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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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Supersonic Wing Nonlinear Aerodynamics computer program, LTSTAR, estimates nonlinear aerodynamics characteristics of wing at supersonic speeds. Program provides generally improved predictions of wing overall force and moment coefficients. LTSTAR is useful in design studies aimed at aerodynamic performance optimization and providing more-realistic tradeoff information for selection of wing planform geometry and airfoil section parameters.
A dc electrical discharge cell for laser optogalvanic spectroscopy has been designed and tested. Signal optimization is achieved through the use of adjustable electrode positions and an orthogonal geometry between the probe laser and the discharge axis. Results are presented for visible dye laser and infrared diode laser studies of selected neutral, radical, and ion species.
The paper reviews the area of body-turbulence interaction with particular emphasis upon the influence of the body upon an incident turublent field. Cases considered include two-dimensional (high and low fineness ratio, porous, and impervious) and three-dimensional bodies in-stream, adjacent to, and attached to walls. Particular physics common to several geometric and incident flow configurations include (1) eddy severing at relatively sharp leading edges, (2) production of vorticity of the opposite sense on bluff bodies, and (3) body region production of control vortices which affect the incident turbulence field for the order of 100 boundary-layer thicknesses downstream. The major local effects of the body upon the incident turbulent field include (1) a blocking effect, (2) influence of the body momentum deficit/near wake, (3) distortion due to the body time-averaged flow field, and (4) unsteady body circulation. The review may be of particular interest for turbulence alteration/control using fixed geometry in applications such as drag reduction, separation control, noise reduction, and augmentor optimization.
Research is summarized on the development of an eye safe Raman conversion system to carry out lidar measurements of aerosol and clouds from an airborne platform. Radiation is produced at the first Stokes wavelength of 1.54 micron in the eye safe infrared, when methane is used as the Raman-active medium, the pump source being a Nd:YAG laser at 1.064 micron. Results are presented for an experimental study of the dependence of the 1.54 micron first Stokes radiation on the focusing geometry, methane gas pressure, and pump energy. The specific new technique developed for optimizing the first Stokes generation involves retroreflecting the backward-generated first Stokes light back into the Raman cell as a seed Stokes beam which is then amplified in the temporal tail of the pump beam. Almost 20 percent conversion to 1.54 micron is obtained. Complete, assembled hardware for the Raman conversion system was delivered to the Goddard Space Flight Center for a successful GLOBE flight (1989) to measure aerosol backscatter around the Pacific basin.
The efforts in geometry modeling and grid generation at the NASA Lewis Research Center, as applied to the computational fluid dynamic (CFD) analysis of aeropropulsion systems, are presented. The efforts are mainly characterized by a focus on the analysis of components of an aeropropulsion system, which involve turbulent viscous flow with heat transfer and chemistry. Thus, this discussion will follow that characterization and will sequence through the components of typical propulsion systems consisting of inlets, compressors, combustors, turbines, and nozzles. For each component, some applications of CFD analysis will be presented to show how CFD is used to compute the desired performance information, how geometry modeling and grid generation are performed, and what issues have developed related to geometry modeling and grid generation. The discussion will illustrate the following needs related to geometry modeling and grid generation as observed in aeropropulsion analysis: (1) accurate and efficient resolution of turbulent viscous and chemically-reacting flowfields; (2) easy-to-use interfaces with CAD data for automated grid generation about complex geometries; and (3) automated batch grid generation software for use with design and optimization software.
The detector response matrices for the Burst And Transient Source Experiment (BATSE) on board the Compton Gamma Ray Observatory (CGRO) are described, including their creation and operation in data analysis. These response matrices are a detailed abstract representation of the gamma-ray detectors' operating characteristics that are needed for data analysis. They are constructed from an extensive set of calibration data coupled with a complex geometry electromagnetic cascade Monte Carlo simulation code. The calibration tests and simulation algorithm optimization are described. The characteristics of the BATSE detectors in the spacecraft environment are also described.
Grand tours are a class of methods for visualizing multivariate data, or any finite set of points in n-space. The idea is to create an animation of data projections by moving a 2-dimensional projection plane through n-space. The path of planes used in the animation is chosen so that it becomes dense, that is, it comes arbitrarily close to any plane. One of the original inspirations for the grand tour was the experience of trying to comprehend an abstract sculpture in a museum. One tends to walk around the sculpture, viewing it from many different angles. A useful class of grand tours is based on the idea of continuously interpolating an infinite sequence of randomly chosen planes. Visiting randomly (more precisely: uniformly) distributed planes guarantees denseness of the interpolating path. In computer implementations, 2-dimensional orthogonal projections are specified by two 1-dimensional projections which map to the horizontal and vertical screen dimensions, respectively. Hence, a grand tour is specified by a path of pairs of orthonormal projection vectors. This paper describes an interpolation scheme for smoothly connecting two pairs of orthonormal vectors, and thus for constructing interpolating grand tours. The scheme is optimal in the sense that connecting paths are geodesics in a natural Riemannian geometry.
