If you’ve ever spent hours rebuilding CAD just to test a small aerodynamic tweak, you’ll understand the appeal of mesh morphing. In this study, we put bramble’s new mesh morphing and optimisation tools to the test by refining the floor turning vanes (aka bargeboards) on a Formula-style race car.
Instead of manually re-modelling geometry, we used bramble to define a local deformation region and ran a mesh morphing-based design study – no CAD remodelling required. The aim? Improve downforce and aerodynamic efficiency with minimal manual input and maximum CFD insight.
Set-up
Geometry morphing without the CAD overhead
We start by defining a deformation volume around the area of interest – in this case, the bargeboard leading edge. Within this volume, bramble generates a grid of control points. When we shift these points, the local mesh geometry morphs smoothly, staying watertight and simulation-ready.
Defining the design space
For this study, we chose two simple parameters:
- X-shift (longitudinal) of the bargeboard leading edge: ±150 mm
- Y-shift (lateral/inboard-outboard): ±100 mm
We grouped relevant control points to create these deformation modes, and fed them into bramble’s Design of Experiments (DoE) generator found in the Map view.
The DoE consisted of 9 geometries: the baseline plus 8 varied configurations. These were distributed evenly across the design space to maximise coverage with a minimal number of CFD runs.
Each geometry was inserted into a full-car assembly, and bramble automatically generated and ran steady-state RANS simulations using the k-omega SST turbulence model.
Predicting performance
Kriging and surrogate models
With the CFD results in hand, we used bramble’s Kriging-based mapping tool to create surrogate models of the performance metrics:
- Downforce (Cl)
- Drag (Cd)
- Efficiency (Cl/Cd)
This enabled us to build response surfaces predicting how bargeboard movement in X and Y affects aerodynamic performance.
The response surface for downforce is shown to the right. This reveals downforce is increased (darker blue) by moving the bargeboard leading edge rearwards and inwards.
Trade-off time
Efficiency vs downforce
Of course, pure downforce isn’t always the goal. To explore the design trade-offs, we used bramble’s Pareto plot tool, which visualises competing objectives, in this case, efficiency (Cl/Cd) vs. downforce.
From the surrogate model, we identified a ‘sweet spot’ at:
- Cl = 4.3
- Cl/Cd = 2.84
Beyond this point, efficiency started to fall off even as downforce increased – classic diminishing returns.
Closing the loop
Predicted performance is great, but as anyone who’s run a study like this knows, we don’t sign off on Kriging alone.
We ‘closed the loop’ by running the predicted optimum geometry in Bramble CFD. The final design – 54mm rearward and 13mm inboard bargeboard shift, delivered the expected gains in downforce and efficiency, validating the surrogate model.
Optimisations like this turn the traditional analysis process on its head. Instead of looking at post-processing and working out what the next test should be, we instead run a test (the optimum design) and then look at the post-processing to work out why it was better.
Our optimium design increases downforce to a Cl of 2.80. The delta Cp plots show increased suction both local to the vanes, but interestingly also along the edge of the floor in front of the rear tyres.
The gif above compares the flow field at a longitudinal position through the bargeboards coloured by contours of total pressure (energy in the flow). It shows that the optimised bargeboard ("DOE009") is producing a smaller, more coherent vortices from both its upper and lower edges.
The result of this change is that higher energy flow (darker reds) are passed along the side of the car and around the edge of the floor. This higher energy flow allows for a stronger vortex to form along the floor edge in front of the rear tyre giving the increased suction, and hence downforce, seen in this region.
Wrapping up
This study was a hands-on trial of bramble’s mesh morphing capabilities applied to a high-impact aerodynamic surface. By bypassing CAD and using mesh morphing directly within the solver, we were able to rapidly explore design space, build surrogate models, and converge on an optimal bargeboard shape with clear aerodynamic benefits.
For CFD engineers and aerodynamicists looking to accelerate their development loop, mesh morphing in bramble offers a smart, simulation-driven workflow. It’s fast, flexible, and, as this case showed, it works.
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