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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
NASA NTRS1984

The accuracy of altimetric surfaces

Estimates of the shape of the sea surface as determined from satellite altimeters have become of increasing importance in geophysics, geodesy and physical oceanography. The most effective error reduction procedures used in producing these surfaces have been some version of a crossing-arc analysis. The problem with this procedure is the absence of an easy means of evaluating the noise structure of the final result. By recasting the problem as one of objective mapping (optimal estimation) the error reduction is optimized and an explicit estimate is obtained of the wave-number structure of the final altimetric surface. Examples, suitable to a small computer, have been generated to show that the error reduction along the sub-satellite tracks is very great, with most of the residual lying in long wavelengths.

Wunsch, C.↗
DOE OSTI2016

Improving Lidar Turbulence Estimates for Wind Energy

Remote sensing devices (e.g., lidars) are quickly becoming a cost-effective and reliable alternative to meteorological towers for wind energy applications. Although lidars can measure mean wind speeds accurately, these devices measure different values of turbulence intensity (TI) than an instrument on a tower. In response to these issues, a lidar TI error reduction model was recently developed for commercially available lidars. The TI error model first applies physics-based corrections to the lidar measurements, then uses machine-learning techniques to further reduce errors in lidar TI estimates. The model was tested at two sites in the Southern Plains where vertically profiling lidars were collocated with meteorological towers. Results indicate that the model works well under stable conditions but cannot fully mitigate the effects of variance contamination under unstable conditions. To understand how variance contamination affects lidar TI estimates, a new set of equations was derived in previous work to characterize the actual variance measured by a lidar. Terms in these equations were quantified using a lidar simulator and modeled wind field, and the new equations were then implemented into the TI error model.

17 WIND ENERGY↗
NASA NTRS1973

Precise timing correlation in telemetry recording and processing systems

Independent PCM telemetry data signals received from missiles must be correlated to within + or - 100 microseconds for comparison with radar data. Tests have been conducted to determine RF antenna receiving system delays; delays associated with wideband analog tape recorders used in the recording, dubbing and repdocuing processes; and uncertainties associated with computer processed time tag data. Several methods used in the recording of timing are evaluated. Through the application of a special time tagging technique, the cumulative timing bias from all sources is determined and the bias removed from final data. Conclusions show that relative time differences in receiving, recording, playback and processing of two telemetry links can be accomplished with a + or - 4 microseconds accuracy. In addition, the absolute time tag error (with respect to UTC) can be reduced to less than 15 microseconds. This investigation is believed to be the first attempt to identify the individual error contributions within the telemetry system and to describe the methods of error reduction within the telemetry system and to describe the methods of error reduction and correction.

Pickett, R. B.↗
DOE OSTI2022

Error rate reduction of single-qubit gates via noise-aware decomposition into native gates

In the current era of Noisy Intermediate-Scale Quantum (NISQ) technology, the practical use of quantum computers remains inhibited by our inability to aptly decouple qubits from their environment to mitigate computational errors. In this paper, we introduce an approach by which knowledge of a qubit’s initial quantum state and the standard parameters describing its decoherence can be leveraged to mitigate the noise present during the execution of a single-qubit gate. We benchmark our protocol using cloud-based access to IBM quantum processors. On ibmq_rome, we demonstrate a reduction of the single-qubit error rate by 38%, from $1.6 \times 10 ^{-3}$ to $1.0 \times 10 ^{-3}$ , provided the initial state of the input qubit is known. On ibmq_bogota, we prove that our protocol will never decrease gate fidelity, provided the system’s T 1 and T 2 times have not drifted above 100 times their assumed values. The protocol can be used to reduce quantum state preparation errors, as well as to improve the fidelity of quantum circuits for which some knowledge of the qubits’ intermediate states can be inferred. This paper presents a pathway to using information about noise levels and quantum state distributions to significantly reduce error rates associated with quantum gates via optimized decomposition into native hardware gates.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2012

Significant reduction in errors associated with nonbonded contacts in protein crystal structures: automated all-atom refinement with PrimeX

