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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

A unified approach to evaluating precipitation frequency estimates with uncertainty quantification: Application to Florida and California watersheds

Although a variety of climate datasets are now available, including observational and model products, there remains a pressing need for the development of a unified, automated and application-focused approach for evaluating and comparing climate extremes within and across these datasets. Precipitation frequency (PF) es- timates for extreme precipitation events are useful decision-relevant quantities among water managers. Our objective and quantitative framework for computing PF estimates of any duration uses known techniques in a novel combination. Our framework consists of three steps: (1) use of a regionalization-based statistical method for precipitation frequency estimation at observed sites; this involves hierarchical clustering (here we used Ward’s method) for delineating homogeneous regions, and regional frequency analysis using L-moments; (2) use of kriging for spatial mapping because of its advantages over other conventional interpolation methods; (3) use of z-scores accounts for estimation uncertainties when comparing PF estimates between datasets. The 24-h PF estimates in the Kissimmee-Southern Florida watershed (flat topography) and the Sacramento-San Joaquin watershed (complex topography) demonstrate the applicability of this approach in regions of any geographical complexity. Our results are shown to be reasonably calibrated using the probability integral transform (PIT) histogram of the best fitted distribution. Our results are compared with that of the NOAA Atlas 14 reports and show that for most of the stations the two estimates are statistically indistinguishable; and, the confidence intervals of our estimates are narrower than those of the NOAA estimates. Our approach is applicable to a variety of datasets and provides a baseline for assessing performance of climate models in historical simulations – a necessary first step towards analyzing future projections.

58 GEOSCIENCES↗
DOE OSTI2017

Transformations based on continuous piecewise-affine velocity fields

Here, we propose novel finite-dimensional spaces of well-behaved Rn → Rn transformations. The latter are obtained by (fast and highly-accurate) integration of continuous piecewise-affine velocity fields. The proposed method is simple yet highly expressive, effortlessly handles optional constraints (e.g., volume preservation and/or boundary conditions), and supports convenient modeling choices such as smoothing priors and coarse-to-fine analysis. Importantly, the proposed approach, partly due to its rapid likelihood evaluations and partly due to its other properties, facilitates tractable inference over rich transformation spaces, including using Markov-Chain Monte-Carlo methods. Its applications include, but are not limited to: monotonic regression (more generally, optimization over monotonic functions); modeling cumulative distribution functions or histograms; time-warping; image warping; image registration; real-time diffeomorphic image editing; data augmentation for image classifiers. Our GPU-based code is publicly available.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

When to use rsync

We have endeavored to show, using a series of data transfer results obtained from two testbeds, when to use the popular data copying tool rsync and related tools. Tests have been conducted in local area network (LAN) and wide area network (WAN) environments. We conclude that for files in a certain size range and network latency ≦ 10 ms round trip time (RTT), rsync is still useful for data moving tasks in the category 4 of the U.S. DOE Technical Report “Data Movement Categories”. For more demanding data movement requirements, tools of different classes are suggested. Sample histograms from two DOE user facilities are provided to further support our conclusions.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1967

A parameter extraction technique

Parameter extraction technique, based on algebraic approach, to video data displayed in histogram form and implemented for IMP flights

IMP↗
NASA NTRS2013

Global Weather States and Their Properties from Passive and Active Satellite Cloud Retrievals

