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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

DETERMINING TYPE Ia SUPERNOVA HOST GALAXY EXTINCTION PROBABILITIES AND A STATISTICAL APPROACH TO ESTIMATING THE ABSORPTION-TO-REDDENING RATIO R{sub V}

We investigate limits on the extinction values of Type Ia supernovae (SNe Ia) to statistically determine the most probable color excess, E(B – V), with galactocentric distance, and use these statistics to determine the absorption-to-reddening ratio, R{sub V}, for dust in the host galaxies. We determined pixel-based dust mass surface density maps for 59 galaxies from the Key Insight on Nearby Galaxies: a Far-infrared Survey with Herschel (KINGFISH). We use SN Ia spectral templates to develop a Monte Carlo simulation of color excess E(B – V) with R{sub V} = 3.1 and investigate the color excess probabilities E(B – V) with projected radial galaxy center distance. Additionally, we tested our model using observed spectra of SN 1989B, SN 2002bo, and SN 2006X, which occurred in three KINGFISH galaxies. Finally, we determined the most probable reddening for Sa–Sap, Sab–Sbp, Sbc–Scp, Scd–Sdm, S0, and irregular galaxy classes as a function of R/R{sub 25}. We find that the largest expected reddening probabilities are in Sab–Sb and Sbc–Sc galaxies, while S0 and irregular galaxies are very dust poor. We present a new approach for determining the absorption-to-reddening ratio R{sub V} using color excess probability functions and find values of R{sub V} = 2.71 ± 1.58 for 21 SNe Ia observed in Sab–Sbp galaxies, and R{sub V} = 1.70 ± 0.38, for 34 SNe Ia observed in Sbc–Scp galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Exploring an Ensemble-Based Approach to Atmospheric Climate Modeling and Testing at Scale

A strict throughput requirement has placed a cap on the degree to which we can depend on the execution of single, long, fine spatial grid simulations to explore global atmospheric climate behavior. Alternatively, running an ensemble of short simulations is computationally more efficient. We test the null hypothesis that the climate statistics of a full-complexity atmospheric model derived from an ensemble of independent short simulation is equivalent to that from an equilibrated long simulation. The climate of short simulation ensembles is statistically distinguishable from that of a long simulation in terms of the distribution of global annual means, largely due to the presence of low-frequency atmospheric intrinsic variability in the long simulation. We also find that model climate statistics of the simulation ensemble are sensitive to the choice of compiler optimizations. While some answer-changing optimization choices do not effect the climate state in terms of mean, variability and extremes, aggressive optimizations can result in significantly different climate states.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Statistical Sampling Considerations in Multifamily Housing

In the past 30 years, some governmental agencies and private-sector owners of multifamily housing properties have limited radon screening to partial screening (e.g., the testing of a subset of the multifamily housing dwelling units). While sampling methods have differed, most have required that if any one unit was found to be “positive,” or have radon levels ≥4 pCi/L, all remaining untested housing units would be tested (e.g. trip-wire approach).

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2014

Stochastic solution of population balance equations for reactor networks

This work presents a sequential modular approach to solve a generic network of reactors with a population balance model using a stochastic numerical method. Full-coupling to the gas-phase is achieved through operator-splitting. The convergence of the stochastic particle algorithm in test networks is evaluated as a function of network size, recycle fraction and numerical parameters. These test cases are used to identify methods through which systematic and statistical error may be reduced, including by use of stochastic weighted algorithms. The optimal algorithm was subsequently used to solve a one-dimensional example of silicon nanoparticle synthesis using a multivariate particle model. This example demonstrated the power of stochastic methods in resolving particle structure by investigating the transient and spatial evolution of primary polydispersity, degree of sintering and TEM-style images. Highlights: •An algorithm is presented to solve reactor networks with a population balance model. •A stochastic method is used to solve the population balance equations. •The convergence and efficiency of the reported algorithms are evaluated. •The algorithm is applied to simulate silicon nanoparticle synthesis in a 1D reactor. •Particle structure is reported as a function of reactor length and time.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