All-atom models derived from moderate-resolution protein crystal structures contain a high frequency of close nonbonded contacts, independent of the major refinement program used for structure determination. All-atom refinement with PrimeX corrects many of these problematic interactions, producing models that are better suited for use in computational chemistry and related applications. All-atom models are essential for many applications in molecular modeling and computational chemistry. Nonbonded atomic contacts much closer than the sum of the van der Waals radii of the two atoms (clashes) are commonly observed in such models derived from protein crystal structures. A set of 94 recently deposited protein structures in the resolution range 1.5–2.8 Å were analyzed for clashes by the addition of all H atoms to the models followed by optimization and energy minimization of the positions of just these H atoms. The results were compared with the same set of structures after automated all-atom refinement with PrimeX and with nonbonded contacts in protein crystal structures at a resolution equal to or better than 0.9 Å. The additional PrimeX refinement produced structures with reasonable summary geometric statistics and similar R{sub free} values to the original structures. The frequency of clashes at less than 0.8 times the sum of van der Waals radii was reduced over fourfold compared with that found in the original structures, to a level approaching that found in the ultrahigh-resolution structures. Moreover, severe clashes at less than or equal to 0.7 times the sum of atomic radii were reduced 15-fold. All-atom refinement with PrimeX produced improved crystal structure models with respect to nonbonded contacts and yielded changes in structural details that dramatically impacted on the interpretation of some protein–ligand interactions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRSDate not supplied

Complementarities of GEO and LEO IR Sounders for Numerical Weather Prediction in an OSSE Framework

Preparations are underway for the United States’ weather satellite program and the expected advancements in the coming decade. Among these changes is the launch of the proposed NOAA/NASA Geostationary eXtended Observations (GeoXO) Sounder (GXS), coordinated with international counterparts, to form a global ring of hyperspectral infrared (IR) sounders. Coincident with the progress of IR sounders from GEO platforms, the future of low Earth orbit (LEO) sounders is being re-envisioned after decades of beneficial impact in numerical weather prediction (NWP) systems. To address questions about the utility of the two platform types in concert, the Global Modeling and Assimilation Office (GMAO) Observing System Simulation Experiment (OSSE) framework was used to examine the roles of GEO and LEO sounders and their impact on forecast error reduction. Overall, inclusion of both types of platforms in the satellite program produces the largest forecast error reduction with LEO sounders having a strong impact on global skill and GEO sounders providing the most benefit on the scale of targeted regions.

Erica McGrath-Spangler↗
NASA NTRS2020

A Brief Assessment of the Impact of Nearly 40 Years of Assimilated Observations Over the Amazon Basin

Adding a Forecast Sensitivity-based Observation Impact component to Version 2 of the Modern Era Retrospective-analysis for Research and Applications, the present study provides an assessment of the impact of nearly 40 years of observations on short-range (24-hr) forecasts over the Amazon basin. Under self-verification, forecast errors are found to slightly increase from the early data-sparse days to the more recent years, when data dramatically increase. Throughout the reanalysis, satellite radiances dominate in volume, but only before 1999 they dominate the impacts. Beyond 1999, over 50% of forecast error reduction is associated with conventional observations (radiosondes). Atmospheric Motion Vectors are also found to be large contributors to error reduction, but their contribution reduces in dry periods. In opposition to Atmospheric Motion Vectors, satellite radiances tend to contribute more in the dry season. Results provide motivation for additional conventional observations and the use of all-sky treatment of radiances.

F. L. R. Diniz↗
DOE OSTI2020

Digital zero noise extrapolation for quantum error mitigation

Zero-noise extrapolation (ZNE) is an increasingly popular technique for mitigating errors in noisy quantum computations without using additional quantum resources. We review the fundamentals of ZNE and propose several improvements to noise scaling and extrapolation, the two key components in the technique. We introduce unitary folding and parameterized noise scaling. Furthermore, these are digital noise scaling frameworks, i.e. one can apply them using only gate-level access common to most quantum instruction sets. We also study different extrapolation methods, including a new adaptive protocol that uses a statistical inference framework. Benchmarks of our techniques show error reductions of 18X to 24X over non-mitigated circuits and demonstrate ZNE effectiveness at larger qubit numbers than have been tested previously. In addition to presenting new results, this work is a self-contained introduction to the practical use of ZNE by quantum programmers.

