Image shows the 2022 F1 car sitting in profile on a racetrack with viewing stands in the background
The F1 engineering team collaborated with AWS to explore the science of how cars interact when racing in close proximity.
F1

The science behind the next-gen FORMULA 1 car

Learn how the F1 engineering team collaborated with AWS to develop new design specifications to help make races more competitive.

When the 2022 FORMULA 1 (F1) racing season revs up in March, teams will take to the track with newly designed cars engineered to give fans — and drivers — more of the wheel-to-wheel action they’ve been seeking.

“Anybody who has followed the sport has heard drivers complain on the radio about not being able to get close enough to the car in front of them,” explains Simon Dodman, principal aerodynamicist at F1. “Essentially what they're reporting in those situations is a lack of grip, or downforce.”

Anybody who has followed the sport has heard drivers complain on the radio about not being able to get close enough to the car in front of them. What they're reporting in those situations is a lack of grip, or downforce.
Simon Dodman

F1 cars are the fastest regulated road-course racing vehicles in the world. While these open-wheel automobiles are only 20 to 30 kilometers (or 12 to 18 miles) per-hour faster than top-of-the-line sports cars, they can speed around corners up to five times as fast due to the powerful aerodynamic downforce they create. Much like the way that aircraft generate lift through their wings, F1 cars use a similar mechanism, except inverted, to generate the downforce they need.

Cars lose up to 50% of this downforce when racing closely behind another car due to the turbulent wake generated by wings and bodywork. Turbulence from the leading car causes the trailing car to slide and lose its grip on the track. The driver behind senses a loss of grip earlier than the driver in front and, ultimately, has to take his foot off the accelerator.

Related content
In its collaboration with the NFL, AWS contributes cloud computing technology, machine learning services, business intelligence services — and, sometimes, the expertise of its scientists.

“This loss of downforce means that even the best drivers in the world can’t overtake the car in front of them, ” says Neil Ashton, a former FORMULA 1 engineer who today is principal computational fluid dynamics (CFD) specialist for Amazon Web Services (AWS).

“It's as simple as an object moving through a fluid — whether that's air or water — and creating a disturbance behind it,” Dodman adds. “Think of a speedboat rushing by on a completely calm lake. Basically, cars do the same through air. The faster cars go, the more downforce they make, and the bigger the wake behind them becomes. And wake is detrimental to what’s behind it. Imagine trying to drive a speedboat behind another speedboat and bouncing around in the water — it’s the same with race cars.”

"Nobody designs a car to come in second"

Over the past three years, the F1 engineering team has collaborated with AWS to explore the science of how cars interact when racing in close proximity and, ultimately, develop new design specifications to deliver a more competitive racing spectacle for fans while keeping drivers safe.

“One criticism often leveled against FORMULA 1 is that, at times, it can be processional and easy to predict who will win on a given race weekend by virtue of the fact that it's quite a cyclical sport in terms of competitiveness,” Dodman said. “Fans want to watch an exciting race with lots of overtaking and, quite simply, the sport hasn’t delivered that. We recognized things had to change to level the playing field and deliver a more compelling spectator experience.”

2022 F1 Car option 1.jpg
Instead of relying on time-consuming and costly physical tests, F1 used computational fluid dynamics, which provides a virtual environment to study the flow of fluids, to help design the 2022 F1 car seen here.
F1

The F1 engineering team was tasked with designing a car that can produce a smaller wake, while maintaining the degree of downforce and peak speeds, but is also not adversely affected by driving through another car’s wake.

“Nobody designs a car to come in second,” observes Pat Symonds, chief technical officer at FORMULA 1. “But for this project, we were looking at how cars perform in the wake of another car, as opposed to running in clean air.”

Instead of relying on time-consuming and costly physical tests, F1 used computational fluid dynamics, which provides a virtual environment to study the flow of fluids (in this case the air around the F1 car) without ever having to manufacture a single part. By numerically solving a form of the Navier-Stokes equations, companies like FORMULA 1 can study the complex nature of turbulent flows from their laptops.

