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  • 39-02 Machine Learning for Turbomachinery Applications & Adjoint-Based Optimization
  • Adjoint-Based Optimization of Rocket Engine Turbine Blades

59580 - Adjoint-Based Optimization of Rocket Engine Turbine Blades 

Axial turbines for rocket engines are characterised by a near zero degree of reaction and highly-loaded stator vanes which are often supersonic. Consequently, the flow pattern downstream of the vanes is characterized strong shock waves which induce high-frequency excitations on the subsequent rotor. For this reason, the optimal design of the stator is crucial to attain high turbine performance and the lifespan of the component that is required for the next generation of rocket engines, especially if re-usability is a design criterion. A thorough comprehension of the loss mechanisms combined with the adoption of automated optimization techniques can therefore enable new stator designs that may provide large benefits in terms of overall turbine performance and lifespan. 

The scope of this study stems from these considerations and its objective is twofold, namely i) the investigation of the loss mechanisms in supersonic axial turbine stator vanes at on- and off-design conditions and ii) the shape optimization of a representative supersonic stator for rocket engines. 
The investigation is performed on stator vanes that are used in the first turbine stage of a gas generator cycle type rocket engine. The stator vanes are therefore optimised in order to reduce the profile losses by exploiting a novel adjoint optimisation framework for turbomachinery implemented in the SU2 open-source code. The effect on the resulting flow field and loss sources is finally investigated. 

Results show that efficiency gains in the order of 10% can be achieved via shape optimization and that the fluid-dynamic performance of these vanes is less sensitive to changes in downstream Mach number as compared to the loss trends observed in transonic vanes.

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Adjoint-Based Optimization of Rocket Engine Turbine Blades

Paper Type

Technical Paper Publication

Description


Session: 39-02 Machine Learning for Turbomachinery Applications & Adjoint-Based Optimization

Paper Number: 59580

Start Time: June 10th, 2021, 12:15 PM

Presenting Author: Bhupinder Singh Sanghera

Authors: Bhupinder Singh Sanghera Delft University of Technology
Nitish Anand Delft University of Technology
Louis Souverein ArianeGroup GmbH
Loic Penin ArianeGroup SAS
Matteo PiniPropulsion & Power, Delft University of Technology
 













 

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