Fast prediction of turbine energy acquisition capacity under combined action of wave and current based on digital twin method

ABSTRACT

Due to the strong nonlinear interaction between the flow field and blades, the prediction of turbine energy acquisition capacity is still quite complex, the traditional methods take too long time to evaluate. In this article, the digital twin model + CFD simulation + monitor data are used for turbine energy efficiency assessment. A self-designed vertical wave flow turbine (VWFT) is taken as the research object, the prediction takes into account the coupling of the VWFT for motion and blade rotation, which is in good agreement with the monitor data at sea. The simulations show that the torque, thrust force and lateral force flow rate data can be loaded from the database into the digital model. If those data are not in the database, the interpolation method is used along with deep learning of recurrent neural network to obtain the energy harvesting parameters, all the error is no more than 10%.

KEYWORDS:

• Digital twin
• vertical wave flow turbine
• deep learning
• recurrent neural network
• energy acquisition capacity

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was supported by the National Natural Science Foundation of China (Grant No. 31972845, 51876114, 52071203), Engineering Guangdong Laboratory (Guangzhou) (grant number GML2019ZD0502), The Open Fund of Key Laboratory of High Performance Ship Technology (Wuhan University of Technology), Ministry of Education (grant number gxnc21112703).