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

Bayat, Saeid

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

Description

Title

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

Author(s)

Bayat, Saeid

Issue Date

2024-07-12

Director of Research (if dissertation) or Advisor (if thesis)

Allison, James

Doctoral Committee Chair(s)

Allison, James

Committee Member(s)

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

Department of Study

Industrial&Enterprise Sys Eng

Discipline

Systems & Entrepreneurial Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Control Co-Design (CCD)
• Optimal Control
• Knowledge Extraction
• Model Predictive Control
• Floating Offshore Wind Turbine CCD

Abstract

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.

Graduation Semester

2024-08

Type of Resource

Thesis

Handle URL

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

Copyright and License Information

Copyright 2024 Saeid Bayat

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