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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 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Uncertainty-aware photovoltaic generation estimation through fusion of physics with harmonics information using Bayesian neural networks

We develop an aggregate photovoltaic generation estimation methodology that uses diverse inputs and can reason on its current input-dependent predictive uncertainty. Named PVPHEst, for PhotoVoltaic Physics- & Harmonics-driven Estimator, the resulting tool is intelligently weighing and fusing information carried by the output of physics models, harmonics, and line sensors, using Bayesian neural networks and related techniques aimed at solving machine learning problems with intrinsic uncertainty quantification. As each of the three input classes carries heterogeneous information that only sheds light on one facet of the estimation problem but its value can diminish in the face of diverse grid phenomena, PV-PHEst with its estimation and uncertainty reasoning capabilities perform a nontrivial and potentially mission-critical task of value to grid operators.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Large Eddy Simulation of turbulent flow in rod bundle with spacer grids

Pressurized water nuclear reactor fuel assemblies are made of fuel rods held by spacer grids, which are the core components of the nuclear power plants. Hot fuel rods are cooled by the coolant water flow, and the spacer grids with mixing vanes distort the mainstream, producing the swirling flow which can enhance the heat transfer to improve CHF (Critical Heat Flux). Heat transfer between fuel rods and coolant is a crucial part of thermal-hydraulic analysis. For the purpose to improve the thermal hydraulic performance of the fuel assemblies, many experiments have been conducted to understand the flow structures and three-dimensional behaviors of the turbulence in the rod bundle sub-channels and then to optimize the design of the spacer grid. In the experimental work, which requires lots of efforts to get reliable data, LDA (Laser Doppler Anemometry) and PIV (Particle Image Velocimetry) are generally used to measure the velocity profile and turbulent intensity in the sub-channels. Computational Fluid Dynamics (CFD) is another useful way to study the characteristics of the detailed flow condition and heat transfer phenomena in the subchannels. The main issue of the single-phase CFD is to accurately predict the flow and turbulence characteristics. RANS method is generally used to simulate the induced lateral velocity and the heat transfer process. With the development of computer technology, some more precise but previously time-consuming turbulent models, such as LES (Large Eddy Simulation), are gradually used for the simulation of rod bundle flow with current advanced CPU resources. Ikeno and Kajishima explained the decay of swirling turbulent flow in the rod bundle using LES method. Benhamadouche acquired the turbulence induced forces on the central rod of the rod bundle through performing the LES of the flow downstream the spacer grid. Chang and Tavoularis conducted hybrid simulations with SAS SST and LES of a 5 x 5 rod bundle, and several vortices were identified in the vicinity of the vanes and the generation mechanisms, evolution progress and influence on the velocities of these vortices were clarified. In the paper we present CFD simulations of turbulent flow in rod bundle sub-channels with spacer grids of two different experiments. One is MATiS-H benchmark experiment with scaled spacer grids, which is conducted by KAERI, and is used as a CFD benchmark test, and the other is a 5 x 5 rod bundle with typical spacer grids using PIV technology, with the cooperation of Harbin Engineering University. The main objectives of the calculations are to validate LES methodology by the benchmark experiments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

An improved multislope MUSCL scheme for solving shallow water equations on unstructured grids

In this paper we describe an improved vector manipulation multislope monotone upstream-centred scheme for conservation laws (MUSCL) reconstruction for solving the shallow water equations on unstructured grids. This improved MUSCL reconstruction method includes a bigger stencil for the interpolation and saves time for determining the geometric relations compared to the original vector manipulation method, so it is computationally more efficient and straightforward to implement. Four examples involving an analytical solution, laboratory experiments and field-scale measurements are used to test the performance of the proposed scheme. It has been proven that the proposed scheme can provide comparable accuracy and higher efficiency compared to the original vector manipulation method. With the increasing of the number of cells, the advantage of the proposed scheme becomes more apparent.

