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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Tough Errors are no Match (TEAM): Optimizing the Quantum Compiler for Noise Resilience

This project builds toward a comprehensive error-mitigating toolkit that makes quantum programming more robust and adaptive to the noisy, resource-limited nature of today’s quantum hardware. To that end, it integrates established error-mitigation methods — such as zero-noise extrapolation and dynamical decoupling — directly into compiler infrastructures. These techniques will be packaged as modules that can automatically adjust and combine based on performance analysis, enabling compilers to explore large design spaces and produce optimized, low-noise quantum programs with minimal manual intervention. In parallel, this project also explores new approaches to analog quantum programming or quantum simulation, and has developed the programming language SimuQ which treats quantum Hamiltonian evolution as the central object.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Error mitigation with Clifford quantum-circuit data

Achieving near-term quantum observables despite significant hardware noise. For this purpose, we propose a novel, scalable error-mitigation method that applies to gate-based quantum computers. The method generates training data { X i noisy , X i exact } via quantum circuits composed largely of Clifford gates, which can be efficiently simulated classically, where X i noisy and X i exact are noisy and noiseless observables respectively. Fitting a linear ansatz to this data then allows for the prediction of noise-free observables for arbitrary circuits. We analyze the performance of our method versus the number of qubits, circuit depth, and number of non-Clifford gates. Here, we obtain an order-of-magnitude error reduction for a ground-state energy problem on 16 qubits in an IBMQ quantum computer and on a 64-qubit noisy simulator.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Error-Level-Controlled Synthetic Forecasts for Renewable Generation

Renewable energy resources, including solar and wind energy, play a significant role in sustainable energy systems. However, the inherent uncertainty and intermittency of renewable generation pose challenges to the safe and efficient operation of power systems. Recognizing the importance of short-term (hours ahead) renewable generation forecasting in power systems operation, it becomes crucial to address the potential inaccuracies in these forecasts. To systematically evaluate the performance of controllers in the presence of imperfect forecasts, we generate synthetic forecasts using actual renewable generation profiles (one from solar and one from wind). These synthetic forecasts incorporate different levels of statistical error, allowing us to control and manipulate the accuracy of the predictions. The primary objective is to employ synthetic forecasts with controlled yet realistic error levels to systematically investigate how controllers adapt to variations in forecast accuracy, providing valuable insights into their robustness and effectiveness under real-world conditions.

Array↗
DOE OSTI2018

Assessing the Temperature Error in Operational Machine Tools

In the proposed experimental assessment of the temperature error in operational machine tools, the workpiece is monitored by means of a measuring probe as it is machined. Experiments show that the temperature error in the machine tool may be compensated by specified thermal displacements of the machine-tool components before machining begins.

42 ENGINEERING↗
DOE OSTI2019

Basing Errors in Machining and Monitoring

This research is focused on sketch bases in machining and monitoring and on basing errors in machining and monitoring. In other words, attention is directed to machining or monitoring errors produced by oscillations in the position of sketch bases relative to the machined surfaces of the components. The use of sketch bases in combination with technological and measurement bases is recommended.

42 ENGINEERING↗
DOE OSTI2016

BEAM-FORMING ERRORS IN MURCHISON WIDEFIELD ARRAY PHASED ARRAY ANTENNAS AND THEIR EFFECTS ON EPOCH OF REIONIZATION SCIENCE

Accurate antenna beam models are critical for radio observations aiming to isolate the redshifted 21 cm spectral line emission from the Dark Ages and the Epoch of Reionization (EOR) and unlock the scientific potential of 21 cm cosmology. Past work has focused on characterizing mean antenna beam models using either satellite signals or astronomical sources as calibrators, but antenna-to-antenna variation due to imperfect instrumentation has remained unexplored. We characterize this variation for the Murchison Widefield Array (MWA) through laboratory measurements and simulations, finding typical deviations of the order of ±10%–20% near the edges of the main lobe and in the sidelobes. We consider the ramifications of these results for image- and power spectrum-based science. In particular, we simulate visibilities measured by a 100 m baseline and find that using an otherwise perfect foreground model, unmodeled beam-forming errors severely limit foreground subtraction accuracy within the region of Fourier space contaminated by foreground emission (the “wedge”). This region likely contains much of the cosmological signal, and accessing it will require measurement of per-antenna beam patterns. However, unmodeled beam-forming errors do not contaminate the Fourier space region expected to be free of foreground contamination (the “EOR window”), showing that foreground avoidance remains a viable strategy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Reading Error Analysis (REA) Software Version 1

This is training presentation for REA (Reading Error Analysis) software. REA is designed for analyzing UGT scope reading errors and for developing reading points optimization algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Efficient Active Learning for Gaussian Process Classification by Error Reduction

