Response Surface Methodology (RSM) Concepts, Optimization by ANSYS Fluent
Free
- Introduction to the Response Surface Methodology (RSM) in ANSYS Fluent software
- Introduction to the Genetic Aggregation
- Introduction to the Full 2nd-Order Polynomials
- Introduction to the Kriging
- Introduction to the Non-Parametric Regression
- Introduction to the Neural Network
- Introduction to the Sparse Grid
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Description
Response Surface Methodology (RSM) Concepts, Optimization by ANSYS Fluent
Introduction
This chapter discusses RSM Concepts and presents a general introduction to the Response Surface Methodology (RSM).
Response surface methodology (RSM) is one of the crucial and important steps in the Optimization process in ANSYS Fluent.
RSM is a main step after the design of experiment (DOE). It means RSM utilizes results obtained from the sample design points so that it can estimate approximate values throughout the design space without needing a complete solution.
Note that response surfaces are functions in which the output parameters are described in terms of the input parameters. Therefore, according to the resulting values at the sample design points in the range of the input parameter variations, the response surfaces can evaluate the output parameters for the entire range of the input parameter variation.
There are several types of response surfaces available in ANSYS optimization. These response surface types include:
- ّGenetic Aggregation
- Full 2nd-Order Polynomials
- Kriging
- Non-Parametric Regression
- Neural Network
- Sparse Grid
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