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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Scalar mixing in a Kelvin-Helmholtz shear layer and implications for Reynolds-averaged Navier-Stokes modeling of mixing layers

Large-eddy simulation of a temporally evolving Kelvin-Helmholtz (KH) mixing layer is performed with the tenth-order compact difference code miranda to examine the steady-state behavior of a passive scalar in a shear-driven mixing layer. It is shown that the integral behavior of scalar variance in a KH mixing layer behaves similarly to the integral behavior of scalar variance in a Rayleigh-Taylor (RT) mixing layer, and mixedness of the simulated KH shear layer tends towards a value of about 0.8. It is further shown that if the k-L-a-V Reynolds-averaged Navier-Stokes (RANS) model [B. E. Morgan et al., Phys. Rev. E 98, 033111 (2018)], calibrated to reproduce steady-state mixing in an RT layer, is applied to simulate a KH mixing layer, the RANS model will significantly overpredict the magnitude of scalar variance in the KH layer. A straightforward addition to the k-L-a-V model is then suggested, and self-similarity analysis is applied to determine constraints on model coefficients. Furthermore, it is shown that with the addition of a buoyancy production term in the model equation for scalar variance, it becomes possible to eliminate the model deficiency and match steady-state mixedness in simulations of both RT and KH mixing layers with a single model calibration.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

MR texture analysis: potential imaging biomarker for predicting the chemotherapeutic response of patients with colorectal liver metastases

Purpose: The purpose of the study was to determine whether the pre-treated MR texture features of colorectal liver metastases (CRLMs) are predictive of therapeutic response after chemotherapy. Methods: The study included twenty-six consecutive patients (a total of 193 liver metastasis) with unrespectable CRLMs at our institution from August 2014 to February 2016. Lesions were categorized into either responding group or non-responding group according to changes in size. Texture analysis was quantified on T2-weighted images by two radiologists with consensus on regions of interest which were manually drawn on the largest cross-sectional area of the lesions. Five histogram features (mean, variance, skewness, kurtosis, and entropy{sup 1}) and five gray level co-occurrence matrix features (GLCM; angular second moment (ASM), entropy{sup 2}, contrast, correlation, and inverse difference moment (IDM)) were extracted. The texture parameters were statistically analyzed to identify the differences between the two groups, and the potential predictive parameters to differentiate the responding group from the non-responding group were subsequently tested using multivariable logistic regression analysis. Results: A total of 107 responding and 86 non-responding lesions were evaluated. A higher variance, entropy{sup 1}, contrast, entropy{sup 2} and a lower ASM, correlation, IDM were independently (P < 0.05) associated with a good response to chemotherapy with the areas under the ROC curves (AUCs) of 0.602–0.784. Variance (P < 0.001) and ASM (P = 0.001) remained potential predictive values to discriminate responding lesions from non-responding lesions when tested using multivariable logistic regression analysis. The highest AUC of the predictors from the association of variance and ASM was 0.814. Conclusion: MR texture features on pre-treated T2 images have the potential to predict the therapeutic response of colorectal liver metastases.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Using Receiver Operating Characteristic Curves to Optimize Discovery-Based Software with Comprehensive Two-Dimensional Gas Chromatography with Time-of-Flight Mass Spectrometry

We report a quantitative approach to optimize implementation of discovery-based software for comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC – TOFMS). The software performs a tile-based Fisher ratio (F-ratio) analysis, and facilitates a supervised non-targeted analysis based upon the experimental design to aid in the discovery of analytes whose variances are statistically different between sample classes. The quantitative approach for software optimization uses receiver operating characteristic (ROC) curves. The area under the curve (AUC) of each ROC curve serves as a quantitative metric to optimize two key algorithm parameters: the signal-to-noise ratio (S/N) threshold of the baseline corrected and normalized data prior to calculating F-ratios at each m/z mass channel, and the number of these F-ratios per m/z to include in the calculation of the average F-ratio of a tile. A total of 25 combinations of S/N threshold by number of m/z were studied. Fifty analytes were spiked into a diesel fuel at two concentration spike levels to produce two sample classes that should in principle produce 50 positive instances in the ROC curves (30 native/20 non-native compounds, at spike levels of 200/20 and100/10 ppm native/non-native, respectively). The “sweet spot” for F-ratio analysis was determined to be a S/N threshold of 10 coupled with a maximum of the 10 most chemically selective m/z (requiring a minimum of 3 m/z), corresponding to a ~ 21% improvement in the discovery rate of true positives relative to prior studies. Furthermore, it was observed that optimization of these software parameters did not depend upon a priori determination of the statistically correct number of positive instances in the sample classes. The AUC metric appears to be suitable for the evaluation of all data analysis methods that utilize the proper experimental design.

