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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 1,261 records · Page 70
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DOE OSTI2022

A model to calculate fatigue damage caused by partial waking during wind farm optimization

Abstract. Wind turbines in wind farms often operate in waked or partially waked conditions, which can greatly increase the fatigue damage. Some fatigue considerations may be included, but currently a full fidelity analysis of the increased damage a turbine experiences in a wind farm is not considered in wind farm layout optimization because existing models are too computationally expensive. In this paper, we present a model to calculate fatigue damage caused by partial waking on a wind turbine that is computationally efficient and can be included in wind farm layout optimization. The model relies on analytic velocity, turbulence, and load models commonly used in farm research and design, and it captures some of the effects of turbulence on the fatigue loading. Compared to high-fidelity simulation data, our model accurately predicts the damage trends of various waking conditions. We also perform example wind farm layout optimizations with our presented model in which we maximize the annual energy production (AEP) of a wind farm while constraining the damage of the turbines in the farm. The results of our optimization show that the turbine damage can be significantly reduced, more than 10 %, with only a small sacrifice of around 0.07 % to the AEP, or the damage can be reduced by 20 % with an AEP sacrifice of 0.6 %.

17 WIND ENERGY↗
DOE OSTI2023

Evolution of eddy viscosity in the wake of a wind turbine

The eddy viscosity hypothesis is a popular method in wind turbine wake modeling for estimating turbulent Reynolds stresses. We document the downstream evolution of eddy viscosity in the wake of a wind turbine from experimental and large-eddy-simulation data. Wake eddy viscosity is isolated from its surroundings by subtracting the inflow profile, and the driving forces are identified in each wake region. Eddy viscosity varies in response to changes in turbine geometry and nacelle misalignment with larger turbines generating stronger velocity gradients and shear stresses. We propose a model for eddy viscosity based on a Rayleigh distribution. Model parameters are obtained from scaling the eddy viscosity hypothesis and demonstrate satisfactory agreement with the reference data. The model is implemented in the curled wake formulation in the FLOw Redirection and Induction in Steady State (FLORIS) framework and assessed through comparisons with the previous formulation. Our approach produced more accurate flow field estimates with lower total error for the majority of cases.

17 WIND ENERGY↗
DOE OSTI2023

Performance of methods for SARS-CoV-2 variant detection and abundance estimation within mixed population samples

The accurate identification of SARS-CoV-2 (SC2) variants and estimation of their abundance in mixed population samples (e.g., air or wastewater) is imperative for successful surveillance of community level trends. Assessing the performance of SC2 variant composition estimators (VCEs) should improve our confidence in public health decision making. Here, we introduce a linear regression based VCE and compare its performance to four other VCEs: two re-purposed DNA sequence read classifiers (Kallisto and Kraken2), a maximum-likelihood based method (Lineage deComposition for Sars-Cov-2 pooled samples (LCS)), and a regression based method (Freyja). We simulated DNA sequence datasets of known variant composition from both Illumina and Oxford Nanopore Technologies (ONT) platforms and assessed the performance of each VCE. We also evaluated VCEs performance using publicly available empirical wastewater samples collected for SC2 surveillance efforts. Bioinformatic analyses were performed with a custom NextFlow workflow (C-WAP, CFSAN Wastewater Analysis Pipeline). Relative root mean squared error (RRMSE) was used as a measure of performance with respect to the known abundance and concordance correlation coefficient (CCC) was used to measure agreement between pairs of estimators. Based on our results from simulated data, Kallisto was the most accurate estimator as it had the lowest RRMSE, followed by Freyja. Kallisto and Freyja had the most similar predictions, reflected by the highest CCC metrics. We also found that accuracy was platform and amplicon panel dependent. For example, the accuracy of Freyja was significantly higher with Illumina data compared to ONT data; performance of Kallisto was best with ARTICv4. However, when analyzing empirical data there was poor agreement among methods and variations in the number of variants detected (e.g., Freyja ARTICv4 had a mean of 2.2 variants while Kallisto ARTICv4 had a mean of 10.1 variants). This work provides an understanding of the differences in performance of a number of VCEs and how accurate they are in capturing the relative abundance of SC2 variants within a mixed sample (e.g., wastewater). Such information should help officials gauge the confidence they can have in such data for informing public health decisions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

