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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 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Understand the Costs of an Energy or Water Outage with the Customer Damage Function Calculator

The Customer Damage Function (CDF) Calculator is a free, publicly available web tool that helps users estimate the costs incurred at their site due to an electric grid or water service outage. Developed by the National Renewable Energy Laboratory (NREL) with support from the Federal Energy Management Program (FEMP), the tool is designed to help federal facility owners, building energy managers, and resilience planners understand and quantify the value of resilience to justify investments that would prevent or lessen the impact of a disruption.

CDF Calculator↗
DOE OSTI2014

Internal ellipsoidal estimates of reachable set of impulsive control systems

A problem of estimating reachable sets of linear impulsive control system with uncertainty in initial data is considered. The impulsive controls in the dynamical system belong to the intersection of a special cone with a generalized ellipsoid both taken in the space of functions of bounded variation. Assume that an ellipsoidal state constraints are imposed. The algorithms for constructing internal ellipsoidal estimates of reachable sets for such control systems and numerical simulation results are given.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

A Group Contribution Method for Estimating Cetane and Octane Numbers

Much of the research on advanced biofuels is devoted to the study of novel chemical pathways for converting nonfood biomass into liquid fuels that can be blended with existing transportation fuels. Many compounds under consideration are not found in the existing fuel supplies. Often, the physical properties needed to assess the viability of a potential biofuel are not available. The only reliable information available may be the molecular structure. Group contribution methods for estimating physical properties from molecular structure have been used for more than 60 years. The most common application is estimation of thermodynamic properties. More recently, group contribution methods have been developed for estimating rate dependent properties including cetane and octane numbers. Often, published group contribution methods are limited in terms of types of function groups and range of applicability. In this study, a new, broadly-applicable group contribution method based on an artificial neural network was developed to estimate cetane number research octane number, and motor octane numbers of hydrocarbons and oxygenated hydrocarbons. The new method is more accurate over a greater range molecular weights and structural complexity than existing group contribution methods for estimating cetane and octane numbers.

09 BIOMASS FUELS↗
DOE OSTI2021

Mixing Cell Model: A One-Dimensional Numerical Model for Assessment of Water Flow and Contaminant Transport in the Unsaturated Zone

This report describes the Mixing Cell Model code, a one dimensional model for water flow and solute transport in the unsaturated zone under steady state or transient flow conditions. The model is based on the principles and assumptions underlying mixing-cell model formulations. The unsaturated zone is discretized into a series of independent mixing cells. Each cell may have unique hydrologic, lithologic, and sorptive properties. Ordinary differential equations describe the material (water and solute) balance within each cell. Water-flow equations are derived from the continuity equation, assuming that unit gradient conditions exist at all times in each cell. Pressure gradients are considered implicitly through model discretization. Unsaturated hydraulic conductivity and moisture contents are determined by the material specific moisture-characteristic curves. Solute-transport processes include explicit treatment of advective processes, first order chain decay, and linear sorption reactions. Dispersion is addressed through implicit and explicit dispersion. Implicit dispersion is an inherent feature of all mixing-cell models and originates from the formulation of the problem in terms of mass balance around fully mixed volume elements. Expressions are provided that relate implicit dispersion to the physical dispersion of the system. Two FORTRAN codes were developed to solve the water flow and solute-transport equations: (1) the Mixing Cell Model for Flow (MCMF) solves transient water-flow problems and (2) the Mixing Cell Model for Transport (MCMT) solves the solute-transport problem. The transient water-flow problem is typically solved first by estimating the water flux through each cell in the model domain as a function of time using the MCMF code. These data are stored in either ASCII or binary files that are later read by the solute transport code (MCMT). Code output includes solute pore water concentrations, water and solute inventories in each cell and at each specified output time, and water and solute fluxes through each cell and specified output time. Computer run times for coupled transient water flow and solute transport were typically several seconds on a 2 GHz Intel Pentium IV desktop computer. The model was benchmarked against analytical solutions and finite element approximations to the partial differential equations (PDE) describing unsaturated flow and transport. Differences between the maximum solute flux estimated by the mixing cell model and the PDE models were typically less than two percent. This revision includes an option for a fixed concentration lower boundary condition for diffusive fluxes for versions 020321 and later.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

