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Results for “explainability”

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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Anomalous magneto-structural behavior of MnBi explained: A path towards an improved permanent magnet

Low-temperature MnBi (hexagonal NiAs phase) exhibits anomalies in the lattice constants (a, c) and bulk elastic modulus (B) below 100 K, spin reorientation and magnetic susceptibility maximum near 90 K, and, importantly for high-temperature magnetic applications, an increasing coercivity (unique to MnBi) above 180 K. We calculate the total energy and magneto-anisotropy energy (MAE) versus (a, c) using DFT+U methods. We reproduce and explain all the above anomalies. We predict that coercivity and MAE increase due to increasing a, suggesting means to improve MnBi permanent magnets.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Hydraulic architecture explains species moisture dependency but not mortality rates across a tropical rainfall gradient

Aim Intensified droughts are affecting tropical forests across the globe. However, the underlying mechanisms of tree drought response and mortality are poorly understood. Hydraulic traits and especially hydraulic safety margins (HSMs), i.e. the extent to which plants buffer themselves from thresholds of water stress, provide insights into species-specific drought vulnerability. Methods We investigated hydraulic traits during an intense drought triggered by the 2015-2016 El Niño on 27 canopy trees across three tropical forest sites with differing precipitation. We capitalized on the drought event as a time when plant water status might approach or exceed thresholds of water stress. We investigated the degree to which these traits varied across the rainfall gradient, as well as relationships amongst hydraulic traits and species-specific optimal moisture and mortality rates. Results There were no differences among sites for any measured trait. There was strong coordination among traits, with a network analysis revealing two major groups of coordinated traits. In one group there were water potentials, turgor loss point, sapwood capacitance and density, HSMs, and mortality rate. In the second group there was leaf mass per area, leaf dry matter content, hydraulic architecture (leaf area to sapwood area ratio), and species-specific optimal moisture. Conclusion These results demonstrated that while species with greater safety from turgor loss had lower mortality rates, hydraulic architecture was the only trait that explained species’ moisture dependency. Species with a greater leaf area to sapwood area ratio were associated with drier sites and reduced their transpirational demand during the dry season via deciduousness.

Hydraulic Saftey Margins, drought, tropical forest↗
DOE OSTI2023

Understanding oxidation of Fe-Cr-Al alloys through explainable artificial intelligence

Abstract The oxidation resistance of FeCrAl based on alloying composition and oxidizing conditions is predicted using a combinatorial experimental and artificial intelligence approach. A neural network (NN) classification model was trained on the experimental FeCrAl dataset produced at GE Research. Furthermore, using the SHapley Additive exPlanations (SHAP) explainable artificial intelligence (XAI) tool, we explore how the NN can showcase further material insights that are unavailable directly from a black-box model. We report that high Al and Cr content forms protective oxide layer, while Mo in FeCrAl creates thick unprotective oxide scale that is vulnerable to spallation due to thermal expansion. Graphical abstract

Materials Science↗
DOE OSTI2016

Equation of state and spin crossover of (Mg,Fe)O at high pressure, with implications for explaining topographic relief at the core-mantle boundary

Iron-bearing periclase is thought to represent a significant fraction of Earth’s lower mantle. However, the concentration of iron in (Mg,Fe)O is not well constrained at all mantle depths. Therefore, understanding the effect of iron on the density and elastic properties of this phase plays a major role in interpreting seismically observed complexity in the deep Earth. Here in this paper, we examine the high-pressure behavior of polycrystalline (Mg,Fe)O containing 48 mol% FeO, loaded hydrostatically with neon as a pressure medium. Using X-ray diffraction and synchrotron Mössbauer spectroscopy, we measure the equation of state to about 83 GPa and hyperfine parameters to 107 GPa at 300 K. A gradual volume drop corresponding to a high-spin (HS) to low-spin (LS) crossover is observed between ~45 and 83 GPa with a volume drop of 1.85% at 68.8(2.7) GPa, the calculated spin transition pressure. Using a newly formulated spin crossover equation of state, the resulting zero-pressure isothermal bulk modulus K 0T,HS for the HS state is 160(2) GPa with a K' 0T,HS of 4.12(14) and a V 0,HS of 77.29(0) Å 3 . For the LS state, the K 0T,LS is 173(13) GPa with a K' 0T,LS fixed to 4 and a V 0,LS of 73.64(94) Å 3 . To confirm that the observed volume drop is due to a spin crossover, the quadrupole splitting (QS) and isomer shift (IS) are determined as a function of pressure. At low pressures, the Mössbauer spectra are well explained with two Fe 2+ -like sites. At pressure between 44 and 84, two additional Fe 2+ -like sites with a QS of 0 are required, indicative of low-spin iron. Above 84 GPa, two low-spin Fe 2+ -like sites with increasing weight fraction explain the data well, signifying the completion of the spin crossover. To systematically compare the effect of iron on the equation of state parameters for (Mg,Fe)O, a spin crossover equation of state was fitted to the pressure-volume data of previous measurements. Our results show that K 0,HS is insensitive to iron concentration between 10 to 60 mol% FeO, while the spin transition pressure and width generally increases from about 50–80 and 2–25 GPa, respectively. A key implication is that iron-rich (Mg,Fe)O at the core-mantle boundary would likely contain a significant fraction of high-spin (less dense) iron, contributing a positive buoyancy to promote observable topographic relief in tomographic images of the lowermost mantle.

