When it comes to designing and developing cars that are not only efficient but also environmentally friendly, one of the most crucial factors is understanding and optimizing the aerodynamics. In the race to reduce fuel consumption and pollutant emissions, we need tools that can accurately predict the aerodynamic performance of vehicles. That's where NablaFlow's AeroCloud comes into play. But, how can we be sure that this advanced CFD (computational fluid dynamics) tool is up to the task? The answer lies in validation. In this blog post, we'll dive into two fascinating validation cases that demonstrate AeroCloud's ability to accurately calculate the drag coefficient of various car models. The first case deals with the widely used DrivAer model, while the second case focuses on a high-performance-type vehicle with modifications to the DrivAer model. These validation studies are vital steps in ensuring the reliability of the simulations and ultimately, the success of the product. So, buckle up and let's explore the world of accurate aerodynamics!
Introducing the DrivAer Model
The DrivAer model
To put AeroCloud's drag coefficient calculations to the test, we turned to the DrivAer model, an open-source benchmark model developed at TU-Munich . This model is based on a combination of two series cars and offers a more detailed geometry than other models commonly used in academia, such as the Ahmed or SAE body. The DrivAer model is also modular, featuring exchangeable rear ends, underbody, and wheel geometries, which makes it an ideal platform for investigating realistic flow features and their nonlinear interactions in various car configurations . This versatility allows us to examine how AeroCloud (pro version) performs across different car configurations, which is essential in understanding its accuracy.
The Validation Process: Comparing AeroCloud with Real-world Experiments
Streamlines over the DrivAer model extracted from the AeroCloud simulation
To validate AeroCloud's drag coefficient calculations, we compared its results with real-world experimental data from studies conducted at various wind tunnel facilities. These studies include the work of Varney et al. , which focuses on the validation of numerical methods, as well as tests conducted at the TUM wind tunnel in Munich , where the DrivAer model was developed , and the University of Stuttgart Göttingen-type wind tunnel . By examining different car configurations, we could gauge how well AeroCloud was able to capture the aerodynamic behavior of each variant. The goal was to see how closely the simulations matched the experimental results, which would demonstrate the tool's accuracy and reliability.
Delving Deeper: The Numbers Behind AeroCloud's Remarkable Accuracy
To truly appreciate the accuracy of AeroCloud's drag coefficient calculations, let's take a closer look at the actual results from both the wind tunnel tests and AeroCloud's simulations. The bar diagram below visually illustrate the match between experimental and CFD results, in the different highlighted configurations of the DrivAer model.
Different configuration of the model used in the experiment:
- Car 1: Fastback rear end, smooth underbody, no cooling system, no mirrors
- Car 2: Notchback rear end, smooth underbody, no cooling system, with mirrors
- Car 3: Estateback rear end, smooth underbody, no cooling system, with mirrors
- Car 4: Notchback rear end, detailed underbody, no cooling system, with mirrors
- Car 5: Estateback rear end, detailed underbody, no cooling system, with mirrors
- Car 6: Notchback rear end, detailed underbody, with engine bay cooling system, with mirrors
- Car 7: Estateback rear end, detailed underbody, with engine bay cooling system, with mirrors
Comparison of drag coefficients for the DrivAer model: Wind Tunnel Experiments vs. AeroCloud Pro CFD Simulations.
The data in the bar diagram demonstrate that the AeroCloud CFD simulations closely match the experimental results in every case, with only minor deviations in percentage: relative differences within +- 4 %. This exceptional level of agreement between the real-world tests and AeroCloud's predictions is a testament to the tool's accuracy and reliability.
Exploring the High-Performance Vehicle Validation Case
In a recent study, a new validation case was conducted for the CFD tool NablaFlow's AeroCloud, which focused on a high-performance enclosed-wheels car based on the DrivAer model . This validation case compares the simulation results of the drag and lift coefficients done in AeroCloud with the results presented in the study by Cranfield University, in which the DrivAer geometry was modified to represent a more realistic high-performance enclosed-wheels car . The results further emphasize the tool's accuracy and capability to handle complex aerodynamic scenarios.
The study demonstrated that the high-performance model produced a higher drag coefficient compared to the baseline model, with an increase of about 0.1, as illustrated in the bar diagram below. This was due to the presence of additional aerodynamic devices mounted on the original baseline model. However, the higher drag also corresponded to greater downforce generation, which is typical in motorsport applications. Interestingly, as the car model approached the ground, the aerodynamic efficiency increased. The study found that the minimum drag occurred at the highest ride height, while the maximum drag was at the lowest ride height, as shown in the bar diagram. Downforce was also found to be more sensitive to ride height changes, especially for the high-performance model equipped with aerodynamic devices designed to generate downforce and enhance ground effect. Refer to the bar diagram below for a visual representation of these findings.
Comparison of drag coefficients for the baseline and the High-Performance model for different elevation levels: Wind Tunnel Experiments vs. AeroCloud Pro CFD Simulations for the DrivAer model.
One key finding of the study was the impact of various aerodynamic devices on the baseline model. The front splitter was found to create a stagnation point above the plate, resulting in increased pressure on its upper surface and generating downforce. The rear spoiler, on the other hand, led to a larger wake and higher drag due to its effect on the base region. The underbody diffuser was found to increase both drag and downforce, while also causing a strong asymmetry in the wake.