EFFICIENT SURROGATE FOR LOW ENERGY DAMAGE EFFECTS IN POLYATOMIC MATERIALS

We present an efficient surrogate for binary-collision Monte-Carlo (BCMC) calculations in polyatomic materials for low ion energies based on Parkin and Coulter’s net displacement function. Most of BCMC’s execution time is spent on the low energy tail of cascades when recoils’ residual travel distance is small compared to the desired resolution of the damage calculation. We propose to curtail an ions’ random walk if its energy reduces below a threshold Et and locally deposit an estimate of the expected number of defects it creates; the estimate of the number of defects is obtained from Parkin and Coulter’s net displacement functions. We find that results obtained with the BCMC code MyTRIM with and without truncation of the random walks compare well for large energy thresholds Et > 5 keV. For Et = 5 keV larger discrepancies are observed because of large differences between the net displacement function and MyTRIM for very low energy recoils. Future work will investigate and resolve this discrepancy. The energy threshold should be chosen so that ions’ average distance to next nuclear scattering is small compared to the desired resolution. A reduction of execution time of damage cascades by over 90 % is observed for an energy threshold of Et = 5 keV and a 1 MeV Xe-135 primary impinging on UO2. The desired application of this surrogate model are coupled FEM-BCMC calculations with varying compositions where net displacement functions are tabulated by composition. For reducing the number of required net displacement functions, derivatives of the net displacement function with respect to number fraction are computed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

EFFICIENT SURROGATE FOR LOW ENERGY DAMAGE EFFECTS IN POLYATOMIC MATERIALS

We present an efficient surrogate for binary-collision Monte-Carlo (BCMC) calculations in polyatomic materials for low ion energies based on Parkin and Coulter’s net displace- ment function. Most of BCMC’s execution time is spent on the low energy tail of cascades when recoils’ residual travel distance is small compared to the desired resolution of the damage calculation. We propose to curtail an ions’ random walk if its energy reduces below a threshold Et and locally deposit an estimate of the expected number of defects it creates; the estimate of the number of defects is obtained from Parkin and Coulter’s net displacement functions. We find that results obtained with the BCMC code MyTRIM with and without truncation of the random walks compare well for large energy thresholds Et > 5 keV. For Et = 5 keV larger discrepancies are observed because of large differ- ences between the net displacement function and MyTRIM for very low energy recoils. Future work will investigate and resolve this discrepancy. The energy threshold should be chosen so that ions’ average distance to next nuclear scattering is small compared to the desired resolution. A reduction of execution time of damage cascades by over 90 % is observed for an energy threshold of Et = 5 keV and a 1 MeV Xe-135 primary impinging on UO2. The desired application of this surrogate model are coupled FEM-BCMC cal- culations with varying compositions where net displacement functions are tabulated by composition. For reducing the number of required net displacement functions, derivatives of the net displacement function with respect to number fraction are computed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Fault isolation and fault-tolerant control for nonlinear stochastic distribution control systems with multiplicative faults

Here, in this paper, a fault isolation, diagnosis and fault tolerant control algorithm is proposed for nonlinear multiple multiplicative faults stochastic distribution control systems employing Takagi–Sugeno fuzzy system. To obtain the detailed fault information, a fault detection algorithm is introduced to discover the fault occurrence time. Then a fault isolation observer is built to produce the residual, and the error system is separated to subsystems affected only by disturbance and multiplicative faults. Moreover, a fault estimation scheme is presented to obtain the fault magnitude information. When faults occur, the system output probability density function will deviate from the desired distribution. So the model predictive control fault tolerant control scheme is needed to minimize the impact of faults as much as possible to make sure that the post fault output probability density function track the desired probability density function. The validity of the designed algorithm is demonstrated through a simulation example, where the fault tolerant control algorithm ensures that the system output probability density function still track the given output probability density function despite the complex case of multiple multiplicative faults occurring simultaneously.

42 ENGINEERING↗
DOE OSTI2022

A reforecasting-based dynamic reserve estimation for variable renewable generation and demand uncertainty

The installed capacity of renewables-based energy sources has been increasing in traditional power systems. In order to accommodate the increased variability and uncertainty associated with the deeper penetration of renewable sources like solar and wind, adjusted amounts of dynamic reserve are needed. Although probabilistic dynamic reserve estimation methods have been previously developed, most of them consider the uncertainty to be represented by parametric density functions that tend to perform poorly under extreme events and, moreover, neglect uncertainty introduced by the forecasting model itself. Toward addressing these limitations, this work presents, for the first time, a dynamic reserve estimation method for flexibility that incorporates nonparametric density estimation and a machine learning based reforecasting to provide a day-ahead prediction of the mean and spread of uncertainty around the base forecast. The prediction is, in turn, used to estimate the up and down reserve relative to the base forecast. Here, the present method takes various endogenous and exogenous features, including the calendar variables, as input to estimate the day-ahead reserve. Using a combination of reforecasting and dynamic reserve estimation techniques, the method is shown to adjust better to the dynamic nature of reserve requirements providing only what is needed to accommodate the expected deviations. Considering California Independent System Operator (CAISO) solar, wind and load data over an 18 month period, up to 67% reduction in the amount of reserve capacity needed for a one day reserve and reserve penalty for solar uncertainty is demonstrated. Additionally, the risk of reserve insufficiency in meeting the net demand is reduced by 20% with the proposed method.

