Finding the Optimum Design of Large Gas Engines Prechambers Using CFD and Bayesian Optimization

TL;DR

This paper presents a computationally efficient method combining CFD and Bayesian optimization to design large gas engine prechambers, enabling effective exploration of complex design spaces with reduced computational cost.

Contribution

The study introduces a Bayesian optimization approach integrated with CFD simulations for optimizing large gas engine prechamber designs, improving efficiency over traditional methods.

Findings

Bayesian optimization effectively identifies optimal prechamber designs.

The combined CFD and Bayesian approach reduces computational costs.

Optimized designs meet target performance criteria.

Abstract

The turbulent jet ignition concept using prechambers is a promising solution to achieve stable combustion at lean conditions in large gas engines, leading to high efficiency at low emission levels. Due to the wide range of design and operating parameters for large gas engine prechambers, the preferred method for evaluating different designs is computational fluid dynamics (CFD), as testing in test bed measurement campaigns is time-consuming and expensive. However, the significant computational time required for detailed CFD simulations due to the complexity of solving the underlying physics also limits its applicability. In optimization settings similar to the present case, i.e., where the evaluation of the objective function(s) is computationally costly, Bayesian optimization has largely replaced classical design-of-experiment. Thus, the present study deals with the computationally efficient Bayesian optimization of large gas engine prechambers design using CFD simulation. Reynolds-averaged-Navier-Stokes simulations are used to determine the target values as a function of the selected prechamber design parameters. The results indicate that the chosen strategy is effective to find a prechamber design that achieves the desired target values.