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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 (April 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 (November 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 (July 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 (July 19, 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, R. V. Nandedkar, Tapas Bandyopadhyay, R. M. Tripathi, P. R. Hannurkar, and D. N. Sharma. "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 (August 4, 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 (November 11, 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 accident postulations on the RDE are water ingress and depressurization accidents. Based on these postulations, source term estimation was performed. It follows the mechanistic source term flow, with conservative assumptions for the radioactive release of fuel into the coolant, reactor building, and finally discharged into the environment. Assumptions for the calculation are taken from conservative removable parameters.The result of source term calculation due to the water ingress accident for Xe-133 noble gas is 8.97E+12 Bq, Cs-137 is 3.59E+07 Bq, and I-131 is 4.34E+10 Bq. As for depressurization accident, the source term activity for Xe-133 is 3.90E+13Bq, Cs-137 is 1.56E+07 Bq, and I-131 is 1.89E+10Bq. The source term calculation results obtained in this work shows a higher number compared to the HTR-10 source term used as a reference. The difference is possibly due to the differences in reactor inventory calculations and the more conservative assumptions for source term calculation.Keywords: RDE, HTGR, Radioactive, Source term, accident

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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 (May 4, 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, R. V. Nandedkar, Tapas Bandyopadhyay, R. M. Tripathi, and P. R. Hannurkar. "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 (July 15, 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 (August 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 (November 1, 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 of source images and source functions because it is inspired by elastic reflection waveform inversion. Thus, we update the source locations, source functions, and velocities simultaneously by using a waveform inversion scheme. In the 2D isotropic case, the source terms are defined by two source image components and three source function components. They provide an alternative representation of the source mechanism, usually defined by the moment tensor. Waveform inversion of passive events has severe nonlinearity due to the unknown source locations in space and their functions in time. We have thus used a source-independent objective function, based on convolving reference traces with modeled and observed data, to avoid cycle skipping caused by the unknown sources. We first synthetically examined our method on a modified Marmousi model. Then, by applying a nested inversion for these variables, our method also produces good estimation of the source and background velocity for real microseismic monitoring data. We use a ball-drop event to test the accuracy because the inverted source location should match the ball-seat location. For the uncontrolled events, the estimated source distribution using waveform inversion agrees with the local stress potential information. Although our method has a higher computational cost than traveltime- or migration-based methods, the estimated event locations have significantly improved accuracy.

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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 (October 1, 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 (April 17, 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 (September 11, 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 quality control and merging with longer accumulations. Verification of the developed algorithms was carried out using monthly accumulations through comparison with precipitation from manual rain gauges. As a result, monthly accumulations estimated by RainGRS Clim were found to be significantly more reliable than accumulations generated operationally. This improvement is particularly noticeable for the winter months, when precipitation estimation is much more difficult due to less reliable radar estimates.

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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 (June 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 aging inaccuracies and assumed source mortalities. Application to walleye (Sander vitreus) sources contributing to the Saginaw Bay, Lake Huron, fishery produced similar recruitment estimates to assessment models. There was greater discrepancy between recruitment estimates for lake trout (Salvelinus namaycush) hatchery strains in northern Lake Michigan when compared with strain stocking levels, although this mismatch may stem from stocking levels being a poor recruitment measure. The estimation approach should prove beneficial for indexing source recruitment based on fishery or assessment collections from mixtures, even when long-term time series of harvest and survey data required for integrated assessments are not available.

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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 (November 29, 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 which a pixel-wise classification is carried out on images of the damaged areas. Using physics-of-failure relations, frequency mismatches associated with sensor- and image-based size estimations are resolved. Finally, in the third module, damage size estimations obtained by the first two modules are fused together for an online remaining useful life estimation of the structure. Performance of the proposed approach is evaluated using sensor and image data obtained from a set of fatigue crack experiments performed on aluminum alloy 7075-T6 specimens. It is shown that using acoustic emission signals obtained from sensors and microscopic images in these experiments, the damage size estimations obtained from the proposed data fusion approach have higher accuracy than the sensor-based and higher frequency than the image-based estimations. Moreover, the accuracy of the data fusion estimations is found to be more than that of image-based estimations for the experiment with the largest sensor dataset. Based on the results obtained, it is concluded that the consideration of longer temporal correlations can lead to improvements in the accuracy of crack size estimations and, thus, a better remaining useful life estimation for structures.

