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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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DOE OSTI2014

Update on a short-distance $D^0$-meson mixing calculation with $N_f=2+1$ flavors

We present an update on our calculation of the short-distance $D^0$-meson mixing hadronic matrix elements. The analysis is performed on the MILC collaboration's $N_f=2+1$ asqtad configurations. We use asqtad light valence quarks and the Sheikoleslami-Wohlert action with the Fermilab interpretation for the valence charm quark. SU(3), partially quenched, rooted, staggered heavy-meson chiral perturbation theory is used to extrapolate to the chiral-continuum limit. Systematic errors arising from the chiral-continuum extrapolation, heavy-quark discretization, and quark-mass uncertainties are folded into the statistical errors from the chiral-continuum fits with methods of Bayesian inference. A preliminary error budget for all five operators is presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2017

Building America Case Study: Indoor Heat Pump Water Heaters During Summer in a Hot-Dry Climate, Redding, California

Heat pump water heaters offer a significant opportunity to improve water heating performance for the over 40% of U.S. households that heat domestic hot water using electric resistance storage water heaters. Numerous field studies have also been completed documenting performance in a variety of climates and applications. More recent evaluation efforts have focused attention on the performance of May through September 2014, with ongoing winter monitoring being sponsored by California utility partners. Summer results show favorable system performance with extrapolated annual water heating savings of 1,466 to 2,300 kWh per year, based on the observed hot water loads. Additional summer space cooling benefits savings of 121 to 135 kWh per year were projected, further increasing the water heating savings by 5-9%. Given the project schedule for 2014 completion, no heating season impacts were able to be monitored. May through September 2014, with ongoing winter monitoring being sponsored by California utility partners. Summer results show favorable system performance with extrapolated annual water heating savings of 1,466 to 2,300 kWh per year, based on the observed hot water loads. Additional summer space cooling benefits savings of 121 to 135 kWh per year were projected, further increasing the water heating savings by 5-9%. Given the project schedule for 2014 completion, no heating season impacts were able to be monitored.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2017

Extracting Neutrino Oscillation Parameters using a Simultaneous Fit of the $\nu_{e}$ Appearance and $\nu_{\mu}$ Disappearance Data in the NOvA Experiment

NOvA is a two detector, long baseline neutrino oscillation experiment designed to study $\nu_{e}$ ($\bar{\nu}_{e}$) appearance and $\nu_{\mu}$ ($\bar{\nu}_{\mu}$) disappearance in a $\nu_{\mu}$ ($\bar{\nu}_{\mu}$) beam produced at Fermilab. The near detector (ND) is located 100 meters underground at a distance of 1 km from the target whereas the far detector (FD) is located on the surface, 810 km away from the beam source in Ash River, MN. The ND is used to measure the beam before oscillations and the FD measures the oscillated spectrum. The ND and the FD are functionally identical detectors and the ND spectra are extrapolated to the FD to predict the signal and background spectra expected in the FD. The extrapolation and data fitting techniques developed for these analyses within NOvA are presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2019

Microscopic calculations of nuclear level densities with the Lanczos method

A new method for computing the density of states in nuclei making use of an extrapolated form of the tri-diagonal matrix obtained from the Lanczos method is presented. It will be shown that the global, average properties of the entire Lanczos matrix can be predicted from just four Lanczos iterations. The extrapolated Lanczos matrix (ELM) approach provides far an accurate computation of the density of states described within the configuration space, which, in many cases, is sufficient to accurately calculate the density of states at, or near, the neutron separation energy. Comparisons between theory and experiment are shown for 57 Fe. In addition, we show results for the J-dependence of the level density in 57 Fe.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

We Just Want to Pump…You Up! Forecasting Grid-Connected Heat Pump Water Heater Energy Savings and Load Shifting Potential for the Southeast U.S.

