flows. The application of LES and DNS methods for real-gas flows in turbomachinery configurations is an open challenge, due to the high Reynolds numbers and the complex thermophysical models required and requires high-quality experimental data for their validation that are not available so far. Wall-resolved and wall-modelled LES methods for organic vapor flows in turbomachinery will be developed in this project based on a combined numerical-experimental approach employing high-accurate numerical solvers, a new organic vapor wind tunnel test facility and a new generation of hot-film surface sensors. The project will explicitly identify and quantify real-gas effects on laminar-to-turbulent transition, flow separation, shock-wave-boundary layer interactions and wake development and, ultimately, their impact on loss mechanisms in the transonic flow regime. Two primary test configurations will be investigated, namely, the flow over a flat plate and though a simplified turbine cascade vane. Flow properties, including turbulence quantities, will be measured by means of hot-wire anemometry, conventional and focusing Schlieren systems, Pitot and five-hole-probes, laser-based anemometry, and by a new generation of miniaturized hot-film surface sensors tailored to the very special needs of organic vapor flows. Thanks to the close collaboration between a theoretical/numerical research group (Paris, France) and an experimental group (Muenster, Germany) and the support by a microsystem technology group (Ilmenau, Germany), the project will enable, for the very first time for organic vapors, several significant advances: 1) characterization of transitional and turbulent flow behavior via simulations and experiments; 2) insight into blade vane loss mechanisms and assessment of the capability of numerical models to capture them; 3) development of innovative high-fidelity CFD tools specifically tailored for ORC turbomachinery; 4) development and release of new thermal surface sensors for measuring flow and turbulence quantities in the very thin wall region for real-gases. The new experimental and CFD data and the outcomes of the present research will constitute benchmark cases for the scientific community and the interested public.

Structured foams are of great interest for different material communities: metallic foams (high resistance), catalysis (high surface/volume ratio) and phononic materials (sound absorption). These foams are fabricated by the solidification of initially liquid foams. A major difficulty in manufacturing such materials is controlling the foam evolution during this first stage. This evolution is due to several phenomena: flow of the continuous phase (drainage), diffusion of gas through the liquid films, and film rupture. Recent publications show that this topic is a crucial issue for both academics and industries. One aspect of the project aims at developing an alternative to control foam drainage. The approach consists in developing a microfluidic system able to control foam drainage in order to generate highly controlled materials (bubble size and volume fraction) and being able either to reinforce or reverse drainage. The advantages of a microfluidic approach are manifold, the most important is the possibility to perform experiments within very short times (few seconds), giving the possibility to span a large range of experimental parameters in order to optimize, in a first step, drainage control. The control of foam drainage has rekindled interest in the Marangoni effect, which refers to the flows induced by a surface tension gradient that can be generated by a surfactant concentration gradient or by a temperature gradient. The objective is to generate a flow either in the direction or in the opposite direction of gravity by using temperature gradients, successively decaying/increasing the liquid fraction. The advantage of generating Marangoni flows stemming from a temperature gradient is to develop a foam drainage control which does not depend on any seeding material (magnetic particles, photo or thermo-responsive surfactant). In the reverse drainage case, the leading material will be more homogeneous, with a well-controlled liquid fraction and bubble size distribution. A challenging aspect of this approach is that a temperature gradient generates several side effects, that can be either advantageous or that need to be neutralized, provided these effects are well understood: we have shown in a recent paper [Selva et al., 2011] that a bubble undergoing a constant temperature gradient generates a flow in the surrounding liquid. The physical phenomena involved in such a system are multifaceted (thermocapillarity, solutocapillarity and potentially cell deformation for soft cell) and may have either complementary or opposite effects depending on the experimental conditions; leading to complex situations involving free interface, surfactant diffusion, heat diffusion and hydrodynamics. For this reason, the project is divided into two main steps: (i) a seeding phase at the elementary level containing a valuation; (ii) the study of foam drainage in a temperature gradient. In a first part (elementary level) we aim to rationalize the contribution of all involved effects under controlled conditions (surfactant concentration and distribution, cavity deformation, level of confinement). Our approach will be threefold: experimental, numerical and modelling legitimizing the contribution of five research complementary groups. This seeding stage will be valued by developing a system able to drive a droplet on a 2D substrate at will (controlled displacement or droplet trapping over space and time). Based on the understanding given by previous analysis, the second objective of the project is developing a study based on the previously understood effects: controlling the foam drainage, a major application in new materials perspectives. More precisely, we aim at developing an experimental set-up allowing to obtain a homogeneous foam of controlled bubble size and controlled liquid fraction stabilized for a typical time larger than the characteristic foam ageing time.