3D-GS/TOFMS, Data discovery, ROC curves, Fisher ra↗
DOE OSTI2013

PROSPECTS FOR MEASURING THE RELATIVE VELOCITIES OF GALAXY CLUSTERS IN PHOTOMETRIC SURVEYS USING THE KINETIC SUNYAEV-ZEL'DOVICH EFFECT

We consider the prospects for measuring the pairwise kinetic Sunyaev-Zel'dovich (kSZ) signal from galaxy clusters discovered in large photometric surveys such as the Dark Energy Survey (DES). We project that the DES cluster sample will, in conjunction with existing mm-wave data from the South Pole Telescope (SPT), yield a detection of the pairwise kSZ signal at the 8{sigma}-13{sigma} level, with sensitivity peaking for clusters separated by {approx}100 Mpc distances. A next-generation version of SPT would allow for a 18{sigma}-30{sigma} detection and would be limited by variance from the kSZ signal itself and the residual thermal Sunyaev-Zel'dovich (tSZ) signal. Throughout our analysis, we assume photometric redshift errors that wash out the signal for clusters separated by {approx}<50 Mpc; a spectroscopic survey of the DES sample would recover this signal and allow for a 26{sigma}-43{sigma} detection, and would again be limited by kSZ/tSZ variance. Assuming a standard model of structure formation, these high-precision measurements of the pairwise kSZ signal will yield detailed information on the gas content of the galaxy clusters. Alternatively, if the gas can be sufficiently characterized by other means (e.g., using tSZ, X-ray, or weak lensing), then the relative velocities of the galaxy clusters can be isolated, thereby providing a precision measurement of gravity on 100 Mpc scales. We briefly consider the utility of these measurements for constraining theories of modified gravity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Dynamical behaviors of structural, constrained and free water in calcium- and magnesium-silicate-hydrate gels

We present the hypothesis that the mechanical properties of cement pastes depend strongly on their porosities. In a saturated paste, the porosity links to the free water volume after hydration. Structural water, constrained water, and free water have different dynamical behavior. Hence, it should be possible to extract information on pore system by exploiting the water dynamics. With our experiments we investigated the slow dynamics of hydration water confined in calcium- and magnesium-silicate-hydrate (C-S-H and M-S-H) gels using high-resolution quasi-elastic neutron scattering (QENS) technique. C-S-H and M-S-H are the chemical binders present in calcium rich and magnesium rich cements. We measured three M-S-H samples: pure M-S-H, M-S-H with aluminum-silicate nanotubes (ASN), and M-S-H with carboxyl group functionalized ASN (ASN-COOH). A C-S-H sample with the same water content (i.e. 0.3) is also studied for comparison. We found that structural water in the gels contributes to the elastic component of the QENS spectrum, while constrained water and free water contribute the quasi-elastic component. The quantitative analysis suggests that the three components vary for different samples and indicate the variance in the system porosity, which controls the mechanical properties of cement pastes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Modeling Data Movement Performance on Heterogeneous Architectures

The cost of data movement on parallel systems varies greatly with machine architecture, job partition, and nearby jobs. Performance models that accurately capture the cost of data movement provide a tool for analysis, allowing for communication bottlenecks to be pinpointed. Modern heterogeneous architectures yield increased variance in data movement as there are a number of viable paths for inter-GPU communication. In this paper, we present performance models for the various paths of inter-node communication on modern heterogeneous architectures, including the trade-off between GPUDirect communication and copying to CPUs. Furthermore, we present a novel optimization for inter-node communication based on these models, utilizing all available CPU cores per node. Finally, we show associated performance improvements for MPI collective operations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Neural Network-Based Electric Vehicle Range Prediction for Smart Charging Optimization

Range prediction is a standard feature in most modern road vehicles, allowing drivers to make informed decisions about when to refuel. Most vehicles make range predictions through data- or model-driven means, monitoring the average fuel consumption rate or using a tuned vehicle model to predict fuel consumption. The uncertainty of future driving conditions makes the range prediction problem challenging, particularly for less pervasive battery electric vehicles (BEV). Most contemporary machine learning-based methods attempt to forecast the battery SOC discharge profile to predict vehicle range. In this work, we propose a novel approach using two recurrent neural networks (RNNs) to predict the remaining range of BEVs and the minimum charge required to safely complete a trip. Each RNN has two outputs that can be used for statistical analysis to account for uncertainties; the first loss function leads to mean and variance estimation (MVE), while the second results in bounded interval estimation (BIE). These outputs of the proposed RNNs are then used to predict the probability of a vehicle completing a given trip without charging, or if charging is needed, the remaining range and minimum charging required to finish the trip with high probability. Training data was generated using a low-order physics model to estimate vehicle energy consumption from historical drive cycle data collected from medium-duty last-mile delivery vehicles. Here, the proposed method demonstrated high accuracy in the presence of day-to-day route variability, with the root-mean-square error (RMSE) below 6% for both RNN models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Update of CEA DES Criticality-Safety Activities and Perspectives [Slides]