Heat pump water heaters (HPWH) can achieve energy savings of 60-70% compared to conventional electric-resistance water heaters. However, even with a favorable simple payback period within the typical product lifetime, HPWHs make up only 1% of all electric water heaters sold in the residential sector. Market adoption is challenged, in part, by the lack of effective energy efficiency programs in the U.S. region with the greatest amount of residential electric water heaters. The recent integration of connected functionality into HPWHs offers the capability to shift load without undue negative impact on customers. This capability can provide value to utilities with peak load constraints, motivating them to promote HPWHs for the first time. This paper will present a methodology to forecast connected HPWH energy use and extrapolate load shifting potential for the Southeast U.S. region. The methodology leverages data from a robust connected HPWH field study conducted in the Pacific Northwest. A coefficient of performance (COP) relationship for HPWHs was developed using ambient and inlet water temperature data to forecast HPWH performance on a month-by-month basis. Using the forecasted COP, HPWH operating hours were calculated for meeting load, which serve as the proxy to extrapolate load shifting potential from the Pacific Northwest to the Southeast. As a use case, analytical results are presented for a specific utility. This methodology, in combination with market analysis, facilitates the development of customized energy savings and load shifting forecasts to understand the potential impacts of launching connected HPWH programs in specific utility service territories.

heat pump water heater, energy savings, load shift↗

DOE OSTI2022

Automated quantum error mitigation based on probabilistic error reduction

Current quantum computers suffer from a level of noise that prohibits extracting useful results directly from longer computations. The figure of merit in many near-term quantum algorithms is an expectation value measured at the end of the computation, which experiences a bias in the presence of hardware noise. A systematic way to remove such bias is probabilistic error cancellation (PEC). PEC requires a full characterization of the noise and introduces a sampling overhead that increases exponentially with circuit depth, prohibiting high-depth circuits at realistic noise levels. Probabilistic error reduction (PER) is a related quantum error mitigation method that systematically reduces the sampling overhead at the cost of reintroducing bias. In combination with zero-noise extrapolation, PER can yield expectation values with an accuracy comparable to PEC.Noise reduction through PER is broadly applicable to near-term algorithms, and the automated implementation of PER is thus desirable for facilitating its widespread use. To this end, we present an automated quantum error mitigation software framework that includes noise tomography and application of PER to user-specified circuits. We provide a multi-platform Python package that implements a recently developed Pauli noise tomography (PNT) technique for learning a sparse Pauli noise model and exploits a Pauli noise scaling method to carry out PER.We also provide software tools that leverage a previously developed toolchain, employing PyGSTi for gate set tomography and providing a functionality to use the software Mitiq for PER and zero-noise extrapolation to obtain error-mitigated expectation values on a user-defined circuit.

McDonough, Benjamin↗

DOE OSTI2015

Neutron structure with (un)polarized deuterons and forward spectator tagging at the Electron-Ion Collider

An Electron-Ion Collider (EIC) would enable novel measurements of neutron structure through deep-inelastic electron-deuteron scattering with coincidence tagging of the forward-moving spectator proton. The free neutron structure functions can be obtained by extrapolating the measured recoil momentum distributions to the on-shell point. Such measurements provide essential information for the flavor separation of the nucleon parton densities, the nucleon spin decomposition, and precision studies of QCD evolution in the flavor-singlet and nonsinglet sectors. The EMC effect in light nuclei can be elucidated by measuring the recoil momentum dependence of the nuclear modification. A Jefferson Lab LDRD project has supported a study of neutron structure measurements with spectator tagging at an EIC, with Monte Carlo simulation of the physical processes and the accelerator and detector requirements. We present results of a model-independent extraction of the free neutron structure function $F_2^n$, as well as the spin structure function $g_1^n$, through on-shell extrapolation in the spectator momentum.