42 ENGINEERING↗
DOE OSTI2023

Learning Distribution Grid Topologies: A Tutorial

Unveiling feeder topologies from data is of paramount importance to advance situational awareness and proper utilization of smart resources in power distribution grids. This tutorial summarizes, contrasts, and establishes useful links between recent works on topology identification and detection schemes that have been proposed for power distribution grids. The primary focus is to highlight methods that overcome the limited availability of measurement devices in distribution grids, while enhancing topology estimates using conservation laws of power-flow physics and structural properties of feeders. Grid data from phasor measurement units or smart meters can be collected either passively in the traditional way, or actively, upon actuating grid resources and measuring the feeder's voltage response. Analytical claims on feeder identifiability and detectability are reviewed under disparate meter placement scenarios. Such topology learning claims can be attained exactly or approximately so via algorithmic solutions with various levels of computational complexity, ranging from least-squares fits to convex optimization problems, and from polynomial-time searches over graphs to mixed-integer programs. Although the emphasis is on radial single-phase feeders, extensions to meshed and/or multiphase circuits are sometimes possible and discussed. Here this tutorial aspires to provide researchers and engineers with knowledge of the current state-of-the-art in tractable distribution grid learning and insights into future directions of work.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Finite spatial-grid effects in energy-conserving particle-in-cell algorithms

Finite-grid (or aliasing) instabilities are pervasive in particle-in-cell (PIC) plasma simulation algorithms, and force the modeler to resolve the smallest (Debye) length scale in the problem regardless of dynamical relevance. These instabilities originate in the aliasing of interpolation errors between mesh quantities and particles (which live in the space–time continuum). Recently, strictly energy-conserving PIC (EC-PIC) algorithms have been developed that promise enhanced robustness against aliasing instabilities. In this study, we confirm by analysis that EC-PIC is stable against aliasing instabilities for stationary plasmas. For drifting plasmas, we demonstrate by analysis and numerical experiments that, while EC-PIC algorithms are not free from these instabilities in principle, they feature a benign stability threshold for finite-temperature plasmas that make them usable in practice for a large class of problems (featuring ambipolarity and realistic ion-electron mass ratios) without the need to consider the size of the Debye length. We also demonstrate that this threshold is absent for the popular momentum-conserving PIC algorithms, which are therefore unstable for both drifting and stationary plasmas beyond a threshold in cell size compared to Debye length. Finite-grid (or aliasing) instabilities are pervasive in particle-in-cell (PIC) plasma simulation algorithms, and force the modeler to resolve the smallest (Debye) length scale in the problem regardless of dynamical relevance. These instabilities originate in the aliasing of interpolation errors between mesh quantities and particles (which live in the space–time continuum). Recently, strictly energy-conserving PIC (EC-PIC) algorithms have been developed that promise enhanced robustness against aliasing instabilities. In this study, we confirm by analysis that EC-PIC is stable against aliasing instabilities for stationary plasmas. For drifting plasmas, we demonstrate by analysis and numerical experiments that, while EC-PIC algorithms are not free from these instabilities in principle, they feature a benign stability threshold for finite-temperature plasmas that make them usable in practice for a large class of problems (featuring ambipolarity and realistic ion-electron mass ratios) without the need to consider the size of the Debye length. Finally, we also demonstrate that this threshold is absent for the popular momentum-conserving PIC algorithms, which are therefore unstable for both drifting and stationary plasmas beyond a threshold in cell size compared to Debye length.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

3D-FE Modeling of 316 SS under Strain-Controlled Fatigue Loading and CFD Simulation of PWR Surge Line

In financial year 2017, we are focusing on developing a mechanistic fatigue model of surge line pipes for pressurized water reactors (PWRs). To that end, we plan to perform the following tasks: (1) conduct stress- and strain-controlled fatigue testing of surge-line base metal such as 316 stainless steel (SS) under constant, variable, and random fatigue loading, (2) develop cyclic plasticity material models of 316 SS, (3) develop one-dimensional (1D) analytical or closed-form model to validate the material models and to understand the mechanics associated with 316 SS cyclic hardening and/or softening, (4) develop three-dimensional (3D) finite element (FE) models with implementation of evolutionary cyclic plasticity, and (5) develop computational fluid dynamics (CFD) model for thermal stratification, thermal-mechanical stress, and fatigue of example reactor components, such as a PWR surge line under plant heat-up, cool-down, and normal operation with/without grid-load-following. This semi-annual progress report presents the work completed on the above tasks for a 316 SS laboratory-scale specimen subjected to strain-controlled cyclic loading with constant, variable, and random amplitude. This is the first time that the accurate 3D-FE modeling of the specimen for its entire fatigue life, including the hardening and softening behavior, has been achieved. We anticipate that this work will pave the way for the development of a fully mechanistic-computer model that can be used for fatigue evaluation of safety-critical metallic components, which are traditionally evaluated by heavy reliance on time-consuming and costly test-based approaches. This basic research will not only help the nuclear reactor industry for fatigue evaluation of reactor components in a cost effective and less time-consuming way, but will also help other safety-related industries, such as aerospace, which is heavily dependent on test-based approaches, where a single full-scale fatigue test can cost millions of dollars and require years of effort to conduct. Toward our goal of demonstration of fully mechanistic fatigue evaluation of reactor components, we also started work on developing a component-level computer model of reactor components, such as 316 SS surge line pipe. This requires developing a thermal-mechanical stress analysis model of the reactor surge line, which, in turn, requires time-dependent temperature and stratification information along the boundary of the pipe. Toward that goal, CFD models of surge lines are being developed. In this report, we also present some preliminary results showing the temperature conditions along the surge line wall under reactor heat-up, cool-down, and steady-state power operation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

