Uncertainty Treatment and OPtimisation In Aerospace Engineering

Informazioni relative al progetto

UTOPIAE

ID dell’accordo di sovvenzione: 722734

DOI

10.3030/722734

Progetto chiuso

Data della firma CE 31 Agosto 2016

Data di avvio 1 Gennaio 2017

Data di completamento 31 Dicembre 2020

Finanziato da

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Costo totale

€ 3 876 853,68

Contributo UE

€ 3 876 853,68

Coordinato da

UNIVERSITY OF STRATHCLYDE
United Kingdom

Periodic Reporting for period 2 - UTOPIAE (Uncertainty Treatment and OPtimisation In Aerospace Engineering)

Periodo di rendicontazione: 2019-01-01 al 2020-12-31

Sintesi del contesto e degli obiettivi generali del progetto

UTOPIAE looked for the ideal compromise between performance and resilience, optimality and reliability in complex engineering systems and, by embracing the inherent stochasticity of Nature, it used optimisation to harness reliability and robustness into a better future. In an expanding world with limited resources, UTOPIAE developed solutions for a more sustainable and resilient future addressing some of the global challenges for sustainable development and the objectives of Europe in the areas of Transport, Advanced Manufacturing & Processing, and Space. The high level objectives were:
To define and develop sets of multi-fidelity models covering key applications in space and aerospace.
To look into the most general representation of uncertainty and develop techniques to reduce the computational cost to quantify uncertainty.
To study optimal techniques to design new experiments, improve the robustness of the numerical simulation and validate the simulation models.
To develop algorithms and methods for worst-case and multi-level optimisation and new techniques for handling expensive many-objective optimisation problems with mixed discrete and continuous decision variables.
To make Evidence-Based Robust Optimisation (EBRO) efficient in high dimensional problems and develop new techniques for large scale constrained Reliability Based Design Optimisation (RBDO) problems.
To use optimisation under uncertainty to optimise multi-phase processes with evolvable requirements, constraints and performance metrics.

UTOPIAE achieved all its objectives by developing new algorithms and mathematical theories that enable the creation of the engineering systems of the future. The immediate impact was to support major aerospace companies like Airbus and Leonardo to design and manufacture safer and better airplanes. The methods developed in UTOPIAE have already been applied to the design of future space missions and have found their way into the design of new medical delivery systems with drones.

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

UTOPIAE training events started with the Opening Training School (OTS), held in Nov 2017 at the University of Strathclyde, with an overview of methods and problems in uncertainty quantification and optimisation under uncertainty. The OTS was followed by the first Local Training workshop (LTW-I) at the University of Strathclyde in Feb 2018 the second training school, TS-II, at the University of Durham in July 2018. TS-II was focused on uncertainty quantification and an introduction to imprecise probabilities.
The third UTOPIAE Training School (TS-III) took place in Sept 2019, at Technische Hochschule Köln (TH Köln) in Gummersbach, Germany, and was focused on optimisation under uncertainty. Finally, the 2nd Local Training Workshop (LTW-II) took place at Politecnico di Milano in Feb 2020. The LTW-II was dedicated to train the ESRs on entrepreneurship and impact. The LTW-II was held alongside the second school of the MSCA ETN Stardust-R.

UTOPIAE conferences started with the GVW-I in Paris and then continued with the Global Virtual Workshop (GVW-II) organised by the Jožef Stefan Institute in Nov 2019 in Ljubljana, Slovenia. The GVW-II was hosted alongside the 5th International Workshop on Optimisation in Space engineering (OSE5). The workshops were followed by the UTOPIAE Winter of Code competition, held in Trieste, Italy on 25–29 Nov 2019 and hosted by ESTECO. The closing event, the International Conference on Uncertainty Quantification & Optimisation (UQOP) was held virtually in Nov 2020. UQOP was run jointly with the 9th International Conference on Bio Inspired Optimisation Methods and their Applications. Both conferences produced a book of proceedings published by Springer Nature.

Over the 4 years of UTOPIAE, the 15 researchers recruited on the action worked together in four project working groups (PWG) sharing their knowledge and expertise and addressing concrete problems of practical importance. Podcasts presenting the PWGs are available on the UTOPIAE website.

As part of the outreach programme, a number of public lectures and public events were organised. In addition, a continuous professional development course for school teachers was offered to local schools and an optimisation app for portable devices was made available to engage students with additional educational material. The app is available to download from the dedicated website OPTIMISATIONPRIME (https://www.optimisationprime.co.uk). UTOPIAE project is present on Research Gate and twitter and the webpage has been used to communicate with the general public and other scholars. More information can be found on the UTOPIAE webpage: http://utopiae.eu/

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

UTOPIAE advancements beyond the state of the art include: a new concept, named Evidence Network Models (ENM), to model complex systems. The accelerated Schwarz method for UQ in CFD. New algorithms for the solution of constrained min-max and multi-level problems, to address worst case scenarios. Computational cost reduction was obtained for mixed-integer problems under uncertainty and the development of a mixed-integer genetic algorithm solver based on structured chromosomes.

Significant theoretical and algorithmic results were obtained on the application of Evidence Based Robust Optimisation in resilience engineering. New theoretical and algorithmic results were achieved on the link between regularization, imprecision and credal classification and efficient computational methods for a large class of general inference problems in imprecise Markov chains, an efficient computational method to compute lower and upper expected hitting times for imprecise Markov chains and the development of a novel robust Bayesian filtering algorithm to incorporate Imprecise Probability.

Further progresses were obtained in the fast robust shape optimisation and the introduction of Value at Risk and Conditional Value at Risk in optimisation under uncertainty. Key advancements were achieved in the computation of the survival signatures of large systems and phased-mission systems, the development of new algorithms with polynomial complexity to solve optimal control under severe uncertainty and a novel optimal control formulation for optimisation problems under uncertainty, called Belief Optimal Control.
In the context of applications to atmospheric entry systems, an efficient Bayesian framework was developed to enhance our understanding of gas-surface interaction phenomena for spacecraft atmospheric entry systems

UTOPIAE achieved the first-ever robust optimization of an electro-thermal ice protection system in collaboration with Leonardo. UTOPIAE was fundamental in the study of design optimization algorithms exploiting codes of different fidelity while taking into account uncertain quantities. This knowledge is going to be coded in the software platform modeFRONTIER. In collaboration with Airbus new methodologies for the quantification of robustness and uncertainty to design future aircraft were developed. Finally, the work on binary credal classification under sparsity constraints was listed in Nature public health emergency collection because of its relevant applicability in ongoing research against covid-19.

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