In this study, the authors apply a clustering algorithm to International Satellite Cloud Climatology Project (ISCCP) cloud optical thickness-cloud top pressure histograms in order to derive weather states (WSs) for the global domain. The cloud property distribution within each WS is examined and the geographical variability of each WS is mapped. Once the global WSs are derived, a combination of CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) vertical cloud structure retrievals is used to derive the vertical distribution of the cloud field within each WS. Finally, the dynamic environment and the radiative signature of the WSs are derived and their variability is examined. The cluster analysis produces a comprehensive description of global atmospheric conditions through the derivation of 11 WSs, each representing a distinct cloud structure characterized by the horizontal distribution of cloud optical depth and cloud top pressure. Matching those distinct WSs with cloud vertical profiles derived from CloudSat and CALIPSO retrievals shows that the ISCCP WSs exhibit unique distributions of vertical layering that correspond well to the horizontal structure of cloud properties. Matching the derived WSs with vertical velocity measurements shows a normal progression in dynamic regime when moving from the most convective to the least convective WS. Time trend analysis of the WSs shows a sharp increase of the fair-weather WS in the 1990s and a flattening of that increase in the 2000s. The fact that the fair-weather WS is the one with the lowest cloud radiative cooling capability implies that this behavior has contributed excess radiative warming to the global radiative budget during the 1990s.

histograms↗
DOE OSTI2016

Convective cloud vertical velocity and mass-flux characteristics from radar wind profiler observations during GoAmazon2014/5: VERTICAL VELOCITY GOAMAZON2014/5

A radar wind profiler data set collected during the 2 year Department of Energy Atmospheric Radiation Measurement Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) campaign is used to estimate convective cloud vertical velocity, area fraction, and mass flux profiles. Vertical velocity observations are presented using cumulative frequency histograms and weighted mean profiles to provide insights in a manner suitable for global climate model scale comparisons (spatial domains from 20 km to 60 km). Convective profile sensitivity to changes in environmental conditions and seasonal regime controls is also considered. Aggregate and ensemble average vertical velocity, convective area fraction, and mass flux profiles, as well as magnitudes and relative profile behaviors, are found consistent with previous studies. Updrafts and downdrafts increase in magnitude with height to midlevels (6 to 10 km), with updraft area also increasing with height. Updraft mass flux profiles similarly increase with height, showing a peak in magnitude near 8 km. Downdrafts are observed to be most frequent below the freezing level, with downdraft area monotonically decreasing with height. Updraft and downdraft profile behaviors are further stratified according to environmental controls. These results indicate stronger vertical velocity profile behaviors under higher convective available potential energy and lower low-level moisture conditions. Sharp contrasts in convective area fraction and mass flux profiles are most pronounced when retrievals are segregated according to Amazonian wet and dry season conditions. During this deployment, wet season regimes favored higher domain mass flux profiles, attributed to more frequent convection that offsets weaker average convective cell vertical velocities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Diagnosing Cloud Biases in the GFDL AM3 Model With Atmospheric Classification

In this paper, we define a set of 21 atmospheric states, or recurring weather patterns, for a region surrounding the Atmospheric Radiation Measurement Program's Southern Great Plains site using an iterative clustering technique. The states are defined using dynamic and thermodynamic variables from reanalysis, tested for statistical significance with cloud radar data from the Southern Great Plains site, and are determined every 6 h for 14 years, creating a time series of atmospheric state. The states represent the various stages of the progression of synoptic systems through the region (e.g., warm fronts, warm sectors, cold fronts, cold northerly advection, and high-pressure anticyclones) with a subset of states representing summertime conditions with varying degrees of convective activity. We use the states to classify output from the NOAA/Geophysical Fluid Dynamics Laboratory AM3 model to test the model's simulation of the frequency of occurrence of the states and of the cloud occurrence during each state. The model roughly simulates the frequency of occurrence of the states but exhibits systematic cloud occurrence biases. Comparison of observed and model-simulated International Satellite Cloud Climatology Project histograms of cloud top pressure and optical thickness shows that the model lacks high thin cloud under all conditions, but biases in thick cloud are state-dependent. Frontal conditions in the model do not produce enough thick cloud, while fair-weather conditions produce too much. Finally, we find that increasing the horizontal resolution of the model improves the representation of thick clouds under all conditions but has little effect on high thin clouds. However, increasing resolution also changes the distribution of states, causing an increase in total cloud occurrence bias.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Value of Formalin Fixation for the Prolonged Preservation of Rodent Myocardial Microanatomical Organization: Evidence by MR Diffusion Tensor Imaging

