Multi-objective Optimization of Heat Transfer Problems

In thermal engineering, the solution of complex heat transfer problems is crucial for system design and operation. As these problems grow in sophistication, numerical tools become vital, especially when including different heat transfer mechanisms such as conduction, convection, and radiation, which require customized analysis methods. A detailed analysis of a system opens the door to possible improvements to state the system, i.e., optimization. Choosing between gradient-based and gradient-free techniques, e.g., genetic algorithms (GAs), depends on system size and complexity. Large-scale systems like industrial heat exchangers benefit from GAs, prioritizing thermal performance optimization. They effectively navigate complex design spaces. Conversely, small-scale systems like microfluidic devices favor precision, making gradient-based techniques more suitable. This chapter serves as a comprehensive guide for readers in defining and selecting an appropriate optimization tool, based on modeling and the system/component scale. It begins by emphasizing the significance of a clear objective setting and constraint identification during the preliminary phase. Throughout the discussion, diverse examples from literature, such as heat exchanger systems, illustrate the complex nature of engineering challenges. Once classifications are provided, the attention shifts to an understanding of multi-objective optimization principles, particularly focusing on GAs. At the core of the exploration lies the crucial decision of selecting the most suitable optimization method. Through a systematic evaluation of current optimization techniques and modern approaches, readers are equipped to make informed choices. Emphasis is placed on the benefits and bottlenecks of presented approaches, based on the desired solution. Thus, the chapter culminates in a detailed examination of several optimization examples, guiding readers towards successful resolutions of heat transfer challenges, offering insights and practical advice on methodologies, solvers, and constraints.