SEARCH · Search NASA

Results for “Spatial Characterization”

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.

SourceResearch connectionFrom yearThrough year

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

At least 181 records · Page 10

All sources NASA NTRS DOE OSTI

NASA NTRS2021

Spatial Registration Assessments for the SNPP and N20 VIIRS Reflective Solar Bands Using Unscheduled Lunar Observations

The Visible Infrared Imaging Radiometer Suite (VIIRS) is a multi-spectral Earth-observing instrument on board the Suomi-NPP (SNPP) and NOAA-20 (N20) spacecraft, with spectral bands ranging in wavelength from 0.41 to 12.2 μm. For the reflective solar bands (RSB), the bands are calibrated on orbit using both solar diffuser (SD) and lunar observations. The lunar observations use near-monthly scheduled spacecraft maneuvers in order to view the Moon within a desired phase angle range. While the primary purpose of the maneuvers is for radiometric calibration, these observations can also be used to characterize the spatial performance of the instrument, including the band-to-band and detector-to-detector registration (BBR/DDR). The Moon can also be observed without spacecraft maneuvers. However, these observations are over a larger phase angle range. While the geometry of these unscheduled observations is more varied, they can still be used to assess the sensor performance. In this work, we will use unscheduled Moon data to analyze the BBR and DDR of the SNPP and N20 VIIRS RSB. For the BBR, we implemented an image cross-correlation approach, which removes the residual oscillations in the trending data when compared to previous methodologies. For the DDR, we developed an edge fitting approach that accounts for the lunar motion across the VIIRS focal plane array on a scan-by-scan basis using lunar and satellite ephemeris data. In our analysis, we find that the BBR and DDR for both VIIRS RSB are stable on orbit.

Band-to-band registration (BBR)↗

NASA NTRS2003

Regional Impacts of Woodland Expansion on Nitrogen Oxide Emissions from Texas Savannahs: Combining Field, Modeling and Remote Sensing Approaches

Woody encroachment has contributed to documented changes world-wide and locally in the southwestern U.S. Specifically, in North Texas rangelands encroaching mesquite (Prosopis glandulosa var. glandulosa) a known N-fixing species has caused changes in aboveground biomass. While measurements of aboveground plant production are relatively common, measures of soil N availability are scarce and vary widely. N trace gas emissions (nitric and nitrous oxide) flom soils reflect patterns in current N cycling rates and availability as they are stimulated by inputs of organic and inorganic N. Quantification of N oxide emissions from savanna soils may depend upon the spatial distribution of woody plant canopies, and specifically upon the changes in N availability and cycling and subsequent N trace gas production as influenced by the shift from herbaceous to woody vegetation type. The main goal of this research was to determine whether remotely sensible parameters of vegetation structure and soil type could be used to quantify biogeochemical changes in N at local, landscape and regional scales. To accomplish this goal, field-based measurements of N trace gases were carried out between 2000-2001, encompassing the acquisition of imaging spectrometer data from the NASA Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) on September 29, 2001. Both biotic (vegetation type and soil organic N) and abiotic (soil type, soil pH, temperature, soil moisture, and soil inorganic N) controls were analyzed for their contributions to observed spatial and temporal variation in soil N gas fluxes. These plot level studies were used to develop relationships between spatially extensive, field-based measurements of N oxide fluxes and remotely sensible aboveground vegetation and soil properties, and to evaluate the short-term controls over N oxide emissions through intensive field wetting experiments. The relationship between N oxide emissions, remotely-sensed parameters (vegetation cover, and soil type), and physical controls (soil moisture, and temperature) permitted the regional scale quantification of soil N oxides emissions. Landscape scale analysis linking N oxide emissions with cover change revealed an alleviation from N limitation following mesquite invasion. This study demonstrated the advantage of using N trace gases as a measure of ecosystem N availability combined with remote sensing to characterize the spatial heterogeneity in ecosystem parameters at a scale commensurate with field-based measurements of these properties. Woody vegetation encroachment provided an opportunity to capitalize on detection of the remotely-sensible parameter of woody cover as it relates to belowground biogeochemical processes that determine N trace gas production. The first spatially-explicit estimates of NO flux were calculated based on Prosopis fractional cover derived from high resolution remote sensing estimates of fractional woody cover (< 4 m) for a 120 sq km region of North Texas. An assessment of both N stocks and fluxes from the study revealed an alleviation of N limitation at this site experiencing recent woody encroachment. Many arid and semi-arid regions of the world are experiencing woody invasions, often of N-fixing species. The issue of woody encroachment is in the center of an ecological and political debate. Improving the links between biogeochemical processes and remote sensing of ecosystem properties will improve our understanding of biogeochemical processes at the regional scale, thus providing a means to address issues of land-use and land-cover change.

