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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Evaluation of Weakly Informed Priors for FLEX Data

On behalf of the U. S. Nuclear Regulatory Commission (NRC), Idaho National Laboratory (INL) has reviewed the development and use of “weakly informed prior” (WIP) probability distributions to obtain industry-wide failure probability and rate distributions for portable “FLEX” equipment, as documented in the Pressurized Water Reactor Owner’s Group (PWROG) report, FLEX Equipment Data Collection and Analysis (PWROG 18043-P) by MM Degonish. The review includes a description of the methods used by the PWROG analysts. It also includes an implementation of those methods for comparison purposes and an implementation of two constrained-noninformative (CN) distributions for further WIP comparisons. The report documents several issues that the review identified in the development of the WIP distributions. They relate to the quality and representativeness of the current portable “FLEX” component data, the choice of informed distributions for related installed components, and the lack of verification for the factors chosen to weaken the information from the installed components. Two errors occurred in the computations: the posterior distribution of the WIP was used to modify the WIP itself, resulting in a lack of independence between the WIP and the data; and an error was made in the conversion of WIP mean and variance values to beta distributions. The INL reviewers think the WIP distributions presented in the report are untenable for use in risk assessments at the present time because of these issues.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2016

Unbiased Ratio Estimator for k-Eigenvalue in Monte Carlo Criticality Calculations

In criticality calculations, the effective multiplication factor (k{sub eff}) is an important parameter for reactor analysis. It can be interpreted as the ratio of the neutron production rate to the loss rate in a reactor core. In Monte Carlo (MC) criticality calculations, k{sub eff} is obtained for each active cycle i as: k-i = P{sub i}/L{sub i} for I = 1 to I where P{sub i} and L{sub i} are the production rate and the loss rate at cycle i, respectively, and they are normalized by the nominal source size for cycle. Since all neutron particles in a cycle will be absorbed or leaks out at the end, the loss rate, L{sub i}, can be set as the source rate, which is the unity. Thus, in MCNP5, k{sub i} is simply calculated with P{sub i} by using three conventional estimators; collision, absorption, and track-length estimators. In domain decomposed MC criticality calculations based on the fission and surface source (FSS) iteration method, k{sub i} is calculated by a ratio of P{sub i} and L{sub i}, where both P{sub i} and L{sub i} are random variables, sampled by using the same particle histories. When the numerator and the denominator are correlated, the variance of the ratio can be smaller than that of the numerator. Then, the final estimate of k{sub eff} is provided as the sample mean of k{sub i}, which will be referred as 'simple' ratio estimator in this paper. In Ref., it is shown that this ratio estimator converges faster and fluctuates less than the conventional estimator. A similar ratio estimator is also used in Serpent 2, as an implicit estimate of k{sub eff}. However, this type of 'simple' ratio estimator is biased, which means that the expectation of the ratio is different from the ratio of the expectation of the numerator and that of the denominator. In this paper, an unbiased ratio estimator which was originally derived in the cytometry analysis is introduced to estimate k{sub eff} and is tested on a continuous-energy UO{sub 2} pin-cell problem. In this test problem, it is shown that the variance of the unbiased ratio estimator is smaller than those of the conventional estimators provided by MCNP5, while the simple ratio estimator can be significantly biased when the number of histories per cycle is too small. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Deep Analysis Net with Causal Embedding for Coal-fired Power Plant Fault Detection and Diagnosis (DANCE4CFDD)

