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At least 397 records · Page 22

Automated Segmentation of Porous Thermal Spray Material CT Scans with Geometric Uncertainty Estimation

Thermal sprayed metal coatings are used in many industrial applications, and characterizing the structure and performance of these materials is vital to understanding their behavior in the field. X-ray Computed Tomography (CT) machines enable volumetric, nondestructive imaging of these materials, but precise segmentation of this grayscale image data into discrete material phases is necessary to calculate quantities of interest related to material structure. In this work, we present a methodology to automate the CT segmentation process as well as quantify uncertainty in segmentations via deep learning. Neural networks (NNs) are shown to accurately segment full resolution CT scans of thermal sprayed materials and provide maps of uncertainty that conservatively bound the predicted geometry. These bounds are propagated through calculations of material properties such as porosity that may provide an understanding of anticipated behavior in the field.

36 MATERIALS SCIENCE↗

From microbial diversity to functional potential using dimensionality reduction

The high dimensionality of microbial diversity data from ‘omics observations can be reduced using Machine Learning, with many recent studies showcasing ML utility for exploratory ecological feature finding and process prediction. Here, we compare the Self Organizing Map (SOM) dimensionality reduction method to the well-documented sample-based Principal Coordinate Analysis (PCoA) and taxa-based Weighted Gene Correlation Network Analysis (WGCNA) using near daily 16S rRNA gene amplicon sequencing data from the 2019 to 2020 MOSAiC International Arctic Drift Expedition. We then map k-means clustering outputs from each method to available metagenomes, extracting functionally distinct seasonal microbial ecotypes in the surface Arctic Ocean. Our results indicate the SOM method better represented expected seasonal transitions and identified a greater number of metabolically distinct functional groups than the more traditional PCoA ordination. Ultimately, we identified four community ecotypes with distinct taxonomic and functional cut-offs driven by seasonality, water mass, and substrate turnover, highlighting the importance of succession in functional diversity for the central Arctic Ocean. These results reinforce ML dimensionality reduction as a meaningful translator in the mining of historical amplicon datasets to address modern mechanistic questions and potentially provide ’omics informed ecotype diversity to leverage in mechanistic biogeochemical models.

Arctic Ocean↗

Water resources

ERTS-1 applications in snow and ice monitoring, surface water monitoring, including monitoring of wetland areas and flood inundated area mapping, and also watershed monitoring for runoff prediction are discussed. Results also indicate that geological features can be noted which relate to ground water. ERTS-1 data can be used successfully in operational situations by water resources management agencies.

Salomonson, V. V.↗

Dislocation model for aseismic crustal deformation at Hollister, California

A model of crustal deformation during the interseismic phase is developed and applied (using the improved Bayesian inversion algorithm described by Jackson and Matsu'ura, 1985) to trilateration data for the USGS Hollister (CA) network. In the model, rigid blocks in motion relative to each other experience friction only in a brittle upper zone, while their ductile lower zones slide freely; the Hollister model comprises five blocks and nine rectangular fault patches. The data and results are presented in tables, graphs, and maps and characterized in detail. The model predicts steady block motion on time scales between 10 yr and 1 Myr, with net motion across the San Andreas/Calaveras fault system 38 + or - 3 mm/yr and brittle/ductile transition depths ranging from 0.4 to 11 km. Two San Andreas segments with higher probabilities of moderate-to-large earthquakes are identified.

Matsuura, Mitsuhiro↗

The Cooperative VAS Program with the Marshall Space Flight Center

Work was divided between the analysis/forecast model development and evaluation of the impact of satellite data in mesoscale numerical weather prediction (NWP), development of the Multispectral Atmospheric Mapping Sensor (MAMS), and other related research. The Cooperative Institute for Meteorological Satellite Studies (CIMSS) Synoptic Scale Model (SSM) has progressed from a relatively basic analysis/forecast system to a package which includes such features as nonlinear vertical mode initialization, comprehensive Planetary Boundary Layer (PBL) physics, and the core of a fully four-dimensional data assimilation package. The MAMS effort has produced a calibrated visible and infrared sensor that produces imager at high spatial resolution. The MAMS was developed in order to study small scale atmospheric moisture variability, to monitor and classify clouds, and to investigate the role of surface characteristics in the production of clouds, precipitation, and severe storms.

