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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Machine-learning from Pseudomonas putida KT2440 transcriptomes reveals its transcriptional regulatory network

Bacterial gene expression is orchestrated by numerous transcription factors (TFs). Elucidating how gene expression is regulated is fundamental to understanding bacterial physiology and engineering it for practical use. In this study, a machine-learning approach was applied to uncover the genome-scale transcriptional regulatory network (TRN) in Pseudomonas putida KT2440, an important organism for bioproduction. We performed independent component analysis of a compendium of 321 high-quality gene expression profiles, which were previously published or newly generated in this study. We identified 84 groups of independently modulated genes (iModulons) that explain 75.7% of the total variance in the compendium. With these iModulons, we (i) expand our understanding of the regulatory functions of 39 iModulon associated TFs (e.g., HexR, Zur) by systematic comparison with 1993 previously reported TF-gene interactions; (ii) outline transcriptional changes after the transition from the exponential growth to stationary phases; (iii) capture group of genes required for utilizing diverse carbon sources and increased stationary response with slower growth rates; (iv) unveil multiple evolutionary strategies of transcriptome reallocation to achieve fast growth rates; and (v) define an osmotic stimulon, which includes the Type VI secretion system, as coordination of multiple iModulon activity changes. Taken together, this study provides the first quantitative genome-scale TRN for P. putida KT2440 and a basis for a comprehensive understanding of its complex transcriptome changes in a variety of physiological states.

09 BIOMASS FUELS↗
DOE OSTI2015

The parametric sensitivity of CAM5's MJO

Abstract We systematically explore the ability of the Community Atmospheric Model version 5 (CAM5) to simulate the Madden‐Julian Oscillation (MJO), through an analysis of MJO metrics calculated from a 1100‐member perturbed parameter ensemble of 5 year simulations with observed sea surface temperatures. Parameters from the deep convection scheme make the greatest contribution to the variance in MJO simulation quality with a much smaller contribution from parameters in the large‐scale cloud, shallow convection, and boundary layer turbulence schemes. Improved MJO variability results from a larger lateral entrainment rate and a reduction in the precipitation efficiency of deep convection that was achieved by a smaller autoconversion of cloud to rainwater and a larger evaporation of convective precipitation. Unfortunately, simulations with an improved MJO also have a significant negative impact on the climatological values of low‐level cloud and absorbed shortwave radiation, suggesting that structural in addition to parametric modifications to CAM5's parameterization suite are needed in order to simultaneously well simulate the MJO and mean‐state climate.

Boyle, J. S.↗
DOE OSTI2017

Global Sensitivity of Simulated Water Balance Indicators Under Future Climate Change in the Colorado Basin

The Colorado River basin is a fundamentally important river for society, ecology and energy in the United States. Streamflow estimates are often provided using modeling tools which rely on uncertain parameters; sensitivity analysis can help determine which parameters impact model results. Despite the fact that simulated flows respond to changing climate and vegetation in the basin, parameter sensitivity of the simulations under climate change has rarely been considered. In this study, we conduct a global sensitivity analysis to relate changes in runoff, evapotranspiration, snow water equivalent and soil moisture to model parameters in the Variable Infiltration Capacity (VIC) hydrologic model. Here, we combine global sensitivity analysis with a space-filling Latin Hypercube sampling of the model parameter space and statistical emulation of the VIC model to examine sensitivities to uncertainties in 46 model parameters following a variance-based approach.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Vibratory Processes in Vessel Steel Damage Kinetics in Nuclear Reactors

The possible reasons for nonstandard embrittlement and significant variance of the experimental data in the study of the impact of neutron irradiation on the properties of reactor vessel materials are analyzed. The authors assume on the basis of experimental data and analysis of foreign research that the fluence dependence of the degree of radiation embrittlement can be of nonmonotonic as well as cyclic. The observation of vibratory processes in the embrittlement kinetics of vessel steel attests periodic transformation of structure and change in the properties of the metal. It is expected that the defect structure formed as a result of neutron bombardment undergoes transformation once or periodically in the direction of losses as well as restoration of the initial properties. The mentioned data shape the search for methods of regulating the radiation resistance of materials.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

