Introduction
Wind tunnel testing provides valuable experimental data that accurately represents the aerodynamic forces acting on a structure. This data is crucial for:
- Validating and Calibrating Simulations: Ensuring that numerical models in RFEM closely match real-world conditions.
- Enhancing Design Accuracy: Providing detailed insights into wind pressures and forces, leading to more accurate and efficient structural designs.
- Safety Assurance: Helping engineers identify potential vulnerabilities and design safer structures.
Importance of Validation Example
Validation is a key step in any simulation process. It ensures that the model accurately represents real-world conditions. By comparing simulation results with experimental data, engineers can identify discrepancies and refine their models, leading to more accurate predictions.
Step-by-Step Implementation in RFEM
1. Collect and Prepare Wind Tunnel Data
Conduct wind tunnel tests to measure wind pressures, forces, and flow patterns on a scaled model of the structure. In this example, we used wind pressure experimental data on probe points.
Organize the data into a structured format, typically CSV or Excel, including wind pressure values.
2. Set Up Model in RFEM
Open RFEM and create a new project, and build the geometry of the experimental model.
- Define a load case for experimental data and enter point probes coordination by using additional surface result points .
- Define Simulation Parameters: Set up the domain size, boundary conditions, mesh density, wind profile and turbulence intensity.
3. Results and Interpolation Methods
Two interpolation methods are available in RWIND: diffusion interpolation and Gaussian interpolation kernel (Figure 3). Only one method must be selected for all probes (see
knowledge base article 1871
). It is possible to transfer experimental wind load data by using interpolation method in order to structural analysis and design in RFEM
The diffusion method distributes the data from the "source" point over the surface. It is suitable for dense mesh of measuring points. In the case of thin open structures, this method interpolates values only on one side of the plate. It is possible to transfer experimental wind load by using motioned technique in order to structural analysis and design.
Here is the results for diffusion interpolation (Figure 4):
Also calculation of statistical parameters and related diagram are provided manually to show how much the results of RWIND and experimental are close to each other. The Simplified Mesh RWIND simulation data shows a slightly better correlation with the experimental wind pressure data than the Exact Mesh RWIND data (Figure 5). However, both meshes exhibit good agreement with the experimental data, making RWIND a reliable tool for predicting wind pressures. The high statistical values (R and R2) demonstrate that both simulation approaches can effectively replicate experimental wind pressure results, with the Simplified Mesh performing slightly better (Figure 6).
Conclusion
Integrating validating data into RWIND is a crucial step in achieving accurate and reliable wind flow predictions. By following a systematic approach to prepare, import, and compare experimental data with simulation results, engineers can refine their models and ensure that their designs are both efficient and safe. This process not only enhances the credibility of RWIND simulations but also contributes to the overall advancement of structural engineering practices.
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