Building a data-driven reduced order model of a chemical vapor deposition process from low-fidelity CFD simulations

Abstract

A computational methodology is introduced for the development of a Reduced Order Model (ROM) using data from low-fidelity, in terms of the spatial discretization, Computational Fluid Dynamics (CFD) simulations. The methodology is applied in the context of investigating efficiently, new chemistry pathways in Chemical Vapor Deposition (CVD) processes by comparing experimental findings with simulation results. The proposed approach involves building a very coarse, yet three-dimensional CFD model of the process that does not include chemical reactions, which provides “snapshots”, i.e. time instances, of the dynamic behavior of the system. This bundle of low-fidelity data is used for generating a reduced order model by implementing Proper Orthogonal Decomposition (POD), for the time-invariant subspace approximation of the data and Artificial Neural Networks (ANN) for the time-dependent coefficients. This last step circumvents the need for Galerkin projection of the equations on the subspace, rendering therefore the approach equation-free and purely data-driven. The ultimate goal addressed in this work is to produce fast approximations of the temperature, pressure and flow field in a CVD reactor, which will then be used as starting points of high-fidelity, in terms of the computational mesh, CFD simulations, that include multiple surface and gas phase reactions. Given the quality of approximations, the model corrects for the species distributions after only a few time-steps, providing thus quick and accurate deposition-rate and uniformity results. This allows for efficient and easy fine-tuning adjustments of the chemistry model diminishing the need to solve the high-fidelity model many times.