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.

Solar Chimney Optimization using Design of Experiments (DOE) in ANSYS

Project Overview

This project demonstrates the optimization of a solar chimney using Design of Experiments (DOE) methodology in ANSYS software.

A solar chimney comprises a tall vertical tower connected to a wide circular collector at its center. Air enters through a gap between the ground and the collector’s absorber plates surrounding the chimney base, while the outlet is located at the tower’s top.

Solar radiation on the absorber plate transfers heat to the airflow beneath the collector. Rising air temperature causes decreased air density and pressure, making buoyancy effects dominant. Consequently, air moves upward at significant velocity.

Methodology

Geometry and Meshing: The 3D solar chimney with simplified construction was modeled in Design Modeler software, followed by mesh generation in ANSYS Meshing software.

Optimization Parameters: The optimization process focuses on three input parameters (geometric factors):

• Tower height
• Collector radius
• Absorber plate angle

The target output parameter is airflow rate.

Optimization Process: The Design Exploration tool was employed for optimization through two stages:

1. Design of Experiments (DOE): Design points were generated using Optimal Space-Filling Design (OSFD). Based on maximum and minimum ranges for all three input parameters, 15 design points were created.

2. Response Surface Methodology (RSM): Output parameter values were estimated using Genetic Aggregation algorithms.

Results and Analysis

Parameter Effects: RSM-generated 2D and 3D plots of mass flow rate reveal the simultaneous effects of the three input parameters. Results demonstrate that increasing tower height, collector radius, and absorber plate angle all increase mass flow rate.

Tower Height Impact: As tower height increases, the pressure difference between base and top increases (ΔP = ρgh). This greater pressure differential enhances buoyancy force, accelerating upward hot air movement.

Collector Radius Impact: Increasing collector radius expands the collector area, enabling greater solar absorption and enhanced heat transfer to air beneath the collector. Higher temperatures reduce air density, strengthening buoyancy forces.

Absorber Plate Angle Impact: Increasing the collector’s slope creates a greater suction effect, facilitating easier hot airflow movement toward the chimney.

Optimal Design: The optimal configuration is achieved at maximum values for height, radius, and angle. Comparison between baseline and optimal cases was performed using velocity contours and vectors.

Validation and Sensitivity: Additional analysis included:

• Local Sensitivity Plots: Quantifying each input parameter’s influence on the output parameter
• Goodness of Fit Plots: Assessing the accuracy of RSM-estimated results compared to actual design point results, confirming the reliability of the optimization process