Dataset for testing the Adaptive Source Term Estimation algorithm

Buchman, Omri1

Published Mar 29, 2023 on Dryad. https://doi.org/10.5061/dryad.4mw6m90fb

Data files

Mar 29, 2023 version files 5.04 MB

average_actual_reduction_test.xlsx

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Backward_C_st_16_xsor_5000.0_ysor_5000.0_zsor_0.0_job_5_seed_-70_time_30.0_npar_400000.txt

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case1_det1_6500_5000_det2_8000_5000.xlsx

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case1_info.txt

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case1_random_placement_for_third_detector.xlsx

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case2_det1_7000_4600_det2_7000_5600.xlsx

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case2_info.txt

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case3_det1_7000_4600_det2_8300_4400.xlsx

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case3_info.txt

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case4_det1_6500_5000_det2_8000_5700.xlsx

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case4_info.txt

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case5_det1_7200_5600_det2_6800_4500.xlsx

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case6_det1_7000_5600_det2_8300_4400.xlsx

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case6_info.txt

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deg_space_Q_sor_1.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_6500_5000_n_1_at_8000_5000_n_2_at_6000_4700_n_3_at_5800_5300.npy

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deg_space_Q_sor_1.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_6500_5000_n_1_at_8000_5000_n_2_at_6000_4700.npy

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deg_space_Q_sor_1.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_6500_5000_n_1_at_8000_5000.npy

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deg_space_Q_sor_1.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_6500_5000_n_1_at_8000_5700_n_2_at_6400_5500_n_3_at_5800_4700.npy

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deg_space_Q_sor_1.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_6500_5000_n_1_at_8000_5700_n_2_at_6400_5500.npy

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deg_space_Q_sor_1.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_6500_5000_n_1_at_8000_5700.npy

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deg_space_Q_sor_1.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_7000_5600_n_1_at_7000_4400_n_2_at_6200_4600_n_3_at_5200_5000.npy

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deg_space_Q_sor_1.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_7000_5600_n_1_at_7000_4400_n_2_at_6200_4600.npy

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deg_space_Q_sor_1.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_7000_5600_n_1_at_7000_4400.npy

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deg_space_Q_sor_1.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_7000_5600_n_1_at_8300_4400_n_2_at_8400_5300_n_3_at_5000_5100.npy

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deg_space_Q_sor_1.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_7000_5600_n_1_at_8300_4400_n_2_at_8400_5300.npy

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deg_space_Q_sor_1.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_7000_5600_n_1_at_8300_4400.npy

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deg_space_Q_sor_1.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_7200_5600_n_1_at_6800_4500_n_2_at_6000_5500_n_3_at_4200_5100.npy

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deg_space_Q_sor_1.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_7200_5600_n_1_at_6800_4500_n_2_at_6000_5500.npy

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deg_space_Q_sor_1.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_7200_5600_n_1_at_6800_4500.npy

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deg_space_Q_sor_2.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_7000_5600_n_1_at_8300_4400_n_2_at_8400_5400_n_3_at_5000_5100.npy

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deg_space_Q_sor_2.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_7000_5600_n_1_at_8300_4400_n_2_at_8400_5400.npy

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deg_space_Q_sor_2.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_7000_5600_n_1_at_8300_4400.npy

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Forward_C_st_1000_xsor_5000.0_ysor_5000.0_zsor_0.0_job_5_seed_-70_time_30.0_npar_1600000.txt

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prediction_map_cycle_1_Q_sor_1.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_6500_5000_n_1_at_8000_5000.npy

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prediction_map_cycle_1_Q_sor_1.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_6500_5000_n_1_at_8000_5700.npy

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prediction_map_cycle_1_Q_sor_1.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_7000_5600_n_1_at_7000_4400.npy

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prediction_map_cycle_1_Q_sor_1.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_7000_5600_n_1_at_8300_4400.npy

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prediction_map_cycle_1_Q_sor_1.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_7200_5600_n_1_at_6800_4500.npy

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prediction_map_cycle_1_Q_sor_2.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_7000_5600_n_1_at_8300_4400.npy

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prediction_map_cycle_2_Q_sor_1.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_6500_5000_n_1_at_8000_5700_n_2_at_6400_5500.npy

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prediction_map_cycle_2_Q_sor_1.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_7000_5600_n_1_at_7000_4400_n_2_at_6200_4600.npy

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prediction_map_cycle_2_Q_sor_1.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_7000_5600_n_1_at_8300_4400_n_2_at_8400_5300.npy

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prediction_map_cycle_2_Q_sor_1.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_7200_5600_n_1_at_6800_4500_n_2_at_6000_5500.npy

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prediction_map_cycle_2_Q_sor_2.0_q_from_0.25_to_15.25_dq_0.15_detector_error_1e-08_chop_at_5e-09_detectors_n_0_at_7000_5600_n_1_at_8300_4400_n_2_at_8400_5400.npy

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README.md

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Abstract

The objective of this work is to study the Adaptive Source Term Estimation algorithm developed by the authors and that will be presented in a future article. Briefly, source term estimation (STE) is a mathematical tool aimed to characterize the source parameters of some gas hazard based on its measurements at given positions. The adaptive STE (ASTE) is aimed to perform this procedure iteratively, i.e. based on the received measurements at a given set of deployed detectors, the algorithm proposes the location for the next detector in order to optimally solve the STE problem.