"A lot of complex physics"

“There are a lot of complex physics involved with how a F1 car moves around a corner, which creates a massive computational challenge with a huge matrix of scenarios,” Ashton said. “This meant that F1 needed access to very large high performance computing (HPC) resources.”

F1's Rob Smedley on using AWS to improve the fan experience

The project kicked off with F1 using CFD at a third-party facility, which meant sharing capacity with other customers and, as a result, limiting the quantity and quality of simulations. Dodman’s team ultimately transitioned to a HPC platform on AWS, using AWS ParallelCluster and a combination of Amazon Elastic Compute Cloud (Amazon EC2) instances including AWS Graviton2-based C6gn instances to run complex simulations modeling the turbulence wake of cars and the impact on trailing cars.

“Moving to AWS enabled us to break away from that serial model and run lots of cases at once without having to queue behind other customers,” Dodman said.This meant the time between receiving and analyzing results and moving to the next step was much shorter. We were able to shortcut a lot of the process.”

Customers use AWS for CFD projects to design everything from aircraft to medical devices. While the most powerful desktops have around 64 processing cores, F1 engineers had access to more than 2,500 AWS cores for every run — often with many jobs running simultaneously.

Image shows an overhead of the right panel of the front wing of the 2022 F1 car, the panel and car are iridescent
The new 2022 F1 car includes a simplified front wing that diverts airflow off the front wheels.
F1

“We quickly realized that the only way we were going to make inroads was to do as many simulations using CFD as possible,” Dodman said. “By using the hugely scalable compute resource AWS offers, we were able to do far more runs and come to conclusions and solutions a lot faster.”

Running the project with AWS removed all barriers related to time and computing capacity, reducing the average simulation run time from 60 hours to 12. It also reduced the cost of running workloads by 30%, delivering supercomputer-level performance for a fraction of the budget.

F1 originally planned to run 20 or 30 simulations a week, but was able to increase that to between 80 and 90 with AWS. “And with access to much more compute resources than even the [F1 racing] teams have, we're able to run two-car simulations and look at the problem in a way that has never been done before,” Dodman added.

Massive data

AWS enabled F1 to run more than 5,000 single- and multi-car simulations over six months, yielding 550 million data points. These insights led to Fédération Internationale de l'Automobile (FIA is the governing body of motor sport) design specifications for a next-gen car with only 15% downforce loss at a one-car-length distance. F1 teams are currently using the regulations to design cars for the 2022 season.

We're confident drivers will be able to race more closely, with potential for far more overtaking.
Simon Dodman

New robust aerodynamic features include wheel wake control devices; a simplified front wing that diverts airflow off the front wheels; a more sculpted rear wing to effectively draw air in from the sides and lift it above the car following behind; simplified suspension; and underfloor tunnels. For the first time, all F1 cars will run on 18-inch wheels (up from 13 inches) with low-profile tires.

This will reduce turbulent airflow from the car ahead, increasing downforce of the following car, and allowing it to close the gap and potentially overtake the leader.

“The new design lifts a car’s wake higher so the following car can drive under it rather than through it,” Dodman said. “We're confident drivers will be able to race more closely, with potential for far more overtaking. And with less distance between the fastest and slowest cars on the track, we see more opportunity for different teams to win week to week.”

F1 2022 - SILVERSTONE - front low angle.jpg
The 2022 F1 car features simplified suspension, underfloor tunnels and, for the first time, all F1 cars will run on 18-inch wheels (up from 13 inches) with low-profile tires.
F1

F1 tested and verified the new design in a wind tunnel. “They found the correlation between the simulation data and the test was very good, which proved that you can do a complicated, high-fidelity engineering design project in CFD,” Ashton said.

