F1 2026: Dirty air — often talked about, now visualized

Drivers often talk about “dirty air” — the disturbed wake that complicates following another car. With high-fidelity CFD, we can move beyond the metaphor and show what that flow field contains.

Here, we present a detached eddy simulation (DES) of our 2026-spec concept and contrast it with steady RANS of the same geometry and conditions. The aim is to illustrate what becomes visible, when the large-scale unsteady structures are resolved, and what insights can be derived from these simulations.

If you are new to this series, be sure to check out our previous post: Chasing 400 km/h: How fast will the 2026 F1 cars be?

From steady to unsteady — what actually changes with DES? 

• RANS (steady): Solves the time-averaged Navier–Stokes equations with a turbulence model and outputs a mean flow field. It is suitable for day-to-day aerodynamic development and for comparing mean forces and surface-averaged quantities. 

• DES (unsteady): Hybrid RANS–LES family; uses RANS near walls and resolves larger turbulent eddies in separated regions. Produces time-dependent fields from which means and variances can be derived.  

The total pressure coefficient cut planes below clearly showcase the difference in the approach and the results. 

Simulation implications 

Capturing this level of flow detail comes at a cost. DES requires: 

• Refined meshes to resolve eddies in detached regions 

• Small time steps to maintain numerical stability (CFL < 1) 

• Consistent monitoring of variance and covariance at reference points to check statistical convergence 

• Proper modeling of turbulent viscosity ratio (TVR) and turbulent kinetic energy (TKE) fraction resolved 

Results: forces and unsteadiness

At the global level, DES captures temporal oscillations in both drag and lift that RANS inherently filters out.

Below is a comparison over 0.15 s of simulated time.

Force signal plot — total drag and lift over 0.15 s, with averages highlighted

CDA and CLA variation for the floor and chassis

The variance signals can then be decomposed in bands, and we can examinate what frequencies contribute most to the variation of the coefficients.

Frequency decomposition of the variance of CDA and CLA

Peaks for contribution can then be associated to periodic structures. For example, the roll hoop and cockpit vortex shedding can be linked to the higher frequency peak on the chassis drag monitor. The floor drag variance is concentred mostly below 75 Hz with the downforce showing further peaks at higher frequencies. 

Making sense of the unsteadiness 

DES provides more than just visual appeal. By quantifying flow unsteadiness, it offers a framework to: 

• Correlate with real-world observations, such as on-track aero oscillations and instabilities. 
• View upgrade-to-upgrade sensitivity. Comparing mean and variance changes between baseline and modified geometries highlights whether a change affects average performance or load stability (or both). 
• Observe ride condition dependence. Repeating the DES at representative ride height / rake / yaw points demonstrates how frequency content shifts with setup. This helps identify conditions where instability grows, even if the mean force is unchanged.

Conclusion — Flow Seen from Above 

From the front, rear, and now top, DES reveals the 3D complexity of the wake behind a 2026 Formula 1 car. It turns a commonly cited concept — dirty air — into something measurable and observable, opening the door to better understand how design decisions translate into race performance and stability. 

The Maya HTT Simulation Team 

DES is not just another simulation mode. It’s a complex, hybrid approach that bridges RANS and LES to capture the real, time-dependent behavior of aerodynamic flow. It requires precise meshing, careful timestep control, and rigorous post-processing to make sense of the resulting data. 

At Maya HTT, we specialize in turning that complexity into clarity. Our standardized CFD workflows and parametric modeling tools let teams extract actionable insight from even the most unsteady flow environments — from race cars to aircraft, and beyond. 

Interested in exploring your own design challenges? 

Get in touch with our team to see how simulation-driven insights can guide your next innovation.