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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The Green's Function Model Intercomparison Project (GFMIP) Protocol

The atmospheric Green's function method is a technique for modeling the response of the atmosphere to changes in the spatial field of surface temperature. While early studies applied this method to changes in atmospheric circulation, it has also become an important tool to understand changes in radiative feedbacks due to evolving patterns of warming, a phenomenon called the “pattern effect.” To better study this method, this paper presents a protocol for creating atmospheric Green's functions to serve as the basis for a model intercomparison project, GFMIP. The protocol has been developed using a series of sensitivity tests performed with the HadAM3 atmosphere-only general circulation model, along with existing and new simulations from other models. Our preliminary results have uncovered nonlinearities in the response of the atmosphere to surface temperature changes, including an asymmetrical response to warming versus cooling patch perturbations, and a change in the dependence of the response on the magnitude and size of the patches. These nonlinearities suggest that the pattern effect may depend on the heterogeneity of warming as well as its location. These experiments have also revealed tradeoffs in experimental design between patch size, perturbation strength, and the length of control and patch simulations. The protocol chosen on the basis of these experiments balances scientific utility with the simulation time and setup required by the Green's function approach. Running these simulations will further our understanding of many aspects of atmospheric response, from the pattern effect and radiative feedbacks to changes in circulation, cloudiness, and precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

How to detect lensing rotation

Gravitational lensing rotation of images is predicted to be negligible at linear order in density perturbations, but can be produced by the post-Born lens-lens coupling at second order. This rotation is somewhat enhanced for Cosmic Microwave Background (CMB) lensing due to the large source path length, but remains small and very challenging to detect directly by CMB lensing reconstruction alone. We show the rotation may be detectable at high significance as a cross-correlation signal between the curl reconstructed with Simons Observatory (SO) or CMB-S4 data, and a template constructed from quadratic combinations of large-scale structure (LSS) tracers. Equivalently, the lensing rotation-tracer-tracer bispectrum can also be detected, where LSS tracers considered include the CMB lensing convergence, galaxy density, and the Cosmic Infrared Background (CIB), or optimal combinations thereof. We forecast that an optimal combination of these tracers can probe post-Born rotation at the level of 5.7σ–6.1σ with SO and 13.6σ–14.7σ for CMB-S4, depending on whether standard quadratic estimators or maximum a posteriori iterative methods are deployed. We also show possible improvement up to 21.3σ using a CMB-S4 deep patch observation with polarization-only iterative lensing reconstruction. However, these cross-correlation signals have non-zero bias because the rotation template is quadratic in the tracers, and exists even if the lensing is rotation free. We estimate this bias analytically, and test it using simple null-hypothesis simulations to confirm that the bias remains subdominant to the rotation signal of interest. Detection and then measurement of the lensing rotation cross-spectrum is therefore a realistic target for future observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Hanford Double Shell Waste Tank Corrosion Studies- Final Report FY2024

For fiscal year (FY) 2024, the Savannah River National Laboratory (SRNL) focused on two experimental tasks related to Hanford Double Shell Tank (DST) chemistry and integrity. The first task focused on understanding risk of corrosion due to formation of either continuous layers or discrete patches of solids on the tanks’ inner sidewalls and bottoms. The objective of this task was to determine the effect of solid deposits on the corrosion risk to the tank bottom, and whether a combination of scale, saltcake and loose solids lead to under deposit corrosion. Electrochemical testing in a single cell arrangement was employed to determine a threshold inhibitor level for the interstitial liquid in the solids, above which carbon steel was not susceptible to corrosion beneath the solids. The inhibitor levels tested were based on the previously determined probability of failure for carbon steel in a simulated waste (i.e., Pitting Factor).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Tomographic assessment of peening for metallurgical bonding in cold spray

