Better combustion for power generation

31st May 2016
Enaie Azambuja

In 2015, the search for efficiency gains led GE to tackle one of the most complex problems in science and engineering—instabilities in gas turbine combustors. The journey led the company to the Titan supercomputer at the Oak Ridge Leadership Computing Facility (OLCF), a US Department of Energy (DOE) Office of Science User Facility located at DOE's Oak Ridge National Laboratory.

Simultaneously increasing the efficiency and reducing the emissions of natural gas-powered turbines is a delicate balancing act. It requires an intricate understanding of these massive energy-converting machines—their materials, aerodynamics, and heat transfer, as well as how effectively they combust, or burn, fuel. Of all these factors, combustion physics is perhaps the most complex.

In an H-class gas turbine, combustion takes place within 6-foot-long chambers at high temperature and pressure. Much like a car engine has multiple cylinders, GE's H-class turbines possess a ring of 12 or 16 combustors, each capable of burning nearly three tons of fuel and air per minute at firing temperatures exceeding 1,500ºC. The extreme conditions make it one of the most difficult processes to test at GE's gas turbine facility in Greenville, South Carolina.

At higher temperatures, gas turbines produce more electricity. They also produce more emissions, such as nitrogen oxides (NOx), a group of reactive gases that are regulated at the state and federal levels. To reduce emissions, GE's Dry Low NOx combustion technology mixes fuel with air before burning it in the combustor.

Such precise burning can lead to other problems, specifically an unstable flame. Inside a combustor, instabilities in the flame can cause deafening acoustic pulsations—essentially noise-induced pressure waves. These pulsations can affect turbine performance.

At their worst, they can wear out the machinery in a matter of minutes. For this reason, whenever a new pulsation is detected, understanding its cause and predicting whether it might affect future products becomes a high priority for the design team.

In 2014, one such pulsation caught researchers' attention during a full-scale test of a gas turbine. The test revealed a combustion instability that hadn't been observed during combustor development testing.

The company determined the instability levels were acceptable for sustained operation and would not affect gas turbine performance. But GE researchers wanted to understand its cause, an investigation that could help them predict how the pulsations could manifest in future designs.

The company suspected the pulsations stemmed from an interaction between adjacent combustors, but they had no physical test capable of confirming this hypothesis. Because of facility airflow limits, GE is able to test only one combustor at a time.

Even if the company could test multiple combustors, access-visibility and camera technology currently limit the researchers' ability to understand and visualise the causes of high-frequency flame instabilities. So GE placed a bet on high-fidelity modeling and simulation to reveal what the physical tests could not.

The company asked its team of computational scientists, led by Yan, to see if it could reproduce the instability virtually using high-performance computers. GE also asked Yan's team to use the resulting model to determine whether the pulsations might manifest in a new GE engine incorporating DOE-funded technology and due to be tested in late 2015, less than a year away.

GE then challenged Yan's team, in collaboration with the software company Cascade Technologies, to deliver these first-of-a-kind results before the 2015 test to demonstrate a truly predictive capability.

Such enhanced modeling and simulation capabilities held the potential to dramatically accelerate future product development cycles and could provide GE with new insights into turbine engine performance earlier in the design process instead of after testing physical prototypes.

In the spring of 2015, GE turned to the OLCF for help. Through the OLCF's Accelerating Competitiveness through Computational Excellence (ACCEL) industrial partnerships program, Yan's team received a Director's Discretionary allocation on Titan, a Cray XK7 system capable of 27 petaflops, or 27 quadrillion calculations per second.

Yan's team began working closely with Cascade Technologies, based in Palo Alto, California, to scale up Cascade's CHARLES code. CHARLES is a high-fidelity flow solver for large eddy simulation, a mathematical model grounded in fluid flow equations known as Navier-Stokes equations.

Using this framework, CHARLES is capable of capturing the high-speed mixing and complex geometries of air and fuel during combustion. The code's efficient algorithms make it ideally suited to leverage leadership-class supercomputers to produce petabytes of simulation data.

Cascade's CHARLES solver can trace its technical roots back to Stanford University's Center for Turbulence Research and research efforts funded through DOE's Advanced Simulation and Computing program. Many of Cascade's engineering team are alumni of these programs.

Although the CHARLES solver was developed to tackle problems like high-fidelity jet engine simulation and supersonic jet noise prediction, it had never been applied to predict combustion dynamics in a configuration as complex as a GE gas turbine combustion system.

Using 11.2 million hours on Titan, members of Yan's team and Cascade's engineering team executed simulation runs that harnessed 8,000 and 16,000 cores at a time, achieving a speedup in code performance 30 times greater than the original code.

Cascade's Sanjeeb Bose, an alumnus of DOE's Computational Science Graduate Fellowship Program, provided significant contributions to the application development effort, upgrading CHARLES' reacting flow solver to work five times faster on Titan's CPUs.

Leveraging CHARLES' massively parallel grid generation capabilities—a new software feature developed by Cascade—Yan's team produced a fine-mesh grid composed of nearly 1 billion cells. Each cell captured microsecond-scale snapshots of the air-fuel mix during turbulent combustion, including particle diffusion, chemical reactions, heat transfer, and energy exchange.

Working with OLCF visualisation specialist Mike Matheson, Yan's team developed a workflow to analyse its simulation data and view the flame structure in high definition. By early summer, the team had made enough progress to view the results: the first ever multicombustor dynamic instability simulation of a GE gas turbine.

The new capability gave GE researchers a clearer picture of the instability and its causes that couldn't be obtained otherwise. Beyond reproducing the instability, the advanced model allowed the team to slow down, zoom in, and observe combustion physics at the sub-millisecond level, something no empirical method can match.

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