GM Revs Up Diesel Combustion Modeling on Titan Supercomputer

February 9, 2018

February 9, 2018 – Most car owners in the United States do not think twice about passing over the diesel pump at the gas station. Instead, diesel fuel mostly powers our shipping trucks, boats, buses, and generators—and that is because diesel engines are about 10 percent more fuel-efficient than gasoline, saving companies money transporting large deliveries.

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

The downside to diesel engines is that they produce more emissions, like soot and nitrogen oxides, than gasoline engines because of how they combust fuel and air. A gasoline engine uses a spark plug to ignite a fuel-air mixture. A diesel engine compresses air until it is hot enough to ignite diesel fuel sprayed into the cylinder, using more air than necessary to burn all the fuel in a process called lean mixing-controlled combustion.

“We can generally clean up emissions for a gasoline engine with a three-way catalyst,” said Ronald Grover, staff researcher at General Motors (GM) Research and Development. “The problem with diesel is that when you operate lean, you can’t use the conventional three-way catalysts to clean up all the emissions suitably, so you have to add a lot of complexity to the after-treatment system.”

That complexity makes diesel engines heavier and more expensive upfront.

Grover and GM colleagues Jian Gao, Venkatesh Gopalakrishnan, and Ramachandra Diwakar are using the Titan supercomputer at the Oak Ridge Leadership Computing Facility, a U.S. Department of Energy Office of Science User Facility at DOE’s Oak Ridge National Laboratory, to improve combustion models for diesel passenger car engines with an ultimate goal of accelerating innovative engine designs while meeting strict emissions standards.

A multinational corporation that delivered 10 million vehicles to market last year, GM runs its research side of the house, Global R&D Laboratories, to develop new technologies for engines and powertrains.

“We work from a clean sheet of paper, asking ‘What if?’” Grover said. From there, ideas move up to advanced engineering, then to product organization where technology is vetted before it goes into the production pipeline.

For every engine design, GM must balance cost and performance for customers while working within the constraints of emissions regulations. The company also strives to develop exciting new ideas.

“The customer is our compass. We’re always trying to design and improve the engine,” Grover said. “We see constraint, and we’re trying to push that boundary.”

But testing innovative engine designs can run up a huge bill.

“One option is to try some designs, make some hardware, go test it, make some more hardware, go test it, and you continue to do this iterative process until you eventually reach the design that you like,” he said. “But obviously, every design iteration costs money because you’re cutting new hardware.”

Meanwhile, competitors might put their own new designs on the market. To reduce R&D costs, automakers use virtual engine models to computationally simulate and calibrate, or adjust, new designs so that only the best designs are built as prototypes for testing in the real world.

Central to engine design is the combustion process, but studying the intricacies of combustion in a laboratory is difficult and significant computational resources are required to simulate it in a virtual environment.

Combustion is critical to drivability and ensuring seamless operation on the road, but combustion also affects emissions production because emissions are chemical byproducts of combustion’s main ingredients: fuel, air, and heat.

“There are hundreds of thousands of chemical species [types of molecules] to be measured that you have to track and tens of thousands of reactions that you need to simulate,” Grover said. “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.”

The combustion process in a car engine—from burning the first drop of fuel to emitting the last discharge of exhaust—can create many thousands of chemical species, including regulated emissions. However, sensors used in experimental testing allow researchers to track only a limited number of species over the combustion process.

“You’re missing a lot of detail in the middle,” Grover said.

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, which is a measure of engine-cycle progress. 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,” he said. “But we still wanted to model the highest fidelity chemistry package that we could.”

To reduce computing time while increasing the complexity of the chemistry calculations, the GM team would need an extremely powerful computer and a new approach.

A richer recipe for combustion

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 nation’s most powerful computer for open scientific research. To make the most of the computing allocation, Grover’s team worked with Dean Edwards and Charles Finney at ORNL’s National Transportation Research Center and Wael Elwasif of ORNL’s Computer Science and Mathematics Division 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 CONVERGE computational fluid dynamics (CFD) software that Grover uses in-house. CONVERGE is the product of a small-business CFD software company called Convergent Science.

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.

“Their goal was to be able to better simulate what actually happens in the engine,” said Edwards, the ORNL principal investigator.

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,” Edwards said.

“And the bigger the recipe, the longer it takes the computer to solve it,” Finney said.

This was also a computationally daunting step because 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,” Grover said.

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,” Grover said. “We found that by using the Zero-RK GPU solver for chemistry, the chemistry computations ran about 33 percent faster.”

These encouraging results led the team to increase the number of chemical species to 766. What had taken the team over 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.

“We gathered a lot of success here,” Grover said.

With the first objective met—to see if they could increase simulation detail within a manageable compute time by using Titan’s GPUs—they moved on to compare accuracy against the experimental data.

They measured emissions including nitrogen oxides, carbon monoxide, soot, and unburned hydrocarbons (fuel that did not burn completely).

“Nitrogen oxide emissions in particular are tied to temperature and how a diesel engine combustion system operates,” Edwards said. “Diesel engines tend to operate at high temperatures and create a lot of nitrogen oxides.”

