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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Porosity-Permeability Evolution During Simultaneous Mineral Dissolution and Precipitation

Permeability in reactive porous media evolves in complex ways that are hard to predict. Macroscopic empirical equations are often used to estimate permeability evolution but fail to reflect the impact of pore scale reactions on permeability. This work aims to understand the evolution of permeability in systems where mineral dissolution and precipitation occur simultaneously. Pore network models are used for permeability simulation of a Paluxy sandstone using pore and pore-throat size distributions from the analysis of X-ray CT images. Pore and pore-throat radii are increased and decreased to reflect the effects of dissolution and precipitation reactions. Varying spatial distributions of reactions are simulated and the resulting porosity and permeability values compared with commonly used macroscopic equations. It is observed that when dissolution occurs at the inlet and precipitation at the outlet, porosity increases while permeability decreases. Similar results are also observed in the respective opposite scenario where precipitation occurs at the inlet and dissolution at the outlet. When dissolution and precipitation reactions are randomly distributed throughout the network, porosity increases with little change in permeability that is not captured by the empirical equations. When reactions are controlled by pore and pore-throat sizes, such as dissolution occurring in pores and pore-throats of small size and precipitation in pores and pore-throats of larger size, porosity and permeability decrease. When precipitation occurs in pores and pore-throats of smaller size and dissolution in pores and pore-throats of larger size, porosity and permeability increase and is described relatively well by the Verma-Pruess porosity-permeability equation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Insights Into the Aerodynamic Versus Radiometric Surface Temperature Debate in Thermal-Based Evaporation Modeling

Global evaporation monitoring from Earth observation thermal infrared satellite missions is historically challenged due to the unavailability of any direct measurements of aerodynamic temperature. State-of-the-art one-source evaporation models use remotely sensed radiometric surface temperature as a substitute for the aerodynamic temperature and apply empirical corrections to accommodate for their inequality. This introduces substantial uncertainty in operational drought mapping over complex landscapes. By employing a non-parametric model, we show that evaporation can be directly retrieved from thermal satellite data without the need of any empirical correction. Independent evaluation of evaporation in a broad spectrum of biome and aridity yielded statistically significant results when compared with eddy covariance observations. While our simplified model provides a new perspective to advance spatio-temporal evaporation mapping from any thermal remote sensing mission, the direct retrieval of aerodynamic temperature also generates the highly required insight on the critical role of biophysical interactions in global evaporation research.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

One Stomatal Model to Rule Them All? Toward Improved Representation of Carbon and Water Exchange in Global Models

Abstract Stomatal conductance schemes that optimize with respect to photosynthetic and hydraulic functions have been proposed to address biases in land‐surface model (LSM) simulations during drought. However, systematic evaluations of both optimality‐based and alternative empirical formulations for coupling carbon and water fluxes are lacking. Here, we embed 12 empirical and optimization approaches within a LSM framework. We use theoretical model experiments to explore parameter identifiability and understand how model behaviors differ in response to abiotic changes. We also evaluate the models against leaf‐level observations of gas‐exchange and hydraulic variables, from xeric to wet forest/woody species spanning a mean annual precipitation range of 361–3,286 mm yr −1 . We find that models differ in how easily parameterized they are, due to: (a) poorly constrained optimality criteria (i.e., resulting in multiple solutions), (b) low influence parameters, (c) sensitivities to environmental drivers. In both the idealized experiments and compared to observations, sensitivities to variability in environmental drivers do not agree among models. Marked differences arise in sensitivities to soil moisture (soil water potential) and vapor pressure deficit. For example, stomatal closure rates at high vapor pressure deficit range between −45% and +70% of those observed. Although over half the new generation of stomatal schemes perform to a similar standard compared to observations of leaf‐gas exchange, two models do so through large biases in simulated leaf water potential (up to 11 MPa). Our results provide guidance for LSM development, by highlighting key areas in need for additional experimentation and theory, and by constraining currently viable stomatal hypotheses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Comparing Model Representations of Physiological Limits on Transpiration at a Semi-arid Ponderosa Pine Site