Cosmology with massive neutrinos II: on the universality of the halo mass function and bias

We use a large suite of N-body simulations to study departures from universality in halo abundances and clustering in cosmologies with non-vanishing neutrino masses. To this end, we study how the halo mass function and halo bias factors depend on the scaling variable σ{sup 2}(M,z), the variance of the initial matter fluctuation field, rather than on halo mass M and redshift z themselves. We show that using the variance of the cold dark matter rather than the total mass field, i.e., σ{sup 2}{sub cdm}(M,z) rather than σ{sup 2}{sub m}(M,z), yields more universal results. Analysis of halo bias yields similar conclusions: when large-scale halo bias is defined with respect to the cold dark matter power spectrum, the result is both more universal, and less scale- or k-dependent. These results are used extensively in Papers I and III of this series.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Parameter constraints from cross-correlation of CMB lensing with galaxy clustering

The lensing convergence measurable with future CMB surveys like CMB-S4 will be highly correlated with the clustering observed by deep photometric large scale structure (LSS) surveys such as the LSST, with cross-correlation coefficient as high as 95%. This will enable use of sample variance cancellation techniques to determine cosmological parameters, and use of cross-correlation measurements to break parameter degeneracies. Assuming large sky overlap between CMB-S4 and LSST, we show that a joint analysis of CMB-S4 lensing and LSST clustering can yield very tight constraints on the matter amplitude σ 8 (z), halo bias, and f NL , competitive with the best stage IV experiment predictions, but using complementary methods, which may carry different and possibly lower systematics. Having no sky overlap between experiments degrades the precision of σ 8 (z) by a factor of 20, and that of f NL by a factor of 1.5 to 2. Without CMB lensing, the precision always degrades by an order of magnitude or more, showing that a joint analysis is critical. Here, our results also suggest that CMB lensing in combination with LSS photometric surveys is a competitive probe of the evolution of structure in the redshift range z ≃ 1–7, probing a regime that is not well tested observationally. We explore predictions against other surveys and experiment configurations, finding that wide patches with maximal sky overlap between CMB and LSS surveys are most powerful for σ 8 (z) and f NL .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Methodology for Sensitivity and Uncertainty-Based Criticality Safety Validation

Monte Carlo radiation transport software is used extensively for determining the effective multiplication factor k of fissionable systems towards assessing the criticality safety of operations. Understanding the predictive capability of the Monte Carlo software and nuclear data libraries for calculations of specific applications is vital towards making these assessments. Recently, sensitivity and uncertainty (S/U) methods have been developed for this purpose. A computational tool has been developed that uses the S/U methods with MCNP6 to select critical experiment benchmarks that have similar sources of computational bias, and extreme value theory is then used to determine suitable and conservative calculational margins. The S/U techniques are then used to perform nuclear data adjustments, which are then used to determine a suitable margin of subcriticality from cross section uncertainties. This document gives an overview of the theory of S/U methods used for criticality safety validation. First an overview is given for using the sensitivity coefficients and nuclear co variance data towards determining neutronic similarity factors is then discussed. Next, the extreme value theory (EVT) to determine a calculational margin is given along with how the similarity factors are used in the analysis. Then, the Generalized Linear Least Squares (GLLS) method for performing data adjustment and how that method is used to reject benchmarks and compute adjusted nuclear data uncertainties in k are discussed. Last, a discussion of the margin of subcriticality is given along with how to apply of the GLLS data adjustment methods to quantify the effect of nuclear data variability on k.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

TBASS: A Robust Adaptation of Bayesian Adaptive Spline Surfaces

The R package TBASS is an extension of the BASS package created by Francom and Sansó (2019). The package is used to fit a Bayesian multivariate adaptive spline to a dataset that either follows a Student’s t-distribution or has outliers. Much of the framework for TBASS is adapted from the concepts of Bayesian Multivariate Adaptive Regression Splines (BMARS), specifically the work done by Denison, Mallick, and Smith (1998). The spline function is fit using a Reversible-Jump Markov Chain Monte Carlo algorithm,. By including this more robust generalization, a dataset with outliers can be accurately fit using the BMARS model, without the possibility of overfitting or variance inflation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