The geometries of the B(n)H(+) clusters for n=1-13 have been optimized at the B3LYP level of theory. Excluding B2H(+) all clusters are low-spin coupled. The structures appear to be related to the bare B(+)(n) clusters in two ways: 1) an H atom is added to the B(+)(n) cluster or 2) a BH is added to the B(+)(n-1) cluster. The B(+)(n)-H binding energies are computed at the B3LYP level of theory, and calibrated n using the CCSD(T) level of theory. The computed results tend to fall between the experimental best estimates and the experimental lower bounds. The B(+)(n)-H binding energies show an inverse correspondence with the B+(n-1)-B values) while the B(+)(n-1)-B and B(+)(n-1)-BH results tend to parallel each other.
The geometry of the left ventricle in patients with cardiomyopathy is often sub-optimal for 2-dimensional ultrasound when assessing left ventricular (LV) function and localized abnormalities such as a ventricular aneurysm. The aim of this study was to report the initial experience of real-time 3-D echocardiography for evaluating patients with cardiomyopathy. A total of 34 patients were evaluated with the real-time 3D method in the operating room (n = 15) and in the echocardiographic laboratory (n = 19). Thirteen of 28 patients with cardiomyopathy and 6 other subjects with normal LV function were evaluated by both real-time 3-D echocardiography and magnetic resonance imaging (MRI) for obtaining LV volumes and ejection fractions for comparison. There were close relations and agreements for LV volumes (r = 0.98, p <0.0001, mean difference = -15 +/- 81 ml) and ejection fractions (r = 0.97, p <0.0001, mean difference = 0.001 +/- 0.04) between the real-time 3D method and MRI when 3 cardiomyopathy cases with marked LV dilatation (LV end-diastolic volume >450 ml by MRI) were excluded. In these 3 patients, 3D echocardiography significantly underestimated the LV volumes due to difficulties with imaging the entire LV in a 60 degrees x 60 degrees pyramidal volume. The new real-time 3D echocardiography is feasible in patients with cardiomyopathy and may provide a faster and lower cost alternative to MRI for evaluating cardiac function in patients.
The efficient detection of single photons at 1.06 micron is of considerable interest for lidar/ladar systems designed for remote sensing an d ranging as well as for free-space optical transmission in photon-st arved applications. However, silicon-based single photon avalanche diodes (SPADs) used at shorter wavelengths have very low single photon d etection efficiency (approximately 1 - 2%) at 1.06 micron, and InP/In GaAs SPADs designed for telecommunications wavelengths near 1.5 micro n exhibit high dark count rates that generally inhibit non-gated (free-running) operation. To bridge this "single photon detection gap" for wavelengths just beyond 1 micron, we have developed high performance , large area (80 - 200 micron diameter) InP-based InGaAsP quaternary absorber SPADs optimized for operation at 1.06 micron and based on a highly reliable planar geometry avalanche photodiode structure. We wil l show that dark count rates are sufficiently low to allow for non-ga ted operation while achieving detection efficiencies far surpassing t hose found for Si SPADs. At a detection efficiency of 10%, 80 micron diameter devices exhibit dark count rates below 1000 Hz and count rate s of at least 3 MHz when operated at -40 C. Significantly higher dete ction efficiencies (30 - 50%) are achievable with acceptable tradeoff s in dark count rate. In this paper, we will also discuss performance modeling for these devices and compare their behavior with longer wav elength InP-based InGaAs ternary absorber SPADs fabricated on a relat ed device design platform.
This presentation reviews recent progress made under NASA s Subsonic Rotary Wing (SRW) propulsion research activities. Advances in engines, drive systems and optimized propulsion systems are discussed. Progress in wide operability compressors, modeling of variable geometry turbine performance, foil gas bearings and multi-speed transmissions are presented.
