Combustor Design Optimization Using Co-Kriging of Steady and Unsteady Turbulent Combustion

Abstract

In the gas turbine industry, computational fluid dynamics (CFD) simulations are often used to predict and visualize the complex reacting flow dynamics, combustion environment and emissions performance of a combustor at the design stage. Given the complexity involved in obtaining accurate flow predictions and due to the expensive nature of simulations, conventional techniques for CFD based combustor design optimization are often ruled out, primarily due to the limits on available computing resources and time. The design optimization process normally requires a large number of analyses of the objective and constraint functions which necessitates a careful selection of fast, reliable and efficient computational methods for the CFD analysis and the optimization process. In this study, given a fixed computational budget, an assessment of a co-Krlging based optimization strategy against a standard Kriging based optimization strategy is presented for the design of a 2D combustor using steady and unsteady Reynolds-averaged Navier Stokes (RANS) formulation. Within the fixed computational budget, using a steady RANS formulation, the Kriging strategy successfully captures the underlying response: however with unsteady RANS the Kriging strategy fails to capture the underlying response due to the e.xistence of a high level of noise. The co-Kriging strategy is then applied to two design problems, one using two levels of grid resolutions in a steady RANS formulation and the other using steady and unsteady RANS formulations on the same grid resolution. With the co-Kriging strategy, the multifidelity analysis is expected to flnd an optimum design in comparatively less time than that required using the high-fldelity model alone since ¡ess high-fidelity function calls should be required. However, using the applied computational setup for co-Kriging, the Kriging strategy beats the co-Kriging strategy under the steady RANS formulation whereas under the unsteady RANS formulation, the high level of noise stalls the co-Kriging optimization process, [

Efficient Strategy for Low NOx Combustor Design in the Spatial Domain Using Multi-Fidelity Solutions

“…The test combustor modeled for this study is the one used by Keller et al [8] in an experimental study of mechanisms of instabilities in turbulent combustion and also used in [3] for developing combustor design strategies. It consists of an oblong rectangular cross-section to model the essential features of planar flow with a profiled backward-facing step designed to act as a flame holder.…”

“…1. 2D computational domain of the combustor with a flame-stabilizer step (All dimensions in mm) [3,8] The main parameters employed in the commercial CFD package ANSYS FLUENT 12.1 are detailed in Table 1. The solver used is pressure based and employs a second-order discretization scheme for space.…”

“…This is mainly to keep the overall computation cost lower [10] and within practical time frames. An investigation into spatial grid dependent accuracy of the CFD solution was carried out in [3] using five grid cell count refinements (mesh1: 11000, mesh2: 46000, mesh3: 190,000, mesh4: 420,000 and mesh5: 800,000) to evaluate a converged reactive solution. Eight processes were used in parallel on a cluster of Intel quad core processors with 2.8GHz clock rate.…”

“…Thus, the strategy employed during combustor design and development has a direct impact on the achievability of design targets within practical time limits. Due to the latest advances in computing power and higher accuracy CFD codes, high-fidelity combustion CFD is now becoming an important and regular part of a combustor design strategy [3]. Since a large number of high-fidelity CFD analyses are generally required during the combustor development phase, there is a need for a computationally efficient strategy where the search algorithm is not coupled directly to expensive CFD simulations.…”

“…Previous studies [3][4][5] based on this methodology acknowledged that Kriging model based combustor design strategies could yield acceptable designs within a manageable timeframe by reducing the total 2 Copyright © 2013 by ASME number of required high-fidelity CFD analyses. Yet, successful combustor design and optimization still largely depends on the total number of design variables, and objective and constraint functions involved.…”