Previous ex vivo diffusion tensor imaging (DTI) studies on formalin-fixed myocardial tissue assumed that, after some initial changes in the first 48 hr since the start of fixation, DTI parameters remain stable over time. Prolonged preservation of cardiac tissue in formalin prior to imaging has been seen many times in the DTI literature as it is considered orderly. Here, our objective is to define the effects of the prolonged cardiac tissue exposure to formalin on tissue microanatomical organization, as this is assessed by DTI parameters. DTI experiments were conducted on eight excised rodent hearts that were fixed by immersion in formalin. The samples were randomly divided into two equinumerous groups corresponding to shorter (~2 weeks) and more prolonged (~6–8 weeks) durations of tissue exposure to formalin prior to imaging. We found that when the duration of cardiac tissue exposure to formalin before imaging increased, water diffusion became less restricted, helix angle (HA) histograms flattened out and exhibited heavier tails (even though the classic HA transmural variation was preserved), and a significant loss of inter-voxel primary diffusion orientation integrity was introduced. The prolonged preservation of cardiac tissue in formalin profoundly affected its microstructural organization, as this was assessed by DTI parameters. The accurate interpretation of diffusivity profiles necessitates awareness of the pitfalls of prolonged cardiac tissue exposure duration to formalin. The acquired knowledge works to the advantage of a proper experimental design of DTI studies of fixed hearts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Predictive analytics of selections of russet potatoes

We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato (Solanum tuberosum L.) clones in breeding trials by predicting their suitability for advancement. This study addresses the challenge of efficiently identifying high-yield, disease-resistant, and climate-resilient potato varieties that meet processing industry standards. Leveraging manually collected data from trials in the state of Oregon, we investigate the potential of a wide variety of state-of-the-art binary classification models. The dataset includes 1086 clones, with data on 38 attributes recorded for each clone, focusing on yield, size, appearance, and frying characteristics, with several control varieties planted consistently across four Oregon regions from 2013 to 2021. We conduct a comprehensive analysis of the dataset that includes preprocessing, feature engineering, and imputation to address missing values. We focus on several key metrics such as accuracy, F1-score, and Matthews correlation coefficient (MCC) for model evaluation. The top-performing models, namely a feedforward neural network classifier (Neural Net), a histogram-based gradient boosting classifier (HGBC), and a support vector machine classifier (SVM), demonstrate consistent and significant results. To further validate our findings, we conducted a simulation study using the aims, data-generating mechanisms, estimands, methods, and performance measures (ADEMP) framework, simulating different data-generating scenarios to assess model robustness and performance through true positive, true negative, false positive, and false negative distributions, area under the receiver operating characteristic curve (AUC-ROC) and MCC. The simulation results highlight that non-linear models like SVM and HGBC consistently show higher AUC-ROC and MCC than logistic regression, thus outperforming the traditional linear model across various distributions, and emphasizing the importance of model selection and tuning in agricultural trials. Variable selection further enhances model performance and identifies influential features in predicting trial outcomes. The findings emphasize the potential of machine learning in streamlining the selection process for potato varieties, offering benefits such as increased efficiency, substantial cost savings, and judicious resource utilization. Our study contributes insights into precision agriculture and showcases the relevance of advanced technologies for informed decision-making in breeding programs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Modeling Hot Spot Experiments on Shocked Octahydro-1,3,5,7-Tetranitro-1,3,5,7-Tetrazocine

