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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Thermodynamic approach to the interpretation of self-consistent pressure profiles in a tokamak

The phenomenon of invariable pressure profiles in tokamaks is interpreted in the framework of the thermodynamic approach suggesting that invariable self-consistent states correspond to the minimum of free energy. Solutions qualitatively consistent with the experiment are obtained under the assumption that the mechanism for the formation of self-consistent profiles is directly related to equilibrium diamagnetic currents. The dynamics of the system and specific transport phenomena, such as energy and particle pinching and a decrease in the local density under auxiliary electron cyclotron resonance heating (density pump-out), are analyzed in the vicinity of an equilibrium state characterized by a stable pressure profile. The scaling for the energy confinement time deduced from the transport model agrees qualitatively with the ITER scaling based on the analysis of experimental data obtained in many tokamaks. The possibility of using generalized Tsallis statistics to analyze pressure profiles is considered.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Precision measurement and interpretation of inclusive W + W - Z /γ* production cross sections with the ATLAS detector

High-precision measurements by the ATLAS Collaboration are presented of inclusive W + →ℓ + νW + →ℓ - $\bar{v}$ and Z/γ*→ℓℓ (ℓ = e,μ) Drell-Yan production cross sections at the LHC. The data were collected in proton–proton collisions at s√=7 TeV with an integrated luminosity of 4.6 fb -1 . Differential W + W - cross sections are measured in a lepton pseudorapidity range |η ℓ | <2.5. Differential Z/γ* cross sections are measured as a function of the absolute dilepton rapidity, for |y ℓℓ |<3.6, for three intervals of dilepton mass, m ℓℓ , extending from 46 to 150 GeV. The integrated and differential electron- and muon-channel cross sections are combined and compared to theoretical predictions using recent sets of parton distribution functions. The data, together with the final inclusive e ± p scattering cross-section data from H1 and ZEUS, are interpreted in a next-to-next-to-leading-order QCD analysis, and a new set of parton distribution functions, ATLAS-epWZ16, is obtained. The ratio of strange-to-light sea-quark densities in the proton is determined more accurately than in previous determinations based on collider data only, and is established to be close to unity in the sensitivity range of the data. Lastly, a new measurement of the CKM matrix element |V cs | is also provided.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Higgs boson production cross-section measurements and their EFT interpretation in the $4\ell $ decay channel at $\sqrt{s}=$13 TeV with the ATLAS detector

Higgs boson properties are studied in the four-lepton decay channel (where lepton = e, μ) using 139 fb -1 of proton–proton collision data recorded at $\sqrt{s}=$13 TeV by the ATLAS experiment at the Large Hadron Collider. The inclusive cross-section times branching ratio for H→ZZ* decay is measured to be 1.34±0.12 pb for a Higgs boson with absolute rapidity below 2.5, in good agreement with the Standard Model prediction of 1.33±0.08 pb. Cross-sections times branching ratio are measured for the main Higgs boson production modes in several exclusive phase-space regions. The measurements are interpreted in terms of coupling modifiers and of the tensor structure of Higgs boson interactions using an effective field theory approach. Exclusion limits are set on the CP-even and CP-odd ‘beyond the Standard Model’ couplings of the Higgs boson to vector bosons, gluons and top quarks.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Das ist der HAMMER: consistent new physics interpretations of semileptonic decays

Abstract Precise measurements of $$b\rightarrow c\tau \bar{\nu }$$ b → c τ ν ¯ decays require large resource-intensive Monte Carlo (MC) samples, which incorporate detailed simulations of detector responses and physics backgrounds. Extracted parameters may be highly sensitive to the underlying theoretical models used in the MC generation. Because new physics (NP) can alter decay distributions and acceptances, the standard practice of fitting NP Wilson coefficients to SM-based measurements of the $$R(D^{(*)})$$ R ( D ( ∗ ) ) ratios can be biased. The newly developed software tool enables efficient reweighting of MC samples to arbitrary NP scenarios or to any hadronic matrix elements. We demonstrate how allows avoidance of biases through self-consistent fits directly to the NP Wilson coefficients. We also present example analyses that demonstrate the sizeable biases that can otherwise occur from naive NP interpretations of SM-based measurements. The library is presently interfaced with several existing experimental analysis frameworks and we provide an overview of its structure.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Measurement of the energy asymmetry in $t\bar{t} j$ production at 13 TeV with the ATLAS experiment and interpretation in the SMEFT framework

