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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Z α 2 correction to superallowed beta decays in effective field theory and implications for | V u d |

Superallowed ( 0 + → 0 + ) beta decays currently provide the most precise extraction of quark mixing in the Standard Model. Their interpretation as a measurement of | V u d | relies on a reliable first-principles computation of QED radiative corrections expressed as a series in Z α and α . In this work, we provide the first model-independent result for two-loop, O ( Z α 2 ) , long-distance radiative corrections where the nuclei are treated as heavy pointlike particles. We use renormalization group analysis to obtain new results at O ( Z α 3 ) for the coefficient of double logarithms in the ratio of the maximal beta energy to the inverse nuclear size, E m / R - 1 . We use the Kinoshita-Lee-Nauenberg theorem to obtain new results at O ( Z 2 α 3 ) for the coefficient of logarithms in the ratio of maximal beta energy to the electron mass, log ( 2 E m / m ) . We identify a structure-dependent, and, therefore, short-distance, contribution to the traditional Z α 2 correction that should be revisited. We provide the first comprehensive update to the long-distance corrections in almost 40 years and comment on the impact of our findings for extractions of | V u d | . We find that shifts in the long-distance corrections are 2.5 × larger than past estimates of their uncertainty, 1.5 × larger than the statistical uncertainty from the combined fit of superallowed decays, and about 1 / 2 the size of estimated systematic error, which stems dominantly from nuclear structure effects.

Cao, Zehua [Kentucky U.] (ORCID:0009000354256423)↗
DOE OSTI2026

A Perspective on Traditional and Data Driven Electrochemical Modeling and Analysis

To understand the behavior of electrochemical systems, we need to reduce the dimensionality of the measured current-voltage-time (I-V-t) data by fitting models, thus enabling us to analyze and compare the governing physics. Traditionally, the process for this is an 'expert first' approach: defining the model and its explicit assumptions based on inductive reasoning or empirical observation, fitting small portions of the I-V-t data where assumptions are most valid or carefully designing experiments to enforce key assumptions, and then interpreting the model parameters. However, modern data-driven methods enable a new paradigm: a 'data first' approach, where the latent behaviors governing the system's measured response are identified directly using machine-learning models that optimize both model structure and parameters from the I-V-t data, guaranteeing that the learned model explains as much of the observed system response as possible. After model identification, the model can then be interrogated by an expert to connect observed behaviors with underlying physics. This talk will review several different types of electrochemical analysis (electrochemical impedance, differential voltage-capacity, electrochemical kinetics) and compare the traditional and data-driven methods for analyzing the data.

42 ENGINEERING↗
DOE OSTI2021

Machine Learned Hückel Theory: Interfacing Physics and Deep Neural Networks

The Hückel Hamiltonian is an incredibly simple tight-binding model known for its ability to capture qualitative physics phenomena arising from electron interactions in molecules and materials. Part of its simplicity arises from using only two types of empirically fit physics-motivated parameters: the first describes the orbital energies on each atom and the second describes electronic interactions and bonding between atoms. By replacing these empirical parameters with machine-learned dynamic values, we vastly increase the accuracy of the extended Hückel model. The dynamic values are generated with a deep neural network, which is trained to reproduce orbital energies and densities derived from density functional theory. The resulting model retains interpretability, while the deep neural network parameterization is smooth and accurate and reproduces insightful features of the original empirical parameterization. Altogether, this work shows the promise of utilizing machine learning to formulate simple, accurate, and dynamically parameterized physics models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Short-term traffic state prediction from latent structures: Accuracy vs. efficiency

Recently, deep learning models have shown promising performances in many research areas, including traffic states prediction, due to their ability to model complex nonlinear relationships. However, deep learning models also have drawbacks that make them less preferable for certain short-term traffic prediction applications. For example, they require a large amount of data for model training, which is also computationally expensive. Moreover, deep learning models lack interpretability of the results. This paper develops a short-term traffic states forecasting algorithm based on partial least square (PLS) to help enhance real-time decision-making and build better insights into traffic data. The proposed model is capable of predicting short-term traffic states accurately and efficiently by capturing dominant spatiotemporal features and day-to-day variations from collinear and correlated traffic data. Three case studies are developed to demonstrate the proposed model in short-term traffic prediction applications.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRSDate not supplied

Performance of Heterogeneous Sulfate Aerosol Formation Mechanisms Against Aircraft and Ground-Based Observations in Seoul, South Korea