Fault detection and diagnosis is critical to power plant operation to ensure attaining high reliability while reducing operation cost. As more renewable power is introduced to the power grid, traditional fossil power plants take on the extra burden of excessive load cycling to compensate the generation variability from renewable power. Such load cycling will pose more reliability challenges to power plant operation. There are a number of challenges faced by today’s asset health management system in coal- fired (or gas) power plants: 1) high-dimensional nonlinear interaction among multiple time series measurements; 2) high measurement variance induced by operational conditions/modes; 3) variation among asset types and plant configurations; and 4) a small number of faulty events to learn from. To cope with these challenges, today’s fielded asset health management systems rely heavily on manual efforts from domain experts and hand-crafted features or rules based on domain knowledge. Despite its role in plant reliability, such a practice is costly and hinders its scalability and sustainability, particularly when a plant undergoes modifications. The objective of this project is to develop a novel end-to-end AI learning system that is trainable (i.e., the AI representation of a complex system behavior can be directly learned from properly labeled data) for accurate fault detection and root cause analysis. The ability to create a fault detection model directly from time series could alleviate the efforts associated with today’s asset management solution development. In the course of this project, we have achieved the following: Created an AI model development environment incorporating state-of-the-art neural network architectures for rapid model development and evaluation; Developed novel learning strategies for training of fault detection model; Developed special-purpose neural network architecture embedded with variable association graph aiming for better interpretability; Developed a learning strategy to leverage a small number of faulty events for enhanced fault detection capability; Conducted detailed experimental study based on public benchmark datasets and demonstrated the effectiveness of the proposed solution; and Validated the developed system with data from both a coal-fired plant boiler dynamic simulation model and real-world coal-fired power plant covering multiple asset and fault types. Overall, the project attained a technology readiness level of TRL 5 from TRL 2 at the beginning of the project.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2020

Uncertainty amplification due to density/refractive index gradients in background-oriented schlieren experiments

Here, we theoretically analyze the effect of density/refractive index gradients on the measurement precision of background-oriented schlieren (BOS) experiments by deriving the Cramer–Rao lower bound (CRLB) for the 2D centroid estimation process. A model is derived for the diffraction limited image of a dot viewed through a medium containing density gradients that includes the effect of the experimental parameters such as the magnification and f-number. It is shown using the model that nonlinearities in the density gradient field lead to blurring of the dot image. This blurring amplifies the effect of image noise on the centroid estimation process, leading to an increase in the CRLB and a decrease in the measurement precision. The ratio of position uncertainties of a dot in the reference and gradient images is shown to be a function of the ratio of the dot diameters and dot intensities. We termed this parameter the amplification ratio (A F ), and a methodology for reporting position uncertainties in tracking-based BOS measurements is proposed. The theoretical predictions of the dot position estimation variance from the CRLB are compared to ray tracing simulations, and agreement is obtained. The uncertainty amplification is also demonstrated on experimental BOS images of flow induced by a spark discharge, where it is seen that regions of high amplification ratio correspond to regions of density gradients. This analysis elucidates the dependence of the position uncertainty on density and refractive index gradient-induced distortion parameters, provides a methodology for accounting its effect on uncertainty quantification and provides a framework for optimizing experiment design.

42 ENGINEERING↗
DOE OSTI2012

CALTECH CORE-COLLAPSE PROJECT (CCCP) OBSERVATIONS OF TYPE II SUPERNOVAE: EVIDENCE FOR THREE DISTINCT PHOTOMETRIC SUBTYPES

We present R-band light curves of Type II supernovae (SNe) from the Caltech Core-Collapse Project (CCCP). With the exception of interacting (Type IIn) SNe and rare events with long rise times, we find that most light curve shapes belong to one of three apparently distinct classes: plateau, slowly declining, and rapidly declining events. The last class is composed solely of Type IIb SNe which present similar light curve shapes to those of SNe Ib, suggesting, perhaps, similar progenitor channels. We do not find any intermediate light curves, implying that these subclasses are unlikely to reflect variance of continuous parameters, but rather might result from physically distinct progenitor systems, strengthening the suggestion of a binary origin for at least some stripped SNe. We find a large plateau luminosity range for SNe IIP, while the plateau lengths seem rather uniform at approximately 100 days. As analysis of additional CCCP data goes on and larger samples are collected, demographic studies of core-collapse SNe will likely continue to provide new constraints on progenitor scenarios.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Indra: a public computationally accessible suite of cosmological N -body simulations

