WP1: the open-source, user-friendly, customizable Multi-disciplinary Design Optimisation (MDO) engine GEMSEO has been enriched by new functionalities and plug-ins to facilitate the access of industry to the most advanced MDO techniques and to ease the setup distributed workflows. Partners have developed advanced physical models and their adjoints (e.g. for laminar flows and engine body-force). They have prepared and tested several numerical enablers to speed-up MDOs such as multi-fidelity and multi-level models, low-cost time/memory approaches to unsteady adjoint, anisotropic mesh adaptation for laminar wing design, tools for computing Pareto fronts using a reduced number of computations. Partners worked on high-dimensional Uncertain Quantification (UQ), assessing the most efficient techniques (gradient-enhanced, compressed sensing, neural network surrogates) to be employed in robust optimisation scenarios in WP5. WP2: two activities were carried out, developing new machine learning methods and assess their limitations, applicability, and performance. Partners focused on different areas such as enhancing CFD solvers with machine-learning turbulence closures, replacing disciplinary high-fidelity models with instant neural network prediction at acceptable accuracy, reconstruct real and damaged geometries from videos/images and point data, making faster their digitalization and performance analysis. WP3: partners have defined the design and optimisation problems for two high-aspect ratio wing aircraft configurations (the short-medium range DLR-F25 and a natural-laminar-flow business jet), generating the required computational models. Partners successfully set up their MDO chains and conducted preliminary optimisation studies on these test cases (newly developed capabilities from WP1 will be employed in the second part of the project). WP4: partners investigated two engine configurations aiming at drastically reducing fuel consumption of future aircraft, an Ultra-By-Pass Ratio turbofan, and an Unducted Single Fan concept. Partners carried out the setup and validation of the corresponding MDO workflows, to start optimisation studies. Some partners also worked on the engine interaction with its environment, thanks to the introduction of a contrail microphysics model (developed in WP1) in the MDO workflow to investigate how contrail formation can be reduced by optimising the nozzle shape and the engine integration on the wing. WP5: industrial partners have defined the uncertainties that will be considered for robust optimisation problems of aircraft and engine test cases, by providing a list of the relevant uncertain parameters, their distributions, and of stochastic objective functions of interest. WP6: two activities were carried out: 1) partners draw upon the advanced methodologies developed in WP1 and WP2, to analyse and model both the manufacturing variations from new blades coming directly from industrial factories, as well as in-service ones (possibly at end-of-life); 2) topology optimisation has been used to design a heat exchanger, creating radically different flow paths than traditional serpentine designs that improve heat transfer properties, reduce pressure losses and the device mass simultaneously