Comparison of drag coefficients for the baseline and the baseline with added parts: Wind Tunnel Experiments vs. AeroCloud Pro CFD Simulations for the DrivAer model
These insights not only provide a better understanding of how aerodynamic devices and solutions influence high-performance vehicles but also highlight the importance of considering ground effect and ride height changes when optimizing vehicle aerodynamics. By utilizing the data obtained from this study, engineers can make more informed decisions about the design of high-performance cars and improve their overall performance on the track.
A closer look at the results reveals the most important effects of different car parts and types on the flow:
- Rear Ends: The Estateback model, characterized by a large slant angle and no boat tailing, exhibited the highest drag due to a larger wake and separation at the sharp trailing edge. The Notchback model showed a slight increase in drag compared to the Fastback, owing to the early separation on the rear windscreen and the resulting longer wake.
- Underbodies: Models with smooth underbodies produced lower drag, highlighting the significance of underbody paneling in reducing aerodynamic losses.
- Cooling Systems: The highest drag among all configurations was observed in models with open front grilles. The cooling air flow caused energy loss due to turbulence, friction, and pressure loss across heat exchangers.
Furthermore, the high-performance vehicle validation case demonstrated the effect of various aerodynamic devices on the drag coefficient and downforce generation. The study also highlighted the importance of considering ground effect and ride height changes when optimizing vehicle aerodynamics, which can contribute to better overall performance on the track. By consistently delivering accurate drag coefficients for different car configurations, AeroCloud proves to be a powerful and dependable tool that can significantly aid in the design and optimization of efficient and eco-friendly vehicles. The consistency between the wind tunnel tests and AeroCloud's simulations showcases the immense value of this cutting-edge technology for the automotive industry.
The Verdict: AeroCloud Delivers Remarkable Accuracy
After comparing the drag coefficients calculated by AeroCloud with those obtained from real-world experiments for both the DrivAer model and the high-performance vehicle validation case presented in  by the Cranfield University, we found that the tool's results were in excellent agreement with the experimental data. This indicates that AeroCloud is highly accurate in predicting the aerodynamic behavior of various car configurations, making it a reliable and powerful tool for designing and optimizing vehicles.
AeroCloud's Impact on the Automotive Industry
As a testament to the effectiveness of AeroCloud, our tool has been used in the development of the world's fastest electric cars, such as Automobili Estrema's Fulminea. The high-performance vehicle validation case showcases the versatility and adaptability of AeroCloud for various automotive applications, further establishing its credibility in the industry. To learn more about how AeroCloud is helping shape the future of sustainable and high-performance vehicles, check out our previous blog post: "The Need for Speed and Sustainability: NablaFlow Joined the Supercars' Race Towards a Greener Future."
With the continuous advancements in AeroCloud's capabilities, we strive to deliver even greater accuracy and reliability to our clients. Our dedication to addressing the challenges faced by the automotive industry, and our commitment to pushing the boundaries of CFD technology, make AeroCloud a driving force for innovation and sustainability.
Use this link to access the interactive result page of an example sport car!
Author of the article: Trond-Ola Hågbo.
Author of the study: Micol Sala.
 Chair of Aerodynamics and Fluid Mechanics. (2022). Geometry - DrivAer Model. Technical University of Munich. Retrieved from https://www.aer.mw.tum.de/en/research-groups/automotive/drivaer/
 Varney, Max & Passmore, Martin & Wittmeier, Felix & Kuthada, Timo. (2020). Experimental Data for the Validation of Numerical Methods: DrivAer Model. Fluids. 5. 236. 10.3390/fluids5040236.
 Mack, Steffen & Indinger, Thomas & Adams, Nikolaus & Blume, Stefan & Unterlechner, Peter. (2012). The Interior Design of a 40% Scaled DrivAer Body and First Experimental Results. 10.1115/FEDSM2012-72371.
 James, Taryn & Krueger, Lothar & Lentzen, Manfred & Woodiga, Sudesh & Chalupa, Karel & Hupertz, Burkhard & Lewington, Neil. (2018). Development of a Full-Scale DrivAer Generic Realistic Wind Tunnel Correlation and Calibration Model. SAE International Journal of Passenger Cars - Mechanical Systems. 11. 10.4271/2018-01-0731.
 Soares, Renan & Knowles, Andrew & Goñalons, Sergio & Garry, Kevin & Holt, Jennifer. (2018). On the Aerodynamics of an Enclosed-Wheel Racing Car: An Assessment and Proposal of Add-On Devices for a Fourth, High-Performance Configuration of the DrivAer Model. 10.4271/2018-01-0725.
Urban Wind Engineer at NablaFlow, Trond-Ola, has a strong background in energy, climate, and environment, and he is currently completing his Ph.D. studies in computational fluid dynamics.
Trond-Ola has spent several years at NablaFlow researching and developing tools to simulate wind flow within the atmospheric boundary layer, focusing on urban areas. These tools form the core of ArchiWind. Trond-Ola has a passion for nature and the environment and believes that ArchiWind can be used to tackle emerging urban wind challenges related to extreme weather events powered by climate change. These include pedestrian wind comfort and safety, city ventilation related to heat waves and pollution, and urban wind power production.