Detroit


05 June 2017

Graham Rahal continued his impressive form to dominate in Detroit, becoming the first man to win both races of IndyCar's Belle Isle double-header.

Starting third, Rahal showed no signs of having lost any pace from Saturday and made short work of Ryan Hunter-Reay, passing him on lap nine of 70 with an audacious move around the outside of Turn 7 for second. After Newgarden pitted again, Rahal claimed the lead and started to build a large gap over the rest of the field, holding a 16-second advantage at the end of the final pitstop phase. Coming up to lap Hunter-Reay - who dropped through the field after contact with Helio Castroneves in the early stages - Rahal was unable to close up to the Andretti driver, allowing Newgarden to whittle away at his advantage. Newgarden - who had made his three-stop strategy work with some blistering pace - closed the gap to just under six seconds, before Hunter-Reay eventually allowed Rahal past.

Newgarden was under threat from Penske team-mate Will Power directly after the restart, but managed to hold onto second place across the line, over a second ahead of the Australian.

Sato was unable to convert his pole into anything greater than fourth place, narrowly beating Simon Pagenaud and Scott Dixon to the line. Alexander Rossi made steady progress through the field to finish seventh as Charlie Kimball just managed to secure eighth, finishing only a tenth ahead of Castroneves. Tony Kanaan rounded out the top 10, after AJ Foyt's Conor Daly dropped down the order following contact at the restart.

Autosport 4 June 2017



Indianapolis 500


31 May 2017

Castroneves Comes So Close to Sealing Legend Four-ever at Indy

The end was too familiar, too agonizing, too close to history. Once more, the bubbly Brazilian driving the No. 3 car would be denied victory number four at Indianapolis Motor Speedway. Helio Castroneves tried to sound positive but couldn’t hide his disappointment after losing a late duel with Andretti Autosport’s Takuma Sato in the 101st Running of the Indianapolis 500 presented by PennGrade Motor Oil on Sunday.

This was another golden opportunity for Team Penske driver Castroneves to join Hall of Famers A.J. Foyt, Rick Mears and Al Unser as celebrated four-time Indy 500 winners. But Castroneves was forced to settle for second — just like three years ago, when he lost to Andretti Autosport’s Ryan Hunter-Reay, as well as in 2003, when he lost to teammate Gil de Ferran.

Castroneves’ No. 3 Shell Fuel Rewards Team Penske Chevrolet had to overcome a career-worst 19th starting position, avoid a major crash by speeding underneath a car, then bounce back from a drive-through penalty for jumping a restart to set up the showdown with Sato. Castroneves overtook Sato on Lap 194 of a race that featured a record 15 different leaders. But Sato’s No. 26 Andretti Autosport Honda never drifted far from the leader’s tail, then regained the lead just before the “Yard of Bricks” start/finish line at the end of Lap 195. And that was it.

Castroneves tried to pass on the outside, but couldn’t hold the grip and drifted back with two laps remaining. Sato, 40, had been in an Indy 500 duel before and lost. Five years ago on the final lap, he pushed inside of Dario Franchitti to try to take the lead in Turn 1. The cars bumped, Sato crashed out, and Franchitti swigged the milk for the third time.

Sato, the first Japanese driver to win the Indy 500, commended Castroneves for maintaining space so they could race. In 2012, Sato accused Franchitti of pinching him, although Sato later said he could have executed a smarter passing attempt. Castroneves said their cars made light contact.

Castroneves’ day almost ended early. On Lap 53, pole sitter Scott Dixon launched over and off Jay Howard into top of the SAFER Barrier inside the south short chute between Turns 1 and 2. As Dixon’s No. 9 Camping World Honda went airborne, Castroneves accelerated underneath on the warm-up lane to escape the carnage. Neither Dixon nor Howard were injured.

Castroneves passed his rival Dixon for the Verizon IndyCar Series points lead. Dixon has won four series titles in addition to the 2008 Indy 500. Castroneves has not yet won a series crown. But the No. 1 objective each year is Indianapolis.

Castroneves drives for legendary team owner Roger Penske, who has a record 16 Indy 500 victories. Like the other Team Penske drivers, Castroneves is mentored by Mears, another daily reminder of team greatness. Castroneves burst onto the “Yard of Bricks’ by winning his first two Indy 500 starts, in 2001 and 2002. “Spiderman” celebrated by climbing the front-straight fence with his crew. The third victory came in 2009.

