By Karthika Swamy Cohen
At a minisymposium on scientific computing and big data at the SIAM Annual Meeting, Shashank Yellapantula (GE Global Research) talked about the “Role of Scientific Computing in Gas Turbine Engine Design.”
“15 years ago, all of aircraft design was based on excel spreadsheets,” Yellapantula said.
“How many of those aircrafts are still in the air?” someone from the audience chimed in.
Many, it turns out, since design to launch takes about 7-8 years.
Scientific computing and big data analytics are also helping make gas turbines energy efficient, he said. A gas turbine has 12 combustor cans, and takes air in the amount of 21 trailers full and fuel in the amount of nine propane tanks. So even a marginal increase in efficiency can make a big difference considering how many gas turbines operate 365 days a year.
About 80 gigabytes of data is generated from experimental testing of these turbines every second and 16 terrabytes of data gathered per month. The process is time consuming and high-performance computing and data analytics have become critical for improvement in experimental diagnostics. GE works with software partners to develop code capable of accurate modeling.
“This is a great opportunity for data analytics, combustion physics, and data analytics to come together,” Yellapantula said.