2026-09-21T02:06:02Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/125803 2025-08-08 col_2142_5131 col_2142_16359 com_2142_5130 com_2142_16358 com_2142_234

application/pdf en text Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-08-01 The student, Saeid Bayat, accepted the attached license on 2024-07-09 at 10:12. The student, Saeid Bayat, submitted this Dissertation for approval on 2024-07-09 at 10:54. This Dissertation was approved for publication on 2024-07-12 at 10:28. DSpace SAF Submission Ingestion Package generated from Vireo submission #21002 on 2025-02-04 at 21:25:43 Optimization, control, and knowledge extraction in engineering systems: Applications in vehicle suspension, thermal management, and floating offshore wind turbines Bayat, Saeid 2024-07-12 Allison, James Allison, James Etesami, Rasoul Wang, Pingfeng Chamorro, Leonardo Control Co-design (ccd) Optimal Control Knowledge Extraction Model Predictive Control Floating Offshore Wind Turbine Ccd eng 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. 2024-08 Thesis https://hdl.handle.net/2142/125803 Copyright 2024 Saeid Bayat Industrial&Enterprise Sys Eng Systems & Entrepreneurial Engr University of Illinois at Urbana-Champaign Ph.D. Dissertation