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At least 379 records · Page 21

All sources NASA NTRS DOE OSTI

NASA NTRS2017

A Physical Interpretation of the Titius-Bode Rule and Its Connection to the Closed Orbits of Bertrand's Theorem

We consider the geometric Titius-Bode rule for the semimajor axes of planetary orbits. We derive an equivalent rule for the midpoints of the segments between consecutive orbits along the radial direction and we interpret it physically in terms of the work done in the gravitational field of the Sun by particles whose orbits are perturbed around each planetary orbit. On such energetic grounds, it is not surprising that some exoplanets in multiple-planet extrasolar systems obey the same relation. However, it is surprising that this simple interpretation of the Titius-Bode rule also reveals new properties of the bound closed orbits predicted by Bertrand's theorem, which has been known since 1873.

Titus-Bode rule↗

NASA NTRS2019

Spectral Interpretation of Magmatic Evolution, Oxidation, and Crystallinity in a Volcanic Planetary Analog System

Volcanic surfaces are common and varied throughout the terrestrial planets. Remote spectroscopy is often the only method for determining surface chemistry and mineralogy of such provinces, and is thus critical for understanding petrologic processes and constraining planetary interior evolution and chemistry. Natural volcanic systems exhibit variability in magmatic chemical evolution, crystallinity, oxidation, and eruption-related alteration (e.g. hydrothermal). The extent to which spectroscopy can identify these characteristics alongside each other is thus a key question for interpreting volcanic processes from orbit. While the effects of each of these on visible/near infrared (VNIR) and thermal infrared (TIR) spectra of igneous rocks has been studied separately to varying degrees, their combined spectral effects (and interpretability of such spectra) are understudied.

Scudder, N. A.↗

NASA NTRSDate not supplied

Simulation-Aided Design and Interpretation of Results of Microgravity-Based Solidification Experiments

The preparation and interpretation of microgravity experiments conducted on the International Space Station (ISS) can be challenging because of the remote location of the equipment. Although a “ground unit” is typically available on Earth, it is not located in the laboratory of the principal investigator. Furthermore, test experiments in the ground unit cannot fully duplicate heat transfer conditions of the weightless environment, and thus be a reliable reference for experiment design. Under such conditions, numerical simulations are a valuable tool both for preparing the experiments and interpreting the results obtained under terrestrial and micro-gravity conditions. In this work, we developed a detailed 3D model of the “SUBSA” furnace located at the ISS, and demonstrate its use in our present investigation which includes melt growth and vapor growth of InI crystals on the ISS. In 2002, the SUBSA furnace was designed and built for the growth of InSb crystals in the Microgravity Science Glovebox (MSG) at the ISS. The SUBSA furnace features a transparent section without heating elements, where the axial temperature gradient is high. In our present investigation, we have used the same furnace to grow four InI crystals from the melt, and two InI crystals from the vapor phase (2018-2021). InI crystals have ~20 times lower thermal conductivity than InSb crystals. As a result, during melt growth, the InI crystals were exposed to extremely high temperature gradients. To reduce the axial temperature gradient, we added external jackets made from high thermal conductivity material to the growth ampoules. The SUBSA furnace could not be modeled as asymmetric because of its rectangular shape and right-angled transparent section, which contains a convoluted geometry. The rectangular shape of the transparent section further complicates modelling of radiative heat transfer, which dominates in the zone. At present, numerical simulations are being conducted to resolve an apparent discrepancy between the readings of the 5 thermocouples located in the SUBSA furnace, and the observed position of the solid and molten material. The 3D simulation will be presented, along with the calibration and growth experiment results conducted in microgravity and in the SUBSA ground unit. The developed model should be useful for upcoming microgravity investigations planned to be conducted in the SUBSA facility.