The objective of this proposed project is to research and develop a prediction tool for advanced additive manufacturing (AAM) processes for advanced materials and develop experimental methods to provide fundamental properties and establish validation data. Aircraft structures and engines demand materials that are stronger, useable at much higher temperatures, provide less acoustic transmission, and enable more aeroelastic tailoring than those currently used. Significant improvements in properties can only be achieved by processing the materials under nonequilibrium conditions, such as AAM processes. AAM processes encompass a class of processes that use a focused heat source to create a melt pool on a substrate. Examples include Electron Beam Freeform Fabrication and Direct Metal Deposition. These types of additive processes enable fabrication of parts directly from CAD drawings. To achieve the desired material properties and geometries of the final structure, assessing the impact of process parameters and predicting optimized conditions with numerical modeling as an effective prediction tool is necessary. The targets for the processing are multiple and at different spatial scales, and the physical phenomena associated occur in multiphysics and multiscale. In this project, the research work has been developed to model AAM processes in a multiscale and multiphysics approach. A macroscale model was developed to investigate the residual stresses and distortion in AAM processes. A sequentially coupled, thermomechanical, finite element model was developed and validated experimentally. The results showed the temperature distribution, residual stress, and deformation within the formed deposits and substrates. A mesoscale model was developed to include heat transfer, phase change with mushy zone, incompressible free surface flow, solute redistribution, and surface tension. Because of excessive computing time needed, a parallel computing approach was also tested. In addition, after investigating various methods, a Smoothed Particle Hydrodynamics Model (SPH Model) was developed to model wire feeding process. Its computational efficiency and simple architecture makes it more robust and flexible than other models. More research on material properties may be needed to realistically model the AAM processes. A microscale model was developed to investigate heterogeneous nucleation, dendritic grain growth, epitaxial growth of columnar grains, columnar-to-equiaxed transition, grain transport in melt, and other properties. The orientations of the columnar grains were almost perpendicular to the laser motion's direction. Compared to the similar studies in the literature, the multiple grain morphology modeling result is in the same order of magnitude as optical morphologies in the experiment. Experimental work was conducted to validate different models. An infrared camera was incorporated as a process monitoring and validating tool to identify the solidus and mushy zones during deposition. The images were successfully processed to identify these regions. This research project has investigated multiscale and multiphysics of the complex AAM processes thus leading to advanced understanding of these processes. The project has also developed several modeling tools and experimental validation tools that will be very critical in the future of AAM process qualification and certification.
The open-source Porous Microstructure Analysis (PuMA) software was implemented to offer an efficient framework for determining material characteristics from 3D microstructures. Its development was inspired by progress in X-ray microtomography, an imaging technology that captures the internal structure of materials in 3D, and even in a 4D temporal context. Over recent years, this method has transformed the domain of materials science due to its capability to non-destructively examine material microstructures while presenting digital data about their geometrical details. It has provided insights into materials relevant to several NASA missions, including heatshields, parachute fabrics, meteorites, and other advanced composites. PuMA, in its current version 3, delivers an array of features, spanning from basic geometric insights of a microstructure to intricate anisotropic thermo-elastic and chemical behavior. Specifically, the software evaluates morphological attributes (specific surface area, volume fractions, mean intercept lengths, orientation) and physical characteristics (conductivity, elasticity, permeability, and tortuosity). Additionally, it can model material degradation processes, such as oxidation and surface chemistry interactions. The software can generate synthetic microstructures, from straightforward geometrical designs to intricate woven and non-woven geometries. Coupling material generation and characterization enable parametric studies and sensitivity analysis to optimize the microstructural performance and inform design decisions and reliability assessment based on uncertainty quantification. A recent addition to PuMA includes the TomoSAM plugin, devised to incorporate the cutting-edge Segment Anything Model (SAM). SAM is a promptable deep learning model that can identify objects and create image masks in a zero-shot manner, based only on a few user clicks. The synergy between these tools aids in the segmentation of complex 3D datasets from tomography or other imaging techniques, which would otherwise require a laborious manual segmentation process.
The open-source Porous Microstructure Analysis (PuMA) software was created to offer an efficient framework for determining material properties from 3D microstructures. Its development was inspired by progress in X-ray microtomography, an imaging technology that captures the internal structure of materials in 3D, and even in a 4D temporal context. Over recent years, this method has transformed the domain of materials science due to its capability to non-destructively examine material microstructures while presenting digital data about their geometrical details. It has provided insights into materials relevant to several NASA missions, including heatshields, parachute fabrics, meteorites, and other advanced composites. PuMA, in its current version 3, delivers an array of features, spanning from basic geometric insights of a microstructure to intricate anisotropic thermo-elastic and chemical behavior. Specifically, the software evaluates morphological attributes (specific surface area, volume fractions, mean intercept lengths, orientation) and physical characteristics (conductivity, elasticity, permeability, and tortuosity). Additionally, it can model material degradation processes, such as oxidation and surface chemistry interactions. The software can generate synthetic microstructures, from straightforward geometrical designs to intricate woven and non-woven geometries. Coupling material generation and characterization enables parametric studies and sensitivity analysis to optimize the microstructural performance and inform design decisions and reliability assessment based on uncertainty quantification. A recent addition to PuMA includes the TomoSAM plugin, devised to incorporate the cutting-edge Segment Anything Model (SAM) into our image segmentation workflow. SAM is a promptable deep learning model that can identify objects and create image masks in a zero-shot manner, based only on a few user clicks. The synergy between these tools aids in the segmentation of complex 3D datasets from tomography and other imaging techniques, which would otherwise require a laborious manual segmentation process.