But in his 20th year of Indy car racing, he’s at times become quite sentimental. He knows his career is running short. Although his 29 career wins are tied with Mears for 12th on IndyCar’s all-time list, Castroneves hasn’t celebrated a victory since 2014, a winless drought of 49 starts. After the latest near-miss in his publicized pursuit of history, Castroneves mentioned the 2003 Indianapolis 500 as the race that truly got away. Specifically, he lamented giving up the lead to de Ferran after slowing for a lapped car.

May 28, 2017 | By Phillip B. Wilson, Indianapolis Motor Speedway



Indycar Grand Prix


15 May 2017

INDIANAPOLIS, Indiana – Team Penske’s Will Power ended the weekend the same way he started it: in first place.

The driver of the No. 12 Verizon Chevrolet capped off a flawless weekend at the INDYCAR Grand Prix with a dominating win today on the Indianapolis Motor Speedway road course. After winning Friday’s Verizon P1 Award with a new track record in qualifying and leading all three practice sessions on the weekend, Power led 61 laps en route to his 30th Indy car win, beating Chip Ganassi Racing’s Scott Dixon (No. 9 NTT Data Honda) by 5.2830 seconds in a caution-free affair, the first such race since Long Beach in 2016.

Power’s win – which came in his 175th Indy car start – breaks a three-way tie with Rick Mears and Helio Castroneves for 11th on the all-time win list. It is Power’s second victory on the IMS 2.439-mile road course and the 190th Indy car win in the history of Team Penske. For Dixon, it was his 35th career runner-up finish, fourth all-time, coming on the day he passed Jimmy Vasser for second on the all-time list with his 212th consecutive start. Teammate Tony Kanaan (No. 10 NTT Data Honda) holds that title with 270 consecutive starts. Andretti Autosport’s Ryan Hunter-Reay (No. 28 DHL Honda) finished third for his first podium of 2017, followed by reigning series champion Simon Pagenaud (No. 1 Menards Team Penske Chevrolet) and Castroneves (No. 3 Verizon Team Penske Chevrolet).

Pagenaud and Dixon remain the only two drivers to score top-fives in every race of 2017. Graham Rahal (No. 15 Soldier Strong / Turns for Troops.com Honda) and Max Chilton (No. 7 Gallagher Honda) came home with much needed sixth- and seventh-place finishes, respectively. Rahal advanced from a 20th-place starting spot and Chilton equaled his career-best finish.

There is little rest for Verizon IndyCar Series teams, as on-track activities for the 101st Indianapolis 500 presented by PennGrade Motor Oil begin in two days. Opening Day practice on the Indianapolis Motor Speedway’s hallowed 2.5-mile oval begins at noon ET Monday.

Indycar.com/news



Technology - Design Simulation


05 May 2017

Technology – Design Simulation

Fluid situation

These days there’s much more to CFD than just colourful spaghetti, as Racecar discovered when it talked to those at the cutting edge of this exciting design technology.

To simulate the behaviour within an engine accurately, the CFD has to overcome more complex challenges than those found in general aerodynamics.

The Sahara Force India F1 Team filed a request to the FIA to modify the Sporting Regulations in favour of CFD last year. “What we have today is a wind tunnel-biased formula, and what we are looking for is a CFD-biased formula”, explained Bob Fenley, its deputy team principal. “We believe that in the foreseeable future – in three years or a bit more – there is a possibility that CFD could become the primary or only aero programme. “Although this was a strategic move to save millions on renting a wind tunnel, it does also demonstrate the importance of CFD. For a team who finished fourth last year to instigate F1’s route to virtual aero development highlights just how realistic modern CFD software is becoming across all areas of the car.

We are all familiar with the mesmerising images of multi-coloured streamlines surrounding a Racecar. What we don’t often realise is that CFD can also be used to optimise internal flows within an engine to increase its performance and efficiency. “Essentially, we use CFD to model the gas exchange and combustion processes within our direct injection engines”, explains Ian Whiteside, chief engineer of Advanced Projects at Ilmor Engineering. “For example, we investigate the amount of turbulence generated in the combustion chamber, the interaction and the distribution of the air and fuel mixture and the volumetric efficiency – all with the aim of optimising the combustion process and therefore generating more engine power”.

