Modeling and probabilistic analysis of civil aircraft operational risk for suborbital disintegration accidents
To reduce the collision risk to civil airliners caused by suborbital vehicle disintegration events, this paper uses a covariance propagation algorithm to model the debris landing point of suborbital disintegration accidents and gives a collision probability analysis method for civil airliners encoun...
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| Veröffentlicht in: | PloS one 2022-04, Vol.17 (4), p.e0266514-e0266514 |
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| creator | Chen, Wantong Tian, Shuyu Ren, Shiyu |
| description | To reduce the collision risk to civil airliners caused by suborbital vehicle disintegration events, this paper uses a covariance propagation algorithm to model the debris landing point of suborbital disintegration accidents and gives a collision probability analysis method for civil airliners encountering debris during the cruise. Collision warning is performed for airborne risk targets to improve the emergency response capability of the ATC surveillance system to hazardous situations. The algorithm models the three-dimensional spatial motion target localization problem as a Gauss-Markov process, quantifying the location of debris landing points in the vicinity of nominal trajectories. By predicting the aircraft trajectory, the calculation of the inter-target collision probability is converted into an integration problem of a two-dimensional normally distributed probability density function in a circular domain. Compared with the traditional Monte Carlo method, the calculation speed of debris drop points is improved, which can meet the requirements of civil aviation for real-time response to unexpected situations. |
| doi_str_mv | 10.1371/journal.pone.0266514 |
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Collision warning is performed for airborne risk targets to improve the emergency response capability of the ATC surveillance system to hazardous situations. The algorithm models the three-dimensional spatial motion target localization problem as a Gauss-Markov process, quantifying the location of debris landing points in the vicinity of nominal trajectories. By predicting the aircraft trajectory, the calculation of the inter-target collision probability is converted into an integration problem of a two-dimensional normally distributed probability density function in a circular domain. Compared with the traditional Monte Carlo method, the calculation speed of debris drop points is improved, which can meet the requirements of civil aviation for real-time response to unexpected situations.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0266514</identifier><identifier>PMID: 35390104</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Accidents ; Accidents, Aviation ; Air traffic control ; Aircraft ; Aircraft accidents & safety ; Algorithms ; Altitude ; Analysis ; Automation ; Aviation ; Causes of ; Civil aviation ; Collision avoidance ; Collision dynamics ; Debris ; Detritus ; Disintegration ; Earth ; Emergency preparedness ; Emergency response ; Engineering and Technology ; Evaluation ; Gaussian process ; Gravity ; Landing ; Localization ; Markov analysis ; Markov processes ; Monte Carlo simulation ; Normal distribution ; Physical Sciences ; Probabilistic analysis ; Probability density function ; Probability density functions ; Propagation ; Research and Analysis Methods ; Risk ; Risk management ; Statistical analysis ; Three dimensional models ; Three dimensional motion ; Time response ; Trajectories ; Vehicles ; Velocity</subject><ispartof>PloS one, 2022-04, Vol.17 (4), p.e0266514-e0266514</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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Collision warning is performed for airborne risk targets to improve the emergency response capability of the ATC surveillance system to hazardous situations. The algorithm models the three-dimensional spatial motion target localization problem as a Gauss-Markov process, quantifying the location of debris landing points in the vicinity of nominal trajectories. By predicting the aircraft trajectory, the calculation of the inter-target collision probability is converted into an integration problem of a two-dimensional normally distributed probability density function in a circular domain. 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probabilistic analysis of civil aircraft operational risk for suborbital disintegration accidents</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2022-04-07</date><risdate>2022</risdate><volume>17</volume><issue>4</issue><spage>e0266514</spage><epage>e0266514</epage><pages>e0266514-e0266514</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>To reduce the collision risk to civil airliners caused by suborbital vehicle disintegration events, this paper uses a covariance propagation algorithm to model the debris landing point of suborbital disintegration accidents and gives a collision probability analysis method for civil airliners encountering debris during the cruise. Collision warning is performed for airborne risk targets to improve the emergency response capability of the ATC surveillance system to hazardous situations. The algorithm models the three-dimensional spatial motion target localization problem as a Gauss-Markov process, quantifying the location of debris landing points in the vicinity of nominal trajectories. By predicting the aircraft trajectory, the calculation of the inter-target collision probability is converted into an integration problem of a two-dimensional normally distributed probability density function in a circular domain. Compared with the traditional Monte Carlo method, the calculation speed of debris drop points is improved, which can meet the requirements of civil aviation for real-time response to unexpected situations.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>35390104</pmid><doi>10.1371/journal.pone.0266514</doi><tpages>e0266514</tpages><orcidid>https://orcid.org/0000-0003-3295-8347</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Accidents Accidents, Aviation Air traffic control Aircraft Aircraft accidents & safety Algorithms Altitude Analysis Automation Aviation Causes of Civil aviation Collision avoidance Collision dynamics Debris Detritus Disintegration Earth Emergency preparedness Emergency response Engineering and Technology Evaluation Gaussian process Gravity Landing Localization Markov analysis Markov processes Monte Carlo simulation Normal distribution Physical Sciences Probabilistic analysis Probability density function Probability density functions Propagation Research and Analysis Methods Risk Risk management Statistical analysis Three dimensional models Three dimensional motion Time response Trajectories Vehicles Velocity |
| title | Modeling and probabilistic analysis of civil aircraft operational risk for suborbital disintegration accidents |
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