Park, Kijun↗

DOE OSTI2023

Machine-Learning-aided Approach for Predicting the Thermal Expansion Behaviors in Advanced Test Reactor Capsules (NURETH-20 full paper)

Instrumented experiments at test reactors are essential to deploying new advanced reactor systems. Designing new experiments and generating data on specific conditions require both time and cost investment. A high-fidelity model of the experiment environment can be created using finite element analysis software to support the actual experiments, but computation time is still a concern in applying outcomes to real-time usage (e.g., a digital twin). This research proposes a machine-learning-aided approach to temperature and displacement predictions, based on the thickness of the outer gas gap on the experimental capsule used for the in-pile demonstration of a novel thermal conductivity probe in the Advanced Test Reactor. The capsule consisted of U10Zr fuel, a rodlet, sodium, and inner and outer capsules. There were gas gaps between the fuel and rodlet and between the inner and outer capsule. The learning data consisted of an experimental capsule’s radial distributions of temperature and displacement, as obtained from Abaqus and the physical features. For the first step, temperature was predicted using three positional parameters. Then the displacement was predicted using six different positional parameters. Each physical feature was normalized to be both nondimensional and standardized. The temperature and displacement predictions showed good agreement in all cases involving interpolation and extrapolation. Also, data similarity enhancement increased the similarity between training and target data increasing the predictive accuracy of machine-learning models. In some cases of extrapolation, the accuracy of the machine-learning model showed limited performance, but still data similarity enhancement improved the accuracy.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Industry Facing PV Degradation Prediction Tool and Database to Enable a 50-Year Life Module

The Photovoltaic (PV) industry constantly aims for lower costs, higher-efficiency cells, and improved module designs. These trends lead to using new materials, designs, and manufacturing processes, resulting in a continually changing technological landscape. These changes can potentially introduce new, unknown degradation mechanisms and failure modes that are difficult to diagnose, analyze, test, and model. This introduces uncertainty into the expected lifetime of PV modules of 25 years. Furthermore, research efforts aim for up to 50 years of service life while keeping performance degradation at a minimum for decades of outdoor weathering - putting additional pressure on improving the accuracy of long-term durability and reliability assessments. Here there is a need to organize the existing degradation data into an accessible format and to provide industry relevant tools for extrapolation from laboratory to field conditions. Because the core of this type of analysis involves calculations that are complicated but ubiquitous for many degradation processes, an enhanced predictive modeling framework will facilitate the analysis to help researchers keep up with the rapid pace of technological changes. In this work, we present an online tool that can be used to search for and analyze degradation information and extrapolate PV module performance and durability to field exposure. A graphical user interface will aid in the understanding of the results. The prediction tool will be built modular and published as open source, enabling users to expand on the existing framework. We use an integration pipeline approach that allows us to leverage weather data from the National Solar Radiation Database to perform geospatial degradation analysis in the US and worldwide. Our repository will contain various degradation models and material parameters suitable for the reliability and durability assessment of materials and components deployed outdoors. We hope to become a repository that can be used for weathering and degradation analysis for various applications beyond the PV industry.

degradation↗

DOE OSTI2022

Application of the Continuum Damage Mechanics Wilshire-Cano-Stewart (WCS) Model

In this study, the applications of the continuum damage mechanics-based Wilshire-Cano-Stewart (WCS) model are explored to predict rupture time, minimum-creep-strain-rate (MCSR), damage, damage evolution, and creep deformation. Increase knowledge in manufacturing methods has pushed the limit of material science and the development of new materials. Conventional testing is required to qualify materials against creep which according to the ASME B&PV III code, 10,000+ hours of experiments are necessary for each heat before materials are put into service. This process is costly and not feasible for new materials. As an alternative, models have been employed to predict creep behaviors and reduce the amount of time necessary for material qualification. Many models have been developed to predict distinct creep behaviors and the question of which model is best remains. Amongst current models, the WCS model has emerge with the ability to predict multiple behaviors using an explicit analytical approach with the ability to predict long-term creep. In this study the novel continuum damage mechanics WCS is employed in multiple applications. The goals of the study are (a) to discuss and determine the framework of the WCS model and validated it mathematically and using parametric simulations, (b) applied the model to accelerated creep data to show the capabilities of the model with non-conventional data, and (c) applied a novel numerical method, the datum temperature method (DTM) to show the model extrapolations and interpolations capabilities with limited and reduce data sets. To accomplish these goals, data is gathered for alloy P91 and Inconel 718 to develop and post-audit validate the model. The benefits of using the WCS model is that it provides an explicit stress and temperature dependency ideal for extrapolations, the coupled equations are suitable for finite element analysis (FEA) implementation, and it follows an explicit calibration approach. The model also proves that it can be applied to accelerated testing data and using the DTM.