V Riabov↗

NASA NTRS2023

Another Geometric Interpretation of Cramer’s Rule

We develop a geometric interpretation of Cramer’s rule as a generalization of projection onto orthogonal basis vectors using the rows of the adjugate. This interpretation makes connections between elementary linear algebra concepts like the solution to linear equations, inner products, and projections. Such connections are useful for introducing broader concepts related to Hilbert spaces and geometric algebras like Grassman algebra. Such connections were essential for the author’s mathematical education as an engineer.

linear algebra↗

NASA NTRSDate not supplied

Interpretable Tree-Based and Graph Neural Network Approaches for Novel Solid State Electrolyte Design

All-solid-state batteries with Li metal anode can address the safety issues surrounding traditional Li-ion batteries as well as the demand for higher energy densities. However, the development of solid electrolytes simultaneously possessing high ionic conductivity and good chemical and electrochemical stabilities has proven to be a challenge. I will present our informatics approach to explore the Li compound space for promising solid electrolytes using high-throughput multi-property screening and interpretable machine learning. This is accomplished through the generation of a large database of battery-related materials properties of Li compounds. We use tree-based ensemble learning methods and graph neural network approaches to accurately learn relationships between crystal structures and corresponding thermodynamic and kinetic properties, with interpretability being a major focus. Our models give us the ability to enable rapid discovery and design of novel solid-state battery chemistries.

Materials discovery↗

NASA NTRS2021

KMT-2021-BLG-0322: Severe Degeneracy Between Triple-lens and Higher-order Binary-lens Interpretations

We investigate the microlensing event KMT-2021-BLG-0322, for which the light curve exhibits three distinctive sets of caustic-crossing features. It is found that the overall features of the light curve are approximately described by a binary-lens (2L1S) model, but the model leaves substantial residuals. We test various interpretations with the aim of explaining the residuals. Methods. We find that the residuals can be explained either by considering a nonrectilinear lens-source motion caused by the microlens-parallax and lens-orbital effects or by adding a low-mass companion to the binary lens (3L1S model). The degeneracy between the higher-order 2L1S model and the 3L1S model is very severe, making it difficult to single out a correct solution based on the photometric data. This degeneracy was known before for two previous events (MACHO-97-BLG-41 and OGLE-2013-BLG-0723),which led to the false detections of planets in binary systems, and thus the identification of the degeneracy for KMT-2021-BLG-0322illustrates that the degeneracy can be not only common but also very severe, emphasizing the need to check both interpretations of deviations from 2L1S models. Results. From the Bayesian analysis conducted with the measured lensing observables of the event timescale, angular Einstein radius, and microlens parallax, it was estimated that the binary lens components have masses(M1,M2)=(0.62+0.25−0.26M,0.07+0.03−0.03M), for both2L1S and 3L1S solutions, and the mass of the tertiary lens component according to the 3L1S solution isM3=6.40+2.64−2.78MJ.

Gravitational lensing: micro↗

NASA NTRSDate not supplied

Interpretable ML Approaches for Novel Solid State Electrolyte Design

Shreyas J Honrao↗

NASA NTRS2023

Interpretation of Probabilistic Surface Ozone Forecasts: A Case Study for Philadelphia

The use of probabilistic forecasting has been growing in a variety of disciplines because of its potential to emphasize the degree of uncertainty inherent in a prediction. Interpretation of probabilistic forecasts, however, is oftentimes difficult, deterring users who may benefit from such forecasts. To encourage broader use of probabilistic forecasts in the field of air quality, a process for interpreting forecasts from a statistical probabilistic air quality surface ozone model [the Regression in Self Organizing Map (REGiS)] is demonstrated. Four procedures to convert probabilistic to deterministic forecasts are explored for the Philadelphia, Pennsylvania, metropolitan area. These procedures calibrate the predicted probability of daily maximum 8-h-average ozone exceeding a standard value by 1) estimating climatological relative frequency, 2) establishing a probability of an exceedance threshold as 50%, 3) maximizing the threat score, and 4) determining the unit bias ratio. REGiS is trained using 2000–11 ozone-season (1 May–30 September) data, calibrated using 2012–14 data, and evaluated using 2015–18 data. Assessment of the calibration data with the Pierce skill score suggests an exceedance threshold based on climatological relative frequency for the conversion from probabilistic to deterministic forecasts. Calibrated REGiS generally compares well to predictions from the U.S. national air quality model and operational “expert” forecasts over the evaluation period. For other probabilistic models and situations, different procedures of converting probabilistic to deterministic forecasts may be more beneficial. The methods presented in this paper represent an approach for operational air quality forecasters seeking to use probabilistic model output to support forecasts designed to protect public health.