Mechanistic representations of biogeochemical processes in ecosystem models are rapidly advancing, requiring advancements in model evaluation approaches. Here we quantify multiple aspects of model functional performance to evaluate improved process representations in ecosystem models. We compare semi-empirical stomatal models with hydraulic constraints against more mechanistic representations of stomatal and hydraulic functioning at a semi-arid pine site using a suite of metrics and analytical tools. We find that models generally perform similarly under unstressed conditions, but performance diverges under atmospheric and soil drought. The more empirical models better capture synergistic information flows between soil water potential and vapor pressure deficit to transpiration, while the more mechanistic models are overly deterministic. Although models can be parameterized to yield similar functional performance, alternate parameterizations could not overcome structural model constraints that underestimate the unique information contained in soil water potential about transpiration. Additionally, both multilayer canopy and big-leaf models were unable to capture the magnitude of canopy temperature divergence from air temperature, and we demonstrate that errors in leaf temperature can propagate to considerable error in simulated transpiration. This study demonstrates the value of merging underutilized observational data streams with emerging analytical tools to characterize ecosystem function and discriminate among model process representations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Lower Urban Humidity Moderates Outdoor Heat Stress

Abstract Surface temperature is often used to examine heat exposure in multi‐city studies and for informing urban heat mitigation efforts due to scarcity of urban air temperature measurements. Cities also have lower relative humidity, traditionally not accounted for in large‐scale observational urban heat risk assessments. Here, using crowdsourced measurements from over 40,000 weather stations in ≈600 urban clusters in Europe, we show the moderating effect of this urbanization‐induced humidity reduction on outdoor heat stress during the 2019 heatwave. We demonstrate that daytime differences in heat index between urban clusters and their surroundings are weak, and associations of this urban‐rural difference with background climate, generally examined from the surface temperature perspective, are diminished due to moisture feedbacks. We also examine the spatial variability of surface temperature, air temperature, and heat index within these clusters—relevant for detecting hotspots and potential disparities in heat exposure—and find that surface temperature is a poor proxy for the intra‐urban distribution of heat index during daytime. Finally, urban vegetation shows much weaker (∼1/6th as strong) associations with heat index than with surface temperature, which has broad implications for optimizing urban heat stress mitigation strategies. These findings are valid for operational metrics of heat stress for shaded conditions (apparent temperature and humidex), thermodynamic proxies (wet‐bulb temperature), and empirical heat indices. Based on this large‐scale empirical evidence, surface temperature, used due to the lack of better alternatives, may not be suitable for accurately informing heat mitigation strategies within and across cities, necessitating more urban‐scale observations and better urban‐resolving models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Contributions to Loss Across the Magnetopause During an Electron Dropout Event

Abstract Dropout events are dramatic decreases in radiation belt electron populations that can occur in as little as 30 minutes. Loss to magnetopause due to a combination of magnetopause shadowing and outward radial transport plays a significant role in these events. We examine the dropout of relativistic electron populations during the October 2012 geomagnetic storm using simulated electron phase space density, evaluating the contribution of different processes to losses across the magnetopause. We compare loss contribution from outward transport calculated using a standard empirical radial diffusion model that assumes a dipolar geomagnetic field to an event‐specific radial diffusion model evaluated with a non‐dipolar geomagnetic field. We additionally evaluate the contribution of Shabansky type 1 particles, which bounce along magnetic field lines with local equatorial maxima, to the loss calculated during this event. We find that the empirical radial diffusion model with a dipolar background field underestimates the contribution of radial diffusion to this dropout event by up to 10% when compared to the event‐specific, non‐dipolar radial diffusion model. We additionally find that including Shabansky type 1 particles in the initial electron phase space density, that is, allowing some magnetic field lines distorted from the typical single‐minima configuration in drift shell construction, increases the calculated loss by an average of 0.75%. This shows that the treatment of the geomagnetic field significantly impacts the calculation of electron losses to the magnetopause during dropout events, with the non‐dipolar treatment of radial diffusion being essential to accurately quantify the loss of outer radiation belt populations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Modeling Isoprene Emission Response to Drought and Heatwaves Within MEGAN Using Evapotranspiration Data and by Coupling With the Community Land Model