Asner, Gregory P.↗

DOE OSTI2022

Nanoindentation mapping defects filtration for heterogeneous materials using generative adversarial networks

Advanced composite materials with multiple phases and heterogeneous microstructure necessitate spatial mapping characterization of elastic modulus to develop constitutive relations and overall mechanical response. Such modulus mapping can be obtained using the nanoindentation technique, where the indenter tip raster over the selected microstructure region. Typically, a surface preparation procedure is done in the specimens to ensure proper contact between the indenter tip and sample surface. However, a near-perfect surface finish is unachievable in heterogeneous materials, primarily with ceramic reinforcements, due to the differential material removal rate during polishing. Thus, the nanoindenter records localized erroneous measurements due to differences in surface roughness and corresponding force response. This study establishes a novel deep learning-based strategy to rectify incorrect experimental spatial measurements acquire during nanoindentation modulus mapping. Here, the integrated bicubic interpolation and generative adversarial networks (GANs) model was trained using 14 ceramic and 18 metallic data sets, each comprising 65,536 measurements. The developed algorithm was validated against experimental measurements on four unknown specimens. The standard deviation in measured elastic modulus reduces by ~50% in ceramics and ~72% in metallic samples. This computational framework proposes a novel approach to reducing uncertainty in materials’ properties using state-of-the-art computer vision techniques.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Characterizing and Mitigating Intraday Variability: Reconstructing Source Structure in Accreting Black Holes with mm-VLBI

The extraordinary physical resolution afforded by the Event Horizon Telescope has opened a window onto the astrophysical phenomena unfolding on horizon scales in two known black holes, M87* and Sgr A*. However, with this leap in resolution has come a new set of practical complications. Sgr A* exhibits intraday variability that violates the assumptions underlying Earth aperture synthesis, limiting traditional image reconstruction methods to short timescales and data sets with very sparse (u, v) coverage. We present a new set of tools to detect and mitigate this variability. We develop a data-driven, model-agnostic procedure to detect and characterize the spatial structure of intraday variability. This method is calibrated against a large set of mock data sets, producing an empirical estimator of the spatial power spectrum of the brightness fluctuations. We present a novel Bayesian noise modeling algorithm that simultaneously reconstructs an average image and statistical measure of the fluctuations about it using a parameterized form for the excess variance in the complex visibilities not otherwise explained by the statistical errors. These methods are validated using a variety of simulated data, including general relativistic magnetohydrodynamic simulations appropriate for Sgr A* and M87*. We find that the reconstructed source structure and variability are robust to changes in the underlying image model. We apply these methods to the 2017 EHT observations of M87*, finding evidence for variability across the EHT observing campaign. The variability mitigation strategies presented are widely applicable to very long baseline interferometry observations of variable sources generally, for which they provide a data-informed averaging procedure and natural characterization of inter-epoch image consistency.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Direct Optoelectronic Imaging of 2D Semiconductor–3D Metal Buried Interfaces

