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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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Data driven investigation to understand the influence of total solids on biological biogas upgrading

In situ biogas upgrading achieves CO 2 conversion to CH 4 via hydrogenotrophic methanogenesis; however, gas-liquid mass transfer constraints limit the upgrading performance. Recognizing that optimization studies often underrepresent the effects of total solids (TS) and organic loading rate (OLR), this study undertook a holistic, statistics driven assessment of operating conditions for in situ H 2 assisted biogas upgrading, centering the analysis on TS and OLR. A dataset of 31 studies was compiled and comprised 99 observations. A rigorous analytical framework was employed, combining data standardization, fixed- and random-effects (REML) weighted regressions with cluster-robust errors, stratified analyses, and machine learning. Mixed-effects meta regression indicated that TS was the main factor explaining differences of methane fraction (CH 4 %) when considering the between studies heterogeneity. Focusing on a near-stoichiometric subset (H 2 /CO 2 ≈ 4:1), TS remained significant. Stratified results showed a stronger negative relationship between TS and CH 4 % in UASB reactors than in CSTRs, with a negative effect under mesophilic conditions and no significant effect under thermophilic conditions. A Random Forest model corroborated the statistical findings, consistently ranking H 2 /CO 2 ratio, OLR, TS, and hydrogen injection rate (HIR) as the most influential predictors. These findings delineate trends across increasing TS levels, particularly between 1% and 10%, and provide preliminary insights for TS above 15% in in situ biogas upgrading. They further provide insights for the influence of TS by reactor type and temperature, thereby advancing the evidence base for implementing biological CO 2 conversion to CH 4 in practice.

In situ biogas upgrading↗
DOE OSTI2014

Transients from initial conditions based on Lagrangian perturbation theory in N-body simulations II: the effect of the transverse mode

We study the initial conditions for cosmological N-body simulations for precision cosmology. In general, Zel'dovich approximation has been applied for the initial conditions of N-body simulations for a long time. These initial conditions provide incorrect higher-order growth. These error caused by setting up the initial conditions by perturbation theory is called transients. We investigated the impact of transient on non-Gaussianity of density field by performing cosmological N-body simulations with initial conditions based on first-, second-, and third-order Lagrangian perturbation theory in previous paper. In this paper, we evaluates the effect of the transverse mode in the third-order Lagrangian perturbation theory for several statistical quantities such as power spectrum and non-Gaussianty. Then we clarified that the effect of the transverse mode in the third-order Lagrangian perturbation theory is quite small.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Collaborative Project: The problem of bias in defining uncertainty in computationally enabled strategies for data-driven climate model development

The objective of the project is to develop strategies for better representing scientific sensibilities within statistical measures of model skill that then can be used within a Bayesian statistical framework for data-driven climate model development and improved measures of model scientific uncertainty. One of the thorny issues in model evaluation is quantifying the effect of biases on climate projections. While any bias is not desirable, only those biases that affect feedbacks affect scatter in climate projections. The effort at the University of Texas is to analyze previously calculated ensembles of CAM3.1 with perturbed parameters to discover how biases affect projections of global warming. The hypothesis is that compensating errors in the control model can be identified by their effect on a combination of processes and that developing metrics that are sensitive to dependencies among state variables would provide a way to select version of climate models that may reduce scatter in climate projections. Gabriel Huerta was responsible for leading the effort to develop statistical methods for evaluating these field dependencies. The UT effort will incorporate these developments into MECS, which is a set of python scripts being developed at the University of Texas for managing the workflow associated with data-driven climate model development over HPC resources. We regularly consult with Peter Gleckler (LLNL), Mark Taylor (SNL), and Rich Neale (NCAR) to provide additional insight into issues concerning CAM5 model development and multi-model evaluation of the AR5 archive. This grant was originally assigned to Indiana University where both the PI (Huerta) and the Postdoc (Nosedal) worked on the project from March 1, 2012-March 31, 2013.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Novel Ultrabright and Air-Stable Photocathodes Discovered from Machine Learning and Density Functional Theory Driven Screening