We introduce two new drought stress algorithms designed to simulate isoprene emission with the Model of Emissions of Gases and Aerosols from Nature (MEGAN) model. The two approaches include the representation of the impact of drought on isoprene emission with a simple empirical approach for offline MEGAN applications and a more process-based approach for online MEGAN in Community Land Model (CLM) simulations. The two versions differ in their implementation of leaf-temperature impacts of mild drought. For the online version of MEGAN that is coupled to CLM, the impact of drought on leaf temperature is simulated directly and the calculated leaf temperature is considered for the estimation of isoprene emission. For the offline version, we apply an empirical algorithm derived from whole-canopy flux measurements for simulating the impact of drought ranging from mild to severe stage. In addition, the offline approach adopts the ratio ($f$ PET ) of actual evapotranspiration to potential evapotranspiration to quantify the severity of drought instead of using soil moisture. We applied the two algorithms in the CLM-CAM-chem (the Community Atmosphere Model with Chemistry) model to simulate the impact of drought on isoprene emission and found that drought can decrease isoprene emission globally by 11% in 2012. We further compared the formaldehyde (HCHO) vertical column density simulated by CAM-chem to satellite HCHO observations. We found that the proposed drought algorithm can improve the match with the HCHO observations during droughts, but the performance of the drought algorithm is limited by the capacity of the model to capture the severity of drought.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Using Convolutional Neural Network to Emulate Seasonal Tropical Cyclone Activity

Abstract It has been widely recognized that tropical cyclone (TC) genesis requires favorable large‐scale environmental conditions. Based on these linkages, numerous efforts have been made to establish an empirical relationship between seasonal TC activities and large‐scale environmental favorability in a quantitative way, which lead to conceptual functions such as the TC genesis index. However, due to the limited amount of reliable TC observations and complexity of the climate system, a simple analytic function may not be an accurate portrait of the empirical relationship between TCs and their ambiences. In this research, we use convolution neural networks (CNNs) to disentangle this complex relationship. To circumvent the limited amount of seasonal TC observation records, we implement transfer‐learning technique to train ensemble of CNNs first on suites of high‐resolution climate model simulations with realistic seasonal TC activities and large‐scale environmental conditions, and then on a state‐of‐the‐art reanalysis from 1950 to 2019. The trained CNNs can well reproduce the historical TC records and yields significant seasonal prediction skills when the large‐scale environmental inputs are provided by operational climate forecasts. Furthermore, by inputting the ensemble CNNs with 20th century reanalysis products and Phase 6 of the Coupled Model Intercomparison Project (CMIP6) simulations, we investigated TC variability and its changes in the past and future climates. Specifically, our ensemble CNNs project a decreasing trend of global mean TC activity in the future warming scenario, which is consistent with our future projections using high‐resolution climate model.

Meteorology & Atmospheric Sciences↗
DOE OSTI2023

Breaking the Window of Detection: Using Multi-Scale Solute Tracer Studies to Assess Mass Recovery at the Detection Limit

Stream solute tracers are commonly injected to assess transport and transformation in study reaches, but results are biased toward the shortest and fastest storage locations. While this bias has been understood for decades, the impact of an experimental constraint on our understanding has yet to be considered. Here, we ask how different our understanding of reach- and segment-scale transport would be if our empirical limits were extended. We demonstrate a novel approach to manipulate experimental conditions and observe mass that is stored at timescales beyond the traditional reach-scale window of detection. We are able to explain the fate of an average of 26% of solute tracer mass that would have been considered as “lost” in a traditional study design across our 14 replicates, extending our detection limits to characterize flowpaths that would have been previously unmeasured. We demonstrate how this formerly lost mass leads to predicting lower magnitudes of gross gains and losses in individual reaches, and ultimately show that the network turnover we infer from solute tracers represents an upper limit on actual, expected behavior. Finally, we review the evolution of tracer studies and their interpretation including this approach and provide a proposed future direction to extend empirical studies to not-before-seen timescales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Baseflow Identification via Explainable AI With Kolmogorov‐Arnold Networks

