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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Online Distribution System State Estimation via Stochastic Gradient Algorithm

Distribution network operation is becoming more challenging because of the growing integration of intermittent and volatile distributed energy resources (DERs). This motivates the development of new distribution system state estimation (DSSE) paradigms that can operate at fast timescale based on real-time data stream of asynchronous measurements enabled by modern information and communications technology. To solve the real-time DSSE with asynchronous measurements effectively and accurately, this paper formulates a weighted least squares DSSE problem and proposes an online stochastic gradient algorithm to solve it. The performance of the proposed scheme is analytically guaranteed and is numerically corroborated with realistic data on IEEE 123-bus feeder.

distribution system state estimation↗
DOE OSTI2025

Inverse rendering of fusion plasmas: Inferring plasma composition from imaging systems

In this work, we develop a differentiable rendering pipeline for visualising plasma emission within tokamaks, and estimating the gradients of the emission and estimating other physical quantities. Unlike prior work, we are able to leverage arbitrary representations of plasma quantities and easily incorporate them into a non-linear optimisation framework. The efficiency of our method enables not only estimation of a physically plausible image of plasma, but also recovery of the neutral Deuterium distribution from imaging and midplane measurements alone. We demonstrate our method with three different levels of complexity showing first that a poloidal neutrals density distribution can be recovered from imaging alone, second that the distributions of neutral Deuterium, electron density and electron temperature can be recovered jointly, and finally, that this can be done in the presence of realistic imaging systems that incorporate sensor cropping and quantisation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2015

Adaptive State Predictor Based Human Operator Modeling on Longitudinal and Lateral Control

Control-theoretic modeling of the human operator dynamic behavior in manual control tasks has a long and rich history. In the last two decades, there has been a renewed interest in modeling the human operator. There has also been significant work on techniques used to identify the pilot model of a given structure. The purpose of this research is to attempt to go beyond pilot identification based on collected experimental data and to develop a predictor of pilot behavior. An experiment was conducted to categorize these interactions of the pilot with an adaptive controller compensating during control surface failures. A general linear in-parameter model structure is used to represent a pilot. Three different estimation methods are explored. A gradient descent estimator (GDE), a least squares estimator with exponential forgetting (LSEEF), and a least squares estimator with bounded gain forgetting (LSEBGF) used the experiment data to predict pilot stick input. Previous results have found that the GDE and LSEEF methods are fairly accurate in predicting longitudinal stick input from commanded pitch. This paper discusses the accuracy of each of the three methods - GDE, LSEEF, and LSEBGF - to predict both pilot longitudinal and lateral stick input from the flight director's commanded pitch and bank attitudes.

Trujillo, Anna C.↗
NASA NTRS1977

Estimation of terrain iso-gradients from a stochastic range data measurement matrix

The problem of estimating terrain iso-gradients for an autonomous roving vehicle is complicated by the measurement error inherent in the range data matrix. In this paper, the in-path and cross-path slopes at each data point in the multivariable range matrix are expressed in terms of parameters at that data point and those at its surrounding points. The sensitivity in the change of these slopes - due to the errors in measurements in range, elevation angle and azimuth angle are formulated. These sensitivities are used to determine the appropriate statistics of the gradient, which are then used to describe the terrain with a known probability of accuracy.

Shen, C. N.↗
NASA NTRS2016

Estimates of Terms in Ohm's Law During an Encounter with an Electron Diffusion Region

We present measurements from the Magnetospheric Multiscale (MMS) mission taken during a reconnection event on the dayside magnetopause which includes a passage through an electron diffusion region (EDR). The four MMS satellites were separated by about 10 km such that estimates of gradients and divergences allow a reasonable estimate of terms in the generalized Ohm's law, which is key to investigating the energy dissipation during reconnection. The strength and character of dissipation mechanisms determines how magnetic energy is released. We show that both electron pressure gradients and electron inertial effects are important, but not the only participants in reconnection near EDRs, since there are residuals of a few mVm (approximately 30-50%) of E+ U(sub e) x B (from the sum of these two terms) during the encounters. These results are compared to a simulation, which exhibits many of the observed features, but where relatively little residual is present.

Torbert, R. B.↗
NASA NTRS1989

Cosmic ray gradients in the heliosphere

Early estimates of the radial gradient made in the inner heliosphere are presented. In a discussion of discoveries in the outer heliosphere, consideration is given to low radial gradients and nonradial transport, the identification of different components, the large-scale organization of the heliospheric magnetic field, propagating modulation features, and latitude gradients. Current estimates of the spatial distribution of the cosmic ray intensity are presented as well.

