Statistically Modeling Fuel Consumption with Heteroscedastic Data

Abstract

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.

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Document Details

Document Type
Technical Report
Publication Date
Jun 16, 2017
Accession Number
AD1055409

Entities

People

  • L. E. Dazzio

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Acquisition
  • Air Force
  • Aircrafts
  • Automatic Pilots
  • Computers
  • Data Acquisition
  • Data Analysis
  • Data Mining
  • Data Science
  • Data Sets
  • Department Of Defense
  • Engineering
  • Fuel Consumption
  • Information Science
  • Network Science
  • Neural Networks
  • Normal Distribution

Readers

  • Petroleum Engineering
  • Regression Analysis.
  • Robotics and Automation.