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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Sources of errors in the simulation of south Asian summer monsoon in the CMIP5 GCMs

Accurate simulation of the South Asian summer monsoon (SAM) is still an unresolved challenge. There has not been a benchmark effort to decipher the origin of undesired yet virtually invariable unsuccessfulness of general circulation models (GCMs) over this region. This study analyzes a large ensemble of CMIP5 GCMs to show that most of the simulation errors in the precipitation distribution and their driving mechanisms are systematic and of similar nature across the GCMs, with biases in meridional differential heating playing a critical role in determining the timing of monsoon onset over land, the magnitude of seasonal precipitation distribution and the trajectories of monsoon depressions. Errors in the pre-monsoon heat low over the lower latitudes and atmospheric latent heating over the slopes of Himalayas and Karakoram Range induce significant errors in the atmospheric circulations and meridional differential heating. Lack of timely precipitation further exacerbates such errors by limiting local moisture recycling and latent heating aloft from convection. Most of the summer monsoon errors and their sources are reproducible in the land–atmosphere configuration of a GCM when it is configured at horizontal grid spacing comparable to the CMIP5 GCMs. While an increase in resolution overcomes many modeling challenges, coarse resolution is not necessarily the primary driver in the exhibition of errors over South Asia. These results highlight the importance of previously less well known pre-monsoon mechanisms that critically influence the strength of SAM in the GCMs and highlight the importance of land–atmosphere interactions in the development and maintenance of SAM.

CMIP5↗
NASA NTRS2013

The Influence of Observation Errors on Analysis Error and Forecast Skill Investigated with an Observing System Simulation Experiment

The Global Modeling and Assimilation Office (GMAO) observing system simulation experiment (OSSE) framework is used to explore the response of analysis error and forecast skill to observation quality. In an OSSE, synthetic observations may be created that have much smaller error than real observations, and precisely quantified error may be applied to these synthetic observations. Three experiments are performed in which synthetic observations with magnitudes of applied observation error that vary from zero to twice the estimated realistic error are ingested into the Goddard Earth Observing System Model (GEOS-5) with Gridpoint Statistical Interpolation (GSI) data assimilation for a one-month period representing July. The analysis increment and observation innovation are strongly impacted by observation error, with much larger variances for increased observation error. The analysis quality is degraded by increased observation error, but the change in root-mean-square error of the analysis state is small relative to the total analysis error. Surprisingly, in the 120 hour forecast increased observation error only yields a slight decline in forecast skill in the extratropics, and no discernable degradation of forecast skill in the tropics.

Observation Error↗
DOE OSTI2021

Correlated charge noise and relaxation errors in superconducting qubits

In This report, the central challenge in building a quantum computer is error correction. Unlike classical bits, which are susceptible to only one type of error, quantum bits (“qubits”) are susceptible to two types of error, corresponding to flips of the qubit state about the X- and Z-directions. While the Heisenberg Uncertainty Principle precludes simultaneous monitoring of X- and Z-flips on a single qubit, it is possible to encode quantum information in large arrays of entangled qubits that enable accurate monitoring of all errors in the system, provided the error rate is low. Another crucial requirement is that errors cannot be correlated. Here, we characterize a superconducting multiqubit circuit and find that charge fluctuations are highly correlated on a length scale over 600 µm; moreover, discrete charge jumps are accompanied by a strong transient suppression of qubit energy relaxation time across the millimeter-scale chip. The resulting correlated errors are explained in terms of the charging event and phonon-mediated quasiparticle poisoning associated with absorption of gamma rays and cosmic-ray muons in the qubit substrate. Robust quantum error correction will require the development of mitigation strategies to protect multiqubit arrays from correlated errors due to particle impacts.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

An error reduction algorithm to improve lidar turbulence estimates for wind energy

