As the use of natural gas grows across the United States, companies like General Electric are working to concurrently increase the efficiency while reducing the emissions impact of their gas turbines. In an effort to model the instabilities found in a gas turbine with multiple combustors, GE partnered with Oak Ridge Leadership Computing Facility (OLCF) to utilize CharLES, our high-fidelity algorithm for large eddy simulations to capture the complexities and hence instabilities that arise from the mixing the fuel and air during combustion. The results are projected to garner a full percentage point gain in efficiency yields.
Below is a clip from an article published today by the DOE/Oak Ridge National Laboratory. To see the full article, go here.
A Computing Breakthrough
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.