The high brightness, low emittance electron beams achieved in modern X-ray free-electron lasers (XFELs) have enabled powerful X-ray imaging tools, allowing molecular systems to be imaged at picosecond time scales and sub-nanometer length scales. One of the most promising directions for increasing the brightness of XFELs is through the development of novel photocathode materials. Whereas past efforts aimed at discovering photocathode materials have typically employed trial-and-error-based iterative approaches, this work represents the first data-driven screening for high brightness photocathode materials. Through screening over 74 000 semiconducting materials, a vast photocathode dataset is generated, resulting in statistically meaningful insights into the nature of high brightness photocathode materials. This screening results in a diverse list of photocathode materials that exhibit intrinsic emittances that are up to 4x lower than currently used photocathodes. In a second effort, multiobjective screening is employed to identify the family of M 2 O (M = Na, K, Rb) that exhibits photoemission properties that are comparable to the current state-of-the-art photocathode materials, but with superior air stability. This family represents perhaps the first intrinsically bright, visible light photocathode materials that are resistant to reactions with oxygen, allowing for their transport and storage in dry air environments.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A hybrid point-particle force model that combines physical and data-driven approaches

Here this study improves upon the physics-based pairwise interaction extended point-particle (PIEP) model. The PIEP model leverages our physical understanding to predict fluid mediated interactions between solid particles. By considering the relative location of neighboring particles, the PIEP model is able to provide better predictions than existing drag models. While the current physical PIEP model is a powerful tool, its assumptions lead to increased error in flows with higher particle volume fractions. To reduce this error, a regression algorithm makes direct use of the results of direct numerical simulations (DNS) of an array of monodisperse solid particles subjected to uniform ambient flow at varying Reynolds numbers. The resulting statistical model and the physical PIEP model are superimposed to construct a hybrid, physics-based data-driven PIEP model. It must be noted that the performance of a pure data-driven approach without the model-form provided by the physical PIEP model is substantially inferior. The hybrid model's predictive capabilities are analyzed using additional DNS data that was not part of training the data-driven model. In every case tested, the hybrid models resulting from the regression were capable of (1) improving upon the physical PIEP model's prediction and (2) recovering underlying relevant physics from the DNS data. As the particle volume fraction increases, the physical PIEP model loses the ability to approximate the forces experienced by the particles, but the statistical model continues to produce accurate approximations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

New axion and hidden photon constraints from a solar data global fit

We present a new statistical analysis that combines helioseismology (sound speed, surface helium and convective radius) and solar neutrino observations (the {sup 8}B and {sup 7}Be fluxes) to place upper limits to the properties of non standard weakly interacting particles. Our analysis includes theoretical and observational errors, accounts for tensions between input parameters of solar models and can be easily extended to include other observational constraints. We present two applications to test the method: the well studied case of axions and axion-like particles and the more novel case of low mass hidden photons. For axions we obtain an upper limit at 3σ for the axion-photon coupling constant of g{sub aγ} < 4.1 · 10{sup −10} GeV{sup −1}. For hidden photons we obtain the most restrictive upper limit available accross a wide range of masses for the product of the kinetic mixing and mass of χ m < 1.8 ⋅ 10{sup −12} eV at 3σ. Both cases improve the previous solar constraints based on the Standard Solar Models showing the power of using a global statistical approach.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

The picasso map-making code: application to a simulation of the QUIJOTE northern sky survey

ABSTRACT Map-making is an important step for the data analysis of cosmic microwave background (CMB) experiments. It consists of converting the data, which are typically a long, complex, and noisy collection of measurements, into a map, which is an image of the observed sky. We present in this paper a new map-making code named picasso (Polarization and Intensity CArtographer for Scanned Sky Observations), which was implemented to construct intensity and polarization maps from the Multi Frequency Instrument (MFI) of the QUIJOTE (Q-U-I Joint TEnerife) CMB polarization experiment. picasso is based on the destriping algorithm, and is suited to address specific issues of ground-based microwave observations, with a technique that allows the fit of a template function in the time domain, during the map-making step. This paper describes the picasso code, validating it with simulations and assessing its performance. For this purpose, we produced realistic simulations of the QUIJOTE-MFI survey of the northern sky (approximately ∼20 000 deg2), and analysed the reconstructed maps with picasso, using real and harmonic space statistics. We show that, for this sky area, picasso is able to reconstruct, with high fidelity, the injected signal, recovering all the scales with ℓ > 10 in TT, EE, and BB. The signal error is better than 0.001 per cent at 20 < ℓ < 200. Finally, we validated some of the methods that will be applied to the real wide-survey data, like the detection of the CMB anisotropies via cross-correlation analyses. Despite that the implementation of picasso is specific for QUIJOTE-MFI data, it could be adapted to other experiments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Ground state energy and magnetization curve of a frustrated magnetic system from real-time evolution on a digital quantum processor

