A Guide to Uncertainty Quantification for Experimental Engine Research and Heat Release Analysis

Citation metadata

Authors: Brian Gainey, Jon P. Longtin and Benjamin Lawler
Date: Dec. 2019
From: SAE International Journal of Engines(Vol. 12, Issue 5)
Publisher: SAE International
Document Type: Article
Length: 7,952 words

Main content

Abstract :

Performing an uncertainty analysis for complex measurement tasks, such as those found in engine research, presents unique challenges. Also, because of the excessive computational costs, modeling-based approaches, such as a Monte Carlo approach, may not be practical. This work provides a traditional statistical approach to uncertainty analysis that incorporates the uncertainty tree, which is a graphical tool for complex uncertainty analysis. Approaches to calculate the required sensitivities are discussed, including issues associated with numerical differentiation, numerical integration, and post-processing. Trimming of the uncertainty tree to remove insignificant contributions is discussed. The article concludes with a best practices guide in the Appendix to uncertainty propagation in experimental engine combustion post-processing, which includes suggested postprocessing techniques and down-selected functional relationships for uncertainty propagation. Keywords Uncertainty analysis, Statistics, Heat release analysis, Experimental engine research, Advanced combustion

Source Citation

Source Citation
Gainey, Brian, et al. "A Guide to Uncertainty Quantification for Experimental Engine Research and Heat Release Analysis." SAE International Journal of Engines, vol. 12, no. 5, 2019, p. 509+. Accessed 1 Aug. 2021.
  

Gale Document Number: GALE|A611825421