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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 (February 5, 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 simulations and in a wind tunnel emulating a quasi-laminar air flow in a controlled environment, in particular by systematically investigating the impact of multiple algorithmic and environmental parameters (wind speed and source release rate) on the overall performance. The outcome of the experiments showed that the algorithm is robust to different environmental conditions in the global framework, but, in the local framework, it is only successful in relatively high wind speeds. In the local framework, on the other hand, the algorithm is less demanding in terms of energy consumption as it does not require any absolute positioning information from the environment and the robot travels less distance compared to the global framework.

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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 (July 31, 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. Correlations of covariates were compared, focusing on rice planting areas (RA). The results showed that (1) The fusion of multi-source data improved the accuracy of model estimations, where the best performance was achieved by the random forest (RF) model combined with the ERA5 combination, with the highest R2 reaching 0.956, the MAE value of 0.996 °C, and the RMSE of 1.365 °C; (2) total precipitation (TP), wind speed (WD), normalized difference vegetation index (NDVI), and land surface temperature (LST) were significant covariates for long-term Ta estimations; (3) Rice planting improved the model performance in estimating Ta, and model accuracy decreased during the crop rotation in summer. This study provides a reference for the selection of temperature estimation models and covariate datasets. It offers a case for subsequent ML studies on remote sensing of temperatures over agricultural areas and the impact of agricultural cultivation on global warming.

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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 (June 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 matrix of the sensors. This inverse is constructed by using an appropriate diagonal weight matrix whose components fulfill the so-called renormalizing conditions. The main contribution of this paper is that it proposes a new compact algorithm (it requires less than 15 lines of MATLAB code) to obtain, in a fast and efficient way, those optimal weights. To show that the algorithm, based on the properties of the resolution matrix, matches the requirements of emergency situations, analysis of the computational complexity and memory requirements is included. Some numerical experiments are also reported to show the efficiency of the algorithm.

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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 (March 25, 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 characterized. Here, we present a top-down approach to estimate mercury emissions based on atmospheric measurements in the city of Zurich, Switzerland. While monitoring the atmospheric mercury concentrations during inversion periods in Zurich, we were able to relate the concentration increase to the mercury emission strength of the city using a box model. By means of this boundary-layer budget approach, we succeeded in narrowing down the emissions of Zurich to range between 41±8 kg a−1 (upper bound) and 24±8 kg a−1 (lower bound). Thereby, we could quantify emissions from mixed, diffuse and point-like sources and derive an annual mercury per capita emission of 0.06 to 0.10 g a−1. The approach presented here has the potential to support authorities in setting up inventories and to validate emission estimations derived from the commonly applied bottom-up approaches. Furthermore, our method is applicable to other compounds and to a wide range of cities or other areas, where sources or sinks for mercury and other atmospheric pollutants are presumed.

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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 (June 10, 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 (December 29, 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-Liouville eigenvalue problem. To achieve this, a linear model is considered in a heterogeneous medium with known and spatially varying physical properties and two heat sources, with both temporal and spatial dependencies, and only spatial dependence. The eigenvalue problem contains information about the heterogeneous properties and is solved using the generalized integral transformation technique. Additionally, an initial interpolation of the sensor data is proposed for each observation time, making the inverse problem computationally lighter. The solutions of the inverse problem exhibit optimal performance, even with noisy input data and sources with abrupt discontinuities. The temperatures recovered by the direct problem considering the recovered source closely match synthetic experimental data, showing errors less than 1%, ensuring the robustness and reliability of the technique for the proposed application.

