Owing to the inherent limitation of the internal pulse ionization chamber within the AlphaGUARD PQ2000 radon monitor,that is,its inability to discriminate the energy levels of α particles,the ingress of^(220)Rn from ...Owing to the inherent limitation of the internal pulse ionization chamber within the AlphaGUARD PQ2000 radon monitor,that is,its inability to discriminate the energy levels of α particles,the ingress of^(220)Rn from the surrounding environment,along with its decay progeny,poses a substantive challenge in accurately determining the^(222)Rn concentration in the measurement outcomes.Among these,the protracted influence primarily stems from the two enduring decay progenies,namely^(212)Pb with a half-life of 10.64 h and^(212)Bi with a half-life of 60.54 min.This study explored the influence of^(220)Rn progeny on the measurement results of an AlphaGUARD PQ2000 radon monitor by developing a theoretical calculation model.The response coefficient related to the residual^(220)Rn progeny within the AlphaGUARD PQ2000 radon monitor was experimentally validated.In addition,this study investigated the effects of temperature and wind speed on the sensitivity of the instrument to^(220)Rn gas.The research findings revealed commendable agreement between the experimentally measured response coefficients of the residual^(220)Rn progeny and the corresponding values derived from the theoretical model.Notably,both the response coefficients of the AlphaGUARD PQ2000 radon monitor to^(220)Rn gas and its internal residual^(220)Rn progeny increased with elevated temperatures and increased wind speeds,providing a reference for correcting the impact of^(220)Rn and its progeny on the measurement results of^(222)Rn concentration obtained using the AlphaGUARD PQ2000 radon monitor.展开更多
Physics-informed neural networks(PINNs)are vital for machine learning and exhibit significant advantages when handling complex physical problems.The PINN method can rapidly predict ^(220)Rn progeny concentration and i...Physics-informed neural networks(PINNs)are vital for machine learning and exhibit significant advantages when handling complex physical problems.The PINN method can rapidly predict ^(220)Rn progeny concentration and is very important for regulating and measuring this property.To construct a PINN model,training data are typically preprocessed;however,this approach changes the physical characteristics of the data,with the preprocessed data potentially no longer directly conforming to the original physical equations.As a result,the original physical equations cannot be directly employed in the PINN.Consequently,an effective method for transforming physical equations is crucial for accurately constraining PINNs to model the ^(220)Rn progeny concentration prediction.This study presents an equation adaptation approach for neural networks,which is designed to improve prediction of ^(220)Rn progeny concentration.Five neural network models based on three architectures are established:a classical network,a physics-informed network without equation adaptation,and a physics-informed network with equation adaptation.The transport equation of the ^(220)Rn progeny concentration is transformed via equation adaption and integrated with the PINN model.The compatibility and robustness of the model with equation adaption is then analyzed.The results show that PINNs with equation adaption converge consistently with classical neural networks in terms of the training and validation loss and achieve the same level of prediction accuracy.This outcome indicates that the proposed method can be integrated into the neural network architecture.Moreover,the prediction performance of classical neural networks declines significantly when interference data are encountered,whereas the PINNs with equation adaption exhibit stable prediction accuracy.This performance demonstrates that the proposed method successfully harnesses the constraining power of physical equations,significantly enhancing the robustness of the resultant PINN models.Thus,the use of a physics-informed network with equation adaption can guarantee accurate prediction of ^(220)Rn progeny concentration.展开更多
基金supported by the National Natural Science Foundation of China(No.12175102)Hunan Provincial Natural Science Foundation(No.2022JJ40346)the 2022 Hunan Provincial University Student Innovation and Entrepreneurship Training Program(No.S202210555144).
文摘Owing to the inherent limitation of the internal pulse ionization chamber within the AlphaGUARD PQ2000 radon monitor,that is,its inability to discriminate the energy levels of α particles,the ingress of^(220)Rn from the surrounding environment,along with its decay progeny,poses a substantive challenge in accurately determining the^(222)Rn concentration in the measurement outcomes.Among these,the protracted influence primarily stems from the two enduring decay progenies,namely^(212)Pb with a half-life of 10.64 h and^(212)Bi with a half-life of 60.54 min.This study explored the influence of^(220)Rn progeny on the measurement results of an AlphaGUARD PQ2000 radon monitor by developing a theoretical calculation model.The response coefficient related to the residual^(220)Rn progeny within the AlphaGUARD PQ2000 radon monitor was experimentally validated.In addition,this study investigated the effects of temperature and wind speed on the sensitivity of the instrument to^(220)Rn gas.The research findings revealed commendable agreement between the experimentally measured response coefficients of the residual^(220)Rn progeny and the corresponding values derived from the theoretical model.Notably,both the response coefficients of the AlphaGUARD PQ2000 radon monitor to^(220)Rn gas and its internal residual^(220)Rn progeny increased with elevated temperatures and increased wind speeds,providing a reference for correcting the impact of^(220)Rn and its progeny on the measurement results of^(222)Rn concentration obtained using the AlphaGUARD PQ2000 radon monitor.
基金supported by the National Natural Science Foundation of China(Nos.12375310,118750356,and 62006110)Graduate Research and Innovation Projects of Hunan Province(CX20230964).
文摘Physics-informed neural networks(PINNs)are vital for machine learning and exhibit significant advantages when handling complex physical problems.The PINN method can rapidly predict ^(220)Rn progeny concentration and is very important for regulating and measuring this property.To construct a PINN model,training data are typically preprocessed;however,this approach changes the physical characteristics of the data,with the preprocessed data potentially no longer directly conforming to the original physical equations.As a result,the original physical equations cannot be directly employed in the PINN.Consequently,an effective method for transforming physical equations is crucial for accurately constraining PINNs to model the ^(220)Rn progeny concentration prediction.This study presents an equation adaptation approach for neural networks,which is designed to improve prediction of ^(220)Rn progeny concentration.Five neural network models based on three architectures are established:a classical network,a physics-informed network without equation adaptation,and a physics-informed network with equation adaptation.The transport equation of the ^(220)Rn progeny concentration is transformed via equation adaption and integrated with the PINN model.The compatibility and robustness of the model with equation adaption is then analyzed.The results show that PINNs with equation adaption converge consistently with classical neural networks in terms of the training and validation loss and achieve the same level of prediction accuracy.This outcome indicates that the proposed method can be integrated into the neural network architecture.Moreover,the prediction performance of classical neural networks declines significantly when interference data are encountered,whereas the PINNs with equation adaption exhibit stable prediction accuracy.This performance demonstrates that the proposed method successfully harnesses the constraining power of physical equations,significantly enhancing the robustness of the resultant PINN models.Thus,the use of a physics-informed network with equation adaption can guarantee accurate prediction of ^(220)Rn progeny concentration.
