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Journal articles on the topic 'Source term estimation'

Author: Grafiati

Published: 25 May 2024

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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 demons

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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

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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

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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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11

Eslinger, Paul W., Jennifer M. Mendez, and Brian T. Schrom. "Source term estimation in the presence of nuisance signals." Journal of Environmental Radioactivity 203 (July 2019): 220–25. http://dx.doi.org/10.1016/j.jenvrad.2019.03.022.

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12

Bieringer, Paul E., George S. Young, Luna M. Rodriguez, Andrew J. Annunzio, Francois Vandenberghe, and Sue Ellen Haupt. "Paradigms and commonalities in atmospheric source term estimation methods." Atmospheric Environment 156 (May 2017): 102–12. http://dx.doi.org/10.1016/j.atmosenv.2017.02.011.

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13

Udiyani, Pande Made, Ihda Husnayani, Mohamad Budi Setiawan, Sri Kuntjoro, Hery Adrial, and Amir Hamzah. "ESTIMATION OF THE RADIOACTIVE SOURCE TERM FROM RDE ACCIDENT POSTULATION." JURNAL TEKNOLOGI REAKTOR NUKLIR TRI DASA MEGA 21, no. 3 (2019): 113. http://dx.doi.org/10.17146/tdm.2019.21.3.5583.

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The design process of Experimental Power Reactor (Reaktor Daya Eksperimental/RDE) has been carried out by BATAN for the last five years, adopting HTGR-type reactor with thermal power of 10 MW. RDE is designed with the reference of similar reactor, namely HTR-10. During this process, source term estimation is required to prove the safety of RDE design, as well as to fulfill the concept of As Low As Reasonably Achievable (ALARA) in radiation protection. The source term is affected by the magnitude of the radioactive substances released from the reactor core due to an accident. Conservative accid

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14

Zhao, Xiangyu, Kuang Cheng, Wang Zhou, Yi Cao, Shuang-hua Yang, and Jianmeng Chen. "Source term estimation with deficient sensors: A temporal augment approach." Process Safety and Environmental Protection 157 (January 2022): 131–39. http://dx.doi.org/10.1016/j.psep.2021.10.042.

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15

Xu, Jinjin, Wenli Du, Qiaoyi Xu, Jikai Dong, and Bing Wang. "Federated learning based atmospheric source term estimation in urban environments." Computers & Chemical Engineering 155 (December 2021): 107505. http://dx.doi.org/10.1016/j.compchemeng.2021.107505.

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16

Labahn, Jeffrey W., Cecile B. Devaud, Timothy A. Sipkens, and Kyle J. Daun. "Inverse analysis and regularisation in conditional source-term estimation modelling." Combustion Theory and Modelling 18, no. 3 (2014): 474–99. http://dx.doi.org/10.1080/13647830.2014.927076.

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17

Nayak, M. K., T. K. Sahu, Haridas G. Nair, et al. "Optimum target source term estimation for high energy electron accelerators." Radiation Physics and Chemistry 122 (May 2016): 77–81. http://dx.doi.org/10.1016/j.radphyschem.2016.01.026.

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18

Qiu, Sihang, Bin Chen, Zhengqiu Zhu, Yuan Wang, and Xiaogang Qiu. "Source term estimation using air concentration measurements during nuclear accident." Journal of Radioanalytical and Nuclear Chemistry 311, no. 1 (2016): 165–78. http://dx.doi.org/10.1007/s10967-016-4941-z.

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19

Li, Hui, and Jianwen Zhang. "Fast source term estimation using the PGA-NM hybrid method." Engineering Applications of Artificial Intelligence 62 (June 2017): 68–79. http://dx.doi.org/10.1016/j.engappai.2017.03.010.

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20

Bailly du Bois, P., P. Laguionie, D. Boust, I. Korsakissok, D. Didier, and B. Fiévet. "Estimation of marine source-term following Fukushima Dai-ichi accident." Journal of Environmental Radioactivity 114 (December 2012): 2–9. http://dx.doi.org/10.1016/j.jenvrad.2011.11.015.

