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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Correction of confidence intervals in excess relative risk models using Monte Carlo dosimetry systems with shared errors

Introduction: Measurement errors are ubiquitous in epidemiological studies, especially for doses arising from environmental and occupational exposures, which are difficult to assess. Correction methods for independent measurement errors, have been comprehensively described elsewhere, including regression calibration, simulation extrapolation (SIMEX), etc. However, when exposure estimates are constructed using complicated physical and biological models, uncertainties in the dosimetry system can become very complex. In such systems, uncertainties in some parameters may affect a large group of study participants simultaneously, and hence are “shared” as opposed to independent dosimetric uncertainties. Representing the structure of the uncertainty in the dosimetry by providing multiple realizations of possible dose has been described for several studies. The goal of this paper is to implement, and explore the properties of, a generalized estimating equation-based approach to shared uncertainties based on principles described in an earlier paper. Methods: The effects of shared and unshared dosimetry error on parameter estimates and confidence intervals in a generalized excess relative risk model are examined. A post-hoc correction method is implemented that constructs corrected confidence intervals based on an underlying variance structure described in an earlier paper. In this correction method it is assumed that the estimated slope parameter in a linear excess relative risk model is distributed as a mixture of normal and lognormal components. A simulation study of the behavior of this correction was developed based on multiple realizations provided by the Monte Carlo dosimetry system used for the plutonium exposures in the Mayak Worker Cohort and a risk model with close similarity to that used in analyses of lung cancer data in that cohort. Results: In the simulation study we found that shared dose errors severely degraded the coverage probabilities of confidence limits for the slope parameter in the linear excess relative risk model relating uncertain exposure to lung cancer risk. Using our correction method to adjust the confidence intervals provided much improved coverage probabilities for this parameter. A smaller amount of degradation in confidence interval coverage for other parameters (e.g. those in the baseline and dose modification terms) was noted, and the proposed correction method also improved coverage for these parameters as well. Discussion: Compared to other recently described methods our proposed correction method relies on relatively familiar software to fit dose response models. The basic modeling philosophy described here first uses existing Poisson regression methods to estimate parameters in the excess relative risk model; in this first stage mean dose (computed from the multiple realizations) is used as the independent variable in the regression. Once the model is fit we then correct (widen) the standard errors for all parameters using the proposed correction method. The simulation studies indicate that the methods described here are an important contribution to the available tools for dealing with shared measurement errors in complex risk analysis.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2015

Shared Dosimetry Error in Epidemiological Dose-Response Analyses

Radiation dose reconstruction systems for large-scale epidemiological studies are sophisticated both in providing estimates of dose and in representing dosimetry uncertainty. For example, a computer program was used by the Hanford Thyroid Disease Study to provide 100 realizations of possible dose to study participants. The variation in realizations reflected the range of possible dose for each cohort member consistent with the data on dose determinates in the cohort. Another example is the Mayak Worker Dosimetry System 2013 which estimates both external and internal exposures and provides multiple realizations of "possible" dose history to workers given dose determinants. This paper takes up the problem of dealing with complex dosimetry systems that provide multiple realizations of dose in an epidemiologic analysis. In this paper we derive expected scores and the information matrix for a model used widely in radiation epidemiology, namely the linear excess relative risk (ERR) model that allows for a linear dose response (risk in relation to radiation) and distinguishes between modifiers of background rates and of the excess risk due to exposure. We show that treating the mean dose for each individual (calculated by averaging over the realizations) as if it was true dose (ignoring both shared and unshared dosimetry errors) gives asymptotically unbiased estimates (i.e. the score has expectation zero) and valid tests of the null hypothesis that the ERR slope β is zero. Although the score is unbiased the information matrix (and hence the standard errors of the estimate of β) is biased for β≠0 when ignoring errors in dose estimates, and we show how to adjust the information matrix to remove this bias, using the multiple realizations of dose. Use of these methods for several studies, including the Mayak Worker Cohort and the U.S. Atomic Veterans Study, is discussed.

epidemiological analysis↗
DOE OSTI2024

Solar and Wind Forecast Error Reserve Sharing in a Multi-Utility Region

As electricity systems transition to higher levels of solar and wind generation, electric system operators will likely need to hold additional reserves to manage the forecast error associated with these resources. Because wind and solar forecast errors tend to be poorly correlated across space, system operators can reduce their reserve requirements by sharing reserves. This paper examines the value of forecast error reserve sharing among balancing areas in the Southeast United States. It finds that forecast error reserve requirements increase linearly with growth in solar and wind generation capacity but that reserve sharing can significantly reduce physical (MW) reserve requirements (from 25%-26% to 18%-19% of average load in high solar scenarios). It finds that the value of forecast error reserve sharing declines with higher levels of solar and wind generation, due to lower wholesale energy and reserve prices. Even with declines in wholesale prices, forecast error reserve sharing can still provide substantial value (as much as $\$$400 million per year in a high solar scenario), though with higher levels of solar, wind, and electricity storage, this value is increasingly tied to avoiding scarcity prices. The results suggest the importance of coordinated capacity expansion planning for forecast error reserve sharing.

