Optimization and uncertainty analysis of a diesel engine operating point using CFD

[+] Author and Article Information
Daniel Probst

Convergence Science, Inc Madison, WI USA

Peter K. Senecal

Convergence Science, Inc Madison, WI USA

Peter Z. Chien

SmartUQ Madison, WI USA

Max Xu

SmartUQ Madison, WI USA

Brian Leyde

SmartUQ Madison, WI USA

1Corresponding author.

ASME doi:10.1115/1.4040006 History: Received February 27, 2017; Revised March 26, 2018


This study describes the use of an analytical model, constructed using sequential design of experiments (DOEs), to optimize and quantify the uncertainty of a Diesel engine operating point. A genetic algorithm (GA) was also used to optimize the design. The engine simulation was completed with a sector mesh in the commercial computational fluid dynamics (CFD) software CONVERGE, which predicted the combustion and emissions using a detailed chemistry solver with a reduced mechanism for n-heptane. The analytical model was constructed using the SmartUQ software using DOE responses to construct kernel emulators of the system. The sequential DOE optimization was compared to an optimization performed using a GA. This study highlighted the strengths of both methods for optimization. The GA (known to be an efficient and effective method) found a better optimum, while the DOE method found a good optimum with fewer total simulations. The DOE method also ran more simulations concurrently, which is an advantage when sufficient computing resources are available. In the second part of the study, the analytical model developed in the first part was used to assess the sensitivity and robustness of the design. The uncertainty propagation was studied over the reduced design region found with the sequential DoE in the first part. Finally, the predictions from the analytical model were validated against CFD results for sweeps of the input parameters. The predictions of the analytical model were found to agree well with the results from the CFD simulation.

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