The semiconductor-metal junction is one of the most critical factors for high performance electronic devices. In two-dimensional (2D) semiconductor devices, minimizing voltage drop at this junction is of particularly challenging and important. Despite numerous studies on contact resistance in 2D semiconductors, the exact nature of the buried interface under a 3D metal remains elusive and unclear. Herein, we report the direct observation of 2D semiconductor-metal buried interface using recently developed metal-assisted transfer technique to expose the buried interface which is then directly investigated using scanning probe techniques. We characterize the spatially varying electronic and optical properties of this buried interface with < 20 nm resolution. To be specific, potential, conductance and photoluminescence at the buried metal/MoS 2 interface are correlated under variety of metal deposition conditions as well as type of metal contacts. We observe that direct evaporation of Au on MoS 2 induces large strain of ~5% in the MoS 2 which coupled with charge transfer leads to degenerate doping of the MoS 2 underneath the contact. These factors lead to improvement of contact resistance to record values of 138 kΩ μm measured using local conductance probes. This approach was adopted to investigate MoS 2 -In/Au alloy interface, which records lowest contact resistance of 63 kΩ μm. Overall, our results highlight that the MoS 2 /Metal interface is very sensitive to method of contact making and provides a universal strategy to characterize buried contact interfaces involving 2D semiconductors.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

NASA NTRS2012

Characterization of Material Response During Arc-Jet Testing with Optical Methods Status and Perspectives

The characterization of ablation and recession of heat shield materials during arc jet testing is an important step towards understanding the governing processes during these tests and therefore for a successful extrapolation of ground test data to flight. The behavior of ablative heat shield materials in a ground-based arc jet facility is usually monitored through measurement of temperature distributions (across the surface and in-depth), and through measurement of the final surface recession. These measurements are then used to calibrate/validate materials thermal response codes, which have mathematical models with reasonably good fidelity to the physics and chemistry of ablation, and codes thus calibrated are used for predicting material behavior in flight environments. However, these thermal measurements only indirectly characterize the pyrolysis processes within an ablative material pyrolysis is the main effect during ablation. Quantification of pyrolysis chemistry would therefore provide more definitive and useful data for validation of the material response codes. Information of the chemical products of ablation, to various levels of detail, can be obtained using optical methods. Suitable optical methods to measure the shape and composition of these layers (with emphasis on the blowing layer) during arc jet testing are: 1) optical emission spectroscopy (OES) 2) filtered imaging 3) laser induced fluorescence (LIF) and 4) absorption spectroscopy. Several attempts have been made to optically measure the material response of ablative materials during arc-jet testing. Most recently, NH and OH have been identified in the boundary layer of a PICA ablator. These species are suitable candidates for a detection through PLIF which would enable a spatially-resolved characterization of the blowing layer in terms of both its shape and composition. The recent emission spectroscopy data will be presented and future experiments for a qualitative and quantitative characterization of the material response of ablative materials during arc-jet testing will be discussed.

Winter, Michael↗

DOE OSTI2025

Mapping wall-to-wall fractional cover of Arctic tundra plant functional types in Alaska using 20-m spatial resolution satellite imagery and harmonized plot observations

Estimates of fractional cover (fCover) across given land surfaces are used to assess, and often model, vegetation composition and diversity, which are crucial for understanding the health and functioning of terrestrial ecosystems. Remote sensing provides a useful means for scaling local, plot-measured fCover estimates to regional scales. Leveraging a recently synthesized and harmonized plot database, this study generated wall-to-wall maps of fCover for six Alaskan-Arctic plant functional types (PFT), including non-vascular plants, forbs, graminoids, and deciduous and evergreen shrubs, using 20-m satellite data (Sentinel-1, Sentinel-2, ArcticDEM) using a machine learning regression approach, specifically the random forest (RF) algorithm, which is well-suited for handling nonlinear relationships and high-dimensional satellite datasets. This study additionally addressed the spatio-temporal inconsistencies e.g., sampling scale, plot size, and collection year in plot measured fCover by adopting a multivariate outlier detection approach—Cook’s distance—to identify high-quality plots for model training and validation. Our approach achieves high accuracy (R 2 = 0.59–0.93, root mean squared errors = 0.02–0.10 for all PFTs) between plot-observed and satellite-derived fCover when using high-quality plot samples. The mapped fCover characterizes the spatial patterns of different PFTs across the tundra biome at a 20-m resolution, providing key information needed for improved representation of Arctic tundra vegetation in terrestrial biosphere models to better understand climate-vegetation feedback across the Arctic tundra.