Ilmor is renowned for its high performance engines that have raced in an impressive array of championships including F1, Indycar, NASCAR and even the WRC. To maintain its competitive edge, Ilmor is utilising the advanced Converge CFD software to not only improve its technologies but also to increase the efficiency of its development process. This is exactly what has been achieved, as Ilmor reduced its development time by 50 per cent which effectively saved eight weeks on the design and manufacture of the 2016 Indycar engine whilst reducing prototype build costs by 75 per cent.

To simulate the behaviour within an engine accurately, the CFD has to overcome more complex challenges than those found in general aerodynamics. “Firstly, the software has to model the movement of components to complex geometries such as pistons and valves”, explains Stephen Ferguson STAR-CC+ marketing director, Siemens PLM Software. “The second complication is simulating the complex physics of the combustion process and the reacting flows which needs to be modelled explicitly. You also have to determine the influence of the extreme heat generated on components and how to manage this by ultimately rejecting the heat to airflow in and around the car. In my opinion, engine simulation is one of the most difficult engineering problems that the motorsport industry has to face”.

One recent development in CFD technology which is providing invaluable for both aero and engine simulations is the concept of Adaptive Mesh Refinement (AMR). This is essentially automatic meshing which occurs during the run time of the simulation.

“For Ilmor, one of the main benefits of the Converge software is the automatic meshing” Whiteside says. “Our CFD experts determine the general grid size and define the areas where they want to refine the resolution of the mesh to capture complex flow physics. The rest is automated, which not only saves time from manually meshing, but also ensures that the same answer is achieved every run, regardless of who is controlling the simulations”.

The Converge software uses an orthogonal mesh that is composed of cubic elements generated from the triangulated approximation of surfaces found in standard CAD output STL files. AMR is also able to refine the mesh itself by subdividing the cubic elements in complex areas where the gradients are high. Therefore, the accuracy of the mesh can be greatly improved but only in areas where it is needed and only at the required times during the simulation. The benefits this gives are dramatic reductions in run time, enabling engineers to be more creative and investigate more ideas within the same time-frame.

For Siemens, highly automated meshing is one of the main reasons why its software is so successful within Formula 1. “F1 is leading the way in terms of automation”, Ferguson says. “Due to the hundreds of simulations run every day, the workflow has to be highly automated to ensure efficiency, so that highly trained engineers can focus their attention on analysing results rather than meshing, which is what our tools aim to achieve. The whole process of sourcing the CAD geometry, generating the mesh and making the simulation is best done automatically to ensure repeatability when comparing results”.

As well as automating a single run, additional optimisation tools such as HEEDS from Siemens allow you to automate a sweep of runs. For example, engineers define particular parameters such as a wing angle and HEEDS drives the STAR-CCM+programme through hundreds or thousands of configurations and then post-processes the results into a report. Therefore, once up and running, the first time an engineer is involved in the processes is right at the end when analysing the results.

 

Design filter

CFD is still mainly used as virtual filter to ensure that only the most effective designs are prototyped and tested. This is particularly true for engine simulations where CFD is utilised in a more fundamental way as dyno testing occurs much later in the development process and can be extremely expensive. “There are some subtle differences between the results from the engine simulations and the dyno but overall we have a lot of confidence in the CFD” says Whiteside. “The question is, can you ever achieve 100 per cent correlation? I think it’s unlikely and although CFD is a fantastic tool for the overall process, Ilmor are still a firm believer that the dyno is the ultimate decider”.

However, with the continuing advances in computing power and simulation efficiency, a fully predictive CFD future is not that far away and in some perspectives, is already here. “In F1, CFD is competing against wind tunnels and engine dynos”, Ferguson says. “Therefore, in practical terms, its accuracy only has to be as good as those test beds, so you could say that CFD tools are already fit for purpose. In general, CFD engineers are more interested in the resulting trends rather than actual values. Once they are satisfied with the accuracy of their CFD they can then go on to simulating scenarios that cannot be tested in real life. For instance, a few years ago, Renault F1 were using STAR-CCM+ to test how hot exhaust plumes influence the rear end aerodynamics, which you would never be able to replicate in a wind tunnel. As the accuracy of the models and solvers continue to increase, engineers can now simulate on-track conditions, overtaking and cornering. Although these might not be 100 per cent accurate, neither are any other engineering tools”.