The cover picture shows a shock-induced reaction in a porous bed of octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX). Our study shows compaction, reaction, and gas jetting during the initial stages of shock in the powder bed. Shock reflection at the glass window interface increases hot spot temperatures in HMX product gas in excess of 7000 K at higher impact speeds which is readily observable using pyrometry techniques. We extract a temperature histogram from simulations at discrete times, using it to calculate an effective emission spectrum and effective temperature, T eff , for comparison to data. Calculating T eff based on Planck's Law with a gray body assumption demonstrates good agreement with hot spot temperature data. The agreement with experimental results validates our mesoscale simulations as useful tools for elucidating the role of hot spot mechanisms in the shock initiation of heterogeneous explosives. Finally, more details can be found in the Full Paper by Harry Springer, Will Bassett, Sorin Bastea, Svjetlana Stekovic, and Craig Tarver on page 330 ff.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Image Distinguishability Analysis Testing Through Principal Components and Its Application to Hot Spot Scale Invariance

Hot spots are spatial regions of intense energy localization that govern initiation of secondary high explosives. Studies that characterize or compare simulated hot spots are frequently either qualitatively descriptive or resort to quantitative distribution functions that neglect stochastic variations and spatial correlations—effects that are also neglected in common comparison tests like the Kolmogorov–Smirnov test. To this end, we develop an image distinguishability analysis (IDA) test based on principal component (PC) analysis that makes pixel-by-pixel comparisons between small, for example, O(<10), image data sets. The IDA test makes comparisons through a generalized distance metric in the PC space and a test statistic that is derived to calculate mathematical equation-values. Here, we derive a statistical distribution and criticality criterion to determine whether images are distinguishable from established baselines. We apply the IDA test on images generated from molecular dynamics simulations of hot spots from pore collapse in TATB to assess scale invariance in the complex patterns of hot spots that form in a representative high explosive crystal. The IDA test shows that TATB hot spot spatial temperature fields and their derived temperature histograms exhibit scale-invariant features over specific intervals of shock orientation, strength, and initial pore diameter. However, the IDA test also shows that qualitatively different conclusions regarding invariance can be reached depending on whether the hot spot is treated as a spatially correlated field as opposed to a distribution function that lacks spatial information.

organic↗
DOE OSTI2021

Parameter inference from event ensembles and the top-quark mass

One of the key tasks of any particle collider is measurement. In practice, this is often done by fitting data to a simulation, which depends on many parameters. Sometimes, when the effects of varying different parameters are highly correlated, a large ensemble of data may be needed to resolve parameter-space degeneracies. An important example is measuring the top-quark mass, where other physical and unphysical parameters in the simulation must be profiled when fitting the top-quark mass parameter. We compare four different methodologies for top-quark mass measurement: a classical histogram fit similar to one commonly used in experiment augmented by soft-drop jet grooming; a 2D profile likelihood fit with a nuisance parameter; a machine-learning method called DCTR; and a linear regression approach, either using a least-squares fit or with a dense linearly-activated neural network. Despite the fact that individual events are totally uncorrelated, we find that the linear regression methods work most effectively when we input an ensemble of events sorted by mass, rather than training them on individual events. Although all methods provide robust extraction of the top-quark mass parameter, the linear network does marginally best and is remarkably simple. For the top study, we conclude that the Monte-Carlo-based uncertainty on current extractions of the top-quark mass from LHC data can be reduced significantly (by perhaps a factor of 2) using networks trained on sorted event ensembles. More generally, machine learning from ensembles for parameter estimation has broad potential for collider physics measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Data-driven high-dimensional statistical inference with generative models

Crucial to many measurements at the LHC is the use of correlated multi-dimensional information to distinguish rare processes from large backgrounds, which is complicated by the poor modeling of many of the crucial backgrounds in Monte Carlo simulations. In this work, we introduce HI-SIGMA, a method to perform unbinned high-dimensional statistical inference with data-driven background distributions. In contradistinction to many applications of Simulation Based Inference in High Energy Physics, HI-SIGMA relies on generative ML models, rather than classifiers, to learn the signal and background distributions in the high-dimensional space. These ML models allow for interpretable inference while also incorporating model errors and other sources of systematic uncertainties. We showcase this methodology on a simplified version of a di-Higgs measurement in the bbγγ final state, where the di-photon resonance allows for background interpolation from sidebands into the signal region. We demonstrate that HI-SIGMA provides improved sensitivity as compared to standard classifier-based methods, and that systematic uncertainties can be straightforwardly incorporated by extending methods which have been used for histogram based analyses.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
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 OSTI2019

