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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS1966

The sonic boom.

Sonic boom reduction by aircraft shape modification

NOISE REDUCTION↗
NASA NTRS2014

LAVA Simulations for the AIAA Sonic Boom Prediction Workshop

Computational simulations using the Launch Ascent and Vehicle Aerodynamics (LAVA) framework are presented for the First AIAA Sonic Boom Prediction Workshop test cases. The framework is utilized with both structured overset and unstructured meshing approaches. The three workshop test cases include an axisymmetric body, a Delta Wing-Body model, and a complete low-boom supersonic transport concept. Solution sensitivity to mesh type and sizing, and several numerical convective flux discretization choices are presented and discussed. Favorable comparison between the computational simulations and experimental data of nearand mid-field pressure signatures were obtained.

Simulations↗
NASA NTRS2017

Cart3D Simulations for the Second AIAA Sonic Boom Prediction Workshop

Simulation results are presented for all test cases prescribed in the Second AIAA Sonic Boom Prediction Workshop. For each of the four nearfield test cases, we compute pressure signatures at specified distances and off-track angles, using an inviscid, embedded-boundary Cartesian-mesh flow solver with output-based mesh adaptation. The cases range in complexity from an axisymmetric body to a full low-boom aircraft configuration with a powered nacelle. For efficiency, boom carpets are decomposed into sets of independent meshes and computed in parallel. This also facilitates the use of more effective meshing strategies - each off-track angle is computed on a mesh with good azimuthal alignment, higher aspect ratio cells, and more tailored adaptation. The nearfield signatures generally exhibit good convergence with mesh refinement. We introduce a local error estimation procedure to highlight regions of the signatures most sensitive to mesh refinement. Results are also presented for the two propagation test cases, which investigate the effects of atmospheric profiles on ground noise. Propagation is handled with an augmented Burgers' equation method (NASA's sBOOM), and ground noise metrics are computed with LCASB.

Prediction↗
NASA NTRS2020

Development of a Weather-Robust Ground-Based System for Sonic Boom Measurements

This report describes Brigham Young University’s (BYU) development and testing of a weather-robust, ground-based system for sonic boom measurements, carried out in support of NASA’s Quiet Supersonic Flight (QSF) initiative. The microphone configuration system, referred to as the Compact Outdoor Unit for Ground-based Acoustical Recordings (COUGAR, for the BYU mascot), was tested at NASA’s 2018 Quiet Supersonic Flight (QSF18) campaign and at the subsequent 2019 Carpet Determination in Entirety Measurements (CarpetDIEM) program.

sonic boom↗
NASA NTRSDate not supplied

Estimating Sonic Boom Metrics Across a Community Using a Kalman Filter

As part of the Quesst mission, NASA will fly the X‑59 aircraft over selected communities to evaluate community responses to low-intensity sonic booms. The purpose of these community tests is to determine the dose-response relationship between the noise exposure metrics and the community response. The independent variables for the dose-response relationship are the noise exposure metrics experienced by survey respondents within the community. Two sources of noise exposure metrics are available in each community: measurements at sparse locations throughout the community, and calculations from propagation models across the community. Both the measurements and calculations are subject to uncertainty. A Kalman filter is proposed to combine the measured and calculated noise exposure metrics to obtain the best estimate of the true noise exposure metrics across the community. The noise exposure metrics estimated by the Kalman filter have lower uncertainty than either the measured or calculated noise exposure metrics alone. Simulations demonstrate that the Kalman filter produces a more accurate estimate of the true noise exposure metrics than other noise estimation methods.

Sonic boom↗