Abstract. Remote-sensing devices such as lidars are currently being investigated as alternatives to cup anemometers on meteorological towers for the measurement of wind speed and direction. Although lidars can measure mean wind speeds at heights spanning an entire turbine rotor disk and can be easily moved from one location to another, they measure different values of turbulence than an instrument on a tower. Current methods for improving lidar turbulence estimates include the use of analytical turbulence models and expensive scanning lidars. While these methods provide accurate results in a research setting, they cannot be easily applied to smaller, vertically profiling lidars in locations where high-resolution sonic anemometer data are not available. Thus, there is clearly a need for a turbulence error reduction model that is simpler and more easily applicable to lidars that are used in the wind energy industry. In this work, a new turbulence error reduction algorithm for lidars is described. The Lidar Turbulence Error Reduction Algorithm, L-TERRA, can be applied using only data from a stand-alone vertically profiling lidar and requires minimal training with meteorological tower data. The basis of L-TERRA is a series of physics-based corrections that are applied to the lidar data to mitigate errors from instrument noise, volume averaging, and variance contamination. These corrections are applied in conjunction with a trained machine-learning model to improve turbulence estimates from a vertically profiling WINDCUBE v2 lidar. The lessons learned from creating the L-TERRA model for a WINDCUBE v2 lidar can also be applied to other lidar devices. L-TERRA was tested on data from two sites in the Southern Plains region of the United States. The physics-based corrections in L-TERRA brought regression line slopes much closer to 1 at both sites and significantly reduced the sensitivity of lidar turbulence errors to atmospheric stability. The accuracy of machine-learning methods in L-TERRA was highly dependent on the input variables and training dataset used, suggesting that machine learning may not be the best technique for reducing lidar turbulence intensity (TI) error. Future work will include the use of a lidar simulator to better understand how different factors affect lidar turbulence error and to determine how these errors can be reduced using information from a stand-alone lidar.

17 WIND ENERGY↗
DOE OSTI2023

Linearization errors in discrete goal-oriented error estimation

This paper is concerned with goal-oriented a posteriori error estimation for nonlinear functionals in the context of nonlinear variational problems solved with continuous Galerkin finite element discretizations. A two-level, or discrete, adjoint-based approach for error estimation is considered. The traditional method to derive an error estimate in this context requires linearizing both the nonlinear variational form and the nonlinear functional of interest which introduces linearization errors into the error estimate. In this paper, we investigate these linearization errors. In particular, we develop a novel discrete goal-oriented error estimate that accounts for traditionally neglected nonlinear terms at the expense of greater computational cost. We demonstrate how this error estimate can be used to drive mesh adaptivity. Here, we show that accounting for linearization errors in the error estimate can improve its effectivity for several nonlinear model problems and quantities of interest. We also demonstrate that an adaptive strategy based on the newly proposed estimate can lead to more accurate approximations of the nonlinear functional with fewer degrees of freedom when compared to uniform refinement and traditional adjoint-based approaches.

42 ENGINEERING↗
DOE OSTI2017

Error modeling for surrogates of dynamical systems using machine learning: Machine-learning-based error model for surrogates of dynamical systems

A machine learning–based framework for modeling the error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high-dimensional regression techniques (eg, random forests, and LASSO) to map a large set of inexpensively computed “error indicators” (ie, features) produced by the surrogate model at a given time instance to a prediction of the surrogate-model error in a quantity of interest (QoI). This eliminates the need for the user to hand-select a small number of informative features. The methodology requires a training set of parameter instances at which the time-dependent surrogate-model error is computed by simulating both the high-fidelity and surrogate models. Using these training data, the method first determines regression-model locality (via classification or clustering) and subsequently constructs a “local” regression model to predict the time-instantaneous error within each identified region of feature space. We consider 2 uses for the resulting error model: (1) as a correction to the surrogate-model QoI prediction at each time instance and (2) as a way to statistically model arbitrary functions of the time-dependent surrogate-model error (eg, time-integrated errors). We then apply the proposed framework to model errors in reduced-order models of nonlinear oil-water subsurface flow simulations, with time-varying well-control (bottom-hole pressure) parameters. The reduced-order models used in this work entail application of trajectory piecewise linearization in conjunction with proper orthogonal decomposition. Moreover, when the first use of the method is considered, numerical experiments demonstrate consistent improvement in accuracy in the time-instantaneous QoI prediction relative to the original surrogate model, across a large number of test cases. When the second use is considered, results show that the proposed method provides accurate statistical predictions of the time- and well-averaged errors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Comparative Analysis of Soft-Error Detection Strategies: A Case Study with Iterative Methods