58 GEOSCIENCES↗
DOE OSTI2024

Demystifying Cyberattacks: Potential for Securing Energy Systems With Explainable AI : Preprint

Modernization of energy systems has led to in- creased interactions among multiple critical infrastructures and diverse stakeholders making the challenge of operational decision making more complex and at times beyond cognitive capabilities of human operators. The state-of-the-art machine learning and deep learning approaches show promise of supporting users with complex decision-making challenges, such as those occurring in our rapidly transforming cyber-physical energy systems. However, successful adoption of data-driven decision support technology for critical infrastructure will be dependent on the ability of these technologies to be trustworthy and contextually interpretable. In this paper, we investigate the feasibility of implementing XAI for interpretable detection of cyberattacks in the energy system. Leveraging a proof-of-concept simulation use case of detection of a data falsification attack on a photovoltaic system using XGBoost algorithm, we demonstrate how Local Interpretable Model-Agnostic Explanations (LIME), a flavor XAI approach, can help provide contextual and actionable interpretation of cyberattack detection.

artificial intelligence↗
DOE OSTI2020

Explaining persistent incomplete mixing in multicomponent reactive transport with Eulerian stochastic model

We present an Eulerian stochastic advection–diffusion–reaction (SADR) model and use it to explain incomplete mixing typically observed in transport experiments with bimolecular reactions. Unlike traditional advection– dispersion–reaction (ADR) models, the SADR model describes mechanical and diffusive mixing as two separate processes. In the SADR model, mechanical mixing is driven by random advective velocity whose variance is given by the coefficient of mechanical dispersion. The diffusive mixing is modeled as a Fickian diffusion process with the effective diffusion coefficient. We demonstrate that the sum of the two coefficients is equal to the dispersion coefficient, but only the effective diffusion coefficient contributes to the mixing-controlled reactions. We use experimental results of Gramling et al. * * to show that for transport and bimolecular reactions in porous media, the SADR model is significantly more accurate than the ADR model, which overestimates the concentration of the reaction product by as much as 60%. We also show that the SADR model predicts an experimentally observed bell- shaped spatial distribution of the reactive product concentration, while the ADR model results in a concentration distribution with an unphysical kink.

Bimolecular reactions, incomplete mixing, Stochast↗
DOE OSTI2023

Theory-Enabled High-Throughput Screening of Ion Dissociation Explains Conductivity Enhancements in Diluted Ionic Liquid Mixtures

The growing demand for room-temperature ionic liquids (RTILs) for energy applications necessitates the development of an efficient screening platform. Here, in this study, we successfully developed a fully automated high-throughput RTIL screening platform specifically designed for assessing ionic conductivity. By utilizing the 96 wells of a microtiter plate as individual electrolysis cells, we measured the ionic conductivity of 22 different RTILs, encompassing various combinations of cations and anions, and benchmarked the values with existing literature. We also employed the screening platform to investigate the conductivities of RTIL mixtures with a nonaqueous solvent, ethylene glycol (EG). Specific combinations of RTILs with EG result in approximately 200% enhancement in the conductivity values compared to the pure RTILs. To understand the underlying mechanisms responsible for this enhancement, we developed a theoretical framework for ionic conductivity that considers factors such as the degree of dissociation, viscous forces, and molal volume of the RTIL-EG mixtures. The optimized electrolyte mixture was then employed in the migration-assisted moisture gradient (MAMG) CO 2 capture process to study the effects of improved ionic conductivity on the energy efficiency of the process. Notably, the enhanced conductivity of the RTIL-EG mixture led to a nearly 50% reduction in energy consumption for capturing CO 2 . These outcomes highlight the effectiveness of our strategy in screening RTILs and improving existing processes. Moreover, this fully automated high-throughput setup, combined with the developed theoretical framework, provides a comprehensive platform for screening and studying RTIL mixtures with different solvents, enabling their application in various fields.

25 ENERGY STORAGE↗
DOE OSTI2020

The structure of the RCAN1:CN complex explains the inhibition of and substrate recruitment by calcineurin

Regulator of calcineurin 1 (RCAN1) is an endogenous inhibitor of the Ser/Thr phosphatase calcineurin (CN). It has been shown that excessive inhibition of CN is a critical factor for Down syndrome and Alzheimer’s disease. Here, we determined RCAN1’s mode of action. Using a combination of structural, biophysical, and biochemical studies, we show that RCAN1 inhibits CN via multiple routes: first, by blocking essential substrate recruitment sites and, second, by blocking the CN active site using two distinct mechanisms. We also show that phosphorylation either inhibits RCAN1-CN assembly or converts RCAN1 into a weak inhibitor, which can be reversed by CN via dephosphorylation. This highlights the interplay between posttranslational modifications in regulating CN activity. Last, this work advances our understanding of how active site inhibition of CN can be achieved in a highly specific manner. Together, these data provide the necessary road map for targeting multiple neurological disorders.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Explaining TeV cosmic-ray anisotropies with non-diffusive cosmic-ray propagation

Constraining the behavior of cosmic ray data observed at Earth requires a precise understanding of how the cosmic rays propagate in the interstellar medium. The interstellar medium is not homogeneous; although turbulent magnetic fields dominate over large scales, small coherent regions of magnetic field exist on scales relevant to particle propagation in the nearby Galaxy. Guided propagation through a coherent field is significantly different from random particle diffusion and could be the explanation of spatial anisotropies in the observed cosmic rays. We present a Monte Carlo code to propagate cosmic particle through realistic magnetic field structures. We discuss the details of the model as well as some preliminary studies which indicate that coherent magnetic structures are important effects in local cosmic-ray propagation, increasing the flux of cosmic rays by over two orders of magnitude at anisotropic locations on the sky. Furthermore, the features induced by coherent magnetic structure could be the cause of the observed TeV cosmic-ray anisotropy.

79 ASTRONOMY AND ASTROPHYSICS↗