Abstract
Existing Reynolds Averaged Navier–Stokes-based transition models do not accurately predict separation induced transition for low pressure turbines. Therefore, in this paper, a novel framework based on computational fluids dynamics (CFD) driven machine learning coupled with multi-expression and multi-objective optimization is explored to develop models which can improve the transition prediction for the T106A low pressure turbine at an isentropic exit Reynolds number of Re2is=100,000. Model formulations are proposed for the transfer and laminar eddy viscosity terms of the laminar kinetic energy transition model using seven non-dimensional pi groups. The multi-objective optimization approach makes use of cost functions based on the suction-side wall-shear stress and the pressure coefficient. A family of solutions is thus developed, whose performance is assessed using Pareto analysis and in terms of physical characteristics of separated-flow transition. Two models are found which bring the wall-shear stress profile in the separated region at least two times closer to the reference high-fidelity data than the baseline transition model. As these models are able to accurately predict the flow coming off the blade trailing edge, they are also able to significantly enhance the wake-mixing prediction over the baseline model. This is the first known study which makes use of ‘CFD-driven’ machine learning to enhance the transition prediction for a non-canonical flow.
Highlights
As air travel is predicted to see continued growth, the aviation industry is focused on further improving the efficiency of gas turbines
This causes a change in the boundary layers from turbulent at takeoff to transitional at cruise, which is the major portion of the flight
Based on the shortcomings of the laminar kinetic energy (LKE) model (Section 2.2.1), we propose to modify the laminar eddy viscosity term from the LKE production term (Pl ) and the transition parameter (ψ) from the transfer term (R) using the non-dimensional pi groups
Summary
As air travel is predicted to see continued growth, the aviation industry is focused on further improving the efficiency of gas turbines. Any technological advances which incrementally increase gas turbine efficiency can save millions of dollars, reduce carbon emissions, and create a sustainable future. If the efficiency of the LPT increases by 1%, the specific fuel consumption will decrease by 0.6–0.8% [1], translating into considerable fuel savings and emissions reductions, considering the volume of operations. LPT design and analysis is challenging as there is a large variation in flow physics between the aircraft taking off and entering cruise conditions. The Reynolds number of LPTs ranges from 0.5 × 105 at cruise to 5 × 105 at takeoff [2]. This causes a change in the boundary layers from turbulent at takeoff to transitional at cruise, which is the major portion of the flight
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