F1 are now starting the process of looking into AWS machine learning services such as Amazon SageMaker to help to optimize the design and performance of the car by using the simulation data to build models with additional insights.

“It’s still early days,” Ashton concluded, “but machine learning is proving to be a compelling additional reason to collaborate with AWS and I’m excited to see what we can achieve together.”

Research areas

Related content

GB, MLN, Edinburgh
We’re looking for a Machine Learning Scientist in the Personalization team for our Edinburgh office experienced in generative AI and large models. You will be responsible for developing and disseminating customer-facing personalized recommendation models. This is a hands-on role with global impact working with a team of world-class engineers and scientists across the Edinburgh offices and wider organization. You will lead the design of machine learning models that scale to very large quantities of data, and serve high-scale low-latency recommendations to all customers worldwide. You will embody scientific rigor, designing and executing experiments to demonstrate the technical efficacy and business value of your methods. You will work alongside a science team to delight customers by aiding in recommendations relevancy, and raise the profile of Amazon as a global leader in machine learning and personalization. Successful candidates will have strong technical ability, focus on customers by applying a customer-first approach, excellent teamwork and communication skills, and a motivation to achieve results in a fast-paced environment. Our position offers exceptional opportunities for every candidate to grow their technical and non-technical skills. If you are selected, you have the opportunity to make a difference to our business by designing and building state of the art machine learning systems on big data, leveraging Amazon’s vast computing resources (AWS), working on exciting and challenging projects, and delivering meaningful results to customers world-wide. Key job responsibilities Develop machine learning algorithms for high-scale recommendations problems. Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative analysis and business judgement. Collaborate with software engineers to integrate successful experimental results into large-scale, highly complex Amazon production systems capable of handling 100,000s of transactions per second at low latency. Report results in a manner which is both statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment.
IN, TS, Hyderabad
Welcome to the Worldwide Returns & ReCommerce team (WWR&R) at Amazon.com. WWR&R is an agile, innovative organization dedicated to ‘making zero happen’ to benefit our customers, our company, and the environment. Our goal is to achieve the three zeroes: zero cost of returns, zero waste, and zero defects. We do this by developing products and driving truly innovative operational excellence to help customers keep what they buy, recover returned and damaged product value, keep thousands of tons of waste from landfills, and create the best customer returns experience in the world. We have an eye to the future – we create long-term value at Amazon by focusing not just on the bottom line, but on the planet. We are building the most sustainable re-use channel we can by driving multiple aspects of the Circular Economy for Amazon – Returns & ReCommerce. Amazon WWR&R is comprised of business, product, operational, program, software engineering and data teams that manage the life of a returned or damaged product from a customer to the warehouse and on to its next best use. Our work is broad and deep: we train machine learning models to automate routing and find signals to optimize re-use; we invent new channels to give products a second life; we develop highly respected product support to help customers love what they buy; we pilot smarter product evaluations; we work from the customer backward to find ways to make the return experience remarkably delightful and easy; and we do it all while scrutinizing our business with laser focus. You will help create everything from customer-facing and vendor-facing websites to the internal software and tools behind the reverse-logistics process. You can develop scalable, high-availability solutions to solve complex and broad business problems. We are a group that has fun at work while driving incredible customer, business, and environmental impact. We are backed by a strong leadership group dedicated to operational excellence that empowers a reasonable work-life balance. As an established, experienced team, we offer the scope and support needed for substantial career growth. Amazon is earth’s most customer-centric company and through WWR&R, the earth is our customer too. Come join us and innovate with the Amazon Worldwide Returns & ReCommerce team!
US, WA, Seattle