 

CFD for all

CFD is not just for F1 teams, though, and it is becoming more widely available, largely thanks to the Cloud. Traditionally, if you wanted to simulate the fluid flow around a Racecar using CFD, you needed two things: 1) A license to use a software programme and 2) Access to high performance computing (HPC). Both of which are expensive, yet both of which can also now be avoided by using the Cloud.

“The whole process of purchasing a license, along with an advanced computer, and then installing the software is very time consuming and expensive” explains Milad Mafi, academic program manager from SimScale. “With SimScale, you simply log in on your web browser, upload your CAD STL file that you want to simulate and everything else is stored and computed within the Cloud. This means you are no longer restricted, because you can work on simulations from wherever you are and on whatever device you want – you just need an internet connection”.

 

Access all aeros

This flexibility and reduced cost has opened the doors for smaller software suppliers, as well as customers and the keen individual, because now everyone can afford to run a simulation. It is this accessibility which is making Cloud based simulation solutions so successful. “Our goal with SimScale was to make simulation accessible to everyone and you can clearly see the effectiveness of this strategy by the number of Formula Student projects we have on our platform” Mafi says. “These teams don’t have the budget of an F1 team and we found that their biggest problem was, despite having access to the software, they had a lack of hardware and knowledge. Therefore, together with the organisers of the competition, we created a four day workshop, teaching the teams how to conduct a fluid flow simulation and then how to utilise this to improve the aerodynamic efficiency of their race cars”.

Over 40 Formula Student teams currently utilise the SimScale platform, which also enables a team to collaboratively develop their design, thus improving the team’s efficiency.

 

Free flow

SimScale’s ethos of accessible simulation is not only achieved through the convenience of the Cloud but also the cost. For you or I to run a simulation, regardless of the complexity of the model or fluid behaviour, will cost us absolutely nothing. That’s right – it’s free and your work will be automatically uploaded to the SimScale library. Cleverly, this has not only resulted in a vast database with hundreds of examples for users to read but also stimulates further ideas for simulations. This perpetuating cycle means that whatever question you have or scenario you are trying to simulate, there is likely to be someone who has already solved your problem. In this way, the platform provides the answers, increasing your knowledge, which is another philosophy behind SimScale’s platform.

However, when in competition, race teams are unlikely to want their designs available with a Google search. Therefore, to ensure privacy, a professional subscription has to be paid. Regardless of your subscription type, everyone has access to up to 32 cores. A core is a processing unit which receives instructions and performs the necessary calculations; in CFD this is using equations to solve large linear algorithms. “To run a CFD simulation, it will take a total amount of computational time and this depends on the complexity of your simulation”, Mafi says. “For example, the higher the number of degrees of freedom or the finer the mesh, the longer the computational time. Let’s say your simulation will take one hour when using one core. If you use two cores, it will take half an hour and so on. Usually, a full F1 car sim using 32 cores can take up to eight to ten hours, at the end of the day it depends on your required accuracy and how fast you need the results”.

To run a full car simulation, imagine that the CAD model is encased in a rectangular box which is the fluid flow domain. On one side there is the inlet to the system and on the other there is the outlet, both of which require specific boundary conditions as well as the floor. The relative velocities of the floor and wheels then need to be considered to ensure the car is actually driving through the air and the wheels are rotating. Many additional complexities can be added, such as the effect of the exhaust flow. It depends on how accurate you need to be.

“Most people prefer to run half car simulations and apply symmetry boundary conditions to save time” Mafi says. “We have three ways of meshing: tetrahedral, hexahedral and then both, so if necessary you have control of every cell within the mesh to refine to your requirements. At the end of the day, all simulation users need to question whether they can trust the results. There is no point in saving money on simulation if you cannot make valid conclusions. SimScale uses the code based on an open source software called OpenFOAM which is used by the Mercedes and Sauber F1 teams. We have added our own GUI and integrated our own improvements”.

 

Clearly cloudy

Cloud based simulation solutions seem to solve many of the current issues found with traditional software programs. In addition to being accessible, it reduces costs, it is convenient and even avoids the need for annoying updates, downloads or restarts, because every time you refresh your web browser you are using the latest version.

But there are always sceptics when it comes to new technology and in this case data security is the main concern. Yet data transferred between your computer and the platform is done via industry standard SSL encryption technology. External data centres and hard drivers on the company’s servers are also encrypted by the same AES technology used to protect government documents.

Clearly, the Cloud is proving to be the next step in the evolution of simulation.

Racecar Engineering June 2017 by Gemma Hatton



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