An algorithm to unveil the inner structure of objects concealed by beam divergence in radiographic image acquisition systems

Two main parameters rule the performance of an Image Acquisition System, namely, spatial resolution and contrast. For radiographic systems using cone beam arrangements, the farther the source, the better the resolution, but the contrast would diminish due to the lower statistics. A closer source would yield a higher contrast but it would no longer reproduce the attenuation map of the object, as the incoming beam flux would be reduced by unequal large divergences and attenuation factors. This work proposes a procedure to correct these effects when the object is comprised of a hull - or encased in it - possessing a shape capable to be described in analytical geometry terms. Such a description allows the construction of a matrix containing the attenuation factors undergone by the beam from the source until its final destination at each coordinate on the 2D detector. Each matrix element incorporates the attenuation suffered by the beam after its travel through the hull wall, as well as its reduction due to the square of distance to the source and the angle it hits the detector surface. When the pixel intensities of the original image are corrected by these factors, the image contrast, reduced by the overall attenuation in the exposure phase, are recovered, allowing one to see details otherwise concealed due to the low contrast. In order to verify the soundness of this approach, synthetic images of objects of different shapes, such as plates and tubes, incorporating defects and statistical fluctuation, have been generated, recorded for further comparison and afterwards processed to improve their contrast. The developed algorithm which, generates processes and plots the images has been written in Fortran 90 language. As the resulting final images exhibit the expected improvements, it therefore seemed worthwhile to carry out further tests with actual experimental radiographies.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

An application of survival analysis to disruption prediction via Random Forests</strong

We report one of the most pressing challenges facing the fusion community is adequately mitigating or, even better, avoiding disruptions of tokamak plasmas. However, before this can be done, disruptions must first be predicted with sufficient warning time to actuate a response. The established field of survival analysis provides a convenient statistical framework for time-to-event (i.e. time-to-disruption) studies. This paper demonstrates the integration of an existing disruption prediction machine learning algorithm with the Kaplan–Meier estimator of survival probability. Specifically discussed are the implied warning times from binary classification of disruption databases and the interpretation of output signals from Random Forest algorithms trained and tested on these databases. This survival analysis approach is applied to both smooth and noisy test data to highlight important features of the survival and hazard functions. In addition, this method is applied to three Alcator C-Mod plasma discharges and compared to a threshold-based scheme for triggering alarms. In one case, both techniques successfully predict the disruption; although, in another, neither warns of the impending disruption with enough time to mitigate. For the final discharge, the survival analysis approach could avoid the false alarm triggered by the threshold method. Limitations of this analysis and opportunities for future work are also presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Single-pixel interior filling function approach for detecting and correcting errors in particle tracking

We present a general method for detecting and correcting biases in the outputs of particle-tracking experiments. Our approach is based on the histogram of estimated positions within pixels, which we term the single-pixel interior filling function (SPIFF). We use the deviation of the SPIFF from a uniform distribution to test the veracity of tracking analyses from different algorithms. Unbiased SPIFFs correspond to uniform pixel filling, whereas biased ones exhibit pixel locking, in which the estimated particle positions concentrate toward the centers of pixels. Although pixel locking is a well-known phenomenon, we go beyond existing methods to show how the SPIFF can be used to correct errors. Here, the key is that the SPIFF aggregates statistical information from many single-particle images and localizations that are gathered over time or across an ensemble, and this information augments the single-particle data. We explicitly consider two cases that give rise to significant errors in estimated particle locations: undersampling the point spread function due to small emitter size and intensity overlap of proximal objects. In these situations, we show how errors in positions can be corrected essentially completely with little added computational cost. Additional situations and applications to experimental data are explored in SI Appendix. In the presence of experimental-like shot noise, the precision of the SPIFF-based correction achieves (and can even exceed) the unbiased Cramér–Rao lower bound. We expect the SPIFF approach to be useful in a wide range of localization applications, including single-molecule imaging and particle tracking, in fields ranging from biology to materials science to astronomy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Approach to Quality Assurance for Complex Environmental Modeling - 20407