Fillius, Walker↗
DOE OSTI2021

Stochastic Gradient-Based Distributed Bayesian Estimation in Cooperative Sensor Networks

Distributed Bayesian inference provides a full quantification of uncertainty offering numerous advantages over point estimates that autonomous sensor networks are able to exploit. However, fully-decentralized Bayesian inference often requires large communication overheads and low network latency, resources that are not typically available in practical applications. In this paper, we propose a decentralized Bayesian inference approach based on stochastic gradient Langevin dynamics, which produces full posterior distributions at each of the nodes with significantly lower communication overhead. We provide analytical results on convergence of the proposed distributed algorithm to the centralized posterior, under typical network constraints. Finally, we also provide extensive simulation results to demonstrate the validity of the proposed approach.

42 ENGINEERING↗
DOE OSTI2023

An Iterative Bidirectional Gradient Boosting Algorithm for CVR Baseline Estimation

This paper presents a novel iterative, bidirectional, gradient boosting (bidirectional-GB) algorithm for estimating the baseline of the Conservation Voltage Reduction (CVR) program. We define the CVR baseline as the load profile during the CVR period if the substation voltage is not lowered. The proposed algorithm consists of two key steps: selection of similar days and iterative bidirectional-GB training. In the first step, preand post-event temperature profiles of the targeted CVR day are used to select similar days from historical non-CVR days. In the second step, the pre-event and post-event similar days are used to train two GBMs iteratively: a forward-GBM and a backwardGBM. After each iteration, the two generated CVR baselines are reconciled and only the first and the last points on the reconciled baseline are kept. The iteration repeats until all CVR baseline points are generated. We tested two gradient boosting methods (i.e., GBM and LighGBM) with two data resolutions (i.e., 15and 30-minute). The results demonstrate that both the accuracy and performance of the algorithm are satisfactory.

Lee, Han Pyo↗
DOE OSTI2024

An iterative bidirectional gradient boosting approach for CVR baseline estimation

Here this paper presents a novel Iterative Bidirectional Gradient Boosting Model (IBi-GBM) for estimating the baseline of Conservation Voltage Reduction (CVR) programs. In contrast to many existing methods, we treat CVR baseline estimation as a missing data retrieval problem. The approach involves dividing the load and its corresponding temperature profiles into three periods: pre-CVR, CVR, and post-CVR. To restore the missing load profile during the CVR period, the method employs a three-step process. First, a forward-pass GBM is executed using data from the pre-CVR period as inputs. Subsequently, a backward-pass GBM is applied using data from the post-CVR period. The two restored load profiles are reconciled, considering pre-calculated weights derived from forecasting accuracy, and only the leftmost and rightmost points are retained. The newly restored points are then included as inputs for the subsequent iteration. This iterative procedure continues until the original load data in the CVR period is fully restored. We develop IBi-GBM using actual smart meter and Supervisory Control and Data Acquisition (SCADA) data. Our results demonstrate that IBi-GBM exhibits robust performance across various data resolutions and in different seasons and outperforms existing methods by achieving a 1-2% reduction in normalized Root Mean Square Error (nRMSE).

42 ENGINEERING↗
NASA NTRS2001

Mitral inertance in humans: critical factor in Doppler estimation of transvalvular pressure gradients

The pressure-velocity relationship across the normal mitral valve is approximated by the Bernoulli equation DeltaP = 1/2 rhoDeltav(2) + M. dv/dt, where DeltaP is the atrioventricular pressure difference, rho is blood density, v is transmitral flow velocity, and M is mitral inertance. Although M is indispensable in assessing transvalvular pressure differences from transmitral flow, this term is poorly understood. We measured intraoperative high-fidelity left atrial and ventricular pressures and simultaneous transmitral flow velocities by using transesophageal echocardiography in 100 beats (8 patients). We computed mean mitral inertance (M) by M = integral((DeltaP)-(1/2 x rho v(2))dt/integral(dv/dt)dt and we assessed the effect of the inertial term on the transmitral pressure-flow relation. ranged from 1.03 to 5.96 g/cm(2) (mean = 3.82 +/- 1.22 g/cm(2)). DeltaP calculated from the simplified Bernoulli equation (DeltaP = 1/2. rhov(2)) lagged behind (44 +/- 11 ms) and underestimated the actual peak pressures (2.3 +/- 1.1 mmHg). correlated with left ventricular systolic pressure (r = -0.68, P < 0.0001) and transmitral pressure gradients (r = 0.65, P < 0.0001). Because mitral inertance causes the velocity to lag significantly behind the actual pressure gradient, it needs to be considered when assessing diastolic filling and the pressure difference across normal mitral valves.