quantum computing↗
NASA NTRS2024

Contributions of irrigation modeling, soil moisture and snow data assimilation to high-resolution water budget estimates over the Po basin: progress towards digital replicas

High-resolution water budget estimates benefit from modeling of human water management and satellite data assimilation (DA) in river basins with a large human footprint. Utilizing the Noah-MP land surface model with dynamic vegetation growth and river routing, in combination with an irrigation module, Sentinel-1 backscatter and snow depth retrievals, we produce a set of 0.7-km2 water budget estimates of the Po river basin (Italy) for 2015–2023. The results demonstrate that irrigation modeling improves the seasonal soil moisture variation and summer streamflow at all gauges in the valley after withdrawal of irrigation water from the streamflow in postprocessing (12% error reduction relative to observed low summer streamflow), even if the basin-wide irrigation amount is underestimated. Sentinel-1 backscatter DA for soil moisture updating strongly interacts with irrigation modeling: when both are activated, the soil moisture updates are limited, and the simulated irrigation amounts are reduced. Backscatter DA systematically reduces soil moisture in the spring, which improves downstream spring streamflow. Assimilating Sentinel-1 snow depth retrievals over the surrounding Alps and Apennines further improves spring streamflow in a complementary way (2% error reduction relative to observed high spring streamflow). Despite the seasonal improvements, irrigation modeling and Sentinel-1 backscatter DA cannot significantly improve short-term or interannual variations in soil moisture, irrigation modeling causes a systematically prolonged high vegetation productivity, and snow depth DA only impacts the deep snowpacks. This study helps advancing the design of digital water budget replicas for river basins.

Gabrielle J M De Lannoy↗
DOE OSTI2013

Depletion GPT-free sensitivity analysis for reactor eigenvalue problems

This manuscript introduces a novel approach to solving depletion perturbation theory problems without the need to set up or solve the generalized perturbation theory (GPT) equations. The approach, hereinafter denoted generalized perturbation theory free (GPT-Free), constructs a reduced order model (ROM) using methods based in perturbation theory and computes response sensitivity profiles in a manner that is independent of the number or type of responses, allowing for an efficient computation of sensitivities when many responses are required. Moreover, the reduction error from using the ROM is quantified in the GPT-Free approach by means of a Wilks' order statistics error metric denoted the K-metric. Traditional GPT has been recognized as the most computationally efficient approach for performing sensitivity analyses of models with many input parameters, e.g. when forward sensitivity analyses are computationally intractable. However, most neutronics codes that can solve the fundamental (homogenous) adjoint eigenvalue problem do not have GPT capabilities unless envisioned during code development. The GPT-Free approach addresses this limitation by requiring only the ability to compute the fundamental adjoint. This manuscript demonstrates the GPT-Free approach for depletion reactor calculations performed in SCALE6 using the 7x7 UAM assembly model. A ROM is developed for the assembly over a time horizon of 990 days. The approach both calculates the reduction error over the lifetime of the simulation using the K-metric and benchmarks the obtained sensitivities using sample calculations. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Higher-order particle representation for particle-in-cell simulations

In this paper we present an alternative approach to the representation of simulation particles for unstructured electrostatic and electromagnetic PIC simulations. In our modified PIC algorithm we represent particles as having a smooth shape function limited by some specified finite radius, r 0 . A unique feature of our approach is the representation of this shape by surrounding simulation particles with a set of virtual particles with delta shape, with fixed offsets and weights derived from Gaussian quadrature rules and the value of r 0 . As the virtual particles are purely computational, they provide the additional benefit of increasing the arithmetic intensity of traditionally memory bound particle kernels. The modified algorithm is implemented within Sandia National Laboratories' unstructured EMPIRE-PIC code, for electrostatic and electromagnetic simulations, using periodic boundary conditions. We show results for a representative set of benchmark problems, including electron orbit, a transverse electromagnetic wave propagating through a plasma, numerical heating, and a plasma slab expansion. In this work, good error reduction across all of the chosen problems is achieved as the particles are made progressively smoother, with the optimal particle radius appearing to be problem-dependent.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1991

