Predicting Complex Erosion Profiles in Steam Distribution Headers with Convolutional and Recurrent Neural Networks
Steve Yang, Zulfikhar A. Ali, Hyuna Kwon, Bryan M. Wong
Abstract
The effects of erosion due to particle impingement continue to be of immense concern in various energy and technology industries. Brute force computational fluid dynamics (CFD) approaches allow accurate predictions of complex erosion processes; however, these large-scale calculations can be very computationally expensive. Specifically, when different initial conditions are required to analyze the system, the CFD simulations must be restarted de novo without recourse to previously converged cases. To address these issues, we harness convolutional neural network (CNN) and long- and short-term memory (LSTM) machine learning approaches to predict complex surface erosion profiles in steam distribution headers for the first time. We show that this hybrid machine learning approach can accurately predict entire particle trajectories and surface erosion profiles when only initial positions and velocities are inputted into the algorithm. Most importantly, our approach is 600 times faster than conventional CFD calculations and gives impressive R2 scores of 0.91 and 0.71 for particle trajectories and surface erosion profiles, respectively. Taken together, our hybrid machine learning approach is a promising technique for accurately predicting surface erosion rates but with a significantly reduced computational cost.