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Program descriptions, flow charts, and program listings for the SIMWEST model generation program, the simulation program, the file maintenance program, and the printer plotter program are given. For Vol 2, see .
This report describes the first study leading to the application of Particle Image Velocimetry to diagnose rotor type flows in the NASA Ames 80x120-foot wind tunnel facility. The necessary steps towards the implementation of PIV to study rotor type flows were first investigated in the Army Rotor Hover Chamber. The model problem consisted of the three-dimensional flow generated by a two-blade rotor at different wake ages.
A Zero-D cycle simulation of the GE90-94B high bypass turbofan engine has been achieved utilizing mini-maps generated from a high-fidelity simulation. The simulation utilizes the Numerical Propulsion System Simulation (NPSS) thermodynamic cycle modeling system coupled to a high-fidelity full-engine model represented by a set of coupled 3D computational fluid dynamic (CFD) component models. Boundary conditions from the balanced, steady state cycle model are used to define component boundary conditions in the full-engine model. Operating characteristics of the 3D component models are integrated into the cycle model via partial performance maps generated from the CFD flow solutions using one-dimensional mean line turbomachinery programs. This paper highlights the generation of the high-pressure compressor, booster, and fan partial performance maps, as well as turbine maps for the high pressure and low pressure turbine. These are actually "mini-maps" in the sense that they are developed only for a narrow operating range of the component. Results are compared between actual cycle data at a take-off condition and the comparable condition utilizing these mini-maps. The mini-maps are also presented with comparison to actual component data where possible.
A prediction scheme was established to compute sound pressure levels in the interior of a simplified cabin model of the second generation Large Civil Tiltrotor (LCTR2) during cruise conditions, while being excited by turbulent boundary layer flow over the fuselage, or by tiltrotor blade loading and thickness noise. Finite element models of the cabin structure, interior acoustic space, and acoustically absorbent (poro-elastic) materials in the fuselage were generated and combined into a coupled structural-acoustic model. Fluctuating power spectral densities were computed according to the Efimtsov turbulent boundary layer excitation model. Noise associated with the tiltrotor blades was predicted in the time domain as fluctuating surface pressures and converted to power spectral densities at the fuselage skin finite element nodes. A hybrid finite element (FE) approach was used to compute the low frequency acoustic cabin response over the frequency range 6-141 Hz with a 1 Hz bandwidth, and the Statistical Energy Analysis (SEA) approach was used to predict the interior noise for the 125-8000 Hz one-third octave bands.
The paper presents a model of two-dimensional cavity flow in which the shear layer over the cavity is represented by discrete rectilinear vortices which are free to move as the flow progresses. Although the model is initially started impulsively, the computation is continued until a statistically steady flow is attained. The broadband noise generation of the cavity is calculated by first running the model until the steady state is reached and then computing a stationary record of far-field density fluctuation through an equation that is suitable for calculating the quadrupole noise generation by the model. The discrete time series obtained can be analyzed by ordinary digital spectral techniques to determine the spectra and overall levels of the noise in airframe noise testing of real aircraft.
Structured grid solvers such as NASA's OVERFLOW compressible Navier-Stokes flow solver can generate large data files that contain convergence histories for flow equation residuals, turbulence model equation residuals, component forces and moments, and component relative motion dynamics variables. Most of today's large-scale problems can extend to hundreds of grids, and over 100 million grid points. However, due to the lack of efficient tools, only a small fraction of information contained in these files is analyzed. OVERSMART (OVERFLOW Solution Monitoring And Reporting Tool) provides a comprehensive report of solution convergence of flow computations over large, complex grid systems. It produces a one-page executive summary of the behavior of flow equation residuals, turbulence model equation residuals, and component forces and moments. Under the automatic option, a matrix of commonly viewed plots such as residual histograms, composite residuals, sub-iteration bar graphs, and component forces and moments is automatically generated. Specific plots required by the user can also be prescribed via a command file or a graphical user interface. Output is directed to the user s computer screen and/or to an html file for archival purposes. The current implementation has been targeted for the OVERFLOW flow solver, which is used to obtain a flow solution on structured overset grids. The OVERSMART framework allows easy extension to other flow solvers.
