AI-enhanced generative design for efficient heat exchangers in thermoelectric generators: revolutionizing waste heat recovery in thermoelectricity / Andrew Robert Martin Hughes ... [et al.]
Thermoelectric generators (TEGs) offer the potential for converting waste heat into electricity, but their efficiency, particularly at low temperatures, remains inadequate. Plate-Fin Heat Exchangers (PFHEs) in TEG systems are not fully optimized, resulting in limited efficiency and applicability. Th...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
UiTM Press
2024
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/105987/1/105987.pdf https://ir.uitm.edu.my/id/eprint/105987/ |
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| Summary: | Thermoelectric generators (TEGs) offer the potential for converting waste heat into electricity, but their efficiency, particularly at low temperatures, remains inadequate. Plate-Fin Heat Exchangers (PFHEs) in TEG systems are not fully optimized, resulting in limited efficiency and applicability. The low conversion efficiency of TEGs means only a small fraction of waste heat is utilized, posing challenges to their long-term viability. While Genetic Algorithms (GAs) have shown promise in optimizing heat exchanger designs, advanced methods like Non-dominated Sorting Genetic Algorithm II (NSGA-II) have yet to be fully applied for PFHE TEG design. This study addresses these challenges by using NSGA-II, combined with a semi-empirical model, to optimize PFHE design in TEG systems. The optimization focuses on refining fin design parameters such as number, width, and height while adhering to constraints on fin area and pressure drop. |
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