Haze events are a common occurrence in East Asia, where local meteorology combines with anthropogenic emissions under certain conditions to create rapid increases in secondary aerosol. Often these events are driven by rapid production of secondary sulfate-nitrate-ammonium (SIA) aerosol. Models have typically included simple parameterizations of the production of sulfate during these events which vary widely in their magnitude. There are a range of different mechanistic descriptions of the chemical processes occurring during these events in large part due to poor constraints on aerosol pH and transition metal content, both of which are important to determining the key pathway(s) driving aerosol production. Here, we use the GEOS-Chem chemical transport model to interpret detailed ground and aircraft observations from May and June 2016 during the Korea-United States Air Quality (KORUS-AQ) field study to evaluate the mechanistic pathways governing sulfate production during a haze event. We apply our model of sulfate production mechanisms to ground-based speciated aerosol measurements in winter, summer, and fall in Seoul.

Katherine R Travis↗
NASA NTRS1991

AIRSAR observations of the Gulf Stream with interpretation from sea truth and modeling

On 20 Jul., JPL/DC-8 synthetic aperture radar (SAR) participated in the 17-21 Jul. 1990 NRL Gulf Stream (GS) experiment in preparation for SIR-C missions in 1993, 1994, and 1996 for calibration purposes and to check modes and techniques for operation at our experimental site off the east coast of the US. During this experiment, coordinated and near simultaneous measurements were performed from ship (R/V Cape Henlopen) and other aircraft (NADC/P-3 and NRL/P-3) to address scientific questions relating to the origin of 'slick-like' features observed by Scully-Power, the refraction and modulation of waves by variable currents, the effect of current and thermal fronts on radar imagery signatures and the modification of Kelvin ship wakes by fronts. The JPL/DC-8 and NADC/P-3 SAR's are fully polarimetric systems. Their composite frequency range varies between P- and X-band. We describe in detail the Airborne SAR (AIRSAR) participation in the Jul. 1990 GS experiment and present preliminary results of the ongoing analysis and interpretation of the radar imagery in the context of ground truth, other remote measurements, and modeling efforts.

Valenzuela, G. R.↗
DOE OSTI2023

Towards physics-informed explainable machine learning and causal models for materials research

From emergent material descriptions to estimation of properties stemming from structures to optimization of process parameters for achieving best performance – all key facets of materials science and related fields have experienced tremendous growth with the introduction of data-driven models. This gradual progression goes at par with developments of machine learning workflows, from purely data-driven shallow models to those that are well-capable in encoding more complex graphs, symbolic representations, invariances, and positional embeddings. Furthermore, this perspective aims at summarizing strategic aspects of such transitions while providing insights into the requirements of bringing in explainable, interpretable predictive models, and causal learning to aid in materials design and discovery. Although the focus remains on a variety of functional materials by providing a handful of case studies, the applications of such integrated methodologies are universal to facilitate fundamental understandings of materials physics while enabling autonomous experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2018

The International Land Model Benchmarking (ILAMB) System: Design, Theory, and Implementation

Abstract The increasing complexity of Earth system models has inspired efforts to quantitatively assess model fidelity through rigorous comparison with best available measurements and observational data products. Earth system models exhibit a high degree of spread in predictions of land biogeochemistry, biogeophysics, and hydrology, which are sensitive to forcing from other model components. Based on insights from prior land model evaluation studies and community workshops, the authors developed an open source model benchmarking software package that generates graphical diagnostics and scores model performance in support of the International Land Model Benchmarking (ILAMB) project. Employing a suite of in situ, remote sensing, and reanalysis data sets, the ILAMB package performs comprehensive model assessment across a wide range of land variables and generates a hierarchical set of web pages containing statistical analyses and figures designed to provide the user insights into strengths and weaknesses of multiple models or model versions. Described here is the benchmarking philosophy and mathematical methodology embodied in the most recent implementation of the ILAMB package. Comparison methods unique to a few specific data sets are presented, and guidelines for configuring an ILAMB analysis and interpreting resulting model performance scores are discussed. ILAMB is being adopted by modeling teams and centers during model development and for model intercomparison projects, and community engagement is sought for extending evaluation metrics and adding new observational data sets to the benchmarking framework.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Parameter Estimation for Electrode Degradation: Learning in the Face of Model-Experiment Discrepancies

