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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Acoustic Mode Decomposition in Rectangular Ducts with Sheared Flow

The performance of new acoustic liner concepts are, in general, characterized and assessed in grazing flow rigs early in the development cycle. These test rigs expose an acoustic liner sample, installed on the side wall of the duct, to a grazing flow and incident acoustic field. The process to characterize these liners involves educing the impedance on the wall where the sample is installed and examining the acoustic power attenuation. Standard approaches to computing impedance or power attenuation generally consider only the effects of a 2D shear flow or uniform flow, on the acoustic field. In this study, the objective is to incorporate 3D shear flow effects in the analysis of acoustic mode attenuation in a rectangular duct flow rig. A modal analysis of microphone measurements obtained on the side walls of the duct upstream and downstream of the test section of the rig is developed. A Galerkin projection of the Pridmore-Brown equation is performed with Chebyshev basis functions in order to incorporate the effects of the Mach number profile on the computation of the axial wavenumber of each mode. Measurements of the Mach number profile are obtained in the test rig and used as input to compute the modes. Comparisons made between the sound field computed with traditional convective Helmholtz modes and the new procedure using Pridmore-Brown modes indicate that the computed acoustic field using Pridmore-Brown modes more accurately reconstruct the acoustic signals at each microphone in the array. The mode structure of the lowest order mode is shown to be significantly impacted by shear flow refraction effects, and higher order mode structures are also effected at higher frequencies and centerline Mach number. An assessment of the acoustic mode attenuation for two acoustic liner samples demonstrates that the computed mode amplitudes for both the traditional and new approach are in agreement for the lowest order mode, but discrepancies arise when higher order modes are the dominant component of the acoustic field.

Duct acoustics↗
NASA NTRSDate not supplied

The Porous Microstructure Analysis (PuMA) software

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.

Tomography↗
NASA NTRS2019

Monolithic Trace-Contaminant Sorbents Fabricated from 3D-Printed Polymer Precursors

The current trace-contaminant (TC) removal technology for use in Extravehicular Activities (EVAs) involves the use of a packed bed of acid-impregnated granular charcoal, which is difficult to regenerate. In this paper, results are presented on the development of vacuum-regenerable TC sorbents for use in the Portable Life Support System (PLSS). The sorbents are derived from 3D-printed polymer monoliths (e.g., honeycomb structures), which are then carbonized and activated in order to develop porosity, and also to enhance the TC-sorption capacity. Results are presented on the following aspects of carbon-sorbent development: (1) precursor selection; (2) monolith fabrication; (3) shape retention and strength; (4) carbon surface and porosity characterization; (5) TC-sorption capacity and vacuum-regeneration; (6) pressure drop; and (7) sub-scale sorbent prototype. The use of predominantly microporous monolithic carbon is associated with the following benefits: (a) high TC-sorption capacity; (b) low pressure drop; (c) rapid vacuum (pressure-swing) desorption due to thin monolith walls and low pressure drop; (d) good thermal management (high thermal conductivity and low adsorption/desorption thermal effects associated with physisorption); and (e) good resistance to dusty environments.

Portable Life Support System (PLSS)↗
NASA NTRSDate not supplied

Modeling Stellar Jitter and its Effects on the Stellar Spectrum for More Precise Radial Velocity Measurements

Detection of Earth-size exoplanets is a technological and data analysis challenge. Future progress in Earth-mass exoplanet detection is expected from the development of extreme precision radial velocity (EPRV) measurements. The required precision of these measurements necessitates developing a new physics-based data analysis methodology to discriminate planetary signals from host-star-related effects, taking stellar variability and instrumental uncertainties into account. In this work, we investigate and quantify stellar disturbances of the planet-hosting solar-type star HD121504 from 3D radiative modeling obtained with the StellarBox code. The model was used to determine the statistical properties of the turbulent plasma and to obtain synthetic spectroscopic observations for several Fe I lines at different locations on the stellar disk to mimic high-resolution spectroscopic observations. Presented are the initial results of our effort to characterize stellar jitter properties as well as future objectives.