During the CACHMAP project, experimental and data processing methods have been developed. The phenomena were identified in the measurements obtained. After having demonstrated the principle of cavitation phenomena, the present project proposes to proceed to the next step which consists in proposing an assembly for the purpose of ballistic protection, much more lighter. The consortium wishes to deal with the case of transparent ballistic protections involving the civilian and military world in multi-impact: personal protections of the combatant, protections for vehicles, protection of infrastructures etc. The various technological bricks from fluids to microstructures would be integrated into a set that makes it possible to perform representative ballistic tests. The objective of the project is to obtain a ballistic plate including the concept of transparent cavitating armor offering a reduction of at least 20% compared to a solution against the threats of Level 1 and 2 of the STANAG 4569 or level EN 166-A for personal protection masks and visors (stop of a steel ball of 6mm diameter and 0.86g mass launched at 190m.s-1). The targeted applications are: - The transparent protections of the fighter to counter ballistic threats of 9 mm or fragments. These protections are currently in PMMA or Polycarbonate. The introduction of a fluid phase in a transparent assembly would reduce the weight of the protection by 20% given that PMMA is 20% heavier than water. - Protections for vehicles (windows) against 7.62 API BZ threats at level 1 and 2 at first.

The sound environment is an important element of quality of life in urban and peri-urban areas. Among the various types of environmental noise, the aeroacoustic noise produced by the interaction of flows and obstacles can be an important source of annoyance. Theses noisy interactions are related to transport noise (landing aircraft, trains, etc.) but also to a more recent problem concerning the noise emitted by the facade elements of modern or renovated buildings. To identify and understand these sources of noise in order to reduce them, mock-ups or samples of obstacles in anechoic wind tunnel are often studied. Sound source imagery is a classic tool for such studies, obtained by using microphone arrays and data processing tools such as beamforming. These techniques are currently developed in very simplified propagation, flow and directivity hypotheses, in order to obtain an analysis that reduces to a two-dimensional vision of the phenomena. Thus, the aim of the MAMIES project is to develop innovative experimental techniques for the study, identification and three-dimensional (3D) imaging of this type of aeroacoustic source in the context of wind tunnel measurements, in configurations for which classical techniques fail: highly 3D configurations, complex and / or unsteady flows, presence of diffracting objects in the noise production zone, complex source directivity. The originality of the project is based on two advances in the identification of environmental aeroacoustic noise: the development of a new generation microphone array (with more than 1,000 microphones) based on MEMS technology, combined with new treatment methods based on the principle of time reversal and associating measurements and numerical simulations. The MAMIES project brings together two partners: the PPRIME Institute (University of Poitiers) where experiments will be carried out in the wind tunnel, and the Jean-Rond d'Alembert Institute (Sorbonne University) where the antenna will be developed. The processing techniques and the production and analysis of the experimental data will be carried out jointly. The developed array will completely encompass the area of the flow containing the noise sources to be identified. Thanks to the large number of microphones the 3D sampling of the radiated acoustic field will give an optimal spatial resolution of the sources. The processing techniques will make it possible to discard the assumptions about the propagation medium because they will be based on a 3D numerical simulation whose input data will be the experimental data, but also the real geometry and flow. This approach will take into account an arbitrary flow, the presence of objects in the flow, and operate in the time domain, allowing the study of transient annoying phenomena from an auditory point of view (eg gust effects). Thus real progress is expected in the ability of analysis of sources of noise. Once the tool is completed and validated, experimental campaigns will be carried out in the wind tunnel, targeting two types of applications. The first concerns the aeroacoustic noise emitted by aircraft wings. A finite wall-mounted airfoil will be studied in depth, then the case of three interacting airfoils which provides a model for high-lifted wings producing a very complex aeroacoustic radiation. The second application concerns the noise of facade elements in modern architectural projects, first through interacting elementary objects, then through the study of real samples.

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.