Understanding and Improving Materials Processing Through Interpreting and Manipulating Predictive Models

Abstract

Most physically based modeling software accepts input in the form of geometry definition, physical parameters, initial conditions, and boundary conditions; and then, on the basis of solving physical conservation equations, predicts the steady-state or transient behavior of a system or process. There is a growing need to create software tools that can themselves control or manipulate the physically based models in certain ways to enhance the usability of models for equipment design and process optimization. These required tools can be described broadly in the following categories: sensitivity analysis, parameter estimation, inverse problems, dynamic optimization, and real-time control. This paper discusses generally the development and application of such modeling tools, drawing examples from a specific RTP reactor design. These techniques accelerate significantly the optimal design of processes and the concurrent engineering of real-time process-control algorithms.

Systematic Strategy for Modeling and Optimization of Thermal Systems With Design Uncertainties

“…Comprehensive reviews on CVD reactor studies are given by Mahajan (Mahajan, 1996) and Jensen et al (Jensen et al, 1991). Kee et al (Kee et al, 1995;Raja et al, 2000) demonstrated that model simulations have much greater flexibility and versatility as compared to experimental counterparts. Experimental studies have also been carried out on the flow in channels for CVD applications (Jensen et al, 1991;Chiu, Richards et al, 2000;Chiu et al, 2001;Chiu et al, 2002).…”

Optimization of Gallium Nitride Metalorganic Chemical Vapor Deposition Process

“…Comprehensive reviews on CVD reactor studies were given by Mahajan [18] and Jensen et al [12]. Numerical modeling is widely used for the optimization of thermal systems as they have much greater flexibility and versatility as compared to experimental counterparts [19]. Examples include: Rashidi et al [20] numerically modeled the heterogeneous heating applied to a cavity containing nanofluid to identify the optimal profile of heat flux; numerical modeling and optimization of the global thermal resistance of a T-Y-shaped cavity were investigated by Lorenzini and Rocha [21]; Hajmohammadi et al [22] investigated the optimization of fluid flow and heat transfer in curved tubes; minimization of peak temperature in a heat generating body by optimizing the N-branch cavities with respect to volume constraints was studied by Hajmohammadi et al [23]; Hajmohammadi et al [24] investigated the optimal architecture of multiple heat sources cooled in a circular shaped fin; minimization of the peak temperatures in thermal systems is investigated by Hajmohammadi et al [25][26][27], and Bejan and Sciubba [28] investigated the optimal spacing for maximum heat transfer from a stack of parallel plates cooled by forced convection.…”