Aircraft operate in unpredictable environmental conditions. As a result, autopilot design is difficult, as optimal responses cannot be anticipated for all conditions. Consequently, the autopilot might overcorrect for conditions, using more fuel than necessary. By analyzing performance data on a subject aircraft, the relationships between environmental condition variables and fuel consumption using linear regression models have been characterized. These relationships are accurate, even though the data is non-normal and heteroscedastic.
Statistically Modeling Fuel Consumption with Heteroscedastic Data
2017
158 pages
Report
No indication
English
Aircraft , Data Files , Data & Information Systems , Aircrafts , Data acquisition , Data analysis , Observation , Regression analysis , Standards , United states , Databases , Fuel consumption , Engineering , Normal distribution , Computers , Data set , Network science , Air force , Digital data , Artificial neural networks , Data mining , Information science , Data science , Aerospace , Linear regression , Heteroscedasticity , Aircraft fuel consumption , Autopilot performance analysis
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