A measurement of the energy asymmetry in jet-associated top-quark pair production is presented using 139fb -1 of data collected by the ATLAS detector at the Large Hadron Collider during pp collisions at $\sqrt{s}$ =13TeV. The observable measures the different probability of top and antitop quarks to have the higher energy as a function of the jet scattering angle with respect to the beam axis. The energy asymmetry is measured in the semileptonic $t\bar{t}$ decay channel, and the hadronically decaying top quark must have transverse momentum above 350GeV. The results are corrected for detector effects to particle level in three bins of the scattering angle of the associated jet. The measurement agrees with the SM prediction at next-to-leading-order accuracy in quantum chromodynamics in all three bins. In the bin with the largest expected asymmetry, where the jet is emitted perpendicular to the beam, the energy asymmetry is measured to be -0.043 ± 0.020, in agreement with the SM prediction of -0.037 ± 0.003. Interpreting this result in the framework of the Standard Model effective field theory (SMEFT), it is shown that the energy asymmetry is sensitive to the top-quark chirality in four-quark operators and is therefore a valuable new observable in global SMEFT fits.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Exploring Hadron Structure with GPDs at EIC: New Topics in Theory, Experiment, Interpretation

Exploring hadron structure with generalized parton distributions (GPDs) and related concepts is an essential part of the physics program of the planned Electron-Ion Collider (EIC). Here, we discuss new topics in GPD physics (theory, experiment, interpretation) that have emerged in the last years and could be included in the EIC program. This includes: (a) D-term and QCD forces in the nucleon; (b) Transversity GPDs in pseudoscalar meson production and high-mass pair production; (c) Pion and kaon form factors and GPDs; (d) Transition distribution amplitudes and backward meson production; (e) Neutron GPDs and nuclear modifications; (f) Nuclear GPDs and quark/gluon imaging of nuclei; (g) Twist-3 GPDs and quark-gluon correlations; (h) Wigner functions and potential observables. We also comment on future theoretical developments needed for the GPD program at EIC and emerging connections with other areas of nuclear physics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Interpreting Black-Box Classifiers Using Instance-Level Visual Explanations

To realize the full potential of machine learning in diverse real- world domains, it is necessary for model predictions to be readily interpretable and actionable for the human in the loop. Analysts, who are the users but not the developers of machine learning models, often do not trust a model because of the lack of transparency in associating predictions with the underlying data space. To address this problem, we propose Rivelo, a visual analytic interface that enables analysts to understand the causes behind predictions of binary classifiers by interactively exploring a set of instance-level explanations. These explanations are model-agnostic, treating a model as a black box, and they help analysts in interactively probing the high-dimensional binary data space for detecting features relevant to predictions. We demonstrate the utility of the interface with a case study analyzing a random forest model on the sentiment of Yelp reviews about doctors.

Tamagnini, Paolo↗
DOE OSTI2024

Topological Interpretability for Deep Learning

With the growing adoption of AI-based systems across everyday life, the need to understand their decision-making mechanisms is correspondingly increasing. The level at which we can trust the statistical inferences made from AI-based decision systems is an increasing concern, especially in high-risk systems such as criminal justice or medical diagnosis, where incorrect inferences may have tragic consequences. Despite their successes in providing solutions to problems involving real-world data, deep learning (DL) models cannot quantify the certainty of their predictions. These models are frequently quite confident, even when their solutions are incorrect. This work presents a method to infer prominent features in two DL classification models trained on clinical and non-clinical text by employing techniques from topological and geometric data analysis. We create a graph of a model's feature space and cluster the inputs into the graph's vertices by the similarity of features and prediction statistics. We then extract subgraphs demonstrating high-predictive accuracy for a given label. These subgraphs contain a wealth of information about features that the DL model has recognized as relevant to its decisions. We infer these features for a given label using a distance metric between probability measures, and demonstrate the stability of our method compared to the LIME and SHAP interpretability methods. This work establishes that we may gain insights into the decision mechanism of a DL model. This method allows us to ascertain if the model is making its decisions based on information germane to the problem or identifies extraneous patterns within the data.

