Ronald Grover, staff researcher at General Motors (GM) Research and Development and GM colleagues Jian Gao, Venkatesh Gopalakrishnan, and Ramachandra Diwakar are the Titan supercomputer at the Oak Ridge Leadership Computing Facility (LCF), a US Department of Energy Office of Science User Facility at DOE’s Oak Ridge National Laboratory (ORNL), to improve combustion models for diesel passenger car engines with an ultimate goal of accelerating innovative engine designs while meeting strict emissions standards.
The team presented a paper on their work at the ASME 2017 Internal Combustion Engine Division Fall Technical Conference.
The combustion process is central to engine design, but studying the intricacies of combustion in a laboratory is difficult and significant computational resources are required to simulate it in a virtual environment.
|In a model of a 1.6 liter engine cylinder, liquid fuel (shown in red and orange) is converted to fuel vapor under high temperatures during ignition. Image courtesy of Ronald Grover. Click to enlarge.|
There are hundreds of thousands of chemical species to be measured that you have to track and tens of thousands of reactions that you need to simulate. We have to simplify the chemistry to the point that we can handle it for computational modeling, and to simplify it, sometimes you have to make assumptions. So sometimes we find the model works well in some areas and doesn’t work well in others.—Ronald Grover
Grover’s team wanted to increase the number of species to better understand the chemical reactions taking place during combustion, but in-house computational resources could not compute such complex chemical changes with high accuracy within a reasonable time frame.
To test the limits of their in-house resources, Grover’s team increased the number of chemical species to 766 and planned to simulate combustion across a span of 280 crank angle degrees. An entire engine cycle, with one combustion event, equals 720 crank angle degrees.
It took 15 days just to compute 150 crank angle degrees. So, we didn’t even finish the calculation in over 2 weeks. But we still wanted to model the highest fidelity chemistry package that we could.—Ronald Grover
Grover and the GM team turned to DOE for assistance. Through DOE’s Advanced Scientific Computing Research Leadership Computing Challenge, a competitive peer-reviewed program, they successfully applied for and were awarded time on Titan during 2015 and 2016.
A 27-petaflop Cray XK7 supercomputer with a hybrid CPU–GPU architecture, Titan is the US’ most powerful computer for open scientific research. To make the most of the computing allocation, Grover’s team worked with Dean Edwards, Wael Elwasif, and Charles Finney at ORNL’s National Transportation Research Center to optimize combustion models for Titan’s architecture and add chemical species. They also partnered with Russell Whitesides at DOE’s Lawrence Livermore National Laboratory.
Whitesides is a developer of a chemical-kinetics solver called Zero-RK, which can use GPUs to accelerate computations. Both the ORNL and LLNL efforts are funded by DOE’s Vehicle Technologies Office.
The team combined Zero-RK with the Convergent Science CONVERGE computational fluid dynamics (CFD) software that Grover uses in-house.
The GM team set out to accomplish three things:
Use Titan’s GPUs so they could increase the complexity of the chemistry in their combustion models;
compare the results of Titan simulations with GM experimental data to measure accuracy; and
identify other areas for improvement in the combustion model.
ORNL’s goal was to help the GM team improve the accuracy of the combustion model, an exercise that could benefit other combustion research down the road. The first step was to improve the emissions predictions by adding detail back into the simulation, said Edwards. This was also a computationally daunting step because the chemistry does not happen in a vacuum.
On top of chemical kinetics, for our engine work, we have to model the movement of the piston, the movement of the valves, the spray injection, the turbulent flow—all of these things in addition to the chemistry.—Ronald Grover
The combustion model also needed to accurately simulate the many different operating conditions created in the engine. To simulate combustion under realistic conditions, GM brought experimental data for about 600 operating conditions—points measuring the balance of engine load (a measure of work output from the engine) and engine speed (revolutions per minute) that mimic realistic driving conditions in which a driver is braking, accelerating, driving uphill or downhill, idling in traffic, and more.
The team simulated a baseline model of 50 chemical species that matched what GM routinely computed in-house, then added 94 chemical species for a total of 144.
On Titan, we almost tripled our number of species. We found that by using the Zero-RK GPU solver for chemistry, the chemistry computations ran about 33 percent faster.—Ronald Grover
These encouraging results led the team to increase the number of chemical species to 766. What had taken the team more than 2 weeks to do in-house—modeling 766 species across 150 crank angle degrees—was completed in 5 days on Titan.
In addition, the team was able to complete the calculations over the desired 280 crank angle degrees, something that wasn’t possible using in-house resources.
With the first objective met, they moved on to compare accuracy against the experimental data. They measured emissions including nitrogen oxides, carbon monoxide, soot, and unburned hydrocarbons.
Compared with the baseline Titan simulation, the refined Titan simulation with 766 species improved nitrogen oxide predictions by 10–20 percent.
Grover said that the team still struggled with increasing predictive accuracy for carbon monoxide and unburned hydrocarbon emissions. Grover and the GM team successfully competed for a new ALCC award. The successful partnership with researchers at ORNL and LLNL and the DOE VTO and ASCR programs will continue to utilize Titan’s GPUs to study the effect of heat transfer and combustion chamber wall temperatures on the formation and oxidation of emissions species.
Future work will be focused on continuing to improve the emission predictions, especially CO and UHC emissions, with higher-fidelity chemical kinetics mechanisms (>1,000 species), careful spray model calibration for multi-component surrogate, and more accurate boundary temperatures by using 3D coupled conjugate heat transfer model. In addition, the potential and feasibility of using a virtual engine model to study engine transient behavior is being explored.—Gao et al.
J. Gao, R. Grover, V. Gopalakrishnan, R. Diwakar, W. Elwasif, K. Edwards, C. Finney, and R. Whitesides, (2017) “Steady-State Calibration of a Diesel Engine in CFD Using a GPU-based Chemistry Solver,” Proceedings of the ASME 2017 Internal Combustion Engine Division Fall Technical Conference, No. 2, doi: .