On Flight Risk Quantitative Evaluation under Icing Conditions
The quantitative assessment of flight risk under icing conditions was taken as the research object. Based on multifactor coupling modeling idea, the pilot-aircraft-environment coupling system was built. Considering the physical characteristics and randomness of aircraft icing, the extreme values of...
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| Veröffentlicht in: | Mathematical problems in engineering 2019-01, Vol.2019 (2019), p.1-14 |
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| creator | Zhang, Zhe Xue, Yuan Xu, Haojun Li, Zhe Duan, Xiaocong |
| description | The quantitative assessment of flight risk under icing conditions was taken as the research object. Based on multifactor coupling modeling idea, the pilot-aircraft-environment coupling system was built. Considering the physical characteristics and randomness of aircraft icing, the extreme values of critical flight risk parameters were extracted by the Monte Carlo flight simulation experiment. The flight characteristics were studied comprehensively and heavy-tail characteristics and the distributions of different flight parameters were verified. Flight risk criterion was developed and one-dimensional extreme flight risk probability was calculated. Further, in order to solve the limitation of one-dimensional extreme value, with the Copula theory, the joint distribution model of flight parameters with three distinct distribution types was built. The optimal Copula model was selected by identification of unknown parameters and goodness of fit tests, and the three-dimensional extreme flight risk probability was defined. Based on the quantitative flight risk, the accident induction mechanism under icing conditions was discussed. Airspeed and roll angle under asymmetry icing conditions were more sensitive and had a more significant impact on flight safety. This method can provide reference for safety manipulation, boundary protection, and risk warning during icing flight. |
| doi_str_mv | 10.1155/2019/2546395 |
| format | Article |
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Based on multifactor coupling modeling idea, the pilot-aircraft-environment coupling system was built. Considering the physical characteristics and randomness of aircraft icing, the extreme values of critical flight risk parameters were extracted by the Monte Carlo flight simulation experiment. The flight characteristics were studied comprehensively and heavy-tail characteristics and the distributions of different flight parameters were verified. Flight risk criterion was developed and one-dimensional extreme flight risk probability was calculated. Further, in order to solve the limitation of one-dimensional extreme value, with the Copula theory, the joint distribution model of flight parameters with three distinct distribution types was built. The optimal Copula model was selected by identification of unknown parameters and goodness of fit tests, and the three-dimensional extreme flight risk probability was defined. Based on the quantitative flight risk, the accident induction mechanism under icing conditions was discussed. Airspeed and roll angle under asymmetry icing conditions were more sensitive and had a more significant impact on flight safety. This method can provide reference for safety manipulation, boundary protection, and risk warning during icing flight.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2019/2546395</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Aerospace engineering ; Aircraft accidents ; Aircraft accidents & safety ; Aircraft icing ; Airspeed ; Civil engineering ; Computer simulation ; Coupling ; Environment models ; Extreme values ; Fatalities ; Flight characteristics ; Flight safety ; Flight simulation ; Goodness of fit ; Identification ; Mathematical models ; Mathematical problems ; Methods ; Neural networks ; Parameter identification ; Physical properties ; Quantitative analysis ; Risk assessment ; Statistical tests ; Stochastic models</subject><ispartof>Mathematical problems in engineering, 2019-01, Vol.2019 (2019), p.1-14</ispartof><rights>Copyright © 2019 Zhe Li et al.