Arctic tundra↗

DOE OSTI2024

Focused‐beam X‐ray fluorescence and diffraction microtomographies for mineralogical and chemical characterization of unsectioned extraterrestrial samples

Abstract This study describes the application of new synchrotron X‐ray fluorescence (XRF) and diffraction (XRD) microtomographies for the 3‐D visualization of chemical and mineralogical variations in unsectioned extraterrestrial samples. These improved methods have been applied to three compositionally diverse chondritic meteorite samples that were between 300 and 400 μm in diameter, including samples prepared from fragments of the CR2 chondrite LaPaz Icefield (LAP) 02342, H5 chondrite MacAlpine Hills (MAC) 88203, and the CM2 chondrite Murchison. The synchrotron‐based XRF and XRD tomographies used are focused‐beam techniques that measure the intensities of fluorescent and diffracted X‐rays in a sample simultaneously during irradiation by a high‐energy microfocused incident X‐ray beam. Measured sinograms of the emitted and diffracted intensities were then tomographically reconstructed to generate 2‐D slices of XRF and XRD intensity through the sample, with reconstructed pixel resolution of 1–2 μm, defined by the resolution of the focused incident X‐ray beam. For sample LAP 02342, primary mineral phases that were visualized in reconstructed slices using these techniques included isolated grains of α‐Fe, orthopyroxene, and olivine. For our sample of MAC 88203, XRF/XRD tomography allowed visualization of forsteritic olivine as a primary mineral phase, a vitrified fusion crust at the sample surface, identification of localized Cr‐rich spinels at spatial resolutions of several micrometers, and imaging of a plagioclase‐rich glassy matrix. In the sample of Murchison, major identifiable phases include clinoenstatite‐ and olivine‐rich chondrules, variable serpentine matrix minerals and small Cr‐rich spinels. Most notable in the tomographic analysis of Murchison is the ability to quantitatively distinguish and visualize the complex mixture of serpentine‐group minerals and associated tochilinite–cronstedtite intergrowths. These methods provide new opportunities for spatially resolved characterization of sample texture, mineralogy, crystal structure, and chemical state in unsectioned samples. This provides researchers an ability to characterize such samples internally with minimal disruption of sample micro‐structures and chemistry, possibly without the need for sample extraction from some types of sampling and capture media.

Geochemistry & Geophysics↗

NASA NTRS2018

AMSR2 Soil Moisture Downscaling Using Temperature and Vegetation Data

Soil moisture (SM) applications in terrestrial hydrology require higher spatial resolution soil moisture products than those provided by passive microwave remote sensing instruments (grid resolution of 9 km or larger). In this investigation, an innovative algorithm that uses visible/infrared remote sensing observations to downscale Advanced Microwave Scanning Radiometer 2 (AMSR2) coarse spatial resolution SM products was developed and implemented for use with data provided by the Advanced Microwave Scanning Radiometer 2 (AMSR2). The method is based on using the Normalized Difference Vegetation Index (NDVI) modulated relationships between day/night SM and temperature change at corresponding times. Land surface model output variables from the North America Land Data Assimilation System (NLDAS), remote sensing data from the Moderate-Resolution Imaging Spectroradiometer (MODIS), and Advanced Very High Resolution Radiometer (AVHRR) were used in this methodology. The functional relationships developed using NLDAS data at a grid resolution of 12.5 km were applied to downscale AMSR2 JAXA (Japan Aerospace Exploration Agency) SM product (25 km) using MODIS land surface temperature (LST) and NDVI observations (1 km) to produce the 1 km SM estimates. The downscaled SM estimates were validated by comparing them with ISMN (International Soil Moisture Network) in situ SM in the Black Bear-Red Rock watershed, central Oklahoma between 2015-2017. The overall statistical variables of the downscaled AMSR2 SM validation R2, slope, RMSE and bias, demonstrate good accuracy. The downscaled SM better characterized the spatial and temporal variability of SM at watershed scales than the original SM product.