Fully automated meshing for Reynolds-Averaged Navier-Stokes Simulations, Mesh generation for complex geometry continues to be the biggest bottleneck in the RANS simulation process; Fully automated Cartesian methods routinely used for inviscid simulations about arbitrarily complex geometry; These methods lack of an obvious & robust way to achieve near wall anisotropy; Goal: Extend these methods for RANS simulation without sacrificing automation, at an affordable cost; Note: Nothing here is limited to Cartesian methods, and much becomes simpler in a body-fitted setting.
Icing calculations were performed for a NACA 0012 swept wing tip using LEWICE3D Version 3.48 coupled with the ANSYS CFX flow solver. The calculated ice shapes were compared to experimental data generated in the NASA Glenn Icing Research Tunnel (IRT). The IRT tests were designed to test the performance of the LEWICE3D ice void density model which was developed to improve the prediction of swept wing ice shapes. Icing tests were performed for a range of temperatures at two different droplet inertia parameters and two different sweep angles. The predicted mass agreed well with the experiment with an average difference of 12%. The LEWICE3D ice void density model under-predicted void density by an average of 30% for the large inertia parameter cases and by 63% for the small inertia parameter cases. This under-prediction in void density resulted in an over-prediction of ice area by an average of 115%. The LEWICE3D ice void density model produced a larger average area difference with experiment than the standard LEWICE density model, which doesn't account for the voids in the swept wing ice shape, (115% and 75% respectively) but it produced ice shapes which were deemed more appropriate because they were conservative (larger than experiment). Major contributors to the overly conservative ice shape predictions were deficiencies in the leading edge heat transfer and the sensitivity of the void ice density model to the particle inertia parameter. The scallop features present on the ice shapes were thought to generate interstitial flow and horse shoe vortices which enhance the leading edge heat transfer. A set of changes to improve the leading edge heat transfer and the void density model were tested. The changes improved the ice shape predictions considerably. More work needs to be done to evaluate the performance of these modifications for a wider range of geometries and icing conditions
Icing calculations were performed for a NACA 0012 swept wing tip using LEWICE3D Version 3.48 coupled with the ANSYS CFX flow solver. The calculated ice shapes were compared to experimental data generated in the NASA Glenn Icing Research Tunnel (IRT). The IRT tests were designed to test the performance of the LEWICE3D ice void density model which was developed to improve the prediction of swept wing ice shapes. Icing tests were performed for a range of temperatures at two different droplet inertia parameters and two different sweep angles. The predicted mass agreed well with the experiment with an average difference of 12%. The LEWICE3D ice void density model under-predicted void density by an average of 30% for the large inertia parameter cases and by 63% for the small inertia parameter cases. This under-prediction in void density resulted in an over-prediction of ice area by an average of 115%. The LEWICE3D ice void density model produced a larger average area difference with experiment than the standard LEWICE density model, which doesn't account for the voids in the swept wing ice shape, (115% and 75% respectively) but it produced ice shapes which were deemed more appropriate because they were conservative (larger than experiment). Major contributors to the overly conservative ice shape predictions were deficiencies in the leading edge heat transfer and the sensitivity of the void ice density model to the particle inertia parameter. The scallop features present on the ice shapes were thought to generate interstitial flow and horse shoe vortices which enhance the leading edge heat transfer. A set of changes to improve the leading edge heat transfer and the void density model were tested. The changes improved the ice shape predictions considerably. More work needs to be done to evaluate the performance of these modifications for a wider range of geometries and icing conditions.