Cano, Jaime A↗

DOE OSTI2023

Machine-Learning Assisted Identification of Battery Life Models

Predictive battery life models are commonly utilized to extrapolate degradation trends observed during accelerated aging tests for simulation of degradation in real-world applications. Thus, fitting accelerated aging data as accurately as possible and with low uncertainty is crucial for making believable projections of battery lifetime, but it is challenging to identify algebraic expressions that accurately fit multivariate degradation trends. A review of models published in literature reveal some common expressions for fitting calendar aging data, which is only dependent on temperature and state-of-charge, but no consistency across many models for fitting cycle aging data, indicating the need for a statistically rigorous data driven approach for developing empirical models. This talk will describe a machine-learning assisted method for identification of predictive battery life models utilizing bilevel optimization and symbolic regression. Bilevel optimization with cross-validation is used to statistically determine cell- and stress-dependent model parameters, while symbolic regression identifies both linear and multiplicative candidate expressions to predict stress-dependent degradation rates by selecting low-order subsets of features from a generated feature library. Because model expressions are identified empirically, it is crucial to ensure resulting models behave according to physical expectations, so the stability of models for interpolation or extrapolation is interrogated qualitatively through simulation and quantitatively through cross-validation and uncertainty quantification via bootstrap resampling. This model identification approach substantially improves upon models identified purely using expert judgement in terms of both accuracy and uncertainty. Model simulation and validation is then conducted by deriving a state-equation form of the predictive model, enabling simulation of battery aging under dynamic stresses. This enables validation of the predictive battery model on lab-based tests with varying conditions or on drive-cycle or application-cycle testing protocols. Parameter uncertainty can be carried forward into model simulation, giving lifetime estimates and confidence windows for cell- or system-level lifetime. The financial impact of battery model uncertainty can be estimated by incorporating uncertainty into a technoeconomic model.

battery↗

DOE OSTI2024

PVDeg: Development of a Streamlined Tool for PV Degradation Modeling

The photovoltaic (PV) industry constantly aims for lower costs, higher-efficiency cells, and improved module designs. These trends lead to using new materials, designs, and manufacturing processes, resulting in a continually changing technological landscape. These changes can potentially introduce new, unknown degradation mechanisms and failure modes that are difficult to diagnose, analyze, test, and model. This introduces uncertainty into the expected lifetime of PV modules of 25 to 50 years. research efforts aim to achieve this while keeping performance degradation at a minimum for decades. This puts considerable pressure on improving the accuracy of long-term durability and reliability assessments. There is a need to organize the existing degradation data into an accessible format and to provide industry relevant tools for extrapolation from laboratory to field conditions. Because the core of this type of analysis involves calculations that are complicated but ubiquitous for many degradation processes, an enhanced predictive modeling framework will facilitate the analysis to help researchers keep up with the rapid pace of technological changes. In this work, we present an online tool that can be used to search for and analyze degradation information and extrapolate PV module performance and durability to field exposure. The tool will simplify many of the routine computational operations that are common to many degradation studies. The prediction tool will be built modular and published as open source, enabling users to expand on the existing framework. This repository will contain various degradation models and material parameters suitable for the reliability and durability assessment of materials and components deployed outdoors.

degradation↗

DOE OSTI2025

FY25 status report on the addition of candidate materials in Class B Code Case

This report provides the time-dependent allowable stress calculation strategy leveraging the limited creep rupture tests data generated to support the allowable stress for 100,000 hours in American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code (BPVC), Section II, Part D. A variable confidence index procedure to extrapolate material properties to support 500,000 hours design life is discussed. Time-dependent allowable stresses for Class B component design and analysis are presented for Grade 1 and Grade 2 of Alloy 625. The presented data extrapolation and allowable stress calculation method will support new material addition using limited creep rupture data in the new ASME Boiler and BPVC, Section III, Division 5, Class B rules.

