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Relevant bibliographies by topics / Source term estimation

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Journal articles

Academic literature on the topic 'Source term estimation'

Author: Grafiati

Published: 25 May 2024

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Journal articles on the topic "Source term estimation"

1

Long, Kerrie J., Sue Ellen Haupt, and George S. Young. "Assessing sensitivity of source term estimation." Atmospheric Environment 44, no. 12 (2010): 1558–67. http://dx.doi.org/10.1016/j.atmosenv.2010.01.003.

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2

Gudiksen, P. H., T. F. Harvey, and R. Lange. "Chernobyl Source Term, Atmospheric Dispersion, and Dose Estimation." Health Physics 57, no. 5 (1989): 697–706. http://dx.doi.org/10.1097/00004032-198911000-00001.

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3

Bushe, W. Kendal, and Helfried Steiner. "Laminar flamelet decomposition for conditional source-term estimation." Physics of Fluids 15, no. 6 (2003): 1564. http://dx.doi.org/10.1063/1.1569483.

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4

Loewenthal, Dan, and Vladimir Shtivelman. "Source signature estimation using fictitious source and reflector." GEOPHYSICS 54, no. 7 (1989): 916–20. http://dx.doi.org/10.1190/1.1442721.

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Source wavelet estimation is an important step in processing and interpreting seismic data. In the context of this work, the term (source wavelet) includes the pure source signature (the source response measured in a homogeneous medium) along with certain model‐related events (such as the ghost and interbed reflections). An estimate of the source wavelet can be used to increase the resolution of seismic data by signature deconvolution, deghosting, and dereverberation. However, pure source signature determination is of particular importance as a first step in direct inversion schemes, as demonstrated by Bube and Burridge (1983) and by Foster and Carrion (1985).

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5

Lu, Jinshu, Mengqing Huang, Wenfeng Wu, Yonghui Wei, and Chong Liu. "Application and Improvement of the Particle Swarm Optimization Algorithm in Source-Term Estimations for Hazardous Release." Atmosphere 14, no. 7 (2023): 1168. http://dx.doi.org/10.3390/atmos14071168.

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Hazardous gas release can pose severe hazards to the ecological environment and public safety. The source-term estimation of hazardous gas leakage serves a crucial role in emergency response and safety management practices. Nevertheless, the precision of a forward diffusion model and atmospheric diffusion conditions have a significant impact on the performance of the method for estimating source terms. This work proposes the particle swarm optimization (PSO) algorithm coupled with the Gaussian dispersion model for estimating leakage source parameters. The method is validated using experimental cases of the prairie grass field dispersion experiment with various atmospheric stability classes. The results prove the effectiveness of this method. The effects of atmospheric diffusion conditions on estimation outcomes are also investigated. The estimated effect in extreme atmospheric diffusion conditions is not as good as in other diffusion conditions. Accordingly, the Gaussian dispersion model is improved by adding linear and polynomial correction coefficients to it for its inapplicability under extreme diffusion conditions. Finally, the PSO method coupled with improved models is adapted for the source-term parameter estimation. The findings demonstrate that the estimation performance of the PSO method coupled with improved models is significantly improved. It was also found that estimated performances of source parameters of two correction models were significantly distinct under various atmospheric stability classes. There is no single optimal model; however, the model can be selected according to practical diffusion conditions to enhance the estimated precision of source-term parameters.

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6

Jing, Yuanqi, Zhonglin Gu, Fei Li, and Kai Zhang. "Gaseous Pollutent Source Term Estimation Based on Adjoint Probability and Regularization Method." E3S Web of Conferences 356 (2022): 05048. http://dx.doi.org/10.1051/e3sconf/202235605048.

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Fast and accurate identification of source locations and release rates is particularly important for improving indoor air quality and ensuring the safety and health of people. Existing methods based on adjoint probability are difficult to distinguish the release rate of dynamic sources, and optimization algorithms based on regularization are limited to analysing only a small amount of potential pollutant source information. Therefore, this study proposed an algorithm combining adjoint equations and regularization models to identify the location and release intensity of pollutant sources in the entire computational domain of a room. Based on the validated indoor CFD computational model, we first obtained a series of response matrices corresponding to the sensor position by solving the adjoint equation, and then used the regularization method and Bayesian inference to extrapolate the release rate and location of dynamic pollutant source in the room. The results shown that the proposed algorithm is convenient and feasible to identify the location and intensity of the indoor pollutant source. Compared with the real source intensity, the identification of constant source intensity is lower than the error threshold (10%) in 97.4% of the time nodes, and the identification of periodic source is lower than the error threshold (10%) in 95.4% of the time nodes. This research provides a new method and perspective for the estimation of indoor pollutant source information.

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7

Cheng, Kuang, Xiangyu Zhao, Wang Zhou, Yi Cao, Shuang-Hua Yang, and Jianmeng Chen. "Source term estimation with deficient sensors: Traceability and an equivalent source approach." Process Safety and Environmental Protection 152 (August 2021): 131–39. http://dx.doi.org/10.1016/j.psep.2021.05.035.

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8

Nayak, M. K., T. K. Sahu, H. G. Nair, et al. "Bremsstrahlung source term estimation for high energy electron accelerators." Radiation Physics and Chemistry 113 (August 2015): 1–5. http://dx.doi.org/10.1016/j.radphyschem.2015.04.004.

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9

Li, Hui, Jianwen Zhang, and Junkai Yi. "Computational source term estimation of the Gaussian puff dispersion." Soft Computing 23, no. 1 (2018): 59–75. http://dx.doi.org/10.1007/s00500-018-3440-2.

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10

Mazzini, Guido, Tadas Kaliatka, Maria Teresa Porfiri, Luigi Antonio Poggi, Andrea Malizia, and Pasqualino Gaudio. "Methodology of the source term estimation for DEMO reactor." Fusion Engineering and Design 124 (November 2017): 1199–202. http://dx.doi.org/10.1016/j.fusengdes.2017.04.101.

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