SMD↗
NASA NTRSDate not supplied

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗
NASA NTRS2023

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗
NASA NTRSDate not supplied

AERACEPT (Aerosol Rapid Analysis Combined Entry Probe/sonde Technology): Enabling Technology for Planetary Atmospheric Science Probes

Aerosols –clouds, hazes, and dusts –are a key part of planetary mass and energy balance, but difficult to study remotely. Current technology limits in situ measurements to once-in-a-lifetime flagship missions, which is not enough to characterize such highly dynamic systems. AERACEPT (AErosol Rapid Analysis Combined Entry Probe/sonde Technology) is an early-stage technology allowing asingle aeroshell body toact as both anentry vehicle and aerosol-samplingpassivedescent sonde, using the aeroshell’sown velocityto drive aerosol capture and separationthrough a series of embedded inlets.It takes advantage ofrecently developed thermal protection materials (3D-CC and 3MDCP)in combination withheritage aerosol sampling technologies fromboth planetary and airborne science (high-speed inlets and particle separation). By eliminating the need for heat shield separation, deployable parachutes, ordescent control, AERACEPT reducesthe mass, volume, and complexity ofplanetary aerosol sampling. Verifying AERACEPT’s performance involves modeling interdependencies between the size and geometry of the sampling inlets, the material response of the hypersonic phase of entry, the thermal conditions throughout the probe’s descent, the subsonic flow and particle sampling efficiency, the needed amount of sample, andthe cadence at which the sample can be analyzed. AERACEPT’s current predictions, using the Nephele Venus cloud mission concept (sampling between 63km to 39km, all subsonic) indicates AERACEPT can obtain >20 μL of particle material (roughly 10x the limit of detection) from droplets larger than 0.2 μm, with sample heating limited to <15 K above ambient. Validation testing for the thermal material response model has recently been performed at the UIUC Plasmatron facility, and for the flow and particle capture models is planned for this fall at the Ames Fluid Mechanics Laboratory.

AERACEPT↗
NASA NTRS2011

Radar and Lidar Radar DEM

Using radar and lidar data, the aim is to improve 3D rendering of terrain, including digital elevation models (DEM) and estimates of vegetation height and biomass in a variety of forest types and terrains. The 3D mapping of vegetation structure and the analysis are useful to determine the role of forest in climate change (carbon cycle), in providing habitat and as a provider of socio-economic services. This in turn will lead to potential for development of more effective land-use management. The first part of the project was to characterize the Shuttle Radar Topography Mission DEM error with respect to ICESat/GLAS point estimates of elevation. We investigated potential trends with latitude, canopy height, signal to noise ratio (SNR), number of LiDAR waveform peaks, and maximum peak width. Scatter plots were produced for each variable and were fitted with 1st and 2nd degree polynomials. Higher order trends were visually inspected through filtering with a mean and median filter. We also assessed trends in the DEM error variance. Finally, a map showing how DEM error was geographically distributed globally was created.

