Linfeng Zhang, Alex Bian, Changmin Jiang, Lingxiao Wu
{"title":"基于飞行轨迹估算飞机耗油量的综合框架","authors":"Linfeng Zhang, Alex Bian, Changmin Jiang, Lingxiao Wu","doi":"arxiv-2409.05429","DOIUrl":null,"url":null,"abstract":"Accurate calculation of aircraft fuel consumption plays an irreplaceable role\nin flight operations, optimization, and pollutant accounting. Calculating\naircraft fuel consumption accurately is tricky because it changes based on\ndifferent flying conditions and physical factors. Utilizing flight surveillance\ndata, this study developed a comprehensive mathematical framework and\nestablished a link between flight dynamics and fuel consumption, providing a\nset of high-precision, high-resolution fuel calculation methods. It also allows\nother practitioners to select data sources according to specific needs through\nthis framework. The methodology begins by addressing the functional aspects of\ninterval fuel consumption. We apply spectral transformation techniques to mine\nAutomatic Dependent Surveillance-Broadcast (ADS-B) data, identifying key\naspects of the flight profile and establishing their theoretical relationships\nwith fuel consumption. Subsequently, a deep neural network with tunable\nparameters is used to fit this multivariate function, facilitating\nhigh-precision calculations of interval fuel consumption. Furthermore, a\nsecond-order smooth monotonic interpolation method was constructed along with a\nnovel estimation method for instantaneous fuel consumption. Numerical results\nhave validated the effectiveness of the model. Using ADS-B and Aircraft\nCommunications Addressing and Reporting System (ACARS) data from 2023 for\ntesting, the average error of interval fuel consumption can be reduced to as\nlow as $3.31\\%$, and the error in the integral sense of instantaneous fuel\nconsumption is $8.86\\%$. These results establish this model as the state of the\nart, achieving the lowest estimation errors in aircraft fuel consumption\ncalculations to date.","PeriodicalId":501172,"journal":{"name":"arXiv - STAT - Applications","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Comprehensive Framework for Estimating Aircraft Fuel Consumption Based on Flight Trajectories\",\"authors\":\"Linfeng Zhang, Alex Bian, Changmin Jiang, Lingxiao Wu\",\"doi\":\"arxiv-2409.05429\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Accurate calculation of aircraft fuel consumption plays an irreplaceable role\\nin flight operations, optimization, and pollutant accounting. Calculating\\naircraft fuel consumption accurately is tricky because it changes based on\\ndifferent flying conditions and physical factors. Utilizing flight surveillance\\ndata, this study developed a comprehensive mathematical framework and\\nestablished a link between flight dynamics and fuel consumption, providing a\\nset of high-precision, high-resolution fuel calculation methods. It also allows\\nother practitioners to select data sources according to specific needs through\\nthis framework. The methodology begins by addressing the functional aspects of\\ninterval fuel consumption. We apply spectral transformation techniques to mine\\nAutomatic Dependent Surveillance-Broadcast (ADS-B) data, identifying key\\naspects of the flight profile and establishing their theoretical relationships\\nwith fuel consumption. Subsequently, a deep neural network with tunable\\nparameters is used to fit this multivariate function, facilitating\\nhigh-precision calculations of interval fuel consumption. Furthermore, a\\nsecond-order smooth monotonic interpolation method was constructed along with a\\nnovel estimation method for instantaneous fuel consumption. Numerical results\\nhave validated the effectiveness of the model. Using ADS-B and Aircraft\\nCommunications Addressing and Reporting System (ACARS) data from 2023 for\\ntesting, the average error of interval fuel consumption can be reduced to as\\nlow as $3.31\\\\%$, and the error in the integral sense of instantaneous fuel\\nconsumption is $8.86\\\\%$. These results establish this model as the state of the\\nart, achieving the lowest estimation errors in aircraft fuel consumption\\ncalculations to date.\",\"PeriodicalId\":501172,\"journal\":{\"name\":\"arXiv - STAT - Applications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - STAT - Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.05429\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - STAT - Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.05429","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Comprehensive Framework for Estimating Aircraft Fuel Consumption Based on Flight Trajectories
Accurate calculation of aircraft fuel consumption plays an irreplaceable role
in flight operations, optimization, and pollutant accounting. Calculating
aircraft fuel consumption accurately is tricky because it changes based on
different flying conditions and physical factors. Utilizing flight surveillance
data, this study developed a comprehensive mathematical framework and
established a link between flight dynamics and fuel consumption, providing a
set of high-precision, high-resolution fuel calculation methods. It also allows
other practitioners to select data sources according to specific needs through
this framework. The methodology begins by addressing the functional aspects of
interval fuel consumption. We apply spectral transformation techniques to mine
Automatic Dependent Surveillance-Broadcast (ADS-B) data, identifying key
aspects of the flight profile and establishing their theoretical relationships
with fuel consumption. Subsequently, a deep neural network with tunable
parameters is used to fit this multivariate function, facilitating
high-precision calculations of interval fuel consumption. Furthermore, a
second-order smooth monotonic interpolation method was constructed along with a
novel estimation method for instantaneous fuel consumption. Numerical results
have validated the effectiveness of the model. Using ADS-B and Aircraft
Communications Addressing and Reporting System (ACARS) data from 2023 for
testing, the average error of interval fuel consumption can be reduced to as
low as $3.31\%$, and the error in the integral sense of instantaneous fuel
consumption is $8.86\%$. These results establish this model as the state of the
art, achieving the lowest estimation errors in aircraft fuel consumption
calculations to date.