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21

Eslinger, Paul W., Justin D. Lowrey, Harry S. Miley, W. Steven Rosenthal, and Brian T. Schrom. "Source term estimation using multiple xenon isotopes in atmospheric samples." Journal of Environmental Radioactivity 204 (August 2019): 111–16. http://dx.doi.org/10.1016/j.jenvrad.2019.04.004.

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22

Xue, Fei, Xiaofeng Li, Ryozo Ooka, Hideki Kikumoto, and Weirong Zhang. "Turbulent Schmidt number for source term estimation using Bayesian inference." Building and Environment 125 (November 2017): 414–22. http://dx.doi.org/10.1016/j.buildenv.2017.09.012.

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23

Bourne, Joseph R., Eric R. Pardyjak, and Kam K. Leang. "Coordinated Bayesian-Based Bioinspired Plume Source Term Estimation and Source Seeking for Mobile Robots." IEEE Transactions on Robotics 35, no. 4 (2019): 967–86. http://dx.doi.org/10.1109/tro.2019.2912520.

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24

Wang, Hanchen, Qiang Guo, Tariq Alkhalifah, and Zedong Wu. "Regularized elastic passive equivalent source inversion with full-waveform inversion: Application to a field monitoring microseismic data set." GEOPHYSICS 85, no. 6 (2020): KS207—KS219. http://dx.doi.org/10.1190/geo2019-0738.1.

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One of the key goals of microseismic processing is accurate estimation of the source location. Using full-waveform information in passive-source data sets can potentially delineate microseismic sources. The accuracy of the compressional-wave and shear-wave velocities has a strong influence on the estimation of source locations and hence the reliability of the fracture detection. We have adopted a methodology for passive source and velocity inversion, in which the conventional source term of the elastic wave equation is represented by an equivalent source. The equivalent source term is composed

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25

Kim, S. G., Y. T. Chen, Z. L. Wu, and G. F. Panza. "A mathematical theorem useful for the direct estimation of seismic source spectra." Bulletin of the Seismological Society of America 87, no. 5 (1997): 1281–87. http://dx.doi.org/10.1785/bssa0870051281.

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Abstract In this note, we prove a mathematical theorem for the direct estimation of the source spectra of earthquakes using a single seismogram. Considering a combination of Fourier spectra, the effects of the “source term” and the “attenuation term” from a single seismogram can be separated. Using signal-processing techniques, such an approach may be expressed in terms of the estimation of the Wigner distribution.

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26

YAMAZAWA, Hiromi. "Source Term Estimation Method Using Long-range Inverse Atmospheric Transport Simulation." Journal of the Atomic Energy Society of Japan / Atomic Energy Society of Japan 40, no. 11 (1998): 885–91. http://dx.doi.org/10.3327/jaesj.40.885.

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27

Fang, Xiao Hang, Riyaz Ismail, Kendal Bushe, and Martin Davy. "Simulation of ECN diesel spray A using conditional source-term estimation." Combustion Theory and Modelling 24, no. 4 (2020): 725–60. http://dx.doi.org/10.1080/13647830.2020.1752942.

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28

Jurczyk, Anna, Katarzyna Ośródka, Jan Szturc, Magdalena Pasierb, and Agnieszka Kurcz. "Long-term multi-source precipitation estimation with high resolution (RainGRS Clim)." Atmospheric Measurement Techniques 16, no. 17 (2023): 4067–79. http://dx.doi.org/10.5194/amt-16-4067-2023.

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Abstract. This paper explores the possibility of using multi-source precipitation estimates for climatological applications. A data-processing algorithm (RainGRS Clim) has been developed to work on precipitation accumulations such as daily or monthly totals, which are significantly longer than operational accumulations (generally between 5 min and 1 h). The algorithm makes the most of additional opportunities, such as the possibility of complementing data with delayed data, access to high-quality data that are not operationally available, and the greater efficiency of the algorithms for data q

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29

Brenden, Travis O., Iyob Tsehaye, James R. Bence, Jeannette Kanefsky, and Kim T. Scribner. "Indexing recruitment for source populations contributing to mixed fisheries by incorporating age in genetic stock identification models." Canadian Journal of Fisheries and Aquatic Sciences 75, no. 6 (2018): 934–54. http://dx.doi.org/10.1139/cjfas-2016-0525.