remote sensing↗
NASA NTRSDate not supplied

Aqueous alteration of Cb-type asteroid Ryugu

The samples from the asteroid Ryugu, which returned to Earth in the winter of 2020 [1], were stored under pure Nitrogen conditions at the Extraterrestrial Materials Curation Center of ISAS. After basic description [2-3], they were distributed to six initial analysis teams in Japan in June 2021. The initial analysis "Stone" team, which was responsible for the analysis of coarse particles (>1 mm in size), consisted of about 150 scientists from Japan and abroad, and conducted a series of analyses We analyzed sixteen coarse Ryugu particles 1 ~ 8 mm in size: six from the 1st touch-down site and ten from the 2nd touch-down site. Individual coarse samples were cut by Xe-FIB or wire-saw to expose particular objects or textures to be exposed on the surface based on 3D structure and element distribution obtained by synchrotron X-ray CT analysis and XRF analysis, respectively. FE-SEM/EDS and FE-EPMA/WDS analysis were made on all polished sections. TEM observation of many FIB sections was made to see microstructures and to compare with carbonaceous chondrites. Many other analyses were carried out to characterize mineralogical and compositional properties and summary of the analysis procedures is described in [4]. X-ray CT analysis showed that all sixteen Ryugu particles are composed of fine-grained material, with no chondrules and CAIs larger than 100 μm in size. FE-EPMA observation showed that Ryugu samples are breccias, consisting of many small rock fragments of different compositions, lithologies, and histories. The most common lithology includes Mg-rich saponite and serpentine, dolomite, magnesite, hydroxyapatite, pyrrhotite, and magnetite as main constituents. The mineralogy of this major lithology supports the classification of Ryugu samples as CI chondrites, which experienced extensive aqueous alteration in Ryugu’s parent asteroid. Based on the obtained mineralogical properties, we performed chemical modeling of aqueous alteration of Ryugu’s parent asteroid [5]. Numerical simulations [6-8] to reproduce thermal history and impact-induced destruction of the Ryugu’s parent asteroid were also carried out using mineralogical and physical properties of the Ryugu samples. In the talk, I would like to explain the conditions of aqueous alteration occurred in the parent asteroid of Ryugu in order to show how the water in the asteroid changed the mineralogy and chemistry of the nebular dust.

T. Nakamura↗
NASA NTRS1992

Multidisciplinary tailoring of hot composite structures

A computational simulation procedure is described for multidisciplinary analysis and tailoring of multilayered multimaterial hot composite engine structural components subjected to simultaneous multiple discipline-specific thermal, structural, vibration, and acoustic loadings including the effect of aggressive environments. The simulation is based on a 3D finite element analysis technique in conjunction with structural mechanics codes, thermal/acoustic analysis methods, and tailoring procedures. The integrated multidisciplinary simulation procedure is general-purpose including the coupled effects of nonlinearities in structure geometry, material, loading, and environmental complexities. The composite material behavior is assessed at all composite scales, i.e., the laminate/ply/constituents (fiber/matrix), via a nonlinear material characterization hygro-thermomechanical model. Sample tailoring cases exhibiting nonlinear material/loading/environmental behavior of aircraft engine fan blades, are presented. The various multidisciplinary loadings lead to different tailored designs, even those opposite of each other, as in the case of minimum material cost versus minimum structure weight and in the case of minimum vibration frequency versus minimum acoustic noise.

Singhal, Surendra N.↗
NASA NTRS2002

Micromere lineages in the glossiphoniid leech Helobdella

In leech embryos, segmental mesoderm and ectoderm arise from teloblasts by lineages that are already relatively well characterized. Here, we present data concerning the early divisions and the definitive fate maps of the micromeres, a group of 25 small cells that arise during the modified spiral cleavage in leech (Helobdella robusta) and contribute to most of the nonsegmental tissues of the adult. Three noteworthy results of this work are as follows. (1) The c"' and dm' clones (3d and 3c in traditional nomenclature) give rise to a hitherto undescribed network of fibers that run from one end of the embryo to the other. (2) The clones of micromeres b" and b"' (2b and 3b in traditional nomenclature) die in normal development; the b" clone can be rescued to assume the normal c" fate if micromere c" or its clone are ablated in early development. (3) Two qualitative differences in micromere fates are seen between H. robusta (Sacramento) and another Helobdella sp. (Galt). First, in Helobdella sp. (Galt), the clone of micromere b" does not normally die, and contributes a subset of the cells arising exclusively from c" in H. robusta (Sacramento). Second, in Helobdella sp. (Galt), micromere c"' makes no definitive contribution, whereas micromere dm' gives rise to cells equivalent to those arising from c"' and dm' in H. robusta (Sacramento).