Spannaus, Adam↗
DOE OSTI2024

Extracting and Interpreting Electrochemical Impedance Spectra (EIS) from Physics-Based Models of Lithium-Ion Batteries

This paper implements a highly efficient algorithm to extract electrochemical impedance spectra (EIS) from physics-based battery models (e.g., a P2D model). The mathematical approach is different from how EIS is practiced experimentally. Experimentally, the voltage (current) is harmonically perturbed over a wide range of frequencies and the amplitude and phase shift of the corresponding current (voltage) is measured. The experimental approach can be implemented in simulation software, but is computationally expensive. The approach here is to determine locally linear state-space models from the full physical model. The four Jacobian matrices that are the basis of the state-space models can be derived by numerical differentiation of the physical model. The EIS is then extracted from the state-space model using computationally efficient matrix-manipulation techniques. The algorithm can evaluate the full EIS at an instant in time during a transient, independent of whether the battery is in a stationary state. The approach is also able to separate the full-cell impedance to evaluate partial EIS, such as for a battery anode alone. Although such partial EIS is difficult to measure experimentally, the partial EIS provides valuable insights in interpreting the full-cell EIS.

25 ENERGY STORAGE↗
DOE OSTI2025

Guidelines for Making, Interpreting, and Quantifying Activity Measurements of Molten Salts Containing Mixed Cations and Anions Using Cation-Based Electrodes by Electromotive Force

Measurements of thermochemical properties in molten salt electrolytes with mixtures of cations and anions are difficult to interpret. Electromotive force measurements of salts containing common anions were driven by the least stable anion compound (e.g., LiCl in LiCl-KCl), while measurements in common cation salts were driven by the most noble anion half-cell reaction (e.g., F − /F 2 in LiCl-LiF). Measurements in mixed cation and anion salts were driven by the least stable anion compound for the most noble anion half-cell reaction (e.g., KF in KCl-LiF). These guidelines enable quantification of the activity of electroactive species in salts containing multiple cations or anions.

Lichtenstein, Timothy [Argonne National Laboratory↗
DOE OSTI2023

EMF Biomass Data Interpreter [SWR-23-06]

EMF Biomass Data Interpreter was developed for the transportation team of the Energy Modeling Forum (EMF) 37: Deep Decarbonization and High Electrification Scenarios for North America project. It parses results from different modeling groups to show how biomass might be employed under electrification.

Wachs, Elizabeth↗
DOE OSTI2026

AIF for Vis (Active Inference for simulating human interpretation of data visualization) [SWR-26-084]

AIF for Vis contains the Active Inference models and analysis scripts used to study a simple visualization-interpretation task: estimating the average value of two bars in a bar chart. The work is a proof of concept for translating hypothesized cognitive strategies into executable, inspectable process models. We implement two idealized strategies inspired by dual-process accounts of visualization-aided decision making: *Fast model: a compressed, heuristic strategy that estimates the visual midpoint of the two bars and maintains a single belief over their average. *Slow model: a sequential, analytic strategy that estimates the two bar heights separately and maintains them in working memory before computing an average. Both models use a common Active-Inference-inspired framework for sequential perception, belief updating, action selection, and reporting. Their different internal representations produce distinct predicted vulnerabilities: *the Fast model is more susceptible to tick-salience bias; *the Slow model is more susceptible to working-memory decay. The repository includes the model implementations, scripts used for the experiments reported in the paper, precomputed trial-level results, and plotting scripts.