ABSTRACT Indra is a suite of large-volume cosmological N-body simulations with the goal of providing excellent statistics of the large-scale features of the distribution of dark matter. Each of the 384 simulations is computed with the same cosmological parameters and different initial phases, with 10243 dark matter particles in a box of length 1 h−1 Gpc, 64 snapshots of particle data and halo catalogues, and 505 time-steps of the Fourier modes of the density field, amounting to almost a petabyte of data. All of the Indra data are immediately available for analysis via the SciServer science platform, which provides interactive and batch computing modes, personal data storage, and other hosted data sets such as the Millennium simulations and many astronomical surveys. We present the Indra simulations, describe the data products and how to access them, and measure ensemble averages, variances, and covariances of the matter power spectrum, the matter correlation function, and the halo mass function to demonstrate the types of computations that Indra enables. We hope that Indra will be both a resource for large-scale structure research and a demonstration of how to make very large data sets public and computationally accessible.

Falck, Bridget↗
DOE OSTI2019

Wireless Sensor Networks for Improved Snow Water Equivalent and Runoff Estimates

We leverage the frontiers of the Internet of Things technology in a recently developed end-to-end wireless sensor network (WSN) system that samples, collects, stores, and displays mountain hydrology measurements in near real-time. At the core of the system lies an ultra-low power, radio channel-hoping, and self-organizing mesh that allows for remote autonomous sampling of snow. Such properties, combined with a rugged weather-sealed design of the devices and multi-level data replication, provides reliable real-time data at spatial and temporal scales previously impractical to achieve in mountain environments. The system was deployed at three 1 km 2 sites across the North Fork of the Feather River basin with a cluster of 12 sensor nodes for each location. Measurements show that existing operational autonomous systems are non-representative spatially, with biases that can reach up to 50%. A comparison between a wet and dry year showed that snow depths exhibit strong multi-scale inter-year spatial stationarity with major rank conservation. Temporally dense analysis using elastic net regression shows that dominant features at the sub-km 2 scale are site-dependent and differ from the watershed scale. Newly introduced explanatory variables, based on the nearest neighbor with a Landsat assimilated historical product, consistently explained up to 90% of the variance in the watershed-scale SWE for both years. At two WSN sites, lagged cross-correlation of snowmelt with stream flow measurements showed a significant improvement of up to 100% compared with existing systems, suggesting that WSNs can be instrumental in improving runoff forecasting and water management.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

EV Profile Capture

NextGen Profiles' EV profile capture efforts aimed to explore the variance in performance and evaluate how different operational conditions influence production EV charging behavior. Data were collected at a frequency of 10 Hz from both the EV and EVSE during each charge session. These charge session parameters were then entered into a time-series database for further analysis. The data were gathered under different operational conditions to examine the effects of various factors such as battery state of charge, battery temperature, vehicle condition, smart charge management, and EVSE limitations. The EV profile capture dataset includes extensive high-power charging data from 16 different EVs—comprising light-, medium-, and heavy-duty vehicles—along with EVSE from various suppliers. To protect confidentiality, the EV and EVSE metadata are anonymized, and the publicly released datasets are aggregated to 0.1-Hz frequency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Evaluation of atmospheric precipitable water from reanalysis products using homogenized radiosonde observations over China