14 SOLAR ENERGY↗
DOE OSTI2018

On Lower Estimates of Solutions and Their Derivatives to a Fourth-Order Linear Integrodifferential Volterra Equation

We examine solutions of the problem on sufficient conditions that guarantee a lower estimate and tending to infinity of solutions and their derivatives up to the third order to a fourth-order linear integrodifferential Volterra equation. For this purpose, we develop a method based on the nonstandard reduction method (S. Iskandarov), the Volterra transformation method, the method of shearing functions (S. Iskandarov), the method of integral inequalities (Yu. A. Ved’ and Z. Pakhyrov), the method of a priori estimates (N. V. Azbelev, V. P. Maksimov, L. F. Rakhmatullina, and P. M. Simonov, 1991, 2001), the Lagrange method for integral representations of solutions to first-order linear inhomogeneous differential equations, and the method of lower estimate of solutions (Yu. A. Ved’ and L. N. Kitaeva).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Computational investigation of incident ion angles and material erosion at rough graphite and silicon carbide divertor surfaces

We present a computational investigation of the dependence of material erosion on the incident ion angle at rough graphite and silicon carbide divertor surfaces. Ion angle distributions (IADs) for D plasmas at NSTX-U and DIII-D divertors were calculated by an equation-of-motion model that traces the ion trajectories in the sheath. Then, the effective sputtering yields and ion shadowed area fractions were calculated by a Monte Carlo micro-patterning and roughness code that applied the calculated IADs to surface topographic data that were obtained from optical confocal microscopy of rough graphite and SiC divertor surfaces from NSTX-U and DIII-D experiments. The calculations found that the effective sputtering yields, the sputtering pattern, and the shadowed area are determined by the detailed surface topology rather than the root mean square roughness R RMS , which represents deviations from a flat surface. The suppression of the effective sputtering yields for rough surfaces compared to the yield for a smooth surface was accounted for by the change of the mean local incident ion angle (LIIA) (θ'). The mean surface inclination angle distribution (SIAD) (δ) was found to be a useful parameter to estimate the LIIA from the calculated IADs. We report global empirical formulas for the mean LIIA and fraction of the area shadowed from the main ions for D plasmas for rough surfaces with B-field incident angles α = 85°–89° as a function of the mean SIAD (δ). We propose the use of the mean LIIA (θ') to estimate the sputtering yield for rough surfaces from the angular dependence of the sputtering yield.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Mixture density network estimation of continuous variable maximum likelihood using discrete training samples

Abstract Mixture density networks (MDNs) can be used to generate posterior density functions of model parameters $$\varvec{\theta }$$ θ given a set of observables $${\mathbf {x}}$$ x . In some applications, training data are available only for discrete values of a continuous parameter $$\varvec{\theta }$$ θ . In such situations, a number of performance-limiting issues arise which can result in biased estimates. We demonstrate the usage of MDNs for parameter estimation, discuss the origins of the biases, and propose a corrective method for each issue.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Learning functional priors and posteriors from data and physics

In this work, we develop a new Bayesian framework based on deep neural networks to be able to extrapolate in space-time using historical data and to quantify uncertainties arising from both noisy and gappy data in physical problems. Specifically, the proposed approach has two stages: (1) prior learning and (2) posterior estimation. At the first stage, we employ the physics-informed Generative Adversarial Networks (PI-GAN) to learn a functional prior either from a prescribed function distribution, e.g., Gaussian process, or from historical data and physics. At the second stage, we employ the Hamiltonian Monte Carlo (HMC) method to estimate the posterior in the latent space of PI-GANs. In addition, we use two different approaches to encode the physics: (1) automatic differentiation, used in the physicsinformed neural networks (PINNs) for scenarios with explicitly known partial differential equations (PDEs), and (2) operator regression using the deep operator network (DeepONet) for PDE-agnostic scenarios. We then test the proposed method for (1) meta-learning for one-dimensional regression, and forward/inverse PDE problems (combined with PINNs); (2) PDE-agnostic physical problems (combined with DeepONet), e.g., fractional diffusion as well as saturated stochastic (100-dimensional) flows in heterogeneous porous media; and (3) spatial-temporal regression problems, i.e., inference of a marine riser displacement field using experimental data from the Norwegian Deepwater Programme (NDP). The results demonstrate that the proposed approach can provide accurate predictions as well as uncertainty quantification given very limited scattered and noisy data, since historical data could be available to provide informative priors. In summary, the proposed method is capable of learning flexible functional priors, e.g., both Gaussian and non-Gaussian process, and can be readily extended to big data problems by enabling mini-batch training using stochastic HMC or normalizing flows since the latent space is generally characterized as low dimensional.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Cyber Attack Resilient HVDC System (CARDS) (Final Scientific/Technical Report)