14 SOLAR ENERGY↗
DOE OSTI2024

Solar and Wind Forecast Error Reserve Sharing in a Multi-Utility Region

As electricity systems transition to higher levels of solar and wind generation, electric system operators will likely need to hold additional reserves to manage solar and wind forecast error. Because solar and wind forecast errors tend to be weakly correlated across space, system operators can reduce their reserve requirements by sharing reserves. This paper examines the benefits of forecast error reserve sharing among balancing areas in the Southeastern United States, in scenarios in which solar and wind generation ranges from 34% to 65% of total generation. It finds that day-ahead forecast error reserve requirements increase linearly with growth in solar and wind generation capacity (6%-10% of total capacity), but that reserve sharing can significantly reduce these requirements (by 6%-29%). It finds that, in economic terms, the value of forecast error reserve sharing ($\$$0.09-$\$$1.24 billion per year, $\$$0.12-$\$$1.68/MWh of load across scenarios) tends to decline with higher levels of solar and wind generation, due to lower reserve and energy prices. Even with declines in reserve prices, forecast error reserve sharing can still provide substantial value, though with higher levels of solar, wind, and electricity storage this value is increasingly tied to avoiding scarcity prices.

14 SOLAR ENERGY↗
DOE OSTI2018

NOvA muon energy scale systematic

The systematic uncertainty on the correspondence between muon range and energy is developed for the NOvA neutrino experiment. NOvA consists of two detectors, the Near Detector at Fermilab and the Far Detector in northern Minnesota. Total errors are developed for the Near Detector, with its Muon Catcher treated separately, the Far Detector, and all combinations of correlated and uncorrelated errors between these three detectors. The absolute errors for the Near Detector (1.0%), the Far Detector (0.9%), and the fully correlated error shared by them (0.9%) are strongly dominated by Geant4’s treatment of the Bethe density effect. At the Near Detector, the next biggest uncertainty is from stray hits caused by neutron capture pile-up. Other contributions are marginally significant, with the biggest, in descending order, being due to external measurements of the mean excitation energies of elements, detector mass accounting, and modification of energy loss by chemical binding. For the Muon Catcher, the absolute error is expressed as an offset instead of a percentage: 21MeV. The density effect (at higher energies) and neutron capture pile-up (at lower energies) are the strongly dominant errors. The relative error between the Near and Far Detectors is 0.4% and is strongly dominated by neutron capture pile-up at the Near Detector, with a subdominant contribution from detector mass accounting.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

NOvA Muon Energy Scale Systematic

The systematic uncertainty on the correspondence between muon range and energy is developed for the NOvA neutrino experiment. NOvA consists of two detectors, the Near Detector at Fermilab and the Far Detector in northern Minnesota. Total errors are developed for the Near Detector, with its Muon Catcher treated separately, the Far Detector, and all combinations of correlated and uncorrelated errors between these three detectors. The absolute errors for the Near Detector (1.0%), the Far Detector (0.9%), and the fully correlated error shared by them (0.9%) are strongly dominated by Geant4's treatment of the Bethe density effect. At the Near Detector, the next biggest uncertainty is from stray hits caused by neutron capture pile-up. Other contributions are marginally significant, with the biggest, in descending order, being due to external measurements of the mean excitation energies of elements, detector mass accounting, and modification of energy loss by chemical binding. For the Muon Catcher, the absolute error is expressed as an offset instead of a percentage: 21 MeV. The density effect (at higher energies) and neutron capture pile-up (at lower energies) are the strongly dominant errors. The relative error between the Near and Far Detectors is 0.4% and is strongly dominated by neutron capture pile-up at the Near Detector, with a subdominant contribution from detector mass accounting.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