It is widely asserted that peening densifies cold spray coatings and improves particle–particle and particle–substrate bonding, yet systematic studies analyzing the evolution of bonding due to peening particle strikes are scarce. We explore peening at the level of individual particle impacts, by creating two-particle Cu-on-Cu stacks using laser-induced particle impact testing. Exploring impact velocities that range from the onset of bonding at the “critical velocity”, V cr , to its peak near 1.4⋅V cr , we perform full 3D tomography of the particle stacks to characterize the bonding state at the particle-substrate interface. Direct labeling of non-bonded patches and the total particle-substrate interface permits quantification and comparison of bonding in single impacts with bonding after subsequent impacts to reveal the origins of the peening effect. The data reveal a surprising finding: although a second impact directly atop the first particle increases the area of the particle-substrate interface (~40% area increase), there is no improvement in metallurgical bonding. Analysis of these data suggest that not only area expansion (and associated oxide rarefication), but also stress-induced extrusion of metal at that interface needs to occur to achieve metal-on-metal contact to see an enhancement in bonding. Furthermore, our analysis suggests that the subsequent impacting particle needs to have ~70% more kinetic energy to produce additional bonding; this implies that particle and velocity distributions within a spray may be helpful, and that peening may rely on unaligned impacts.

Bonding↗
DOE OSTI2024

ESM data downscaling: a comparison of super-resolution deep learning models

Abstract Climate projections at fine spatial resolutions are required to conduct accurate risk assessment for critical infrastructure and design adaptation planning. Generating these projections using advanced Earth system models (ESM) requires significant computational resources. To address this issue, various statistical downscaling techniques have been introduced to generate fine-resolution data from coarse-resolution simulations. In this study, we evaluate and compare five deep learning-based downscaling techniques, namely, super-resolution convolutional neural networks, fast super-resolution convolutional neural network ESM, efficient sub-pixel convolutional neural network, enhanced deep residual network (EDRN), and super-resolution generative adversarial network (SRGAN). These techniques are applied to a dataset generated by the Energy Exascale Earth System Model (E3SM), focusing on key surface variables such as surface temperature, shortwave heat flux, and longwave heat flux. Models are trained and validated using paired fine-resolution (0.25 $$^{\circ }$$ ∘ ) and coarse-resolution (1 $$^{\circ }$$ ∘ ) monthly data obtained from a 9-year simulation. Next, blind testing is performed using monthly data obtained from two different years outside of the training and validation set. To evaluate the efficiency of each technique, different statistical metrics are used, including mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and learned perceptual image patch similarity (LPIPS). The results show that EDRN outperforms other algorithms in terms of PSNR, SSIM, and MSE, but struggles to capture fine-scale features in the data. In contrast, SRGAN, a generative model that uses perceptual loss, excels in capturing fine details at boundaries and internal structures, resulting in lower LPIPS than other methods.

Pawar, Nikhil M. (ORCID:0000000211613289)↗
DOE OSTI2021

Connectivity and edge effects increase bee colonization in an experimentally fragmented landscape

Though landscape corridors increase dispersal of many animals and plants, it remains unknown whether these positive effects extend to the process of colonization and establishment of new populations in fragments. Working in experimentally fragmented landscapes, we tested how two aspects of habitat fragments altered by corridors – connectivity and edge-to-area ratio – determine patterns of colonization by a solitary, cavity-nesting bee Megachile rotundata. We found that though connectivity initially affected rates of nest-site occupation, edge-to-area ratio ultimately determined the final patterns of patch occupation and nest building, likely due to habitat selection by our focal species. Bee colonization was also higher in patches with higher abundances of their preferred food resources, flowers from the Fabaceae family. Our results show the importance of considering the effects of both connectivity and edge on population dynamics in habitat-based conservation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Hanford Double Shell Waste Tank Corrosion Studies (Final Report FY2023)

For fiscal year (FY) 2023, the Savannah River National Laboratory (SRNL) focused on two experimental tasks related to Hanford Double Shell Tank (DST) chemistry and integrity. The first task focused on understanding risk of corrosion due to formation of either continuous layers or discrete patches of solids on the tanks’ inner sidewalls and bottoms. Differences in the conductivity between various layers of the tank (e.g., solids, liquid, etc.) could result in differences in the electrochemical potential of the tank metal at various locations. The electrochemical potential difference may result in a corrosion current between the coupled surfaces. In FY23, SRNL investigated test configurations and protocols that could evaluate the presence of a galvanic couple between the tank bottom and the tank wall.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Atmospheric-river-induced precipitation in California as simulated by the regionally refined Simple Convective Resolving E3SM Atmosphere Model (SCREAM) Version 0

