Shape Optimization of a Low Pressure Turbine Cascade Endwall Using a Genetic Algorithm

Abstract

Various approaches have been used to shape the endwall and blade profile in order to reduce the endwall losses in turbine passages. This study describes the workflow used to design an optimized endwall contour for a front-loaded high-lift low pressure turbine research profile. Endwall contours were defined using a series of Bezier curves across the passage to create a smooth contoured surface. A new computational mesh was generated for each design configuration by morphing the baseline mesh of a passage with a flat endwall until it matched each new contour shape. Two different contour designs were fabricated and installed in a low speed linear cascade wind tunnel to validate the workflow. One contour shape was designed using approaches described in open literature. The second shape was optimized using a genetic algorithm using passage total pressure loss as the objective function. The optimization process produced a unique and aggressive contour shape. Comparisons between the planar and the contoured endwall shapes are presented both experimentally and computationally. Although the two different contours produced a similar reduction in passage loss, the analysis shows that the effect of the two contours on the endwall flow field are significantly different providing insight into the fluid dynamic mechanism responsible for the reduction in endwall losses.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2019
Accession Number
AD1103478

Entities

People

  • Christopher R. Marks
  • Jacob Dickel
  • Mitch Wolff
  • Rolf Sondergaard

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Aerodynamic Characteristics
  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Boundary Layer
  • Computational Fluid Dynamics
  • Flow
  • Fluid Flow
  • Gas Turbines
  • Genetic Algorithms
  • High Lift
  • Optimization
  • Pressure Distribution
  • Pressure Gradients
  • Three Dimensional
  • Turbines
  • Wind Tunnels

Fields of Study

  • Physics

Readers

  • Aerodynamics.
  • Distributed Systems and Data Platform Development

Technology Areas

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