FY25 Theory and Simulation Performance Target: Development of an integrated modeling framework for fusion reactor design and assessment (Final Report)

This report documents the FY25 Theory and Simulation Performance Target (TSPT) of developing an integrated modeling framework for fusion reactor design and assessment (FREDA). Over Q1-Q4, new capabilities were developed across both plasma and engineering domains and demonstrated on an example representation of a Compact Advanced Tokamak with a Dual Cooled Lead Lithium blanket. This represents a first-of-a-kind demonstration of coupled core-to-wall-to-engineering for a reactor. Self-consistent CESOL workflows were applied to provide core, pedestal, and SOL prediction; new modules were developed for energetic particle stability (FAR3D) and transport (TGLF-EP) analysis; and boundary plasma modeling (SOLPS-ITER, BOUT++/Hermes-3) was expanded to evaluate wall and divertor heat fluxes and interface with engineering thermal analysis. A parameterized CAD tool, TRACER, was expanded to generate medium-fidelity divertor, blanket, and coil geometries; OpenFOAM and Diablo workflows were applied for first-wall and divertor thermal analyses with helium cooling; and reduced-order models were created for high-mass-flux divertor cooling. Magnet multiphysics capabilities were verified between Elmer, Diablo, and a new MFEM-based solver, and workflows enable stress, thermal, and neutron-fluence analysis of TF coils with neutronics-driven heating. Nuclear and blanket analysis workflows were demonstrated, including tritium breeding, transport, and CFD-informed thermo-mechanical assessment. Preliminary multi-fidelity uncertainty quantification workflows were applied to boundary modeling codes and shown to achieve variance reductions with fewer high-fidelity boundary simulations. Key findings highlight the challenges of resolving the ITEP gap to find suitable balance between wall and divertor loads, neutron heating, and practical limits of PFC cooling. Next step priorities are to develop automated workflows to check boundary code convergence and detachment, implement tighter physics-engineering CAD provenance tracking, and inclusion of plasma-material interface models for SLAG and tungsten cracking behavior. Collectively, these developments establish sophisticated capabilities for predictive, multi-fidelity, whole-device modeling that integrates plasma physics, materials, magnets, and nuclear engineering to guide pathways to viable Fusion Pilot Plant design points.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Structuring Nutrient Yields throughout Mississippi/Atchafalaya River Basin Using Machine Learning Approaches

To minimize the eutrophication pressure along the Gulf of Mexico or reduce the size of the hypoxic zone in the Gulf of Mexico, it is important to understand the underlying temporal and spatial variations and correlations in excess nutrient loads, which are strongly associated with the formation of hypoxia. This study’s objective was to reveal and visualize structures in high-dimensional datasets of nutrient yield distributions throughout the Mississippi/Atchafalaya River Basin (MARB). For this purpose, the annual mean nutrient concentrations were collected from thirty-three US Geological Survey (USGS) water stations scattered in the upper and lower MARB from 1996 to 2020. Eight surface water quality indicators were selected to make comparisons among water stations along the MARB over the past two decades. Principal component analysis (PCA) was used to comprehensively evaluate the nutrient yields across thirty-three USGS monitoring stations and identify the major contributing nutrient loads. The results showed that all samples could be analyzed using two main components, which accounted for 81.6% of the total variance. The PCA results showed that yields of orthophosphate (OP), silica (SI), nitrate–nitrites (NO 3 -NO 2 ), and total suspended sediment (TSS) are major contributors to nutrient yields. It also showed that land-planted crops, density of population, domestic and industrial discharges, and precipitation are fundamental causes of excess nutrient loads in MARB. These factors are of great significance for the excess nutrient load management and pollution control of the Mississippi River. It was found that the average nutrient yields were stable within the sub-MARB area, but the large nitrogen yields in the upper MARB and the large phosphorus yields in the lower MARB were of great concern. t-distributed stochastic neighbor embedding (t-SNE) revealed interesting nonlinear and local structures in nutrient yield distributions. Clustering analysis (CA) showed the detailed development of similarities in the nutrient yield distribution. Moreover, PCA, t-SNE, and CA showed consistent clustering results. This study demonstrated that the integration of dimension reduction techniques, PCA, and t-SNE with CA techniques in machine learning are effective tools for the visualization of the structures of the correlations in high-dimensional datasets of nutrient yields and provide a comprehensive understanding of the correlations in the distributions of nutrient loads across the MARB.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Semi-automatic Methods for Airway and Adjacent Vessel Measurement in Bronchiectasis Patterns in Lung HRCT Images of Cystic Fibrosis Patients

