A Deep Learning Simulation Framework for Building Digital Twins of Wind Farms: Concepts and Roadmap

Simulation-based Digital Twins are often limited by the difficulties encountered in the real-time simulation of continuous physical systems, for example, fluid flow simulations in complex domains. Classical methods used to simulate such systems, such as the mesh-based methods, typically require state-of-the-art computing infrastructure to get a rapid estimation of the trajectory of the system dynamics if the problem size is large. We propose a simulation framework comprising of a Physics Informed Neural Network (PINN) and a model order reduction strategy based on the Dynamic Mode Decomposition (DMD) technique for rapid simulation of fluid flows, such as air, in complex domains. This framework is primarily targeted at realizing a Digital Twin of a wind farm in terms of the aerodynamics aspects. However, the framework will be flexible and capable of creating simulation-based Digital Twins of other systems involving continuous physics. The reduced order model aims to make this framework lightweight, such that a trained model will be able to run even on compact edge devices. In this paper, we present the building blocks of this framework, a few key concepts, and a roadmap for completing the framework. We illustrate our approach with the help of an example in transient heat transfer.