Abstract Hydrological models often involve constitutive laws that may not be optimal in every application. We propose to replace such laws with the Kolmogorov‐Arnold networks (KANs), a class of neural networks designed to identify symbolic expressions. We demonstrate KAN's potential on the problem of baseflow identification, a notoriously challenging task plagued by significant uncertainty. KAN‐derived functional dependencies of the baseflow components on the aridity index outperform their original counterparts; they demonstrate that water availability, rather than potential evapotranspiration, drives baseflow by constraining actual evapotranspiration under arid conditions. On a test set, they increase the Nash‐Sutcliffe efficiency (NSE) by 65%, decrease the root mean squared error by 29%, and increase the Kling‐Gupta efficiency by 34%. This superior performance is achieved while reducing the number of fitting parameters from three to two. Next, we use data from 378 catchments across the continental United States to refine the water‐balance equation at the mean‐annual scale. The KAN‐derived equations based on the refined water balance outperform both the current aridity index model, with up to a 105% increase in NSE, and the KAN‐derived equations based on the original water balance. While the performance of our model and tree‐based machine learning methods is similar, KANs offer the advantage of simplicity and transparency and require no specific software or computational tools. This case study focuses on the aridity index formulation, but the approach is flexible and transferable to other hydrological processes. Plain Language Summary Equations used in hydrologic model are often suboptimal, resulting in reduced prediction accuracy and efficiency. We implemented Kolmogorov‐Arnold networks (KAN), a machine learning algorithm for deriving symbolic formulations, to estimate groundwater recharge and showed that it outperforms an existing state‐of‐the‐art semi‐empirical formulation. In hydrology, Nash‐Sutcliffe efficiency (NSE), root mean squared error (RMSE), and Kling‐Gupta efficiency (KGE) are commonly used to evaluate model performance. Higher NSE and KGE values indicate better performance, while lower RMSE values are preferable. Our results show that NSE increased by 71%, RMSE decreased by 32%, and KGE improved by 25%. In addition, KAN identifies an optimal functional form and can be used to derive new analytical formulas using the prior knowledge. The KAN‐inspired equation outperformed the original formulation and reduced the fitting parameters. Furthermore, we refined the water‐balance equation at the mean‐annual scale and showed that, based on the new water‐balance equation, KAN can derive new formulations that are superior to the original aridity index formulations (up to 105% increase in NSE) and KAN‐derived equations based on the original water balance. These findings highlight the significant potential of KAN to advance the scientific understanding of a wide range of hydrologic processes. Key Points Kolmogorov‐Arnold networks (KANs) enhance interpretability of machine‐learned hydrological models KAN‐derived symbolic formulations outperform state‐of‐the‐art semi‐empirical aridity indices KAN‐identified functional form yields an analytical index with fewer fitting parameters and improved performance

baseflow↗
DOE OSTI2021

Deconstructing criterion a of the alternative model for personality disorders.

Here, the construct composition of the Level of Personality Functioning Scale (LPFS; Criterion A) of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition alternative model for personality disorders (American Psychiatric Association, 2013) was examined in a clinical vignette rating study. Multiple indices of level of personality functioning, psychiatric and psychosocial impairment, Criterion B maladaptive personality traits, and conceptually divergent variables (intellectual level, socioeconomic status, and likability) were used to deconstruct the LPFS. Most variables were highly intercorrelated, but partial correlational analyses showed the LPFS possesses meaningful personality construct variance not fully explained by severity of pathological traits, psychiatric and psychosocial impairment, or the conceptually divergent variables. This exploratory study offers initial evidence that the LPFS contains substantive LPF variance beyond PD severity. Results are framed and discussed in terms of the known conceptual and empirical overlap between Criterion A and Criterion B as well as the differing ways a dimension of personality disorder (PD) severity may be interpreted. Finally, we propose the LPFS is more than statistical artifact created by empirical covariation but less than a true latent dimension of PD severity. The LPFS may be understood as a methodologically pragmatic but theoretically substantive dimension of PD severity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

