High-output diesel engine heat transfer: Part 2 – instantaneous spatially averaged heat transfer correlation

Abstract

A new temporally resolved spatially averaged heat transfer correlation was developed using the local piston heat flux data presented in the first part of this paper. The new correlation extends previous correlations that relate the Nusselt and Reynolds numbers through a power law by adding a dimensionless chemical energy release rate term. The new term, which arises from dimensional analysis, should enable similitude for diesel engines. Additionally, the characteristic velocity used in the Reynolds number was modified to include the integrated fuel mass injection rate. The new correlation was calibrated to the experimental data by minimizing the least squares error, and compared to existing correlations from the literature. On average, the new formulation was found to match the experimental data better than the existing models even when the existing models’ constants were adjusted to best fit the measured data.

High-output diesel engine heat transfer: Part 1 - comparison between piston heat flux and global energy balance

“…The next section will summarize the results for all operating conditions; for a more detailed comparison similar to that presented for operating condition 3, see Gingrich. 17…”

“…A graphical representation of the entire data reduction, filtering and averaging method described in this section can be found in Gingrich. 17…”

“…Soot witness marks on the piston indicated that the swirl effectively shifted the plumes close to the 5.5° offset to get nearly on-, off- or split-plume targeting. 17…”

“…Overall, the agreement between the left- and right-side data was good and confirmed that grouping thermocouples radially was reasonable. 17…”

“…For a full list of detailed substitutions see Gingrich. 17 When greater than one data set was available, the mean was taken to produce a single temporally varying data set (heat flux and temperature). Heat flux was calculated before averaging to prevent any artificial biasing of the data.…”