Computational Fluid Dynamics: Simulating the Future
Computational Fluid Dynamics: Simulating the Future
Jason Xu Thomas Jefferson High School for Science and Technology
This article was originally included in the 2021 print publication of the Teknos Science Journal.
4.8 million. 4.9 million. 5 million. I watched in fascination as the step counter on my screen slowly but surely increased, signaling the successful simulation of air flow around a wedge moving incredibly fast high up in the atmosphere. After sending the data to a visualization program and being greeted by a beautiful, successfully rendered flow model, I leaned far back in my chair, letting out a sigh of relief now that this 3-day long simulation was complete. It was another step forward in my computational physics research project at my mentorship site, the US Naval Research Lab, where I aim to advance our understanding of fluid dynamics and air chemistry by running simulations for a specific airfoil-esque shape traveling through the air at a very high speed and altitude.
On December 17, 1903, the Wright brothers achieved the impossible. They had successfully created and flown the world’s first powered aircraft. Since that day, aircraft design has become faster, more advanced, and more powerful. The most advanced jets are designed to fly high above the clouds at many times the speed of sound, but these designs aren’t created by hand. Rather, they’re built and tested through computational fluid dynamics (CFD) methods. It is thanks to CFD that aircrafts can fly across the globe in mere hours rather than days or weeks, though this is not the only application of the method. It can be used in any situation in which a liquid or gas flows, including in designing things as commonplace as ventilation systems or coffee pots.
So, how does CFD work? How does a computer crunching numbers have anything to do with aircraft design? A study by Gu et al. (2018) sought to explore these questions and gauge the effectiveness of an automated CFD process when applied to drag and fuel consumption estimates for a large passenger aircraft. Their process involved a flowchart, beginning with generating the mesh (a set of data points outlining the physical object), solving the fluid dynamics equations by simulating millions of very short timesteps, using that data to estimate drag, and then combining that data with engine performance metrics to calculate fuel efficiency. This process is very similar to the research I am conducting at the Naval Research Lab: I started by making a mesh and continued by simulating a couple seconds of movement at a time to find molecular concentrations in the air. In the aforementioned study, these methods resulted in a slight overestimation of drag forces and thus a slight underestimation of fuel efficiency, likely due to an inadequately refined mesh generated early on (Gu et al., 2018). However, this flaw does not mean CFD is not useful. Especially in the early stages of the design process, CFD can help create a suitable prototype and reduce future need for redesigning. By using CFD, much of the manual testing and calculations that normally go into designing aircraft can be replaced by automatic processes.
While it has certain applications in overall aircraft design, CFD is best utilized when applied to very specific cases, such as at extremely high speeds. When an object travels through the air at faster than roughly 0.3 times the speed of sound, the flow around it becomes compressible (Oosthuizen, 2019). This means that the air starts to “compress” and its density changes. These changes are extremely difficult to calculate by hand, so this is where CFD comes in. This issue was the premise of a study by Wang et al. (2018), in which the researchers analyzed the accuracy of using CFD to model drag along airfoils compared to the conventional method of using surface integrals. This roughly outlines what my research is focused on; my goal is to simulate a generic airfoil moving at many times the speed of sound high up in the atmosphere. However, unlike my research, Wang’s team was able to greatly simplify the process of 2D flow calculations while also increasing the accuracy of the results through a method known as entropy generation. More importantly, they were able to determine that this process was possible without detailed surface geometry, meaning it can be applied to many cases, not just the specific airfoils used in this study (Wang et al., 2018).
Computational fluid dynamics becomes even more powerful when combined with other methods to achieve a more niche solution to certain problems. For example, a study by Jia and Lee (2020) combined CFD with a similar field known as computational structural dynamics (CSD) and an acoustic modeling software to investigate the effects of grid resolution on the simulated air-load of a 2-rotor helicopter. CSD is generally used when the simulated object goes through structural deformations, which is certainly the case with coaxial helicopter blades, where one set of blades is spinning clockwise and the other is spinning counterclockwise. This causes interference and deflection between the blades. Henry Jia (personal communication, Feb. 3, 2021) was able to combine CFD and CSD through a loose coupling method in order to achieve a more accurate result that accounted for the deformed rotor blades. Through this method, he found that when modifying wall spacing in the CFD mesh, the chord length at the tip of each blade, Ctip, reached acceptable accuracy levels for air-load engineering purposes at around 10%.
As previously mentioned, computational fluid dynamics can be applied to more than just aircraft design. Recently, it has also been used to simulate ambient airflow inside of cars in an effort to reduce the spread of COVID-19 in these confined areas (Anthes, 2021). The researchers studied a generic Prius traveling at 50 mph with one driver and one passenger in the back left seat. Such simulations are run at much slower speeds, resulting in incompressible flows and requiring a different kind of CFD. In this case, a set of equations known as the Navier-Stokes equations were used to model incompressible flows (Paterson & Stern, 2020). The study found that even with only 2 people in the car, there was a great risk of viral transmission via airborne particles. This all changed when all the windows were opened, however, with as little as 0.2% of particles traveling between the driver and passenger (Anthes, 2021).
Computational fluid dynamics is a relatively new field of study that undoubtedly holds a vast expanse of currently undiscovered knowledge. By continuing to use CFD methods and researching new ways to simulate fluid flow, not only will we be able to build faster planes, but we will also be able to improve our own everyday lives. More gas-efficient cars, inexpensive heating systems, and the prevention of disease transmission are all possible thanks to computational fluid dynamics.
References
Anthes, E. (2021, January 16). How to (Literally) Drive the Coronavirus Away. The New York Times. https://www.nytimes.com/2021/01/16/health/coronavirus-transmission-cars.html
Gu, X., Ciampa, P. D., & Nagel, B. (2018). An automated CFD analysis workflow in overall aircraft design applications. CEAS Aeronautical Journal, 9(1), 3-13. http://dx.doi.org/10.1007/s13272-017-0264-1
Jia, Z., & Lee, S. (2020). Impulsive Loading Noise of a Lift-Offset Coaxial Rotor in High-Speed Forward Flight. American Institute of Aeronautics and Astronautics Journal, 58(2), 687-701. http://dx.doi.org/10.2514/1.J058295
Oosthuizen, P. H. (2019, November). Compressible flow. Access Science. Retrieved January 18, 2021, from https://www.accessscience.com/content/compressible-flow/153300
Paterson, E., & Stern, F. (2020, January). Computational fluid dynamics. University of Iowa. https://doi.org/10.1036/1097-8542.757259
Wang, W., Wang, J., Liu, H., & Jiang, B.-Y. (2018). CFD Prediction of Airfoil Drag in Viscous Flow Using the Entropy Generation Method. Mathematical Problems in Engineering. http://dx.doi.org/10.1155/2018/4347650
