{"id":"https://openalex.org/W4414733201","doi":"https://doi.org/10.48550/arxiv.2509.15736","title":"Aircraft Fuel Flow Modelling with Ageing Effects: From Parametric Corrections to Neural Networks","display_name":"Aircraft Fuel Flow Modelling with Ageing Effects: From Parametric Corrections to Neural Networks","publication_year":2025,"publication_date":"2025-09-19","ids":{"openalex":"https://openalex.org/W4414733201","doi":"https://doi.org/10.48550/arxiv.2509.15736"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2509.15736","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.15736","pdf_url":"https://arxiv.org/pdf/2509.15736","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2509.15736","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026770201","display_name":"\ufeffGabriel Jarry","orcid":"https://orcid.org/0000-0002-7294-4217"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jarry, Gabriel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079756809","display_name":"Ramon Dalmau","orcid":"https://orcid.org/0000-0003-3587-7331"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dalmau, Ramon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093506299","display_name":"Philippe Very","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Very, Philippe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5102932619","display_name":"Junzi Sun","orcid":"https://orcid.org/0000-0003-3888-1192"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Junzi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10597","display_name":"Nuclear reactor physics and engineering","score":0.9246000051498413,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10597","display_name":"Nuclear reactor physics and engineering","score":0.9246000051498413,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.9067999720573425,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/airframe","display_name":"Airframe","score":0.7957000136375427},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6898999810218811},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.6294999718666077},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5090000033378601},{"id":"https://openalex.org/keywords/fuel-efficiency","display_name":"Fuel efficiency","score":0.4876999855041504},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.4772000014781952},{"id":"https://openalex.org/keywords/parametric-model","display_name":"Parametric model","score":0.45410001277923584},{"id":"https://openalex.org/keywords/representativeness-heuristic","display_name":"Representativeness heuristic","score":0.4383000135421753}],"concepts":[{"id":"https://openalex.org/C205167488","wikidata":"https://www.wikidata.org/wiki/Q222946","display_name":"Airframe","level":2,"score":0.7957000136375427},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6898999810218811},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.6294999718666077},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5942000150680542},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5090000033378601},{"id":"https://openalex.org/C45882903","wikidata":"https://www.wikidata.org/wiki/Q5042317","display_name":"Fuel efficiency","level":2,"score":0.4876999855041504},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.4772000014781952},{"id":"https://openalex.org/C24574437","wikidata":"https://www.wikidata.org/wiki/Q7135228","display_name":"Parametric model","level":3,"score":0.45410001277923584},{"id":"https://openalex.org/C37381756","wikidata":"https://www.wikidata.org/wiki/Q20203288","display_name":"Representativeness heuristic","level":2,"score":0.4383000135421753},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.390500009059906},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3560999929904938},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.3449000120162964},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3222000002861023},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3156000077724457},{"id":"https://openalex.org/C129364497","wikidata":"https://www.wikidata.org/wiki/Q3042561","display_name":"Prognostics","level":2,"score":0.30300000309944153},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.2924000024795532},{"id":"https://openalex.org/C42747912","wikidata":"https://www.wikidata.org/wiki/Q1048447","display_name":"Multiplicative function","level":2,"score":0.28450000286102295},{"id":"https://openalex.org/C2780695315","wikidata":"https://www.wikidata.org/wiki/Q3799040","display_name":"Unobservable","level":2,"score":0.2612000107765198},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C78297888","wikidata":"https://www.wikidata.org/wiki/Q7449607","display_name":"Semiparametric model","level":3,"score":0.25690001249313354},{"id":"https://openalex.org/C103402496","wikidata":"https://www.wikidata.org/wiki/Q1106171","display_name":"Prediction interval","level":2,"score":0.2531999945640564},{"id":"https://openalex.org/C133199616","wikidata":"https://www.wikidata.org/wiki/Q25386885","display_name":"Empirical modelling","level":2,"score":0.2515999972820282}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2509.15736","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.15736","pdf_url":"https://arxiv.org/pdf/2509.15736","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2509.15736","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.15736","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2509.15736","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.15736","pdf_url":"https://arxiv.org/pdf/2509.15736","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"modelling":[1],"of":[2,49,109,130,135,146,154,159,171,191],"aircraft":[3,26],"fuel-flow":[4,39],"is":[5],"crucial":[6],"for":[7,41,104,156,182],"both":[8],"operational":[9,172],"planning":[10],"and":[11,75,113,119,132,144,163,173],"environmental":[12,174],"impact":[13],"assessment,":[14],"yet":[15],"standard":[16],"parametric":[17,162],"models":[18,99,115],"often":[19],"neglect":[20],"performance":[21],"deterioration":[22],"that":[23,80,96,186],"occurs":[24],"as":[25,84,89],"age.":[27],"This":[28,149],"paper":[29],"investigates":[30],"multiple":[31],"approaches":[32],"to":[33,167],"integrate":[34],"engine":[35,193],"ageing":[36,160],"effects":[37,158],"into":[38],"prediction":[40,121],"the":[42,107,127,133,142,152,157,169,180,189],"Airbus":[43],"A320-214,":[44],"using":[45],"a":[46],"comprehensive":[47],"dataset":[48],"approximately":[50],"nineteen":[51],"thousand":[52],"Quick":[53],"Access":[54],"Recorder":[55],"flights":[56],"from":[57,126],"nine":[58],"distinct":[59],"airframes":[60,131],"with":[61],"varying":[62],"years":[63],"in":[64,161],"service.":[65],"We":[66],"systematically":[67],"evaluate":[68],"classical":[69],"physics-based":[70],"models,":[71],"empirical":[72],"correction":[73,111],"coefficients,":[74],"data-driven":[76],"neural":[77,114],"network":[78],"architectures":[79],"incorporate":[81],"age":[82],"either":[83],"an":[85,90],"input":[86],"feature":[87],"or":[88],"explicit":[91],"multiplicative":[92],"bias.":[93],"Results":[94],"demonstrate":[95],"while":[97],"baseline":[98],"consistently":[100],"underestimate":[101],"fuel":[102],"consumption":[103],"older":[105],"aircraft,":[106],"use":[108],"age-dependent":[110],"factors":[112],"substantially":[116],"reduces":[117],"bias":[118],"improves":[120],"accuracy.":[122],"Nevertheless,":[123],"limitations":[124],"arise":[125],"small":[128],"number":[129],"lack":[134],"detailed":[136],"maintenance":[137],"event":[138],"records,":[139],"which":[140],"constrain":[141],"representativeness":[143],"generalization":[145],"age-based":[147],"corrections.":[148],"study":[150,177],"emphasizes":[151],"importance":[153],"accounting":[155],"machine":[164],"learning":[165],"frameworks":[166],"improve":[168],"reliability":[170],"assessments.":[175],"The":[176],"also":[178],"highlights":[179],"need":[181],"more":[183],"diverse":[184],"datasets":[185],"can":[187],"capture":[188],"complexity":[190],"real-world":[192],"deterioration.":[194]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}