Goldwyn, Harrison [National Laboratory of the Rock↗
DOE OSTI2023

Interpretable inverse-designed cavity for on-chip nonlinear photon pair generation

Inverse design is a powerful tool in wave physics for compact, high-performance devices. To date, applications in photonics have mostly been limited to linear systems and it has rarely been investigated or demonstrated in the nonlinear regime. In addition, the “black box” nature of inverse design techniques has hindered the understanding of optimized inverse-designed structures. We propose an inverse design method with interpretable results to enhance the efficiency of on-chip photon generation rate through nonlinear processes by controlling the effective phase-matching conditions. We fabricate and characterize a compact, inverse-designed device using a silicon-on-insulator platform that allows a spontaneous four-wave mixing process to generate photon pairs at a rate of 1.1 MHz with a coincidence to accidental ratio of 162. Our design method accounts for fabrication constraints and can be used for scalable quantum light sources in large-scale communication and computing applications.

42 ENGINEERING↗
DOE OSTI2023

An accurate and interpretable model for antimicrobial resistance in pathogenic Escherichia coli from livestock and companion animal species

Understanding the microbial genomic contributors to antimicrobial resistance (AMR) is essential for early detection of emerging AMR infections, a pressing global health threat in human and veterinary medicine. Here we used whole genome sequencing and antibiotic susceptibility test data from 980 disease causing Escherichia coli isolated from companion and farm animals to model AMR genotypes and phenotypes for 24 antibiotics. We determined the strength of genotype-to-phenotype relationships for 197 AMR genes with elastic net logistic regression. Model predictors were designed to evaluate different potential modes of AMR genotype translation into resistance phenotypes. Our results show a model that considers the presence of individual AMR genes and total number of AMR genes present from a set of genes known to confer resistance was able to accurately predict isolate resistance on average (mean F 1 score = 98.0%, SD = 2.3%, mean accuracy = 98.2%, SD = 2.7%). However, fitted models sometimes varied for antibiotics in the same class and for the same antibiotic across animal hosts, suggesting heterogeneity in the genetic determinants of AMR resistance. We conclude that an interpretable AMR prediction model can be used to accurately predict resistance phenotypes across multiple host species and reveal testable hypotheses about how the mechanism of resistance may vary across antibiotics within the same class and across animal hosts for the same antibiotic.

Chung, Henri C.↗
DOE OSTI2017

Treatment with integrase inhibitor suggests a new interpretation of HIV RNA decay curves that reveals a subset of cells with slow integration

The kinetics of HIV-1 decay under treatment depends on the class of antiretrovirals used. Mathematical models are useful to interpret the different profiles, providing quantitative information about the kinetics of virus replication and the cell populations contributing to viral decay. We modeled proviral integration in short- and long-lived infected cells to compare viral kinetics under treatment with and without the integrase inhibitor raltegravir (RAL). We fitted the model to data obtained from participants treated with RAL-containing regimes or with a four-drug regimen of protease and reverse transcriptase inhibitors. Our model explains the existence and quantifies the three phases of HIV-1 RNA decay in RAL-based regimens vs. the two phases observed in therapies without RAL. Our findings indicate that HIV-1 infection is mostly sustained by short-lived infected cells with fast integration and a short viral production period, and by long-lived infected cells with slow integration but an equally short viral production period. We propose that these cells represent activated and resting infected CD4+ T-cells, respectively, and estimate that infection of resting cells represent ~4% of productive reverse transcription events in chronic infection. RAL reveals the kinetics of proviral integration, showing that in short-lived cells the pre-integration population has a half-life of ~7 hours, whereas in long-lived cells this half-life is ~6 weeks. We also show that the efficacy of RAL can be estimated by the difference in viral load at the start of the second phase in protocols with and without RAL. Altogether, we provide a mechanistic model of viral infection that parsimoniously explains the kinetics of viral load decline under multiple classes of antiretrovirals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Utah FORGE 8-3637: Integrated Diagnostics for Interpreting Doublet Heat Sweep Efficiency - 2024 Annual Workshop Presentation

This is a presentation on the Integrated Diagnostics for Interpreting Doublet Heat Sweep Efficiency by Texas Tech University, presented by Smith Leggett. This video slide presentation discusses the ID squared technical objectives to develop and integrate diagnostic tools to determine (1) the number of fractures, (2) the uniformity of flow distribution, (3) heat exchange areas, and (4) heat efficiency. This presentation was featured in the Utah FORGE R&D Annual Workshop on August 15, 2024.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Data from: "Towards CONUS-Wide ML-Augmented Conceptually-Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics"

