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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 OSTI2019

Error Analysis of ZFP Compression for Floating-Point Data

Compression of floating-point data will play an important role in high-performance computing as data bandwidth and storage become dominant costs. Lossy compression of floating-point data is powerful, but theoretical results are needed to bound its errors when used to store look-up tables, simulation results, or even the solution state during the computation. In this paper, we analyze the round-off error introduced by ZFP, a lossy compression algorithm. Here, the stopping criteria for ZFP depends on the compression mode specified by the user: fixed rate, fixed accuracy, or fixed precision [P. Lindstrom, ZFP 0.5.3 Documentation, 2018]. While most of our discussion is focused on the fixed precision mode of ZFP, we establish a bound on the error introduced by all three compression modes. In order to tightly capture the error, first we introduce a vector space that allows us to work with binary representations of components. Under this vector space, we define operators that implement each step of the ZFP compression and decompression to establish a bound on the error caused by ZFP. To conclude, numerical tests are provided to demonstrate the accuracy of the established bounds.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Estimating Aboveground Biomass in Tropical Forests: Field Methods and Error Analysis for the Calibration of Remote Sensing Observations

Mapping and monitoring of forest carbon stocks across large areas in the tropics will necessarily rely on remote sensing approaches, which in turn depend on field estimates of biomass for calibration and validation purposes. Here, we used field plot data collected in a tropical moist forest in the central Amazon to gain a better understanding of the uncertainty associated with plot-level biomass estimates obtained specifically for the calibration of remote sensing measurements. In addition to accounting for sources of error that would be normally expected in conventional biomass estimates (e.g., measurement and allometric errors), we examined two sources of uncertainty that are specific to the calibration process and should be taken into account in most remote sensing studies: the error resulting from spatial disagreement between field and remote sensing measurements (i.e., co-location error), and the error introduced when accounting for temporal differences in data acquisition. We found that the overall uncertainty in the field biomass was typically 25% for both secondary and primary forests, but ranged from 16 to 53%. Co-location and temporal errors accounted for a large fraction of the total variance (>65%) and were identified as important targets for reducing uncertainty in studies relating tropical forest biomass to remotely sensed data. Although measurement and allometric errors were relatively unimportant when considered alone, combined they accounted for roughly 30% of the total variance on average and should not be ignored. Lastly, our results suggest that a thorough understanding of the sources of error associated with field-measured plot-level biomass estimates in tropical forests is critical to determine confidence in remote sensing estimates of carbon stocks and fluxes, and to develop strategies for reducing the overall uncertainty of remote sensing approaches.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Error analysis in nuclear density functional theory

Nuclear density functional theory (DFT) is the only microscopic, global approach to the structure of atomic nuclei. It is used in numerous applications, from determining the limits of stability to gaining a deep understanding of the formation of elements in the Universe or the mechanisms that power stars and reactors. The predictive power of the theory depends on the amount of physics embedded in the energy density functional as well as on efficient ways to determine a small number of free parameters and solve the DFT equations. In this article, we discuss the various sources of uncertainties and errors encountered in DFT and possible methods to quantify these uncertainties in a rigorous manner.

Schunck, Nicolas↗
DOE OSTI2022

Error analysis of a hybrid control drum worth model

This paper presents a perturbation-based model for control drum worth prediction which employs both physics-based and statistics-based components. Control drums, or control shims, are cylindrical in shape and span the axial length of the core. A portion of the cylinder is coated in neutron absorbing material and the drum can rotate to introduce the absorbing material to the body of the core to reduce reactivity. The model can be expensive to create due to the requirement for full-core Monte Carlo eigenvalue calculations. Therefore, it is important to analyze how the errors in Monte Carlo calculated k{sub eff} used for model training affect model performance. It was found that the error in predicted criticalities could average to 70 pcm in the most complex form of the model and 215 pcm in the simplest form of the model. Furthermore, it was found that the Monte Carlo uncertainty in quantities calculated with Serpent used to train the models had minimal impact on the error observed from the model. Lastly, one of the forms of the hybrid model could be trained in considerably less computational time if the Monte Carlo calculations were run to higher uncertainty in k{sub eff} with a small penalty to model performance.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Error analysis of low-fidelity models for wake steering based on field measurements

The observations collected by two scanning lidars deployed on the roof of a 2.8-MW turbine undergoing a series of imposed yaw offsets are analyzed. The wake lateral displacement detected by the rear-facing lidar correlates well with the yaw offset sensed by the forward-facing lidar. We find that the high-frequency part of the yaw offset signal is connected to wake meandering, whereas the low frequency component is a good predictor for wake displacement due to yaw misalignment. Conditionally averaged wake velocity data for different yaw offsets are used as benchmarks for the validation of a linearized Reynolds-averaged Navier-Stokes and an empirical wake model. A mean error as low as 2% and a good prediction of the wake trajectory are achieved, provided that the wake recovery rate matches the observations.

17 WIND ENERGY↗
DOE OSTI2021

Spectral sensor error analysis for measuring x-ray radiation drive using the DANTE diagnostic toward inertial confinement fusion experiments

DANTE is a diagnostic used to measure the x-radiation drive produced by heating a high-Z cavity (“hohlraum”) with high-powered laser beams. It records the spectrally and temporally resolved radiation flux at x-ray energies between 50 eV and 20 keV. Each sensor configuration on DANTE is composed of filters, mirrors, and x-ray diodes to define 18 different x-ray channels whose output is voltage as a function of time. The absolute flux is then determined from the photometric calibration of the sensor configuration and a spectral reconstructing algorithm. The reconstruction of the spectra vs time from the measured voltages and known response of each channel has presented challenges. Here we demonstrate a novel approach here for quantifying the error on the determined flux based on the channel sensor configuration and most commonly used reconstruction algorithm. In general, we find that the integrated spectral flux from a hohlraum can robustly be reconstructed (within ~14%) using a traditional unfold approach with as few as ten channels due to the underlying assumption of a largely Planckian spectral intensity distribution.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Error Analysis of the Thornado Supernova Simulation Code

The iron core collapse of a supernova throws 1.5 solar masses at speeds up to 35% the speed of light into a space with radius 50km, which creates a high density environment capable of generating neutrinos, producing gravitational waves, and driving chemical evolution of matter past iron. By designing a simulation of the process, we can better understand the origins and properties of these rare phenomena which are not seen anywhere else in the universe. Typically, supernovas are simulated with a finite volume method, but we want to incorporate a Nodal Discontinuous Galerkin Method to better utilize high performance computing because the DG method allows us to increase precision without increasing the number of computational elements. This project focuses on the fluid dynamics during collapse and the resulting shockwaves. To understand this, we improved on an existing simulation by comparing benchmark tests against published and theoretical results to identify and correct sources of error. The DG Method creates discontinuous functions across computational elements by solving the Euler fluid equations computed over Runge-Kutta time steps. The DG method allows for discontinuous functions to be built from polynomials defined in individual elements; however, it can lead to unphysical oscillation near discontinuities. We apply the characteristic slope limiter to reduce these affects, but when applied globally it leads to a 1% increase in absolute error. By introducing a Trouble Cell Indicator, which marks certain fluid elements based on the rate of change of observables, we can constrain the limiter to elements near shocks and manage the resulting absolute error. By constraining the limiter to discontinuous regions, we can remove what we know will be unrealistic without impacting naturally occurring oscillations.

79 ASTRONOMY AND ASTROPHYSICS↗