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We describe a methodology for estimating relative recruitments for source populations (sources) contributing to mixed fisheries by incorporating age into genetic stock identification models. The approach produced recruitment estimates that were strongly correlated (median correlation = 0.849; 2.5 and 97.5 percentile in correlations = 0.613 and 0.951, respectively) with simulated recruitments across various design factors, including number of sources, genetic divergence among sources, and temporal variation in source recruitments. Sensitivity analyses indicated that the approach was robust to a

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30

Aria, Amin, Enrique Lopez Droguett, Shapour Azarm, and Mohammad Modarres. "Estimating damage size and remaining useful life in degraded structures using deep learning-based multi-source data fusion." Structural Health Monitoring 19, no. 5 (2019): 1542–59. http://dx.doi.org/10.1177/1475921719890616.

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In this article, a new deep learning-based approach for online estimation of damage size and remaining useful life of structures is presented. The proposed approach consists of three modules. In the first module, a long short-term memory regression model is used to construct a sensor-based estimation of the damage size where different ranges of temporal correlations are considered for their effects on the accuracy of the damage size estimations. In the second module, a convolutional neural network semantic image segmentation approach is used to construct automated damage size estimations in wh

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31

Rahbar, Faezeh, Ali Marjovi, and Alcherio Martinoli. "Design and Performance Evaluation of an Algorithm Based on Source Term Estimation for Odor Source Localization." Sensors 19, no. 3 (2019): 656. http://dx.doi.org/10.3390/s19030656.

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Finding sources of airborne chemicals with mobile sensing systems finds applications across safety, security, environmental monitoring, and medical domains. In this paper, we present an algorithm based on Source Term Estimation for odor source localization that is coupled with a navigation method based on partially observable Markov decision processes. We propose a novel strategy to balance exploration and exploitation in navigation. Moreover, we study two variants of the algorithm, one exploiting a global and the other one a local framework. The method was evaluated through high-fidelity simu

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32

Lin, Minghao, Qiang Fang, Jizhe Xia, and Chenyang Xu. "Air Temperature Monitoring over Low Latitude Rice Planting Areas: Combining Remote Sensing, Model Assimilation, and Machine Learning Techniques." Remote Sensing 15, no. 15 (2023): 3805. http://dx.doi.org/10.3390/rs15153805.

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Air temperature (Ta) is essential for studying surface processes and human activities, particularly agricultural cultivation, which is strongly influenced by temperature. Remote sensing techniques that integrate multi-source data can estimate Ta with a high degree of accuracy, overcoming the shortcomings of traditional measurements due to spatial heterogeneity. Based on in situ measurements in Guangdong Province from 2012 to 2018, this study applied three machine learning (ML) models and fused multi-source datasets to evaluate the performance of four data combinations in Ta estimation. Correla

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33

Turbelin, Grégory, Sarvesh Singh, Jean Pierre Issartel, Xavier Busch, and Pramod Kumar. "Computation of Optimal Weights for Solving the Atmospheric Source Term Estimation Problem." Journal of Atmospheric and Oceanic Technology 36, no. 6 (2019): 1053–61. http://dx.doi.org/10.1175/jtech-d-18-0145.1.

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AbstractIn case of a release of a hazardous material (e.g., a chemical or a biological agent) in the atmosphere, estimation of the source from concentration observations (provided by a network of sensors) is a challenging inverse problem known as the atmospheric source term estimation (STE) problem. This study emphasizes a method, known in the literature as the renormalization inversion technique, for addressing this problem. This method provides a solution that has been interpreted as a weighted minimal norm solution and can be computed in terms of a generalized inverse of the sensitivity mat

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34

Denzler, Basil, Christian Bogdal, Cyrill Kern, Anna Tobler, Jing Huo, and Konrad Hungerbühler. "Urban source term estimation for mercury using a boundary-layer budget method." Atmospheric Chemistry and Physics 19, no. 6 (2019): 3821–31. http://dx.doi.org/10.5194/acp-19-3821-2019.

