Unlock the future of 3D geometry predictions with cutting-edge PI-BO optimization technology, now available through the innovative Stochos App.
Challenges
Despite the advancements in 3D modeling technologies, several challenges persist. One of the primary issues is the computational complexity involved in generating accurate geometric predictions. Traditional methods often require significant processing power and time, which can be a bottleneck in the design process.
Additionally, achieving a balance between accuracy and computational efficiency remains a critical challenge. Ensuring that predictions are not only precise but also generated within a reasonable timeframe is essential for practical applications in engineering and design.
Engineering Solution
The implementation of optimization within the Stochos App addresses these challenges head-on. By utilizing probabilistic inference, the app can efficiently navigate the vast design space to identify optimal geometric configurations. This approach reduces the computational load while maintaining high accuracy levels.
Case Study:
Previously, a 2D benchmark case of a simplified shower head (Figure 1) was utilized to demonstrate a Probabilistic Inference for Bayesian Optimization (PI-BO) application in optiSLang1.
Figure 1. 2D Benchmark Case
The study explored the optimal design (i.e. the diameters of internal holes) to satisfy two output conditions of maximum pressure drop and minimum uniformity. Out of 20 designs, a Pareto Front solution suggested, design 19 as the optimal one (Figure 2).
Figure 2. PI-BO Optimization Solution
Current study utilizes the data from the optimization work to generate a geometry prediction app using stand-alone Stochos. For this purpose, the optimization table from Ansys Workbench, the exported data from Ansys CFD-Post with coordinates and field variables of pressure and velocity, and geometry files exported from Ansys Discovery were utilized to create a visualization toolkit (vtk) file. That was done with a Python code using the Stochos functions including dimgp and bayesian optimization. An additional Python code was utilized to generate the app (Figure 3).
Figure 3. Schematic Representation of the App Generation
Once the app is active, the first step is to enter the path for the data where the corresponding vtk files are found for all 20 design points (Figure 4).
Figure 4. The GUI of the App to Enter the Path for the Data
Then, clicking the Load and predict button brings the predicted design points to visualize the geometric features colored with a selected parameter, such as pressure (Figure 5). Note that, we used a coarse mesh for this study, and therefore the predicted geometry field is demonstrated with scattered dots. A finer mesh would lead to more of a regular contour plot. Also note that, the Stochos code uses only a group of design points for training, to predict the entire set.
Figure 5. The Section of the design, and Visualization of the Geometry and Pressure Distribution
Stochos predicted geometry and pressure field was found to in fairly good agreement with the CFD-Post contour from Fluent simulation for design 19 (Figure 6).
Figure 6. The Comparison of Stochos App Predicted Geometry and Pressure Field to the Fluent Simulation for Design 19
The next step will be, generating another Stochos App with the genAI application to predict the geometry and pressure field from any given pressure drop and uniformity parameters.
Benefits
The integration of PI-BO optimization technology in the Stochos App offers numerous benefits. First and foremost, it enhances the accuracy of 3D geometry predictions, providing users with reliable and precise models. This leads to better design outcomes and reduces the need for costly revisions.
Additionally, the app's efficiency in processing complex calculations ensures that users can iterate quickly, speeding up the overall design cycle. This efficiency translates to cost savings and improved productivity for engineering teams. Furthermore, the user-friendly interface makes advanced optimization accessible to a broader audience, democratizing high-level computational tools.
Ozen Engineering Expertise
Ozen Engineering Inc. leverages its extensive consulting expertise in CFD, FEA, optics, photonics, and electromagnetic simulations to achieve exceptional results across various engineering projects, addressing complex challenges like multiphase flows, erosion modeling, and channel flows using Ansys software.
We offer support, mentoring, and consulting services to enhance the performance and reliability of your mining equipment and systems. Trust our proven track record to accelerate projects, optimize performance, and deliver high-quality, cost-effective results for both new and existing systems. For more information, please visit https://ozeninc.com.
The details of the application can be found in below video:
The video can be reached from Ozen Engineering YouTube channel with the following link:
Geometry Prediction using Data from Optimization: A Stochos App Application
Reference
1 Ozen Engineering Blog: Probabilistic Inference for Bayesian Optimization Application on a 2D Benchmark Case
Note
Special thanks to Dr.-Ing Kevin Cremanns from PI Probaligence GmbH