Nikolay Balashov↗

NASA NTRS2024

Interpretable Machine Learning for Molecular Biosignatures: a Novel Single-Sample Feature Importance Method That Is Sensitive To Statistical Interactions

Isotope ratio mass spectrometry (IRMS) of volatiles (e.g., CO 2 ) promises to be a powerful tool for potential biosignature detection for future missions to ocean worlds (OW) such as Europa and Enceladus. Machine learning (ML) methods for IRMS data could enable science autonomy by onboard prediction of seawater chemistry and biosignature presence. However, ML models are likely to be complex and involve statistical interactions between features (variables), which can make predictions seem opaque and enigmatic. For ML predictions as significant as extraterrestrial biosignatures, we must place extraordinary confidence in models. It is therefore essential that these models make interpretable predictions (i.e., human-understandable) and include false-prediction diagnostics. We achieve high accuracy and interpretability in ML biosignature and seawater chemistry models for OW through a nearest-neighbors feature selection tool that detects statistical interactions between predictors, constructs interaction networks for visualization of selected features working together to make a prediction, and reports single-sample feature importance scores for false-detection diagnostics. Here we develop a novel single-sample nearest-neighbors projected distance regression(ssNPDR) feature selection method that improves upon existing single-sample algorithms through the inclusion of statistical interactions while providing false-prediction diagnostics for ML models.

geochemistry↗

DOE OSTI2018

Evolution, Current State of the Art, and Interpretation of Aircraft-Based Methane Emission Quantification at the Natural Gas Basin-Level

Accurately quantifying methane (CH4) emissions from the oil and gas (O&G) industry is challenging given the existence of many potential emission sources, differences in industry practices and regulations, and often incomplete or out-of-date activity data. It is thus important to regularly assess emission inventories and support improvements by deriving independent estimates using state-of-the-science approaches. Recent research has provided CH4 emission estimates from the O&G supply chains based on different measurement techniques at multiple spatio-temporal scales. Natural gas basin-scale aircraft-based atmospheric measurements indicate greater CH4 emissions than emission inventories based on up-scaling of component-/facility-level emission measurements from a relatively small sample size. Here we present results from a coordinated field study that compares concurrent CH4 emission measurements using different techniques and scales to better understand the strengths and weaknesses of each approach, with a focus on the aircraft-based estimation method. We describe the importance of (i) decades of measurement technology development, (ii) experience in designing measurement sampling strategies and aligning system boundaries, and (Hi) properly interpreting CH4 emission estimates for successfully reconciling aircraft-based measurements with emission inventories and for identifying CH4 emission mitigation potential. We illustrate that aircraft-based top-down estimates represent a brief snapshot of total basin-level CH4 emissions. Thus, a high spatio-temporally resolved bottom-up emission inventory is needed to identify key CH4 sources (including episodic sources) at the process level. While this paper provides a brief overview of the overall study design and findings, its focus is on the implementation of the aircraft measurements and its interpretation and results. A separate paper describes in more detail the overall organizational structure of the study as well as the study implications from an industry perspective.

03 NATURAL GAS↗

DOE OSTI2023

An Interpretable Index of Social Vulnerability to Environmental Hazards

Index-based measures of social vulnerability to environmental hazards are commonly modeled from composites of population-level risk factors. These models overlook individual context in communities' experiences of environmental hazards, producing metrics that may hinder spatial decision support for mitigating and responding to hazards. This paper introduces an interpretable, high-resolution model for generating an individual-oriented social vulnerability index (IOSVI) for the United States built on synthetic populations that couples individual and social determinants of vulnerability. The IOSVI combines an individual vulnerability index (IVI) that ranks individuals in an area’s synthetic population based on intersecting risk factors, with a social vulnerability index (SVI) based on the population’s cumulative distribution of IVI scores. Interpretability of the IOSVI procedure is demonstrated through examples of national, metropolitan, and neighborhood (census tract) level spatial variation in index scores and IVI themes, as well as an exploratory analysis examining risk factors affecting a specific sub-population (military veterans) in areas of high social and environmental vulnerability.

Tuccillo, Joe↗

DOE OSTI2024

MicroBooNE investigations on the photon interpretation of the MiniBooNE low energy excess

The MicroBooNE experiment is a liquid argon time projection chamber with 85-ton active volume at Fermilab, operated from 2015 to 2020 to collect neutrino data from Fermilab's Booster Neutrino Beam. One of MicroBooNE's physics goals is to investigate possible explanations of the low-energy excess observed by the MiniBooNE experiment in $\nu_{\mu}\rightarrow \nu_{e}$ neutrino oscillation measurements. MicroBooNE has performed searches to test hypothetical interpretations of the MiniBooNE low-energy excess, including the underestimation of the photon background or instrinic $\nu_{e}$ background. This thesis presents MicroBooNE's searches for two neutral current (NC) single-photon production processes that contribute to the photon background of the MiniBooNE measurement: NC $\Delta$ resonance production followed by $\Delta$ radiative decay: $\Delta \rightarrow N\gamma$, and NC coherent single-photon production. Both searches take advantage of boosted decision trees to yield efficient background rejection, and a high-statistic NC $\pi^0$ measurement to constrain dominant background, and make use of MicroBooNE's first three years of data. The NC $\Delta \rightarrow N\gamma$ measurement yielded a bound on the $\Delta$ radiative decay process at 2.3 times the predicted nominal rate at 90\% confidence level(C.L.), disfavoring a candidate photon interpretation of the MiniBooNE low-energy excess as a factor of 3.18 times the nominal NC Δ radiative decay rate at the 94.8\% C.L. The NC coherent single photon measurement leads to the world's first experimental limit on the cross-section of this process below 1 GeV, of $1.49 \times 10^{-41} \text{cm}^2$ at 90\% C.L., corresponding to 24.0 times the nominal prediction.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Nonparametric Multiparticle Set Methods for Interpreting Environmental Samples

Collection and analysis of environmental samples is commonly used by a range of stakeholders in nuclear safeguards and security contexts. While the ubiquity of samples and their transport in the environment allow regular collection, developing and demonstrating methods for analyzing these samples is difficult. In this work, an environmental sample consists of a set of one or more individual particles. Recent advances in reactor simulation have allowed us to generate data that are more representative of real-world environmental samples, enabling statistically defensible method development and testing. The most notable of these advances is a drastic increase in the number of material depletion regions, which allows our simulations to capture the variation in isotopic composition seen at length scales consistent with environmental samples. Traditional approaches for handling multiparticle samples treat each particle in the sample individually, estimating the quantity of interest (e.g., core-average burnup) resulting from measurement and analysis of signatures (e.g., nuclide assays) from each individual particle. Individual estimates are then averaged to generate a single estimate of the quantity of interest over the entire sample. In this presentation, we introduce two novel approaches for interpreting environmental samples that comprise of multiple particles: (1) the Quantile-Quantile Comparator, which uses a multivariate generalization of quantile-quantile plots for comparing unknown statistical distributions, and (2) the Set Transformer, an attention-based neural network module designed to model interactions among elements (particles) in the input set (sample). Statistically representative sampling cannot be guaranteed as samples are passively collected and are beholden to what particles are available in the environment. These new analysis methods for set-input problems are expected to be more robust than traditional approaches to issues of sampling bias where particles are not uniformly distributed throughout regions of interest, as well as generally outperform traditional approaches by jointly considering all elements in the set. We will present results comparing the performance of traditional single particle approaches and the novel Quantile-Quantile Comparator and Set Transformer for interpretation of simulated environmental samples.

Phathanapirom, Birdy↗

DOE OSTI2025

A multiscale design method using interpretable machine learning for phononic materials with closely interacting scales

Manipulating the dispersive characteristics of vibrational waves is beneficial for many applications, e.g., high-precision instruments. architected hierarchical phononic materials have sparked promise tunability of elastodynamic waves and vibrations over multiple frequency ranges. In this article, hierarchical unit-cells are obtained, where features at each length scale result in a band gap within a targeted frequency range. Our novel approach, the ‘‘hierarchical unit-cell template method,’’ is an interpretable machine-learning approach that uncovers global unit-cell shape/topology patterns corresponding to predefined band-gap objectives. A scale-separation effect is observed where the coarse-scale band-gap objective is mostly unaffected by the fine-scale features despite the closeness of their length scales, thus enabling an efficient hierarchical algorithm. Moreover, the hierarchical patterns revealed are not predefined or self-similar hierarchies as common in current hierarchical phononic materials. Furthermore, our approach offers a flexible and efficient method for the exploration of new regions in the hierarchical design space, extracting minimal effective patterns for inverse design in applications targeting multiple frequency ranges.

Architected materials↗

DOE OSTI2025

TRANSP integrated modeling code for interpretive and predictive analysis of tokamak plasmas

This paper provides a comprehensive review of the TRANSP code, a sophisticated tool for interpretive and predictive analysis of tokamak plasmas, detailing its major capabilities and features. It describes the equations for particle, power, and momentum balance analysis, as well as the poloidal field diffusion equations. The paper outlines the spatial and time grids used in TRANSP and details the equilibrium assumptions and solvers. Various models for heating and current drive and radiation, including updates to the NUBEAM model, are discussed. The handling of large-scale events such as sawtooth crashes and pellet injections is examined, along with the predictive capabilities for advancing plasma profiles. The integration of TRANSP with the ITER Integrated Modeling and Analysis Suite (IMAS) is highlighted, demonstrating enhanced data access and analysis capabilities. Additionally, the paper discusses best practices and continuous integration techniques to enhance TRANSP's robustness. The suite of TRANSP tools, designed for efficient data analysis and simulation, further supports the optimization of tokamak operations and coupling with other tokamak codes. Continuous development and support ensure that TRANSP remains a major code for the analysis of experimental data for controlled thermonuclear fusion, maintaining its critical role in supporting the optimization of tokamak operations and advancing fusion research.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

How to see hidden patterns in metamaterials with interpretable machine learning

Machine learning models can assist with metamaterials design by approximating computationally expensive simulators or solving inverse design problems. However, past work has usually relied on black box deep neural networks, whose reasoning processes are opaque and require enormous datasets that are expensive to obtain. Here, in this work, we develop two novel machine learning approaches to metamaterials discovery that have neither of these disadvantages. These approaches, called shape-frequency features and unit-cell templates, can discover 2D metamaterials with user-specified frequency band gaps. Our approaches provide logical rule-based conditions on metamaterial unit-cells that allow for interpretable reasoning processes, and generalize well across design spaces of different resolutions. The templates also provide design flexibility where users can almost freely design the fine resolution features of a unit-cell without affecting the user’s desired band gap.

36 MATERIALS SCIENCE↗

DOE OSTI2026

End-To-End Decentralized Transmission Line Protection in IBR-Dominated Weak Grids Using Interpretable Data-Driven Methods

Traditional transmission line protection relies on predictable synchronous-based fault signatures, which frequently fail under the non-standard, current-limited fault characteristics of Inverter-Based Resources (IBRs). This study investigates how to achieve secure, communication-free fault isolation in IBR-dominated weak grids without relying on opaque, computationally heavy "black-box" machine learning algorithms. To address this, we propose a novel, standalone, and inherently interpretable data-driven protection framework. Unlike centralized methods requiring multi-terminal communication, this decentralized approach relies solely on local measurements using a hierarchical linear-kernel Support Vector Machine (SVM). The methodology decomposes the protection task into four sequential stages that mimic traditional protection elements: fault detection and fault direction identification, fault type classification, zone classification, and location estimation. This multi-stage architecture allows for specialized feature engineering at each stage, combining high computational efficiency with logic traceability. The framework's end-to-end performance was validated via C-code and PSCAD/EMTDC co-simulation, utilizing a real-world utility network and an OEM black-box IBR model. The proposed relay achieves 97.2% overall accuracy and provides a reliable trip decision within a 2.5-cycle window. The results confirm 100% accuracy in fundamental fault detection, reliable zone selectivity across low to moderate fault resistances, and robust security against non-fault transients, proving its immediate viability for integration into commercial numerical relays.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

Above zone pressure interpretation for leaky well characterization and its identification from leaky caprock/fault

Pressure transient analysis has long been used for reservoir characterization. Above-zone (AZ) pressure has been recently investigated for inferring leakage pathway characteristics in leakage events from subsurface injection operations. The recorded pressure in the AZ should be purely related to leakage and therefore it can be safely inverted to deduce leakage characteristics. It is crucial to evaluate fluid leakage through abandoned wells to plan for further measurements of leakage prevention. However, the recorded AZ leakage signal may not be related to leaky well(s). Therefore, identification and spatial investigation of well leakage is required for leakage evaluation. Here in this paper, we propose a pressure interpretation method for early detection of leaky pathways, applying two observation points in the AZ. We distinguish leaky well, fault and caprock based on their corresponding flow regime identification. We show that the pressure difference of the two observation wells can be applied as a proxy for unknown leakage rate, which is crucial for leakage identification as well as characterization. Results show that the estimated location of the leaky well, leakage coefficient, and the leakage rate are in good agreement with the actual values. The estimated leakage coefficient of the leaky well can be used to evaluate well leakage in multiphase systems such as carbon dioxide leakage in deep saline aquifers.

02 PETROLEUM↗