Improving Algorithmic Efficiency of Aircraft Engine Design for Optimal Mission Performance

Abstract

Automated techniques for selecting jet engines that minimize overall fuel consumption for a given aircraft mission have recently been developed. However, the current techniques lack the efficiency required by Wright Laboratories. Two noted dependencies between turbine engine fan pressure ratio, bypass ratio, high pressure compressor pressure ratio and overall engine mass flow allows for a reduction in the number of independent design variables searched in the optimization process. Additionally, through the use of spatial statistics (specifically kriging estimation), it is possible to significantly reduce the number of time consuming response function evaluations required to obtain an optimal combination of engine parameters. A micro Genetic Algorithm (microGA) is employed to perform the non linear optimization process with these two computation saving techniques. Optimal engine solutions were obtained. in 25 percent of the time required by previous automated search algorithms.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1998
Accession Number
ADA341915

Entities

People

  • Paul T. Millhouse

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Space

DTIC Thesaurus Topics

  • Air Force
  • Aircraft Engines
  • Algorithms
  • Compressors
  • Computations
  • Genetic Algorithms
  • High Pressure
  • High Pressure Compressors
  • Jet Engines
  • Linear Programming
  • Mass Flow
  • Mathematical Models
  • Operations Research
  • Statistics
  • Turbines
  • Turbofan Engines
  • Two Dimensional

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Aerodynamics.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Biotechnology