AMSR2; passive microwave soil moisture; soil moist↗

DOE OSTI2021

Effective kinetics driven by dynamic concentration gradients under coupled transport and reaction

Biogeochemical reaction kinetics are generally established from batch reactors where concentrations are uniform. In natural systems, many biogeochemical processes are characterized by spatially and temporally variable concentration gradients that often occur at scales which are not resolved by field measurements or biogeochemical and reactive transport models. Yet, it is not clear how these sub-scale chemical gradients affect reaction kinetics compared to batch kinetics. Here we investigate this question by studying the paradigmatic case of localized pulses of solute reacting with a solid or a dissolved species in excess. Additionally, we consider non-linear biogeochemical reactions, representative of mineral dissolution, adsorption and redox reactions, which we quantify using simplified power-law kinetics. The combined effect of diffusion and reaction leads to effective kinetics that differ quantitatively and qualitatively from the batch kinetics. Depending on the nonlinearity (reaction order) of the local kinetics, these effects lead to either enhancement or decrease of the overall reaction rate, and result in a rich variety of reaction dynamics. We derive analytical results for the effective kinetics, which are validated by comparison to direct numerical simulations for a broad range of Damköhler numbers and reaction order. Our findings provide new insights into the interpretation of imperfectly mixed lab experiments, the effective kinetics of field systems characterized by intermittent reactant release and the integration of sub-scale concentration gradients in reactive transport models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1993

The national geomagnetic initiative

The Earth's magnetic field, through its variability over a spectrum of spatial and temporal scales, contains fundamental information on the solid Earth and geospace environment (the latter comprising the atmosphere, ionosphere, and magnetosphere). Integrated studies of the geomagnetic field have the potential to address a wide range of important processes in the deep mantle and core, asthenosphere, lithosphere, oceans, and the solar-terrestrial environment. These studies have direct applications to important societal problems, including resource assessment and exploration, natural hazard mitigation, safe navigation, and the maintenance and survivability of communications and power systems on the ground and in space. Studies of the Earth's magnetic field are supported by a variety of federal and state agencies as well as by private industry. Both basic and applied research is presently supported by several federal agencies, including the National Science Foundation (NSF), U.S. Geological Survey (USGS), U.S. Department of Energy (DOE), National Oceanic and Atmospheric Administration (NOAA), National Aeronautics and Space Administration (NASA), and U.S. Department of Defense (DOD) (through the Navy, Air Force, and Defense Mapping Agency). Although each agency has a unique, well-defined mission in geomagnetic studies, many areas of interest overlap. For example, NASA, the Navy, and USGS collaborate closely in the development of main field reference models. NASA, NSF, and the Air Force collaborate in space physics. These interagency linkages need to be strengthened. Over the past decade, new opportunities for fundamental advances in geomagnetic research have emerged as a result of three factors: well-posed, first-order scientific questions; increased interrelation of research activities dealing with geomagnetic phenomena; and recent developments in technology. These new opportunities can be exploited through a national geomagnetic initiative to define objectives and encourage coordination of efforts among federal and state agencies, academic institutions, and industry to systematically characterize the spatial and temporal behavior of the Earth's magnetic field on local, regional, and global scales in order to understand the physical processes in the solid earth and geospace environment, and to apply this understanding to a variety of scientific problems and to technical and societal needs.