Airway and vessel characterization of bronchiectasis patterns in lung high-resolution computed tomography (HRCT) images of cystic fibrosis (CF) patients is very important to compute the score of disease severity. We propose a hybrid and evolutionary optimized threshold and model-based method for characterization of airway and vessel in lung HRCT images of CF patients. First, the initial model of airway and vessel is obtained using the enhanced threshold-based method. Then, the model is fitted to the actual image by optimizing its parameters using particle swarm optimization (PSO) evolutionary algorithm. The experimental results demonstrated the outperformance of the proposed method over its counterpart in R-squared, mean and variance of error, and run time. Moreover, the proposed method outperformed its counterpart for airway inner diameter/vessel diameter (AID/VD) and airway wall thickness/vessel diameter (AWT/VD) biomarkers in R-squared and slope of regression analysis.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Lens covariance effects on likelihood analyses of CMB power spectra

Non-Gaussian correlations induced in CMB power spectra by gravitational lensing needs to be included in likelihood analyses for future CMB experiments. We present a simple but accurate likelihood model which includes these correlations and use it for Markov Chain Monte Carlo parameter estimation from simulated lensed CMB maps in the context of ΛCDM and extensions which include the sum of neutrino masses or the dark energy equation of state w. If lensing-induced covariance is not taken into account for a CMB-S4 type experiment, the errors for one combination of parameters in each case would be underestimated by more then a factor of two and lower limits on w could be misestimated substantially. The frequency of falsely ruling out the true model or finding tension with other data sets would also substantially increase. Our analysis also enables a separation of lens and unlensed information from CMB power spectra, which provides for consistency tests of the model and, if combined with other such measurements, a nearly lens-sample-variance free test for systematics and new physics in the unlensed spectrum. This parameterization also leads to a simple effective likelihood that can be used to assist model building in case consistency tests of ΛCDM fail.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Texture analysis on the fluence map to evaluate the degree of modulation for volumetric modulated arc therapy