A transformed path integral approach for solution of the Fokker–Planck equation

A novel path integral (PI) based method for solution of the Fokker–Planck equation is presented. The proposed method, termed the transformed path integral (TPI) method, utilizes a new formulation for the underlying short-time propagator to perform the evolution of the probability density function (PDF) in a transformed computational domain where a more accurate representation of the PDF can be ensured. The new formulation, based on a dynamic transformation of the original state space with the statistics of the PDF as parameters, preserves the non-negativity of the PDF and incorporates short-time properties of the underlying stochastic process. New update equations for the state PDF in a transformed space and the parameters of the transformation (including mean and covariance) that better accommodate nonlinearities in drift and non-Gaussian behavior in distributions are proposed (based on properties of the SDE). Owing to the choice of transformation considered, the proposed method maps a fixed grid in transformed space to a dynamically adaptive grid in the original state space. The TPI method, in contrast to conventional methods such as Monte Carlo simulations and fixed grid approaches, is able to better represent the distributions (especially the tail information) and better address challenges in processes with large diffusion, large drift and large concentration of PDF. Additionally, in the proposed TPI method, error bounds on the probability in the computational domain can be obtained using the Chebyshev's inequality. The benefits of the TPI method over conventional methods are illustrated through simulations of linear and nonlinear drift processes in one-dimensional and multidimensional state spaces. The effects of spatial and temporal grid resolutions as well as that of the diffusion coefficient on the error in the PDF are also characterized.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Medial axis and local thickness computation using the Fast Sweeping Method

This report describes an efficient and robust voxel-based methodology for computing the medial axis, local thickness, and distance-to-skeleton of arbitrary three-dimensional geometries. It is assumed that the object can be represented by an exact or approximate signed distance function on a discrete grid. The gradient of such function is used to formulate a hyperbolic partial differential equation (PDE) that models the collapse of the position vector in space. By exploiting the causality property of the PDE, the Fast Sweeping Method is able to obtain the solution in a finite number of sweeps independent of the mesh resolution. The intersection of characteristic lines leads to the formation of shocks and a discrete bisector function is used to identify the medial axis. The same PDE approach is used to compute the local thickness inside the object and obtain the distance-to-skeleton field. Multiple examples are given in two and three dimensions along with a resolution study. The methodology has optimal complexity and yields subsecond computational times for geometries with over a million zones on a single core. The methodology is also capable of parallelization across shared and distributed memory architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Two-loop mixed QCD-electroweak amplitudes for Z+jet production at the LHC: bosonic corrections

Abstract We present a calculation of the bosonic contribution to the two-loop mixed QCD-electroweak scattering amplitudes forZ-boson production in association with one hard jet at hadron colliders. We employ a method to calculate amplitudes in the ’t Hooft-Veltman scheme that reduces the amount of spurious non-physical information needed at intermediate stages of the computation, to keep the complexity of the calculation under control. We compute all the relevant Feynman integrals numerically using the Auxiliary Mass Flow method. We evaluate the two-loop scattering amplitudes on a two-dimensional grid in the rapidity and transverse momentum of theZboson, which has been designed to yield a reliable numerical sampling of the boosted-Zregion. This result provides an important building block for improving the theoretical modelling of a key background for monojet searches at the LHC.