Non-NASA Center↗
NASA NTRSDate not supplied

Experimental Observations of Damage States in Unnotched and Notched 3D Orthogonal Woven Coupons Loaded in Tension

This paper describes a series of unnotched and notched tensile tests on two 3D orthogonal woven architectures. Dog bone specimens were used for the unnotched tests, and open-hole specimens were used for the notched tests. For each architecture and test type, two specimens were loaded under monotonic displacement control to failure and two specimens were incrementally loaded to failure in five steps. X-ray computed tomography (CT) was used to characterize the woven architecture and defects on each specimen prior to testing. Digital image correlation (DIC) images were obtained from both sides of the specimen throughout the loading and unloading. Acoustic emission (AE) data were also continuously acquired throughout the loading. For the incremental tests, X-ray CT was used to document the damage progression after each load increment. The damage progression was analyzed based on the results from these three techniques. The results for both architectures and test specimens followed a pattern where matrix cracks formed at the surface perpendicular to the tensile load direction. New surface cracks formed continuously throughout the loading and cracks extended in depth and width. In general, fiber failure was not detected until specimen failure.

Wade C Jackson↗
NASA NTRS2000

The Use of Vegetation For Estimating Broken-Cloud Optical Properties From Surface Measurements

It is known that because of complex three-dimensional (3D) radiative effects of broken clouds, the retrieval of cloud optical properties from upward measurements based on a one-dimensional (1D) inversion technique almost surely fails. To remove radiative effects of 3D cloud structure, we have developed a new technique that retrieves cloud optical thickness for broken clouds above green vegetation from simultaneous surface measurements in the VIS and Near Infrared (NIR) spectral regions. The theoretical basis of the method is the very different spectral behavior of cloud liquid water drops and green vegetation. For example, cloud optical properties, and hence cloud reflectivities, change little between 650 and 860 nm, while the vegetated surface albedo changes from 0.05 to 0.5 between the same two wavelengths. This spectral contrast in surface albedo suggests using ground measurements at both wavelengths not independently, but as an algebraic combination (a spectral index). For a spectral band in the NIR region, the green vegetation acts as a powerful reflector that "illuminates" horizontally inhomogeneous clouds from below. This provides the extra information needed to largely remove the 3D radiative effects, especially in the case of broken clouds; this in turn allows the retrieval of cloud optical depth using traditional 1D radiative transfer theory. This approach is similar to the so-called Green's function problem for radiative transfer where a laser beam illuminates clouds and the resulting "spot-size" of the reflected light around the beam characterizes cloud properties. We generalize Green's function theory to surf ace-cloud interaction and develop new spectral indices from which broken-cloud optical depth can be retrieved.

Marshak, A.↗
NASA NTRS2018

Optical Characterization of Fuel Injection in a Flame Tube Combustor Using a GE TAPS Injector Configuration

This paper presents results in which we compare fuel staging and its effect on fuel spray pattern, velocity and speciation during combustion for several inlet conditions using a GE TAPS injector configuration. Planar laser-induced fluorescence (PLIF), particle image velocimetry (PIV) and phase Doppler interferometry (PDI) were used to investigate spray patterns and velocity. The 2D PIV provides slices in the flow of axial-vertical or axial horizontal velocity components. With 3D PDI, we obtained 3 components of velocity, and fuel drop sizes. Chemiluminescence imaging and spontaneous Raman scattering (SRS) were used to investigate flame structure, species location and relative species concentration. Phase Doppler and PIV data were acquired using scatter from fuel droplets; therefore, those data were obtained only at the pilot-only test points. Raman measurements were acquired only at 10/90 split points to avoid droplets.

optical diagnostics↗
NASA NTRS2021

Newmark-Type Pseudo-Three-Dimensional Back-Analysis of Co-Seismic Landslides in Egkremnoi, Lefkada, Greece

A pseudo-three-dimensional (pseudo-3D) methodology to back-analyze coseismic landslides was developed and applied to 68mapped landslides, which occurred over approximately 1 km2area in Egkremnoi, Greece, during the 2015 Mw6.5 Lefkada earthquake. The methodology is based on a one-dimensional (1D) Newmark-type sliding block model to assess instability and a spatial projection in 3D topography to derive landslide geometry. The strength parameters for modeled landslides that best match the landslide location, area, and volume were derived through an iterative scheme that optimizes the match using predefined matching criteria. The range of different-sized landslides produced shear strength estimates from ~10 to 300 kPa and led to the derivation of a regionally averaged strength envelope characterized by a cohesion of 6 kPa and a friction angle of 53°for the highly fractured limestones that are encountered in this area. Compared to previous full 3Dslope stability analyses in this area, the friction angle using this methodology was found to be generally consistent, but the cohesion was lower. The presented methodology can provide a computationally efficient method to estimate the average shear strength of a geologic unit over large areas, especially where extensive field and laboratory tests on the materials are unavailable or difficult to conduct.

