Parallel Multi-Cycle LES of an Optical Pent-Roof DISI Engine Under Motored Operating Conditions

Abstract

The use of Large-eddy Simulations (LES) has increased due to their ability to resolve the turbulent fluctuations of engine flows and capture the resulting cycle-to-cycle variability. One drawback of LES, however, is the requirement to run multiple engine cycles to obtain the necessary cycle statistics for full validation. The standard method to obtain the cycles by running a single simulation through many engine cycles sequentially can take a long time to complete. Recently, a new strategy has been proposed by our research group to reduce the amount of time necessary to simulate the many engine cycles by running individual engine cycle simulations in parallel. With modern large computing systems this has the potential to reduce the amount of time necessary for a full set of simulated engine cycles to finish by up to an order of magnitude. In this paper, the Parallel Perturbation Methodology (PPM) is used to simulate up to 35 engine cycles of an optically accessible, pent-roof Direct-injection Spark-ignition (DISI) engine at two different motored engine operating conditions, one throttled and one un-throttled. Comparisons are made against corresponding sequential-cycle simulations to verify the similarity of results using either methodology. Mean results from the PPM approach are very similar to sequential-cycle results with less than 0.5% difference in pressure and a magnitude structure index (MSI) of 0.95. Differences in cycle-to-cycle variability (CCV) predictions are larger, but close to the statistical uncertainty in the measurement for the number of cycles simulated. PPM LES results were also compared against experimental data. Mean quantities such as pressure or mean velocities were typically matched to within 5–10%. Pressure CCVs were under-predicted, mostly due to the lack of any perturbations in the pressure boundary conditions between cycles. Velocity CCVs for the simulations had the same average magnitude as experiments, but the experimental data showed greater spatial variation in the root-mean-square (RMS). Conversely, circular standard deviation results showed greater repeatability of the flow directionality and swirl vortex positioning than the simulations.

Large-Eddy Simulations of Spray Variability Effects on Flow Variability in a Direct-Injection Spark-Ignition Engine Under Non-Combusting Operating Conditions

“…The individual simulations are then run for two cycles, using only the second cycle for data analysis. This same methodology was also used for the previous study by the authors on pure motored flow and found the correlation between the parallel and sequential cycles to be very high while significantly reducing the wall-clock time to complete the same number of engine cycles [48]. For the current study, each engine cycle took approximately 40 hours on 48 processors, or about 100 hours per usable cycle including the initial PPM cycles.…”

“…This happens despite the fact that the simulations also over-predict the trapped mass (304 mg in simulations vs. an estimated 280 mg for experiments). Previously published comparisons of motored engine simulations without fuel spray showed an under-prediction of the cylinder pressure of 1.5% with similar differences in trapped mass [48]. A possible explanation for the greater difference in peak pressure with the fuel injection is the uncertainty associated with the fuel's heat of vaporization (HoV).…”

“…The set-up for the current engine simulations was based on a set-up used for an earlier motored simulation study [48]. Engine geometry used for CFD simulations came from a combination of X-ray scans and nominal geometry.…”

Quantitative metrics for comparison of in-cylinder velocity fields using particle image velocimetry

“…A more general approach is to consider the entire flow field by calculating differences in RMS velocities over the whole field (Van Dam et al 2018). Statistical methods such as the circular standard deviation can also be used to investigate cyclic variability in velocity magnitudes and directions (Van Dam and Rutland 2015) and flow field differences between sequential and parallel LES simulations (Van Dam et al 2017).…”

“…The MSI produces values between 0 and 1 with larger values corresponding to better alignment. This metric has been used to investigate the convergence of simulated OH mass fraction and soot mass fraction distributions of an n-dodecane spray flame (Ameen et al 2016) and to quantify flow field differences between simulations and experiments (Ameen et al 2017;Van Dam et al 2017). Similarly to the MSI, the local magnitude index (LMI), defined using magnitude subtraction rather than vector subtraction, has recently been used to compare LES simulations to PIV measurements across five planes within an optical engine (Zhao et al 2019).…”