Physics↗
DOE OSTI2018

A phase model approach for thermostatically controlled load demand response

A significant portion of electricity consumed worldwide is used to power thermostatically controlled loads (TCLs) such as air conditioners, refrigerators, and water heaters. Because the short-term timing of operation of such systems is inconsequential as long as their long-run average power consumption is maintained, they are increasingly used in demand response (DR) programs to balance supply and demand on the power grid. Here, we present an ab initio phase model for general TCLs, and use the concept to develop a continuous oscillator model of a TCL and compute its phase response to changes in temperature and applied power. This yields a simple control system model that can be used to evaluate control policies for modulating the power consumption of aggregated loads with parameter heterogeneity and stochastic drift. We demonstrate this concept by comparing simulations of ensembles of heterogeneous loads using the continuous state model and an established hybrid state model. The developed phase model approach is a novel means of evaluating DR provision using TCLs, and is instrumental in estimating the capacity of ancillary services or DR on different time scales. We further propose a novel phase response based open-loop control policy that effectively modulates the aggregate power of a heterogeneous TCL population while maintaining load diversity and minimizing power overshoots. This is demonstrated by low-error tracking of a regulation signal by filtering it into frequency bands and using TCL sub-ensembles with duty cycles in corresponding ranges. As a result, control policies that can maintain a uniform distribution of power consumption by aggregated heterogeneous loads will enable distribution system management (DSM) approaches that maintain stability as well as power quality, and further allow more integration of renewable energy sources.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

Verification of sub-grid filtered drag models for gas-particle fluidized beds with immersed cylinder arrays

The accuracy of coarse-grid multiphase CFD simulations of fluidized beds may be improved via the inclusion of filtered constitutive models. In our previous study (Sarkar et al., Chem. Eng. Sci., 104, 399-412), we developed such a set of filtered drag relationships for beds with immersed arrays of cooling tubes. Verification of these filtered drag models is addressed in this work. Predictions from coarse-grid simulations with the sub-grid filtered corrections are compared against accurate, highly-resolved simulations of full-scale turbulent and bubbling fluidized beds. The filtered drag models offer a computationally efficient yet accurate alternative for obtaining macroscopic predictions, but the spatial resolution of meso-scale clustering heterogeneities is sacrificed.

42 ENGINEERING↗
DOE OSTI2017

Memory-optimized shift operator alternating direction implicit finite difference time domain method for plasma

Through introducing the alternating direction implicit (ADI) technique and the memory-optimized algorithm to the shift operator (SO) finite difference time domain (FDTD) method, the memory-optimized SO-ADI FDTD for nonmagnetized collisional plasma is proposed and the corresponding formulae of the proposed method for programming are deduced. In order to further the computational efficiency, the iteration method rather than Gauss elimination method is employed to solve the equation set in the derivation of the formulae. Complicated transformations and convolutions are avoided in the proposed method compared with the Z transforms (ZT) ADI FDTD method and the piecewise linear JE recursive convolution (PLJERC) ADI FDTD method. The numerical dispersion of the SO-ADI FDTD method with different plasma frequencies and electron collision frequencies is analyzed and the appropriate ratio of grid size to the minimum wavelength is given. The accuracy of the proposed method is validated by the reflection coefficient test on a nonmagnetized collisional plasma sheet. The testing results show that the proposed method is advantageous for improving computational efficiency and saving computer memory. The reflection coefficient of a perfect electric conductor (PEC) sheet covered by multilayer plasma and the RCS of the objects coated by plasma are calculated by the proposed method and the simulation results are analyzed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Nowcasting Earthquakes: Imaging the Earthquake Cycle in California With Machine Learning

We propose a new machine learning-based method for nowcasting earthquakes to image the time-dependent earthquake cycle. The result is a timeseries that may correspond to the process of stress accumulation and release. The timeseries are constructed by using principal component analysis of regional seismicity. The patterns are found as eigenvectors of the cross-correlation matrix of a collection of seismicity timeseries in a coarse grained regional spatial grid (pattern recognition via unsupervised machine learning). The eigenvalues of this matrix represent the relative importance of the various eigenpatterns. Using the eigenvectors and eigenvalues, we compute the weighted correlation timeseries of the regional seismicity. This timeseries has the property that the weighted correlation generally decreases prior to major earthquakes in the region, and increases suddenly just after a major earthquake occurs. As in a previous paper, we find that this method produces a nowcasting timeseries that resembles the hypothesized regional stress accumulation and release process characterizing the earthquake cycle. We then address the problem of whether the timeseries contain information regarding future large earthquakes. For this, we compute a receiver operating characteristic and determine the decision thresholds for several future time periods of interest (optimization via supervised machine learning). We find that signals can be detected that can be used to characterize the information content of the timeseries. These signals may be useful in assessing present and near-future seismic hazards.