Diak, George R.↗

Preliminary analyses of space radiation protection for lunar base surface systems

Radiation shielding analyses are performed for candidate lunar base habitation modules. The study primarily addresses potential hazards due to contributions from the galactic cosmic rays. The NASA Langley Research Center's high energy nucleon and heavy ion transport codes are used to compute propagation of radiation through conventional and regolith shield materials. Computed values of linear energy transfer are converted to biological dose-equivalent using quality factors established by the International Commision of Radiological Protection. Special fluxes of heavy charged particles and corresponding dosimetric quantities are computed for a series of thicknesses in various shield media and are used as an input data base for algorithms pertaining to specific shielded geometries. Dosimetric results are presented as isodose contour maps of shielded configuration interiors. The dose predictions indicate that shielding requirements are substantial, and an abbreviated uncertainty analysis shows that better definition of the space radiation environment as well as improvement in nuclear interaction cross-section data can greatly increase the accuracy of shield requirement predictions.

Nealy, John E.↗

Lightning studies using LDAR and companion data sets

Research was conducted to use the KSC Lightning Detection and Ranging (LDAR) system, together with companion data, in four subprojects: weather forecasting and advisory applications of LDAR, LDAR in relation to field mill readings, lightning flash and stroke detection using LDAR, and LDAR in relation to radar reflectivity patterns and KSC wind profiler vertical velocities. The research is aimed at developing rules, algorithms, and training materials that can be used by the operational weather forecasters who issue weather advisories for daily ground operations and launches by NASA and the United States Air Force. During the summer of 1993, LDAR data was examined on an hourly basis from 14 thunderstorm days and compared to ground strike data measured by the Lightning Location and Protection (LLP) system. These data were re-examined during 1994 to identify, number, and track LDAR-detected storms continually throughout the day and avoid certain interpretation problems arising from the use of hourly files. An areal storm growth factor was incorporated into a scheme to use current mappings of LDAR-defined thunderstorms to predict future ground strikes. During the summer of 1994, extensive sets of LDAR and companion data have been collected for 16 thunderstorm days, including a variety of meteorological situations. Detailed case studies are being conducted to relate the occurence of LDAR to the radar structure and evolution of thunderstorms. Field mill (LPWS) data are being examined to evaluate the complementary nature of LDAR and LPLWS data in determining the time of beginning and ending of the ground strike threat at critical sites. A computerized lightning flash and stroke discrimination algorithm has been written that can be used to help locate the points of origin of the electrical discharges, help distinguish in-cloud, cloud-ground, and upward flashes, and perhaps determine when the threat of ground strikes has ceased. Surface wind tower (mesonet), radar, sounding, and KSC wind profiler data will be used to develop schemes to help anticipate the timing and location of new thunderstorm development. Analysis of this data will continue in graduate student research projects.

Forbes, Gregory S.↗

Assessment of a remote sensing-based model for predicting malaria transmission risk in villages of Chiapas, Mexico

A blind test of two remote sensing-based models for predicting adult populations of Anopheles albimanus in villages, an indicator of malaria transmission risk, was conducted in southern Chiapas, Mexico. One model was developed using a discriminant analysis approach, while the other was based on regression analysis. The models were developed in 1992 for an area around Tapachula, Chiapas, using Landsat Thematic Mapper (TM) satellite data and geographic information system functions. Using two remotely sensed landscape elements, the discriminant model was able to successfully distinguish between villages with high and low An. albimanus abundance with an overall accuracy of 90%. To test the predictive capability of the models, multitemporal TM data were used to generate a landscape map of the Huixtla area, northwest of Tapachula, where the models were used to predict risk for 40 villages. The resulting predictions were not disclosed until the end of the test. Independently, An. albimanus abundance data were collected in the 40 randomly selected villages for which the predictions had been made. These data were subsequently used to assess the models' accuracies. The discriminant model accurately predicted 79% of the high-abundance villages and 50% of the low-abundance villages, for an overall accuracy of 70%. The regression model correctly identified seven of the 10 villages with the highest mosquito abundance. This test demonstrated that remote sensing-based models generated for one area can be used successfully in another, comparable area.