Abstract. Using the regionally refined mesh (RRM) configuration of the US Department of Energy's Simple Cloud-Resolving Energy Exascale Earth System Model (E3SM) Atmosphere Model (SCREAM), we simulate and evaluate four meteorologically distinct atmospheric river events over California. We test five different RRM configurations, each differing in terms of the areal extent of the refined mesh and the resolution (ranging from 800 m to 3.25 km). We find that SCREAM RRM generally has a good representation of the AR-generated precipitation in CA, even for the control simulation which has a very small 3 km refined patch, and is able to capture the fine-scale regional distributions that are controlled largely by the fine-scale topography of the state. It is found that SCREAM generally has a wet bias over topography, most prominently over the Sierra Nevada mountain range, with a corresponding dry bias on the lee side. We find that refining the resolution beyond 3 km (specifically 1.6 km and 800 m) has virtually no benefit towards reducing systematic precipitation biases but that improvements can be found when increasing the areal extent of the upstream refined mesh. However, these improvements are relatively modest and only realized if the size of the refined mesh is expanded to the scale where employing RRM no longer achieves the substantial cost benefit it was intended for.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Radiation hot spot formation on UF 6 30B cylinders

The accurate material accountancy of a country's enriched uranium hexafluoride (UF 6 ) stockpiles is imperative to the International Atomic Energy Agency (IAEA) for determining appropriate material safeguards. Uranium hexafluoride is typically stored in large cylinders with enrichment verification typically performed with a NaI gamma detector using the enrichment meter method. This approach for calculating enrichment assumes that the UF 6 is homogeneous inside the cylinder. Here, the measurements and tests presented in this manuscript show that the UF 6 in cylinders left outside in the elements can experience fractionation, leading to inhomogeneity, and subsequent development of radiation hot spots. These hot spot locations can produce errant enrichment measurements. In dark-colored cylinders that experience significant surface heating from the sun, UF 6 sublimates off the cylinder walls, leaving only the uranium daughter products in a horizontal patch (stripe) along the cylinder's midline. This daughter patch is hypothesized to be the source of the radiation hot spots. The patch forms during the summer and tends to decay away during the winter, when solar intensity and temperatures are reduced.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

An ecologically-constrained deep learning model for tropical leaf phenology monitoring using PlanetScope satellites

In tropical forests, leaf phenology signals leaf-on/off status and exhibits considerable variability across scales from a single tree-crown to the entire forest ecosystem. Such phenology signals importantly regulate large-scale biogeochemical cycles and regional climate. PlanetScope CubeSats data with a 3-m resolution and near-daily global coverage provide an unprecedented opportunity to monitor both fine- and ecosystem-scale phenology variability along large environmental gradients. However, a scalable method that accurately characterizes leaf phenology from PlanetScope with biophysically meaningful metrics remains lacking. Here we developed an index-guided, ecologically constrained autoencoder (IG-ECAE) method to automatically derive a deciduousness metric (i.e. percentage of tree canopies with leaf-off status within an image pixel) from PlanetScope. The IG-ECAE first estimated the reflectance spectra of leafy/leafless canopies based on their spectral indices characteristics, then used the derived reflectance spectra to guide an autoencoder deep learning method with additional ecological constraints to refine the reflectance spectra, and finally used linear spectral unmixing to estimate the relative abundance of leafless canopies (or deciduousness) per PlanetScope image pixel. We tested the IG-ECAE method at 16 tropical forest sites spanning multiple continents and a large precipitation gradient (1470-2819 mm year -1 ). Among these sites, we evaluated the PlanetScope-derived deciduousness with corresponding measures derived from WorldView-2 (n=9 sites) and local phenocams (n=9 sites). Our results show that PlanetScope-derived deciduousness agrees: 1) with that derived from WorldView-2 at the patch level (90m×90m) with r 2 =0.89 across all sites; and 2) with that derived from phenocams to quantify ecosystem-scale seasonality with r 2 ranging from 0.62-0.96. These results demonstrate the effectiveness and scalability of IG-ECAE in characterizing the wide variability in deciduousness across scales from pixels to forest ecosystems, and from a single date to the full annual cycle, indicating the potential for using high-resolution satellites to track the large-scale phenological patterns and response of tropical forests to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

4D Multimodal Co-attention Fusion Network with Latent Contrastive Alignment for Alzheimer’s Diagnosis