Model-based tomographic reconstruction

A model-based approach to estimating wall positions for a building is developed and tested using simulated data. It borrows two techniques from geophysical inversion problems, layer stripping and stacking, and combines them with a model-based estimation algorithm that minimizes the mean-square error between the predicted signal and the data. The technique is designed to process multiple looks from an ultra wideband radar array. The processed signal is time-gated and each section processed to detect the presence of a wall and estimate its position, thickness, and material parameters. The floor plan of a building is determined by moving the array around the outside of the building. In this paper we describe how the stacking and layer stripping algorithms are combined and show the results from a simple numerical example of three parallel walls.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Using Machine Learning to Create Turbine Performance Models (Presentation)

Wind turbine power output is known to be a strong function of wind speed, but is also affected by turbulence and shear. In this work, new aerostructural simulations of a generic 1.5 MW turbine are used to explore atmospheric influences on power output. Most significant is the hub height wind speed, followed by hub height turbulence intensity and then wind speed shear across the rotor disk. These simulation data are used to train regression trees that predict the turbine response for any combination of wind speed, turbulence intensity, and wind shear that might be expected at a turbine site. For a randomly selected atmospheric condition, the accuracy of the regression tree power predictions is three times higher than that of the traditional power curve methodology. The regression tree method can also be applied to turbine test data and used to predict turbine performance at a new site. No new data is required in comparison to the data that are usually collected for a wind resource assessment. Implementing the method requires turbine manufacturers to create a turbine regression tree model from test site data. Such an approach could significantly reduce bias in power predictions that arise because of different turbulence and shear at the new site, compared to the test site.

17 WIND ENERGY↗
DOE OSTI2014

Computational Tools to Assess Turbine Biological Performance

Public Utility District No. 2 of Grant County (GCPUD) operates the Priest Rapids Dam (PRD), a hydroelectric facility on the Columbia River in Washington State. The dam contains 10 Kaplan-type turbine units that are now more than 50 years old. Plans are underway to refit these aging turbines with new runners. The Columbia River at PRD is a migratory pathway for several species of juvenile and adult salmonids, so passage of fish through the dam is a major consideration when upgrading the turbines. In this paper, a method for turbine biological performance assessment (BioPA) is demonstrated. Using this method, a suite of biological performance indicators is computed based on simulated data from a CFD model of a proposed turbine design. Each performance indicator is a measure of the probability of exposure to a certain dose of an injury mechanism. Using known relationships between the dose of an injury mechanism and frequency of injury (dose–response) from laboratory or field studies, the likelihood of fish injury for a turbine design can be computed from the performance indicator. By comparing the values of the indicators from proposed designs, the engineer can identify the more-promising alternatives. We present an application of the BioPA method for baseline risk assessment calculations for the existing Kaplan turbines at PRD that will be used as the minimum biological performance that a proposed new design must achieve.

hydropower, turbine, fish, CFD, Columbia River, pr↗
DOE OSTI2015

Coolant Density and Control Blade History Effects in Extended BWR Burnup Credit

Oak Ridge National Laboratory and the US Nuclear Regulatory Commission have initiated a multiyear project to investigate the application of burnup credit (BUC) for boiling water reactor (BWR) fuel in storage and transportation casks. This project includes two phases. The first phase investigates the applicability of peak reactivity methods currently used for spent fuel pools to spent fuel storage and transportation casks and the validation of reactivity (k eff ) calculations and predicted spent fuel compositions. The second phase focuses on extending BUC beyond peak reactivity. This paper documents work performed to date investigating some aspects of extended BUC. (The technical basis for application of peak reactivity methods to BWR fuel in storage and transportation systems is presented in a companion paper.) Two reactor operating parameters are being evaluated to establish an adequate basis for extended BWR BUC: (1) the effect of axial void profile and (2) the effect of control blade utilization during operation. A detailed analysis of core simulator data for one cycle of a modern operating BWR plant was performed to determine the range of void profiles and the variability of the profile experienced during irradiation. Although a single cycle does not provide complete data, the data obtained are sufficient to determine the primary effects and to identify conservative modeling approaches. These data were used in a study of the effect of axial void profile. The first stage of the study was determination of the necessary moderator density temporal fidelity in depletion modeling. After the required temporal fidelity was established, multiple void profiles were used to examine the effect on cask reactivity. The results of these studies are being used to develop recommendations for conservatively modeling the void profile effects for BWR depletion calculations. The second operational parameter studied was control blade history. Control blades are inserted in various locations and at varying degrees during BWR operation based on the core loading pattern. When present during depletion, control blades harden the neutron spectrum locally because they displace the moderator and absorb thermal neutrons. The investigation of the effect of control blades on post operational cask reactivity is documented herein, as is the effect of multiple (continuous and intermittent) exposure periods with control blades inserted. The coupled effects of control blade presence on power density, void profile, or burnup profile will be addressed in future work.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