Insect Vectors/growth & development↗

A PdMn Based High Resolution Thermometer for the Temperature Range of 0.7-1 K

Experiments Along the Coexistence near Tricriticality (EXACT) will test exact predictions made by Renormalization Group theory by mapping the phase diagram of liquid He-3 and He-4 mixtures at the tricritical point, Tcp=0.867 K. A PdMn based High Resolution Thermometer (HRT) will be utilized by EXACT to make accurate measurements with a resolution that has never been attained for the temperature range 0.7-1 K. The basic design of this mini high resolution thermometer comprises a sensing element whose magnetic susceptibility changes with temperature, a thermal connector, magnetic shielding, and some permanent magnets to apply a constant magnetic field. In this study, we will quantitatively determine the resolution of possible sensing element candidates of 0.15%, 0.20%, and 0.25% ppm Mn and compare them with an annealed group of PdMn with the corresponding concentrations to see how this processing technique affects sensitivity.

Koo, Peter K.↗

Lunar Water ISRU Measurement Study (LWIMS): Establishing a Measurement Plan for Identification and Characterization of a Water Reserve

NASA’s Artemis program aims to achieve a sustainable lunar presence by 2028. To carry out sustained crewed surface operations, In-Situ Resource Utilization (ISRU), which would use lunar resources (e.g., water) to produce mission consumables, will be critical. Water-bearing materials have been identified at both lunar poles, butthe nature and extent of this resource is not well understood. Identification of the presence of water alone is not adequate for ISRU architecture planning and engineering design. The Lunar Water ISRU Measurement Study (LWIMS) assessed and defined the type, amount, and fidelity of the information and measurements needed to select mining locations for lunar water ISRU and to define requirements for ISRU hardware and architecture development. Current ISRU requirements were used to define a water ‘reserve’ in this context. A measurement plan to achieve these goals includes three key elements; a predictive ‘water favorability’ model to identify and map potential deposits, continued assessment of orbital data, and three types of landed missions to make direct ground measurements. Corresponding mission scenarios and instrument suites will depend on risk posture and timelines for ISRU implementation.

In situ resource utilization↗

Simulation-Based Model for Facility-Scale Eagle Presence Mapping

This talk presents the recent progress in building a predictive movement model for predicting conflict between soaring raptors and wind turbines. In particular, it details the recently developed numerical tool called Stochastic Soaring Raptor Simulator for simulating updraft-subsidized paths for soaring raptors through variety of wind conditions and the associated directional intent.

agent-based model↗

Mapping the scattered family tree of fission neutrons

A statistically predicted tendency for neutrons produced inside fission reactors to form in clusters can cause asymmetrical energy production that is counterbalanced, at least in part, by the spontaneous fission of radioactive material in the reactor. This neutron-clustering effect theory has been demonstrated in a nuclear reactor for the first time and was described in an article published by Los Alamos National Laboratory. The findings of a study supported by the Department of Energy’s Nuclear Criticality Safety Program, funded through the National Nuclear Security Administration, and carried out in collaboration with two French nuclear agencies—the Institute for Radiological Protection and Nuclear Safety (IRSN) and the Atomic Energy Commission (CEA)—could improve reactor safety and lead to more accurate simulations, according to LANL. The team found that a complete die-off was avoided in the small reactor because spontaneous fission—nuclear splitting of radioactive material inside reactors that is not caused by direct impact from a fission neutron—creates more neutrons. The balance of fission chain reactions and spontaneous fission lessens the impact of energy bursts created by clustering neutrons.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NORAD/SPACECMD comments concerning the atmospheric density models

Recent models do not produce more accurate neutral densities (although they require more computer time), regardless of the level of solar activity. This implies that there has been no measurable improvement of the calculation of neutral density since the early 60s. With known solar flux and geomagnetic activity (Ap) inputs, the density evaluation needs improvement. For highly eccentric orbits which span low to high altitude, the accuracy generated which has an altitude limitation of 1000 km remains comparable with those obtained by more recent models. This implies that either the density at 1000 km and above is insignificant or that the values provided by the recent models at high altitudes may not be reliable or both. Prediction accuracies obtained through the use of precision data from the Defense Mapping Agency are generally comparable to those obtained by using operational sensor data. This implies that the prediction accuracy problem is not necessarily caused by less accurate observations. The definition of the mean solar flux F10.7 is not universal.

