I always liked the combination of CFD results and photographs; so I decided it was about time I learned how to do them myself. And that’s where Blender comes in. The advantage of using Blender over photo editors like GIMP or Photoshop is the control over the 3D scene —camera, light, and geometry.
I just started this weekend with Blender and already managed to render a couple of nice pictures using data from the Shelby GT350R Mustang simulation.
You can’t be a true petrolhead until you’ve owned an Alfa Romeo —Jeremy Clarkson.
And what best Alfa Romeo than an evolution of the first model to win the inaugural Automobile World Championship in 1925 marking the beginning of Alfa Romeo’s legend?
In this Alfa Romeo P2 CFD analysis I will compare drag for different configurations:
You can have a look at the simulation setup in SimScale.
The 3D model of this italian race car is available in GrabCAD. The #30 was driven by Achille Varzi at the 1030 Targa Florio in what would be the P2’s latest victory. The car features a flat radiator, a slot cut down the fuel tank at the rear to host a spare wheel, and the rear springs placed outside the chassis rails.
It’s been a really long time since my last post as I got a new job and didn’t find time to post anything interesting. Until recently, that I came across a with the ideal geometry for a CFD simulation; the Shelby GT350R Mustang.
As have no longer access to STAR-CCM+ student’s license, and considering myself an open-source advocate. I thought it was about time to come back to OpenFOAM, but I lack the computational resources to carry out large simulations. Cloud computing seems to be the solution. I’ve known the SimScale platform for quite some time —as you can see from some of my older posts—. I did not take advantage of the free community plan for carrying out my own projects until now, although can not consider this one entirely mine, as I was not involved in the creation of the CAD geometry used for the simulations.
The car is the 2016 Ford Shelby GT350R Mustang, introduced at the 2015 North American International Auto Show in Detroit. It is a more refined, hardcore and limited production version of the GT350; and of all the upgrades, the most interesting from the CFD standpoint is the aerodynamic package which in the R introduces a larger splitter and rear wing to help improve downforce.
Gorgeous, isn’t it?
Check out our #F1 #ItalianGP Pit Stop Strategy Infographic! pic.twitter.com/grkkUMhqIe
— Pirelli Motorsport (@pirellisport) septiembre 6, 2015
Following, I provide some race charts so you may draw your own conclusions. You can also compare with last year’s Italian Grand Prix.
This plot shows the difference to the average pace of the race winner. That is, the difference to the average lap time, including pit stops.
The steeper the curve, the faster the lap; and as the curves are generated from cumulative sums of lap times, a negative slope implies a lap time which is quicker that the average.
Following, I provide some plots so you may draw your own conclusions. You can also compare with last year’s Belgian Grand Prix.
This plot shows the difference to the average pace of the race winner. That is, the difference to the average lap time, including pit stops.
The steeper the curve, the faster the lap; and as the curves are generated from cumulative sums of lap times, a negative slope implies a lap time which is quicker that the average.
Following, I provide some plots so you may draw your own conclusions. You can also compare with last year’s Hungarian Grand Prix.
This plot shows the difference to the average pace of the race winner. That is, the difference to the average lap time, including pit stops.
The steeper the curve, the faster the lap; and as the curves are generated from cumulative sums of lap times, a negative slope implies a lap time which is quicker that the average.
On the occasion of their three-week F1 workshops, SimScale and Nic Perrinn are launching a competition to look for the most creative visualization of airflow around a Formula 1 car. The rules?
These are my three images.
Close-up of Perrinn’s F1 car coloured by pressure coefficient in a scale of greens. The symmetry plane is a surface LIC visualization of the air, coloured by speed in a scale of blues. We can clearly appreciate the air bending around the rear wing.
The two F1 cars are very close to each other. Body surface color represents the pressure coefficient in the classic rainbow scale. In addition, a contour plot representing the reference pressure has been included to show how differently the leading and trailing car interact with the freestream.
What would the leading driver see from his mirror o a rainy day? Probably just a white cloud, but streamlines give us a close approximation of rain’s trajectory if its Stokes number were much smaller than 1 ($\text{Stk} \ll 1$). That’s why wind tunnels use smoke, with low Stokes number, to visualize complex dynamic flow phenomena.
Pressure through the boundary layer in a direction perpendicular to the surface is constant.
This phenomenon, which holds for thin boundary layers that remain attached to the body surface, bears strong implications in aerodynamics.
Since 2008 the British Grand Prix has not seen a British manufacturer —McLaren Mercedes— win the race. This year, Williams Mercedes had the chance to amend the situation but, somehow, they messed it up. The race start was superb, with Massa overtaking both Mercedes and Bottas following close behind taking P2 from Hamilton after the Safety Car went out in lap 3. But what could’ve been a 1-2 for the Grove team ended up being a 4-5 finish.
Mercedes pace has no match; but I keep thinking that Williams could have done better to protect at least one of the drivers from Hamilton’s undercut. At least while the track was dry, because Williams’s wet pace was significantly inferior. They at least had the chance to make it to the podium, but decided to stop for intermediate rain tyres a little bit too late. Vettel opted to change tyres a lap earlier on lap 43, the same as Hamilton, which turned out to be the sweet spot, allowing the German to undercut Massa.
Kimi opted to pit in too early, during the first light shower. This decision cost him the 5th place as he was the only driver in the top 5 to do so. Kvyat and Hulkenberg both delayed the move into intermediates to lap 44. The cost of pitting on lap 44 was, in the best case, about 7 seconds. A huge difference, and the key for Vettel climbing to the podium. Perez could have also posed a thread to Kimi’s position, but stopping late for intermediates and a poor performance in the wet gave him 2 points.
And 1 point for Alonso in the Driver’s Championship. The first one this season for the Spaniard. This was unexpected as McLaren Honda seemed to have taken a step back in terms of performance. Not to forget the huge impact against Button’s car during the first lap mêlée, which sent the Briton to watch the race from the Paddock.
Tyre strategy was key for @MercedesAMGF1. See all the strategies here.
Can you count the pit-stops during #BritishGP? pic.twitter.com/qYvlntUzyx
— Pirelli Motorsport (@pirellisport) julio 5, 2015
Following, I provide some plots so you may draw your own conclusions. You can also compare with last year’s British Grand Prix.
This plot shows the difference to the average pace of the race winner. That is, the difference to the average lap time, including pit stops.
The steeper the curve, the faster the lap; and as the curves are generated from cumulative sums of lap times, a negative slope implies a lap time which is quicker that the average.
SimScale started, a couple of weeks ago, a series of workshops on F1 aerodynamics in collaboration with Nic Perrinn, creator of the open source LM P1 and Formula 1 cars. You can watch the recording of the first session on Fundamentals of F1 aerodynamics in the following video.
A really interesting workshop to understand a little bit more about the constraints that drive the aerodynamic design of a Formula 1. Concepts such as the Y250 vortex (33:00) and the whole point behind the multiple cascade wing elements and necessity to weaken and burst the outboard vortex (35:20) in order to reduce the drag of the car are briefly explained.
Post-processing is done locally in ParaView, but we have no information on how much drag and downforce the car is generating. Those are aerodynamic forces acting on the body and are due to only two basic sources (Anderson, 2007):