Identification Method of Source Term Parameters of Nuclear Explosion Based on GA and PSO for Lagrange-Gaussian Puff Model
Many well-established models exist for predicting the dispersion of radioactive particles that will be generated in the surrounding environment after a nuclear weapon explosion. However, without exception, almost all models rely on accurate source term parameters, such as DELFIC, DNAF-1, and so on....
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| Format: | Article |
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MDPI AG
2023-05-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/14/5/877 |
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| author | Yang Zheng Yuyang Wang Longteng Wang Xiaolei Chen Lingzhong Huang Wei Liu Xiaoqiang Li Ming Yang Peng Li Shanyi Jiang Hao Yin Xinliang Pang Yunhui Wu |
| author_facet | Yang Zheng Yuyang Wang Longteng Wang Xiaolei Chen Lingzhong Huang Wei Liu Xiaoqiang Li Ming Yang Peng Li Shanyi Jiang Hao Yin Xinliang Pang Yunhui Wu |
| author_sort | Yang Zheng |
| collection | DOAJ |
| description | Many well-established models exist for predicting the dispersion of radioactive particles that will be generated in the surrounding environment after a nuclear weapon explosion. However, without exception, almost all models rely on accurate source term parameters, such as DELFIC, DNAF-1, and so on. Unlike nuclear experiments, accurate source term parameters are often not available once a nuclear weapon is used in a real nuclear strike. To address the problems of unclear source term parameters and meteorological conditions during nuclear weapon explosions and the complexity of the identification process, this article proposes a nuclear weapon source term parameter identification method based on a genetic algorithm (GA) and a particle swarm optimization algorithm (PSO) by combining real-time monitoring data. The results show that both the PSO and the GA are able to identify the source term parameters satisfactorily after optimization, and the prediction accuracy of their main source term parameters is above 98%. When the maximum number of iterations and population size of the PSO and GA were the same, the running time and optimization accuracy of the PSO were better than those of the GA. This study enriches the theory and method of radioactive particle dispersion prediction after a nuclear weapon explosion and is of great significance to the study of environmental radioactive particles. |
| first_indexed | 2024-03-11T03:57:21Z |
| format | Article |
| id | doaj.art-1036fe30602f483bba144b5164afe804 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-11T03:57:21Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-1036fe30602f483bba144b5164afe8042023-11-18T00:26:13ZengMDPI AGAtmosphere2073-44332023-05-0114587710.3390/atmos14050877Identification Method of Source Term Parameters of Nuclear Explosion Based on GA and PSO for Lagrange-Gaussian Puff ModelYang Zheng0Yuyang Wang1Longteng Wang2Xiaolei Chen3Lingzhong Huang4Wei Liu5Xiaoqiang Li6Ming Yang7Peng Li8Shanyi Jiang9Hao Yin10Xinliang Pang11Yunhui Wu12State Key Laboratory of NBC Protection for Civilian, Beijing 102205, ChinaCollege of Computer and Data Science, Fuzhou University, Fuzhou 350108, ChinaPeking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program, School of Life Sciences, Peking University, Beijing 100871, ChinaState Key Laboratory of NBC Protection for Civilian, Beijing 102205, ChinaState Key Laboratory of NBC Protection for Civilian, Beijing 102205, ChinaState Key Laboratory of NBC Protection for Civilian, Beijing 102205, ChinaState Key Laboratory of NBC Protection for Civilian, Beijing 102205, ChinaState Key Laboratory of NBC Protection for Civilian, Beijing 102205, ChinaState Key Laboratory of NBC Protection for Civilian, Beijing 102205, ChinaState Key Laboratory of NBC Protection for Civilian, Beijing 102205, ChinaState Key Laboratory of NBC Protection for Civilian, Beijing 102205, ChinaState Key Laboratory of NBC Protection for Civilian, Beijing 102205, ChinaState Key Laboratory of NBC Protection for Civilian, Beijing 102205, ChinaMany well-established models exist for predicting the dispersion of radioactive particles that will be generated in the surrounding environment after a nuclear weapon explosion. However, without exception, almost all models rely on accurate source term parameters, such as DELFIC, DNAF-1, and so on. Unlike nuclear experiments, accurate source term parameters are often not available once a nuclear weapon is used in a real nuclear strike. To address the problems of unclear source term parameters and meteorological conditions during nuclear weapon explosions and the complexity of the identification process, this article proposes a nuclear weapon source term parameter identification method based on a genetic algorithm (GA) and a particle swarm optimization algorithm (PSO) by combining real-time monitoring data. The results show that both the PSO and the GA are able to identify the source term parameters satisfactorily after optimization, and the prediction accuracy of their main source term parameters is above 98%. When the maximum number of iterations and population size of the PSO and GA were the same, the running time and optimization accuracy of the PSO were better than those of the GA. This study enriches the theory and method of radioactive particle dispersion prediction after a nuclear weapon explosion and is of great significance to the study of environmental radioactive particles.https://www.mdpi.com/2073-4433/14/5/877genetic algorithmparticle swarm optimizationnuclear explosionradioactive diffusion |
| spellingShingle | Yang Zheng Yuyang Wang Longteng Wang Xiaolei Chen Lingzhong Huang Wei Liu Xiaoqiang Li Ming Yang Peng Li Shanyi Jiang Hao Yin Xinliang Pang Yunhui Wu Identification Method of Source Term Parameters of Nuclear Explosion Based on GA and PSO for Lagrange-Gaussian Puff Model Atmosphere genetic algorithm particle swarm optimization nuclear explosion radioactive diffusion |
| title | Identification Method of Source Term Parameters of Nuclear Explosion Based on GA and PSO for Lagrange-Gaussian Puff Model |
| title_full | Identification Method of Source Term Parameters of Nuclear Explosion Based on GA and PSO for Lagrange-Gaussian Puff Model |
| title_fullStr | Identification Method of Source Term Parameters of Nuclear Explosion Based on GA and PSO for Lagrange-Gaussian Puff Model |
| title_full_unstemmed | Identification Method of Source Term Parameters of Nuclear Explosion Based on GA and PSO for Lagrange-Gaussian Puff Model |
| title_short | Identification Method of Source Term Parameters of Nuclear Explosion Based on GA and PSO for Lagrange-Gaussian Puff Model |
| title_sort | identification method of source term parameters of nuclear explosion based on ga and pso for lagrange gaussian puff model |
| topic | genetic algorithm particle swarm optimization nuclear explosion radioactive diffusion |
| url | https://www.mdpi.com/2073-4433/14/5/877 |
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