Models of interacting many-body quantum systems that may realize new exotic phases of matter, notably quantum spin liquids, are challenging to study using even state-of-the-art classical methods such as tensor network simulations. Quantum computing provides a promising route for overcoming these difficulties to find ground states, dynamics, and more. In this paper, we argue that recently developed hybrid quantum-classical algorithms based on real-time evolution are promising methods for solving a particularly important model in the search for spin liquids, the antiferromagnetic Heisenberg model on the two-dimensional kagome lattice. We show how to construct efficient quantum circuits to implement time evolution for the model and to evaluate key observables on the quantum computer, and we argue that the method has favorable scaling with increasing system size. We then restrict to a 12-spin star plaquette from the kagome lattice and a related 8-spin system, and we give an empirical demonstration on these small systems that the hybrid algorithms can efficiently find the ground state energy and the magnetization curve. For these demonstrations, we use four levels of approximation: exact state vectors, exact state vectors with statistical noise from sampling, noisy classical emulators, and (for the 8-spin system only) real quantum hardware, specifically the Quantinuum H1-1 processor; for the noisy simulations and hardware demonstration, we also employ error mitigation strategies based on the symmetries of the Hamiltonian. Our results strongly suggest that these hybrid algorithms present a promising direction for studying quantum spin liquids and more generally for resolving important unsolved problems in condensed matter theory and beyond.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Evidence for Universality in the Initial Planetesimal Mass Function

Planetesimals may form from the gravitational collapse of dense particle clumps initiated by the streaming instability. We use simulations of aerodynamically coupled gas–particle mixtures to investigate whether the properties of planetesimals formed in this way depend upon the sizes of the particles that participate in the instability. Based on three high-resolution simulations that span a range of dimensionless stopping times 6×10{sup −3}⩽τ⩽2, no statistically significant differences in the initial planetesimal mass function are found. The mass functions are fit by a power law, dN/dM{sub p}∝M{sub p}{sup −p}, with p = 1.5–1.7 and errors of Δp≈0.1. Comparing the particle density fields prior to collapse, we find that the high-wavenumber power spectra are similarly indistinguishable, though the large-scale geometry of structures induced via the streaming instability is significantly different between all three cases. We interpret the results as evidence for a near-universal slope to the mass function, arising from the small-scale structure of streaming-induced turbulence.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Data Collection and Analysis Challenges and Mitigation Strategies for Quantitative Human Factors Research Studies in Nuclear Power Plant Modernization

The United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) program Plant Modernization Pathway is conducting targeted research and development (R&D) to address aging and reliability concerns with the legacy instrumentation and control and related information systems of the U.S. LWR fleet. In this effort, the application of human factors engineering (HFE) provides an important role in ensuring new digital plant technologies enable broad innovation and business improvement with continued operational safety. Evaluation is a key activity in HFE, which often occurs iteratively through the system design lifecycle. While qualitative methods are important in collecting information of how users perform tasks through observations, quantitative methods are equally important in assessing system design based on performance. In collecting and analyzing this quantitative data, there are notable challenges in the nuclear HFE domain that may threaten the validity and reliability of the inferences made in these studies. Notable challenges include small sample size and limited resources, large error variance and small effect size, an “adding test features to losing degrees of freedom” dilemma, non-normal distribution, and heterogeneity of variance. The results in control room usability studies are often statistically non-significant, which makes it hard to interpret. This work discusses these challenges across different scientific viewpoints and provides real-world examples of these challenges in practice. Collectively, the objective of this work is to position these challenges to the larger data science community as a means of identifying future opportunities to address these issues.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Quantitative assessment of fitting errors associated with streak camera noise in Thomson scattering data analysis

Thomson scattering measurements in high energy density experiments are often recorded using optical streak cameras. In the low-signal regime, noise introduced by the streak camera can become an important and sometimes the dominant source of measurement uncertainty. In this paper, we present a formal method of accounting for the presence of streak camera noise in our measurements. We present a phenomenological description of the noise generation mechanisms and present a statistical model that may be used to construct the covariance matrix associated with a given measurement. This model is benchmarked against simulations of streak camera images. We demonstrate how this covariance may then be used to weight fitting of the data and provide quantitative assessments of the uncertainty in the fitting parameters determined by the best fit to the data and build confidence in the ability to make statistically significant measurements in the low-signal regime, where spatial correlations in the noise become apparent. These methods will have general applicability to other measurements made using optical streak cameras.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Evaluation of reconstruction errors and identification of artefacts for JET gamma and neutron tomography

