This chapter discusses DOE Concepts and presents a general introduction to the Design of Experiments (DOE). Creating and utilizing a design of experiment (DOE) is one of the crucial and important steps in the Optimization process in ANSYS Fluent. DOE is a technique for defining sample design points to conduct experiments, so that the output variables will be obtained based on the input variables at these sample design points. DOE algorithms attempt to determine sample design points in a way that the entire space of the input parameters’ ranges is explored to obtain the output parameters. DOE is a main step before response surface methodology (RSM). So, building DOE efficiently causes improvement in the accuracy of the response surface derived from the sample design points. There are different DOE types available in ANSYS optimization. These DOE types determine the method or algorithm required to define the sample design points. These DOE types include: ّCentral Composite Design (CCD) Box-Behnken Design (BBD) Optimal Space-Filling Design (OSF) Sparse Grid Initialization Latin Hypercube Sampling Design (LHS) In CCD, there are different design types available. Including: Rotatable VIF-Optimality G-Optimality Face-Centered In OSF and LHS, there are different sample types available. Including: CCD Samples Linear Model Samples Pure Quadratic Model Samples Full Quadratic Samples

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

In this project, we present the optimization process for improving the performance of a combustion chamber using the Design of Experiment (DOE) in ANSYS software. We intend to optimize the design of a combustion chamber. Therefore, we defined 8 input parameters, including the cone angular velocity, outer diameter, cone height, cone length, air inlet diameter, fuel inlet diameter, air inlet offset, and fuel inlet offset. Then, we defined the outlet temperature, CO2 mass fraction, CO mass fraction, average temperature, total heat generation, and chamber heat flux as the target output parameters. We used the Design Exploration tool to perform the optimization process. First, we start with the Design of Experiment (DOE). We generated the design points using the Central Composite Design (CCD). According to the maximum and minimum ranges for all three input parameters, design points are generated. Then, we continue with the Response Surface Methodology (RSM). We estimated the output parameter values ​​based on the Genetic Aggregation type.

In this project, we present the optimization process of a compressor cascade using the Design of Experiments (DOE) in ANSYS software. We intend to optimize the design of a solar chimney. Therefore, we defined 3 input parameters: 2 geometric factors, pitch and angle of attack, and 1 operating factor, velocity inlet. Then, we defined the drag and lift forces as the target output parameter. We used the Design Exploration tool to perform the optimization process. First, we start with the Design of Experiment (DOE). We generated the design points using the Box-Behsnken Design (BBD). According to the maximum and minimum ranges for all three input parameters, 13 design points are generated. Then, we continue with the Response Surface Methodology (RSM). We estimated the output parameter values ​​based on the Genetic Aggregation type.

In this project, we present the optimization process of a solar chimney using the Design of Experiments (DOE) in ANSYS software. We intend to optimize the design of a solar chimney. Therefore, we defined 3 input parameters (geometric factors), including tower height, collector radius, and the angle of the absorber plate. Then, we defined the airflow rate as the target output parameter. We used the Design Exploration tool to perform the optimization process. First, we start with the Design of Experiment (DOE). We generated the design points using the Optimal Space-Filling Design (OSPF). According to the maximum and minimum ranges for all three input parameters, 15 design points are generated. Then, we continue with the Response Surface Methodology (RSM). We estimated the output parameter values ​​based on the Genetic Aggregation type.

In this project, we present the optimization process for improving the thermal performance of a microchannel heat sink using the Design of Experiment (DOE) in ANSYS software. We intend to optimize the design of a microchannel heat sink. Therefore, we defined 3 input parameters: Two geometric factors, including the length and height sizes of the rectangular cross-section of the cooling fluid channel, and one operating factor, i.e., porosity of the porous medium of the channel. Then, we defined the maximum temperature of the microchannel surface as the target output parameter. We used the Design Exploration tool to perform the optimization process. First, we start with the Design of Experiment (DOE). We generated the design points using the Latin Hypercube Sampling Design (LHSD). According to the maximum and minimum ranges for all three input parameters, 10 design points are generated. Then, we continue with the Response Surface Methodology (RSM). We estimated the output parameter values ​​based on the Genetic Aggregation type.