![]() ![]() The same happens (as experienced by the Author in the 90s) when making small manual shape modifications during expensive laboratory testing (velocimetry, wind tunnel testing). However, it will be so coarse that no helpful comparison can be made between the two car designs. The human brain can intuitively guess a numerical value for downforce this intuition will be immediate and more accurate for more experienced engineers. “What will be the aerodynamic downforce on the car?” Intuition Is Real-Time but Biased Computations Are Accurate but Slow The new How - Machine Learning and Deep Learning can speed up traditional CFD simulation not by a factor of 10 but by several orders of magnitude.This solution comes at the cost of huge infrastructure investments, not to mention maintenance costs. The old How - The traditional approach is to increase the number of processors that crunch numbers to give answers faster. ![]() Meanwhile, a compute farm needs “number crunching” time before giving the results. They access a portal, submit a batch job, and await the result. The Why - Aerodynamicists want to select the best F1 car shapes for downforce and streamline structure.Traditionally, computational aerodynamics of F1 cars is delegated to CFD specialists or aerodynamicists. how is Artificial Intelligence now accelerating CFD simulation?Īs a first example, let us illustrate the aerodynamics of F1 cars.In this article, we will describe the impact of the “Deep Learning Revolution” on engineers who need CFD but can’t keep on waiting for it.ĭeep Learning is an enabler of democratization and acceleration of CFD. How can Machine Learning and its newest evolution, Deep Learning, help CFD address these challenges? the skills required to use it at a reliable level (the so-called high-fidelity level).the time to complete a numerical simulation.Two major drawbacks are hindering CFD's democratization: So why isn't CFD on any engineer's desktop? the most efficient geometrical configuration of heat exchanger fins.the most performing shape of a ship propeller.This is why design engineers are interested in predicting the performance of a product.Ĭomputational Fluid Dynamics (CFD) simulations are helping corporations with several design decisions. ![]() When design engineers model objects in 3D with CAD, they must decide on product shapes. This is why it is considered the most productive branch of modern Artificial Intelligence. In Machine Learning, computers learn to make predictions and decisions based on data. Artificial Intelligence is changing how we use computing power to solve complex problems. ![]()
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