ooDACE toolbox: a flexible object-oriented Kriging implementation

When analyzing data from computationally expensive simulation codes, surrogate modeling methods are firmly established as facilitators for design space exploration, sensitivity analysis, visualization and optimization. Kriging is a popular surrogate modeling technique used for the Design and Analysis of Computer Experiments (DACE). Hence, the past decade Kriging has been the subject of extensive research and many extensions have been proposed, e.g., co-Kriging, stochastic Kriging, blind Kriging, etc. However, few Kriging implementations are publicly available and tailored towards scientists and engineers. Furthermore, no Kriging toolbox exists that unifies several Kriging flavors. This paper addresses this need by presenting an efficient object-oriented Kriging implementation and several Kriging extensions, providing a flexible and easily extendable framework to test and implement new Kriging flavors while reusing as much code as possible

Modeling and Optimization of M-cresol Isopropylation for Obtaining N-thymol: Combining a Hybrid Artificial Neural Network with a Genetic Algorithm

The application of a hybrid framework based on the combination, artificial neural network-genetic algorithm (ANN-GA), for n-thymol synthesis modeling and optimization has been developed. The effects of molar ratio propylene/cresol (X1), catalyst mass (X2) and temperature (X3) on n-thymol selectivity Y1 and m-cresol conversion Y2 were studied. A 3-8-2 ANN model was found to be very suitable for reaction modeling. The multiobjective optimization, led to optimal operating conditions (0.55 ≤X1≤0.77; 1.773 g ≤ X2 ≤1.86 g; 289.74 °C ≤ X3 ≤291.33 °C) representing good solutions for obtaining high n-thymol selectivity and high m-cresol conversion. This optimal zone corresponded to n-thymol selectivity and m-cresol conversion ranging respectively in the interval [79.3; 79.5]% and [13.4 %; 23.7]%. These results were better than those obtained with a sequential method based on experimental design for which, optimum conditions led to n-thymol selectivity and m-cresol conversion values respectively equal to 67%and 11%. The hybrid method ANN-GA showed its ability to solve complex problems with a good fitting

Reynolds-averaged Navier-Stokes simulation of turbulent flow in a circular pipe using OpenFOAM®

A RANS simulation of flow through a pipe is performed and validated against experimental data and previous DNS results. A mesh refinement study is performed to illustrate the near wall mesh size needed to correctly predict mean flow characteristics. In addition, aspects of the model are changed to study their impact on the results as well as the computational requirements. Comparisons are made between a two-dimensional analysis with axisymmetric boundary conditions, a one-eighth axisymmetric model, a one-fourth axisymmetric model, and a full three-dimensional pipe. The two-dimensional model provides the best match to past data; however, it is noted that the model may not be well tuned for a three-dimensional mesh. The simulation is also performed using three different turbulence models and the results of each model are compared. The purpose of the model is to create a tool that can be used for design iterations. While the model does not fully capture the complexities of turbulent flow, it is able to predict the mean flow accurately enough to be useful in a design setting. The goal of this work is to create a foundation upon which further studies of pipe flow with internal obstructions can build. The overall results show the model is able to predict the mean flow well for the validation case. However, the model does not perform well when certain aspects are changed. Increasing the robustness of the model and the determination of more usable boundary conditions remains a subject for future studies