A strong Quality Assurance (QA) program for complex environmental modeling is essential for regulatory and public acceptance and trust, but it does not have to be onerous. Neptune and Company, Inc. (Neptune) has developed a strong QA program that improves transparency, traceability, reproducibility, and therefore, defensibility and trust. Neptune's QA program has evolved over the past 27 years, transitioning from an ad hoc QA program, to a program that is currently Nuclear Quality Assurance-1 (NQA-1) compliant, and will soon be NQA-1/DOE approved for DOE EM modeling work. In addition, Neptune is a qualified laboratory assessor and qualified auditor for reference materials, a proficiency testing provider, and fully complies with the Environmental Protection Agency (EPA) QA program. Neptune's President and CEO, Kelly Black, was recently named the Chairperson of the International Organization for Standardization (ISO) Technical Committee 69, Application of Statistical Methods. A strong QA program has been developed for Neptune's radiological performance assessment (PA) program. Although pieces of their QA program are currently in development, their current program includes document control using Subversion, issue tracking and work flow tracking using JIRA, and transparency and traceability via a system of 'calc sheets' for documentation of all data analysis and modeling combined with 'check print' documentation of QA checking, and rigorous model testing and configuration control. All work is reviewed by an independent subject matter expert who is not associated with the collection and assembly of information, for an internal peer review. The program is enforced with a handful of Standard Operation Procedures (SOPs), Work Instructions, QA Project Plans (QAPPs), and Quality Management Plans (QMPs) that are updated frequently, with required annual training and acknowledgment. In addition, effective communication of modeling approaches and results to clients and stakeholders are integral to their QA program. Many of their models are built using the GoldSim modeling platform, for which their models are well-known for their level of transparency, documentation, and QA traceability. This level of QA is also applied to their process-level models. Neptune has taken some lessons learned from the extremely rigorous QA program required for the Yucca Mountain Project (YMP), and imposed the YMP requirements of complete traceability, transparency, and reproducibility, but avoided the inflexibility of a QA program that likely contributed to the suspect e-mails in 2005 that resulted in loss of trust in the YMP, and in nuclear waste disposal (and nuclear power) in general. There are far too many examples of loss of public trust due to poor QA that could have been easily avoided with a simple and straight-forward QA program combined with stakeholder engagement. The purpose of this paper is to share features of Neptune's Radiological Performance Assessment program QA program, and some of their QA related lessons learned over the past 27 years of QA for complex environmental modeling. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

A Data-Driven Passive Islanding Detection Scheme

This paper proposes a novel statistical parameters based data driven approach for passive islanding detection. The proposed scheme relies in the voltage data, measured at the point of common coupling, and is comprised of three stages. The Stage-1 quickly detects what is termed as coarse islanding, Stage-2 computes the decaying DC detector (DDCD), and 3) finally, the digital relay logic (DRL) is devised in Stage-3. The proposed scheme is tested as per the IEEE-1547, UL 1741 and IEC-62116 standards. Various tests are investigated on the modified IEEE-13 bus system, modeled in PSCAD/EMTDC, under stringent test scenarios such as power mismatch, induction motor starting effect, capacitor bank switching and various level of loads changes, considered for both single and multiple DG systems. The test results reveal that the proposed scheme can detect islanding in less than 2 cycles, and results in nearly zero non detection zone (NDZ). Also, the scheme can very well be used for single-phase microgrids too as it relies only on the single phase voltage information.

anti-islanding protection↗
DOE OSTI2022

Optimization of the deep neural network parameters for generating homogenized fuel assembly data for nodal codes

