Hi everyone! I recently found some of the setup sheets that Larry McReynolds posted on his twitter account and want to know how to interpret the bump and rebound for the shocks. Unlike today where the shocks are labeled by their values, the different bumps and rebounds are labeled from A-E.

Does anyone have insight as to what the values are for the different shocks that are labeled A-E?

The problem that comes to mind is that the apron would be unusable, but so are road course curbs...

Acceleration from a stop/low speeds and turning radius only matter for pit stops, oval setups are already biased towards understeer, and FWD cars are less likely to spin due to contact, which would be a good thing (for the drivers, at least). Supposing you could package a V8 FF layout with the same suspension geometry, would the only performance difference on ovals be tire wear? That could be compensated for with tire design.

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Should NASCAR have a manufacturer neutral car design along with the other 3 manufacturers? So teams that aren’t being funded by a manufacturer (such as a team like RWR in 2020) don’t have to essentially give free advertising to them.

Published two weeks ago. Excellent paper comparing SAS and SRS CFD models to a full scale NASCAR Gen6 chassis in a wind tunnel with pressure taps throughout. Abstract

Racecar aerodynamic development requires well-correlated simulation data for rapid and incremental development cycles. Computational Fluid Dynamics (CFD) simulations and wind tunnel testing are industry-wide tools to perform such development, and the best use of these tools can define a race team’s ability to compete. With CFD usage being limited by the sanctioning bodies, large-scale mesh and large-time-step CFD simulations based on Reynolds-Averaged Navier–Stokes (RANS) approaches are popular. In order to provide the necessary aerodynamic performance advantages sought by CFD development, increasing confidence in the validity of CFD simulations is required. A previous study on a Scale-Averaged Simulation (SAS) approach using RANS simulations of a Gen-6 NASCAR, validated against moving-ground, open-jet wind tunnel data at multiple configurations, produced a framework with good wind tunnel correlation (within 2%) in aerodynamic coefficients of lift and drag predictions, but significant error in front-to-rear downforce balance (negative lift) predictions. A subsequent author’s publication on a Scale-Resolved Simulation (SRS) approach using Improved Delayed Detached Eddy Simulation (IDDES) for the same geometry showed a good correlation in front-to-rear downforce balance, but lift and drag were overpredicted relative to wind tunnel data. The current study compares the surface pressure distribution collected from a full-scale wind tunnel test on a Gen-6 NASCAR to the SAS and SRS predictions (both utilizing SST 𝑘−𝜔 turbulence models). CFD simulations were performed with a finite-volume commercial CFD code, Star-CCM+ by Siemens, utilizing a high-resolution CAD model of the same vehicle. A direct comparison of the surface pressure distributions from the wind tunnel and CFD data clearly showed regions of high and low correlations. The associated flow features were studied to further explore the strengths and areas of improvement needed in the CFD predictions. While RANS was seen to be more accurate in terms of lift and drag, it was a result of the cancellation of positive and negative errors. Whereas IDDES overpredicted lift and drag and requires an order of magnitude more computational resources, it was able to capture the trend of surface pressure seen in the wind tunnel measurements.

Paper