Weibing Gong↗
NASA NTRS2022

Vegetation and vantage point influence visibility across diverse ecosystems: Implications for animal ecology

Visual information can influence animal behavior and habitat use in diverse ways. Visibility is the property that relates 3D habitat structure to accessibility of visual information. Despite the importance of visibility in animal ecology, this property remains largely unstudied. Our objective was to assess how habitat structure from diverse environments and animal position within that structure can influence visibility. We gathered terrestrial lidar data (1 cm at 10 m) in four ecosystems (forest, shrub-steppe, prairie, and desert) to characterize viewsheds (i.e., estimates of visibility based on spatially explicit sightlines) from multiple vantage points. Both ecosystem-specific structure and animal position influenced potential viewsheds. Generally, as height of the vantage point above the ground increased, viewshed extent also increased, but the relationships were not linear. In low-structure ecosystems (prairie, shrub-steppe, and desert), variability in viewsheds decreased as vantage points increased to heights above the vegetation canopy. In the forest, however, variation in viewsheds was highest at intermediate heights, and markedly lower at the lowest and highest vantage points. These patterns are likely linked to the amount, heterogeneity, and distribution of vegetation structure occluding sightlines. Our work is the first to apply a new method that can be used to estimate viewshed properties relevant to animals (i.e., viewshed extent and variability). We demonstrate that these properties differ across terrestrial landscapes in complex ways that likely influence many facets of animal ecology and behavior.

Rachel Stein↗
NASA NTRSDate not supplied

The Kinematic Navigation and Cartography Knapsack (KNaCK): Demonstrating SLAM (Simultaneous Localization and Mapping) LiDAR as a Tool for Exploration and Mapping of Lunar Pits and Caves.

Renewed interest in deep space exploration has made establishing a persistent human presence on the Moon and eventually Mars a priority. Sustained habitation of the Moon will require in-situ resource utilization (ISRU) and protection from radiation at the lunar surface. Lunar caves are of interest to the space community because they could provide shelter from radiation, access to water deposits, and a space for habitation, as well as hold information about the geology, volcanology, and evolution of the Moon. Developing the tools to explore, map, and characterize these voids is key to understanding the Moon as a planetary body and to establishing a permanent human presence there. Our team is currently developing tools that enable ultra-high resolution terrain mapping and navigation using mobile light detection and ranging (LIDAR) technology and simultaneous localization and mapping (SLAM) algorithms in fully GPS-denied and no-light environments. 3D mapping with LiDAR and SLAM is relatively under-used, particularly in the context of planetary exploration and planetary analog environments. The backpack mounted LiDAR instrument currently under development by the KNaCK (Kinematic Navigation and Cartography Knapsack) team has demonstrated the potential of mobile SLAM LiDAR for lunar, planetary, and terrestrial cave exploration, study, and utilization. A description of the instrument and associated SLAM algorithms used for mapping in GPS-denied environments are referenced here.

LiDAR↗
NASA NTRS1993

Plume structures in the hard-turbulent regime of three-dimensional infinite Prandtl number convection

Numerical simulations of three-dimensional infinite Prandtl number thermal convection with Rayleigh number (Ra) up to 10 exp 8 are reported. Convection with Ra higher than 10 exp 7 is characterized by the appearance of disconnected thermal plumes. The smaller plumes are detached by the currents produced by the larger plumes. The low wavenumber portion of a thermal power spectrum near the boundary layer becomes flat at high Ra, while the spectrum measured in the interior shows a positive slope for low wavenumbers. Differences are found in the thermal spectra between 2D and 3D models.

Malevsky, A. V.↗