Part D↗

DOE OSTI2026

Measurements of three-flavor neutrino oscillations from a PISCES two-detector fit to the NOvA Experiment data

NOvA is a long-baseline neutrino oscillation experiment with two functionally identical detectors: a Near Detector (ND) at Fermilab, placed 1 km from the neutrino source, and a Far Detector (FD) located 810 km away from the ND in Minnesota. NOvA s primary physics goals are to measure the neutrino oscillation parameters $\theta_{23}$ and $\Delta m^2_{32}$ with high precision, determine the neutrino mass hierarchy, and constrain the value of $\delta_{CP}$, primarily via the study of muon neutrino to electron neutrino oscillation. Extracting values for oscillation parameters from fits to data usually relies on treating systematic uncertainties as nuisance parameters, a strategy that suffers from poor scalability as the number of uncertainties becomes larger. This work introduces PISCES (Parameter Inference with Systematic Covariance and Exact Statistics), a novel method that circumvents this scalability problem by encoding systematic uncertainties into a covariance matrix. PISCES utilizes a nested minimization in which optimal systematic pulls are first computed using the covariance matrix in an inner minimization step, then the oscillation parameters are profiled over in the outer minimization. PISCES also uses a Poisson Likelihood term, making it ideal for the inclusion of low-statistic samples in the fits. PISCES is a flexible framework that also supports complex fits, such as a joint Near and Far detector fit. In the standard NOvA analysis, oscillation parameters are extracted using an extrapolation technique in which the ND data indirectly constrain the FD prediction via a ratio method. PISCES, on the other hand, enables a simultaneous ND+FD fit, allowing the high-statistics ND data to directly constrain systematic uncertainties across all samples. This thesis presents the full PISCES joint ND+FD fit for the NOvA three-flavor analysis, details its implementation, and evaluates its performance through extensive robustness tests and fake data studies. It also provides a comparison between the PISCES joint ND+FD results and the standard NOvA extrapolation method using the full NOvA 10-year data set. The results demonstrate that PISCES can successfully fit NOvA data while incorporating the constraints from the ND detectors consistently, using physically motivated systematic uncertainties to account for data/MC discrepancies.

Rajaoalisoa, Miriama [Cincinnati U.]↗

DOE OSTI2024

Dynamic Scaling Analysis of Accelerated Irradiation Testing on Additive Manufacturing Materials by Positron Annihilation

The timely applications of Additive Manufacturing (AM) materials in nuclear environments require accelerated irradiation tests, mainly ion irradiation to enable rapid prototyping. Low dose ion irradiation would cause sub-nanostructure changes by generation of lattice defects, vacancies, vacancy clusters and voids and void swelling caused by cellular dislocations. Positron Annihilation Lifetime (PAL), a novel technology, sensitive towards sub-nanostructure morphology with high accuracy (about 10-7 vacancy per atom), supported by Transition Electron Microscope (TEM) would be applied to identify the type and total size of the defects. The subsequent PAL measurements and TEM surface studies would be followed by PAL analysis that includes sophisticated trapping model. The PAS results would become an input to dynamic scaling analysis (that predicts radiation effects from low dose studies for high dose effects), which incorporate mean-field theory model. The final effect is an in-depth understanding of the microstructure evolution of AM materials under ion irradiation which can be extrapolated to the studies of neutron irradiation, since ion-irradiation takes less time and do not cause the irradiation hazard. The working hypothesis is that PAL technology, that have excellent sensitivity to low-defect concentration would help to identify ion-induced material damage on the atomic and nano-scale level, which then could be extrapolated to understand the neutron damage better.