The impact of low-mode symmetry on inertial fusion energy output in the burning plasma state

Indirect Drive Inertial Confinement Fusion Experiments on the National Ignition Facility (NIF) have achieved a burning plasma state with neutron yields exceeding 170 kJ, roughly 3 times the prior record and a necessary stage for igniting plasmas. The results are achieved despite multiple sources of degradations that lead to high variability in performance. Results shown here, for the first time, include an empirical correction factor for mode-2 asymmetry in the burning plasma regime in addition to previously determined corrections for radiative mix and mode-1. Analysis shows that including these three corrections alone accounts for the measured fusion performance variability in the two highest performing experimental campaigns on the NIF to within error. Here we quantify the performance sensitivity to mode-2 symmetry in the burning plasma regime and apply the results, in the form of an empirical correction to a 1D performance model. Furthermore, we find the sensitivity to mode-2 determined through a series of integrated 2D radiation hydrodynamic simulations to be consistent with the experimentally determined sensitivity only when including alpha-heating.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Learning earthquake ground motions via conditional generative modeling

Predicting high-fidelity ground motions for future earthquakes is crucial for seismic hazard assessment and infrastructure resilience. Conventional empirical simulations suffer from sparse sensor distribution and geographically localized earthquake locations, while physics-based methods are computationally intensive and require accurate representations of Earth structures and earthquake sources. We propose an artificial intelligence (AI) spectrogram generator, Conditional Generative Modeling for Ground Motion (CGM-GM). CGM-GM leverages earthquake magnitudes and geographic coordinates of earthquakes and sensors as inputs, when postprocessed with phase information, capturing spatially continuous Fourier amplitude spectra (FAS) as well as properties such as P and S arrivals, and waveform durations, without explicit physics constraints. This is achieved through a probabilistic autoencoder that extracts latent distributions in the time-frequency domain and variational sequential models for prior and posterior distributions. We evaluate the performance of CGM-GM using small-magnitude earthquake records from the San Francisco Bay Area, a region with high seismic risks. Here, we report that CGM-GM demonstrates potential for complementing physics-based simulations and non-ergodic empirical ground motion models, as well as shows promise in seismology and beyond.

geophysics↗
DOE OSTI2020

Organizing principles for vegetation dynamics

Understanding vegetation dynamics is very challenging because of the multitude of contributing processes at 64widely different spatial and temporal scales. In this Perspective we propose that understanding of vegetation dynamics can be improved, permitting better predictions, based on organizing principles that constrain plant and ecosystem be haviour: natural selection, self-organization, and entropy maximization. Although these ideas are increasingly used,a limited common understanding of their theoretical basis has prevented their full potential to be realized. We explain the power of natural selection-based optimality to predict photosynthesis and carbon allocation responses to multiple environmental drivers, and how individual plasticity leads to the predictable self-organization of forest canopies. We show how models of natural selection acting on a few key traits can generate realistic plant communities, and how entropy maximization can distinguish between stochastic and deterministic drivers of vegetation patterns. In combination with empirical exploration of patterns in plant functional variation, these principles can accelerate the development of dynamic vegetation models as well as trait-based ecology resting on strengthened theoretical and empirical foundations.

Geosciences↗
DOE OSTI2023

Ultra-fast interpretable machine-learning potentials

Abstract All-atom dynamics simulations are an indispensable quantitative tool in physics, chemistry, and materials science, but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and predictive accuracy. To address this challenge, we combine effective two- and three-body potentials in a cubic B-spline basis with regularized linear regression to obtain machine-learning potentials that are physically interpretable, sufficiently accurate for applications, as fast as the fastest traditional empirical potentials, and two to four orders of magnitude faster than state-of-the-art machine-learning potentials. For data from empirical potentials, we demonstrate the exact retrieval of the potential. For data from density functional theory, the predicted energies, forces, and derived properties, including phonon spectra, elastic constants, and melting points, closely match those of the reference method. The introduced potentials might contribute towards accurate all-atom dynamics simulations of large atomistic systems over long-time scales.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Double Chooz {theta}13 measurement via total neutron capture detection

