Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Geophysical”

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.

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

At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Multi-arrival infrasound from meteoroids: Fragmentation signatures versus propagation effects in a fine-scale layered atmosphere

Infrasonic signatures of meteoroid fragmentation are frequently ambiguous: do multiple arrivals signify a complex breakup or merely the distorting effects of a layered atmosphere? Resolving this ambiguity is critical for accurate energy estimates and source reconstruction. In this study, we address this challenge by analyzing a unique regional dataset of well-constrained meteoroid events observed by the Southern Ontario Meteor Network and the co-located Elginfield Infrasound Array. We employ pseudo-differential parabolic equation (PPE) simulations to quantify how fine-scale gravity-wave structures in the stratosphere and lower thermosphere modify acoustic waveforms at ranges <300 km. Our modeling reveals that while fine-scale layering can stretch signals and generate diffuse oscillatory tails, it does not produce discrete, high-amplitude pulse splitting at ranges below ∼140 km. By applying these results to the rare multi-arrival event 20060305, we demonstrate that its distinct double arrival at 100 km range is inconsistent with atmospheric multipathing and provides definitive evidence of separate fragmentation episodes. These findings establish new diagnostic criteria for separating source physics from propagation artifacts, improving the reliability of infrasound as a monitoring tool for natural bolides, space debris re-entries, and catastrophic launch failures.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Comparison of machine learning and electrical resistivity arrays to inverse modeling for locating and characterizing subsurface targets

Here, this study evaluates the performance of multiple machine learning (ML) algorithms and electrical resistivity (ER) arrays for inversion with comparison to a conventional Gauss-Newton numerical inversion method. Four different ML models and four arrays were used for the estimation of only six variables for locating and characterizing hypothetical subsurface targets. The combination of dipole-dipole with Multilayer Perceptron Neural Network (MLP-NN) had the highest accuracy. Evaluation showed that both MLP-NN and Gauss-Newton methods performed well for estimating the matrix resistivity while target resistivity accuracy was lower, and MLP-NN produced sharper contrast at target boundaries for the field and hypothetical data. Both methods exhibited comparable target characterization performance, whereas MLP-NN had increased accuracy compared to Gauss-Newton in prediction of target width and height, which was attributed to numerical smoothing present in the Gauss-Newton approach. MLP-NN was also applied to a field dataset acquired at U.S. DOE Hanford site.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Relationships among diurnal variations of polar night cloud, precipitation, surface temperatures, and the fair-weather return current of the global electric circuit (GEC)

Over the past five years (June 2017-current), the vertical electric field (E z ) as well as numerous cloud, precipitation and radiation properties have been monitored at the Department of Energy-Atmospheric Radiation Measurement (DOE ARM) North Slope of Alaska (NSA) field site. Comparisons between the composite diurnal averaged fair-weather E z , and composite ceilometer derived cloud base height during the polar night, reveal a significant correlation between the parameters (r = 0.62), supporting previous studies that there is high correlation between local electric field and cloud properties. With the use of extensive instrumentation at the site, such as the Micro Pulse Lidar (MPL), Ka-band Zenith Radar (KAZR), ceilometer, SKYRAD, Precipitation Imaging Package (PIP), among others, this study provides a more comprehensive examination of the diurnal cycle of cloud and precipitation properties along with the localized fair-weather return current of the larger Global Electric Circuit (GEC) system. Comparisons between the composite diurnal averaged fair-weather E z , and cloud thickness, maximum column backscatter, and precipitation particle counts all show similar diurnal variability during the polar night, indicating that during the largest magnitude fair weather E z time periods, clouds bases tend to be higher, clouds are thicker, have a larger column backscatter, and display more precipitating particles. Furthermore, a slight diurnal variability in the polar night surface temperature was found to be highly correlated (r = 0.87) to the longwave downwelling irradiance measured by SKYRAD, indicating that the variations in the physical properties of local clouds could modulate the diurnal polar night surface temperature variability on the order of 0.5 °C/day. In conclusion, these findings emphasize the importance and global nature of the GEC system, with the global aggregate of thunderstorms and electrified clouds potentially influencing polar night cloud properties as well as diurnal wintertime polar surface temperature variation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Spectral induced polarization signatures of smoldering remediation enhanced with colloidal activated carbon: An experimental study

Monitoring the remediation of soil and groundwater contaminated by organic compounds remains highly challenging. The spectral induced polarization (SIP) method exhibits significant promise for monitoring changes to the electrical properties of soils that are undergoing remediation. Thermal treatments, such as smoldering combustion, have become established remediation techniques for destroying contaminants. Smoldering combustion is now being supported by colloidal activated carbon (CAC), with CAC able to adsorb contaminants and supplement the fuel source for destroying contaminants. The objective of this study is to investigate the potential of SIP for tracking the smoldering remediation of imitated field soils supplemented with CAC. SIP column experiments were first conducted to assess the response of SIP to varying concentrations of CAC in field soils that contain, or do not contain, organic material (OM). Here these results demonstrate that increasing OM and CAC contents increase both the real and imaginary components of the complex conductivity, with the imaginary conductivity also showing frequency dependence. Next, a suite of smoldering and SIP column experiments was conducted to investigate if SIP can detect changes in imitated field soils of varying OM and CAC contents that have been remediated by smoldering combustion. The SIP results on the examined soils both before and after smoldering show that SIP can detect changes in the real conductivity, and particularly the imaginary conductivity, between different soil compositions and different stages of the remediation process. High resolution scanning electron microscopy (SEM) imaging was performed on all samples to validate the SIP and smoldering experiments, confirming significant reductions in carbon after smoldering. Overall, this study suggests that SIP has potential to detect changes in the electrical properties of field soils due to the addition of remedial fluids like CAC and contaminant destruction by smoldering remediation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Climate-extreme modeling framework for sustainable flood management in the Arabian Peninsula

