Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Flow Conditions”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Airfoil wake and linear theory gust response including sub and superresonant flow conditions

The unsteady aerodynamic gust response of a high solidity stator vane row is examined in terms of the fundamental gust modeling assumptions with particular attention given to the effects near an acoustic resonance. A series of experiments was performed with gusts generated by rotors comprised of perforated plates and airfoils. It is concluded that, for both the perforated plate and airfoil wake generated gusts, the unsteady pressure responses do not agree with the linear-theory gust predictions near an acoustic resonance. The effects of the acoustic resonance phenomena are clearly evident on the airfoil surface unsteady pressure responses. The transition of the measured lift coefficients across the acoustic resonance from the subresonant regime to the superresonant regime occurs in a simple linear fashion.

Henderson, Gregory H.↗
NASA NTRS1993

Heating analysis for a Lunar Transfer Vehicle at near-equilibrium flow conditions

A heating analysis for a 15.2 m diameter Lunar Transfer Vehicle (LTV) at 0 and 10.6 deg angle of attack for a nominal trajectory through the earth's atmosphere is described. The analysis utilizes the Langley Aerothermodynamic Upwind Relaxation Algorithm (LAURA) with thin-layer, Navier-Stokes, thermochemical nonequilibrium options. Radiative heating levels are calculated using the Langley Optimized RAdiative Nonequilibrium (LORAN) and the Non-EQuilibrium AIr Radiation (NEQAIR) codes. At peak heating, the shock layer is substantially in equilibrium. Comprehensive spatial and spectral grid convergence studies have been implemented to quantify grid effects on the convective and radiative heating levels. Axisymmetric tests including the coupled effects of radiative energy transfer show negligible change to the convective heating and a 20 percent reduction in the radiative heating.

Gnoffo, Peter A.↗
NASA NTRS1955

Component Performance Investigation of J71 Type II Turbines V - Internal Flow Conditions of J71 Type IIA Turbine

An experimental investigation of the J71 Type IIA turbine was conducted at the equivalent design speed and work output. The design stagework distribution was closely approached, although the design vector diagram was not attained. Efficiencies of 0.904, 0.851, and 0.806 were obtained for the first, second, and third stages, respectively. Losses occurred near the hub and tip for all three rotors. The third-stage losses, however, were significantly greater than those of either the first or second stage.

Schum, Harold J.↗
NASA NTRS2019

Numerical Investigation and Optimization of a Flushwall Injector for Scramjet Applications at Hypervelocity Flow Conditions

An investigation utilizing Reynolds-averaged simulations (RAS) was performed in order to find optimal designs for an interdigitated flushwall injector suitable for scramjet applications at hypervelocity conditions. The flight Mach number, duct height, spanwise width, and injection angle were the design variables selected to maximize two objective functions: the thrust potential and combustion efficiency. A Latin hypercube sampling design-of-experiments method was used to select design points for RAS. A methodology was developed that automated building geometries and generating grids for each design. The ensuing RAS analysis generated the performance database from which the two objective functions of interest were computed using a one-dimensional performance utility. The data were fitted using four surrogate models: an artificial neural network (ANN) model, a cubic polynomial, a quadratic polynomial, and a Kriging model. Variance-based decomposition showed that both objective functions were primarily driven by changes in the duct height. Multiobjective design optimization was performed for all four surrogate models via a genetic algorithm method. Optimal solutions were obtained at the upper and lower bounds of the flight Mach number range. The Kriging model obtained an optimal solution set that predicted high values for both objective functions. Additionally, three challenge points were selected to assess the designs on the Pareto fronts. Further sampling among the designs of the Pareto fronts are required in order to lower the errors and perform more accurate surrogate-based optimization. sed optimization.

Shenoy, Rajiv R.↗
DOE OSTI2025

A comparison of probabilistic generative frameworks for molecular simulations

Generative artificial intelligence is now a widely used tool in molecular science. Despite the popularity of probabilistic generative models, numerical experiments benchmarking their performance on molecular data are lacking. Here, in this work, we introduce and explain several classes of generative models, broadly sorted into two categories: flow-based models and diffusion models. We select three representative models: neural spline flows, conditional flow matching, and denoising diffusion probabilistic models, and examine their accuracy, computational cost, and generation speed across datasets with tunable dimensionality, complexity, and modal asymmetry. Our findings are varied, with no one framework being the best for all purposes. In a nutshell, (i) neural spline flows do best at capturing mode asymmetry present in low-dimensional data, (ii) conditional flow matching outperforms other models for high-dimensional data with low complexity, and (iii) denoising diffusion probabilistic models appear the best for low-dimensional data with high complexity. Our datasets include a Gaussian mixture model and the dihedral torsion angle distribution of the Aib9 peptide, generated via a molecular dynamics simulation. We hope our taxonomy of probabilistic generative frameworks and numerical results may guide model selection for a wide range of molecular tasks.

Artificial intelligence↗