Digital Inject Book v. 1.7

Digital Inject Book is a software program designed to generate and managed simulated data for radiation detectors, used to increase the realism of training where real radiation sources are impractical, expensive, or simply not available.

Eldridge, Bryce↗
DOE OSTI2017

Massive Associative K-biclustering (MAK) v1.0

Massive Associative K-biclustering (MAK) is a method to address the key limitations in other biclustering approaches. In a formal evaluation against new simulated data sets with more realistic properties MAK improves recovery of biclusters exhibiting common behavior across conditions, with the highest F1 scores, a measure of a test's accuracy, and lowest false positive rates, the probability of falsely rejecting the null hypothesis, compared to established methods. The developers show how MAK can be applied to a popular data set from Saccharomyces cerevisiae to uncover new properties not captured by other methods. MAK biclusters showed increased statistical enrichments for known regulatory associations and gene functions, including for nearly 30% of the novel MAK biclusters not found by other methods. Compared to other methods, MAK results had at least two-fold higher coverage of differential expression in the overall dataset, an overlapping bicluster network with distinct modules for protein expression and energy metabolism, and biologically relevant overlapping biclusters including cases of combinatorial regulation. MAK is suited for large datasets with many potentially noisy and overlapping patterns of varying sizes.

JOACHIMIAK, MARCIN↗
DOE OSTI2016

ACCELEROMETERS IN FLOW FIELDS: A STRUCTURAL ANALYSIS OF THE CHOPPED DUMMY INPILE TUBE

Four tests characterizing the structural response of the Chopped-Dummy In-Pile tube (CDIPT) experiment design were measured in the Hydro-Mechanical Fuel Test Facility (HMFTF). Four different test configurations were tried. These configurations tested the pressure drop and flow impact of various plate configurations and flow control orifices to be used later at different reactor power levels. Accelerometers were placed on the test vehicle and flow simulation housing. A total of five accelerometers were used with one on the top and bottom of the flow simulator and vehicle, and one on the outside of the flow simulator. Data were collected at a series of flow rates for 5 seconds each at an acquisition rate of 2 kHz for a Nyquist frequency of 1 kHz. The data were then analyzed using a Fast Fourier Transform (FFT) algorithm. The results show very coherent vibrations of the CDIPT experiment on the order of 50 Hz in frequency and 0.01 m/s2 in magnitude. The coherent vibrations, although small in magnitude pose a potential design problem if the frequencies coincide with the natural frequency of the fueled plates or test vehicle. The accelerometer data was integrated and combined to create a 3D trace of the experiment during the test. The merits of this data as well as further anomalies and artifacts are also discussed as well as their relation to the instrumentation and experiment design.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

In Situ Visualization for Computational Science

In situ visualization, i.e., visualizing simulation data as it is generated, is an emerging processing paradigm in response to trends in the area of high-performance computing. This paradigm holds great promise in its ability to access increased spatio-temporal resolution and leverage extensive computational power. However, the paradigm is also widely viewed as limiting when it comes to exploration-oriented use cases and further will require visualization systems to become more and more complicated and constrained. Additionally, there are many open research topics with in situ visualization. The Dagstuhl seminar 18271 "In Situ Visualization for Computational Science" brought together researchers and practitioners from three communities (computational science, high-performance computing, and scientific visualization) to share interesting findings, to identify lines of open research, and to determine a medium-term research agenda that addresses the most pressing problems. This report summarizes the outcomes and findings of the seminar.