Purpose: Texture analysis on fluence maps was performed to evaluate the degree of modulation for volumetric modulated arc therapy (VMAT) plans. Methods: A total of six textural features including angular second moment, inverse difference moment, contrast, variance, correlation, and entropy were calculated for fluence maps generated from 20 prostate and 20 head and neck VMAT plans. For each of the textural features, particular displacement distances (d) of 1, 5, and 10 were adopted. To investigate the deliverability of each VMAT plan, gamma passing rates of pretreatment quality assurance, and differences in modulating parameters such as multileaf collimator (MLC) positions, gantry angles, and monitor units at each control point between VMAT plans and dynamic log files registered by the Linac control system during delivery were acquired. Furthermore, differences between the original VMAT plan and the plan reconstructed from the dynamic log files were also investigated. To test the performance of the textural features as indicators for the modulation degree of VMAT plans, Spearman’s rank correlation coefficients (r{sub s}) with the plan deliverability were calculated. For comparison purposes, conventional modulation indices for VMAT including the modulation complexity score for VMAT, leaf travel modulation complexity score, and modulation index supporting station parameter optimized radiation therapy (MI{sub SPORT}) were calculated, and their correlations were analyzed in the same way. Results: There was no particular textural feature which always showed superior correlations with every type of plan deliverability. Considering the results comprehensively, contrast (d = 1) and variance (d = 1) generally showed considerable correlations with every type of plan deliverability. These textural features always showed higher correlations to the plan deliverability than did the conventional modulation indices, except in the case of modulating parameter differences. The r{sub s} values of contrast to the global gamma passing rates with criteria of 2%/2 mm, 2%/1 mm, and 1%/2 mm were 0.536, 0.473, and 0.718, respectively. The respective values for variance were 0.551, 0.481, and 0.688. In the case of local gamma passing rates, the r{sub s} values of contrast were 0.547, 0.578, and 0.620, respectively, and those of variance were 0.519, 0.527, and 0.569. All of the r{sub s} values in those cases were statistically significant (p < 0.003). In the cases of global and local gamma passing rates, MI{sub SPORT} showed the highest correlations among the conventional modulation indices. For global passing rates, r{sub s} values of MI{sub SPORT} were −0.420, −0.330, and −0.632, respectively, and those for local passing rates were −0.455, −0.490 and −0.502. The values of r{sub s} of contrast, variance, and MI{sub SPORT} with the MLC errors were −0.863, −0.828, and 0.795, respectively, all with statistical significances (p < 0.001). The correlations with statistical significances between variance and dose-volumetric differences were observed more frequently than the others. Conclusions: The contrast (d = 1) and variance (d = 1) calculated from fluence maps of VMAT plans showed considerable correlations with the plan deliverability, indicating their potential use as indicators for assessing the degree of modulation of VMAT plans. Both contrast and variance consistently showed better performance than the conventional modulation indices for VMAT.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Mitigating the noise of DESI mocks using analytic control variates

In order to address fundamental questions related to the expansion history of the Universe and its primordial nature with the next generation of galaxy experiments, we need to model reliably large-scale structure observables such as the correlation function and the power spectrum. Cosmological N-body simulations provide a reference through which we can test our models, but their output suffers from sample variance on large scales. Fortunately, this is the regime where accurate analytic approximations exist. To reduce the variance, which is key to making optimal use of these simulations, we can leverage the accuracy and precision of such analytic descriptions using Control Variates (CV). The power of control variates stems from utilizing inexpensive but highly correlated surrogates of the statistics one wishes to measure. The stronger the correlation between the surrogate and the statistic of interest, the larger the variance reduction delivered by the method. We apply two control variate formulations to mock catalogs generated in anticipation of upcoming data from the Dark Energy Spectroscopic Instrument (DESI) to test the robustness of its analysis pipeline. Our CV-reduced measurements offer a factor of 5-10 improvement in the measurement error compared with the raw measurements. We explore the relevant properties of the galaxy samples that dictate this reduction and comment on the improvements we find on some of the derived quantities relevant to Baryon Acoustic Oscillation (BAO) analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Analysis of Global Horizontal Irradiance in Version 3 of the National Solar Radiation Database

We report an analysis that compares global horizontal irradiance (GHI) estimates from version 3 of the National Solar Radiation Database (NSRDB v3) with surface measurements of GHI at a wide variety of locations over the period spanning from 2005 to 2012. The NSRDB v3 estimate of GHI are derived from the Physical Solar Model (PSM) which employs physics-based models to estimate GHI from measurements of reflected visible and infrared irradiance collected by Geostationary Operational Environment Satellites (GOES) and several other data sources. Because the ground measurements themselves are uncertain our analysis does not establish the absolute accuracy for PSM GHI. However by examining the comparison for trends and for consistency across a large number of sites, we may establish a level of confidence in PSM GHI and identify conditions which indicate opportunities to improve PSM. We focus our evaluation on annual and monthly insolation because these quantities directly relate to prediction of energy production from solar power systems. We find that generally, PSM GHI exhibits a bias towards overestimating insolation, on the order of 5% when all sky conditions are considered, and somewhat less (-3%) when only clear sky conditions are considered. The biases persist across multiple years and are evident at many locations. In our opinion the bias originates with PSM and we view as less credible that the bias stems from calibration drift or soiling of ground instruments. We observe that PSM GHI may significantly underestimate monthly insolation in locations subject to broad snow cover. We found examples of days where PSM GHI apparently misidentified snow cover as clouds, resulting in significant underestimates of GHI during these days and hence leading to substantial understatement of monthly insolation. Analysis of PSM GHI in adjacent pixels shows that the level of agreement between PSM GHI and ground data can vary substantially over distances on the order of 2 km. We conclude that the variance most likely originates from dramatic contrasts in the ground's appearance over these distances.