XO-2b: A HOT JUPITER WITH A VARIABLE HOST STAR THAT POTENTIALLY AFFECTS ITS MEASURED TRANSIT DEPTH

The transiting hot Jupiter XO-2b is an ideal target for multi-object photometry and spectroscopy as it has a relatively bright (V-mag = 11.25) K0V host star (XO-2N) and a large planet-to-star contrast ratio (R{sub p}/R{sub s} ≈ 0.015). It also has a nearby (31.″21) binary stellar companion (XO-2S) of nearly the same brightness (V-mag = 11.20) and spectral type (G9V), allowing for the characterization and removal of shared systematic errors (e.g., airmass brightness variations). We have therefore conducted a multiyear (2012–2015) study of XO-2b with the University of Arizona’s 61″ (1.55 m) Kuiper Telescope and Mont4k CCD in the Bessel U and Harris B photometric passbands to measure its Rayleigh scattering slope to place upper limits on the pressure-dependent radius at, e.g., 10 bar. Such measurements are needed to constrain its derived molecular abundances from primary transit observations. We have also been monitoring XO-2N since the 2013–2014 winter season with Tennessee State University’s Celestron-14 (0.36 m) automated imaging telescope to investigate stellar variability, which could affect XO-2b’s transit depth. Our observations indicate that XO-2N is variable, potentially due to cool star spots, with a peak-to-peak amplitude of 0.0049 ± 0.0007 R-mag and a period of 29.89 ± 0.16 days for the 2013–2014 observing season and a peak-to-peak amplitude of 0.0035 ± 0.0007 R-mag and 27.34 ± 0.21 day period for the 2014–2015 observing season. Because of the likely influence of XO-2N’s variability on the derivation of XO-2b’s transit depth, we cannot bin multiple nights of data to decrease our uncertainties, preventing us from constraining its gas abundances. This study demonstrates that long-term monitoring programs of exoplanet host stars are crucial for understanding host star variability.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

High Impedance Fault Detection Through Quasi-Static State Estimation: A Parameter Error Modeling Approach

This paper presents a model for detecting high impedance faults using parameter error modeling and a two step per-phase weighted-least squares state estimation process. The proposed scheme leverages the use of Phasor Measurement Units and synthetic measurements to identify per-phase power flow and injection measurements which indicate a parameter error through ?2 Hypothesis Testing applied to the composed measurement error. Although current and voltage waveforms are commonly analyzed for high-impedance fault detection, wide area power flow and injection measurements, which are already inherent to the state estimation process, also show promise for real-world high-impedance fault detection applications. The error distributions after detection share the measurement function error spread observed in proven parameter error diagnostics and can be applied to high-impedance fault identification. Further, this error spread across measurement functions related to the fault will be clearly discerned from measurement error. Case studies are performed on the IEEE 33-Bus Distribution System along with the proposed model in Simulink.

Cooper, Austin↗
DOE OSTI2025

Solar and Storage Integration in the U.S. Southeast: Implications for Resource Adequacy

Resource adequacy concerns may be very different in electricity systems that have higher levels of solar and storage, requiring changes to existing planning models. This study explores a novel approach to evaluating resource adequacy under future scenarios with higher solar and storage in the Southeast U.S. It uses NREL’s Probabilistic Resource Adequacy Suite (PRAS), a collection of probabilistic resource adequacy modeling tools, and compares results when interacting PRAS and a portfolio planning tool with a more traditional modeling approach. The results suggest that traditional models perform reasonably well with lower levels of solar PV, but at higher levels of solar probabilistic tools better capture the changes in resource adequacy concerns—such as winter energy availability—associated with higher solar systems. This is the final study in the Preparing Southeast Markets for Reliable and Affordable Integration of Solar into Operations and Planning project. Two prior reports can be found at: Solar and Storage Integration in the Southeastern United States: Economics, Reliability, and Operations. https://emp.lbl.gov/publications/solar-and-storage-integration Solar and Wind Forecast Error Reserve Sharing in a Multi-Utility Region. https://eta-publications.lbl.gov/sites/default/files/2024-11/multiutility_fe_reserve_sharing_final.pdf

14 SOLAR ENERGY↗
DOE OSTI2015

Ensemble-type numerical uncertainty information from single model integrations

We suggest an algorithm that quantifies the discretization error of time-dependent physical quantities of interest (goals) for numerical models of geophysical fluid dynamics. The goal discretization error is estimated using a sum of weighted local discretization errors. The key feature of our algorithm is that these local discretization errors are interpreted as realizations of a random process. The random process is determined by the model and the flow state. From a class of local error random processes we select a suitable specific random process by integrating the model over a short time interval at different resolutions. The weights of the influences of the local discretization errors on the goal are modeled as goal sensitivities, which are calculated via automatic differentiation. The integration of the weighted realizations of local error random processes yields a posterior ensemble of goal approximations from a single run of the numerical model. From the posterior ensemble we derive the uncertainty information of the goal discretization error. This algorithm bypasses the requirement of detailed knowledge about the models discretization to generate numerical error estimates. The algorithm is evaluated for the spherical shallow-water equations. For two standard test cases we successfully estimate the error of regional potential energy, track its evolution, and compare it to standard ensemble techniques. The posterior ensemble shares linear-error-growth properties with ensembles of multiple model integrations when comparably perturbed. The posterior ensemble numerical error estimates are of comparable size as those of a stochastic physics ensemble.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Monte Carlo MP2-F12 for Noncovalent Interactions: The C 60 Dimer