Source record↗

NASA NTRS2006

Modeling of Transient Flow Mixing of Streams Injected into a Mixing Chamber

Ignition is recognized as one the critical drivers in the reliability of multiple-start rocket engines. Residual combustion products from previous engine operation can condense on valves and related structures thereby creating difficulties for subsequent starting procedures. Alternative ignition methods that require fewer valves can mitigate the valve reliability problem, but require improved understanding of the spatial and temporal propellant distribution in the pre-ignition chamber. Current design tools based mainly on one-dimensional analysis and empirical models cannot predict local details of the injection and ignition processes. The goal of this work is to evaluate the capability of the modern computational fluid dynamics (CFD) tools in predicting the transient flow mixing in pre-ignition environment by comparing the results with the experimental data. This study is a part of a program to improve analytical methods and methodologies to analyze reliability and durability of combustion devices. In the present paper we describe a series of detailed computational simulations of the unsteady mixing events as the cold propellants are first introduced into the chamber as a first step in providing this necessary environmental description. The present computational modeling represents a complement to parallel experimental simulations' and includes comparisons with experimental results from that effort. A large number of rocket engine ignition studies has been previously reported. Here we limit our discussion to the work discussed in Refs. 2, 3 and 4 which is both similar to and different from the present approach. The similarities arise from the fact that both efforts involve detailed experimental/computational simulations of the ignition problem. The differences arise from the underlying philosophy of the two endeavors. The approach in Refs. 2 to 4 is a classical ignition study in which the focus is on the response of a propellant mixture to an ignition source, with emphasis on the level of energy needed for ignition and the ensuing flame propagation issues. Our focus in the present paper is on identifying the unsteady mixing processes that provide the propellant mixture in which the ignition source is to be placed. In particular, we wish to characterize the spatial and temporal mixture distribution with a view toward identifying preferred spatial and temporal locations for the ignition source. As such, the present work is limited to cold flow (pre-ignition) conditions

Voytovych, Dmytro M.↗

DOE OSTI2025

Improving Ionic Conformality Across Polymer Electrolyte|Electrode Interfaces

Maintaining uniform ionic transport at electrode|electrolyte interfaces, i.e., ionic conformality, remains challenging in polymer electrolyte (PE)-based solid-state batteries. Morphological conformality does not necessarily imply ionic conformality. In PEs, which typically consist of a mechanically supporting component and distinct ionically conductive components, the rearrangement or depletion of mobile ion-conductive domains at interfaces can disrupt ionic transport pathways. Such localized ionic depletion contributes to interfacial instability and capacity degradation in high-voltage lithium-metal batteries. Herein, an electrolyte design approach aimed at minimizing interfacial heterogeneities is demonstrated through compositional adjustments, characterized by spatially resolved structural and chemical X-ray techniques and NMR diffusometry to elucidate ion transport dynamics. This approach improves ionic conformality at electrode interfaces, enhancing cycling stability in Li||LiNi 0.8 Co 0.1 Mn 0.1 O 2 (NMC811) coin and pouch cells cycled at high voltages. These results contribute to understanding interfacial behaviors in multiphase PEs and inform strategies for improving stability across solid-state battery interfaces.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Multiphase layered transition metal oxide positive electrodes for sodium ion batteries

Abstract Multiphase layered transition metal oxides (LTMOs) for sodium ion battery (SIB) positive electrodes with phase interfaces across multiple length scales are a promising avenue toward practical, high‐performance SIBs. Combinations of phases can complement each other's strengths and mitigate their weaknesses if their interfaces are carefully controlled. Intra‐ and interparticle phase interactions from nanoscale to macroscale must be carefully tuned to generate distinct effects on properties and performance. An informed design strategy must be paired with relevant synthesis techniques and complemented by spatially resolved characterization tools to manipulate different length scales and interfaces. This review examines the design, synthesis, and characterization strategies that have been demonstrated for the preparation of heterogeneous, multiphasic LTMOs with phase interfaces across varied length scales.

25 ENERGY STORAGE↗

DOE OSTI2021

Electrochemical Degradation Modes in Bifacial Silicon Photovoltaic Modules

Motivated by the rapidly rising deployment of bifacial monocrystalline-silicon photovoltaics (PV), we investigate the durability of various PV module packaging configurations with transparent coverings on both the front and rear sides of the module. We use a series of bifacial passivated emitter and rear cell (p-PERC) mini-modules with systematically varying outer cover materials (glass/glass, G/G, or glass/transparent backsheet, G/TB) and encapsulant chemistries (poly [ethylene-co-vinyl acetate], EVA; or polyolefin, POE). We study degradation modes over 1,000 hours of combined damp heat (DH) exposure and high system voltages that can cause potential-induced degradation (PID) under positive, zero, or negative 1,000 V cell-to-frame bias. We analyze the degradation modes using a combination of current-voltage measurements, impedance spectroscopy, external quantum efficiency, and spatially resolved luminescence and thermal imaging. Our results highlight various types of degradation including shunting, enhanced recombination, and series resistance increases, and we use spatially resolved characterization to separately identify the localized effects. We show that multiple PID and moisture-ingress degradation modes severely affect EVA-containing modules, with previously reported PID processes under negative-bias DH and a unique observation of rear-side surface recombination in G/EVA/G modules under positive-bias DH. We observe significantly less degradation in POE-containing modules, where the G/POE/G configuration exhibits minimal degradation under all stress conditions that we employ.