NASA Discipline Cardiopulmonary↗
DOE OSTI2017

Gradient boosting machine for modeling the energy consumption of commercial buildings

Accurate savings estimations are important to promote energy efficiency projects and demonstrate their cost-effectiveness. The increasing presence of advanced metering infrastructure (AMI) in commercial buildings has resulted in a rising availability of high frequency interval data. These data can be used for a variety of energy efficiency applications such as demand response, fault detection and diagnosis, and heating, ventilation, and air conditioning (HVAC) optimization. This large amount of data has also opened the door to the use of advanced statistical learning models, which hold promise for providing accurate building baseline energy consumption predictions, and thus accurate saving estimations. The gradient boosting machine is a powerful machine learning algorithm that is gaining considerable traction in a wide range of data driven applications, such as ecology, computer vision, and biology. In the present work an energy consumption baseline modeling method based on a gradient boosting machine was proposed. To assess the performance of this method, a recently published testing procedure was used on a large dataset of 410 commercial buildings. The model training periods were varied and several prediction accuracy metrics were used to evaluate the model's performance. The results show that using the gradient boosting machine model improved the R-squared prediction accuracy and the CV(RMSE) in more than 80 percent of the cases, when compared to an industry best practice model that is based on piecewise linear regression, and to a random forest algorithm.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Gradient-Informed Design Optimization of Select Nuclear Systems

In this work, we present a gradient-informed design optimization of nuclear reactor core components based on neutronics objectives with both continuous and discrete materials. The main argument in favor of using gradient-informed design optimization is that it scales well with increasing dimensionality of the design space. First, a challenge problem with 121 free parameters is solved with a gradient-informed method and then with a genetic algorithm. Then, a challenge problem to optimize the flux profile of a simplified assembly with eight axial zones is solved. Both challenge problems are solved using directly calculated derivatives from Tools for Sensitivity and Uncertainty Analysis Methodology Implementation (TSUNAMI) in the SCALE package. Furthermore, we demonstrate how a discrete optimization problem—selection of materials for 121 voxels—can be lifted into a continuous problem with mixed materials. In the continuous space, adjoint-based gradients are well-defined, and gradient descent is applicable. Then, a forcing function is introduced that with the selection of an appropriately sized hyperparameter can be used to guide the optimized continuous solution back into a discrete solution. This paper presents an account of the challenges that were faced when applying a gradient-informed optimization algorithm using a Monte Carlo calculation to estimate the gradient information and compares a gradient descent optimization method to a genetic algorithm optimization of the same geometry. Overall, this work demonstrates the potential use of adjoint-based gradient calculations in design optimization of nuclear systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2003

Revisiting the Least-squares Procedure for Gradient Reconstruction on Unstructured Meshes

The accuracy of the least-squares technique for gradient reconstruction on unstructured meshes is examined. While least-squares techniques produce accurate results on arbitrary isotropic unstructured meshes, serious difficulties exist for highly stretched meshes in the presence of surface curvature. In these situations, gradients are typically under-estimated by up to an order of magnitude. For vertex-based discretizations on triangular and quadrilateral meshes, and cell-centered discretizations on quadrilateral meshes, accuracy can be recovered using an inverse distance weighting in the least-squares construction. For cell-centered discretizations on triangles, both the unweighted and weighted least-squares constructions fail to provide suitable gradient estimates for highly stretched curved meshes. Good overall flow solution accuracy can be retained in spite of poor gradient estimates, due to the presence of flow alignment in exactly the same regions where the poor gradient accuracy is observed. However, the use of entropy fixes has the potential for generating large but subtle discretization errors.

