Biao Wang, Meihua Fang, Dingyi Song, Jianfei Cheng, Kang Wu
{"title":"Rapid assessment of cosmic radiation exposure in aviation based on BP neural network method.","authors":"Biao Wang, Meihua Fang, Dingyi Song, Jianfei Cheng, Kang Wu","doi":"10.1093/rpd/ncae126","DOIUrl":null,"url":null,"abstract":"<p><p>Cosmic radiation exposure is one of the important health concerns for aircrews. In this work, we constructed a back propagation neural network model for the real-time and rapid assessment of cosmic radiation exposure to the public in aviation. The multi-dimensional dataset for this neural network was created from modeling the process of cosmic ray transportation in magnetic field by geomagnetic cutoff rigidity method and air shower simulation by a Monte Carlo based Geant4 code. The dataset was characterized by parameters including cosmic ray energy spectrum, Kp-index, coordinated universal time, altitude, latitude, and longitude. The effective dose and dose rate was finally converted from the particle fluxes at flight position by the neural network. This work shows a good agreement with other models from International Civil Aviation Organization. It is also illustrated that the effective dose rate by galactic cosmic ray is <10 μSv h-1 and the value during ground level enhancement (GLE) 42 is 4 ~ 10 times larger on the routes calculated in this work. In GLE 69, the effective dose rate reaches several mSv h-1 in the polar region. Based on this model, a real-time warning system is achieved.</p>","PeriodicalId":20795,"journal":{"name":"Radiation protection dosimetry","volume":null,"pages":null},"PeriodicalIF":0.8000,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radiation protection dosimetry","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1093/rpd/ncae126","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Cosmic radiation exposure is one of the important health concerns for aircrews. In this work, we constructed a back propagation neural network model for the real-time and rapid assessment of cosmic radiation exposure to the public in aviation. The multi-dimensional dataset for this neural network was created from modeling the process of cosmic ray transportation in magnetic field by geomagnetic cutoff rigidity method and air shower simulation by a Monte Carlo based Geant4 code. The dataset was characterized by parameters including cosmic ray energy spectrum, Kp-index, coordinated universal time, altitude, latitude, and longitude. The effective dose and dose rate was finally converted from the particle fluxes at flight position by the neural network. This work shows a good agreement with other models from International Civil Aviation Organization. It is also illustrated that the effective dose rate by galactic cosmic ray is <10 μSv h-1 and the value during ground level enhancement (GLE) 42 is 4 ~ 10 times larger on the routes calculated in this work. In GLE 69, the effective dose rate reaches several mSv h-1 in the polar region. Based on this model, a real-time warning system is achieved.
期刊介绍:
Radiation Protection Dosimetry covers all aspects of personal and environmental dosimetry and monitoring, for both ionising and non-ionising radiations. This includes biological aspects, physical concepts, biophysical dosimetry, external and internal personal dosimetry and monitoring, environmental and workplace monitoring, accident dosimetry, and dosimetry related to the protection of patients. Particular emphasis is placed on papers covering the fundamentals of dosimetry; units, radiation quantities and conversion factors. Papers covering archaeological dating are included only if the fundamental measurement method or technique, such as thermoluminescence, has direct application to personal dosimetry measurements. Papers covering the dosimetric aspects of radon or other naturally occurring radioactive materials and low level radiation are included. Animal experiments and ecological sample measurements are not included unless there is a significant relevant content reason.