Computed tomography-based texture analysis of bladder cancer: differentiating urothelial carcinoma from micropapillary carcinoma

Purpose: The purpose of the study is to determine the feasibility of using computed tomography-based texture analysis (CTTA) in differentiating between urothelial carcinomas (UC) of the bladder from micropapillary carcinomas (MPC) of the bladder. Methods: Regions of interests (ROIs) of computerized tomography (CT) images of 33 MPCs and 33 UCs were manually segmented and saved. Custom MATLAB code was used to extract voxel information corresponding to the ROI. The segmented tumors were input to a pre-existing radiomics platform with a CTTA panel. A total of 58 texture metrics were extracted using four different texture extraction techniques and statistically analyzed using a Wilcoxon rank-sum test to determine the differences between UCs and MPCs. Results: Of the 58 texture metrics extracted using the gray level co-occurrence matrix (GLCM) and gray level difference matrix (GLDM), 28 texture metrics were statistically significant (p < 0.05) for differences in tumor textures and 27 texture metrics were statistically significant (p < 0.05) for peritumoral fat textures. The remaining nine metrics extracted using histogram and fast Fourier transform analyses did not show significant differences between the textures of the tumors and their peritumoral fat. Conclusions: CTTA shows that MPC have a more heterogeneous texture compared to UC. As visual discrimination of MPC from UC from clinical CT scans are difficult, results from this study suggest that tumor heterogeneity extracted using GLCM and GLDM may be a good imaging aid in segregating MPC from UC. This tool can aid clinicians in further sub-classifying bladder cancers on routine imaging, a process which has potential to alter treatment and patient care.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Can MRI be used to diagnose histologic grade in T1a (< 4 cm) clear cell renal cell carcinomas?

Objective: To assess whether MRI can differentiate low-grade from high-grade T1a cc-RCC. Materials and methods: With IRB approval, 49 consecutive solid < 4 cm cc-RCC (low grade [Grade 1 or 2] N = 38, high grade [Grade 3] N = 11) with pre-operative MRI before nephrectomy were identified between 2013 and 2018. Tumor size, apparent diffusion coefficient (ADC) histogram analysis, enhancement wash-in and wash-out rates, and chemical shift signal intensity index (SI index) were assessed by a blinded radiologist. Subjectively, two blinded Radiologists also assessed for (1) microscopic fat, (2) homogeneity (5-point Likert scale), and (3) ADC signal (relative to renal cortex); discrepancies were resolved by consensus. Outcomes were studied using Chi square, multivariate analysis, logistic regression modeling, and ROC. Inter-observer agreement was assessed using Cohen’s kappa. Results: Tumor size was 24 ± 7 (13–39) mm with no association to grade (p = 0.45). Among quantitative features studied, corticomedullary phase wash-in index (p = 0.015), SI index (p = 0.137), and tenth-centile ADC (p = 0.049) were higher in low-grade tumors. 36.8% (14/38) low-grade tumors versus zero high-grade tumors demonstrated microscopic fat (p = 0.015; Kappa = 0.67). Microscopic fat was specific for low-grade disease (100.0% [71.5–100.0]) with low sensitivity (36.8% [21.8–54.6]). Other subjective features did not differ between groups (p > 0.05). A logistic regression model combining microscopic fat + wash-in index + tenth-centile-ADC yielded area under ROC curve 0.98 (Confidence Intervals 0.94–1.0) with sensitivity/specificity 87.5%/100%. Conclusion: The combination of microscopic fat, higher corticomedullary phase wash-in and higher tenth-centile ADC is highly accurate for diagnosis of low-grade disease among T1a clear cell RCC.

62 RADIOLOGY AND NUCLEAR MEDICINE↗