The loss of the Space Shuttle Challenger was caused by the failure of the aft joint O-ring seals in its right solid rocket booster. It has been suggested by several sources that wind conditions through a reduction in temperature of the right solid rocket booster caused by the wind blowing across the cold external tank, played a role in the O-ring failure. To check the plausibility of the wind theory, an experiment was carried out in a water towing tank to visualize the flow past a two-dimensional model representing a cross section of the Space Shuttle launch configuration. The periodic formation of vortices was found to characterize the wake generated by the model. It is suggested that this organized motion in the flow is the dominant mechanism that accomplishes heat transfer from the external tank to the right solid rocket booster. Flow visualization results consisting of photographs that show instantaneous streamline patterns of the flow are presented.
Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. At the NASA Earth eXchange (NEX), we build deep learning methods to learn from cross sensor satellite-based Earth observations for generating new datasets with efficient processing techniques. Using current generation geostationary satellites on NEX, we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow. These tools are used to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.
Schlieren methods of flow visualization and hot-wire anemometry for velocity measurements were used to investigate the wakes generated by hovering model propellers and rotors. The research program was directed toward investigating (1) the stability of the tip vortex, (2) the effects produced by various tip shapes on performance and tip vortex characteristics, and (3) the shock formation and noise characteristics associated with various tip shapes. A free-wake analysis was also conducted for comparison with the vortex stability experimental results. Schlieren photographs showing wake asymmetry, interaction, and instability are presented along with a discussion of the effects produced by the number of blades, collective pitch, and tip speed. Two hot-wire anemometer techniques, used to measure the maximum circumferential velocity in the tip vortex, are discussed.
A series of Reynolds-averaged Navier-Stokes (RANS) simulations were performed of two Mach 2.5 axisymmetric shock-wave/boundary-layer interactions (SWBLIs) based off of the NASA Turbulent Computational Fluid Dynamics Validation Experiment (TCFDVE). The goals of the simulations were to (1) determine how well RANS can predict the flow physics of an axisymmetric SWBLI and (2) generate a new validation case for the NASA Turbulence Modeling Resource (TMR). The simulations were run with two different turbulence models and three different computational fluid dynamics (CFD) codes while looking at two different conical shock generator angles, one of which was shown to cause flow separation in the experiment. The results in this paper show that, as expected, the CFD simulations agreed well with each other and the experiment when SWBLI-induced separation was not present. When separation was present, differences in the predicted separation characteristics were noted. These included a stronger interaction and subsequently longer separation extent for the Menter Shear-Stress Transport k-ω (SST) turbulence model simulations compared to both the Spalart-Allmaras (SA) turbulence model simulations and the experiment.
We have previously proposed a computational neural-network model by which the complex patterns of retinal image motion generated during locomotion (optic flow) can be processed by specialized detectors acting as templates for specific instances of self-motion. The detectors in this template model respond to global optic flow by sampling image motion over a large portion of the visual field through networks of local motion sensors with properties similar to neurons found in the middle temporal (MT) area of primate extrastriate visual cortex. The model detectors were designed to extract self-translation (heading), self-rotation, as well as the scene layout (relative distances) ahead of a moving observer, and are arranged in cortical-like heading maps to perform this function. Heading estimation from optic flow has been postulated by some to be implemented within the medial superior temporal (MST) area. Others have questioned whether MST neurons can fulfill this role because some of their receptive-field properties appear inconsistent with a role in heading estimation. To resolve this issue, we systematically compared MST single-unit responses with the outputs of model detectors under matched stimulus conditions. We found that the basic physiological properties of MST neurons can be explained by the template model. We conclude that MST neurons are well suited to support heading estimation and that the template model provides an explicit set of testable hypotheses which can guide future exploration of MST and adjacent areas within the primate superior temporal sulcus.