Bauer, Andy↗
DOE OSTI2019

DeepCMB: Lensing Reconstruction of the CMB with Deep Neural Networks

Next-generation cosmic microwave background (CMB) experiments will have lower noise and therefore increased sensitivity, enabling improved constraints on fundamental physics parameters such as the sum of neutrino masses and the tensor-to-scalar ratio r. Achieving competitive constraints on these parameters requires high signal-to-noise extraction of the projected gravitational potential from the CMB maps. Standard methods for reconstructing the lensing potential employ the quadratic estimator (QE). However, the QE performs suboptimally at the low noise levels expected in upcoming experiments. Other methods, like maximum likelihood estimators (MLE), are under active development. In this work, we demonstrate reconstruction of the CMB lensing potential with deep convolutional neural networks (CNN) - ie, a ResUNet. The network is trained and tested on simulated data, and otherwise has no physical parametrization related to the physical processes of the CMB and gravitati onal lensing. We show that, over a wide range of angular scales, ResUNets recover the input gravitational potential with a higher signal-to-noise ratio than the QE method, reaching levels comparable to analytic approximations of MLE methods. We demonstrate that the network outputs quantifiably different lensing maps when given input CMB maps generated with different cosmologies. We also show we can use the reconstructed lensing map for cosmological parameter estimation. This application of CNN provides a few innovations at the intersection of cosmology and machine learning. First, while training and regressing on images, we predict a continuous-variable field rather than discrete classes. Second, we are able to establish uncertainty measures for the network output that are analogous to standard methods. We expect this approach to excel in capturing hard-to-model non-Gaussian astrophysical foreground and noise contributions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Studying the effects of overlapping objects in dark energy

Observing the clustering of galaxies allows us to calculate cosmological parameters necessary for understanding dark energy. However, as the density of observed objects increases, the probability of these objects blending likewise increases, causing multiple galaxies to be observed as one. This affects the inferred values of parameters such as the galaxy bias (b) and the matter energy density (ΩM). To see whether the bias from incorrectly inferring the galaxy count is significant, we compare the correlation functions in simulated data for “true” and “observed” data sets with one-to-one and multiple-to-one correspondences, respectively. For each data set, we create two correlation functions: one “measured” function directly relying on the galaxies’ positions using the TreeCorr python library, and one “model” derived mathematically from the galaxies’ power spectrum using the Cosmological Core Library (CCL). By minimizing the residual between these two functions, we compute the ideal values for b and ΩM across the various possible redshifts that position the galaxies in three dimensional space. This minimization is done with an Markov chain Monte Carlo (MCMC) estimate that finds one value of ΩM and ten values for b corresponding to the ten redshift bins ranging from z = 0.2 to z = 1.2. We find that neither b nor ΩM is particularly affected by inclusion of blended galaxies. Though there is room for improvement, the data suggests that the fluctuations we found are a result of noise or limitations on the modeling rather than blending explicitly.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Cutting the Deployment Costs of Physics-Based MPC in Buildings by Simulation-Based Imitation Learning

It has been shown that model predictive control (MPC) is a promising solution for energy-efficient building operations. However, the deployment of MPC in a large portion of the building stock has not been possible partially because of high installation costs. Every building is unique and requires a tailored MPC solution. The best performing solutions are often based on physics-based modeling, which is, however, computationally expensive and requires dedicated software. A promising direction that tackles this problem is to train a neural network-based optimal control policy to imitate the behavior of physics-based MPC from the simulation data generated offline. The neural networks give control actions that closely approximate those produced by physics-based MPC, but with a fraction of the computational and memory requirements and without the need for licensed software. The main advantage of the proposed approach stems from simple evaluation at execution time, leading to low computational footprints and easy deployment on embedded HW platforms. In the case study, we present the energy savings potential of physics-based MPC applied to an office building in Belgium. We demonstrate how neural network approximators can be used to cut the implementation and maintenance costs of MPC deployment without compromising performance. We also critically assess the presented approach by pointing out the remaining challenges and open research questions.