Neutronics staff at CEA DES are in charge of a whole set of modeling and simulation tools for neutronics: nuclear data evaluation and processing, transport codes development, calculation sequences edition, verification, validation and uncertainty analysis. Advanced user groups are in charge of specific nuclear analysis in reactor physics, fuel cycle, criticality-safety, radiation shielding and nuclear instrumentation for CEA and its partners, at all stages of nuclear facilities life cycle. CEA is willing to participate/collaborate on the following issues: detector design, experiments, analysis and evaluation, DH validation by combining TAGS measurements with Fission Yield variance-covariance matrices.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

Estimation of uncertainties for subcritical benchmark measurements - 14387

Subcritical measurements are used for many applications including nonproliferation, treaty verification, in-situ monitoring for criticality, and reactor subcriticality monitoring. Most analysis methods involve correlated neutron detection, based upon the property that fission events can create multiple neutrons that are born at nearly the same time (on a scale of 10{sup -13} or 10{sup -14} seconds). Many different time-correlated methods have been used since the 1950's and are still widely utilized today. This particular work describes an uncertainty analysis of measured data using the Hage-Cifarelli formalism of the Feynman Variance-to-Mean method. The Feynman Variance-to-Mean method was introduced by Feynman et al. in 1956 and is based upon the differences between the detector count data and the data that would be expected from measurements taken from a Poissonian source. The Hage-Cifarelli formalism relates moments of the Feynman histograms to several parameters of a system: leakage multiplication (M{sub L}), spontaneous fission rate (F{sub S}), (α,n) neutron emission rate (S{sub α}), detector efficiency (ε), and the moments of the number of neutrons emitted per fission (ν). The first part of the uncertainty method described in this work will show how to determine uncertainties of the first (R{sub 1}) and second (R{sub 2}) reduced factorial moments of the Feynman histograms. The second part of the method will describe how to determine the uncertainties in the other parameters given uncertainties in the reduced factorial moments; in particular, this work will focus on determining the uncertainties in M{sub L} and F{sub S}. This work will utilize measured data from a recent benchmark evaluation with a 4.5 kg sphere of α-phase plutonium to validate the uncertainty analysis. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Assessing the predictive impact of factor fixing with an adaptive uncertainty-based approach .

Despite widespread use of factor fixing in environmental modeling, its effect on model predictions has received little attention and is instead commonly presumed to be negligible. We propose a proof-of-concept adaptive method for systematically investigating the impact of factor fixing. The method uses Global Sensitivity Analysis methods to identify groups of sensitive parameters, then quantifies which groups can be safely fixed at nominal values without exceeding a maximum acceptable error, demonstrated using the 21-dimensional Sobol’ G-function. Furthermore, three error measures are considered for quantities of interest, namely Relative Mean Absolute Error, Pearson Product-Moment Correlation and Relative Variance. Results demonstrate that factor fixing may cause large errors in the model results unexpectedly, when preliminary analysis suggests otherwise, and that the default value selected affects the number of factors to fix. To improve the applicability and methodological development of factor fixing, a new research agenda encompassing five opportunities is discussed for further attention.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Solving the structure of “single-atom” catalysts using machine learning – assisted XANES analysis

We show that "single-atom” catalysts (SACs) have demonstrated excellent activity and selectivity in challenging chemical transformations such as photocatalytic CO 2 reduction. For heterogeneous photocatalytic SAC systems, it is essential to obtain sufficient information of their structure at the atomic level in order to understand reaction mechanisms. In this work, a SAC was prepared by grafting a molecular cobalt catalyst on a light-absorbing carbon nitride surface. Due to the sensitivity of the X-ray absorption near edge structure (XANES) spectra to subtle variances in the Co SAC structure in reaction conditions, different machine learning (ML) methods, including principal component analysis, K-means clustering, and neural network (NN), were utilized for in situ Co XANES data analysis. As a result, we obtained quantitative structural information of the SAC nearest atomic environment thereby extending the NN-XANES approach previously demonstrated for nanoparticles and size-selective clusters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Sources of error in detonation calorimeters and error analysis for neat 2,4,6-triamino-1,3,5- trinitrobenzene (TATB)