Neutrinos were assumed to be massless particles until the discovery of the neutrino oscillation process. This phenomenon indicates that the neutrinos have non-zero masses and the mass eigenstates (nu(1), nu(2), nu(3)) are mixtures of their flavour eigenstates (nu(e), nu(mu), nu(tau)). The oscillations between different flavour eigenstates are described by three mixing angles (theta(12), theta(23), theta(13)), two differences of the squared neutrino masses of the nu(2)/nu(1) and nu(3)/nu(1) pairs and a charge conjugation parity symmetry violating phase delta(CP). The Double Chooz experiment, located near the Chooz Electricite de France reactors, measures the oscillation parameter theta(13) using reactor neutrinos. Here, the Double Chooz collaboration reports the measurement of the mixing angle theta(13) with the new total neutron capture detection technique from the full data set, yielding sin(2)(2 theta(13)) = 0.105 +/- 0.014. This measurement exploits the multidetector configuration, the isoflux baseline and data recorded when the reactors were switched off. In addition to the neutrino mixing angle measurement, Double Chooz provides a precise measurement of the reactor neutrino flux, given by the mean cross-section per fission <sigma(f)& rang; = (5.71 +/- 0.06) x 10(-43) cm(2) per fission, and reports an empirical model of the distortion in the reactor neutrino spectrum. The Double Chooz collaboration reports the neutrino oscillation parameter theta(13) from a measurement of the disappearance of reactor anti-electron neutrinos with the total neutron capture technique.

Djurcic, Z.↗
DOE OSTI2020

The role of geography in the complex diffusion of innovations

The urban–rural divide is increasing in modern societies calling for geographical extensions of social influence modelling. Improved understanding of innovation diffusion across locations and through social connections can provide us with new insights into the spread of information, technological progress and economic development. In this work, we analyze the spatial adoption dynamics of iWiW, an Online Social Network (OSN) in Hungary and uncover empirical features about the spatial adoption in social networks. During its entire life cycle from 2002 to 2012, iWiW reached up to 300 million friendship ties of 3 million users. We find that the number of adopters as a function of town population follows a scaling law that reveals a strongly concentrated early adoption in large towns and a less concentrated late adoption. We also discover a strengthening distance decay of spread over the life-cycle indicating high fraction of distant diffusion in early stages but the dominance of local diffusion in late stages. The spreading process is modelled within the Bass diffusion framework that enables us to compare the differential equation version with an agent-based version of the model run on the empirical network. Although both model versions can capture the macro trend of adoption, they have limited capacity to describe the observed trends of urban scaling and distance decay. We find, however that incorporating adoption thresholds, defined by the fraction of social connections that adopt a technology before the individual adopts, improves the network model fit to the urban scaling of early adopters. Controlling for the threshold distribution enables us to eliminate the bias induced by local network structure on predicting local adoption peaks. Finally, we show that geographical features such as distance from the innovation origin and town size influence prediction of adoption peak at local scales in all model specifications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Methods to capture proteomic and metabolomic signatures from cerebrospinal fluid and serum of healthy individuals

Discovery of reliable signatures for the empirical diagnosis of neurological diseases—both infectious and non-infectious—remains unrealized. One of the primary challenges encountered in such studies is the lack of a comprehensive database representative of a signature background that exists in healthy individuals, and against which an aberrant event can be assessed. For neurological insults and injuries, it is important to understand the normal profile in the neuronal (cerebrospinal fluid) and systemic fluids (e.g., blood). Here, we present the first comparative multi-omic human database of signatures derived from a population of 30 individuals (15 males, 15 females, 23–74 years) of serum and cerebrospinal fluid. In addition to empirical signatures, we also assigned common pathways between serum and CSF. Together, our findings provide a cohort against which aberrant signature profiles in individuals with neurological injuries/disease can be assessed—providing a pathway for comprehensive diagnostics and therapeutics discovery.

59 BASIC BIOLOGICAL SCIENCES↗