accelerated irradiation testing↗

DOE OSTI2015

A Measurement and Analysis of Buildup Region Dose for Open Field Photon Beams (Cobalt-60 through 24 MV)

The central axis depth dose in the build-up region (surface to d{sub max}) of single open field photon beams (cobalt-60 through 24 MV) has been measured utilizing parallel plate and extrapolation chamber methodology. These data were used to derive, for a prescription dose of 100 cGy, values of surface dose, the maximum value of dose along the central axis (D{sub max}) and the depth (nearest the surface) at which 90% of the prescription dose occurs (d{sub 90}). For both single and parallel opposed pair (POP) open field configurations, data are presented at field sizes of 5 × 5, 15 × 15 and 25 × 25 cm{sup 2} for prescription depths of 10, 15 and 20 cm (midplane for POP). For the treatment machines, field sizes, and prescription depths studied, it is possible to conclude that: for single open field irradiation, surface dose values (as a percentage of the prescription dose) can be either low (<10%) or comparable to the prescription dose itself; for POP open fields, surface dose values are relatively independent of photon energy and midplane depth, and range between 30% and 70% of prescription dose, being principally dependent on field size; the depth of the initial 90 cGy point for a prescription dose of 100 cGy, d{sub 90}, was larger for POP fields. For either single or POP open field treatments, d{sub 90} was always less than 22 mm, while for 6 MV or less, values of d{sub 90} were less than 4 mm; D{sub max} values can be very large (e.g., above 300 cGy) for certain treatment situations and are reduced significantly for POP treatments; for open field POP treatments, the percent reduction in D{sub max} with each increment in beam energy above 10 MV is reduced over that seen at 10 MV or less and, possibly, this further reduction may be clinically insignificant; for open field POP treatments, changes in surface dose, d{sub 90} and D{sub max} with beam energy above 10 MV do not suggest, with regard to these specific build-up curve parameters, any obvious advantage for treatment with beam energies greater than 10 MV for the specific machines and situations studied.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2018

Analytical Energy Dispersive X-Ray Fluorescence Measurements with a Scanty Amounts of Plant and Soil Materials

Elemental evaluations in scanty powdered material have been made using energy dispersive X-ray fluorescence (EDXRF) measurements, for which formulations along with specific procedure for sample target preparation have been developed. Fractional amount evaluation involves an itinerary of steps; (i) collection of elemental characteristic X-ray counts in EDXRF spectra recorded with different weights of material, (ii) search for linearity between X-ray counts and material weights, (iii) calculation of elemental fractions from the linear fit, and (iv) again linear fitting of calculated fractions with sample weights and its extrapolation to zero weight. Thus, elemental fractions at zero weight are free from material self absorption effects for incident and emitted photons. The analytical procedure after its verification with known synthetic samples of macro-nutrients, potassium and calcium, was used for wheat plant/ soil samples obtained from a pot experiment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Study of Magnetic Entropy Change in Nd{sub 0.67}Ba{sub 0.33}Mn{sub 0.98}Fe{sub 0.02}O{sub 3} by Means of Theoretical Models

Magnetic entropy change (−ΔS{sub M}) of Nd{sub 0.67} Ba{sub 0.33}Mn{sub 0.98}Fe{sub 0.02}O{sub 3} perovskite have been analyzed by means of theoretical models. An excellent agreement has been found between the (ΔS{sub M}) values estimated by Landau theory and those obtained using the classical Maxwell relation. In order to estimate the spontaneous magnetization M{sub spont}(T), we used the mean-field theory to analyses the (ΔS{sub M}) vs. M{sup 2} data. The obtained M{sub spont}(T) values are in good agreement with those found from the classical extrapolation from the Arrott plots(H/M vs. M{sup 2}), confirming that the magnetic entropy is a valid approach to estimate the spontaneous magnetization in our system. At a relatively low magnetic field, a phenomenological model has been used to estimate the values of the magnetic entropy change. The results are in good agreement with those obtained from the experimental data using Maxwell relation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