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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 (December 1, 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 using additional information. We consider two kinds of additional information: the prior source term, also known as the first guess, and regularization parameters for the shape of the source term. While the first guess is based on information independent of the measurements, such as the physics of the potential release or previous estimations, the regularization parameters are often selected by the designers of the optimization procedure. In this paper, we provide a sensitivity study of two inverse methodologies on the choice of the prior source term and regularization parameters of the methods. The sensitivity is studied in two cases: data from the European Tracer Experiment (ETEX) using FLEXPART v8.1 and the caesium-134 and caesium-137 dataset from the Chernobyl accident using FLEXPART v10.3.

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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 wind power estimation efficiency of a few machine learning approaches. At first, the performance of a Feed Forward Neural Network with different activation functions is considered. Next, Support Vector Regression Machine with different kernels is utilized for estimating the wind power. Then, deep Learning networks such as Long Short-Term Memory network, Convolutional Neural Network and Recurrent Neural Network are employed for assessing the future wind power and their ability is analyzed. Finally, a comparative chart is prepared to evaluate the efficacy and usefulness of the different machine learning techniques employed for estimating wind power.

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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 (November 10, 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 method provides a probabilistic source term estimate based on the early available observations of air concentration and gamma dose rate by monitoring systems. The method is intended for operational use in case of a nuclear accident, where no reliable source term estimate exists. We demonstrate how the probabilistic formulation can be used to provide estimates of the released amounts of each radionuclide as well as estimates of future gamma dose rates. The method is applied to an artificial case of a radioactive release from the Loviisa nuclear power plant in southern Finland, considering the most important dose-contributing nuclides. The case demonstrates that only limited air concentration measurement data may be available shortly after the release, and that to a large degree one will have to rely on gamma dose rate observations from a frequently reporting denser monitoring network. Further, we demonstrate that information about the core inventory of the nuclear power plant can be used to constrain the release rates of certain radionuclides, thereby decreasing the number of free parameters of the source term.

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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 (March 11, 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 several types of Artificial Neural Networks in estimating Directed Connectivity between Reconstructed EEG Sources and assessed their accuracy with respect to several ground truths. We show that a Long Short-Term Memory neural network with Non-Uniform Embedding yields the most promising results due to its relative robustness to differing dipole locations. We conclude that certain network architectures can compete with the already established methods for brain connectivity analysis.

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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 (May 3, 2021): 713–40. http://dx.doi.org/10.1007/s10652-021-09794-6.

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Ham, Jaehyun, Sang Ho Kim, Sung Il Kim, Byeonghee Lee, Jong-Hwa Park, Rae-Joon Park, and Jaehoon Jung. "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 containment condition variables using MELCOR code. It was estimated that there is no containment failure based on pressure, hydrogen mole fraction, and ablation depth, so that the release fractions of the 12 classes of fission products in MELCOR were evaluated considering design leak only for all cases. The overall source term of the case in which the integrity of the reactor vessel is maintained by the early initiation of the cavity flooding system was similar to that of the reactor vessel failure case. While the release fraction of cesium to the environment was analyzed to increase when there is no water in the radioactivity removal tanks, the fraction is small enough at which the radioactivity of the released cesium-137 remains well below 100 TBq, a regulatory limit. Moreover, it was found that the source term can be cut in half if the ancillary containment spray system is available. The results of this study verify the safety performance of the SMART under the small break loss-of-coolant severe accident condition with respect to the source term of interest.

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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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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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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 (November 11, 2021): 679–91. http://dx.doi.org/10.1007/s00477-021-02128-4.

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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 (January 2016): A2334—A2356. http://dx.doi.org/10.1137/15m1034829.

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Kim, Minsik, Ryohji Ohba, Masamichi Oura, Shinsuke Kato, Masayuki Takigawa, Paul E. Bieringer, Bent Lauritzen, and Martin Drews. "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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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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