Active learning sequentially selects the best instance for labeling by optimizing an acquisition function to enhance data/label efficiency. The selection can be either from a discrete instance set (pool-based scenario) or a continuous instance space (query synthesis scenario). In this work, we study both active learning scenarios for Gaussian Process Classification (GPC). The existing active learning strategies that maximize the Estimated Error Reduction (EER) aim at reducing the classification error after training with the new acquired instance in a onestep-look-ahead manner. The computation of EER-based acquisition functions is typically prohibitive as it requires retraining the GPC with every new query. Moreover, as the EER is not smooth, it can not be combined with gradient-based optimization techniques to efficiently explore the continuous instance space for query synthesis. To overcome these critical limitations, we develop computationally efficient algorithms for EER-based active learning with GPC. Further, we derive the joint predictive distribution of label pairs as a one-dimensional integral, as a result of which the computation of the acquisition function avoids retraining the GPC for each query, remarkably reducing the computational overhead. We also derive the gradient chain rule to efficiently calculate the gradient of the acquisition function, which leads to the first query synthesis active learning algorithm implementing EER-based strategies. Our experiments clearly demonstrate the computational efficiency of the proposed algorithms. We also benchmark our algorithms on both synthetic and real-world datasets, which show superior performance in terms of sampling efficiency compared to the existing state-of-the-art algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Determination of a Generic Human Error Probability Distribution, Part 2: A Dynamic SPAR-H Example Application

Humans are an integral part of a nuclear power plant (NPP) from its construction, to its everyday maintenance and operation. In the nuclear industry there are many approaches to quantifying component reliability, which is called probability risk assessment (PRA). In PRA there are two considerations given to the quantification, static and dynamic. Static is quantified before or after an operation; and dynamic is time dependent, in that the probability changes as real time data are provided. In PRA, the importance of the components is calculated, but what of the importance of the human component? There are several methods for human reliability analysis (HRA), which include but are not limited to: THERP, ATHEANA, CREAM, SPAR-H, ASEP, and SLIM. Each of these methods aims to provide a more accurate and objective human error probability (HEP) quantification. Of the methods listed above, ASEP, THERP, and SPAR-H are all inextricably linked. The Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) method was generated to better generalize the Technique for Human Error Rate Prediction (THERP) and Accident Sequence Precursor (ASEP). THERP is mapped to specific scenario templates, and ASEP is a simplified screening method, while SPAR-H employs generalizations to describe the spectrum of human behavior while maintaining the same theoretical underpinnings. SPAR-H is a well-known and accepted HRA method that uses performance shaping factors (PSFs) to classify inputs to the human component. PSFs capture a wide variety of input data, including plant status, crew dynamics, task description, and psychological aspects of the human operator. Current efforts have been focused to further generalize SPAR-H in to a dynamic framework. These methods can fit into dynamic NPP simulations such as RAVEN / RELAP-7. In order to do this, a dynamic methodology is applied to model the human as would be applied to the components. This method is applied in part one in the accompanying paper.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Mechanism of Error-Free Bypass of the Environmental Carcinogen N -(2'-Deoxyguanosin-8-yl)-3-aminobenzanthrone Adduct by Human DNA Polymerase η

The environmental pollutant 3-nitrobenzanthrone produces bulky aminobenzanthrone (ABA) DNA adducts with both guanine and adenine nucleobases. A major product occurs at the C8 position of guanine (C8-dG-ABA). These adducts present a strong block to replicative polymerases but, remarkably, can be bypassed in a largely error-free manner by the human Y-family polymerase η (hPol η). Here, we report the crystal structure of a ternary Pol•DNA•dCTP complex between a C8-dG-ABA-containing template:primer duplex and hPol η. The complex was captured at the insertion stage and provides crucial insight into the mechanism of error-free bypass of this bulky lesion. Specifically, bypass involves accommodation of the ABA moiety inside a hydrophobic cleft to the side of the enzyme active site and formation of an intra-nucleotide hydrogen bond between the phosphate and ABA amino moiety, allowing the adducted guanine to form a standard Watson–Crick pair with the incoming dCTP.

Patra, Amritraj↗
DOE OSTI2019

Quantification and Statistical Analysis of Errors Related to the Approximate Description of Active Site Models in Metal‐Exchanged Zeolites

Abstract Metal‐exchanged zeolites are complex catalytic systems with great potential for applications in hydrocarbon conversion processes. The numerous combinations of zeotypes and metal‐based Lewis acid motifs create a vast material space that can only be effectively navigated with robust structure‐function relationships. Exhaustive modeling of extraframework metal sites in all crystallographically distinct framework positions is not practical; thus, we quantified the uncertainties arising from the use of simplified active site representations using the C−H bond activation in methane as a probe reaction. Density functional theory calculations and statistical analysis of associated error ensembles suggest that the Lewis acid identity primarily determines the reactivity of the zeolite towards methane activation, with other factors such as zeolite model characteristics deemed secondary. The error ensemble distributions were used to predict the relative probability of creating active C−H bond scission fragments in metal‐exchanged zeolites, and thus can be used to efficiently screen various metal‐exchange candidates in their efficacy towards hydrocarbon conversion.