Use of physics-based models to interpret battery degradation data over the course of cycling can provide deeper physical insight into the internal states of the system and how they evolve. We present a neural network trained on simulations generated by a previously published physics-based model for a lithium trivanadate (LVO) cathode to estimate parameters that evolve over the course of cycling. We focus on the robustness of the neural network through two case studies that probe different kinds of discrepancies between model and experiment: nonideal data and imperfect model. In the former, the experimental protocols do not meet the assumption made in the training data generated by the physics-based model, while in the latter, the physics-based model fails to describe all of the measured cathode behavior even under ideal conditions. When there is total model-experiment agreement, a neural network estimates parameters with improved accuracy compared to a maximum likelihood analysis using the same set of simulations. However, in both types of model-experiment discrepancy, the neural network returned biased parameter estimates. We introduce a data augmentation procedure into the neural network training to mitigate these effects and improve robustness, and employ it to estimate parameters for a cycling LVO cathode.

Mayilvahanan, Karthik S. (ORCID:0000000316282332)↗
DOE OSTI2020

High-throughput phase-field simulations and machine learning of resistive switching in resistive random-access memory

Metal oxide-based Resistive Random-Access Memory (RRAM) exhibits multiple resistance states, arising from the activation/deactivation of a conductive filament (CF) inside a switching layer. Understanding CF formation kinetics is critical to achieving optimal functionality of RRAM. Here a phase-field model is developed, based on materials properties determined by ab initio calculations, to investigate the role of electrical bias, heat transport and defect-induced Vegard strain in the resistive switching behavior, using MO 2- x systems such as HfO 2- x as a prototypical model system. It successfully captures the CF formation and resultant bipolar resistive switching characteristics. High-throughput simulations are performed for RRAMs with different material parameters to establish a dataset, based on which a compressed-sensing machine learning is conducted to derive interpretable analytical models for device performance (current on/off ratio and switching time) metrics in terms of key material parameters (electrical and thermal conductivities, Vegard strain coefficients). These analytical models reveal that optimal performance (i.e., high current on/off ratio and low switching time) can be achieved in materials with a low Lorenz number, a fundamental material constant. This work provides a fundamental understanding to the resistive switching in RRAM and demonstrates a computational data-driven methodology of materials selection for improved RRAM performance, which can also be applied to other electro-thermo-mechanical systems.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

On the Importance of Laboratory Astrophysics and Astrochemistry and Interdisciplinary Research: Two Success Stories

Here we present two examples that demonstrate how cross-disciplinary research projects, where experimentalists, modelers and observers work together to answer science questions, allow expertise to be shared and misconceptions or missing key elements to be tackled by looking at the problem from different perspectives. They also show how, by working in unison, the group can accomplish more than the sum of its parts by combining results into a higher-level understanding of the chemical processes taking place. The first project is a collaborative study between experimentalists, modelers and observers to1) produce laboratory analogs of cosmic grains and planetary aerosols (Titan, Pluto...) from different gas mixtures in cold astrophysically relevant conditions (<200 K); and 2) characterize them with scanning electron microscopy and visible-to-far-infrared spectroscopy in order to assess the impact of the precursors on their growth structure and optical properties. We can then produce and study analogs that are representative of different formation stages or environments, and provide their complex refractive indices to the scientific community. We will show how, by using these experimental optical constants of more representative analogs in radiative transfer and reflectance spectra models, better interpretations of (exo)planetary atmosphere- and surface observations are possible. The second project is an interdisciplinary study of the formation of benzene clouds in the atmosphere of Saturn’s largest moon, Titan. We will show how combining Earth and Planetary Science laboratory expertise, modeling and observations has led to providing to the scientific community, for the first time, experimental vapor pressures for benzene at cold temperatures(<200K) relevant to Titan’s atmospheric conditions. These have been used in microphysical models instead of the higher temperature extrapolations used previously, allowing a better match to observations.

