Electrification of vehicles and improved efficiency of internal combustion engines (ICE) are the main levers to reduce greenhouse gas emissions. Recent studies indicate that in 2040 thermal cars sales will still remain an important part of the market and the spark-ignition engine (SIE) is seen as the most interesting ICE technology. However, technological challenges must be tackled before meeting real driving emissions expectation due to the diversification and complexity of hybrid applications. For flow aerodynamics, mixing and combustion down to the individual engine cycle, challenges are for example associated to robustness of concepts on a cycle basis, rapid variations of engine loads observed in hybrid technologies during transients, the occurrence of extreme cycles for a wider range of operating conditions. Numerical, experimental and analyzing tools have made significant progress in recent years for the analysis of spatial and temporal scales of the unsteady in-cylinder flows. Large-Eddy Simulation (LES) is an essential tool for the design of robust concepts. While LES has been validated against well-defined experiments, the prediction of internal turbulent dynamics and combustion during a cycle is affected by epistemic uncertainties. Therefore, progress is still needed to obtain optimal and robust design. The main objective of ALEKCIA is to develop game-changing tools for augmented prediction and analysis of turbulent reactive flows with a focus on real SIE operations to better capture time-resolved events and increase understanding and control of the origins of undesired behaviors. The key hypothesis is that future progress and success is tied to the synergistic, strong combination of experimental and numerical tools at every stage of the project, which will provide advancement in the analysis of physical scales and boundary conditions (BCs). The major scientific challenges addressed by ALEKCIA are to 1/ quantify and reduce uncertainties (UQ) due to model parameters and BCs, 2/ develop new Data Assimilation (DA) approaches for coupling LES with experimental measurements, 3/ develop new decomposition methods to analyse big data generated by LES and high-speed PIV, 4/ combine them with UQ and DA methods for detailed analysis of individual SIE cycles during steady operations and fast transients. We stress that this methodology could also be used more widely for industry and energy applications. To achieve its ambitious objectives, work in ALEKCIA is structured into one management task (T0) and three technical tasks (T1 to T3). We will address non-cyclic phenomena under transient and fired operations and develop novel analysis from the acquired experimental and LES databases of a SIE performed respectively at PRISME (T1) and IFPEN (T3) laboratories. The partners of the project will also collaborate on the development of crank-angle resolved spatio-temporal EMD decomposition (T1 and T3) for engine flows to obtain an unprecedented detailed understanding of the mechanisms involved in the generation of in-cylinder flow, turbulent dynamics and their impact on combustion. The development of UQ tools to quantify and reduce uncertainties in complex LES of SIE flows is also targeted (T3). Finally, the capabilities of DA methods to calibrate realistic BCs on-the-fly is investigated by PPRIME (T2 and T3). This task is particularly relevant when assimilating experimental data (in the form of BC and in-cylinder large-scale flow patterns from EMD) obtained in extreme cycles. EMD obtained from a selected number of measured cycles presenting very slow or fast combustion rates will be coupled with UQ and DA tools for their inclusion in LES (T3). In this scenario, LES will be able to properly follow the assimilated aerodynamic behaviour of these cycles while turbulent dynamic will be modelled. Finally, the application of the developed tools will allow to identify the main key parameters controlling internal aerodynamics.

Three-dimensional bluff-body wakes are of key importance due to their relevance to the automotive industry. Such wakes contribute to consumption and greenhouse gas emissions. Drastic European Union limitations concerning these two mechanisms conduct the car industry to think about efficient vehicles. In this project, we propose robust drag and fuel reduction solutions for road-vehicles by closed-loop control of turbulent flows working efficiently for a range of operating conditions including changing oncoming velocity and transient side winds. To achieve this goal, we combine passive, active control and closed-loop strategies by using compliant deflectors, unsteady micro-jets and Machine Learning techniques. This project aims to prove a feasibility of the control from laboratory scale up to a full-scale industrial demonstrator. The main repercussions of the project will be on the reduction of the environmental impacts of transport industry and the gain of competitiveness and employment. This project consists on experiments in wind and water tunnels, numerical simulations and control strategies. Two models will be used, the square back bluff body and a reduced scale car model. The latter is representative of SUV and is inspired from the model used in collaborative work between POAES and PRISME. Control strategies will be tested in both configurations by combining passive and active actuation i.e. fixed or moving flaps and micro jet actuators. Closed-loop control will also be developed in these situations. Control strategies will be mainly developed by PPRIME. Experiments will be done in PRISME and PPRIME. LHEEA will take in charge numerical simulations and optimization. Finally, PSA will provide the vision of an automobile manufacturer on the industrial feasibility of the developed control strategies.

FlowCon is a project devoted to the development and the experimental demonstration of innovative methods for the closed loop control of turbulent fluid flows using machine learning. Applications of flow control are ubiquitous, in particular in aerospace, both civilian and military: aerodynamic drag reduction, jets mixing and vectorization, mitigation of noise emission due to impacting vortices, reduction of the vortex-induced vibrations and suppression of the hyper-lift flaps to name only a few examples. In contrast with most current control strategies, this project aims to develop and offer control methods applicable to operational configurations. In particular, the proposed methods could be applied to systems governed by strongly nonlinear equations. Information on the system to be controlled only comes from a very few, wall-mounted, sensors. The environment of operation is potentially severely noisy and non-stationary (drift over time). Further, controllers must be synthesized and evaluated in real-time (closed-loop) with a very limited computational power, compatible with embedded hardware. We plan to develop control methods using Machine Learning. The available computational power and the real-time constraint make solving the governing equations of high Reynolds number turbulent flows intractable. Standard control methods rely on the knowledge of the state of the system (state vector). Instead of such a rich information, our approach is to use a statistical description, with no need for a detailed information about the flow, of the map between actions from the actuators and effects onto the flow over time. This class of methods lies in the Machine Learning framework and does not require a model a priori but only information available from sensors (data-driven). Some members of the present project are experts in flow control and have obtained very encouraging early results with this class of methods (for instance, re-attachment of the turbulent flow behind a descending ramp). However, they have also identified some limitations, such as the time required for learning or the difficulty in guarantying robustness of the control with respect to a drift of the system's dynamics. While they are key enablers of these early successes, current machine learning methods are not suitable for addressing the identified issues and limitations. The control of a system as complex as a turbulent flow is far from the usual applicability framework of this class of algorithms, which typically includes robotics, language processing, image segmentation, etc. A significant effort, gathering both researchers from the machine learning community, in a wide sense, and experts in flow control, is necessary. As a result, thanks to a close interaction and feedbacks from experiments to the theory, significant innovations are expected for the turbulent flow control community, as well as developments of innovative aspects for the machine learning community. FlowCon is a highly interdisciplinary project involving, for the first time, researchers from the fluid mechanics community as well as machine learning who will work in close interaction to develop innovative solutions allowing to reach the ambitious goals we have set. These developments will be tested on an experimental open cavity flow experiment of moderate complexity (low Reynolds number) and next on a demonstrator consisting of two configurations as realistic as possible: a turbulent flow behind a descending ramp and a NACA profile with a varying angle of attack at high Reynolds number. This project is supported by Dassault Aviation (see enclosed letter).