Many imaging techniques, particularly in environmental transmission electron microscopy (ETEM), generate images with degraded signal-to-noise ratio, contrast and spatio-temporal resolution, which hamper quantification and reliable interpretation of data. Moreover, the extraction of structural information from these images relies on manual acquisition and local structural identification which does not allow statistical analysis of the data and necessarily introduces a human bias carried out at the post-processing stage. The general aim of the ARTEMIA project is to develop a ground-breaking deep learning-based framework for in situ microscopy in liquid and gaseous media allowing the automated, high throughput, real-time acquisition and analysis of ETEM image sequences.Our framework will integrate aberration-corrected in situ ETEM imaging using windowed liquid/gas nanoreactors with denoising and resolution enhancement scheme set up using convolutional neural network (CNN). For model training, datasets consisting of simulated liquid- and gas-phase TEM images will be generated by by atomistic simulations including instrumental noise and imperfections of the microscope optics. In the ARTEMIA project, the CNN models will be applied to the study of two crystalline samples with complementary structural characteristics and electron beam sensitivity, model gold nanoparticles (Au NPs) and microporous zeolite, in reactive gas and/or liquid environments. Our scientific aim will be to gain further mechanistic understanding ofthe growth of model Au NPs in liquid phase and their reactivity in oxidizing and reducing gas environments on one hand and the steaming process of beam-sensitive zeolite on the other hand. The consortium comprises three academic partners (MPQ, LEM, IPCMS) and an EPIC partner (IFPEN) with complementary expertise in liquid and gas ETEM, data science and image processing with special focus on deep learning approaches, atomic modelling and TEM image simulation.

This proposal addresses the vital issue of prediction of multiphase flows in large diameter risers in off-shore hydrocarbon recovery. The riser is essentially a vertical or near-vertical pipe connecting the sea-bed collection pipe network (the flowlines) to a sea-surface installation, typically a floating receiving and processing vessel. In the early years of oil and gas exploration and production, the oil and gas companies selected the largest and most accessible off-shore fields to develop first. In these systems, the risers were relatively short and had modest diameters. However, as these fields are being depleted, the oil and gas companies are being forced to look further afield for replacement reserves capable of being developed economically. This, then, has led to increased interest in deeper waters, and harsher and more remote environments, most notably in the Gulf of Mexico, the Brazilian Campos basin, West of Shetlands and the Angolan Aptian basin. Many of the major deepwater developments are located in water depths exceeding 1km (e.g. Elf's Girassol at 1300m or Petrobras' Roncador at 1500-2000m). To transport the produced fluids in such systems with the available pressure driving forces has led naturally to the specification of risers of much greater diameter (typically 300 mm) than those used previously (typically 75 mm). Investments in such systems have been, and will continue to be, huge (around $35 billion up to 2005) with the riser systems accounting for around 20% of the costs. Prediction of the performance of the multiphase flow riser systems is of vital importance but, very unfortunately, available methods for such prediction are of doubtful validity. The main reason for this is that the available data and methods have been based on measurements on smaller diameter tubes (typically 25-75 mm) and on the interpretation of these measurements in terms of the flow patterns occurring in such tubes. These flow patterns are typically bubble, slug, churn and annular flows. The limited amount of data available shows that the flow patterns in larger tubes may be quite different and that, within a given flow pattern, the detailed phenomena may also be different. For instance, there are reasons to believe that slug flow of the normal type (with liquid slugs separated by Taylor bubbles of classical shape) may not exist in large pipes. Methods to predict such flows with confidence will be improved significantly by means of an integrated programme of work at three universities (Nottingham, Cranfield and Imperial College) which will involve both larger scale investigations as well as investigations into specific phenomena at a more intimate scale together with modelling studies. Large facilities at Nottingham and Cranfield will be used for experiments in which the phase distribution about the pipe cross section will be measured using novel instrumentation which can handle a range of fluids. The Cranfield tests will be at a very large diameter (250 mm) but will be confined to vertical, air/water studies with special emphasis on large bubbles behaviour. In contrast those at Nottingham will employ a slightly smaller pipe diameter (125 mm) but will use newly built facilities in which a variety of fluids can be employed to vary physical properties systematically and can utilise vertical and slightly inclined test pipes. The work to be carried out at Imperial College will be experimental and numerical. The former will focus on examining the spatio-temporal evolution of waves in churn and annular flows in annulus geometries; the latter will use interface-tracking methods to perform simulations of bubbles in two-phase flow and will also focus on the development of a computer code capable of predicting reliably the flow behaviour in large diameter pipes. This code will use as input the information distilled from the other work-packages regarding the various flow regimes along the pipe.

The decrease of CO2 & particulates emissions is a main challenge of the automotive sector. European OEMs and automotive manufacturers need new long term technologies, still to be implemented by 2030. Currently, hybrid powertrains are considered as the main trend to achieve clean and efficient vehicles. EAGLE project is to improve energy efficiency of road transport vehicles by developing an ultra-lean Spark Ignition gasoline engine, adapted to future electrified powertrains. This new concept using a conventional engine architecture will demonstrate more than 50% peak brake thermal efficiency while reducing particulate and NOx emissions. It will also reach real driving Euro 6 values with no conformity factor. This innovative approach will consequently support the achievement of long term fleet targets of 50 g/km CO2 by providing affordable hybrid solution. EAGLE will tackle several challenges focusing on: • Reducing engine thermal losses through a smart coating approach to lower volumetric specific heat capacity under 1.5 MJ/m3K • Reaching ultra-lean combustion (lambda > 2) with very low particulate (down to 10 nm) emission by innovative hydrogen boosting • Developing breakthrough ignition system for ultra-lean combustion • Investigating a close loop combustion control for extreme lean limit stabilization • Addressing and investigating NOx emissions reduction technologies based on a tailor made NOx storage catalyst and using H2 as a reducing agent for SCR. A strong engine modeling approach will allow to predict thermal and combustion performances to support development and assess engine performances prior to single and multi-cylinder test bench application. An interdisciplinary consortium made of nine partners from four different countries (France, Germany, Italy, Spain) will share its cutting-edge know-how in new combustion process, sensing, control, engine manufacturing, ignition system, simulation & modeling, advanced coating, as well as after-treatment systems.

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.