Evaluating extreme precipitation events (EPEs) is essential for building climate-resilient water management strategies, but it remains a major challenge in ungauged basins. Using the 26,070 km 2 Wadi al-Rummah basin in central Saudi Arabia as a case study, we developed an alternative, reliable, cost-effective satellite-based framework that combines empirically derived EPE thresholds, imagery-calibrated 2D hydrodynamic modeling, GRACE water-storage diagnostics, and bias-corrected CMIP6 projections to assess flood hazards and recharge potential under current and future climate scenarios in ungauged basins. The integrated approach and the resulting findings followed four key steps: (1) Identified a 22.5 mm EPE threshold, the 80th percentile of 3-day GPM/IMERG rainfall (2000–2024), aligned with flood-triggering events (Nov 2018: 23–28 mm; Apr, 2023: 42 mm); (2) Developed and calibrated a RiverFlow2D model using Sentinel-2 and PlanetScope imagery for the November 2018 flood, accurately reproducing flood depth and extent (RMSE ≤0.31 m; fuzzy-Dice ≥0.91), and estimating runoff (41 %), infiltration (25 %), and evaporation (34 %); (3) Independently validated the model with the April 2023 event (RMSE ≤0.35 m; fuzzy-Dice ≥0.86); (4) Conducted climate projections (2025–2100) from five bias-corrected NEX-GDDP CMIP6 models that revealed a 34 % increase in EPE intensity under SSP2-4.5 and 48 % under SSP5-8.5 scenarios, relative to 20th-century baselines. Our findings indicate that while intensifying extremes in the 21st century increase flood risk, the results highlight the potential for episodic recharge if effective retention strategies are employed, and offer a transferable model for climate-informed planning in data-scarce arid regions.

CMIP6↗
DOE OSTI2026

Nuclear Structure and Decay Data for A=220 Isobars

Here, this work presents a comprehensive and critical evaluation of experimental nuclear spectroscopic data from reactions and decays for all 11 known nuclides with mass number A=220 (Pb, Bi, Po, At, Rn, Fr, Ra, Ac, Th, Pa, and Np). Recommended values are produced for level energies, spins and parities, half-lives, and radiation properties including energies, branching ratios, and multipolarities of γ rays, as well as characteristics of β and α radiation decays, based on all available experimental data. This work supersedes previous A=220 evaluations: 2011Br05, 1997Ar04, 1986Ma45, 1976El04.

Chen, J. [Michigan State University, East Lansing,↗
DOE OSTI2021

Practical considerations for amino acid isotope analysis

Over the last few decades, isotopic analysis of amino acids at the compound- and position-specific levels has been rapidly advancing across diverse fields. As these techniques progress, evaluation of isotopic fractionation associated with sample workup is essential. This critical review of analytical methods through the lens of isotope geochemistry provides a benchmark for researchers across disciplines seeking to make compound- and position-specific amino acid isotope measurements. We focus on preparation, acid hydrolysis, clean-up, derivatization, separation, and C, H, N, and S isotope measurement. Despite substantial customizability across these steps, the following general recommendations should maximize recovery while minimizing isotopic fractionation. Samples should be freeze-dried and stored anoxically at ≤ –20 °C prior to conventional acid hydrolysis (6N HCl, 110 °C, 20–24 h, anoxic), which suffices for many residues. Both gas and liquid chromatographic (GC and LC, respectively) techniques are well-established and separate about 15 amino acids; LC bypasses the need for derivatization, while GC provides higher sensitivity. Furthermore, when derivatization is needed, n-acetyl and alkoxycarbonyl esters provide the most reproducible C isotope ratios. For compound-specific analyses, online GC–IRMS and LC–IRMS systems offer the easiest workflow, but EA–IRMS enables potential multi-element isotope analysis. Emerging techniques like high-resolution mass spectrometry are also promising for multi-element analysis and recover position-specific isotopic information. Looking forward to the next decade of innovation, isotope geochemists and ecologists can improve amino acid isotope analysis by focusing on streamlining multi-element analysis and standardizing calibration practices across laboratories.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

The Geothermal Artificial Intelligence for geothermal exploration

Exploration of geothermal resources involves analysis and management of a large number of uncertainties, which makes investment and operations decisions challenging. Remote Sensing (RS), Machine Learning (ML) and Artificial Intelligence (AI) have potential in managing the challenges of geothermal exploration. In this paper, we present a methodology that integrates RS, ML and AI to create an initial assessment of geothermal potential, by resorting to known indicators of geothermal areas namely mineral markers, surface temperature, faults and deformation. We demonstrated the implementation of the method in two sites (Brady and Desert Peak geothermal sites) that are close to each other but have different characteristics (Brady having clear surface manifestations and Desert Peak being a blind site). Here, we processed various satellite images and geospatial data for mineral markers, temperature, faults and deformation and then implemented ML methods to obtain pattern of surface manifestation of geothermal sites. We developed an AI that uses patterns from surface manifestations to predict geothermal potential of each pixel. We tested the Geothermal AI using independent data sets obtaining accuracy of 92-95%; also tested the Geothermal AI trained on one site by executing it for the other site to predict the geothermal / non-geothermal delineation, the Geothermal AI performed quite well in prediction with 72-76% accuracy.

15 GEOTHERMAL ENERGY↗