Published: March 22, 2022

David Darmofal
Jerome C. Hunsaker Professor of Aeronautics and Astronautics at MIT & Smead Aerospace Byram Distinguished Visiting Professor
Friday, Apr. 1 | 12:00 P.M. | AERO 114

Abstract: A key consideration in the design of supersonic aircraft is reducing the effects of sonic boom. In the past 15 years, significant progress has led to the demonstration of geometric shaping to produce supersonic aircraft with lower boom impact. This progress has been in part a result of improvements in sonic boom analysis and design techniques.

The common approach for sonic boom noise prediction divides the problem into a nearfield and a propagation portion. Computational fluid dynamics (CFD) is utilized for nearfield simulation to correctly account for the strong nonlinearities and geometric complexities of the aircraft. Recently developed adaptive methods have been critical in achieving the high levels of automation and accuracy that is now possible for nearfield simulations. Farther from the aircraft, flowfield disturbances diminish facilitating the modeling of acoustic wave propagation along individual ray paths. Currently, boom propagation is modeled by solving an augmented Burgers equation that includes the effects of second-order nonlinearity, absorption, molecular relaxation, atmospheric stratification, and spreading. A recent advance in sonic boom design has been the development of gradient-based optimization techniques utilizing an adjoint approach to efficiently determine design sensitivities from the geometry through the nearfield CFD analysis and finally the propagation to the ground. 

Even with these methodological improvements, the computational cost to analyze and design the next generation of low boom aircraft is significant as a direct result of the increasingly tighter control required on sonic boom noise generation. In this talk, we will provide a general overview of the sonic boom modeling process and then focus on the development of an adaptive finite element method to solve the boom propagation problem. Results demonstrate that the new adaptive method can efficiently and reliably produce high accuracy solutions to the boom propagation problem.

Bio: David Darmofal is the Jerome C. Hunsaker Professor of Aeronautics and Astronautics at MIT and a member of the MIT Aerospace Computational Design Laboratory and the MIT Center for Computational Science & Engineering. His principal areas of interest are computational methods for partial differential equations, especially fluid dynamics; and engineering education innovation. He has written approximately 80 technical publications in peer-reviewed journals and conferences. He teaches graduate and undergraduate courses in fluid dynamics and computational methods.

He was appointed as an MIT MacVicar Faculty Fellow in 2004, and in 2012 received the MIT Bisplinghoff Fellow. Darmofal received an NSF CAREER Award in 1997 and is an Associate Fellow of AIAA. He received his BS in Aerospace Engineering from the University of Michigan (1989), and SM (1991) and PhD (1993) in Aeronautics and Astronautics from MIT.


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