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Abstract. Mercury is a heavy metal of particular concern due to its adverse effects on human health and the environment. Recognizing this problem, the UN Minamata Convention on Mercury was recently adopted, where signatory countries agreed to reduce anthropogenic mercury emissions. To evaluate the effectiveness of the convention, quantitative knowledge on mercury emissions is crucial. So far, bottom-up approaches have successfully been applied to quantify mercury emission – especially for point sources. Distributed sources make up a large share of the emission; however, they are still poorly c

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35

Liu, Zhenzhe, and Xiaofeng Li. "The impact of sensor layout on Source Term Estimation in urban neighborhood." Building and Environment 213 (April 2022): 108859. http://dx.doi.org/10.1016/j.buildenv.2022.108859.

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36

Tsui, Hong Pak, and William Kendal Bushe. "Conditional Source-term Estimation using dynamic ensemble selection and parallel iterative solution." Combustion Theory and Modelling 20, no. 5 (2016): 812–33. http://dx.doi.org/10.1080/13647830.2016.1178811.

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37

Jensen, Derek D., Donald D. Lucas, Katherine A. Lundquist, and Lee G. Glascoe. "Sensitivity of a Bayesian source-term estimation model to spatiotemporal sensor resolution." Atmospheric Environment: X 3 (July 2019): 100045. http://dx.doi.org/10.1016/j.aeaoa.2019.100045.

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38

Oliveira, André José Pereira de, Luiz Alberto da Silva Abreu, and Diego Campos Knupp. "Explicit scheme based on integral transforms for estimation of source terms in diffusion problems in heterogeneous media." Journal of Engineering and Exact Sciences 9, no. 10 (2023): 17811. http://dx.doi.org/10.18540/jcecvl9iss10pp17811.

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The estimation of source terms present in differential equations has various applications, ranging from structural assessment, industrial process monitoring, equipment failure detection, environmental pollution source detection to identification applications in medicine. Significant progress has been made in recent years in methodologies capable of estimating this parameter. This work employs a methodology based on an explicit formulation of the integral transformation to characterize the unknown source term, reconstructing it through the expansion in known eigenfunctions of the Sturm-Liouvill

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39

Tichý, Ondřej, Lukáš Ulrych, Václav Šmídl, Nikolaos Evangeliou, and Andreas Stohl. "On the tuning of atmospheric inverse methods: comparisons with the European Tracer Experiment (ETEX) and Chernobyl datasets using the atmospheric transport model FLEXPART." Geoscientific Model Development 13, no. 12 (2020): 5917–34. http://dx.doi.org/10.5194/gmd-13-5917-2020.

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Abstract. Estimation of the temporal profile of an atmospheric release, also called the source term, is an important problem in environmental sciences. The problem can be formalized as a linear inverse problem wherein the unknown source term is optimized to minimize the difference between the measurements and the corresponding model predictions. The problem is typically ill-posed due to low sensor coverage of a release and due to uncertainties, e.g., in measurements or atmospheric transport modeling; hence, all state-of-the-art methods are based on some form of regularization of the problem us

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40

Selvi B., Dora Arul, and Kannan S. "A Performance Comparison of Machine Learning Methods For Short-Range Wind Power Estimation." E3S Web of Conferences 387 (2023): 05013. http://dx.doi.org/10.1051/e3sconf/202338705013.

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Renewable energy generation is increasingly employed nowadays for multitudes of reasons such as global warming, depletion of conventional sources of energy and emission constraints. Even though the wind generators constitute a potential source of energy, the uncertainties associated with them make the operation complex. As a consequence, the successful operation and planning of the present distributed generation dominated power systems requires exact estimate of wind power. Numerous wind power estimation techniques based on Machine Learning were available. This work attempts to compare the win

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41

Tølløse, Kasper Skjold, and Jens Havskov Sørensen. "Bayesian Inverse Modelling for Probabilistic Multi-Nuclide Source Term Estimation Using Observations of Air Concentration and Gamma Dose Rate." Atmosphere 13, no. 11 (2022): 1877. http://dx.doi.org/10.3390/atmos13111877.