Mavriplis, Dimitri J.↗
NASA NTRS1989

Numerical optimization in Hilbert space using inexact function and gradient evaluations

Trust region algorithms provide a robust iterative technique for solving non-convex unstrained optimization problems, but in many instances it is prohibitively expensive to compute high accuracy function and gradient values for the method. Of particular interest are inverse and parameter estimation problems, since function and gradient evaluations involve numerically solving large systems of differential equations. A global convergence theory is presented for trust region algorithms in which neither function nor gradient values are known exactly. The theory is formulated in a Hilbert space setting so that it can be applied to variational problems as well as the finite dimensional problems normally seen in trust region literature. The conditions concerning allowable error are remarkably relaxed: relative errors in the gradient error condition is automatically satisfied if the error is orthogonal to the gradient approximation. A technique for estimating gradient error and improving the approximation is also presented.

Carter, Richard G.↗
NASA NTRS1997

Large Volcanic Rises on Venus

Large volcanic rises on Venus have been interpreted as hotspots, or the surface manifestation of mantle upwelling, on the basis of their broad topographic rises, abundant volcanism, and large positive gravity anomalies. Hotspots offer an important opportunity to study the behavior of the lithosphere in response to mantle forces. In addition to the four previously known hotspots, Atla, Bell, Beta, and western Eistla Regiones, five new probable hotspots, Dione, central Eistla, eastern Eistla, Imdr, and Themis, have been identified in the Magellan radar, gravity and topography data. These nine regions exhibit a wider range of volcano-tectonic characteristics than previously recognized for venusian hotspots, and have been classified as rift-dominated (Atla, Beta), coronae-dominated (central and eastern Eistla, Themis), or volcano-dominated (Bell, Dione, western Eistla, Imdr). The apparent depths of compensation for these regions ranges from 65 to 260 km. New estimates of the elastic thickness, using the 90 deg and order spherical harmonic field, are 15-40 km at Bell Regio, and 25 km at western Eistla Regio. Phillips et al. find a value of 30 km at Atla Regio. Numerous models of lithospheric and mantle behavior have been proposed to interpret the gravity and topography signature of the hotspots, with most studies focusing on Atla or Beta Regiones. Convective models with Earth-like parameters result in estimates of the thickness of the thermal lithosphere of approximately 100 km. Models of stagnant lid convection or thermal thinning infer the thickness of the thermal lithosphere to be 300 km or more. Without additional constraints, any of the model fits are equally valid. The thinner thermal lithosphere estimates are most consistent with the volcanic and tectonic characteristics of the hotspots. Estimates of the thermal gradient based on estimates of the elastic thickness also support a relatively thin lithosphere (Phillips et al.). The advantage of larger estimates of the thermal lithospheric thickness is that they provide an explanation for the apparently modest levels of geologic activity on Venus over the last half billion years.

Smrekar, Suzanne E.↗
NASA NTRS1983

Plasmapause diffusion

The Bohm diffusion coefficient and observed electrostatic wave scattering are used as the bases of estimates of the smoothing effect that diffusion may have on steep plasmapause density gradients. The estimate for diffusion resulting from scattering by observed electrostatic waves is found to be much lower than that of the perpendicular Bohm diffusion coefficient for characteristic plasma temperatures and magnetic fields. This diffusion rate estimate may be too small, however, if the wave amplitudes are significantly higher for steep plasmapauses. The effects are therefore negligible for most considerations of macroscopic plasmapause dynamics, but may be significant in limiting drift wave instabilities and similar phenomena driven by the steepness of the plasmapause density gradient.

Horwitz, J. L.↗
NASA NTRS2021

Multi-Pass Sequential Mini-Batch Stochastic Gradient Descent Algorithms for Noise Covariance Estimation in Adaptive Kalman Filtering

Estimation of unknown noise covariances in a Kalman filter is a problem of significant practical interest in a wide array of applications. Although this problem has a long history, reliable algorithms for their estimation were scant, and necessary and sufficient conditions for identifiability of the covariances were in dispute until recently. Necessary and sufficient conditions for covariance estimation and a batch estimation algorithm were presented in our previous study. This paper presents stochastic gradient descent algorithms for noise covariance estimation in adaptive Kalman filters that are an order of magnitude faster than the batch method for similar or better root mean square error. More significantly, these algorithms are applicable to non-stationary systems where the noise covariances can occasionally jump up or down by an unknown magnitude. The computational efficiency of the new algorithms stems from adaptive thresholds for convergence, recursive fading memory estimation of the sample cross-correlations of the innovations, and accelerated stochastic gradient descent algorithms. The comparative evaluation of the proposed methods on a number of test cases demonstrates their computational efficiency and accuracy.

Adaptive Kalman filtering↗