Multidisciplinary and Multiobjective Optimization in Conceptual Design for Mixed-Stream Turbofan Engines

Abstract

Despite major advances in design tools such as engine cycle analysis software and computer aided design, conceptual gas turbine engine design is essentially a trial-and-error process based on the experience of engineers. Modern optimization concepts, such as multidisciplinary optimization (MDO), and multiobjective optimization (MOO), linked with sequential quadratic programming (SQP) methods and genetic algorithms (GA), were applied to the conceptual engine design process to automate the conceptual design phase. Robust integrated computer codes were created to find the optimal values of eight engine parameters in order to minimize fuel usage, aircraft cost and engine annulus area over a given mission. The engine cycle selected for study was the mixed stream, low bypass turbofan. SQP and GA optimization algorithms were integrated with on-design and off- design engine cycle analysis and mission analysis computer codes created by the authors to obtain the optimized conceptual engine design for an imaginary short range interceptor and the Global Strike Aircraft U.S. Air Force concept. The process used a nonspecific approach that can be applied to a wide variety of missions and aircraft. All the codes were written in Matlab, and so operate under the same programming architecture and can be easily upgraded or modified.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1996
Accession Number
ADA320800

Entities

People

  • Luc J. Nadon

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Aircrafts
  • Airframes
  • Algorithms
  • Computer Programming
  • Computers
  • Engineering
  • Fighter Aircraft
  • Gas Turbines
  • Genetic Algorithms
  • Low Bypass Turbofans
  • Multiobjective Optimization
  • Operating Systems
  • Optimization
  • Propulsion Systems
  • Standards
  • Turbines

Readers

  • Aerospace Engineering
  • Computational Fluid Dynamics (CFD)
  • Database Systems and Applications

Technology Areas

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