14 SOLAR ENERGY↗
DOE OSTI2019

Multifidelity Uncertainty Quantification with Applications in Wind Turbine Aerodynamics: Preprint

The propagation of input uncertainty through engineering models allows designers to better understand the range of possible outcomes resulting from design decisions. This could lead to greater trust between modelers and stakeholders in the wind energy industry. In this study, we apply multilevel-multifidelity Monte Carlo sampling to flow over an airfoil, assuming uncertainty in the inflow conditions, and characterize the associated computational savings compared to standard Monte Carlo approaches. The truth model is provided by an airfoil simulation with a very fine computational time step, and auxiliary lower-level models are provided by simulations with coarser time steps. Reynolds-averaged Navier Stokes and detached eddy simulations are used to obtain two different model fidelities. The primary quantity of interest for this analysis is the lift force, which is examined for a range of angles of attack. We launch an initial set of 'trial' samples to determine the optimal allocation of model evaluations, and these trial evaluations are used to inform a larger sampling effort. Using the multilevel-multifidelity approach, we achieve roughly an order of magnitude variance reduction in expected lift as compared to the standard Monte Carlo approach with an equivalent computational cost.

17 WIND ENERGY↗
DOE OSTI2015

Dependence of Achievable Plan Quality on Treatment Technique and Planning Goal Refinement: A Head-and-Neck Intensity Modulated Radiation Therapy Application

Purpose: To develop a practical workflow for retrospectively analyzing target and normal tissue dose–volume endpoints for various intensity modulated radiation therapy (IMRT) delivery techniques; to develop technique-specific planning goals to improve plan consistency and quality when feasible. Methods and Materials: A total of 165 consecutive head-and-neck patients from our patient registry were selected and retrospectively analyzed. All IMRT plans were generated using the same dose–volume guidelines for TomoTherapy (Tomo, Accuray), TrueBeam (TB, Varian) using fixed-field IMRT (TB-IMRT) or RAPIDARC (TB-RAPIDARC), or Siemens Oncor (Siemens-IMRT, Siemens). A MATLAB-based dose–volume extraction and analysis tool was developed to export dosimetric endpoints for each patient. With a fair stratification of patient cohort, the variation of achieved dosimetric endpoints was analyzed among different treatment techniques. Upon identification of statistically significant variations, technique-specific planning goals were derived from dynamically accumulated institutional data. Results: Retrospective analysis showed that although all techniques yielded comparable target coverage, the doses to the critical structures differed. The maximum cord doses were 34.1 ± 2.6, 42.7 ± 2.1, 43.3 ± 2.0, and 45.1 ± 1.6 Gy for Tomo, TB-IMRT, TB-RAPIDARC, and Siemens-IMRT plans, respectively. Analyses of variance showed significant differences for the maximum cord doses but no significant differences for other selected structures among the investigated IMRT delivery techniques. Subsequently, a refined technique-specific dose–volume guideline for maximum cord dose was derived at a confidence level of 95%. The dosimetric plans that failed the refined technique-specific planning goals were reoptimized according to the refined constraints. We observed better cord sparing with minimal variations for the target coverage and other organ at risk sparing for the Tomo cases, and higher parotid doses for C-arm linear accelerator–based IMRT and RAPIDARC plans. Conclusion: Patient registry–based processes allowed easy and systematic dosimetric assessment of treatment plan quality and consistency. Our analysis revealed the dependence of certain dosimetric endpoints on the treatment techniques. Technique-specific refinement of planning goals may lead to improvement in plan consistency and plan quality.

62 RADIOLOGY AND NUCLEAR MEDICINE↗