Prometheus: A Geometry-Centric Optimization System for Combustor Design

Journal of Engineering for Gas Turbines and Power

Keane

et al. 2015

Self Cite

The prometheus combustor design system aims to reduce the complexity of evaluating combustor designs by automatically defining preprocessing, simulation, and postprocessing tasks based on the automatic identification of combustor features within the computer-aided design (CAD) environment. This system enables best practice to be codified and topological changes to a combustor's design to be more easily considered within an automated design process. The following paper presents the prometheus combustor design system and its application to the multiobjective isothermal optimization of a combustor prediffuser and the multifidelity isothermal optimization of a fuel injector feed arm in combination with a surrogate modeling strategy accelerated via a high-performance graphical processing unit (GPU).

Section: An Overview Of the Prometheus Design Systemmentioning

confidence: 99%

Section: An Overview Of the Prometheus Design Systemmentioning

confidence: 99%

Section: An Overview Of the Prometheus Design Systemmentioning

confidence: 99%

“…5. An example mesh generated using an ICEM script created automatically by Prometheus [11] ple, the combustor exit height.…”

Section: An Overview Of the Prometheus Design Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Zhang

Journal of Engineering for Gas Turbines and Power

Keane

et al. 2015

Self Cite

Applications of multi-fidelity multi-output Kriging to engineering design optimization

2023

Struct Multidisc Optim

Surrogate modelling is a popular approach for reducing the number of high fidelity simulations required within an engineering design optimization. Multi-fidelity surrogate modelling can further reduce this effort by exploiting low fidelity simulation data. Multi-output surrogate modelling techniques offer a way for categorical variables e.g. the choice of material, to be included within such models. While multi-fidelity multi-output surrogate modelling strategies have been proposed, to date only their predictive performance rather than optimization performance has been assessed. This paper considers three different multi-fidelity multi-output Kriging based surrogate modelling approaches and compares them to ordinary Kriging and multi-fidelity Kriging. The first approach modifies multi-fidelity Kriging to include multiple outputs whereas the second and third approaches model the different levels of simulation fidelity as different outputs within a multi-output Kriging model. Each of these techniques is assessed using three engineering design problems including the optimization of a gas turbine combustor in the presence of a topological variation, the optimization of a vibrating truss where the material can vary and finally, the parallel optimization of a family of airfoils.

The Potential of a Multifidelity Approach to Gas Turbine Combustor Design Optimization

Journal of Engineering for Gas Turbines and Power

Zhang

Keane

et al. 2021

Self Cite

The desire to reduce gas turbine emissions drives the use of design optimization approaches within the combustor design process. However, the relative cost of combustion simulations can prohibit such optimizations from being carried out within an industrial setting. Strategies which can significantly reduce the cost of such studies can enable designers to further improve emissions performance. The following paper investigates the application of a multi-fidelity surrogate modelling approach to the design optimization of a typical gas turbine combustor from a civil airliner engine. Results over three different case studies of varying problem dimensionality indicate that a multi-fidelity surrogate modelling based design optimization, whereby the simulation fidelity is varied by adjusting the coarseness of the mesh, can indeed improve optimization performance. These results indicate that such an approach has the potential to significantly reduce design optimization cost whilst achieving similar, or in some cases superior, design performance.