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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Surface Pressure Fluctuations Induced by a Hypersonic Turbulent Boundary Layer on a Sharp Cone at Angle of Attack

High-fidelity simulations are performed to characterize the turbulence-induced wall pressure fluctuations on a sharp cone at a 5.5° angle-of-attack in a Mach 8 flow. Wall-resolved large-eddy simulation (LES) and wall-modeled large-eddy simulation (WMLES) results are compared to measurements at several locations on the cone body, where comparisons with high-frequency PCB sensors are good, while comparison with low-frequency kulite sensors varies depending on the location. For a given streamwise location, simulation results show significant azimuthal variation in the wall pressure fluctuation statistics. Comparisons between LES and WMLES results indicate that WMLES can accurately reproduce the low-frequency component of the autospectra quite well, while slight sensitivity is apparent in the coherence. A grid sensitivity study for WMLES further demonstrated sensitivity in the wall pressure fluctuation coherence, but not in the autospectra.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Using Deep Learning to Develop a High Resolution Planetary Boundary Layer Model for Infrasound Propagation

Infrasound, with frequencies less than 20 Hz, is generated by both natural and anthropogenic sources. When one of these sources exerts a force on the atmosphere, infrasonic waves are generated. The propagation of these waves largely depends on temperature, wind speed, and wind direction. Previous work has used deep learning to accurately predict atmospheric specifications to altitudes of ~40 km. However, this model breaks down for local distances because it is too low resolution. Here we use a high-resolution meteorological dataset collected in Las Vegas, Nevada, USA to develop a deep learning model that can predict temperature, wind speed, and wind direction. Predictions are compared to ground truth observations to show that the model performs well at predicting temperature and wind direction but struggles with prediction wind speed. Model limitations and improvements are also discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Mixed-phase Convective Clouds in the Polar Marine Boundary Layer

This proposal, a collaboration between two universities, firstly aims to describe the high-latitude CAO cloud regime using the array of instruments at the NSA (Barrow, Alaska) and at two mobile ARM sites (MARCUS 2017-18, and COMBLE 2020). These ARM data, and the multi-sensor variables we propose to derive, allow detailed descriptions of mesoscale organization of precipitation, thermodynamic profiles, vertical velocity, cloud depth, and cloud and precipitation properties. These observations are key for our second goal, which is to use output from validated cloud- and eddy-resolving simulations to gain insights into the linkages between microphysical processes, the MBL vertical structure, and the MBL dynamics that control cloud macrostructure.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Turbulent Processes that Influence Boundary-Layer Cloud Structure (Final Technical Report)

Many models of the atmosphere simulate vertical profiles of observable fields, such as temperature, moisture, and vertical velocity. To assess the realism of such model output, it is important to evaluate the modeled profiles against observations. However, many extant evaluations consist of comparing time averaged profiles from the model and the observations. Such time averages can smooth out unrealistic features that appear in instantaneous profiles. In this way, the time averages may obscure certain model errors from detection. For instance, many models misrepresent turbulent processes, leading to profiles that are too smoothed out or too noisy. E.g., if a model has coarse vertical resolution and overly strong diffusion, then the model may be unable to produce observed sharp peaks. On the other hand, a model may have artificial approximations that produce spurious jumps in the vertical. Much can be learned by browsing through instantaneous snapshots of model output. But the volume of output produced by a global model is far too great to rely purely on visual inspection. In addition, we need a way to statistically characterize the profiles, so that anomalies can be detected more conveniently.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Generalized Integral Boundary Layer Equations in Streamline Curvilinear Coordinates

The MEIT equations are derived in orthogonal streamline curvilinear coordinates defined by the inviscid velocity at the wall and the surface-normal direction. The resulting formulations is (1) is not restricted to flat-plate or axisymmetric geometries, (2) accommodates vortical inviscid flows (e.g., induced by curved shocks) and wall mass blowing, and (3) allows for an arbitrary definition of the edge state. The streamline ODEs are also extended to a surface PDE framework.

97 MATHEMATICS AND COMPUTING↗