Multimodal neuroimaging provides complementary structural and functional insights into both human brain organization and disease-related dynamics. Recent studies demonstrate enhanced diagnostic sensitivity for Alzheimer’s disease (AD) through synergistic integration of neuroimaging data (e.g., sMRI, fMRI) with tabular data (e.g., behavioral and cognitive tests). However, the intrinsic heterogeneity across modalities (e.g., 4D spatiotemporal fMRI dynamics vs. 3D anatomical sMRI structure) presents critical challenges for discriminative feature fusion, often leading to information loss or biased fusion. To bridge this gap, we propose M2M-AlignNet: a multimodal co-attention network with latent alignment for early AD diagnosis using sMRI and fMRI. At the core of our approach is a multi-patch-to-multi-patch (M2M) contrastive loss function that quantifies and reduces representational discrepancies via weighted patch correspondence, explicitly aligning fMRI components across brain regions with their sMRI structural substrates without one-to-one constraints. Additionally, we propose a latent-as-query co-attention module to autonomously discover fusion patterns, circumventing modality prioritization biases while minimizing feature redundancy. We conduct extensive experiments to confirm the effectiveness of our method and highlight the correspondence between fMRI and sMRI as AD biomarkers.

Wei, Yuxiang [Georgia Institute of Technology]↗
DOE OSTI2022

Performance Evaluation of Distributed Energy Resource Management Algorithm in Large Distribution Networks

This paper presents performance evaluation of hierarchical optimization and control for distributed energy resource management system (DERMS) in large distribution networks via an advanced hardware-in-the-loop (HIL) platform. The HIL platform provides realistic testing in a laboratory envi-ronment, including the accurate modeling of a full-scale dis-tribution system of 11,000 nodes, the DERMS software con-troller, and 90 power hardware photovoltaics (PVs) and bat-tery inverters. The applied DERMS algorithm is designed based on a real-time optimal power flow algorithm and im-plemented with acceleration design that performs fast dis-patch of simulated PVs and real physical hardware DER devices every 4 seconds.

distributed energy management system (DERMS)↗
DOE OSTI2023

Attention-Augmented Parametric Kernel Graph Neural Network (APKGNN) for Node Classification

We present a new graph neural network, the Attention-based Parametric-Kernel augmented Graph Neural Network (APKGNN), developed for node classification tasks. Despite extensive work on modeling multi-faceted relationships between connected nodes of a graph, the effect of attention on edge features mapped to relationships has not yet been analyzed through learning representation. This study derives such an attention vector by first calculating node features corresponding to endpoints of an edge and then aggregating these with extracted local intrinsic patches of a given graph to generate augmented local patch vectors. This process uses a parametric kernel based on Gaussian mixture models (GMMs) to embed local neighborhoods of the graph in local patches. The patch vectors then convolve with the above node features to produce an updated node representation. We show that this new learning representation (APKGNN) achieves higher node classification accuracy on tasks - both standard benchmarks (Cora, PubMed, Citeseer) and new experimental short text corpora where nodes correspond to text documents and words. This implementation of the GNN convolution layer outperforms state-of-the-art (SOTA) algorithms, achieving higher training, validation, and test accuracy by a significant margin on three standard benchmark data sets under both SOTA experimental settings and those for new testbeds.

Bose, Avishek↗
DOE OSTI2023

Antenna With Embedded Die in Glass Interposer for 6G Wireless Applications

This article presents the antenna-integrated glass interposer for D-band 6G wireless applications using dieembedding technology. Here, we implement the die-embedded package on glass substrates and characterize the electrical performance in the D-band. The electrical characterization employs embedded test dies with the 50-Ω ground–signal–ground (GSG) ports and coplanar waveguides. We achieve low-loss die-to-package transitions by using staggered dielectric vias, which are compared with the transitions of wire-bonding and flip-chip assembly. This article provides detailed information on the design, modeling, fabrication, and characterization of the die-to-package interconnects. This article also demonstrates the integration of microstrip patch antenna array and embedded dies in the D-band. The results show superior electrical performance provided by the die-embedded glass interposer. The die-to-package interconnect exhibits good matching (less than —10-dB S11) and low loss (0.2-dB loss) in the D-band. The integrated 1 × 8 patch antenna array shows 11.6-dB broadside gain and good matching with the embedded die. In addition, by using a temporary carrier, the antenna-integrated glass interposer also has great potential for further heterogeneous integration and thermal management.

