Computational-Fluid-Dynamics-Based Kriging Optimization Tool for Aeronautical Combustion Chambers

Abstract

Current state-of-the-art in Computational Fluid Dynamics (CFD) provides reasonable reacting flow predictions and is already used in industry to evaluate new concepts of gas turbine engines. In parallel, optimization techniques have reached maturity and several industrial activities benefit from enhanced search algorithms.However, coupling a physical model with an optimization algorithm to yield a decision making tool, needs to be undertaken with care to take advantage of the current computing power while satisfying the gas turbine industrial constraints. Among the many delicate issues for such tools to contribute efficiently to the gas turbine industry, combustion is probably the most challenging and optimization algorithms are not easily applicable to such problems. In our study, a fully encapsulated algorithm addresses the issue by making use of a new multi-objective optimization strategy based on an iteratively enhanced meta-model (Kriging) coupled to a Design of Experiments (DoE) method and a fully parallel three dimensional (3D) CFD solver to model turbulent reacting flows. With this approach, the computer cost needed for thousands of CFD computations is greatly reduced while ensuring an automatic error reduction of the approximated response function. Preliminary assessments of the search algorithm against simple analytical test functions prove the strategy to be efficient and robust. Application to a 3D industrial aeronautical combustion chamber demonstrates the approach to be feasible with currently available computing power. One result of the optimization is that possible design changes can improve performance and durability of the studied engine. With the advent of * Post. of 43American Institute of Aeronautics and Astronautics massively parallel architectures, the intersection between these two advanced techniques seems a logical path to yield fully automated decision making tools for the design of gas turbine engines.

Isothermal Combustor Prediffuser and Fuel Injector Feed Arm Design Optimization Using the prometheus Design System

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Robust Numerical Coupling of Pressure and Pressureless Gas Dynamics Equations for Eulerian Spray DNS and LES

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Resampling strategies to improve surrogate model‐based uncertainty quantification: Application to LES of LS89

“…Design process and robust optimization are the major purposes of most engineering works dealing with computational fluid dynamics (CFD), especially in aeronautical or automotive industry. 1 Despite the large amount of work that has been devoted to the design of efficient optimization techniques, the design process still requires important investments (financial and human). 2 As a consequence, design errors appear after the industrialization phase, 3 and the implications that these can have may be critical.…”