Scania, a world-leading provider of sustainable transport solutions, is looking for answers to speed up the prediction and optimization of vehicle aerodynamic drag while handling large amounts of data. We would like to hear from you if you can provide proposals on new vehicle designs while quickly processing many alternatives.
The aerodynamic vehicle design process aims to optimize vehicle shape to minimize aerodynamic drag while relying heavily on computational fluid dynamics (CFD) simulations. This process involves creating a digital model of the vehicle and its surroundings and then simulating how the air flows around and interacts with the car with mathematical equations. Design improvements take time and effort, especially with heavy vehicles such as trucks. However, technological advancements, such as machine learning, deep learning, and artificial intelligence, can significantly enhance the aerodynamic vehicle design process by providing faster, more cost-effective ways to predict aerodynamic performance and propose optimized designs. These technologies can help overcome the limitations of traditional CFD-based approaches and enable design teams to explore a broader design space in a shorter time frame.
Scania is looking for partners to support predicting aerodynamic drag utilizing ML or AI models on large datasets such as existing vehicle simulation data to generate new vehicle designs quicker to improve vehicle efficiency, performance, and sustainability. The partners we are looking for have ML or AI expertise while also possessing previous experience in CFD simulations and vehicle aerodynamics. The envisioned solution should handle large amounts of data with high-performance computational capabilities and would integrate into existing tools and systems for training with new data. In addition, the ability to onboard new users and provide continuous solution support is essential.
Your opportunity with Scania
New technologies provide an excellent opportunity to reduce the required time and effort in the design process of vehicle aerodynamics. Scania is looking for innovative solutions or technology providers as long-term partners that combine most of the focus areas listed below. While Scania is open to different solution approaches, the goal is eventually to combine design generation, ML or AI, and data management in an integrated solution adjustable to Scania’s needs.
Examples we're looking for
Machine learning, deep learning, or AI algorithms can be trained on existing CFD simulation data to build predictive models estimating aerodynamic properties like drag coefficients. These models can quickly and accurately predict drag for various design iterations, reducing the need for resource-intensive simulations.
Vehicle Aerodynamics Design
The envisioned solution assists in quickly optimizing parameters and generating optimal vehicle designs, allowing Scania experts to explore a broader range of design possibilities in a shorter time frame, leading to more innovative vehicle designs.
Effective data management plays a crucial role in the aerodynamic vehicle design process as the complexity of designs and the volume of data increases, particularly when incorporating Machine Learning and CFD simulations. The data should eventually be available, organized, and accessible in the correct format.
At Scania, we are developing and enabling the automation of digital, sustainable end-to-end supply chains and logistics. If you work on solutions that contribute to developing connected digital threads of goods and articles in intelligent transport, be sure to contact us!Learn more
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