Search NASA⌕ Search

Engineering topics

Freund, J. B.

Publications and source records attributed to Freund, J. B..

Perspective on machine learning for advancing fluid mechanics

In this paper, a perspective is presented on how machine learning (ML), with its burgeoning popularity and the increasing availability of portable implementations, might advance fluid mechanics. As with any numerical or experimental method, ML methods have strengths and limitations, which are acknowledged. Their potential impact is high so long as outcomes are held to the long-standing critical standards that should guide studies of flow physics.

42 ENGINEERING↗

The Robustness of Acoustic Analogies

Acoustic analogies for the prediction of flow noise are exact rearrangements of the flow equations N(right arrow q) = 0 into a nominal sound source S(right arrow q) and sound propagation operator L such that L(right arrow q) = S(right arrow q). In practice, the sound source is typically modeled and the propagation operator inverted to make predictions. Since the rearrangement is exact, any sufficiently accurate model of the source will yield the correct sound, so other factors must determine the merits of any particular formulation. Using data from a two-dimensional mixing layer direct numerical simulation (DNS), we evaluate the robustness of two analogy formulations to different errors intentionally introduced into the source. The motivation is that since S can not be perfectly modeled, analogies that are less sensitive to errors in S are preferable. Our assessment is made within the framework of Goldstein's generalized acoustic analogy, in which different choices of a base flow used in constructing L give different sources S and thus different analogies. A uniform base flow yields a Lighthill-like analogy, which we evaluate against a formulation in which the base flow is the actual mean flow of the DNS. The more complex mean flow formulation is found to be significantly more robust to errors in the energetic turbulent fluctuations, but its advantage is less pronounced when errors are made in the smaller scales.

Freund, J. B.↗

Ray Traces Through Unsteady Jet Turbulence

Results of an ongoing effort to quantify the role turbulence in scattering sound in jets are reported. Using a direct numerical simulation database to provide the flow data, ray paths traced through the mean flow are compared with those traced through the actual time evolving turbulent flow. Significant scattering by the turbulence is observed. The most notable effect is that upstream traveling waves that are trapped in the potential core by the mean flow, which acts as a wave guide, easily escape in the turbulent flow. A crude statistical estimate based on ray number density suggests that directivity is modified by the turbulence, but no rigorous treatment of non-uniformities in the high-frequency approximation is attempted.

Freund, J. B.↗