To reduce weight and lower greenhouse gas emissions, modern jet engines are designed to use as few low-pressure turbine blades as possible. However, as the number of blades is reduced, individual blades are subjected to higher loading, which can introduce complex, unsteady airflow patterns that lead to an increase in fuel consumption.
Using high-order computational fluid dynamics, the research team will be simulating flow over the MTU-T161 low-pressure turbine linear cascade under various operating conditions and at unprecedented scale and resolution. The team’s open-source Python-based code, PyFR, combines highly accurate numerical methods with a highly flexible, portable, and scalable code implementation that makes efficient use of GPU accelerators.
Moreover, MTU Aero Engines will make a comprehensive experimental dataset openly available for the first time for this test case. Results will be used to validate the performance of high-order implicit large eddy simulation as a predictive technology for cascades and turbine blade rows. Results will also be used to obtain unprecedented insight into the physics of three-dimensional unsteady flow over low-pressure turbines, including the effect of inlet turbulence on transition, the effect of inlet wake generators on transition, the shape and behavior of the separation bubble on the suction-side of the blade, and the effect of non-parallel end walls on transition and losses.
Finally, results will provide an extended database to enable development of improved models for lower fidelity—but significantly cheaper—Reynolds-Averaged Navier–Stokes simulations of low pressure turbine configurations.
|Source||Hours||Start Date||End Date|
|DOE INCITE PROGRAM||2,350,000||2018-01-01||2019-01-31|
|DOE INCITE PROGRAM||5,000||2018-01-01||2019-01-31|
|DOE INCITE PROGRAM||80,000,000||2018-01-01||2019-01-31|
|DOE INCITE PROGRAM||2,500||2017-06-30||2019-01-31|
|DOE INCITE PROGRAM||20,000||2017-01-01||2017-12-31|
|DOE INCITE PROGRAM||100,000,000||2017-01-01||2017-12-31|
|DOE INCITE PROGRAM||2,350,000||2017-01-01||2017-12-31|