Undetected soft errors caused by transient bit flips can lead to silent data corruption (SDC), an undesirable outcome where invalid results pass for valid ones. This has motivated the design of soft error detectors to minimize SDCs. However, the detectors have been studied under different contexts, making comparative evaluation difficult. In this paper, we present the first comprehensive evaluation of four online soft error detection techniques in detecting the adverse impact of soft errors on iterative methods. We observe that, across five iterative methods, the detectors studied achieve high but not perfect detection rates. To understand the potential for improved detection, we evaluate a machine-learning based detector that takes as features that are the runtime features observed by the individual detectors to arrive at their conclusions. Our evaluation demonstrates improved but still far from perfect detection accuracy for the machine learning based detectors. This extensive evaluation demonstrates the need for designing error detectors to handle the evolutionary behavior exhibited by iterative solvers.

Silent data corruption, Soft error detection, Iter↗
NASA NTRS1980

Effect of rawinsonde errors on rocketsonde density and pressure profiles: An error analysis of the Rawinsonde System

An initial value of pressure is required to derive the density and pressure profiles of the rocketborne rocketsonde sensor. This tie-on pressure value is obtained from the nearest rawinsonde launch at an altitude where overlapping rawinsonde and rocketsonde measurements occur. An error analysis was performed of the error sources in these sensors that contribute to the error in the tie-on pressure. It was determined that significant tie-on pressure errors result from radiation errors in the rawinsonde rod thermistor, and temperature calibration bias errors. To minimize the effect of these errors radiation corrections should be made to the rawinsonde temperature and the tie-on altitude should be chosen at the lowest altitude of overlapping data. Under these conditions the tie-on error, and consequently the resulting error in the Datasonde pressure and density profiles is less tha 1%. The effect of rawinsonde pressure and temperature errors on the wind and temperature versus height profiles of the rawinsonde was also determined.

Luers, J. K.↗
DOE OSTI2019

Error field impact on mode locking and divertor heat flux in NSTX-U

During the 2016 NSTX-U experimental campaign, locked modes in the plasma edge presented clear evidence of the presence of error fields. Extensive metrology and plasma response modeling with IPEC and M3D-C1 have been conducted to understand the various sources of error fields in NSTX-U as built in 2016, and to determine which of these sources have the greatest effect on the plasma. In particular, modeling finds that the error field from misalignment of the toroidal field (TF) coils may have a significant effect on the plasma. The response to the TF error field is shown to depend on the presence of a q = 1 surface, in qualitative agreement with experimental observations. It is found that certain characteristics of the TF error field present new challenges for error field correction. Specifically, the error field spectrum differs significantly from that of coils on the low-field side (such as the NSTX-U error field correction coils), and does not resonate strongly with the dominant kink mode, thus potentially requiring a multi-mode correction. Moreover, to mitigate heat fluxes using poloidal flux expansion, the pitch angle at the divertor plates must be small (~1°). It is shown that uncorrected error fields may result in potentially significant local perturbation to the pitch angle. Estimates for coil alignment tolerances in NSTX-U are derived based on consideration of both heat flux and core resonant fields independently.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Error statistics and scalability of quantum error mitigation formulas

Quantum computing promises advantages over classical computing in many problems. Nevertheless, noise in quantum devices prevents most quantum algorithms from achieving the quantum advantage. Quantum error mitigation provides a variety of protocols to handle such noise using minimal qubit resources. While some of those protocols have been implemented in experiments for a few qubits, it remains unclear whether error mitigation will be effective in quantum circuits with tens to hundreds of qubits. In this paper, we apply statistics principles to quantum error mitigation and analyse the scaling behaviour of its intrinsic error. We find that the error increases linearly O(ϵN) with the gate number N before mitigation and sublinearly O(ϵ'N γ ) after mitigation, where γ ≈ 0.5, ϵ is the error rate of a quantum gate, and ϵ' is a protocol-dependent factor. The $\sqrt{N}$ scaling is a consequence of the law of large numbers, and it indicates that error mitigation can suppress the error by a larger factor in larger circuits. We propose the importance Clifford sampling as a key technique for error mitigation in large circuits to obtain this result.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty

This paper presents four algorithms to generate random forecast error time series, including a truncated-normal distribution model, a state-space based Markov model, a seasonal autoregressive moving average (ARMA) model, and a stochastic-optimization based model. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets, used for variable generation integration studies. A comparison is made using historical DA load forecast and actual load values to generate new sets of DA forecasts with similar stoical forecast error characteristics. This paper discusses and compares the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.

