Prediction of Crashworthiness Performance Using Multi-Fidelity Machine Learning Techniques

This study investigates the development and application of meta-models for crashworthiness assessment of helicopter structures and components. It aims to address the challenges associated with scarcity of data from computationally expensive simulations and experimental drop-tests, and enable the use of surrogates in a crashworthiness...

Probabilistic Regression of wind turbine loads using Conditional Generative Adversarial Networks

Site analysis to determine the loads experienced by wind turbines based on site-specific environmental conditions is typically done using either coupled aero-servo-elastic simulations for onshore wind turbines or coupled aero-servo-hydroelastic simulations in the case of offshore wind turbines. These simulations become computationally expensive...

On leveraging physical knowledge to augment neural network-based surrogate models for simulations

The computational cost of high-fidelity engineering simulations, for example CFD, is prohibitive if the application requires frequent design iterations or even fully fledged optimization. A popular way to reduce the computational cost and enable fast iteration cycles is to use surrogate models that are trained to predict simulation results from...

Fatigue Load Emulation of Offshore Wind Turbines Using Advanced Surrogate Models

Understanding the fatigue load history of wind turbines is critical for taking decisions regarding the lifetime of a project. However, direct measurement of fatigue loads at each turbine in a wind farm is unfeasible. For this reason, surrogate models offer a useful alternative. In this thesis, a methodology for creating surrogate models for...

Online Surrogate Models for the Constrained Optimization of Interplanetary Low-Thrust Trajectories

The optimization of interplanetary, low-­thrust trajectories is a computationally expensive aspect of preliminary mission design. To reduce the computational burden associated with it, surrogate models can be used as cheap approximations of the original fitness function. Training the surrogate models in a fully online manner can be done to...

Surrogate modelling for multi-carrier distribution networks

The thesis mainly concentrates on exploring the suitability and applicability of surrogate models for a multi-carrier energy system (MCES). The climate crisis is being paid more attention owing to its notable effects on the environment globally. For the sake of facing the challenge, the European Union (EU) has developed different strategies. One...

Surrogate Constitutive Models with Multi-fidelity Gaussian Processes for Composite Micromodels

Various engineering applications rely on efficient, high performance materials to overcome design challenges. This high performance can be achieved by engineering micro-heterogenous materials also known as composites. Since the behavior of composites relies heavily on micro-scale interactions between different components, modeling...

Humble Giants: Computational Intelligence for Designing More Sustainable High-rise Buildings using Surrogate Models

As urbanization increases around the world, high-rise buildings will continue to become a more prevailing typology, nonetheless, due in part to cumbersome computational simulations, rarely do designers have enough information during the early stages of design, which is the time when their choices affect the most the efficiency of their building....