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AR模型在阵风载荷飞行试验中的应用.docx

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AR模型在阵风载荷飞行试验中的应用 Title:ApplicationofARModelsinGustLoadFlightTesting Abstract: Gustloadflighttestingplaysacrucialroleinensuringthesafetyandperformanceofaircraftundervaryingweatherconditions.Accurateestimationandpredictionofgustloadsareessentialfordesigningrobustandreliableaircraftstructures.Thispaperexplorestheapplicationofautoregressive(AR)modelsingustloadflighttesting.TheARmodelshaveproventobeaneffectivetoolforanalyzinggustdataandpredictinggustloads.Thepaperdiscussestheadvantages,limitations,andfutureprospectsofARmodelsinthisdomain. 1.Introduction: Gustloadsareunsteadyaerodynamicforcescausedbyatmosphericturbulencethatactonaircraftduringflight.Theseloadscanhavedetrimentaleffectsontheaircraft'sstructureandaffectitsoverallstabilityandmaneuverability.Gustloadflighttestinginvolvessubjectinganaircrafttovariousgustpatterns,assimulatedbyaerodynamicists,tomeasureandevaluatethestructuralresponseundersuchconditions.Theaccuratemodelingandpredictionofgustloadsarecrucialinensuringthestructuralintegrityandsafetyoftheaircraft. 2.Autoregressive(AR)Models: ARmodelsarewidelyusedintime-seriesanalysisandhavefoundapplicationsinvariousfields.TheprimarycharacteristicofanARmodelisthatitpredictsfuturevaluesbasedonpastobservations.Inthecontextofgustloadflighttesting,ARmodelscanbeusedtoestimateandforecastgustloadsbasedonhistoricaldata.Thesemodelshavetheadvantageofbeingsimple,computationallyefficient,andflexibleforcapturingtheunderlyingdynamicsofgustloads. 3.AdvantagesofARModelsinGustLoadFlightTesting: a.AccurateGustLoadEstimation:ARmodelscaneffectivelyestimatethegustloadsbyanalyzingthetemporalpatternsandrelationshipsbetweenpastgustdata.Thisenablestheidentificationofimportantparametersandfactorsaffectingtheloads. b.Real-timePrediction:ARmodelscanprovidereal-timepredictionsofgustloads,allowingflightengineersandpilotstomakeinformeddecisionsregardingaircraftcontrolandmaneuveringduringflight.Thisreal-timecapabilityenhancessafetyandimprovestheflightenvelopeprotectionsystem. c.FlexibleModelStructur