Lu, Ning↗
DOE OSTI2013

Automated evaluation of setup errors in carbon ion therapy using PET: Feasibility study

Purpose: To investigate the possibility of detecting patient mispositioning in carbon-ion therapy with particle therapy positron emission tomography (PET) in an automated image registration based manner. Methods: Tumors in the head and neck (H and N), pelvic, lung, and brain region were investigated. Biologically optimized carbon ion treatment plans were created with TRiP98. From these treatment plans, the reference β{sup +}-activity distributions were calculated using a Monte Carlo simulation. Setup errors were simulated by shifting or rotating the computed tomography (CT). The expected β{sup +} activity was calculated for each plan with shifts. Finally, the reference particle therapy PET images were compared to the “shifted” β{sup +}-activity distribution simulations using the Pearson's correlation coefficient (PCC). To account for different PET monitoring options the inbeam PET was compared to three different inroom scenarios. Additionally, the dosimetric effects of the CT misalignments were investigated. Results: The automated PCC detection of patient mispositioning was possible in the investigated indications for cranio-caudal shifts of 4 mm and more, except for prostate tumors. In the rather homogeneous pelvic region, the generated β{sup +}-activity distribution of the reference and compared PET image were too much alike. Thus, setup errors in this region could not be detected. Regarding lung lesions the detection strongly depended on the exact tumor location: in the center of the lung tumor misalignments could be detected down to 2 mm shifts while resolving shifts of tumors close to the thoracic wall was more challenging. Rotational shifts in the H and N and lung region of +6° and more could be detected using inroom PET and partly using inbeam PET. Comparing inroom PET to inbeam PET no obvious trend was found. However, among the inroom scenarios a longer measurement time was found to be advantageous. Conclusions: This study scopes the use of various particle therapy PET verification techniques in four indications. The automated detection of patients' setup errors was investigated in a broad accumulation of data sets. The evaluation of introduced setup errors is performed automatically, which is of utmost importance to introduce highly required particle therapy monitoring devices into the clinical routine.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Quantifying structural errors in cloud condensation nuclei activity from reduced representation of aerosol size distributions

Aerosol effects on clouds and radiation are the dominant contribution to uncertainty in radiative forcing relative to the pre-industrial atmosphere. While previous studies have assessed the impact of parametric uncertainty on modeled forcing, structural errors from the numerical representation of particle distributions have not been well quantified. Here we present a framework for quantifying error in aerosol size distributions and cloud condensation nuclei activity, which we apply to the widely used 4-mode version of the Modal Aerosol Module (MAM4). Box model predictions from the MAM4 are evaluated against the Particle Monte Carlo Model for Simulating Aerosol Interactions and Chemistry (PartMC-MOSAIC), a benchmark model that tracks the evolution of individual particles. We show that size distributions simulated by MAM4 diverge from those simulated by PartMC-MOSAIC after only a few hours of aging by condensation and coagulation in polluted conditions, which leads to large errors in modeled cloud condensation nuclei concentrations. We find that differences between MAM4 and PartMC-MOSAIC are largest under polluted conditions, where the size distribution evolves rapidly though aging by condensation of semi-volatile substances and coagulation among particles. These findings suggest that structural error in modeled aerosol properties contributes to the large inter-model variability in aerosol radiative forcing.