Here, a calorimeter for measuring heats of detonation at Lawrence Livermore National Laboratory is described. A calibration precision of 0.2 % at the 95 % confidence interval (CI) is reported. Sources of uncertainty are discussed, including nonequivalent sources, which are those arising from differences between calibration and experimental tests. The systematic error due to nonequivalent sources is bounded to 0.19–0.22 % of the measured heat for a standard detonation test where the confinement material is gold. The recommendation is to correct for the systematic error by adding 0.19 % to the reported value and adding 0.03 % to the uncertainty. It is demonstrated that the precision of a detonation test is variable with testing duration because a source of uncertainty resides in the thermodynamic correction factor k6, which accounts for the contribution to heat by stirring; the generated power is additive and therefore highly impacted by total test time. The relative proportion also varies with the magnitude of heat release and sample size, adding variance to the weight of the error arising from the correction factor. A full error analysis based on the described sources of uncertainty is developed. The methodology is applied to a test series on neat 2,4,6-triamino-1,3,5- trinitrobenzene (TATB), demonstrating an ultimate precision of 0.7 % (single test) for materials testing and a relative standard deviation of 1.8 %.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Co-optimized machine-learned manifold models for large eddy simulation of turbulent combustion

Many modeling approaches in large eddy simulation (LES) of turbulent combustion employ a projection of the thermochemical state onto a low-dimensional manifold within state space to reduce the number of transported variables and hence computational cost. Flamelet-generated manifolds (FGM) is an example of a well-established, physics-based approach, but increasingly, principal component analysis (PCA) is being used as a data-driven method for generating manifold models. For both approaches, the nonlinear relationship between the location on the predefined manifold and the outputs of interest, such as reaction rates, can be tabulated or encoded in a neural network. This work proposes a new approach for manifold modeling that extends these existing approaches. A modified neural network structure simultaneously encodes the definition of the manifold variables, the nonlinear mapping, and the subfilter closure for LES. This allows all three of these aspects of the model to be co-optimized, generating a model from any source of combustion thermochemical state data. The manifold parameterizing variables are constrained to be linear combinations of species, as in FGM and PCA-based models, to aid in interpretability and implementation. For LES, subfilter variances of the manifold variables are also included as inputs. Two types of a priori analysis are performed to evaluate the new approach. In the first, the model is trained on data from one-dimensional premixed flames. In this case, the approach recovers the behavior of flamelet-based manifold approaches, and in fact slightly improves performance by identifying an optimized progress variable. The approach is also applied to data from direct numerical simulations of spherical ignition kernels in isotropic turbulence. For any specified manifold dimensionality, the new approach provides substantially lower prediction errors than a PCA-based model developed from the same data set. Additionally, the LES formulation of the new approach can provide accurate predictions for filtered reaction rates across a variety of filter widths.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Polynomial chaos expansions on principal geodesic Grassmannian submanifolds for surrogate modeling and uncertainty quantification

In this work we introduce a manifold learning-based surrogate modeling framework for uncertainty quantification in high-dimensional stochastic systems. Our first goal is to perform data mining on the available simulation data to identify a set of low-dimensional (latent) descriptors that efficiently parameterize the response of the high-dimensional computational model. To this end, we employ Principal Geodesic Analysis on the Grassmann manifold of the response to identify a set of disjoint principal geodesic submanifolds, of possibly different dimension, that captures the variation in the data. Since operations on the Grassmann require the data to be concentrated, we propose an adaptive algorithm based on Riemannian K-means and the minimization of the sample Fréchet variance on the Grassmann manifold to identify “local” principal geodesic submanifolds that represent different system behavior across the parameter space. Polynomial chaos expansion is then used to construct a mapping between the random input parameters and the projection of the response on these local principal geodesic submanifolds. Here, the method is demonstrated on four test cases, a toy-example that involves points on a hypersphere, a Lotka-Volterra dynamical system, a continuous-flow stirred-tank chemical reactor system, and a two-dimensional Rayleigh-Bénard convection problem.