Homogenized fuel assembly (FA) data is a typical input data for nodal codes. Generating that data, however, could be time-consuming. One of promising ways to mitigate the computational burden of generating macroscopic cross-sections is to use trained artificial neural network (ANN) models for predicting nuclear data. However, there is a challenge to make the model support variable FA geometry. In this work, two most common types of FA were combined in one ANN model. Since there could be multiple ways of converting 2-dimensional FA data into 1-dimensional input vector for ANN, three different approaches of data flattening were evaluated. The input parameters included each fuel pin enrichment, fuel temperature, moderator temperature and boron concentration. The output parameters were 2-group macroscopic cross-sections (XS) and pin power distribution (HFF). A fully connected deep neural network (DNN) model was trained and tested using pre-generated data obtained with lattice physics code STREAM. The results of this study showed no statistically significant difference in the accuracy of XS and HFF generation for all 3 tested input vector orders. This means that fully connected DNN for XS generation demonstrated input sequence invariance. Results of comparing predicted XS data with reference solutions were found sufficiently close considering the reduction of computation time offered by ANN. Mean relative difference (MRD) for all output XS parameters was found below 0.7%, while HFF MRD was found higher compared to XS values, in some cases slightly exceeding 1%, mostly near guide tube locations. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

An application of survival analysis to disruption prediction via Random Forests

One of the most pressing challenges facing the fusion community is adequately mitigating or, even better, avoiding disruptions of tokamak plasmas. However, before this can be done, disruptions must first be predicted with sufficient warning time to actuate a response. The established field of survival analysis provides a convenient statistical framework for time-to-event (i.e. time-to-disruption) studies. This paper demonstrates the integration of an existing disruption prediction machine learning algorithm with the Kaplan-Meier estimator of survival probability. Specifically discussed are the implied warning times from binary classification of disruption databases and the interpretation of output signals from Random Forest algorithms trained and tested on these databases. This survival analysis approach is applied to both smooth and noisy test data to highlight important features of the survival and hazard functions. In addition, this method is applied to three Alcator C-Mod plasma discharges and compared to a threshold-based scheme for triggering alarms. In one case, both techniques successfully predict the disruption; although, in another, neither warns of the impending disruption with enough time to mitigate. For the final discharge, the survival analysis approach could avoid the false alarm triggered by the threshold method. Limitations of this analysis and opportunities for future work are also presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Dark Energy Survey Year 3 results: optimized $w$CDM simulation-based inference with weak lensing map-level hybrid statistics

We present cosmological constraints from the Dark Energy Survey Year 3 (DES Y3) weak lensing data using hierarchical hybrid statistics within a Bayesian simulation-based inference framework that is based on the Gower Street simulations. To maximize the precision of the inference, we have developed a new, information-theory based, data compression of the weak lensing maps to just seven highly informative summary statistics. The hybrid scheme exploits the high information content of the power spectrum, compressing both the power spectrum and neural-based summaries that are designed to extract further information. Our simulation-based approach enables principled forward modelling of all major sources of systematic uncertainty and survey properties into realistic mock observations, including the survey mask, photometric redshift uncertainties, intrinsic galaxy alignments, multiplicative shear calibration bias, source galaxy clustering, non-Gaussian shape noise, and non-linear structure formation. The summary statistics are then used in a Bayesian simulation-based inference pipeline. The inference is validated through coverage tests and checks for robustness against baryonic feedback. Assuming a $w$CDM cosmology, our analysis yields $S_8 = 0.808 \pm 0.017$, $Ω_{\rm m} = 0.325 \pm 0.024$, and $w < -0.766$ (marginalized posterior 68 per cent credible intervals). This rigorous combination of information theory, physics- and neural network-based extreme data compression, and principled Bayesian analysis improves the figure of merit for $(Ω_{\rm m}, S_8, w)$ by 60 per cent over the previous state-of-the-art, and by almost a factor of 3 over two-point analyses of the same data. They are the most precise joint constraints on $(Ω_{\rm m}, S_8, w)$ from weak gravitational lensing data alone of any survey to date. We intend to apply this analysis to the more recent DES Y6 data.

Williamson, J. [University Coll. London]↗
DOE OSTI2024

How do mirror charge radii constrain density dependence of the symmetry energy?