Ella Sciamma-o'Brien↗
NASA NTRS1988

Heat Flow and Magnetization in the Oceanic Lithosphere

Two aspects of the processing and interpretation of satellite measurements of the geomagnetic field are described. One deals with the extraction of the part of the geomagnetic field that originates from sources in the earth's atmosphere. The other investigates the possibility of using the thermal state of the oceanic lithosphere to further constrain modelling and interpretation of magnetic anomalies. It is shown that some of the magnetic signal in crustal anomaly maps can be an artifact of the mathematical algorithms that have been used to separate the crustal field from the observed data. Strong magnetic anomalies can be distorted but are probably real, but weak magnetic anomalies can arise from leakage of power from short wavelengths, and will also appear in anomaly maps as repetitions of the strong crustal anomaly. The distortion and the ghost anomalies follow the magnetic dip lines in a way that is similar to actual MAGSAT anomaly fields. This phenomenon will also affect the lower degree spherical harmonic terms in the power spectrum of the crustal field. A model of the magnetic properties of the oceanic crust that has been derived from direct measurements of the rock magnetic properties of oceanic rocks is presented. The average intensity of magnetization in the oceanic crust is not strong enough to explain magnetic anomalies observed over oceanic areas. This is the case for both near surface observations (ship and aeromagnetic data) and satellite altitude observations. It is shown that magnetic sources in the part of the upper mantle that is situated above the Curie isotherm, if sufficiently strong, can produce satellite magnetic anomalies that are comparable to MAGSAT data. The method developed for the study of depth to the Curie isotherm and magnetic anomalies can also be used in inverse modelling of satellite magnetic anomalies when the model is to be adjusted with an annihilator.

Hayling, Kjell Lennart↗
DOE OSTI2023

Measurements and Model Improvement: Insight into NWP Model Error Using Doppler Lidar and Other WFIP2 Measurement Systems

Abstract Doppler-lidar wind-profile measurements at three sites were used to evaluate NWP model errors from two versions of NOAA’s 3-km-grid HRRR model, to see whether updates in the latest version 4 reduced errors when compared against the original version 1. Nested (750-m grid) versions of each were also tested to see how grid spacing affected forecast skill. The measurements were part of the field phase of the Second Wind Forecasting Improvement Project (WFIP2), an 18-month deployment into central Oregon–Washington, a major wind-energy-producing region. This study focuses on errors in simulating marine intrusions, a summertime, 600–800-m-deep, regional sea-breeze flow found to generate large errors. HRRR errors proved to be complex and site dependent. The most prominent error resulted from a premature drop in modeled marine-intrusion wind speeds after local midnight, when lidar-measured winds of greater than 8 m s −1 persisted through the next morning. These large negative errors were offset at low levels by positive errors due to excessive mixing, complicating the interpretation of model “improvement,” such that the updates to the full-scale versions produced mixed results, sometimes enhancing but sometimes degrading model skill. Nesting consistently improved model performance, with version 1’s nest producing the smallest errors overall. HRRR’s ability to represent the stages of sea-breeze forcing was evaluated using radiation budget, surface-energy balance, and near-surface temperature measurements available during WFIP2. The significant site-to-site differences in model error and the complex nature of these errors mean that field-measurement campaigns having dense arrays of profiling sensors are necessary to properly diagnose and characterize model errors, as part of a systematic approach to NWP model improvement. Significance Statement Dramatic increases in NWP model skill will be required over the coming decades. This paper describes the role of major deployments of accurate profiling sensors in achieving that goal and presents an example from the Second Wind Forecast Improvement Program (WFIP2). Wind-profile data from scanning Doppler lidars were used to evaluate two versions of HRRR, the original and an updated version, and nested versions of each. This study focuses on the ability of updated HRRR versions to improve upon predicting a regional sea-breeze flow, which was found to generate large errors by the original HRRR. Updates to the full-scale HRRR versions produced mixed results, but the finer-mesh versions consistently reduced model errors.

Meteorology & Atmospheric Sciences↗
NASA NTRS1994

Investigation of passive atmospheric sounding using millimeter and submillimeter wavelength channels

Progress by the Georgia Institute of Technology's Laboratory for Radio-science and Remote Sensing in developing techniques for passive microwave retrieval of water vapor profiles and cloud and precipitation parameters using millimeter and submillimeter wavelength channels is reviewed. Channels of particular interest are in the tropospheric transmission windows at 90, 166, 220, 340, and 410 GHz and centered around the water vapor lines at 183 and 325 GHz. Collectively, these channels have potential application in high-resolution precipitation mapping (e.g., from geosynchronous orbit), remote sensing of cloud and precipitation parameters, including cirrus ice mass, and improved retrieval of water vapor profiles. During the period from January 1, 1994 through June 30, 1994 research activities focussed on calibrating and interpreting data from the Millimeter-Wave Imaging Radiometer (MIR). The MIR was deployed on the NASA ER-2 during the Convective Atmospheric Moisture Experiment (CAMEX, September-October 1993) to obtain the first submillimeter-wave tropospheric imagery of convective precipitations. A 325-GHz radiometer consisted of a submillimeter-wave DSB receiver with three IF channels at +/- 1, 3, and 8.5 GHz, and approximately 14 dB DSB noise figure was successfully operated during these experiments. Activities supported under this grant include a study of the impact of local oscillator reflections from the MIR calibration loads, the development of optimal gain and offset filters for radiometric calibration, and the modeling and interpretation of the MIR 325-GHz data over both clear and cloudy atmospheres. In addition, polarimetric radiometer measurements and modeling for ocean surface and atmospheric cloud-ice studies_were supported.