</rights><rights>Copyright © 2019 Zhe Li et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c360t-9016967294318e7a8002988abd9f16d2b9b1e606d91b05bf4cbfe67f1d372ad93</citedby><cites>FETCH-LOGICAL-c360t-9016967294318e7a8002988abd9f16d2b9b1e606d91b05bf4cbfe67f1d372ad93</cites><orcidid>0000-0003-4439-6855 ; 0000-0003-3763-0818 ; 0000-0001-8495-2831 ; 0000-0002-5523-3202 ; 0000-0001-7923-1693</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27915,27916</link.rule.ids></links><search><contributor>Okarma, Krzysztof</contributor><contributor>Krzysztof Okarma</contributor><creatorcontrib>Zhang, Zhe</creatorcontrib><creatorcontrib>Xue, Yuan</creatorcontrib><creatorcontrib>Xu, Haojun</creatorcontrib><creatorcontrib>Li, Zhe</creatorcontrib><creatorcontrib>Duan, Xiaocong</creatorcontrib><title>On Flight Risk Quantitative Evaluation under Icing Conditions</title><title>Mathematical problems in engineering</title><description>The quantitative assessment of flight risk under icing conditions was taken as the research object. Based on multifactor coupling modeling idea, the pilot-aircraft-environment coupling system was built. Considering the physical characteristics and randomness of aircraft icing, the extreme values of critical flight risk parameters were extracted by the Monte Carlo flight simulation experiment. The flight characteristics were studied comprehensively and heavy-tail characteristics and the distributions of different flight parameters were verified. Flight risk criterion was developed and one-dimensional extreme flight risk probability was calculated. Further, in order to solve the limitation of one-dimensional extreme value, with the Copula theory, the joint distribution model of flight parameters with three distinct distribution types was built. The optimal Copula model was selected by identification of unknown parameters and goodness of fit tests, and the three-dimensional extreme flight risk probability was defined. Based on the quantitative flight risk, the accident induction mechanism under icing conditions was discussed. Airspeed and roll angle under asymmetry icing conditions were more sensitive and had a more significant impact on flight safety. This method can provide reference for safety manipulation, boundary protection, and risk warning during icing flight.</description><subject>Aerospace engineering</subject><subject>Aircraft accidents</subject><subject>Aircraft accidents & safety</subject><subject>Aircraft icing</subject><subject>Airspeed</subject><subject>Civil engineering</subject><subject>Computer simulation</subject><subject>Coupling</subject><subject>Environment models</subject><subject>Extreme values</subject><subject>Fatalities</subject><subject>Flight characteristics</subject><subject>Flight safety</subject><subject>Flight simulation</subject><subject>Goodness of fit</subject><subject>Identification</subject><subject>Mathematical models</subject><subject>Mathematical problems</subject><subject>Methods</subject><subject>Neural networks</subject><subject>Parameter identification</subject><subject>Physical properties</subject><subject>Quantitative analysis</subject><subject>Risk assessment</subject><subject>Statistical tests</subject><subject>Stochastic models</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqF0M9LwzAUB_AgCs7pzbMUPGpdXn61OXiQselgMBQFbyVt0i1zpjNpJ_73ZnTg0dP78vjwHnwRugR8B8D5iGCQI8KZoJIfoQFwQVMOLDuOGROWAqHvp-gshDXGBDjkA3S_cMl0Y5erNnmx4SN57pRrbatauzPJZKc2XYyNSzqnjU9mlXXLZNw4bffbcI5OarUJ5uIwh-htOnkdP6XzxeNs_DBPKypwm0oMQoqMSEYhN5nK43uZ56rUsgahSSlLMAILLaHEvKxZVdZGZDVomhGlJR2i6_7u1jdfnQltsW467-LLghAgguaMsKhue1X5JgRv6mLr7afyPwXgYl9QsS-oOBQU-U3PV9Zp9W3_01e9NtGYWv1pkCyPzf4CmNhtgA</recordid><startdate>20190101</startdate><enddate>20190101</enddate><creator>Zhang, Zhe</creator><creator>Xue, Yuan</creator><creator>Xu, Haojun</creator><creator>Li, Zhe</creator><creator>Duan, Xiaocong</creator><general>Hindawi Publishing Corporation</general><general>Hindawi</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0003-4439-6855</orcidid><orcidid>https://orcid.org/0000-0003-3763-0818</orcidid><orcidid>https://orcid.org/0000-0001-8495-2831</orcidid><orcidid>https://orcid.org/0000-0002-5523-3202</orcidid><orcidid>https://orcid.org/0000-0001-7923-1693</orcidid></search><sort><creationdate>20190101</creationdate><title>On Flight Risk Quantitative Evaluation under Icing Conditions</title><author>Zhang, Zhe ; Xue, Yuan ; Xu, Haojun ; Li, Zhe ; Duan, Xiaocong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c360t-9016967294318e7a8002988abd9f16d2b9b1e606d91b05bf4cbfe67f1d372ad93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Aerospace