Many multidecadal atmospheric reanalysis products are available now, but their consistencies and reliability are far from perfect. In this work, atmospheric precipitable water (PW) from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR), NCEP/Department of Energy (DOE), Modern Era Retrospective-Analysis for Research and Applications (MERRA), Japanese 55 year Reanalysis (JRA-55), JRA-25, ERA-Interim, ERA-40, Climate Forecast System Reanalysis (CFSR), and 20th Century Reanalysis version 2 is evaluated against homogenized radiosonde observations over China during 1979–2012 (1979–2001 for ERA-40). Results suggest that the PW biases in the reanalyses are within ~20% for most of northern and eastern China, but the reanalyses underestimate the observed PW by 20%–40% over western China and by ~60% over the southwestern Tibetan Plateau. The newer-generation reanalyses (e.g., JRA25, JRA55, CFSR, and ERA-Interim) have smaller root-mean-sq uare error than the older-generation ones (NCEP/NCAR, NCEP/DOE, and ERA-40). Most of the reanalyses reproduce well the observed PW climatology and interannual variations over China. However, few reanalyses capture the observed long-term PW changes, primarily because they show spurious wet biases before about 2002. This deficiency results mainly from the discontinuities contained in reanalysis relative humidity fields in the middle-lower troposphere due to the wet bias in older radiosonde records that are assimilated into the reanalyses. An empirical orthogonal function (EOF) analysis revealed two leading modes that represent the long-term PW changes and El Niño–Southern Oscillation-related interannual variations with robust spatial patterns. The reanalysis products, especially the MERRA and JRA-25, roughly capture these EOF modes, which acco unt for over 50% of the total variance. The results show that even during the post-1979 satellite era, discontinuities in radiosonde data can still induce large spurious long-term changes in reanalysis PW and other related fields. Thus, more efforts are needed to remove spurious changes in input data for future long-term reanalyses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

An Evaluation of the Large‐Scale Atmospheric Circulation and Its Variability in CESM2 and Other CMIP Models

Abstract The Community Earth System Model 2 (CESM2) is the latest Earth System Model developed by the National Center for Atmospheric Research in collaboration with the university community and is significantly advanced in most components compared to its predecessor (CESM1). Here, CESM2's representation of the large‐scale atmospheric circulation and its variability is assessed. Further context is providedthrough comparison to the CESM1 large ensemble and other models from the Coupled Model Intercomparison Project (CMIP5 and CMIP6). This includes an assessment of the representation of jet streams and storm tracks, stationary waves, the global divergent circulation, the annular modes, the North Atlantic Oscillation, and blocking. Compared to CESM1, CESM2 is substantially improved in the representation of the storm tracks, Northern Hemisphere (NH) stationary waves, NH winter blocking and the global divergent circulation. It ranks within the top 10% of CMIP class models in many of these features. Some features of the Southern Hemisphere (SH) circulation have degraded, such as the SH jet strength, stationary waves, and blocking, although the SH jet stream is placed at approximately the correct location. This analysis also highlights systematic deficiencies in these features across the new CMIP6 archive, such as the continued tendency for the SH jet stream to be placed too far equatorward, the North Atlantic westerlies to be too strong over Europe, the storm tracks as measured by low‐level meridional wind variance to be too weak and a lack of blocking in the North Atlantic sector.

Simpson, Isla R.↗
DOE OSTI2014

Task-based detectability in CT image reconstruction by filtered backprojection and penalized likelihood estimation

Purpose: Nonstationarity is an important aspect of imaging performance in CT and cone-beam CT (CBCT), especially for systems employing iterative reconstruction. This work presents a theoretical framework for both filtered-backprojection (FBP) and penalized-likelihood (PL) reconstruction that includes explicit descriptions of nonstationary noise, spatial resolution, and task-based detectability index. Potential utility of the model was demonstrated in the optimal selection of regularization parameters in PL reconstruction. Methods: Analytical models for local modulation transfer function (MTF) and noise-power spectrum (NPS) were investigated for both FBP and PL reconstruction, including explicit dependence on the object and spatial location. For FBP, a cascaded systems analysis framework was adapted to account for nonstationarity by separately calculating fluence and system gains for each ray passing through any given voxel. For PL, the point-spread function and covariance were derived using the implicit function theorem and first-order Taylor expansion according toFessler [“Mean and variance of implicitly defined biased estimators (such as penalized maximum likelihood): Applications to tomography,” IEEE Trans. Image Process. 5(3), 493–506 (1996)]. Detectability index was calculated for a variety of simple tasks. The model for PL was used in selecting the regularization strength parameter to optimize task-based performance, with both a constant and a spatially varying regularization map. Results: Theoretical models of FBP and PL were validated in 2D simulated fan-beam data and found to yield accurate predictions of local MTF and NPS as a function of the object and the spatial location. The NPS for both FBP and PL exhibit similar anisotropic nature depending on the pathlength (and therefore, the object and spatial location within the object) traversed by each ray, with the PL NPS experiencing greater smoothing along directions with higher noise. The MTF of FBP is isotropic and independent of location to a first order approximation, whereas the MTF of PL is anisotropic in a manner complementary to the NPS. Task-based detectability demonstrates dependence on the task, object, spatial location, and smoothing parameters. A spatially varying regularization “map” designed from locally optimal regularization can improve overall detectability beyond that achievable with the commonly used constant regularization parameter. Conclusions: Analytical models for task-based FBP and PL reconstruction are predictive of nonstationary noise and resolution characteristics, providing a valuable framework for understanding and optimizing system performance in CT and CBCT.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model