Compared with the baseline Titan simulation, the refined Titan simulation with 766 species improved nitrogen oxide predictions by 10–20 percent.

“That was one of our objectives: Can we model bigger chemistry and learn anything? Yes, we can,” Grover said, noting that the team saw some improvements for soot predictions as well but still struggled with increasing predictive accuracy for carbon monoxide and unburned hydrocarbon emissions.

“That’s not a bad result because we were able to see that maybe there’s something we’re missing other than chemistry,” Grover said.

To determine what that something missing might be, 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.

“We need to spend more time evaluating the validity of those wall temperatures,” Grover said. “We’re actually going to compute the wall temperatures by simulating the effect of the coolant flow around the engine. We’re hoping better heat transfer predictions will give us a big jump in combination with better chemistry.”

Another result was the demonstration of the GPUs’ ability to solve new problems.

The parallelism boosted by Titan’s GPUs enabled the throughput necessary to calculate hundreds of chemical species across hundreds of operating points. “Applying GPUs for computer-aided engineering could open up another benefit,” Grover said.

If GPUs can help reduce design time, that could boost business.

“That’s faster designs to market,” Grover said. “Usually a company will go through a vehicle development process from end-to-end that could take 4 or 5 years. If you could develop the powertrain faster, then you could get cars to market faster and more reliably.”

Related Publication: J. Gao, R. Grover, V. Gopalakrishnan, R. Diwakar, W. Elwasif, K. Edwards, C. Finney, and R. Whitesides, “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: 10.1115/ICEF2017-3631.

ORNL is managed by UT-Battelle for the Department of Energy’s Office of Science, the single largest supporter of basic research in the physical sciences in the United States. DOE’s Office of Science is working to address some of the most pressing challenges of our time. For more information, please visit http://science.energy.gov/.


Source: Oak Ridge National Laboratory

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

Fluid HPC: How Extreme-Scale Computing Should Respond to Meltdown and Spectre

February 15, 2018

The Meltdown and Spectre vulnerabilities are proving difficult to fix, and initial experiments suggest security patches will cause significant performance penalties to HPC applications. Even as these patches are rolled o Read more…

By Pete Beckman

Intel Touts Silicon Spin Qubits for Quantum Computing

February 14, 2018

Debate around what makes a good qubit and how best to manufacture them is a sprawling topic. There are many insistent voices favoring one or another approach. Referencing a paper published today in Nature, Intel has offe Read more…

By John Russell

Brookhaven Ramps Up Computing for National Security Effort

February 14, 2018

Last week, Dan Coats, the director of Director of National Intelligence for the U.S., warned the Senate Intelligence Committee that Russia was likely to meddle in the 2018 mid-term U.S. elections, much as it stands accused of doing in the 2016 Presidential election. Read more…

By John Russell

HPE Extreme Performance Solutions

Safeguard Your HPC Environment with the World’s Most Secure Industry Standard Servers

Today’s organizations operate in an environment with ever-evolving threats, and in order to protect themselves they must continuously bolster their security strategy. Hewlett Packard Enterprise (HPE) and Intel® are addressing modern security challenges with the world’s most secure industry standard servers powered by the latest generation of Intel® Xeon® Scalable processors. Read more…

AI Cloud Competition Heats Up: Google’s TPUs, Amazon Building AI Chip

February 12, 2018

Competition in the white hot AI (and public cloud) market pits Google against Amazon this week, with Google offering AI hardware on its cloud platform intended to make it easier, faster and cheaper to train and run machi Read more…

By Doug Black

Fluid HPC: How Extreme-Scale Computing Should Respond to Meltdown and Spectre

February 15, 2018

The Meltdown and Spectre vulnerabilities are proving difficult to fix, and initial experiments suggest security patches will cause significant performance penal Read more…

By Pete Beckman

Brookhaven Ramps Up Computing for National Security Effort

February 14, 2018

Last week, Dan Coats, the director of Director of National Intelligence for the U.S., warned the Senate Intelligence Committee that Russia was likely to meddle in the 2018 mid-term U.S. elections, much as it stands accused of doing in the 2016 Presidential election. Read more…

By John Russell

AI Cloud Competition Heats Up: Google’s TPUs, Amazon Building AI Chip

February 12, 2018

Competition in the white hot AI (and public cloud) market pits Google against Amazon this week, with Google offering AI hardware on its cloud platform intended Read more…

By Doug Black

Russian Nuclear Engineers Caught Cryptomining on Lab Supercomputer

February 12, 2018

Nuclear scientists working at the All-Russian Research Institute of Experimental Physics (RFNC-VNIIEF) have been arrested for using lab supercomputing resources to mine crypto-currency, according to a report in Russia’s Interfax News Agency. Read more…

By Tiffany Trader

The Food Industry’s Next Journey — from Mars to Exascale

February 12, 2018

Global food producer and one of the world's leading chocolate companies Mars Inc. has a unique perspective on the impact that exascale computing will have on the food industry. Read more…

By Scott Gibson, Oak Ridge National Laboratory

Singularity HPC Container Start-Up – Sylabs – Emerges from Stealth

February 8, 2018

The driving force behind Singularity, the popular HPC container technology, is bringing the open source platform to the enterprise with the launch of a new vent Read more…

