Heat dissipation performance is critical to the design of high-end equipment,such as integrated chips and high-precision machine tools.Owing to the advantages of artificial intelligence in solving complex tasks involv...Heat dissipation performance is critical to the design of high-end equipment,such as integrated chips and high-precision machine tools.Owing to the advantages of artificial intelligence in solving complex tasks involving a large number of variables,researchers have exploited deep learning to expedite the optimization of material properties,such as the heat dissipation of solid isotropic materials with penalization(SIMP).However,because the approach is limited by discrete datasets and labeled training forms,ensuring the continuous adaptation of the condition domain and maintaining the stability of the design structure remain major challenges in the current intelligent design methodology for thermally conductive structures.In this study,we propose an innovative intelligent design fram-ework integrating Conditional Deep Convolutional Generative Adversarial Networks(CDCGAN)with SIMP,capable of creating topology structures that meet prescribed thermal conduction performance.This proposed design strategy significantly reduces the computational time required to solve symmetric and random heat sink problems compared with existing design approaches and is approximately 98%faster than standard SIMP methods and 55.5%faster than conventional deep-learning-based methods.In addition,we benchmarked the design performance of the proposed framework against theoretical structural designs via experimental measurements.We observed a 50.1%reduction in the average temperature and a 28.2%reduction in the highest temperature in our designed topology compared with those theoretical structure designs.展开更多
The western Sichuan hydrothermal area is located at the northeastern margin of the eastern syntaxis of the Qinghai-Tibet Plateau, which is also the eastern end of the Mediterranean-Himalayan geothermal activity zone. ...The western Sichuan hydrothermal area is located at the northeastern margin of the eastern syntaxis of the Qinghai-Tibet Plateau, which is also the eastern end of the Mediterranean-Himalayan geothermal activity zone. There are 248 warm or hot springs in this area, and 11 have temperatures beyond the local boiling temperature. Most of these hot springs are distributed along the Jinshajiang, Dege-Xiangcheng, Ganzi-Litang, and Xianshuihe faults, forming a NW-SE hydrothermal belt. A geothermal analysis of this high-temperature hydrothermal area is an important basis for understanding the deep geodynamic process of the eastern syntaxis of the Qinghai-Tibet Plateau. In addition, this study offers an a priori view to utilize geothermal resources, which is important in both scientific research and application. We use gravity, magnetic, seismic, and helium isotope data to analyze the crust-mantle heat flow ratio and deep geothermal structure. The results show that the background terrestrial heat flow descends from southwest to northeast. The crustal heat ratio is not more than 60%. The high temperature hydrothermal active is related to crustal dynamics processes. Along the Batang-Litang-Kangding line, the Moho depth increases eastward, which is consistent with the changing Qc/Qm(crustal/mantle heat flow) ratio trend. The geoid in the hydrothermal zone is 4–6 km higher than the surroundings, forming a local "platform". The NW-SE striking local tensile stress zone and uplift structure in the upper and middle crust corresponds with the surface hydrothermal active zone. There is an average Curie Point Depth(CPD) of 19.5–22.5 km in Batang, Litang, and Kangding. The local shear-wave(S-wave) velocity is relatively low in the middle and lower crust. The S-wave shows a low velocity trap(Vs<3.2 km s.1) at 15–30 km, which is considered a high-temperature partial melting magma, the crustal source of the hydrothermal active zone. We conclude that the hydrothermal system in this area can be divided into Batang-type and Kangding-type, both of which rely on a crustal heating cycle of atmospheric precipitation and surface water along the fracture zone. The heat is derived from the middle and lower crust: groundwater penetrates the deep faults bringing geothermal energy back to the surface and forming high-temperature springs.展开更多
基金Supported by National Natural Science Foundation of China(Grant Nos.52222508 and 52335011)。
文摘Heat dissipation performance is critical to the design of high-end equipment,such as integrated chips and high-precision machine tools.Owing to the advantages of artificial intelligence in solving complex tasks involving a large number of variables,researchers have exploited deep learning to expedite the optimization of material properties,such as the heat dissipation of solid isotropic materials with penalization(SIMP).However,because the approach is limited by discrete datasets and labeled training forms,ensuring the continuous adaptation of the condition domain and maintaining the stability of the design structure remain major challenges in the current intelligent design methodology for thermally conductive structures.In this study,we propose an innovative intelligent design fram-ework integrating Conditional Deep Convolutional Generative Adversarial Networks(CDCGAN)with SIMP,capable of creating topology structures that meet prescribed thermal conduction performance.This proposed design strategy significantly reduces the computational time required to solve symmetric and random heat sink problems compared with existing design approaches and is approximately 98%faster than standard SIMP methods and 55.5%faster than conventional deep-learning-based methods.In addition,we benchmarked the design performance of the proposed framework against theoretical structural designs via experimental measurements.We observed a 50.1%reduction in the average temperature and a 28.2%reduction in the highest temperature in our designed topology compared with those theoretical structure designs.
基金supported by the National Natural Science Foundation of China (Grant Nos. 41574074, 41174085, 41430319)the Innovation Team Project of Chinese Academy of Sciences (Grant No. KZZD-EW-TZ-19)the Strategic Pilot Technology of Chinese Academy of Sciences (Grant No. XDA1103010102)
文摘The western Sichuan hydrothermal area is located at the northeastern margin of the eastern syntaxis of the Qinghai-Tibet Plateau, which is also the eastern end of the Mediterranean-Himalayan geothermal activity zone. There are 248 warm or hot springs in this area, and 11 have temperatures beyond the local boiling temperature. Most of these hot springs are distributed along the Jinshajiang, Dege-Xiangcheng, Ganzi-Litang, and Xianshuihe faults, forming a NW-SE hydrothermal belt. A geothermal analysis of this high-temperature hydrothermal area is an important basis for understanding the deep geodynamic process of the eastern syntaxis of the Qinghai-Tibet Plateau. In addition, this study offers an a priori view to utilize geothermal resources, which is important in both scientific research and application. We use gravity, magnetic, seismic, and helium isotope data to analyze the crust-mantle heat flow ratio and deep geothermal structure. The results show that the background terrestrial heat flow descends from southwest to northeast. The crustal heat ratio is not more than 60%. The high temperature hydrothermal active is related to crustal dynamics processes. Along the Batang-Litang-Kangding line, the Moho depth increases eastward, which is consistent with the changing Qc/Qm(crustal/mantle heat flow) ratio trend. The geoid in the hydrothermal zone is 4–6 km higher than the surroundings, forming a local "platform". The NW-SE striking local tensile stress zone and uplift structure in the upper and middle crust corresponds with the surface hydrothermal active zone. There is an average Curie Point Depth(CPD) of 19.5–22.5 km in Batang, Litang, and Kangding. The local shear-wave(S-wave) velocity is relatively low in the middle and lower crust. The S-wave shows a low velocity trap(Vs<3.2 km s.1) at 15–30 km, which is considered a high-temperature partial melting magma, the crustal source of the hydrothermal active zone. We conclude that the hydrothermal system in this area can be divided into Batang-type and Kangding-type, both of which rely on a crustal heating cycle of atmospheric precipitation and surface water along the fracture zone. The heat is derived from the middle and lower crust: groundwater penetrates the deep faults bringing geothermal energy back to the surface and forming high-temperature springs.
