Intelligent Design Method for Thermal Conductivity Topology Based on a Deep Generative Network
Abstract 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 learnin...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-04-01
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| Series: | Chinese Journal of Mechanical Engineering |
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| Online Access: | https://doi.org/10.1186/s10033-025-01222-w |
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| _version_ | 1850187363830988800 |
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| author | Qiyin Lin Feiyu Gu Chen Wang Hao Guan Tao Wang Kaiyi Zhou Lian Liu Desheng Yao |
| author_facet | Qiyin Lin Feiyu Gu Chen Wang Hao Guan Tao Wang Kaiyi Zhou Lian Liu Desheng Yao |
| author_sort | Qiyin Lin |
| collection | DOAJ |
| description | Abstract 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 framework 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. |
| format | Article |
| id | doaj-art-a2404da5dd7841bb874da356e9e36938 |
| institution | OA Journals |
| issn | 2192-8258 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Chinese Journal of Mechanical Engineering |
| spelling | doaj-art-a2404da5dd7841bb874da356e9e369382025-08-20T02:16:06ZengSpringerOpenChinese Journal of Mechanical Engineering2192-82582025-04-0138111610.1186/s10033-025-01222-wIntelligent Design Method for Thermal Conductivity Topology Based on a Deep Generative NetworkQiyin Lin0Feiyu Gu1Chen Wang2Hao Guan3Tao Wang4Kaiyi Zhou5Lian Liu6Desheng Yao7Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, School of Mechanical Engineering, Xi’an Jiaotong UniversityKey Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, School of Mechanical Engineering, Xi’an Jiaotong UniversityKey Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, School of Mechanical Engineering, Xi’an Jiaotong UniversityKey Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, School of Mechanical Engineering, Xi’an Jiaotong UniversityKey Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, School of Mechanical Engineering, Xi’an Jiaotong UniversityKey Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, School of Mechanical Engineering, Xi’an Jiaotong UniversityKey Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, School of Mechanical Engineering, Xi’an Jiaotong UniversityDepartment of Material Science and Engineering, University of California, BerkeleyAbstract 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 framework 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.https://doi.org/10.1186/s10033-025-01222-wTopology optimizationIntelligent predictionThermal conductivity structureGenerative adversarial networkInstantaneous prediction |
| spellingShingle | Qiyin Lin Feiyu Gu Chen Wang Hao Guan Tao Wang Kaiyi Zhou Lian Liu Desheng Yao Intelligent Design Method for Thermal Conductivity Topology Based on a Deep Generative Network Chinese Journal of Mechanical Engineering Topology optimization Intelligent prediction Thermal conductivity structure Generative adversarial network Instantaneous prediction |
| title | Intelligent Design Method for Thermal Conductivity Topology Based on a Deep Generative Network |
| title_full | Intelligent Design Method for Thermal Conductivity Topology Based on a Deep Generative Network |
| title_fullStr | Intelligent Design Method for Thermal Conductivity Topology Based on a Deep Generative Network |
| title_full_unstemmed | Intelligent Design Method for Thermal Conductivity Topology Based on a Deep Generative Network |
| title_short | Intelligent Design Method for Thermal Conductivity Topology Based on a Deep Generative Network |
| title_sort | intelligent design method for thermal conductivity topology based on a deep generative network |
| topic | Topology optimization Intelligent prediction Thermal conductivity structure Generative adversarial network Instantaneous prediction |
| url | https://doi.org/10.1186/s10033-025-01222-w |
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