{"id":1709,"date":"2023-06-15T09:13:15","date_gmt":"2023-06-15T09:13:15","guid":{"rendered":"https:\/\/nag.com\/?post_type=case-studies&p=1709"},"modified":"2023-07-03T16:13:06","modified_gmt":"2023-07-03T16:13:06","slug":"team-sonnenwagen-optimize-solar-racing-car-aerodynamics-using-nag-algorithmic-differentiation-software-tool-dco-c","status":"publish","type":"case-studies","link":"https:\/\/nag.com\/case-studies\/team-sonnenwagen-optimize-solar-racing-car-aerodynamics-using-nag-algorithmic-differentiation-software-tool-dco-c\/","title":{"rendered":"Team Sonnenwagen Optimize Solar Racing Car Aerodynamics Using n<\/i><\/span>AG Algorithmic Differentiation Software Tool \u2013 dco\/c++"},"content":{"rendered":"\n
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Team Sonnenwagen Optimize Solar Racing Car Aerodynamics Using n<\/i><\/span>AG Algorithmic Differentiation Software Tool \u2013 dco\/c++<\/h1>\n \n

Case Study<\/p>\n<\/div>\n\n \n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n<\/div>\n\n\n

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\u00a0<\/h3>\n

Case Study Background<\/h3>\n
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Team Sonnenwagen<\/a>\u00a0is a group of students from the Aachen Universities. They participated in the \u2018World Solar Challenge\u2019 (WSC) for the first time in 2017. The WSC is considered the most important race for solar-car teams on the international circuit. 40 teams from five\u00a0continents compete against each other, all trying to complete a gruelling 3022 km stretch through the Australian outback \u2013 all the way from Darwin to Adelaide. The 2019 WSC sees the team bring an additional car to the race in the \u2018Challenger\u2019 class of vehicles, in their quest to capture a Top 3 podium finish. A very intensive development period is currently underway in the hope of the team achieving their mission.<\/p>\n

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\u00a0<\/div>\n

Fundamental Challenge \u2013 Racing Car Aerodynamics Optimization<\/h3>\n
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Of course, the aerodynamics of the team\u2019s cars are of paramount importance, and as such, this area has been put under a strict process. In early 2018, the team began a concept study. To validate\u00a0the CFD (computational fluid dynamics) simulations, wind tunnel tests were conducted on the older car. After a nerve-wracking decision phase involving the concepts, a parameter study was initiated based on the validated simulation model. Various parameters, including for instance the radii of the edge fillets, or the angle of the trailing edge were observed in isolation. This allowed the team to perform the optimization step-by-step.<\/p>\n

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Utilization of n<\/i><\/span>AG Adjoint Algorithmic Differentiation Software<\/h3>\n

An adjoint solver based on n<\/i><\/span>AG\u2019s\u00a0dco\/c++<\/a>\u00a0software tool was a pivotal part in this phase.<\/em>\u00a0dco\/c++<\/a>\u00a0also calculated the surface sensitivity of the geometry to the fluid flow solution, ultimately becoming an optimization problem (in this\u00a0case, a minimization problem for air resistance). The sensitivities could be used to do an automated optimization of the geometry, thus giving the engineers qualitative information: Visualization could very easily show how the surface had to be altered in order to obtain a reduction in aerodynamic resistance. The use of\u00a0dco\/c++<\/a>\u00a0was of major benefit to Team Sonnenwagen.<\/p>\n

In the hardware department, Team Sonnenwagen employed a Rack-Server with 256 GB RAM. Through the use of a filetape function and a RAID 0 of 8 SSDs, another 2 TB of fast-writing memory was made available. In terms of processors, the server is equipped with 2×6 cores, with a clock speed of 2.66 GHz. With this configuration, the adjoint simulation can handle up to 16 million mesh cells.<\/p>\n

The new approaches and solution ideas that use of\u00a0dco\/c++<\/a>\u00a0brings to Team Sonnenwagen\u2019s design process are very exciting. New suggestions for the cars aerodynamics that would otherwise have gone unnoticed were detected and implemented. At the same time, many optimization results could be confirmed later by the adjoint calculations.<\/p>\n

Background Information<\/h3>\n
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Adjoint calculations are a useful supplement to classical parameter studies. However, it should be noted that adjoint solutions exclusively represent a local sensitivity of the surface. Large geometric changes still have to be optimized with a parameter study. In addition, the students work with clearly defined boundary conditions, such as the size of the driver. These are not stored in the adjoint solutions as ancillary conditions. Consequently,\u00a0dco\/c++<\/a>\u00a0is better suited for problems that allow more design freedom.<\/p>\n<\/div>\n

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<\/div>\n\n \n Learn More about Automatic Differentiation <\/i><\/a> <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Discover how a team of students from the Aachen Universities maximized racing car aerodynamics using automatic differentiation tool dco\/c++ and competed in the world solar challenge race. <\/p>\n","protected":false},"author":8,"featured_media":1710,"parent":0,"menu_order":0,"template":"","meta":{"content-type":"","footnotes":""},"post-tag":[16,17,26],"class_list":["post-1709","case-studies","type-case-studies","status-publish","has-post-thumbnail","hentry"],"yoast_head":"\nTeam Sonnenwagen Optimize Solar Racing Car Aerodynamics Using NAG Algorithmic Differentiation Software Tool \u2013 dco\/c++ - nAG<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/nag.com\/case-studies\/team-sonnenwagen-optimize-solar-racing-car-aerodynamics-using-nag-algorithmic-differentiation-software-tool-dco-c\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Team Sonnenwagen Optimize Solar Racing Car Aerodynamics Using NAG Algorithmic Differentiation Software Tool \u2013 dco\/c++ - 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