Multi-objective-mean clustering fin optimization method and double-layer micro-channel radiator
Through the multi-objective-mean clustering fin optimization method, the fin length is accurately determined, which solves the limitations of traditional microchannel radiators in flow resistance and thermal resistance optimization, and realizes efficient and stable operation and energy consumption reduction of double-layer microchannel radiators.
Patent Information
- Application Number
- CN202511080260.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-04
- Publication Date
- 2025-09-05
- Estimated Expiration
- 2045-08-04
AI Technical Summary
Traditional microchannel heat sinks have optimization limitations in fin parameter settings, making it difficult to optimize both flow resistance and thermal resistance simultaneously. This results in the peak temperature being unable to drop to the theoretical minimum value, resulting in excessive pressure drop and energy dissipation, affecting equipment stability and R&D cycles.
The multi-objective-mean clustering fin optimization method is adopted to generate the Pareto front through the multi-objective optimization algorithm. Combined with the thermal performance model and decision matrix, the fin length is accurately determined, and a double-layer microchannel radiator is constructed to balance the heat dissipation performance and fluid resistance.
It achieves the goal of maintaining a lower peak temperature while reducing the voltage drop, improving the heat dissipation efficiency and equipment stability, shortening the R&D cycle, and adapting to the development trend of miniaturization and integration.