14 SOLAR ENERGY↗

DOE OSTI2024

Offshore application of landslide susceptibility mapping using gradient-boosted decision trees: a Gulf of Mexico case study

Abstract Among natural hazards occurring offshore, submarine landslides pose a significant risk to offshore infrastructure installations attached to the seafloor. With the offshore being important for current and future energy production, there is a need to anticipate where future landslide events are likely to occur to support planning and development projects. Using the northern Gulf of Mexico (GoM) as a case study, this paper performs Landslide Susceptibility Mapping (LSM) using a gradient-boosted decision tree (GBDT) model to characterize the spatial patterns of submarine landslide probability over the United States Exclusive Economic Zone (EEZ) where water depths are greater than 120 m. With known spatial extents of historic submarine landslides and a Geographic Information System (GIS) database of known topographical, geomorphological, geological, and geochemical factors, the resulting model was capable of accurately forecasting potential locations of sediment instability. Results of a permutation modelling approach indicated that LSM accuracy is sensitive to the number of unique training locations with model accuracy becoming more stable as the number of training regions was increased. The influence that each input feature had on predicting landslide susceptibility was evaluated using the SHapely Additive exPlanations (SHAP) feature attribution method. Areas of high and very high susceptibility were associated with steep terrain including salt basins and escarpments. This case study serves as an initial assessment of the machine learning (ML) capabilities for producing accurate submarine landslide susceptibility maps given the current state of available natural hazard-related datasets and conveys both successes and limitations.

Dyer, Alec S. (ORCID:0000000219813904)↗

DOE OSTI2020

Machine learning and ligand binding predictions: A review of data, methods, and obstacles

We report that computational predictions of ligand binding is a difficult problem, with more accurate methods being extremely computationally expensive. The use of machine learning for drug binding predictions could possibly leverage the use of biomedical big data in exchange for time-intensive simulations. This paper reviews current trends in the use of machine learning for drug binding predictions, data sources to develop machine learning algorithms, and potential problems that may lead to overfitting and ungeneralizable models. A few popular datasets that can be used to develop virtual high-throughput screening models are characterized using spatial statistics to quantify potential biases. We can see from evaluating some common benchmarks that good performance correlates with models with high-predicted bias scores and models with low bias scores do not have much predictive power. A better understanding of the limits of available data sources and how to fix them will lead to more generalizable models that will lead to novel drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Microstructure prediction for Ti-22Al-25Nb in laser powder bed fusion

This work presents a physics-informed framework for predicting solidification morphology and defect susceptibility in additively manufactured Ti–22Al–25Nb across a broad processing space. The framework integrates solidification microstructure selection (SMS) analysis with a single-track defect-based printability map to establish a unified methodology linking processing parameters to both interfacial morphology and manufacturability. Thermal gradients G and solidification rates R are first computed using the Thermo-Calc Additive Manufacturing (TC-AM) module, a finite-interface-dissipation (FID) phase-field (PF) model coupled with CALPHAD method is then employed to systematically distinguish planar and dendritic regimes as functions of $G$ and $R$. By superimposing the printability map onto the morphology projections, a comprehensive process–structure framework is obtained. Across most processing conditions, the predicted microstructure is predominantly dendritic, while planar growth emerges only under selected laser power $P$ and scan speed $v$ combinations. In addition to morphology classification, the framework quantifies the dendritic area fraction and introduces a width-based morphology descriptor to characterize the spatial extent of planar/dendritic regions within the melt pool. It provides mechanistic insight into the interplay between solidification physics and defect formation, offering practical guidance for parameter selection and microstructural control in Ti–22Al–25Nb additive manufacturing (AM).

36 MATERIALS SCIENCE↗