We have proposed previously a computational neural-network model by which the complex patterns of retinal image motion generated during locomotion (optic flow) can be processed by specialized detectors acting as templates for specific instances of self-motion. The detectors in this template model respond to global optic flow by sampling image motion over a large portion of the visual field through networks of local motion sensors with properties similar to those of neurons found in the middle temporal (MT) area of primate extrastriate visual cortex. These detectors, arranged within cortical-like maps, were designed to extract self-translation (heading) and self-rotation, as well as the scene layout (relative distances) ahead of a moving observer. We then postulated that heading from optic flow is directly encoded by individual neurons acting as heading detectors within the medial superior temporal (MST) area. Others have questioned whether individual MST neurons can perform this function because some of their receptive-field properties seem inconsistent with this role. To resolve this issue, we systematically compared MST responses with those of detectors from two different configurations of the model under matched stimulus conditions. We found that the characteristic physiological properties of MST neurons can be explained by the template model. We conclude that MST neurons are well suited to support self-motion estimation via a direct encoding of heading and that the template model provides an explicit set of testable hypotheses that can guide future exploration of MST and adjacent areas within the superior temporal sulcus.
A case history depicting the effort to generate a computational grid for the simulation of transonic flow about an F-16 aircraft at realistic flight conditions is presented. The flow solver for which this grid is designed is a zonal one, using the Reynolds averaged Navier-Stokes equations near the surface of the aircraft, and the Euler equations in regions removed from the aircraft. A body conforming global grid, suitable for the Euler equation, is first generated using 3-D Poisson equations having inhomogeneous terms modeled after the 2-D GRAPE code. Regions of the global grid are then designated for zonal refinement as appropriate to accurately model the flow physics. Grid spacing suitable for solution of the Navier-Stokes equations is generated in the refinement zones by simple subdivision of the given coarse grid intervals. That grid generation project is described, with particular emphasis on the global coarse grid.
TARGET (Task Analysis/Rule Generation Tool) represents a new breed of tool that blends graphical process flow modeling capabilities with the function of a top-down reporting facility. Since NASA personnel frequently perform tasks that are primarily procedural in nature, TARGET models mission or task procedures and generates hierarchical reports as part of the process capture and analysis effort. Historically, capturing knowledge has proven to be one of the greatest barriers to the development of intelligent systems. Current practice generally requires lengthy interactions between the expert whose knowledge is to be captured and the knowledge engineer whose responsibility is to acquire and represent the expert's knowledge in a useful form. Although much research has been devoted to the development of methodologies and computer software to aid in the capture and representation of some types of knowledge, procedural knowledge has received relatively little attention. In essence, TARGET is one of the first tools of its kind, commercial or institutional, that is designed to support this type of knowledge capture undertaking. This paper will describe the design and development of TARGET for the acquisition and representation of procedural knowledge. The strategies employed by TARGET to support use by knowledge engineers, subject matter experts, programmers and managers will be discussed. This discussion includes the method by which the tool employs its graphical user interface to generate a task hierarchy report. Next, the approach to generate production rules for incorporation in and development of a CLIPS based expert system will be elaborated. TARGET also permits experts to visually describe procedural tasks as a common medium for knowledge refinement by the expert community and knowledge engineer making knowledge consensus possible. The paper briefly touches on the verification and validation issues facing the CLIPS rule generation aspects of TARGET. A description of efforts to support TARGET's interoperability issues on PCs, Macintoshes and UNIX workstations concludes the paper.
In an earlier study, a prediction model for jet noise in dual stream jets was proposed that is founded on velocity scaling laws in single stream jets and similarity features of the mean velocity and turbulent kinetic energy in dual stream flows. The model forms a composite spectrum from four component single-stream jets each believed to represent noise-generation from a distinct region in the actual flow. While the methodology worked effectively at conditions considered earlier, recent examination of acoustic data at some unconventional conditions indicate that further improvements are necessary in order to expand the range of applicability of the model. The present work demonstrates how these predictions compare with experimental data gathered by NASA and industry for the purpose of examining the aerodynamic and acoustic performance of such nozzles for a wide range of core and fan stream conditions. Of particular interest are jets with inverted velocity and temperature profiles and the appearance of a second spectral peak at small aft angles to the jet under such conditions. It is shown that a four-component spectrum succeeds in modeling the second peak when the aft angle refraction effects are properly incorporated into the model. A tradeoff of noise emission takes place between two turbulent regions identified as transition and fully mixed regions as the fan stream velocity exceeds that of the core stream. The effect of nozzle discharge coefficients will also be discussed.