The Glenn Icing Computational Environment (GlennICE) is a computational tool designed to calculate ice growth on complex three-dimensional geometries. It utilizes user-supplied computational fluid dynamics solutions for the geometry of interest. Key developments include advancements in convergence of collection efficiency, trajectory optimization, and refinement methodology. These improvements have significantly enhanced GlennICE’s efficiency for practical engineering applications. A recent study focused on benchmarking GlennICE’s scalability in a parallel environment using static scheduling. Findings indicated a potential twofold increase in efficiency through workload balance enhancements. This paper presents an analysis of the solver’s new workload balancing improvements, incorporating shared memory and dynamic scheduling routines. Results demonstrate a highly efficient and consistent algorithm across high-performance computing clusters.
The Glenn Icing Computational Environment (GlennICE) is a computational tool designed to calculate ice growth on complex three-dimensional geometries. It utilizes user-supplied computational fluid dynamics solutions for the geometry of interest. Key developments include advancements in convergence of collection efficiency, trajectory optimization, and refinement methodology. These improvements have significantly enhanced GlennICE’s efficiency for practical engineering applications. A recent study focused on benchmarking GlennICE’s scalability in a parallel environment using static scheduling. Findings indicated a potential twofold increase in efficiency through workload balance enhancements. This paper presents an analysis of the solver’s new workload balancing improvements, incorporating shared memory and dynamic scheduling routines. Results demonstrate a highly efficient and consistent algorithm across high-performance computing clusters.
In Silva et al. (2011) and Alidoost et al. (2020), the authors developed an approximation of the energy release rate field associated with a small edge or surface crack at any boundary location and with any orientation using the topological derivative. The approximation is computationally attractive because it requires only a single analysis on the non-cracked domain in contrast with conventional boundary-element and finite-element-based methods, which require a separate and costlier analysis for each crack length-location-orientation combination. Here, a shape optimization scheme for fracture-resistant structures is developed using the energy release rate approximation. In the gradient-based optimization scheme, the domain and its boundary are defined implicitly using level-set functions. The level-set functions of arbitrary geometries are constructed using Boolean operations from the level-set functions of simple primitives. This geometrical representation has the dual advantage of (i) allowing shapes to intersect and/or separate during the optimization and (ii) simplifying the computation of the shape sensitivities.
In this work, we present the results of two-dimensional modeling of the effects of non-equilibrium excitation and electrode geometry on H 2 /air ignition in a nanosecond plasma discharge. A multiscale adaptive reduced chemistry solver for plasma assisted combustion (MARCS-PAC) based on PASSKEy discharge modeling package and compressible multi-component reactive flow solver ASURF+ is developed and validated. This model is applied to simulate the impact of non-equilibrium plasma excitation and electrode geometry and heat loss on the dynamics of the discharge from streamer to spark and ignition kernel development in a H 2 /air mixture with a pair of cylindrical electrodes. The results show that the plasmagenerated species (N 2 (A), N 2 (B), N 2 (a'), N 2 (C), O( 1 D), O and H) in the spark and afterglow significantly accelerate the ignition kernel development. The increase of discharge voltage at the same total discharge energy promotes the non-equilibrium active species production. It is found that the production of electronically excited species at higher reduced electric field strength is more efficient in enhancing ignition in comparison to the vibrational excitation and heating. Moreover, the 2D simulation clearly reveals that the electric field and active species distribution are highly non-uniform. The streamers are initiated at the sharp outer edges of the negative and positive electrodes by a strong electric field while the electric field is much weaker at the centerline of the electrodes. Furthermore, the simulations reveal that the ignition enhancement is sensitive to the variation of electrode shape, diameter, and gap size due to the changes of electric field distribution and location of streamer formation. A cylindrical electrode produces a larger discharge volume and ignition kernel than the parabolic and spherical electrodes, when the discharge is localized near the axis of the gap. It is found that there is a non-monotonic dependence of ignition kernel size on the electrode diameter and inter-electrode distance. The increase of electrode diameter and gap size above the optimal conditions leads to the reduction of ignition kernel volume, due to the decrease of active species concentration and gas temperature. At a larger electrode surface area and electrode diameter as well as smaller electrode gap size, the heat loss to electrode plays a greater role in reducing the ignition kernel size and slowing ignition kernel development. This work provides insights and guidance to understand the kinetic enhancement of non-equilibrium plasma and the effects of electrode geometries on ignition for the optimization ignitors in advanced engines.