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In case of a release of hazardous radioactive matter to the atmosphere from e.g., a nuclear power plant accident, atmospheric dispersion models are used to predict the spatial distribution of radioactive particles and gasses. However, at the early stages of an accident, only limited information about the release may be available. Thus, there is a need for source term estimation methods suitable for operational use shortly after an accident. We have developed a Bayesian inverse method for estimating the multi-nuclide source term describing a radioactive release from a nuclear power plant. The m

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42

Faes, Axel, Iris Vantieghem, and Marc M. Van Hulle. "Neural Networks for Directed Connectivity Estimation in Source-Reconstructed EEG Data." Applied Sciences 12, no. 6 (2022): 2889. http://dx.doi.org/10.3390/app12062889.

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Directed connectivity between brain sources identified from scalp electroencephalography (EEG) can shed light on the brain’s information flows and provide a biomarker of neurological disorders. However, as volume conductance results in scalp activity being a mix of activities originating from multiple sources, the correct interpretation of their connectivity is a formidable challenge despite source localization being applied with some success. Traditional connectivity approaches rely on statistical assumptions that usually do not hold for EEG, calling for a model-free approach. We investigated

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43

Ryan, Sydney D., and Chris J. Arisman. "Uncertainty quantification of steady and transient source term estimation in an urban environment." Environmental Fluid Mechanics 21, no. 3 (2021): 713–40. http://dx.doi.org/10.1007/s10652-021-09794-6.

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44

Ham, Jaehyun, Sang Ho Kim, Sung Il Kim, et al. "Source Term Estimation under the SBLOCA-Induced Severe Accident Condition in the SMART." Science and Technology of Nuclear Installations 2021 (April 12, 2021): 1–14. http://dx.doi.org/10.1155/2021/6686615.

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The SMART is a system-integrated modular reactor in which a nuclear steam supply system with a thermal power of 365 MW is contained inside of the reactor vessel. Although the probability is very low, the reactor core can be damaged during a small break loss-of-coolant accident when both the passive safety injection system and the passive residual heat removal system are completely unavailable. In this work, a total of five cases were analyzed considering the reactor vessel condition and the availability of the radioactivity removal tanks and the ancillary containment spray system as containmen

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45

Septier, François, Patrick Armand, and Christophe Duchenne. "Sequential Monte Carlo sampler applied to source term estimation in complex atmospheric environments." Atmospheric Environment 269 (January 2022): 118822. http://dx.doi.org/10.1016/j.atmosenv.2021.118822.

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46

Zhou, Wang, Xiangyu Zhao, Kuang Cheng, Yi Cao, Shuang-Hua Yang, and Jianmeng Chen. "Source term estimation with deficient sensors: Error analysis and mobile station route design." Process Safety and Environmental Protection 154 (October 2021): 97–103. http://dx.doi.org/10.1016/j.psep.2021.08.001.

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47

Liu, Suning, Haiyun Shi, and Bellie Sivakumar. "Long-term mean river discharge estimation with multi-source grid-based global datasets." Stochastic Environmental Research and Risk Assessment 36, no. 2 (2021): 679–91. http://dx.doi.org/10.1007/s00477-021-02128-4.

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48

Zhuk, Sergiy, Tigran T. Tchrakian, Stephen Moore, Rodrigo Ordón͂ez-Hurtado, and Robert Shorten. "On Source-Term Parameter Estimation for Linear Advection-Diffusion Equations with Uncertain Coefficients." SIAM Journal on Scientific Computing 38, no. 4 (2016): A2334—A2356. http://dx.doi.org/10.1137/15m1034829.

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49

Kim, Minsik, Ryohji Ohba, Masamichi Oura, et al. "A source term estimation method for a nuclear accident using atmospheric dispersion models." International Journal of Environment and Pollution 58, no. 1/2 (2015): 39. http://dx.doi.org/10.1504/ijep.2015.076582.

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50

Tinarelli, Gianni, Francesco Uboldi, and Giuseppe Carlino. "Source term estimation using an adjoint model: a comparison of two different algorithms." International Journal of Environment and Pollution 64, no. 1/2/3 (2018): 209. http://dx.doi.org/10.1504/ijep.2018.099157.

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