Channel coding for digital HDTV terrestrial broadcasting

The Federal Communications Commission of the United States has ruled that high-definition television (HDTV) will occupy no more than 6 MHz of the VHF and UHF bands now used for conventional TV. In order to transmit the HDTV signal in 6 MHz, the four United States digital HDTV proponents, the DigiCipher, DSC-HDTV, ADTV, and ATVA-P systems, are reducing the video data rate of HDTV to 15-17 Mb/s, a compression ratio of approximately 60-70 times. The high compression dictates that channel coding be used to avoid block errors and multiframe error propagation. High efficiency in channel utilization required by the 6-MHz limitation means that the channel must be properly equalized and that the multipath and interfering signals must be severely limited. The channel coding techniques used for error reduction include data interleaving, error detection and replacement, and error correction at different levels of protection for bits and blocks of unequal importance.

Beakley, Guy W.↗
NASA NTRSDate not supplied

Assimilation of Soil Moisture Observations Over Land Improves Analysis and Prediction of Tropical Cyclone Idai

Soil moisture conditions can impact the circulation and structure of a tropical cyclone (TC) when part or all of the circulation is over land. Dry land surface conditions may lead to faster dissipation of a TC over land, whereas very wet conditions may lead to a prolonged maintenance of its intensity. While this relationship is relatively well understood in theory, applications of these findings in the context of numerical weather prediction (NWP) have been limited. Here we present a case study that explores the potential of improving TC predictions through an improved soil moisture initialization in an NWP framework. Specifically, we examine the impact of assimilating observations from the NASA Soil Moisture Active Passive (SMAP) mission into the NASA Goddard Earth Observing System (GEOS) global weather model on the prediction of South-West Indian Ocean TC Idai (2019). SMAP provides accurate L-band (1.4 GHz) brightness temperatures (Tb) observations that are sensitive to soil moisture globally and at high revisit times of 2-3 days. It has previously been shown that the assimilation of SMAP Tbs significantly improves modeled land surface states. Here we evaluate: (i) forecasts initialized from an analysis that is comparable to the GEOS operational analysis (without SMAP Tb assimilation) and (ii) forecasts initialized from an analysis that additionally assimilates SMAP Tb observations. We find that in the analysis with SMAP assimilation, the TC has a better-defined, more aligned vertical structure over land relative to the control run; moreover, the analyzed TC size, as measured by the wind speed radius, better matches the observed TC size. We further find significant reductions in the forecast intensity error and the forecast along-track error, measured against observations. The largest error reductions occur at lead times of 36 to 72 hours, suggesting that the land with its longer memory gains in importance as a source of predictability at this timescale. An investigation of the underlying mechanisms leading to the skill improvements from SMAP data assimilation revealed that the assimilation of SMAP leads to wetter soil moisture conditions and an increased latent heat flux in the SMAP analysis, which results in a TC with higher column-integrated total moisture content and total energy compared to the control analysis.

Jana Kolassa↗
DOE OSTI2023

Multifidelity Neural Network Formulations for Prediction of Reactive Molecular Potential Energy Surfaces

Here, this paper focuses on the development of multifidelity modeling approaches using neural network surrogates, where training data arising from multiple model forms and resolutions are integrated to predict high-fidelity response quantities of interest at lower cost. We focus on the context of quantum chemistry and the integration of information from multiple levels of theory. Important foundations include the use of symmetry function-based atomic energy vector constructions as feature vectors for representing structures across families of molecules and single-fidelity neural network training capabilities that learn the relationships needed to map feature vectors to potential energy predictions. These foundations are embedded within several multifidelity topologies that decompose the high-fidelity mapping into model-based components, including sequential formulations that admit a general nonlinear mapping across fidelities and discrepancy-based formulations that presume an additive decomposition. Methodologies are first explored and demonstrated on a pair of simple analytical test problems and then deployed for potential energy prediction for C 5 H 5 using B2PLYP-D3/6-311++G(d,p) for high-fidelity simulation data and Hartree–Fock 6-31G for low-fidelity data. For the common case of limited access to high-fidelity data, our computational results demonstrate that multifidelity neural network potential energy surface constructions achieve roughly an order of magnitude improvement, either in terms of test error reduction for equivalent total simulation cost or reduction in total cost for equivalent error.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