The Joint European Torus (JET) neutron profile monitor ensures 2D coverage of the gamma and neutron emissive region that enables tomographic reconstruction. Due to the availability of only two projection angles and to the coarse sampling, tomographic inversion is a limited data set problem. Several techniques have been developed for tomographic reconstruction of the 2-D gamma and neutron emissivity on JET, but the problem of evaluating the errors associated with the reconstructed emissivity profile is still open. The reconstruction technique based on the maximum likelihood principle, that proved already to be a powerful tool for JET tomography, has been used to develop a method for the numerical evaluation of the statistical properties of the uncertainties in gamma and neutron emissivity reconstructions. The image covariance calculation takes into account the additional techniques introduced in the reconstruction process for tackling with the limited data set (projection resampling, smoothness regularization depending on magnetic field). The method has been validated by numerically simulations and applied to JET data. Different sources of artefacts that may significantly influence the quality of reconstructions and the accuracy of variance calculation have been identified.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2013

GPS Data Filtration Method for Drive Cycle Analysis Applications

When employing GPS data acquisition systems to capture vehicle drive-cycle information, a number of errors often appear in the raw data samples, such as sudden signal loss, extraneous or outlying data points, speed drifting, and signal white noise, all of which limit the quality of field data for use in downstream applications. Unaddressed, these errors significantly impact the reliability ofsource data and limit the effectiveness of traditional drive-cycle analysis approaches and vehicle simulation software. Without reliable speed and time information, the validity of derived metrics for drive cycles, such as acceleration, power, and distance, become questionable. This study explores some of the common sources of error present in raw onboard GPS data and presents a detailedfiltering process designed to correct for these issues. Test data from both light and medium/heavy duty applications are examined to illustrate the effectiveness of the proposed filtration process across the range of vehicle vocations. Graphical comparisons of raw and filtered cycles are presented, and statistical analyses are performed to determine the effects of the proposed filtration processon raw data. Finally, an evaluation of the overall benefits of data filtration on raw GPS data and present potential areas for continued research is presented.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Collaborative Project: The problem of bias in defining uncertainty in computationally enabled strategies for data-driven climate model development. Final Technical Report.

The objective of the project is to develop strategies for better representing scientific sensibilities within statistical measures of model skill that then can be used within a Bayesian statistical framework for data-driven climate model development and improved measures of model scientific uncertainty. One of the thorny issues in model evaluation is quantifying the effect of biases on climate projections. While any bias is not desirable, only those biases that affect feedbacks affect scatter in climate projections. The effort at the University of Texas is to analyze previously calculated ensembles of CAM3.1 with perturbed parameters to discover how biases affect projections of global warming. The hypothesis is that compensating errors in the control model can be identified by their effect on a combination of processes and that developing metrics that are sensitive to dependencies among state variables would provide a way to select version of climate models that may reduce scatter in climate projections. Gabriel Huerta at the University of New Mexico is responsible for developing statistical methods for evaluating these field dependencies. The UT effort will incorporate these developments into MECS, which is a set of python scripts being developed at the University of Texas for managing the workflow associated with data-driven climate model development over HPC resources. This report reflects the main activities at the University of New Mexico where the PI (Huerta) and the Postdocs (Nosedal, Hattab and Karki) worked on the project.

58 GEOSCIENCES↗
DOE OSTI2022

Hydrologic investigations of radar-rainfall error propagation to rainfall-runoff model hydrographs

Rainfall is arguably the most important yet most variable input for rainfall-runoff hydrologic models. In this study, the authors search for the characteristics of radar-rainfall estimates that are most important for skillful streamflow predictions. They perform comprehensive hydrologic investigations of radar-rainfall characteristics, including spatiotemporal resolution, radar range visibility, statistical characterization of rainfall variability, all vis-a-vis basin characteristics such as size and river network topology. Since the true rainfall fields are unknown, the authors exploit a paradigm of using two independently constructed radar-rainfall products i.e., Multi-Radar Multi-Sensor and IFC-ZR used operationally by the Iowa Flood Center (IFC). Using the distributed hydrologic model called the Hillslope-Link Model for the domain of the state of Iowa, they evaluate streamflow prediction at 140 USGS gauge stations that monitor rivers in Iowa. Through spatial and temporal rainfall aggregation experiments, the authors show that the impact of spatial and temporal resolution of rainfall is significant typically for smaller basins while starts reducing significantly for basins larger than 1,000 km 2 . Other rainfall characteristics they explored do not reveal a strong signature in the relationship of rainfall differences between the two products and hydrograph errors. However, exploring the product similarities rather than differences reveals that the basin-wide rainfall volume has the most significant effect on streamflow prediction. The results from this study are generalizable for all rainfall observing systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Toward Predictive RANS and SRS Computations of Turbulent External Flows of Practical Interest

