The aim of this project is to design a computational toolbox for the Adaptive Surrogate Modelling & Optimisation (ASMO) of problems described by data, where surrogate model refers to both meta-models and data-driven models. In the former case, they are emulators of other models whilst in the latter they can be viewed as surrogates for unknown mechanistic models. Thus, the ASMO toolbox would receive input data −either experimental or generated via HF models− and return meaningful optimal response(s).
While the ASMO toolbox may be applied to an unlimited number of real-world problems, the focus in this project will be on “optimal design” and “decision-making” within a comprehensive spectrum of applications spanning disciplines such as operational research; aerospace and civil materials and structures; automotive powertrains; and electrical engineering. In particular, we are interested in the following specific objectives:
- Surrogate-based optimal design of civil and aerospace structures composed of complex engineering materials (e.g. composites, concrete, wood, soil) initially modelled using classical and multi-scale methods.
- Optimal design of FRP-reinforced concrete beams from experimental data as well as from computationally intensive numerical models.
- Data-driven modelling and optimisation in soil mechanics.
- Optimal Management of Waterflooding in Petroleum Fields.
- Decision-making in smart manufacturing.
- Surrogate-assisted optimal design of power converters.
- Optimal design of automotive powertrain system under real driving emissions (RDE) conditions.
Related with this project, SVeCLab actively participated in the InnovateUK & KTN Uncertainty Quantification & Management (UQ&M) Study Group with Industry in Liverpool 2017, leading the Set-Based & Multi-Objective Design work-package of a problem presented by Airbus titled “Climb-Cruise Engine Matching – An Optimisation Approach“. The latter involved Surrogate Modelling, Uncertainty Propagation, and Chance-Constrained Set-Based and Multi-Objective Optimal Design of Airframe-Engine Matching aiming for minimum noise, fuel consumption, and gaseous emissions.