By George Leopold

Dell EMC Debuts PowerEdge Servers with AMD EPYC Chips

February 6, 2018

AMD notched another EPYC processor win today with Dell EMC’s introduction of three PowerEdge servers (R6415, R7415, and R7425) based on the EPYC 7000-series p Read more…

By John Russell

‘Next Generation’ Universe Simulation Is Most Advanced Yet

February 5, 2018

The research group that gave us the most detailed time-lapse simulation of the universe’s evolution in 2014, spanning 13.8 billion years of cosmic evolution, is back in the spotlight with an even more advanced cosmological model that is providing new insights into how black holes influence the distribution of dark matter, how heavy elements are produced and distributed, and where magnetic fields originate. Read more…

By Tiffany Trader

Inventor Claims to Have Solved Floating Point Error Problem

January 17, 2018

"The decades-old floating point error problem has been solved," proclaims a press release from inventor Alan Jorgensen. The computer scientist has filed for and Read more…

By Tiffany Trader

Japan Unveils Quantum Neural Network

November 22, 2017

The U.S. and China are leading the race toward productive quantum computing, but it's early enough that ultimate leadership is still something of an open questi Read more…

By Tiffany Trader

AMD Showcases Growing Portfolio of EPYC and Radeon-based Systems at SC17

November 13, 2017

AMD’s charge back into HPC and the datacenter is on full display at SC17. Having launched the EPYC processor line in June along with its MI25 GPU the focus he Read more…

By John Russell

Researchers Measure Impact of ‘Meltdown’ and ‘Spectre’ Patches on HPC Workloads

January 17, 2018

Computer scientists from the Center for Computational Research, State University of New York (SUNY), University at Buffalo have examined the effect of Meltdown Read more…

By Tiffany Trader

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

IBM Begins Power9 Rollout with Backing from DOE, Google

December 6, 2017

After over a year of buildup, IBM is unveiling its first Power9 system based on the same architecture as the Department of Energy CORAL supercomputers, Summit a Read more…

By Tiffany Trader

Fast Forward: Five HPC Predictions for 2018

December 21, 2017

What’s on your list of high (and low) lights for 2017? Volta 100’s arrival on the heels of the P100? Appearance, albeit late in the year, of IBM’s Power9? Read more…

By John Russell

Russian Nuclear Engineers Caught Cryptomining on Lab Supercomputer

February 12, 2018

Nuclear scientists working at the All-Russian Research Institute of Experimental Physics (RFNC-VNIIEF) have been arrested for using lab supercomputing resources to mine crypto-currency, according to a report in Russia’s Interfax News Agency. Read more…

By Tiffany Trader

Leading Solution Providers

Chip Flaws ‘Meltdown’ and ‘Spectre’ Loom Large

January 4, 2018

The HPC and wider tech community have been abuzz this week over the discovery of critical design flaws that impact virtually all contemporary microprocessors. T Read more…

By Tiffany Trader

Perspective: What Really Happened at SC17?

November 22, 2017

SC is over. Now comes the myriad of follow-ups. Inboxes are filled with templated emails from vendors and other exhibitors hoping to win a place in the post-SC thinking of booth visitors. Attendees of tutorials, workshops and other technical sessions will be inundated with requests for feedback. Read more…

By Andrew Jones

How Meltdown and Spectre Patches Will Affect HPC Workloads

January 10, 2018

There have been claims that the fixes for the Meltdown and Spectre security vulnerabilities, named the KPTI (aka KAISER) patches, are going to affect applicatio Read more…

By Rosemary Francis

GlobalFoundries, Ayar Labs Team Up to Commercialize Optical I/O

December 4, 2017

GlobalFoundries (GF) and Ayar Labs, a startup focused on using light, instead of electricity, to transfer data between chips, today announced they've entered in Read more…

By Tiffany Trader

Tensors Come of Age: Why the AI Revolution Will Help HPC

November 13, 2017

Thirty years ago, parallel computing was coming of age. A bitter battle began between stalwart vector computing supporters and advocates of various approaches to parallel computing. IBM skeptic Alan Karp, reacting to announcements of nCUBE’s 1024-microprocessor system and Thinking Machines’ 65,536-element array, made a public $100 wager that no one could get a parallel speedup of over 200 on real HPC workloads. Read more…

By John Gustafson & Lenore Mullin

Flipping the Flops and Reading the Top500 Tea Leaves

November 13, 2017

The 50th edition of the Top500 list, the biannual publication of the world’s fastest supercomputers based on public Linpack benchmarking results, was released Read more…

By Tiffany Trader

V100 Good but not Great on Select Deep Learning Aps, Says Xcelerit

November 27, 2017

Wringing optimum performance from hardware to accelerate deep learning applications is a challenge that often depends on the specific application in use. A benc Read more…

By John Russell

2017 Gordon Bell Prize Finalists Named

October 23, 2017

The three finalists for this year’s Gordon Bell Prize in High Performance Computing have been announced. They include two papers on projects run on China’s Read more…

By John Russell

  • arrow
  • Click Here for More Headlines
  • arrow
Share This