Simulating to track soot formations in diesel engines

  • 11-Jun-2014 01:53 EDT

Streamlines showing intake process for an SI engine in a FORTÉ simulation.

It is not easy to see what is going on inside an engine—it is a hot and hostile environment. But if you are an engine designer seeking to optimize engines for fuel efficiency and emissions control, you need visibility into the way combustion progresses, how the fuel breaks down, and how pollutants are formed. To gain these insights, the time-honored approach of physical prototyping can guide engine design, but such methods require a series of direct tests on real hardware with many differential settings. While experiments and measurements yield important information, they are very costly both in time and resources.

Combustion simulation is a valuable aid to engine designers, but only if the results of modeling can give true insight into engine behavior. Obtaining accurate and predictive results from combustion simulation requires a detailed understanding of the chemical makeup of fuels and the physical geometries of engines.

To demonstrate the predictive qualities of its engine simulations, Reaction Design worked with a German premium automotive company to build cylinder-combustion simulations for a high-performance diesel engine. The goal was to accurately and quickly predict combustion performance and the effects of varied operating conditions on soot emissions.

Soot emissions are particularly challenging to simulate using conventional CFD technologies, as soot formation results from a combination of processes within the engine, including fuel-injection dynamics, auto-ignition kinetics, and fluid-wall interactions. It is also strongly affected by the chemical composition of the fuel. Using engine and fuel specifications, engineers from Reaction Design demonstrated a CFD simulation that could predict soot formation over a wide range of designs and operating conditions, using consistent model parameters and practices for all cases.

Reaction Design generated a computational mesh to represent the unique piston-bowl and combustion chamber of the diesel test engine. Care was taken to assure the precise location of the fuel-injection nozzle holes were used, after discovering a sensitivity of the results to the hole location. Reaction Design then proposed a standardized set of spray and injection model settings to be used in all subsequent modeling.

The greatest simulation challenge was creating a detailed multi-component fuel surrogate model that could accurately predict engine behavior and soot creation for the test engine. Reaction Design worked with data from the Model Fuel Library, a compendium of detailed chemical mechanisms for 56 fully validated, self-consistent fuel components, developed by the Model Fuels Consortium.

The European diesel fuel was represented with a four-component fuel surrogate that matched key fuel properties. From a well-validated master reaction mechanism that considered interactions among over 4000 chemical species, Reaction Design extracted an appropriate mechanism for the selected fuel surrogate, which required 394 species. The advanced solver capabilities in the FORTÉ CFD Package allowed the simulation to run in less than half a day for most of the cases. Because Reaction Design’s solution drew from the Model Fuel Library’s extensively validated chemistry models, it was able to deliver fast and accurate engine simulations with the level of detail required to predict complex combustion behavior.

When compared to real-world empirical results of emissions for the same engine conditions, FORTÉ performed well, producing emissions rates that tracked observed trends from detailed performance testing in lab situations, despite the very wide range of operating conditions considered. Since the measurements are downstream of the engine-out model predictions, it is expected that the engine-out soot values will differ quantitatively to the measured values, depending on when the peak soot formation occurs relative to the exhaust valve opening. Nonetheless, the prediction of trends without change to model input parameters enables the model to be used in pre-development studies.

Ellen Meeks, Director, Development, Reaction Design, ANSYS, wrote this article for Automotive Engineering.

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