engineering</topic><topic>Aircraft accidents</topic><topic>Aircraft accidents & safety</topic><topic>Aircraft icing</topic><topic>Airspeed</topic><topic>Civil engineering</topic><topic>Computer simulation</topic><topic>Coupling</topic><topic>Environment models</topic><topic>Extreme values</topic><topic>Fatalities</topic><topic>Flight characteristics</topic><topic>Flight safety</topic><topic>Flight simulation</topic><topic>Goodness of fit</topic><topic>Identification</topic><topic>Mathematical models</topic><topic>Mathematical problems</topic><topic>Methods</topic><topic>Neural networks</topic><topic>Parameter identification</topic><topic>Physical properties</topic><topic>Quantitative analysis</topic><topic>Risk assessment</topic><topic>Statistical tests</topic><topic>Stochastic models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Zhe</creatorcontrib><creatorcontrib>Xue, Yuan</creatorcontrib><creatorcontrib>Xu, Haojun</creatorcontrib><creatorcontrib>Li, Zhe</creatorcontrib><creatorcontrib>Duan, Xiaocong</creatorcontrib><collection>الدوريات العلمية والإحصائية - e-Marefa Academic and Statistical Periodicals</collection><collection>معرفة - المحتوى العربي الأكاديمي المتكامل - e-Marefa Academic Complete</collection><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Middle East & Africa Database</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>Mathematical problems in engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Zhe</au><au>Xue, Yuan</au><au>Xu, Haojun</au><au>Li, Zhe</au><au>Duan, Xiaocong</au><au>Okarma, Krzysztof</au><au>Krzysztof Okarma</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>On Flight Risk Quantitative Evaluation under Icing Conditions</atitle><jtitle>Mathematical problems in engineering</jtitle><date>2019-01-01</date><risdate>2019</risdate><volume>2019</volume><issue>2019</issue><spage>1</spage><epage>14</epage><pages>1-14</pages><issn>1024-123X</issn><eissn>1563-5147</eissn><abstract>The quantitative assessment of flight risk under icing conditions was taken as the research object. Based on multifactor coupling modeling idea, the pilot-aircraft-environment coupling system was built. Considering the physical characteristics and randomness of aircraft icing, the extreme values of critical flight risk parameters were extracted by the Monte Carlo flight simulation experiment. The flight characteristics were studied comprehensively and heavy-tail characteristics and the distributions of different flight parameters were verified. Flight risk criterion was developed and one-dimensional extreme flight risk probability was calculated. Further, in order to solve the limitation of one-dimensional extreme value, with the Copula theory, the joint distribution model of flight parameters with three distinct distribution types was built. The optimal Copula model was selected by identification of unknown parameters and goodness of fit tests, and the three-dimensional extreme flight risk probability was defined. Based on the quantitative flight risk, the accident induction mechanism under icing conditions was discussed. Airspeed and roll angle under asymmetry icing conditions were more sensitive and had a more significant impact on flight safety. This method can provide reference for safety manipulation, boundary protection, and risk warning during icing flight.</abstract><cop>Cairo, Egypt</cop><pub>Hindawi Publishing Corporation</pub><doi>10.1155/2019/2546395</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0003-4439-6855</orcidid><orcidid>https://orcid.org/0000-0003-3763-0818</orcidid><orcidid>https://orcid.org/0000-0001-8495-2831</orcidid><orcidid>https://orcid.org/0000-0002-5523-3202</orcidid><orcidid>https://orcid.org/0000-0001-7923-1693</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Aerospace engineering Aircraft accidents Aircraft accidents & safety Aircraft icing Airspeed Civil engineering Computer simulation Coupling Environment models Extreme values Fatalities Flight characteristics Flight safety Flight simulation Goodness of fit Identification Mathematical models Mathematical problems Methods Neural networks Parameter identification Physical properties Quantitative analysis Risk assessment Statistical tests Stochastic models |
| title | On Flight Risk Quantitative Evaluation under Icing Conditions |
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