By Derek Yang, edited by Archisha Das
Euless, TX
In any typical NASCAR race, 42 cars are entered initially into the competition. After 26 grueling races of up to five hours each, a single driver is crowned the NASCAR Champion (who can expect a substantial $2 million dollars!). To win, drivers must not only drive incredibly skillfully, but they must also drive the best race cars in the industry. The winning race cars are typically crafted using top-of-the-line materials, tested repeatedly to ensure absolute perfection. But these tests aren’t any ordinary tests—they usually involve a complex process known as computational fluid dynamics.
Computational fluid dynamics (CFD) has its roots in the Navier-Stokes equations, a set of equations developed from 1822 to 1850 that describes the momentum balance of Newtonian fluids. CFD’s development and societal applications greatly accelerated during the mid-20th century with the invention of computers, of which CFD’s primary purpose depends upon.
CFD is one of the three primary methods to simulate the behavior of liquids and gasses in their interaction with the surrounding environment, which means it can help predict the efficiency of the flow around a race car, for example. Compared to the other two more traditional methods, one of which is solving complex equations by hand, CFD is the most cost-effective and efficient method, meaning it can be applied to a variety of engineering fields on a much larger scale. That way, vehicles that must be streamlined in design to work effectively (eg., missiles, airplanes, race cars, and motorcycles) can be tested at an in-depth level, allowing scientists deep insights into the physics and physical science involved.
But how does CFD work? The CFD simulation process involves using the fundamental laws of mechanics, fluid dynamics equations, and complex math models to simulate a physical problem. Then, different CFD software programs are employed to solve these equations and optimize liquid/gas flow, finding important variables like temperature, density, and pressure. Finally, engineers will attempt to integrate these variable measurements into the physical prototype. Because physical prototypes are only built after rigorous simulation testing, CFD allows engineers to test and improve their designs without wasting expensive resources.
Besides race cars and planes, CFD software can be widely applied to various engineering and technology industries including chemical engineering, engineering of consumer products, marine vessels like research ships and submarines, energy tools like hydroelectric dam turbines and wind turbines, building design and architecture, and sports. In general, any field that needs to test the efficacy of an object in withstanding wind or water will use CFD for its high efficiency and cost-effectiveness.
While CFD is undoubtedly a powerful simulation modeling tool, it is a relatively new technology with a few inherent flaws. For example, the word simulation itself implies that the model may not perfectly match real-world environmental conditions, so a degree of prototype testing is still needed. Additionally, CFD-equation solving software can make numerical calculation errors, some of which can be very costly and difficult to correct, once integrated into a prototype or final design. However, CFD is also evolving with increasing advancements in computing power, such as those facilitated by machine learning, and the models are becoming more realistic by the day.
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