| Abstract: |
Flat Plate Solar Collectors (FPSCs) continue to be a leading technology for the collection of low-to-medium temperature thermal energy. Improving their thermal efficiency while reducing pressure drop, material expenses, and environmental effects poses a complex, multi-faceted challenge. This review article thoroughly investigates the use of Multi-Objective Optimization (MOO) methods in the design and enhancement of FPSCs. It consolidates recent studies aimed at optimizing essential factors such as absorber plate shape, fin configuration, riser tube arrangement, properties of the working fluid, and glazing. The review emphasizes the common application of evolutionary algorithms like NSGA-II (Non-dominated Sorting Genetic Algorithm-II) in conjunction with computational fluid dynamics (CFD) and artificial neural network (ANN) surrogate models. A detailed analysis indicates that the main conflicting goals are to maximize thermal efficiency (η) while minimizing pressure drop (ΔP) or overall cost. Additionally, the paper provides a structured literature review in tabular format, identifies existing research gaps, and discusses the key findings with an emphasis on Pareto-optimal solutions that reconcile the competing objectives. The conclusion highlights the importance of system-specific MOO frameworks to achieve cost-effective and high-performance FPSC designs that are appropriate for various climatic and operational scenarios. |