This report summarizes the activities conducted under the DOE-OE funded project DE-OE0000824, where ABB Inc. (ABB), in collaboration with University of Illinois at Urbana-Champaign (UIUC), Bonneville Power Administration (BPA), Argonne National Laboratories (ANL), and University of Idaho (UI) pursued the development of a system of defense for High Voltage Direct Current transmission systems against cyber-attacks (CARDS). An HVDC substation with CARDS features will be more capable of mitigating cyber-attacks especially those that seek to control or interrupt the power flow in the HVDC line. It empowers existing HVDC intelligent electronic devices to collaborate in identifying and blocking malicious intents to trip HVDC AC circuit breakers, block thyristor valves, misconfigure devices settings, even though the commands and the measurements comply with correct syntax. The CARDS functions utilize the physics of electromagnetic AC and DC systems, electric power engineering principles, and state estimation to bring more in-depth cyber defense closer to the protected HVDC substation devices. With the aim of advancing the state of the art, the team proceeded to develop a demonstrable system against cyber-attacks in an HVDC substation environment, via functional upgrades to existing firmware of commercial off-the-shelf HVDC station devices, and/or embedded hardware, without the need for additional instrumentation nor new instrumentation technologies. It is to ensure that the solutions resulting from the project will support the HVDC devices currently installed in the US electricity delivery infrastructure.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

High-Throughput Computation and the Applicability of Monte Carlo Integration in Fatigue Load Estimation of Floating Offshore Wind Turbines

Long-term fatigue loads for floating offshore wind turbines are hard to estimate because they require the evaluation of the integral of a highly nonlinear function over a wide variety of wind and wave conditions. Current design standards involve scanning over a uniform rectangular grid of metocean inputs (e.g., wind speed and direction and wave height and period), which becomes intractable in high dimensions as the number of required evaluations grows exponentially with dimension. Monte Carlo integration offers a potentially efficient alternative because it has theoretical convergence proportional to the inverse of the square root of the number of samples, which is independent of dimension. In this paper, we first report on the integration of the aeroelastic code FAST into NREL's systems engineering tool, WISDEM, and the development of a high-throughput pipeline capable of sampling from arbitrary distributions, running FAST on a large scale, and postprocessing the results into estimates of fatigue loads. Second, we use this tool to run a variety of studies aimed at comparing grid-based and Monte Carlo-based approaches with calculating long-term fatigue loads. We observe that for more than a few dimensions, the Monte Carlo approach can represent a large improvement in computational efficiency, but that as nonlinearity increases, the effectiveness of Monte Carlo is correspondingly reduced. The present work sets the stage for future research focusing on using advanced statistical methods for analysis of wind turbine fatigue as well as extreme loads.

17 WIND ENERGY↗
DOE OSTI2020

A Dynamic Amplitude-Correcting Gradient Estimation Technique to Align X-ray Focusing Optics

High-brightness X-rays, as produced at synchrotrons and X-ray free electron laser (XFEL) facilities, are used to characterize materials in a variety of scientific experiments. In most cases, effective use of the high-energy light requires precisely-aligned focusing optics; one example being a compound refractive lens (CRL). To align a CRL, the position and rotation must be optimized along four axes. In practice, this is a labor-intensive, time-consuming manual process that can monopolize scarce experimental time at the necessary X-ray facilities. Models of the expected Xray transmission function suggest that this task can be automated; however, the temporally-varying intensity at X-ray free electron laser facilities preclude the direct use of standard implementations of optimization solvers such as steepest descent algorithms. In this paper, we propose a novel technique to estimate the gradient of noisy functions with temporally-varying amplitudes. We construct this dynamicamplitude correction by systematically sampling a fixed central location within the standard finite difference stencil, accounting for the observed changes in time, and normalizing the difference quotients against those fluctuations. In addition to a rigorous error analysis of the sampling technique, we demonstrate its efficacy in stochastic descent optimization methods. Further, we demonstrate how this approach may be implemented to optimize X-ray focusing optics at synchrotrons or XFEL facilities

