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With the improvement of the integration degree of power modules, the optimization of their heat transfer structures has become a focus in the development. The topology optimization(TO) can maximize the cooling performance by transforming the morphology and structure of heat sinks, thus receiving extensive attention. However, in the TO process, the temperature distribution of modules and heat sinks needs to be calculated in each iteration step, consuming a large amount of computing resource and calculation time. To accelerate the TO process of traditional heat sinks, a fast iterative method combining neural network (NN) synchronous learning and the traditional solid isotropic material with penalization (SIMP)-based TO methods is put forward. First, an NN prediction model based on the encoder-decoder structure is constructed, which can iteratively evolve the shape of heat sinks to achieve a fast prediction of optimized structures. Second, the NN model is integrated into the TO process of the heat sink based on the SIMP method, and the NN is trained synchronously using the intermediate morphology obtained in the iteration process. Finally, aimed at the single-chip and dual-chip modules, the results obtained by the new method and traditional iterative methods are compared to validate the accuracy and rapidity of the proposed NN synchronous leaning method.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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