Liu, Joseph J. F.↗

The application of ERTS imagery to mapping snow cover in the western United States

The author has identified the following significant results. In much of the western United States a large part of the utilized water comes from accumulated mountain snowpacks; thus, accurate measurements of snow distributions are required for input to streamflow prediction models. The application of ERTS-1 imagery for mapping snow has been evaluated for two geographic areas, the Salt-Verde watershed in central Arizona and the southern Sierra Nevada in California. Techniques have been developed to identify snow and to differentiate between snow and cloud. The snow extent for these two drainage areas has been mapped from the MSS-5 (0.6 - 0.7 microns) imagery and compared with aerial survey snow charts, aircraft photography, and ground-based snow measurements. The results indicate that ERTS imagery has substantial practical applications for snow mapping. Snow extent can be mapped from ERTS-1 imagery in more detail than is depicted on aerial survey snow charts. Moreover, in Arizona and southern California cloud obscuration does not appear to be a serious deterrent to the use of satellite data for snow survey. The costs involved in deriving snow maps from ERTS-1 imagery appear to be very reasonable in comparison with existing data collection methods.

Barnes, J. C.↗

Symplectic machine learning model for fast simulation of space-charge effects

Symplectic simulation of space-charge effects is crucial for the design and operation of high-intensity particle accelerators. Traditional methods for simulating these effects are often computationally expensive, resulting in significant overhead. In this work, we introduce a generative model based on a U-Net architecture within a generative adversarial network framework to efficiently simulate space-charge effects. The model is trained to predict the transverse multiparticle space-charge Hamiltonian, which can be physically computed using a gridless spectral method. The one-step symplectic transverse transfer map for the particles is then obtained by differentiating the predicted Hamiltonian. Benchmarking results demonstrate that this generative model achieves an order of magnitude higher computational efficiency compared to the spectral method, providing a highly efficient alternative for simulating space-charge effects with a large number of particles. By maintaining symplecticity, the model effectively preserves the phase-space structure and mitigates nonphysical errors in long-term simulations. This model has been integrated into jutrack, a novel autodifferentiable accelerator modeling code developed in the julia programming language.

Beam code development & simulation techniques↗

Comparison of Model Prediction with Measurements of Galactic Background Noise at L-Band

The spectral window at L-band (1.413 GHz) is important for passive remote sensing of surface parameters such as soil moisture and sea surface salinity that are needed to understand the hydrological cycle and ocean circulation. Radiation from celestial (mostly galactic) sources is strong in this window and an accurate accounting for this background radiation is often needed for calibration. Modem radio astronomy measurements in this spectral window have been converted into a brightness temperature map of the celestial sky at L-band suitable for use in correcting passive measurements. This paper presents a comparison of the background radiation predicted by this map with measurements made with several modem L-band remote sensing radiometers. The agreement validates the map and the procedure for locating the source of down-welling radiation.

LeVine, David M.↗

A physics-constrained neural network for multiphase flows

The present study develops a physics-constrained neural network (PCNN) to predict sequential patterns and motions of multiphase flows (MPFs), which includes strong interactions among various fluid phases. To predict the order parameters, which locate individual phases in the future time, a neural network (NN) is applied to quickly infer the dynamics of the phases by encoding observations. The multiphase consistent and conservative boundedness mapping algorithm (MCBOM) is next implemented to correct the predicted order parameters. This enforces the predicted order parameters to strictly satisfy the mass conservation, the summation of the volume fractions of the phases to be unity, the consistency of reduction, and the boundedness of the order parameters. Then, the density of the fluid mixture is updated from the corrected order parameters. Finally, the velocity in the future time is predicted by another NN with the same network structure, but the conservation of momentum is included in the loss function to shrink the parameter space. The proposed PCNN for MPFs sequentially performs (NN)-(MCBOM)-(NN), which avoids nonphysical behaviors of the order parameters, accelerates the convergence, and requires fewer data to make predictions. Numerical experiments demonstrate that the proposed PCNN is capable of predicting MPFs effectively.

Mechanics↗

Real-Time Prognostics of a Rotary Valve Actuator

Valves are used in many domains and often have system-critical functions. As such, it is important to monitor the health of valves and their actuators and predict remaining useful life. In this work, we develop a model-based prognostics approach for a rotary valve actuator. Due to limited observability of the component with multiple failure modes, a lumped damage approach is proposed for estimation and prediction of damage progression. In order to support the goal of real-time prognostics, an approach to prediction is developed that does not require online simulation to compute remaining life, rather, a function mapping the damage state to remaining useful life is found offline so that predictions can be made quickly online with a single function evaluation. Simulation results demonstrate the overall methodology, validating the lumped damage approach and demonstrating real-time prognostics.

actuator↗