In this work, we investigate the main challenges to prediction of turbulent external flows of practical interest with Reynolds-Averaged Navier–Stokes equations (RANS) and Scale-Resolving Simulation (SRS) models. This represents a crucial step toward further developing and establishing these formulations so they can be confidently utilized in engineering problems without reference data. The study initiates by identifying the major challenges to prediction. A literature review is performed to illustrate their effects in RANS and SRS computations. Afterward, we evaluate the impact of the challenges to prediction by analyzing representative statistically steady and unsteady flows with prominent RANS and SRS methods. These include multiple turbulent viscosity and second-moment RANS closures, and hybrid and bridging SRS models. The results demonstrate the potential of the selected SRS models to predict engineering flows. Yet, they also show the importance of considering the challenges to prediction during the setup and conduction of numerical experiments. These can suppress the advantages of using SRS formulations. The data also indicate that only SRS models can confidently predict statistically unsteady flows. In contrast, the results demonstrate that mean-flow quantities of statistically steady flows can be efficiently calculated with RANS closures, especially second-moment closures. Among the selected SRS methods, bridging models reveal better suited for prediction due to their ability to prevent commutation errors and enable the robust evaluation of numerical and modeling errors. This last property allows the use of a new validation technique that does not require reference data.

42 ENGINEERING↗
DOE OSTI2015

Design of experiments and data analysis challenges in calibration for forensics applications

Forensic science aims to infer characteristics of source terms using measured observables. Our focus is on statistical design of experiments and data analysis challenges arising in nuclear forensics. More specifically, we focus on inferring aspects of experimental conditions (of a process to produce product Pu oxide powder), such as temperature, nitric acid concentration, and Pu concentration, using measured features of the product Pu oxide powder. The measured features, Y, include trace chemical concentrations and particle morphology such as particle size and shape of the produced Pu oxide power particles. Making inferences about the nature of inputs X that were used to create nuclear materials having particular characteristics, Y, is an inverse problem. Therefore, statistical analysis can be used to identify the best set (or sets) of Xs for a new set of observed responses Y. One can fit a model (or models) such as Υ = f(Χ) + error , for each of the responses, based on a calibration experiment and then “invert” to solve for the best set of Xs for a new set of Ys. This perspectives paper uses archived experimental data to consider aspects of data collection and experiment design for the calibration data to maximize the quality of the predicted Ys in the forward models; that is, we assume that well-estimated forward models are effective in the inverse problem. In addition, we consider how to identify a best solution for the inferred X, and evaluate the quality of the result and its robustness to a variety of initial assumptions, and different correlation structures between the responses. In addition, we also briefly review recent advances in metrology issues related to characterizing particle morphology measurements used in the response vector, Y.

36 MATERIALS SCIENCE↗
DOE OSTI2012

POSSIBLE DETECTION OF AN EMISSION CYCLOTRON RESONANCE SCATTERING FEATURE FROM THE ACCRETION-POWERED PULSAR 4U 1626-67

We present analysis of 4U 1626-67, a 7.7 s pulsar in a low-mass X-ray binary system, observed with the hard X-ray detector of the Japanese X-ray satellite Suzaku in 2006 March for a net exposure of {approx}88 ks. The source was detected at an average 10-60 keV flux of {approx}4 Multiplication-Sign 10{sup -10} erg cm{sup -2} s{sup -1}. The phase-averaged spectrum is reproduced well by combining a negative and positive power-law times exponential cutoff (NPEX) model modified at {approx}37 keV by a cyclotron resonance scattering feature (CRSF). The phase-resolved analysis shows that the spectra at the bright phases are well fit by the NPEX with CRSF model. On the other hand, the spectrum in the dim phase lacks the NPEX high-energy cutoff component, and the CRSF can be reproduced by either an emission or an absorption profile. When fitting the dim phase spectrum with the NPEX plus Gaussian model, we find that the feature is better described in terms of an emission rather than an absorption profile. The statistical significance of this result, evaluated by means of an F test, is between 2.91 Multiplication-Sign 10{sup -3} and 1.53 Multiplication-Sign 10{sup -5}, taking into account the systematic errors in the background evaluation of HXD-PIN. We find that the emission profile is more feasible than the absorption one for comparing the physical parameters in other phases. Therefore, we have possibly detected an emission line at the cyclotron resonance energy in the dim phase.

79 ASTRONOMY AND ASTROPHYSICS↗