Optimization, control, and knowledge extraction in engineering systems: Applications in vehicle suspension, thermal management, and floating offshore wind turbines

This study focuses on deriving valuable insights from Engineering Design Optimization (EDO) processes for systems with active components, referred to here as Controller. These components can alter the system’s dynamics, thereby changing its constraints, objectives, etc. The significance of EDO extends beyond the optimal solutions it provides; it can reveal how the system behaves in various scenarios, which is driven by design variables, the primary constraints driving the design, the degree of coupling between different design variables, and more. Extracting these insights from EDO studies can be achieved through expert human interpretation of optimization data or by applying machine learning techniques. These insights are invaluable for facilitating the design of new systems, especially when there is limited or no design heritage. Even in systems with rich design histories, existing designs may not be optimal, requiring new designs based on optimization driven insights to achieve optimal performance. By exploring and leveraging these insights, this research aims to advance the understanding of optimal engineering design and its practical implications. The proposed methodology is tested in three distinct applications: thermal management of multi-split systems, floating offshore wind turbines, and vehicle suspensions, all of which involve at least one active component. Optimization problems for these systems are formulated and solved, and insights are derived through expert human interpretation or the application of machine learning. Additionally, toolsets, including a Model Predictive Control (MPC) toolset in MATLAB and Python, are developed to facilitate the analysis and implementation of the proposed methods across various domains.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Systems & Entrepreneurial Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Bayat, Saeid

Contributors dc:contributor

• Allison, James
• Etesami, Rasoul
• Wang, Pingfeng
• Chamorro, Leonardo

• Control Co-design (ccd)
• Optimal Control
• Knowledge Extraction
• Model Predictive Control
• Floating Offshore Wind Turbine Ccd

Statement dc:rights

• Copyright 2024 Saeid Bayat

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/125803

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F125803