Wind tunnels require advanced data visualization techniques for aerodynamic modeling to handle the real-time data from test environments. Wind tunnels are deployed across the F1 grid, but also in aerospace and defense, with NASA Ames Research Center having some of the largest wind tunnels globally.
In F1, wind tunnels are essential for testing vehicles and creating aerodynamic dashboards to monitor performance. All teams on the grid are allowed an amount of wind tunnel tests based on their current position and can use these simulation environments to optimize their vehicle across design and environment – catering towards races under a variety of environmental conditions. As a result, optimizing the technology behind these limited resources, and extracting more information than your competitor is integral to taking full advantage of wind tunnel simulations.
As a supplier to the entire F1 grid, as well as broader automotive sector alongside aerospace and defense, SciChart caters to building bespoke Wind Tunnel and Aerodynamic Dashboards.
What Data Needs to Be Visualized from F1 Wind Tunnels?
Before we dive into the list of essential F1 wind tunnel data to measure, we’ll also explore the significance of collecting all these data points for enhancing performance on the track. From minimizing drag to adapting to regulatory changes, F1 teams consistently face challenges that require adaptive enhancements to vehicle performance and efficiency to bring back those trophies.
Aerodynamic Forces
Lift, Drag and Side Force measure the respective forces on the vehicle to understand how downforce is generated and how to better optimise straight line speed. This also assists with stability and handling corners. With this data in hand, engineers can make modifications to simulate lap times with real vehicles. This helps teams play “what-if” scenarios for exciting and performance-enhancing data analysis.
Most of the chart types leveraged here are variations of line charts, however, the volume of data created by wind tunnels requires a high performance chart library capable of rendering these points not just quickly, but also accurately. Additionally, logarithmic scales, or precision time-series requirements are often needed. SciChart provides a 64-bit solution catering to high-precision data sets as well as enabling multiple axis variations.
Whilst simulation setups and sensors typically create continuous data streams, engineers need to be able to handle gaps in data sets and include complex overlays and tooltips. Below we set out some of the variations in the SciChart Javascript library that cater to this needs. Similarly, many users will need to zoom and pan through historic data sets and run comparative analysis, whilst keeping overlays and annotations in tact. SciChart enables this functionality as standard, ensuring performance isn’t a limitation in reviewing vast data sets.
Flow Visualization
On the track, turbulence is one of the major disruptors to vehicle performance. There are various methods our clients leverage in a wind tunnel and through simulations to adjust this, however, first they need reliable data sets they can interact with and data analysts can interact with to extract information.
Pressure Distribution
Pressure taps on the vehicle’s surface reveal how air flows over the vehicle under different conditions. Pressure distribution supports your decision to tweak the vehicle design for optimum aerodynamics. This is just one of the ways you can reduce fuel consumption, helping to meet the sustainability criteria.
SciChart is used to monitor pressure distribution with overlayed contoured heatmaps. This helps to track pressure and forces acting on the vehicle. Implementations leverage this against a time series to monitor force interactions on the chassis in real time and in comparative data sets.
Balance and Stability
Lap time is often split into two segments: time on the straights and time on the corners. To get a better picture of how a vehicle performs during dynamic speed changes—namely when accelerating, braking or navigating a corner—Yaw Angle alongside Pitch and Roll data helps analysts understand how the car’s aerodynamic balance profile changes under these conditions.
Running real-world tests provides data sets, but the ability to critically investigate this data comes through data visualisation solutions. SciChart provides a highly customised solution that allows charts to be built into custom dashboards, as per the data teams requirements.
Force and Moment Coefficients
Measuring the forces acting on your vehicle also has a lot to reveal about the performance of the car and how fine-tuned the design is.
Lift coefficient (CL) is crucial for determining how much downforce a car generates, which directly affects its grip on the track. On the flip side, the drag coefficient (CD), measures the resistance a car experiences as it moves through air. To establish the car’s centre of gravity and detect any imbalances, moment coefficients are the ones to track.
Essential metrics to play around with include lift, drag, pitch, roll and yawing moments. This supports your understanding of the race car’s stability and control through simulation changes.
By creating specific dashboards comparing lift coefficient to drag ratios between simulations, teams can extract vital insights on the impact of their changes. SciChart enables multi-screen, multi-chart type, and multi-axis chart surfaces all syncable with the option for linked legends for isolating specific series, or panning back through time-series data sets.
With the inclusion of overlays, or on-click behavior enabled through our API, race engineers can input points of interest directly onto the chart surface, and even run simulations with slightly changed parameters directly from the chart surface
Wheel & Tire Data
The only point of contact between the vehicle and the track, collecting wheel and tire data is a must for responding to changing track conditions. No two races are the same, and monitoring how tires perform under various conditions in real-time (think acceleration, braking, and cornering in all weather conditions) helps your team make dynamic decisions. By studying the tire footprint and seeing how it changes with different loads and pressures, you’re in the perfect position to maximize grip and handling.
Environmental Conditions
As races happen outdoors, it’s worth capturing the environmental and climate elements that come with that. For instance, you can compare the wind speed and air temperature from real-life locations to correlate aerodynamic forces with vehicle speed. This will help you understand how different locations or wind speeds would change your approach to tackling the race. Wind tunnels enable simulation that accommodates various environmental conditions that could be present on a race day.
Model Movement Data
Models could well be the tipping point that wins the race, and present a more cost effective solution against repeat wind tunnel tests. In advanced wind tunnels, models can be moved or adjusted dynamically to simulate real-world conditions. Essentially, you can leverage models for forecasting performance based on real-time output in an advanced aerodynamic simulation.
SciChart provides a rich-interactions demo, seen below which showcases some of the user driven behaviour possible with our technology. Users can create multi-screen simulation dashboards that update on user interaction to monitor the outcome. Examples include adjusting the size or shape of spoiler sections to simulate the flow of air over the vehicle at various air densities based on temperature changes.
Find the Right Chart Visualizations
All this quality data, once collected, deserves a powerful rendering engine to ensure stability and accuracy of real-time visualizations. SciChart’s cross-platform chart library has worked with Formula student teams and every single F1 team. Our insights result in lighter and faster racecars, as well as smarter decisions on the track. Interested in finding out more?
Get the right insights with real-time, high-performance JavaScript and WPF charts from SciChart. We support reliable, accurate data with no lag in performance, even with low-memory hardware. Every facet of our rendering engine is built to support the ultimate big-data performance that’s trusted by F1 teams.
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