Changes in the high-latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with midlatitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. We present a quantitative way to assess uncertainty in complex computer models, which is a new approach in the analysis of sea ice models. We characterize parametric uncertainty in the Los Alamos sea ice model (CICE) in a standalone configuration and quantify the sensitivity of sea ice area, extent, and volume with respect to uncertainty in 39 individual model parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one at a time, this study uses a global variance-based approach in which Sobol' sequences are used to efficiently sample the full 39-dimensional parameter space. We implement a fast emulator of the sea ice model whose predictions of sea ice extent, area, and volume are used to compute the Sobol' sensitivity indices of the 39 parameters. Main effects and interactions among the most influential parameters are also estimated by a nonparametric regression technique based on generalized additive models. A ranking based on the sensitivity indices indicates that model predictions are most sensitive to snow parameters such as snow conductivity and grain size, and the drainage of melt ponds. Lastly, it is recommended that research be prioritized toward more accurately determining these most influential parameter values by observational studies or by improving parameterizations in the sea ice model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Development and evaluation of a device for simultaneous uniaxial compression and optical imaging of cartilage samples in vitro

In this paper, we present a system that allows imaging of cartilage tissue via optical coherence tomography (OCT) during controlled uniaxial unconfined compression of cylindrical osteochondral cores in vitro. We describe the system design and conduct a static and dynamic performance analysis. While reference measurements yield a full scale maximum deviation of 0.14% in displacement, force can be measured with a full scale standard deviation of 1.4%. The dynamic performance evaluation indicates a high accuracy in force controlled mode up to 25 Hz, but it also reveals a strong effect of variance of sample mechanical properties on the tracking performance under displacement control. In order to counterbalance these disturbances, an adaptive feed forward approach was applied which finally resulted in an improved displacement tracking accuracy up to 3 Hz. A built-in imaging probe allows on-line monitoring of the sample via OCT while being loaded in the cultivation chamber. We show that cartilage topology and defects in the tissue can be observed and demonstrate the visualization of the compression process during static mechanical loading.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Portfolio Analysis with General Commission

The well-known problem of finding explicit formulas for the expected return and risk of portfolios with general commission is completely solved. It is assumed that the commission depends on the asset and the asset position, and on whether the given position is opened or closed. For portfolios with only the budget constraint and initial commission, we prove that the function of expected portfolio return and portfolio variance function are bounded.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Photometric redshifts and intrinsic alignments: Degeneracies and biases in the 3 × 2 pt analysis