42 ENGINEERING↗
DOE OSTI2022

Electrical Substation Configuration Effect on Substation Reliability

The nation’s electrical grid must be reliable because of its critical role in the economy, defense, and security of the nation. Many common tasks and industrial operations rely on proper electrical power distribution. In the design of new bus systems or evaluation of existing bus systems, the type of bus configuration affects the risk, reliability, maintenance, and cost of the system. This work utilizes Systems Analysis Programs for Hands-on Integrated Reliability Evaluations (SAPHIRE) to perform a probabilistic risk assessment (PRA) based sensitivity study on the reliability of substations with differing bus configurations. This study examines single bus, main and transfer, breaker and a half, double bus/double breaker, and ring bus configurations applied to busses of varying number of lines. The case variances, methods, and rankings presented can provide a guide for bus configuration for future electrical grid design and analysis.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

Sources of Technical Variability in Quantitative LC-MS Proteomics: Human Brain Tissue Sample Analysis.

To design a robust quantitative proteomics study, an understanding of both the inherent heterogeneity of the biological samples being studied as well as the technical variability of the proteomics methods and platform is needed. Additionally, accurately identifying the technical steps associated with the largest variability would provide valuable information for the improvement and design of future processing pipelines. We present an experimental strategy that allows for a detailed examination of the variability of the quantitative LC-MS proteomics measurements. By replicating analyses at different stages of processing, various technical components can be estimated and their individual contribution to technical variability can be dissected. This design can be easily adapted to other quantitative proteomics pipelines. Herein, we applied this methodology to our label-free workflow for the processing of human brain tissue. For this application, the pipeline was divided into four critical components: Tissue dissection and homogenization (extraction), protein denaturation followed by trypsin digestion and SPE clean-up (digestion), short-term run-to-run instrumental response fluctuation (instrumental variance), and long-term drift of the quantitative response of the LC-MS/MS platform over the 2 week period of continuous analysis (instrumental stability). From this analysis, we found the following contributions to variability: extraction (72%) >> instrumental variance (16%) > instrumental stability (8.4%) > digestion (3.1%). Furthermore, the stability of the platform and its’ suitability for discovery proteomics studies is demonstrated.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2025

Polynomial Chaos Surrogate Construction for Random Fields with Parametric Uncertainty

Engineering and applied science rely on computational experiments to rigorously study physical systems. The mathematical models used to probe these systems are highly complex, and sampling-intensive studies often require prohibitively many simulations for acceptable accuracy. Surrogate models provide a means of circumventing the high computational expense of sampling such complex models. In particular, polynomial chaos expansions (PCEs) have been successfully used for uncertainty quantification studies of deterministic models where the dominant source of uncertainty is parametric. We discuss an extension to conventional PCE surrogate modeling to enable surrogate construction for stochastic computational models that have intrinsic noise in addition to parametric uncertainty. We develop a PCE surrogate on a joint space of intrinsic and parametric uncertainty, enabled by Rosenblatt transformations, which are evaluated via kernel density estimation of the associated conditional cumulative distributions. Furthermore, we extend the construction to random field data via the Karhunen–Loève expansion. We then take advantage of closed-form solutions for computing PCE Sobol indices to perform a global sensitivity analysis of the model which quantifies the intrinsic noise contribution to the overall model output variance. Additionally, the resulting joint PCE is generative in the sense that it allows generating random realizations at any input parameter setting that are statistically approximately equivalent to realizations from the underlying stochastic model. The method is demonstrated on a chemical catalysis example model and a synthetic example controlled by a parameter that enables a switch from unimodal to bimodal response distributions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Towards retrieving critical relative humidity from ground-based remote sensing observations

Nearly all parameterisations of large-scale cloud require the specification of the critical relative humidity (RHcrit). This is the gridbox-mean relative humidity at which the subgrid fluctuations in temperature and water vapour become so large that part of a subsaturated gridbox becomes saturated and cloud starts to form. Until recently, the lack of high-resolution observations of temperature and moisture variability has hindered a reasonable estimate of the RHcrit from observations. However, with the advent of ground-based measurements from Raman lidar, it becomes possible to obtain long records of temperature and moisture (co-)variances with sub-minute sample rates. Lidar observations are inherently noisy and any analysis of higher-order moments will be very dependent on the ability to quantify and remove this noise. We present an exporatory study aimed at understanding whether current noise levels of lidar-retrieved temperature and water vapour are sufficient to obtain a reasonable estimate of the RHcrit. We show that vertical profiles of RHcrit can be derived for a gridbox length of up to about 30 km (120) with an uncertainty of about 4 % (2 %). RHcrit tends to be smallest near the scale height and seems to be fairly insensitive to the horizontal grid spacing at the scales investigated here (30 - 120 km). However, larger sensitivity was found to the vertical grid spacing. As the grid spacing decreases from 400 to 100 m, RHcrit is observed to increase by about 6 %, which is more than the uncertainty in the RHcrit retrievals.

Van Weverberg, Kwinten↗