42 ENGINEERING↗
DOE OSTI2021

Hanford Double Shell Waste Tank Corrosion Studies FY2020 (Final Report)

For fiscal year (FY) 2020, the Savannah River National Laboratory (SRNL) focused on three experimental tasks related to Hanford Double Shell Tank (DST) chemistry and integrity. The first task focused on understanding risk of corrosion due to formation of either continuous layers or discrete patches of deposits on the tanks’ inner sidewalls and bottoms. The second task studied vapor space corrosion (VSC) of the secondary liner using two commercially available vapor corrosion inhibitors (VCIs) at manufacturer recommended dosages. Finally, the third task investigated the long-term open circuit potential (OCP) drift for mill-scale coupons, coupons with polished surfaces, and partial mill-scale coupons that were exposed to tank waste simulants. Additional tests were performed to evaluate the OCP drift as a function of the quantity and composition of organic compounds added to the simulant. A test that was included in the matrix that established the new chemistry limits for tank corrosion, but was previously unreported, was presented and reviewed as well.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

INGRID: An interactive grid generator for 2D edge plasma modeling

A fusion boundary-plasma domain is defined by axisymmetric magnetic surfaces where the geometry is often complicated by the presence of one or more X-points; and modeling boundary plasmas usually relies on computational grids that account for the magnetic field geometry. The new grid generator INGRID (Interactive Grid Generator) presented here is a Python-based code for calculating grids for fusion boundary plasma modeling, for a variety of configurations with one or two X-points in the domain. INGRID first performs partitioning over the domain consisting of a small number of patches conforming to the magnetic field and wall geometry; then it generates a subgrid on each of the patches and joins them into a global grid. This domain partitioning strategy makes possible a uniform treatment of various configurations with one or two X-points in the domain. This includes single-null, double-null, and other configurations with two X-points in the domain. The INGRID design allows generating grids either interactively, via a parameterfile driven GUI, or using a non-interactive script-controlled workflow. Results of testing demonstrate that INGRID is a flexible, robust, and user-friendly grid-generation tool for fusion boundary-plasma modeling.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

INGRID: An interactive grid generator for 2D edge plasma modeling

A fusion boundary-plasma domain is defined by axisymmetric magnetic surfaces where the geometry is often complicated by the presence of one or more X-points; and modeling boundary plasmas usually relies on computational grids that account for the magnetic field geometry. The new grid generator INGRID (Interactive Grid Generator) presented in this work is a Python-based code for calculating grids for fusion boundary plasma modeling, for a variety of configurations with one or two X-points in the domain. INGRID first performs partitioning over the domain consisting of a small number of patches conforming to the magnetic field and wall geometry; then it generates a subgrid on each of the patches and joins them into a global grid. This domain partitioning strategy makes possible a uniform treatment of various configurations with one or two X-points in the domain. This includes single-null, double-null, and other configurations with two X-points in the domain. The INGRID design allows generating grids either interactively, via a parameter-file driven GUI, or using a non-interactive script-controlled workflow. Results of testing demonstrate that INGRID is a flexible, robust, and user-friendly grid-generation tool for fusion boundary-plasma modeling.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Ecological connectivity and habitat loss shape patterns of genetic diversity in a threatened salamander

Context The maintenance of genetic diversity is essential for preserving adaptive potential in populations, yet it is increasingly threatened by landscape alteration. The field of landscape genetics offers a framework for assessing how patch-level landscape conditions, modeled at multiple scales, influence genetic diversity. Objectives We sought to assess how local environmental features and connectivity influence genetic diversity across 74 four-toed salamander (Hemidactylium scutatum) breeding wetlands in the southeastern United States. Methods Using next-generation sequencing data and hierarchical Bayesian models, we examined genome-wide heterozygosity in relation to local landscape features and ecological connectivity. We also assessed the scale of effect of landscape features and tested for temporal lag effects. Results Genetic diversity was lower in wetlands with higher levels of historic deforestation and lower connectivity. An interaction between deforestation and connectivity indicated that deforestation had stronger negative effects in isolated wetlands but weaker effects in well-connected wetlands. Accounting for scale of effect and temporal lags was critical for detecting these relationships. Conclusions Our analyses highlight the importance of assessing the spatial scale (scale of effect) and temporal lag of landscape features to detect key drivers of genetic diversity. In line with population genetic theory, our results indicate that the genetic consequences of habitat loss do not affect populations uniformly and are most severe in isolated populations where gene flow cannot buffer against loss of diversity. Altogether, we highlight the importance of considering the interaction of habitat loss and connectivity in conservation genetic management.

Hemidactylium scutatum↗