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! In Prime Video READI, our mission is to automate infrastructure scaling and operational readiness. We are growing a team specialized in time series modeling, forecasting, and release safety. This team will invent and develop algorithms for forecasting multi-dimensional related time series. The team will develop forecasts on key business dimensions with optimization recommendations related to performance and efficiency opportunities across our global software environment. As a founding member of the core team, you will apply your deep coding, modeling and statistical knowledge to concrete problems that have broad cross-organizational, global, and technology impact. Your work will focus on retrieving, cleansing and preparing large scale datasets, training and evaluating models and deploying them to production where we continuously monitor and evaluate. You will work on large engineering efforts that solve significantly complex problems facing global customers. You will be trusted to operate with complete independence and are often assigned to focus on areas where the business and/or architectural strategy has not yet been defined. You must be equally comfortable digging in to business requirements as you are drilling into design with development teams and developing production ready learning models. You consistently bring strong, data-driven business and technical judgment to decisions. You will work with internal and external stakeholders, cross-functional partners, and end-users around the world at all levels. Our team makes a big impact because nothing is more important to us than delivering for our customers, continually earning their trust, and thinking long term. You are empowered to bring new technologies to your solutions. If you crave a sense of ownership, this is the place to be.
US, WA, Seattle
Amazon Advertising operates at the intersection of eCommerce and advertising, and is investing heavily in building a world-class advertising business. We are defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long-term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products to improve both shopper and advertiser experience. With a broad mandate to experiment and innovate, we grow at an unprecedented rate with a seemingly endless range of new opportunities. The Ad Response Prediction team in Sponsored Products organization build advanced deep-learning models, large-scale machine-learning pipelines, and real-time serving infra to match shoppers’ intent to relevant ads on all devices, for all contexts and in all marketplaces. Through precise estimation of shoppers’ interaction with ads and their long-term value, we aim to drive optimal ads allocation and pricing, and help to deliver a relevant, engaging and delightful ads experience to Amazon shoppers. As the business and the complexity of various new initiatives we take continues to grow, we are looking for talented Applied Scientists to join the team. Key job responsibilities As a Applied Scientist II, you will: * Conduct hands-on data analysis, build large-scale machine-learning models and pipelines * Work closely with software engineers on detailed requirements, technical designs and implementation of end-to-end solutions in production * Run regular A/B experiments, gather data, perform statistical analysis, and communicate the impact to senior management * Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving * Provide technical leadership, research new machine learning approaches to drive continued scientific innovation * Be a member of the Amazon-wide Machine Learning Community, participating in internal and external MeetUps, Hackathons and Conferences
US, WA, Bellevue
mmPROS Surface Research Science seeks an exceptional Applied Scientist with expertise in optimization and machine learning to optimize Amazon's middle mile transportation network, the backbone of its logistics operations. Amazon's middle mile transportation network utilizes a fleet of semi-trucks, trains, and airplanes to transport millions of packages and other freight between warehouses, vendor facilities, and customers, on time and at low cost. The Surface Research Science team delivers innovation, models, algorithms, and other scientific solutions to efficiently plan and operate the middle mile surface (truck and rail) transportation network. The team focuses on large-scale problems in vehicle route planning, capacity procurement, network design, forecasting, and equipment re-balancing. Your role will be to build innovative optimization and machine learning models to improve driver routing and procurement efficiency. Your models will impact business decisions worth billions of dollars and improve the delivery experience for millions of customers. You will operate as part of a team of innovative, experienced scientists working on optimization and machine learning. You will work in close collaboration with partners across product, engineering, business intelligence, and operations. Key job responsibilities - Design and develop optimization and machine learning models to inform our hardest planning decisions. - Implement models and algorithms in Amazon's production software. - Lead and partner with product, engineering, and operations teams to drive modeling and technical design for complex business problems. - Lead complex modeling and data analyses to aid management in making key business decisions and set new policies. - Write documentation for scientific and business audiences. About the team This role is part of mmPROS Surface Research Science. Our mission is to build the most efficient and optimal transportation network on the planet, using our science and technology as our biggest advantage. We leverage technologies in optimization, operations research, and machine learning to grow our businesses and solve Amazon's unique logistical challenges. Scientists in the team work in close collaboration with each other and with partners across product, software engineering, business intelligence, and operations. They regularly interact with software engineering teams and business leadership.
IL, Tel Aviv