Gasiewski, Albin J.↗
DOE OSTI2023

A new method for predicting hurricane rapid intensification based on co-occurring environmental parameters

Abstract Tropical cyclones (TCs) that undergo Rapid Intensification (RI) can pose serious socioeconomic threats and can potentially result in major damaging impacts along coastal areas. Considering the complexity of various physical mechanisms that play a role in RI and its relatively low probability of occurrence, predicting RI remains a major operational challenge. In this study, we propose a simple deterministic binary classification model based on the co-occurrence of environmental parameters (MCE) to predict an RI event. More specifically, the model determines the possibility of RI based on a simple count of the number of environmental predictors deemed favorable and unfavorable. We compare our model results to logistic regression (LR) and decision tree (DT) models, well-trained using the same set of environmental predictors. Results reveal that at an RI threshold of 30 kt, the MCE exhibits a critical success index score of 0.233 which is 14% higher than DT and LR model performances. When tested at multiple RI thresholds, the MCE displays relatively higher skill scores across multiple metrics. By simultaneously evaluating the favorability of predictors, the MCE is able to comparatively reduce the number of false alarms predicted when certain predictors are unfavorable toward RI. Interpreting these model results to gain a physical understanding of how co-occurring environmental parameters can affect RI, we highlight future directions for using models based on the MCE approach to understand and predict TC RI as well as other meteorological extremes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1984

A Crustal Structure Study of South America

Variations in the average crustal thickness of the South American platform were determined by measuring fundamental mode Rayleigh wave dispersion between nine pairs of South American WWSSN seismograph stations and inverting the measurements to obtain average shear wave velocity-depth models across the platform. Additional models were derived from the Rayleigh wave dispersion data previously measured over eastern South America. Both sets of models were interpreted in terms of average crustal thickness and average crustal shear wave velocity. The results obtained were depicted in a contour map of crustal thickness. Average values for seismic velocities in the crust and upper mantle were calculated and compared with maps of regional gravity models, MAGSAT magnetic anomalies, and major tectonic features. The correlations and relationships discovered are discussed.

Renbarger, K. S.↗
DOE OSTI2020

Electron inelastic mean free path in water

Liquid phase transmission electron microscopy (LPTEM) is rapidly developing as a powerful tool for probing processes in liquid environments with close to atomic resolution. Knowledge of the water thickness is needed for reliable interpretation and modelling of analytical studies in LPTEM, and is particularly essential when using thin liquid layers, required for achieving the highest spatial resolutions. Furthermore, the log-ratio method in electron energy-loss spectroscopy (EELS) is often applied in TEM to quantify the sample thickness, which is measured relative to the inelastic mean free path ( λ IMFP ). However, λ IMFP itself is dependent on sample material, the electron energy, and the convergence and divergence angles of the microscope electronoptics. Here, we present a detailed quantitative analysis of the λ IMFP of water as functions of the EELS collection angle ( β ) at 120 keV and 300 keV in a novel nanochannel liquid cell. We observe good agreement with earlier studies conducted on ice, but find that the most widely used theoretical models significantly underestimate λ IMFP of water. We determine an adjusted average energy-loss term E m, water , and characteristic scattering angle θ E, water that improve the accuracy. The results provide a comprehensive knowledge of the λ IMFP of water (or ice) for reliable interpretation and quantification of observations in LPTEM and cryo-TEM studies.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

SOHIP Abel Transform and Onion Peeling Model Module

This software provides tools for analyzing and modeling physical systems using mathematical transforms and layered models. It includes (1) functions for performing the Abel transform, which is used to relate measurements of bending angles to properties such as refractive index and radius in a medium. The code can compute bending angles from input profiles and also reconstruct these profiles from observed data; (2) the functions for modeling systems with multiple layers using an onion-peeling approach, allowing users to simulate and analyze the behavior of layered materials or structures. These capabilities are useful for researchers and engineers working in fields such as optics, atmospheric science, and materials analysis, enabling them to interpret and model data from experiments or simulations relates to refraction in spherical symmetric medium.

Xu, Shuang [Lawrence Livermore National Laboratory↗