Abstract
This study develops and validates a machine-learned (ML) actuator line model (ALM) as a promising advancement in turbine/rotor modeling that can be applied to diverse engineering applications. The model alleviates two limitations of the standard ALM, namely, its reliance on the predefined lift and drag coefficient tables and its inability to account for flow unsteadiness. The ML-ALM model is trained using a blade-resolved simulation database of forces acting on blade elements for unsteady inflow conditions. The model is validated for solitary turbine performance and wake predictions against experimental data and is verified for an inline turbine case for the performance and wake predictions of the downstream turbine against blade-resolved simulations. Its engineering applicability is demonstrated for an eight-turbine array farm simulation. The ML-ALM predicts turbine performance and wakes within 10% of blade-resolved results along with credible advection of tip vortical structures including breakdown and turbulent kinetic energy burst, using 92% less computational time than corresponding blade-resolved simulations.
Issue Section:
Techniques and Procedures
Topics:
Actuators,
Additive manufacturing,
Blades,
Machinery,
Rotors,
Simulation,
Turbines,
Wakes,
Flow (Dynamics)
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