This data package was generated to support the manuscript “Towards CONUS-Wide Machine Learning-Augmented Conceptually Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics.” It provides input files, model outputs, plotting data, scripts, notebooks, and documentation used to develop, evaluate, and reproduce Mass-Conserving Perceptron (MCP)-based hydrologic modeling experiments across 513 selected Catchment Attributes and Meteorology for Large-sample Studies in the United States (CAMELS-US) basins. The files are organized by modeling component and analysis purpose, including rainfall–runoff experiments, snow module experiments, coupled hydrologic-snow experiments, Long Short-Term Memory (LSTM) benchmark results, model skill metrics, initialization and epoch records, cell-state normalization files, Akaike Information Criterion (AIC)-based model comparison files, and data used to generate manuscript figures. Tabular files can be opened using standard spreadsheet software or Python/R data-analysis tools. Python scripts, Jupyter notebooks, and selected MATLAB scripts are included for model execution, postprocessing, plotting, and statistical analysis. Quality assurance and quality control were conducted through the source-data selection and modeling workflow. Meteorological forcing, streamflow, and static catchment attributes were derived from the CAMELS-US dataset, and snow water equivalent data were derived from the University of Arizona (UA) Snow Water Equivalent dataset. Selected basins and time periods were screened during the associated research workflow to avoid missing observations or poor-quality cases. Static geospatial features were processed primarily using Quantum Geographic Information System (QGIS) and Geospatial Data Abstraction Library (GDAL) workflows. Additional details are provided in the associated manuscript and documentation.

ESS-DIVE CSV File Formatting Guidelines Reporting ↗
DOE OSTI2021

A Systematic Interpretation of Subsurface Proppant Concentration from Drilling Mud Returns: Case Study from Hydraulic Fracturing Test Site (HFTS-2) in Delaware Basin

The aim of this study is generation and validation of a proppant log using analysis of drilling mud returns for child wells. Proppant log provides qualitative as well as quantitative insights into spatial distribution of proppant sand particles from prior stimulation of parent wells. While the basic methodology was developed and formalized during analysis of material collected from through fracture cores at Hydraulic Fracturing Test Site in Midland Basin (HFTS – 1), the test wells at HFTS – 2 in the neighboring Delaware Basin allowed the opportunity to validate the workflow on actual mud return samples from subsurface. As a child well is being drilled, periodic mud return samples are collected at the rig site and preserved for analysis. The workflow involves systematic cleaning of the samples including various steps such as washing, drying and segregation of samples into relevant size fractions of interest (< Mesh 20) based on specifications of pumped sand during stimulation of the parent well. Clean samples are imaged using high resolution transparency scanning. Scan images are then systematically analyzed for particles of interest using computer vision techniques. Sample counts are further validated using elemental analysis of smaller sub-samples at various depths of interest. This step is necessary to isolate proppant versus other naturally occurring minerals such as sulphates and carbonates which show similar optical properties. We successfully correlated proppant distribution against the existing parent well and validated propped versus relatively un-propped zones for a child well at the test site. The advantage of testing the proppant log concept at the HFTS – 2 site is the plethora of additional diagnostic data that is available to validate our primary observations. We can correlate spatial proppant distribution against variability in stimulation response based on independent observations such as image logs, microseismic attributes as well as DAS response, all of which tend to corroborate one another. One of our significant successes was being able to describe varying degrees of impact of the parent well along the lateral length of a stimulated child well. Our workflow represents a systematic and one-of-a-kind interpretation of spatial proppant distribution while drilling child wells. This provides unique opportunities to better understand the current state of the Downloaded from http://onepetro.org/URTECONF/proceedings-pdf/21URTC/2-21URTC/D021S031R003/2477415/urtec-2021-5189-ms.pdf/1 by Carol Worster on 28 February 2022 URTeC 5189 2 reservoir being targeted including zones which are likely more drained relative to others and how the planned completion of the child well can be improved. Lastly, this log can be useful is validating optimal well spacing in relatively new fields under development.

58 GEOSCIENCES↗