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

US, WA, Bellevue
Conversational AI ModEling and Learning (CAMEL) team is part of Amazon Devices organization where our mission is to build a best-in-class Conversational AI that is intuitive, intelligent, and responsive, by developing superior Large Language Models (LLM) solutions and services which increase the capabilities built into the model and which enable utilizing thousands of APIs and external knowledge sources to provide the best experience for each request across millions of customers and endpoints. We are looking for a passionate, talented, and resourceful Senior Applied Scientist in the field of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP), Recommender Systems and/or Information Retrieval, to invent and build scalable solutions for a state-of-the-art context-aware conversational AI. A successful candidate will have strong machine learning background and a desire to push the envelope in one or more of the above areas. The ideal candidate would also have hands-on experiences in building Generative AI solutions with LLMs, enjoy operating in dynamic environments, be self-motivated to take on challenging problems to deliver big customer impact, moving fast to ship solutions and then iterating on user feedback and interactions. Key job responsibilities As a Senior Applied Scientist, you will leverage your technical expertise and experience to demonstrate leadership in tackling large complex problems, setting the direction and collaborating with other talented applied scientists and engineers to research and develop LLM modeling and engineering techniques to reduce friction and enable natural and contextual conversations. You will analyze, understand and improve user experiences by leveraging Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in artificial intelligence. You will work on core LLM technologies, including Prompt Engineering, Model Fine-Tuning, Reinforcement Learning from Human Feedback (RLHF), Evaluation, etc. Your work will directly impact our customers in the form of novel products and services .
US, CA, Pasadena
The Amazon Web Services (AWS) Center for Quantum Computing (CQC) is a multi-disciplinary team of scientists, engineers, and technicians, on a mission to develop a fault-tolerant quantum computer. We are looking to hire a Research Scientist with fabrication and data analysis experience working on all elements of a superconducting circuit. The position is on-site at our lab, located on the in Pasadena, CA. The ideal candidate will have had prior experience building software tools for data analysis and visualization to enable deep diving into fabrication details, electrical test data. We are looking for candidates with strong engineering principles, resourcefulness and data science experience. Organization and communication skills are essential. Key job responsibilities * Develop and automate data pipeline pertinent to superconducting device fabrication. * Develop analytical tools to uncover new information about established and new processes. * Develop new or contribute to modifying existing data visualization tools. * Utilize machine learning to enable better deeper dives into fabrication and related data. * Interface with various software, design, fabrication and electrical test teams to enable new functionalities. A day in the life The role will be vital to the fabrication team and quantum computing device integration mechanism. The candidate will develop software based analytical tools to enable data driven decisions across projects related to fabrication and supporting infrastructure. Each fabrication run delivers additional data. The candidate will stay close to the details of fabrication providing data analysis and quick feedback to key stakeholders. At the end of fabrication runs custom and standardized reports will be generated by the candidate to provide insights into data generated from the run. This position may require occasional weekend work. About the team AWS values diverse experiences. Even if you do not meet all of the 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. 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. 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. Mentorship & 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. 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. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices.
US, WA, Seattle
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations, Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches
CA, ON, Toronto
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! As an Applied Scientist on this team, you will: - Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity. - Perform hands-on analysis and modeling of enormous data sets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Run A/B experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Research new and innovative machine learning approaches. - Recruit Applied Scientists to the team and provide mentorship. Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. Team video https://youtu.be/zD_6Lzw8raE
US, WA, Seattle
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! As the Data Science Manager on this team, you will: - Lead of team of scientists, business intelligence engineers, etc., on solving science problems with a high degree of complexity and ambiguity. - Develop science roadmaps, run annual planning, and foster cross-team collaboration to execute complex projects. - Perform hands-on data analysis, build machine-learning models, run regular A/B tests, and communicate the impact to senior management. - Hire and develop top talent, provide technical and career development guidance to scientists and engineers in the organization. - Analyze historical data to identify trends and support optimal decision making. - Apply statistical and machine learning knowledge to specific business problems and data. - Formalize assumptions about how our systems should work, create statistical definitions of outliers, and develop methods to systematically identify outliers. Work out why such examples are outliers and define if any actions needed. - Given anecdotes about anomalies or generate automatic scripts to define anomalies, deep dive to explain why they happen, and identify fixes. - Build decision-making models and propose effective solutions for the business problems you define. - Conduct written and verbal presentations to share insights to audiences of varying levels of technical sophistication. Why you will love this opportunity: Amazon has invested heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. Team video ~ https://youtu.be/zD_6Lzw8raE
US, WA, Seattle
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! As an Applied Science Manager in Machine Learning, you will: - Directly manage and lead a cross-functional team of Applied Scientists, Data Scientists, Economists, and Business Intelligence Engineers. - Develop and manage a research agenda that balances short term deliverables with measurable business impact as well as long term investments. - Lead marketplace design and development based on economic theory and data analysis. - Provide technical and scientific guidance to team members. - Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative and business judgment - Advance the team's engineering craftsmanship and drive continued scientific innovation as a thought leader and practitioner. - Develop science and engineering roadmaps, run annual planning, and foster cross-team collaboration to execute complex projects. - Perform hands-on data analysis, build machine-learning models, run regular A/B tests, and communicate the impact to senior management. - Collaborate with business and software teams across Amazon Ads. - Stay up to date with recent scientific publications relevant to the team. - Hire and develop top talent, provide technical and career development guidance to scientists and engineers within and across the organization. Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. Team video ~ https://youtu.be/zD_6Lzw8raE
US, WA, Seattle
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations, Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches
US, CA, San Diego
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations, Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches
US, CA, San Diego
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations, Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches
US, WA, Seattle
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations, Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches