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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Modeling Water Electrolysis in Bipolar Membranes

Bipolar membranes (BPMs) have proven useful in numerous electrochemical energy conversion and storage applications, including fuel cells and electrolyzers. However, water dissociation in bipolar membrane electrolysis cells (BPMECs) is a complicated phenomenon that occurs via several different pathways. In this work, we develop a model based on the Poisson-Nernst-Planck system that includes a multistep water-dissociation mechanism to observe the fundamental processes that contribute to BPMEC performance. The model, which is validated to in-house experimental data, demonstrates that the junction potential is the most significant contributor to the total electrolysis voltage. We investigated the effects of water-dissociation catalysts and found that the optimal catalyst $p{K}_{a}$ depends on how the catalyst is integrated into the BPM (although values near 7 are typically best, in accordance with conventional wisdom). We also simulated the water content across the BPM and found that dry-out is not a significant issue when the membrane is in contact with liquid water on both sides. The species conservation approach taken here leads to a physical understanding of the system without using any fitting parameters.

25 ENERGY STORAGE↗
DOE OSTI2025

Overview of the SCEC/USGS Community Stress Drop Validation Study Using the 2019 Ridgecrest Earthquake Sequence

We present initial findings from the ongoing Community Stress Drop Validation Study to compare spectral stress-drop estimates for earthquakes in the 2019 Ridgecrest, California, sequence. This study uses a unified dataset to independently estimate earthquake source parameters through various methods. Stress drop, which denotes the change in average shear stress along a fault during earthquake rupture, is a critical parameter in earthquake science, impacting ground motion, rupture simulation, and source physics. Spectral stress drop is commonly derived by fitting the amplitude-spectrum shape, but estimates can vary substantially across studies for individual earthquakes. Sponsored jointly by the U.S. Geological Survey and the Statewide (previously, Southern) California Earthquake Center our community study aims to elucidate sources of variability and uncertainty in earthquake spectral stress-drop estimates through quantitative comparison of submitted results from independent analyses. The dataset includes nearly 13,000 earthquakes ranging from M 1 to 7 during a two-week period of the 2019 Ridgecrest sequence, recorded within a 1° radius. Here, in this article, we report on 56 unique submissions received from 20 different groups, detailing spectral corner frequencies (or source durations), moment magnitudes, and estimated spectral stress drops. Methods employed encompass spectral ratio analysis, spectral decomposition and inversion, finite-fault modeling, ground-motion-based approaches, and combined methods. Initial analysis reveals significant scatter across submitted spectral stress drops spanning over six orders of magnitude. However, we can identify between-method trends and offsets within the data to mitigate this variability. Averaging submissions for a prioritized subset of 56 events shows reduced variability of spectral stress drop, indicating overall consistency in recovered spectral stress-drop values.

58 GEOSCIENCES↗
DOE OSTI2024

Physics-Based Machine Learning Methods for U-235 Forensics Signatures

Signatures of low-intensity U-235 sources have been recently studied by utilizing a variety of machine learning (ML) classifiers using features derived from gamma spectral measurements collected under structured campaigns. Several ML classifiers, such as ensemble of tress and classification trees, revealed misleadingly-optimistic training error due to over-fitting, and furthermore, their performance is not directly relatable to the physical properties due to their data-driven, opaque designs. We present a regression-based ML method that first estimates the inverse distance to the source and then utilizes a threshold to infer its presence, by representing the background as a source located at an infinite distance. For the inverse distance estimation, we study the ensemble of trees and Gaussian process regression methods, and a hyper parameter auto-tuning and selection method that employs five regression estimators. These methods avoid the over-fitting observed in several ML classifiers, while providing the classification error nearly comparable to them based on independent test data. Their error is directly related to estimates of the inverse physical distance to source, and the precision of error determines the seperability property that determines the false alarm and missed detection rates. The property of monotonic decrease of the source strength with increasing detector distance combined with Poisson distribution of measurements is utilized to analytically validate these methods by deriving the generalization equations of underlying regression methods.

Rao, Nageswara↗
DOE OSTI2024

Gluon moment and parton distribution function of the pion from 𝑁 𝑓 = 2 +1 +1 lattice QCD

We present the first calculation of the pion gluon moment from lattice QCD in the continuum-physical limit. The calculation is done using clover fermions for the valence action with three pion masses, 220, 310 and 690 MeV, and three lattice spacings, 0.09, 0.12, and 0.15 fm, using ensembles generated by MILC Collaboration with 2+1+1 flavors of highly improved staggered quarks (HISQ). On the lattice, we nonperturbatively renormalize the gluon operator in RI/MOM scheme using the cluster-decomposition error reduction (CDER) technique to enhance the signal-to-noise ratio of the renormalization constant. We extrapolate the pion gluon moment to the continuum-physical limit and obtain ⟨𝑥⟩ 𝑔 = 0.394⁢(58) stat+NPR ⁢(39) mixing in the $\overline{MS}$ scheme at 2 GeV, with first error being the statistical error and uncertainties in nonperturbative renormalization, and the second being a systematic uncertainty estimating the effect of ignoring quark mixing. Our pion gluon momentum fraction has a central value lower than two recent single-ensemble lattice-QCD results near physical pion mass but is consistent with the recent global fits by JAM and xFitter and with most QCD-model estimates.

Astronomy & Astrophysics↗
DOE OSTI2020

Moments of nucleon isovector structure functions in 2+1+1-flavor QCD

We present results on the isovector momentum fraction, $\langle x \rangle_{u–d}$ , helicity moment, $\langle x \rangle_{Δ u–Δd}$ , and the transversity moment, $\langle x \rangle_{δu–δd}$, of the nucleon obtained using nine ensembles of gauge configurations generated by the MILC Collaboration using 2 + 1 + 1 -flavors of dynamical highly improved staggered quarks. The correlation functions are calculated using the Wilson-Clover action, and the renormalization of the three operators is carried out nonperturbatively on the lattice in the RI'–MOM scheme. The data have been collected at lattice spacings a ≈ 0.15 , 0.12, 0.09, and 0.06 fm and $M_π$ ≈ 310 , 220, and 135 MeV, which are used to obtain the physical values using a simultaneous chiral-continuum-finite-volume fit. The final results, in the $\overline{\text{MS}}$ scheme at 2 GeV, are $\langle$ x $\rangle$ u – d = 0.173 ( 14 ) ( 07 ) , $\langle x \rangle_{Δu–Δd}$ = 0.213 ( 15 ) ( 22 ) , and $\langle x \rangle_{δu–δd}$ = 0.208 ( 19 ) ( 24 ) , where the first error is the overall analysis uncertainty and the second is an additional systematic uncertainty due to possible residual excited-state contributions. These results are consistent with other recent lattice calculations and phenomenological global fit values.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter

A novel method to reconstruct the energy of hadronic showersin the CMS High Granularity Calorimeter (HGCAL) is presented. TheHGCAL is a sampling calorimeter with very fine transverse andlongitudinal granularity. The active media are silicon sensors andscintillator tiles readout by SiPMs and the absorbers are acombination of lead and Cu/CuW in the electromagnetic section, andsteel in the hadronic section. The shower reconstruction method isbased on graph neural networks and it makes use of a dynamicreduction network architecture. It is shown that the algorithm isable to capture and mitigate the main effects that normally hinderthe reconstruction of hadronic showers using classicalreconstruction methods, by compensating for fluctuations in themultiplicity, energy, and spatial distributions of the shower'sconstituents. The performance of the algorithm is evaluated usingtest beam data collected in 2018 prototype of the CMS HGCALaccompanied by a section of the CALICE AHCAL prototype. Thecapability of the method to mitigate the impact of energy leakagefrom the calorimeter is also demonstrated.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Coupling to rotational manifolds to improve gas-phase pump–probe spectroscopic models

The physical picture of gas-phase optical transitions is normally presented as an isolated two-level system balanced by upward and downward processes. Isolated models assume a phenomenological treatment of collisional dephasing but do not strictly account for collisional population exchange with the rotational baths. While this assumption is valid under low-intensity conditions, where excitation is rate-limiting, isolated models can deviate from Beer’s Law at sufficient pressures and monochromatic intensities when both collisional broadening and power broadening are comparable to (or greater than) lifetime broadening, which are not uncommon conditions for cavity enhanced spectroscopies in the mid-IR spectral range. Although this problem has been addressed by rate-equation models for linear absorption measurements, a general treatment for multi-level quantum mechanical models suitable for non-linear absorption measurements (two-photon/two-color/pump–probe) is lacking. Isolated models require physical parameter inputs that disagree with expected values by at least an order of magnitude. These non-physical models undermine the ability to predict non-linear signal strengths under untested conditions and thereby limit the potential to optimize the sensitivity of non-linear spectroscopies and to expand their analytical applications (e.g., new analytes and/or buffer gases, changes in cavity free-spectral-range, changes in intracavity powers or wavelengths, and accurate investigation of physical phenomena). In this study, we derive bath-coupled models for gaseous pump–probe spectroscopy by application of the quantum Lindblad equation and detailed balance. Bath-coupled models are shown to fit data consistently across variations in intensity and agree with all physically expected values.

Cavity ring-down spectroscopy↗
DOE OSTI2022

On the implications of positive W mass shift

We investigate the phenomenological implications of the recent W mass measurement by the CDF collaboration, which exhibits tension with the standard model (SM) electroweak fit. Performing the fit to the electroweak observables within the SM effective field theory, we find that the new physics that contributes either to the determination of the electroweak vacuum expectation value, or to the oblique parameters, can improve the agreement with data. The best description is obtained from a fit where flavor universality is not required in the new physics operators, with 2 to 3 σ indications for several nonzero Wilson coefficients. We point out that top partners with order TeV masses could lead to the observed shift in the W mass.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Two-loop electroweak corrections to the Top-Quark Contribution to ϵ K

The parameter ϵ K measures CP violation in the neutral kaon system. It is a sensitive probe of new physics and plays a prominent role in the global fit of the Cabibbo-Kobabyashi-Maskawa matrix. The perturbative theory uncertainty is currently dominated by the top-quark contribution. Here, we present the calculation of the full two-loop electroweak corrections to the top-quark contribution to ϵ K , including the resummation of QED-QCD logarithms. We discuss different renormalization prescriptions for the electroweak input parameters. In the traditional normalization of the weak Hamiltonian with two powers of the Fermi constant G F , the top-quark contribution is shifted by –1%.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Parameter inference from event ensembles and the top-quark mass

One of the key tasks of any particle collider is measurement. In practice, this is often done by fitting data to a simulation, which depends on many parameters. Sometimes, when the effects of varying different parameters are highly correlated, a large ensemble of data may be needed to resolve parameter-space degeneracies. An important example is measuring the top-quark mass, where other physical and unphysical parameters in the simulation must be profiled when fitting the top-quark mass parameter. We compare four different methodologies for top-quark mass measurement: a classical histogram fit similar to one commonly used in experiment augmented by soft-drop jet grooming; a 2D profile likelihood fit with a nuisance parameter; a machine-learning method called DCTR; and a linear regression approach, either using a least-squares fit or with a dense linearly-activated neural network. Despite the fact that individual events are totally uncorrelated, we find that the linear regression methods work most effectively when we input an ensemble of events sorted by mass, rather than training them on individual events. Although all methods provide robust extraction of the top-quark mass parameter, the linear network does marginally best and is remarkably simple. For the top study, we conclude that the Monte-Carlo-based uncertainty on current extractions of the top-quark mass from LHC data can be reduced significantly (by perhaps a factor of 2) using networks trained on sorted event ensembles. More generally, machine learning from ensembles for parameter estimation has broad potential for collider physics measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Physics-Based Machine Learning Methods for U-235 Forensics Signatures

Signatures of low-intensity U-235 sources have been recently studied by utilizing a variety of machine learning (ML) classifiers using features derived from gamma spectral measurements collectedunder structured campaigns. Several ML classifiers, such as ensemble of tress and classification trees, revealed misleadingly-optimistic training error due to over-fitting, and furthermore,their performance is not directly relatable to the physical properties due to their data-driven, opaque designs. We present a regression-based ML method that first estimates the inverse distanceto the source and then utilizes a threshold to infer its presence, by representing the background as a source located at an infinite distance. For the inverse distance estimation, we study the ensembleof trees and Gaussian process regression methods, and a hyper parameter auto-tuning and selection method that employs five regression estimators. These methods avoid the over-fittingobserved in several ML classifiers, while providing the classification error nearly comparable to them based on independent test data. Their error is directly related to estimates of the inversephysical distance to source, and the precision of error determines the seperability property that determines the false alarm and missed detection rates. The property of monotonic decrease of thesource strength with increasing detector distance combined with Poisson distribution of measurements is utilized to analytically validate these methods by deriving the generalization equations ofunderlying regression methods.

Rao, Nageswara↗
DOE OSTI2021

Combined selection of the dynamic model and modeling error in nonlinear aeroelastic systems using Bayesian Inference

Here, we report a Bayesian framework for concurrent selection of physics-based models and (modeling) error models. We investigate the use of colored noise to capture the mismatch between the predictions of calibrated models and observational data that cannot be explained by measurement error alone within the context of Bayesian estimation for stochastic ordinary differential equations. Proposed models are characterized by the average data-fit, a measure of how well a model fits the measurements, and the model complexity measured using the Kullback–Leibler divergence. The use of a more complex error models increases the average data-fit but also increases the complexity of the combined model, possibly over-fitting the data. Bayesian model selection is used to find the optimal physical model as well as the optimal error model. The optimal model is defined using the evidence, where the average data-fit is balanced by the complexity of the model. The effect of colored noise process is illustrated using a nonlinear aeroelastic oscillator representing a rigid NACA0012 airfoil undergoing limit cycle oscillations due to complex fluid–structure interactions. Several quasi-steady and unsteady aerodynamic models are proposed with colored noise or white noise for the model error. The use of colored noise improves the predictive capabilities of simpler models.

42 ENGINEERING↗
DOE OSTI2026

TEM Approaches for Microstructure-Informed Prediction of Mechanical Properties in Structural Alloys

Predicting the mechanical performance of structural alloys from their evolving microstructure remains a major challenge in materials science, particularly for nuclear structural materials, where irradiation-induced defects span multiple types and length scales and interact through complex mechanisms. The dispersed barrier hardening (DBH) [1] and Friedel–Kroupa–Hirsch (FKH) [2,3] models have been widely used to evaluate the hardening contributions of individual obstacles and to estimate tensile strength from quantified microstructures; however, when multiple size-dependent obstacles coexist and evolve, predicting temperature-dependent tensile strength becomes significantly more complex, and a fully consistent hardening model is still lacking. Transmission electron microscopy (TEM) plays a central role in refining hardening models and enabling predictive assessments of tensile strength evolution by providing quantitative characterization of dislocations, irradiation-induced defects (e.g., dislocation loops and cavities), precipitates, and grain structure (Fig. 1.). These experimentally measured defect densities are incorporated into physically based hardening models with size- and shape- dependent obstacle strengths [4], using root-sum-square superposition for obstacles of comparable strength and linear superposition for dissimilar ones [5]. In addition, recent advances in TEM [6-8], including high-resolution imaging, 4D-STEM strain mapping, EDS/EELS elemental analysis, and flash-polishing-based TEM specimen preparation and extraction-replica methods (Fig. 2), further improve the accuracy of microstructural quantification. By comparison with prior studies as well as our own results, we show that when TEM-derived microstructural information is carefully integrated with physically grounded hardening models, yield strength (or irradiation-induced hardening) measured at room temperature can be predicted with good quantitative agreement across multiple alloy classes. In-situ TEM combined with high-temperature mechanical testing represents an important next step for refining hardening models by directly probing dislocation–obstacle interactions across varying irradiation doses and temperatures [9]. Because the barrier strength factor (α) depends on both temperature and obstacle size, it should not be treated as a constant fitting parameter; rather, it must be explicitly evaluated to achieve physically meaningful predictions of mechanical behaviour at operating temperatures. This presentation therefore discusses why all strengthening contributions (e.g., Peierls stress, solid-solution strengthening, voids, bubbles, dislocation loops, dislocation lines, and grain boundaries) must be considered collectively, why appropriate superposition methods are essential when obstacles possess different barrier strength factors, how hardness measurements can be meaningfully related to tensile properties, and how TEM-derived microstructural information can be systematically incorporated into hardening models. More broadly, it outlines a pathway toward microstructure-informed prediction of mechanical properties and supports the goal of establishing science-based tools for evaluating structural materials in extreme environments [10].

Lin, Yan-Ru [ORNL] (ORCID:0000000339991473)↗
DOE OSTI2021

A rigorous physics-based enhanced parameter estimation (EPE) methodology for calibration of building energy simulations

Buildings rarely perform as designed/simulated and there are numerous tangible benefits if this gap is reconciled. A new scientifically rigorous yet pragmatic methodology for calibrating building energy simulations - called Enhanced Parameter Estimation (EPE) - is proposed that allows physically relevant parameter estimation rather than a blind force-fit to energy use data. Starting with a rapidly created simulation model, calibration is performed in two stages: (a) building shell calibration with the HVAC system replaced by an ideal system that meets the loads (b) HVAC system calibration with the building shell and all internal loads replaced by a box with only process loads. In the first stage, EPE identifies a small number of high-level heat flows in the energy balance, calculates them with specifically tailored individual driving functions, introduces physically significant parameters to best accomplish energy balance, and, estimates the parameters and their uncertainty bounds. Calibration is thus done with corrective heat flows without any arbitrary tuning of input parameters. Calibration accuracy is enhanced by machine learning of the residual errors. The EPE methodology is demonstrated by means of: a synthetic building and an actual 75,000 sq. ft. building in Pennsylvania. Future work needed for widespread application is discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

HD 63021: Chromospheric Activity and Mass Transfer in a Close Binary

Prompted by X-ray detections from multiple surveys, we investigated the A-type star HD 63021 and found that it is a double-lined spectroscopic binary with highly variable emission associated with the primary star. Analysis of our multiepoch spectroscopic observations, the majority of which were carried out on small-aperture telescopes, indicates a very short orbital period of just 2.9 days and a mass ratio M {sub 2}/M {sub 1} of 0.23. The A1 V star is a slow rotator, with a rotational speed of ∼34 km s{sup −1}. Assuming that its mass is 2.3 M {sub ⊙}, the present-day secondary is an evolved star of ∼0.5 M {sub ⊙} that nearly fills its Roche lobe. This secondary star rotates comparatively rapidly at ∼44 km s{sup −1}, and we see evidence that it is chromospherically active. Analysis of a photometric light curve from TESS reveals two strong periods, one at the orbital period for the system and another at half the orbital period. These findings suggest that HD 63021 is a close binary system undergoing mass transfer from the secondary star onto the primary star—in all ways like an Algol eclipsing binary system, except without the eclipse. We discuss the system’s mass transfer, which is not steady but seems to run in fits and bursts, and infer the system’s basic physical properties from an orbital parameter study, the Roche lobe geometry, and its extant X-ray emission.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Transport coefficients for magnetic-field evolution in inviscid magnetohydrodynamics

The magnetized resistivity and electrothermal tensors when substituted into the induction equation lead to electrothermal magnetic field generation, resistive magnetic diffusion, and magnetic field advection due to resistivity gradients, temperature gradients and currents. Here, the advection terms driven by temperature gradient and current have cross-field components (perpendicular to both the magnetic field and the driving term) that depend on significantly modified versions of Braginskii’s transport coefficients. The improved fits to Braginskii’s coefficients given by Epperlein and Haines and Ji and Held give physically incorrect results for cross-field advection at small Hall parameters (product of cyclotron frequency and collision time). The errors in Epperlein and Haines’ fits are particularly severe, giving increasing advection velocities below a Hall parameter of one when they should decrease linearly to zero. Epperlein and Haines’ fits can also give erroneous advection terms due to variations in effective atomic number. The only serious error in Braginskii’s fits is an overestimate in advection due to perpendicular resistivity. New fits for the cross-field advection terms are obtained from a direct numerical solution of the Fokker-Planck equation and Ji and Held’s higher order expansion approach that are continuous functions of the effective atomic number.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Atomistic Assessment of Solute-Solute Interactions during Grain Boundary Segregation

Grain boundary solute segregation is becoming increasingly common as a means of stabilizing nanocrystalline alloys. Thermodynamic models for grain boundary segregation have recently revealed the need for spectral information, i.e., the full distribution of environments available at the grain boundary during segregation, in order to capture the essential physics of the problem for complex systems like nanocrystalline materials. However, there has been only one proposed method of extending spectral segregation models beyond the dilute limit, and it is based on simple, fitted parameters that are not atomistically informed. In this work, we present a physically motived atomistic method to measure the full distribution of solute-solute interaction energies at the grain boundaries in a polycrystalline environment. We then cast the results into a simple thermodynamic model, analyze the Al(Mg) system as a case study, and demonstrate strong agreement with physically rigorous hybrid Monte Carlo/molecular statics simulations. This approach provides a means of rapidly measuring key interactions for non-dilute grain boundary segregation for any system with an interatomic potential.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Interface diagnostics platform for thin-film solid-state batteries

Understanding the impedances of battery materials and their interfaces remains a major challenge, usually addressed by electrochemical impedance spectroscopy (EIS) where frequency-dependent complex impedance of full battery cells is measured and then modeled by a network of connected electrical elements. As conventionally applied, this approach produces ambiguity in that (1) multiple different network configurations may fit the data convincingly and (2) the method offers no direct association of the electrical elements with physical features of the battery. Here we present a new methodology that resolves both sources of ambiguity, enabled by expanding the experimental scope to directly inform the configuration of elements and their parameters in the network model. We demonstrate this methodology using thin film fabrication of solid state battery devices patterned by shadow masked sputter deposition, so that diagnostic devices corresponding to individual interface and material components can be fabricated simultaneously with full cell batteries. EIS models for the diagnostic devices can then be connected to form full cell networks whose topology matches the well-known physical configuration of the battery. When connected in this way, the full network model – made from connecting the diagnostic device EIS models – fits the full cell EIS data. For the case of a thin film solid state battery composed of amorphous silicon anode, lithium phosphorus oxynitride (LiPON) solid electrolyte, and lithium vanadium oxide (Li x V 2 O 5 ) cathode, we show that the approach allows us to identify ionic impedance/conductivity of the cathode/electrolyte as a limiting impedance and the anode/electrolyte interface cycling instability as a primary degradation factor.

25 ENERGY STORAGE↗