We present a systematic study of cosmological parameter bias in weak lensing and large-scale structure analyses for upcoming imaging surveys induced by the interplay of intrinsic alignments (IA) and photometric redshift (photo-z) model misspecification error. We first examine the degeneracies between the parameters of the tidal alignment–tidal torquing (TATT) model for IA and of a photo-z model including a mean shift ( Δ z ¯ ) and variance ( σ z ) for each tomographic bin of lenses and sources, under a variety of underlying true IA behaviors. We identify strong degeneracies between: (1) the redshift scaling of the tidal alignment amplitude and the mean shift and variances of source bins, (2) the redshift scaling of the tidal torquing amplitude and the variance of the lowest- z source bin, and (3) the IA source density weighting and the mean shift and variance of several source bins. We then use this information to guide our exploration of the level of cosmological parameter bias which can be induced given incorrect modeling of IA, photo-z, or both. We find that marginalizing over all the parameters of TATT is generally sufficient to preclude cosmological parameter bias in the scenarios we consider. However, this does not necessarily mean that IA and photo-z parameters are themselves unbiased, nor does it mean that the best-fit model is a good fit to the data. We also find scenarios where the inferred parameters produce χ d . o . f . 2 values indicative of a good fit but cosmological parameter bias is significant, particularly when the IA source density weighting parameter is not marginalized over. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2012

Toward Joint Hypothesis-Tests Seismic Event Screening Analysis: Ms|mb and Event Depth

Well established theory can be used to combine single-phenomenology hypothesis tests into a multi-phenomenology event screening hypothesis test (Fisher's and Tippett's tests). Commonly used standard error in Ms:mb event screening hypothesis test is not fully consistent with physical basis. Improved standard error - Better agreement with physical basis, and correctly partitions error to include Model Error as a component of variance, correctly reduces station noise variance through network averaging. For 2009 DPRK test - Commonly used standard error 'rejects' H0 even with better scaling slope ({beta} = 1, Selby et al.), improved standard error 'fails to rejects' H0.

58 GEOSCIENCES↗
DOE OSTI2015

Minima of the fluctuations of the order parameter of global seismicity

It has been recently shown [N. V. Sarlis, Phys. Rev. E 84, 022101 (2011) and N. V. Sarlis and S.-R. G. Christopoulos, Chaos 22, 023123 (2012)] that earthquakes of magnitude M greater or equal to 7 are globally correlated. Such correlations were identified by studying the variance κ{sub 1} of natural time which has been proposed as an order parameter for seismicity. Here, we study the fluctuations of this order parameter using the Global Centroid Moment Tensor catalog for a magnitude threshold M{sub thres} = 5.0 and focus on its behavior before major earthquakes. Natural time analysis reveals that distinct minima of the fluctuations of the order parameter of seismicity appear within almost five and a half months on average before all major earthquakes of magnitude larger than 8.4. This phenomenon corroborates the recent finding [N. V. Sarlis et al., Proc. Natl. Acad. Sci. U.S.A. 110, 13734 (2013)] that similar minima of the seismicity order parameter fluctuations had preceded all major shallow earthquakes in Japan. Moreover, on the basis of these minima a statistically significant binary prediction method for earthquakes of magnitude larger than 8.4 with hit rate 100% and false alarm rate 6.67% is suggested.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Spherical nanoindentation stress-strain analysis, Version 1

Nanoindentation is a tool that allows the mechanical response of a variety of materials at the nano to micron length scale to be measured. Recent advances in spherical nanoindentation techniques have allowed for a more reliable and meaningful characterization of the mechanical response from nanoindentation experiments in the form on an indentation stress-strain curve. This code base, Spin, is written in MATLAB (The Mathworks, Inc.) and based on the analysis protocols developed by S.R. Kalidindi and S. Pathak [1, 2]. The inputs include the displacement, load, harmonic contact stiffness, harmonic displacement, and harmonic load from spherical nanoindentation tests in the form of an Excel (Microsoft) spreadsheet. The outputs include indentation stress-strain curves and indentation properties as well their variance due to the uncertainty of the zero-point correction in the form of MATLAB data (.mat) and figures (.png). [1] S. Pathak, S.R. Kalidindi. Spherical nanoindentation stress–strain curves, Mater. Sci. Eng R-Rep 91 (2015). [2] S.R. Kalidindi, S. Pathak. Determination of the effective zero-point and the extraction of spherical nanoindentation stress-strain curves, Acta Materialia 56 (2008) 3523-3532.

Weaver, JordanS.↗