Drgona, Jan↗
DOE OSTI2018

Semi-inclusive Neutral Current Neutral Pion Production Selection at the NOvA (NuMI Off-axis Electron Neutrino Appearance) Near Detector Using Prong Level Convolutional Neural Networks

The NONA neutrino experiment based in Fermilab is designed to measure ν µ →ν e neutrino oscillations. This experiment will give us insight into the properties of massive neutrinos. Neutral current (NC) ν µ,e neutral pion production events can mimic the ν µ →ν e oscillation signal and therefore are an important background for NONA to understand. Neutral pions decay into two photons which can fake a single electron shower (νe appearance signal) in two ways: either the 2 photons can merge together or one of them may escape detection. In order to constrain this background, NONA utilizes the Near Detector to measure neutral current π 0 neutrino interactions. In this analysis, neutrino-Nucleus (ν µ →N) NC π 0 interactions with total pion energy greater then 0.3 GeV are studied by selecting two prong events with two final state photons as determined by prong based Convolutional Visual Networks (CVN). The analysis is performed on 3.54x10 21 Protons On Target (POT) of NONA Near Detector simulated data and compared to 8.09x10 20 POT of data. Optimization of the selection based on fractional cross-section uncertainty and an initial energy resolution study of the final sample are presented. The final 2 prong selection using prong based CVN gave a purity of 74%, selection efficiency of ~ 1.8%, and an expected (NC) ν µ,e neutral pion cross section of ~ 15.2%.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Evaluation of Portable Programming Models to Accelerate LArTPC Detector Simulations

The Liquid Argon Time Projection Chamber (LArTPC) technology is widely used in high energy physics experiments, including the upcoming Deep Underground Neutrino Experiment (DUNE). Accurately simulating LArTPC detector responses is essential for analysis algorithm development and physics model interpretations. Accurate LArTPC detector response simulations are computationally demanding, and can become a bottleneck in the analysis workflow. Compute devices such as General-Purpose Graphics Processing Units (GPGPUs) have the potential to substantially accelerate simulations compared to traditional CPU-only processing. The software development that requires often carries the cost of specialized code refactorization and porting to match the target hardware architecture. With the rapid evolution and increased diversity of the computer architecture landscape, it is highly desirable to have a portable solution that also maintains reasonable performance. We report our ongoing effort in evaluating Kokkos as a basis for this portable programming model using LArTPC simulations in the context of the Wire-Cell Toolkit, a C++ library for LArTPC simulations, data analysis, reconstruction and visualization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

The Cherenkov Telescope Array Will Test Whether Pulsars Generate the Galactic Center Gamma-Ray Excess

The GeV-scale gamma-ray excess observed from the region surrounding the Galactic Center has been interpreted as either the products of annihilating dark matter particles, or as the emission from a large population of faint and centrally-located millisecond pulsars. If pulsars are responsible for this signal, they should also produce detectable levels of TeV-scale emission. In this study, we employ a template-based analysis of simulated data in an effort to assess the ability of the Cherenkov Telescope Array (CTA) to detect or constrain the presence of this emission, providing a new and powerful means of testing whether millisecond pulsars are responsible for the observed excess. We find that after even a relatively brief observation of the Inner Galaxy, CTA will be able to definitively detect this TeV-scale emission, or rule out pulsars as the source of the Galactic Center Gamma-Ray Excess.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Graphical Methods for Separating Beam and Target Fragmentation Regions

For reactions involving three or more final-state particles, graphical methods can help to elucidate the dominant reaction mechanism. Van Hove [1] introduced a longitudinal phase space plot, which categorizes reaction products in terms of their longitudinal moments. We review the construction of such plots, and show how they are useful in separating beam and target fragmentation regimes. We summarize the information that can be obtained from Van Hove plots, and use these plots to analyze reactions with three or four strongly-interacting particles in the final state. As an example, we apply these methods to simulated data for the reaction ??+p???+?(??)+p. We show how cuts in the Van Hove plot can be utilized to isolate various two-body processes that contribute to this reaction. We also show how the dominant reaction processes change with the beam energy.\\[4pt] [1] L. Van Hove, Phys. Lett. B28, 429 (1969).

Londergan, J.T.↗