It has recently been suggested that differences in the charge radii of mirror nuclei (Δ$R^{mirr}_{ch}$) are strongly correlated with the neutron-skin thickness ($R_{skin}$) of neutron-rich nuclei and with the slope of the symmetry energy ($L$). To test this assumption, we present ab initio calculations of $R_{skin}$ in 48 Ca and 208 Pb, Δ$R^{mirr}_{ch}$ in 36 Ca$-$ 36 S, 38 Ca$-$ 38 Ar, 41 Sc$-$ 41 Ca, 48 Ni$-$ 48 Ca, 52 Ni$-$ 52 Cr, and 54 Ni$-$ 54 Fe mirror pairs, and $L$. Employing the recently developed 34 chiral interaction samples, identified by the history matching approach, we conduct rigorous statistical analysis of correlations among Δ$R^{mirr}_{ch}$, $R_{skin}$ and $L$, accounting for quantified uncertainties from low-energy constants of chiral interaction, chiral effective field theory truncation and many-body method approximation. The ab initio results reveal an appreciable Δ$R^{mirr}_{ch}$ $-$ $L$ correlation in $fp$-shell mirror pairs. However, contrary to previous studies, the present calculation finds that the studied $sd$-shell mirror pairs do not exhibit any Δ$R^{mirr}_{ch}$ $-$ $L$ correlation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

On the reliability of microvariability tests in quasars

Microvariations probe the physics and internal structure of quasars. Unpredictability and small flux variations make this phenomenon elusive and difficult to detect. Variance-based probes such as the C and F tests, or a combination of both, are popular methods to compare the light curves of the quasar and a comparison star. Recently, detection claims in some studies have depended on the agreement of the results of the C and F tests, or of two instances of the F-test, for rejecting the non-variation null hypothesis. However, the C-test is a non-reliable statistical procedure, the F-test is not robust, and the combination of tests with concurrent results is anything but a straightforward methodology. A priori power analysis calculations and post hoc analysis of Monte Carlo simulations show excellent agreement for the analysis of variance test to detect microvariations as well as the limitations of the F-test. Additionally, the combined tests yield correlated probabilities that make the assessment of statistical significance unworkable. However, it is possible to include data from several field stars to enhance the power in a single F-test, increasing the reliability of the statistical analysis. This would be the preferred methodology when several comparison stars are available. An example using two stars and the enhanced F-test is presented. These results show the importance of using adequate methodologies and avoiding inappropriate procedures that can jeopardize microvariability detections. Power analysis and Monte Carlo simulations are useful tools for research planning, as they can demonstrate the robustness and reliability of different research approaches.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A probabilistic-phase field model for the fracture of brittle materials

Abstract We develop a computational method to determine the failure probability of brittle materials under general mechanical loading conditions. The method is a combination of two parts: (1) numerical simulations of materials with multiple cracks using phase field theory, where the complete fracture process is viewed as ‘damage percolation’ along critical paths or clusters of cracks, rather than the traditional weak-link failure mechanism of Weibull, and (2) an extension of the Batdorf statistical theory of fracture to finite domains, where it is implemented within the finite element framework. The results of phase-field simulations at the ‘percolation threshold’ are used as failure data in the Batdorf theory to determine the overall probability of failure. The input to this approach is the size distribution of cracks in a pristine material. An example is shown, where alumina samples that were previously tested by Abe and coworkers (Abeet al2003J. Am. Ceram. Soc.861019–21) in four-point loading are compared to the results of our numerical simulations. The approach developed here has the advantage of being extendable to more complex thermomechanical loading.

