Publications Details
Massively parallel computation of 3D flow and reactions in chemical vapor deposition reactors
Computer modeling of Chemical Vapor Deposition (CVD) reactors can greatly aid in the understanding, design, and optimization of these complex systems. Modeling is particularly attractive in these systems since the costs of experimentally evaluating many design alternatives can be prohibitively expensive, time consuming, and even dangerous, when working with toxic chemicals like Arsine (AsH{sub 3}): until now, predictive modeling has not been possible for most systems since the behavior is three-dimensional and governed by complex reaction mechanisms. In addition, CVD reactors often exhibit large thermal gradients, large changes in physical properties over regions of the domain, and significant thermal diffusion for gas mixtures with widely varying molecular weights. As a result, significant simplifications in the models have been made which erode the accuracy of the models` predictions. In this paper, the authors will demonstrate how the vast computational resources of massively parallel computers can be exploited to make possible the analysis of models that include coupled fluid flow and detailed chemistry in three-dimensional domains. For the most part, models have either simplified the reaction mechanisms and concentrated on the fluid flow, or have simplified the fluid flow and concentrated on rigorous reactions. An important CVD research thrust has been in detailed modeling of fluid flow and heat transfer in the reactor vessel, treating transport and reaction of chemical species either very simply or as a totally decoupled problem. Using the analogy between heat transfer and mass transfer, and the fact that deposition is often diffusion limited, much can be learned from these calculations; however, the effects of thermal diffusion, the change in physical properties with composition, and the incorporation of surface reaction mechanisms are not included in this model, nor can transitions to three-dimensional flows be detected.