Fierce, Laura M.↗
DOE OSTI2021

Securing against errors in an error correcting code (ECC) implemented in an automotive system

In general, data is susceptible to errors caused by faults in hardware (i.e. permanent faults), such as faults in the functioning of memory and/or communication channels. To detect errors in data caused by hardware faults, the error correcting code (ECC) was introduced, which essentially provides a sort of redundancy to the data that can be used to validate that the data is free from errors caused by hardware faults. In some cases, the ECC can also be used to correct errors in the data caused by hardware faults. However, the ECC itself is also susceptible to errors, including specifically errors caused by faults in the ECC logic. A method, computer readable medium, and system are thus provided for securing against errors in an ECC.

Saxena, Nirmal Raj↗
DOE OSTI2015

Dependence of the compensation error on the error of a sensor and corrector in an adaptive optics phase-conjugating system

In the framework of a statistical model of an adaptive optics system (AOS) of phase conjugation, three algorithms based on an integrated mathematical approach are considered, each of them intended for minimisation of one of the following characteristics: the sensor error (in the case of an ideal corrector), the corrector error (in the case of ideal measurements) and the compensation error (with regard to discreteness and measurement noises and to incompleteness of a system of response functions of the corrector actuators). Functional and statistical relationships between the algorithms are studied and a relation is derived to ensure calculation of the mean-square compensation error as a function of the errors of the sensor and corrector with an accuracy better than 10%. Because in adjusting the AOS parameters, it is reasonable to proceed from the equality of the sensor and corrector errors, in the case the Hartmann sensor is used as a wavefront sensor, the required number of actuators in the absence of the noise component in the sensor error turns out 1.5 – 2.5 times less than the number of counts, and that difference grows with increasing measurement noise. (adaptive optics)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

On the Correspondence between Mean Forecast Errors and Climate Errors in CMIP5 Models

The present study examines the correspondence between short- and long-term systematic errors in five atmospheric models by comparing the 16 five-day hindcast ensembles from the Transpose Atmospheric Model Intercomparison Project II (Transpose-AMIP II) for July–August 2009 (short term) to the climate simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) and AMIP for the June–August mean conditions of the years of 1979–2008 (long term). Because the short-term hindcasts were conducted with identical climate models used in the CMIP5/AMIP simulations, one can diagnose over what time scale systematic errors in these climate simulations develop, thus yielding insights into their origin through a seamless modeling approach. The analysis suggests that most systematic errors of precipitation, clouds, and radiation processes in the long-term climate runs are present by day 5 in ensemble average hindcasts in all models. Errors typically saturate after few days of hindcasts with amplitudes comparable to the climate errors, and the impacts of initial conditions on the simulated ensemble mean errors are relatively small. This robust bias correspondence suggests that these systematic errors across different models likely are initiated by model parameterizations since the atmospheric large-scale states remain close to observations in the first 2–3 days. However, biases associated with model physics can have impacts on the large-scale states by day 5, such as zonal winds, 2-m temperature, and sea level pressure, and the analysis further indicates a good correspondence between short- and long-term biases for these large-scale states. Therefore, improving individual model parameterizations in the hindcast mode could lead to the improvement of most climate models in simulating their climate mean state and potentially their future projections.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1974

Error control in the GCF: An information-theoretic model for error analysis and coding

The structure of data-transmission errors within the Ground Communications Facility is analyzed in order to provide error control (both forward error correction and feedback retransmission) for improved communication. Emphasis is placed on constructing a theoretical model of errors and obtaining from it all the relevant statistics for error control. No specific coding strategy is analyzed, but references to the significance of certain error pattern distributions, as predicted by the model, to error correction are made.

Adeyemi, O.↗
DOE OSTI2016

Error recovery to enable error-free message transfer between nodes of a computer network

An error-recovery method to enable error-free message transfer between nodes of a computer network. A first node of the network sends a packet to a second node of the network over a link between the nodes, and the first node keeps a copy of the packet on a sending end of the link until the first node receives acknowledgment from the second node that the packet was received without error. The second node tests the packet to determine if the packet is error free. If the packet is not error free, the second node sets a flag to mark the packet as corrupt. The second node returns acknowledgement to the first node specifying whether the packet was received with or without error. When the packet is received with error, the link is returned to a known state and the packet is sent again to the second node.

97 MATHEMATICS AND COMPUTING↗