Come join the AWS Agentic AI science team in building the next generation models for intelligent automation. AWS, the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems that will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. We are looking for world class researchers with experience in one or more of the following areas - autonomous agents, API orchestration, Planning, large multimodal models (especially vision-language models), reinforcement learning (RL) and sequential decision making. Key job responsibilities PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience 3+ years of building models for business application experience Experience in patents or publications at top-tier peer-reviewed conferences or journals Experience programming in Java, C++, Python or related language Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
IL, Haifa
Come join the AWS Agentic AI science team in building the next generation models for intelligent automation. AWS, the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems that will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. We are looking for world class researchers with experience in one or more of the following areas - autonomous agents, API orchestration, Planning, large multimodal models (especially vision-language models), reinforcement learning (RL) and sequential decision making.
US, NY, New York
Join us in a historic endeavor to make Generative AI accessible to the world with breakthrough research! The AWS AI team has a world-leading team of researchers and academics, and we are looking for world-class colleagues to join us and make the AI revolution happen. Our team of scientists drives the innovation that enables external and internal SageMaker customers to train their next generation models on both GPU and Trainium instances. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. AWS is the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems which will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. About the team Why AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Utility Computing (UC) AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (IoT), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Mentorship and Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
US, WA, Seattle
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video team member, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Key job responsibilities As an Applied Scientist in the Content Understanding Team, you will lead the end-to-end research and deployment of video and multi-modal models applied to a variety of downstream applications. More specifically, you will: - Work backwards from customer problems to research and design scientific approaches for solving them - Work closely with other scientists, engineers and product managers to expand the depth of our product insights with data, create a variety of experiments to determine the high impact projects to include in planning roadmaps - Stay up-to-date with advancements and the latest modeling techniques in the field - Publish your research findings in top conferences and journals About the team Our Prime Video Content Understanding team builds holistic media representations (e.g. descriptions of scenes, semantic embeddings) and apply them to new customer experiences supply chain problems. Our technology spans the entire Prime Video catalogue globally, and we enable instant recaps, skip intro timing, ad placement, search, and content moderation.
US, MA, Westborough
We are seeking a Principal Applied Scientist to lead the development of our autonomous driving stack for last-mile delivery vehicles. In this role, you will drive technical innovation, architect advanced autonomous systems, and lead a team of researchers and engineers in pushing the boundaries of what's possible in autonomous delivery. Key job responsibilities As the Principal Applied Scientist, you will architect and evolve LMDA's autonomous driving stack for last-mile delivery vehicles. Your role involves driving research and development in key areas such as perception, prediction, planning, and control. You will develop novel algorithms and approaches to solve complex challenges in urban autonomous navigation. A critical aspect of your role will be leading system-level architecture decisions and setting technical direction for the autonomous systems team. You will mentor and develop a team of scientists and engineers, fostering a culture of innovation and excellence. This involves close collaboration with cross-functional teams including hardware, safety, and operations to ensure seamless integration of autonomous systems. As a senior technical leader, you will represent LMDA's technical capabilities to partners, customers, and at industry conferences. In this role, you will define and execute the technical roadmap for LMDA's autonomous systems. This includes identifying key research areas and technological advancements that will drive LMDA's competitive advantage. A crucial aspect of your role will be balancing long-term research goals with near-term product delivery needs. You will lead the integration of various autonomous subsystems into a cohesive, performant stack. This includes developing and implementing strategies for optimizing system performance across hardware and software. You will also design and oversee testing and validation frameworks for autonomous systems. About the team Last Mile Delivery Automation (LMDA) is at the forefront of revolutionizing the logistics industry through advanced autonomous vehicle technology. Our mission is to create safe, efficient, and scalable autonomous solutions for last-mile delivery, reducing costs and environmental impact while improving delivery speed and reliability.