Enabling the environmentally clean air transportation of the future: A vision of computational fluid dynamics in 2030

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Abstract

As global air travel expands rapidly to meet demand generated by economic growth, it is essential to continue to improve the efficiency of air transportation to reduce its carbon emissions and address concerns about climate change. Future transports must be 'cleaner' and designed to include technologies that will continue to lower engine emissions and reduce community noise. The use of computational fluid dynamics (CFD) will be critical to enable the design of these new concepts. In general, the ability to simulate aerodynamic and reactive flows using CFD has progressed rapidly during the past several decades and has fundamentally changed the aerospace design process. Advanced simulation capabilities not only enable reductions in ground-based and flight-testing requirements, but also provide added physical insight, and enable superior designs at reduced cost and risk. In spite of considerable success, reliable use of CFD has remained confined to a small region of the operating envelope due, in part, to the inability of current methods to reliably predict turbulent, separated flows. Fortunately, the advent of much more powerful computing platforms provides an opportunity to overcome a number of these challenges. This paper summarizes the findings and recommendations from a recent NASA-funded study that provides a vision for CFD in the year 2030, including an assessment of critical technology gaps and needed development, and identifies the key CFD technology advancements that will enable the design and development of much cleaner aircraft in the future. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

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Slotnick, J. P., Khodadoust, A., Alonso, J. J., Darmofal, D. L., Gropp, W. D., Lurie, E. A., … Venkatakrishnan, V. (2014). Enabling the environmentally clean air transportation of the future: A vision of computational fluid dynamics in 2030. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 372(2022). https://doi.org/10.1098/rsta.2013.0317

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