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Transition-state correlations for predicting thermochemistry of adsorbates and surface reactions

Estimating thermochemical properties from linear correlations may provide a pathway to circumvent expensive density functional theory (DFT) calculations for quantities such as pre-exponentials and temperature corrections to DFT energies. Here, in this work, we construct thermochemical scaling relations between C 1 –C 6 n-alkanes in the gas phase and adsorbed alkyl chains extending from several transition metal surfaces, and examine changes in the slope and fit between metals and adsorption sites. We subsequently add –OH, –NH 2 , C$=$O, and C$=$C functional groups to the C 1 –C 6 molecules and demonstrate strong linear correlations for thermochemistry across all species. We broaden the correlations to incorporate transition states of C 1 –C 6 n-alkane dehydrogenation reactions, where thermochemistry for computationally prohibitive transition-state calculations can be quickly assessed. Additionally, we rationalize the linearity of thermochemical correlations based on the composition of the homologous series and theoretical assessments. As an application of the correlations, we estimate pre-exponentials for elementary surface reactions of ethane and propane hydrogenolysis on Ru(0001), which is of relevance to plastic hydrogenolysis. Depending on kinetically important steps, entropic contributions may be necessary to include in certain reaction mechanisms; in contrasting examples, entropies are found to be relatively insignificant for ethane hydrogenolysis but pertinent for propane hydrogenolysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Optimal intrinsic alignment estimators in the presence of redshift-space distortions

We present estimators for quantifying intrinsic alignments in large spectroscopic surveys that efficiently capture line-of-sight (LOS) information while being relatively insensitive to redshift-space distortions (RSD). We demonstrate that changing the LOS integration range, pimax, as a function of transverse separation outperforms the conventional choice of a single pimax value. This is further improved by replacing the flat pimax cut with a LOS weighting based on shape projection and RSD. Although these estimators incorporate additional LOS information, they are projected correlations that exhibit signal-to-noise ratios comparable to 3D correlation functions, such as the IA quadrupole. Using simulations from Abacus Summit, we evaluate these estimators and provide recommended pimax values and weights for projected separations of 1 - 100 Mpc/h. These will improve measurements of intrinsic alignments in large cosmological surveys and the constraints they provide for both weak lensing and direct cosmological applications.

cosmology↗
DOE OSTI2015

SU-E-T-631: Preliminary Results for Analytical Investigation Into Effects of ArcCHECK Setup Errors

Purpose: As three-dimensional diode arrays increase in popularity for patient-specific quality assurance for intensity-modulated radiation therapy (IMRT), it is important to evaluate an array’s susceptibility to setup errors. The ArcCHECK phantom is set up by manually aligning its outside marks with the linear accelerator’s lasers and light-field. If done correctly, this aligns the ArcCHECK cylinder’s central axis (CAX) with the linear accelerator’s axis of rotation. However, this process is prone to error. This project has developed an analytical expression including a perturbation factor to quantify the effect of shifts. Methods: The ArcCHECK is set up by aligning its machine marks with either the sagittal room lasers or the light-field of the linear accelerator at gantry zero (IEC). ArcCHECK has sixty-six evenly-spaced SunPoint diodes aligned radially in a ring 14.4 cm from CAX. The detector response function (DRF) was measured and combined with inverse-square correction to develop an analytical expression for output. The output was calculated using shifts of 0 (perfect alignment), +/−1, +/−2 and +/−5 mm. The effect on a series of simple inputs was determined: unity, 1-D ramp, steps, and hat-function to represent uniform field, wedge, evenly-spaced modulation, and single sharp modulation, respectively. Results: Geometric expressions were developed with perturbation factor included to represent shifts. DRF was modeled using sixth-degree polynomials with correlation coefficient 0.9997. The output was calculated using simple inputs such as unity, 1-D ramp, steps, and hat-function, with perturbation factors of: 0, +/−1, +/−2 and +/−5 mm. Discrepancies have been observed, but large fluctuations have been somewhat mitigated by aliasing arising from discrete diode placement. Conclusion: An analytical expression with perturbation factors was developed to estimate the impact of setup errors on an ArcCHECK phantom. Presently, this has been applied to simple functions, but is being developed to handle more realistic clinical cases.

62 RADIOLOGY AND NUCLEAR MEDICINE↗