Thirumalai, Hari↗
DOE OSTI2025

Celestial Quantum Error Correction. Part II. From qudits to celestial CFT

A holographic CFT description of asymptotically flat spacetimes inherits vacuum degeneracies and IR divergences from its gravitational dual. We devise a Quantum Error Correcting (QEC) framework to encode both effects as correctable fluctuations on the CFT dual. The framework is physically motivated by embedding a chain of qudits in the so-called Klein spacetime and then taking a continuum 𝑁 → ∞ limit. At finite 𝑁 the qudit chain 1) enjoys a discrete version of celestial symmetries and 2) supports a Gottesman-Kitaev-Preskill (GKP) code. The limit results in hard states with quantized BMS hair in the celestial torus forming the logical subspace, robust under errors induced by soft radiation. Technically, the construction leverages the recently studied 𝑤 1+∞ hierarchy of soft currents and its realization from a sigma model in twistor space.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Diagnosing and Correcting Mass Accuracy and Signal Intensity Error Due to Initial Ion Position Variations in a MALDI TOFMS

Frustrated by worse than expected error for both peak area and time-of-flight (TOF) in matrix assisted laser desorption ionization (MALDI) experiments using samples prepared by electrospray deposition, it was finally determined that there was a correlation between sample location on the target plate and the measured TOF/peak area. Variations in both TOF and peak area were found to be due to small differences in the initial position of ions formed in the source region of the TOF mass spectrometer. These differences arise largely from misalignment of the instrument sample stage, with a smaller contribution arising from the non-ideal shape of the target plates used. By physically measuring the target plates used and comparing TOF data collected from three different instruments, an estimate of the magnitude and direction of the sample stage misalignment was determined for each of the instruments. A correction method was developed to correct the TOFs and peak areas obtained for a given combination of target plate and instrument. Two correction factors are determined, one by initially collecting spectra from each sample position used and another by using spectra from a single position for each set of samples on a target plate. For TOF and mass values, use of the correction factor reduced the error by a factor of 4, with the relative standard deviation (RSD) of the corrected masses being reduced to 12-24 ppm. For the peak areas, the RSD was reduced from 28% to 16% for samples deposited twice onto two target plates over two days. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Optimization of an Impedance-Matched Test Fixture with the Modal Projection Error

Across many industries and engineering disciplines, components and systems are designed and deployed into their operational environment of intended use. It is the desire of the design agency to be able to predict whether their component or system will function in its shock and vibration environments or if it will fail due to mechanical stresses. One method to determine if the component will survive the shock and vibration environments is to expose the component to the operational environment in a laboratory. One difficulty in executing a representative laboratory test is that the component may not have the same boundary condition in the laboratory as in the operational configuration. This paper examines the use of parameterized optimization to design the test fixture in order to better match the operational configuration. Several frequency and modal-based objective functions are examined for the optimization. The study shows that the Modal Projection Error objective function performs the best of the functions studied. The efficacy of the Modal Projection Error is demonstrated with respect to dynamic test fixture design on several analytical and experimental exemplars.

dynamic↗
DOE OSTI2016

Error correction in short time steps during the application of quantum gates

We propose a modification of the standard quantum error-correction method to enable the correction of errors that occur due to the interaction with a noisy environment during quantum gates without modifying the codification used for memory qubits. Using a perturbation treatment of the noise that allows us to separate it from the ideal evolution of the quantum gate, we demonstrate that in certain cases it is necessary to divide the logical operation in short time steps intercalated by correction procedures. A prescription of how these gates can be constructed is provided, as well as a proof that, even for the cases when the division of the quantum gate in short time steps is not necessary, this method may be advantageous for reducing the total duration of the computation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Online randomized interpolative decomposition with a posteriori error estimator for temporal PDE data reduction

Traditional low-rank approximation is a powerful tool for compressing large data matrices that arise in simulations of partial differential equations (PDEs), but suffers from high computational cost and requires several passes over the PDE data. The compressed data may also lack interpretability thus making it difficult to identify feature patterns from the original data. Here, to address these issues, we present an online randomized algorithm to compute the interpolative decomposition (ID) of large-scale data matrices in situ. Compared to previous randomized IDs that used the QR decomposition to determine the column basis, we adopt a streaming ridge leverage score-based column subset selection algorithm that dynamically selects proper basis columns from the data and thus avoids an extra pass over the data to compute the coefficient matrix of the ID. In particular, we adopt a single-pass error estimator based on the non-adaptive Hutch++ algorithm to provide real-time error approximation for determining the best coefficients. As a result, our approach only needs a single pass over the original data and thus is suitable for large and high-dimensional matrices stored outside of core memory or generated in PDE simulations. A strategy to improve the accuracy of the reconstructed data gradient, when desired, within the ID framework is also presented. We provide numerical experiments on turbulent channel flow and ignition simulations, and on the NSTX Gas Puff Image dataset, comparing our algorithm with the offline ID algorithm to demonstrate its utility in real-world applications.

Column subset selection↗