Materials Science↗
DOE OSTI2024

Improved Subseasonal Forecasting of Extreme Polar Vortices Using Machine Learning

Our research was focused on forecasting the position and shape of the winter stratospheric polar vortex at a subseasonal timescale of 15 days in advance. To achieve this, we employed both statistical and neural network machine learning techniques. The analysis was performed on 42 winter seasons of reanalysis data provided by NASA giving us a total of 6,342 days of data. The state of the polar vortex for determined by using geometric moments to calculate the centroid latitude and the aspect ratio of an ellipse fit onto the vortex. Timeseries for thirty additional precursors were calculated to help improve the predictive capabilities of the algorithm. Feature importance of these precursors was performed using random forest to measure the predictive importance and the ideal number of precursors. Then, using the precursors identified as important, various statistical methods were tested for predictive accuracy with random forest and nearest neighbor performing the best. An echo state network, a type of recurrent neural network that features sparsely connected hidden layer and a reduced number of trainable parameters that allows for rapid training and testing, was also implemented for the forecasting problem. Hyperparameter tuning was performed for each methods using a subset of the training data. The algorithms were trained and tuned on the first 41 years of data, then tested for accuracy on the final year. In general, the centroid latitude of the polar vortex proved easier to predict than the aspect ratio across all algorithms. Random forest outperformed other statistical forecasting algorithms overall but struggled to predict extreme values. Forecasting from echo state network suggested a strong predictive capability past 15 days, but further work is required to fully realize the potential of recurrent neural network approaches.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

STAT7 v1.2 User Guide: The STAT7 Code for Statistical Propagation of Uncertainties in Steady-State Thermal Hydraulics Analysis of Plate-Fueled Reactors

The STAT7 software was developed to perform steady-state, single-phase thermal hydraulics analysis of plate-fueled reactors based on statistical propagation of uncertainties. Application of the software is for non-power research and test reactors, including conversion to low-enriched uranium fuel of U.S. High-Performance Research Reactors such as Massachusetts Institute of Technology Research Reactor. Since it can be necessary to repeat analysis during fuel reloading, STAT7 accommodates flexibility in analyzing many realistic aspects of reactor fuel management. STAT7 uses a Monte Carlo approach to model uncertainty in common fuel fabrication parameters and other key reactor operating parameters required for thermal hydraulics analyses of research and test reactors. These safety calculations are ultimately intended to protect against high fuel plate temperatures due to critical heat flux, or onset of flow instability. STAT7 supports water properties based on the IAPWS-IF97 functions (The International Association for the Properties of Water and Steam Industrial Formulation 1997 for the Thermodynamic Properties of Water and Steam) in addition to the fit functions. STAT7 predicts axial profiles of fuel, cladding, and coolant temperature along a lateral stripe that runs the full length of the fuel plate from the bottom to the top. STAT7 can simultaneously analyze all of the axial nodes of all of the fuel plates and all of the coolant channels for one latera stripe of a fuel element. Power splits are calculated for each axial node of each plate to determine how much of the power goes out each face of the plate. By running STAT7 multiple times, full core analysis can be performed by analyzing the margin to onset of nucleate boiling and onset of flow instability for each axial node of each stripe of each plate of each fuel element in the core.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Hydrogen Dispersion Modeling for Development of Smart Distributed Monitoring

Studying hydrogen dispersion is crucial for ensuring the safe and effective deployment of hydrogen as an energy carrier. This study presents a comprehensive CFD modeling framework for simulating hydrogen dispersion at a real-world hydrogen production, storage, and utilization facility. Utilizing the Hydrogen Research Facility under the Advanced Research on Integrated Energy Systems (ARIES) at the National Renewable Energy Laboratory's (NREL) Flatirons campus, controlled hydrogen releases at 27 kg-H2/hr were simulated. The model incorporated site-specific atmospheric conditions, including hourly wind speeds and temperatures recorded between 8 AM and 8 PM from October to December 2023. To reduce computational demands, a statistical reduction technique was applied to condense the dataset to 100 representative scenarios, validated by statistical tests for wind speeds and power law coefficients. Simulations were conducted using the Reynolds-Averaged Navier-Stokes equations. Results demonstrated that wind speed substantially influences hydrogen dispersion, with low wind conditions forming concentrated clouds and higher wind speeds stretching the plume. Additionally, clustering analysis informed optimal sensor placement at various elevations with up to 10 sensor locations on each elevation. This framework offers a robust approach for understanding hydrogen behavior in ambient conditions and informing detection strategies.

08 HYDROGEN↗