58 GEOSCIENCES↗
DOE OSTI2024

A Non‐Intrusive Machine Learning Framework for Debiasing Long‐Time Coarse Resolution Climate Simulations and Quantifying Rare Events Statistics

Abstract Due to the rapidly changing climate, the frequency and severity of extreme weather is expected to increase over the coming decades. As fully‐resolved climate simulations remain computationally intractable, policy makers must rely on coarse‐models to quantify risk for extremes. However, coarse models suffer from inherent bias due to the ignored “sub‐grid” scales. We propose a framework to non‐intrusively debias coarse‐resolution climate predictions using neural‐network (NN) correction operators. Previous efforts have attempted to train such operators using loss functions that match statistics. However, this approach falls short with events that have longer return period than that of the training data, since the reference statistics have not converged. Here, the scope is to formulate a learning method that allows for correction of dynamics and quantification of extreme events with longer return period than the training data. The key obstacle is the chaotic nature of the underlying dynamics. To overcome this challenge, we introduce a dynamical systems approach where the correction operator is trained using reference data and a coarse model simulation nudged toward that reference. The method is demonstrated on debiasing an under‐resolved quasi‐geostrophic model and the Energy Exascale Earth System Model (E3SM). For the former, our method enables the quantification of events that have return period two orders longer than the training data. For the latter, when trained on 8 years of ERA5 data, our approach is able to correct the coarse E3SM output to closely reflect the 36‐year ERA5 statistics for all prognostic variables and significantly reduce their spatial biases.

Barthel Sorensen, B.↗
DOE OSTI2019

Multilevel Techniques for Compression and Reduction of Scientific Data---The Multivariate Case

We develop a technique for multigrid adaptive reduction of data (MGARD). Special attention is given to the case of tensor product grids, where our approach permits the use of nonuniformly spaced grids in each direction, which can prove problematic for many types of data reduction methods. An important feature of our approach is the provision of guaranteed, computable bounds on the loss incurred by the reduction of the data. Many users are leery of lossy algorithms and will only consider using them provided that numerical bounds on the pointwise difference between the original and the reduced datasets are given. Accordingly, we develop techniques for bounding the loss measured in the $L^{\infty}(\Omega)$ norm, and we show that these bounds are realistic in the sense that they do not significantly overestimate the actual loss. Here, the resulting loss indicators are used to guide the adaptive reduction of the data so that the reduced dataset meets a user-prescribed tolerance or memory constraint. Illustrative numerical examples, including the reduction of data arising from the simulation of a nonlinear reaction-diffusion problem, a turbulent channel flow, and a climate simulation, are provided.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Plant density and enhanced plant vigor estimation for high-resolution images v1

The package contains algorithms for the estimation of sprout/plant density and the corresponding enhanced plant vigor from remotely sensed (RS) images. These algorithms aim to improve the plant characterization and monitoring in the early season when the soil component is significant compared to the plant spatial coverage. In such a scenario, the identification of sprouts (or plants) and the estimation of plant vegetation indexes can be performed only using UAV-acquired images. Additionally, because of the extremely high resolution, gridding becomes necessary to improve visualization and provide more informative maps. However, to capture the real range of values of the VIs, it is important to avoid the inclusion of those pixels belonging to the soil component. The sprout/plant density estimation algorithm takes in input a classification map and uses the vegetation class to estimates the plant density within a user-defined grid using a floating window. Similarly, the enhanced plant vigor algorithm takes in input the vegetation index and the classification map. The enhanced vegetation index is computed for each pixel as the average value among those pixels that belong to the vegetation class within a floating window. The software is developed for the MATLAB environment.

Falco, Nicola↗
DOE OSTI2020

On Mixed-Integer Programming Formulations for the Unit Commitment Problem

We provide a comprehensive overview of mixed-integer programming formulations for the unit commitment (UC) problem. UC formulations have been an especially active area of research over the past 12 years due to their practical importance in power grid operations, and this paper serves as a capstone for this line of work. We additionally provide publicly available reference implementations of all formulations examined. We computationally test existing and novel UC formulations on a suite of instances drawn from both academic and real-world data sources. Driven by our computational experience from this and previous work, we contribute some additional formulations for both generator production upper bounds and piecewise linear production costs. By composing new UC formulations using existing components found in the literature and new components introduced in this paper, we demonstrate that performance can be significantly improved—and in the process, we identify a new state-of-the-art UC formulation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Enhancing the EVI-X National Framework to Address Emerging Energy Questions

This project advances the state-of-the-art in infrastructure analysis to better inform deployment strategies. It enhances NLR's EVI-X Suite, a set of tools supporting national network planning, local site design, and financial evaluation. These capabilities will position DOE to provide timely analysis and inform the strategic buildout of the national charging network. EVI-X development is closely coordinated with other DOE-funded efforts to ensure consistent, integrated use of key inputs and outputs, including EV adoption scenarios from NLR's TEMPO model.

24 POWER TRANSMISSION AND DISTRIBUTION↗