Date of Award
Master of Science (MS)
Mechanical and Civil Engineering
Anand Balu Nellippallil, Ph.D.
Luis Daniel Otero, Ph.D.
Chiradeep Sen, Ph.D.
Ashok Pandit, Ph.D.
Designing a complex engineered system is challenging due to many conflicting goals, uncertainties, and multiple interactions. Traditional optimization approaches often yield single-point solutions, which may not be suitable for early design stages due to their susceptibility to changes in conditions and uncertainties. To address this challenge, a satisficing approach is employed. This approach enables designers to effectively navigate the design space and identify satisficing solutions that balance conflicting goals in the face of uncertainties and changes in conditions. From a systems design perspective, we view design as an iterative process that involves making informed decisions based on available information and supported by simulations. The Decision-Based Design (DBD) paradigm is the foundation for design methodology in this thesis, empowering designers to navigate the complex design landscape by making informed decisions grounded in available information. In this thesis, the DBD technique called compromise Decision Support Problem Technique (cDSP) is employed to address the issue of many (more than three) goals and uncertainty in the system.
Model-based complex system design involves one crucial step: exploring and visualizing the solution space. This procedure provides insightful information about the system's behavior, enabling designers to make informed decisions. Accurately predicting future states is difficult because of the intrinsic constraints of models and the inherent uncertainty in search methods and solvers. By exploring the solution space and displaying its complexities, designers find satisficing solutions relatively insensitive to uncertainties.
In this thesis, a Decision-based Design framework is proposed. The novelty of this framework is that it integrates the compromise Decision Support Problem (cDSP) technique accounting for many conflicting goals, incorporated with robust design metrics to address the issue of uncertainty with a machine-learning-based visualization technique called interpretable Self-Organizing Maps (iSOM) to visualize and explore the solution space for many goals effectively. The efficacy of this framework is validated, considering vehicular crashworthiness problems as an example.
Once the DBD framework has identified feasible solutions, selecting a standard satisficing solution proves crucial for understanding the system's behavior and performance. This thesis presents a systematic approach for identifying common satisficing solutions from the visualized plots generated by the DBD framework. The effectiveness of this approach is demonstrated through the design of a composite beam as an illustrative example.
Balaji, Niharika, "Design Space Visualization And Exploration For Many Goal Problems Under Uncertainity" (2023). Theses and Dissertations. 1382.