Engineering Design Optimisation Using Computational Fluid Dynamics and Human-AI Collaboration

The primary contributions of this thesis are a comparison of a ducted winglet to two other geometries and a user study which investigated the relationship between engineers and an optimisation algorithm. With an ever increasing emphasis on sustainable and renewable energy production, new technology can increase the efficiency of existing infrastructure such as wind turbines. Aerodynamic devices known as winglets can be fitted to the end of wind turbine blades to increase efficiency and reduce negative downstream effects. A patented winglet introduces a duct that channels freestream air from the bottom of the winglet to the top. The effectiveness of this design is simulated using computational fluid dynamics and compared to two alternative designs across a range of angles of attack. The patented winglet had lower induced drag across a range of angles of attack. Following the comparison, optimisation methods including evolutionary algorithms are employed to further increase the efficiency of the winglet. These algorithms can be used when engineers would otherwise rely on intuition, preconceptions, and theory to try and find optimal designs. The manner in which engineers utilise and engage with an evolutionary algorithm known as MAP-Elites is evaluated through a user study. 12 participants were given 20 minutes each to design a car that could travel over an inclined course with obstacles as far as possible. Their behaviour was compared to survey answers they provided before and after designing cars. Participants who engaged with the MAP-Elites algorithm outperformed baseline designs created by the computer and participants who did not engage with the algorithm. Participants were more likely to use the MAP-Elites algorithm even if they were unaware they were using it or if they had stated that they did not trust optimisation algorithms. Shortening the optimisation cycle is explored during the optimisation of the ducted winglet. The effects of increasing uncertainty in the results are explored through two studies on the robustness of evolutionary algorithms and a study on 2-dimensional aerofoils. The studies show that the optimisation cycle can be reduced to a certain extent while maintaining the original ranking of the designs

A user-centered approach to evolutionary algorithms and their use in industry

The key contribution of this article to the domain of engineering optimisation is establishing best practices with respect to the development of evolutionary algorithms in the context of design engineering. Despite their various uses, the uptake of evolutionary algorithms in industry remains limited. In order to understand why uptake is low a survey of engineers was undertaken, the results of which are presented here. A total of 23 participants (N = 23) took part in the 3-section mixed methodssurvey. Reflexive thematic analysis was conducted on the open-ended questions. A common thread throughout participants responses is that there is a question of trust towards evolutionary algorithms within industry. Perhaps surprising is that the key to gaining this trust is not producing good results, but creating algorithms which explain the process they take in reaching a result. Based on this, recommendations have been made to increase their use in industry

How Engineers Use Evolution to Invent Things

You may have heard of evolution in terms of plants and animals, but did you know that this natural process can also be used by engineers to invent things? Animals and plants have evolved in amazing ways to survive in their environments. Biologists have been investigating how evolution works for a long time. Mathematicians and computer scientists have worked alongside biologists to create computer programs that can evolve designs, to help engineers invent things. These are called evolutionary optimization algorithms, and they can be used to evolve faster airplanes, stronger bridges, or even better video games. In this article, we will explain how these algorithms work and what their strong and weak points are