A scalable stochastic algorithm is presented that can evaluate explicitly correlated (F12) second-order many-body perturbation (MP2) energies of weak, noncovalent, intermolecular interactions. It first transforms the formulas of the MP2 and F12 energy differences into a short sum of high-dimensional integrals of Green’s functions in real space and imaginary time. Furthermore, these integrals are then evaluated by the Monte Carlo method augmented by parallel execution, redundant-walker convergence acceleration, direct-sampling autocorrelation elimination, and control-variate error reduction. By sharing electron-pair walkers across the supermolecule and its subsystems spanned by the joint basis set, the statistical uncertainty is reduced by one to 2 orders of magnitude in the MP2 binding energy corrected for the basis-set incompleteness and superposition errors. The method predicts the MP2-F12/aug-cc-pVDZ binding energy of 19.1 ± 4.0 kcal mol –1 for the C 60 dimer at the center distance of 9.748 Å.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Unifying and benchmarking state-of-the-art quantum error mitigation techniques

Error mitigation is an essential component of achieving a practical quantum advantage in the near term, and a number of different approaches have been proposed. In this work, we recognize that many state-of-the-art error mitigation methods share a common feature: they are data-driven, employing classical data obtained from runs of different quantum circuits. For example, Zero-noise extrapolation (ZNE) uses variable noise data and Clifford-data regression (CDR) uses data from near-Clifford circuits. We show that Virtual Distillation (VD) can be viewed in a similar manner by considering classical data produced from different numbers of state preparations. Observing this fact allows us to unify these three methods under a general data-driven error mitigation framework that we call UNIfied Technique for Error mitigation with Data (UNITED). In certain situations, we find that our UNITED method can outperform the individual methods (i.e., the whole is better than the individual parts). Specifically, we employ a realistic noise model obtained from a trapped ion quantum computer to benchmark UNITED, as well as other state-of-the-art methods, in mitigating observables produced from random quantum circuits and the Quantum Alternating Operator Ansatz (QAOA) applied to Max-Cut problems with various numbers of qubits, circuit depths and total numbers of shots. We find that the performance of different techniques depends strongly on shot budgets, with more powerful methods requiring more shots for optimal performance. For our largest considered shot budget (10 10 ), we find that UNITED gives the most accurate mitigation. Hence, our work represents a benchmarking of current error mitigation methods and provides a guide for the regimes when certain methods are most useful.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Regulatory Considerations for Nuclear Energy Applications of Digital Twin Technologies

Digital twins (DTs) in complex industrial and engineering applications have potential benefits that include increased operational efficiencies, enhanced safety and reliability, improved security engineering, reduced errors, faster information sharing, and better predictions. The interest in DT technologies continues to grow, and many of these advanced technologies are expected to experience rapid and wide industry adoption in the near future. Some of the potential application areas for DTs in the nuclear industry are design, licensing, plant construction, training simulators, predictive operations and maintenance, autonomous operation and control, failure and degradation prediction, physical protection modeling and simulation, and safety and reliability analyses. The Office of Nuclear Regulatory Research at the U.S. Nuclear Regulatory Commission (NRC) has initiated a future-focused research project to assess the regulatory viability of DTs for nuclear power plants and other NRC-regulated activities, such as fuel cycle facilities and operations. This report explores the potential impact of DT technologies in nuclear applications on NRC-regulated activities of interest. This report describes a nuclear DT system and its capabilities for nuclear power plant applications, followed by identification and discussion of some regulated activities that merit special consideration and present opportunities in implementing DT-enabling technologies and capabilities.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

BUILD: Binary Understanding and Integration Logic for Dependencies (Final Report)

Increasingly diverse mission needs, the emergence of AI and cloud, and increasing hardware diversity are driving HPC software to be more complex. Modern codes are built from hundreds of small, complex components, and much of the software development process involves integrating these components rather than developing new components from scratch. The goal of the BUILD project was to ease the task of software integration for developers across LLNL’s programs. The project focused on (1) modeling software compatibility, (2) modeling ABI compatibility with binary analysis, (3) developing solver techniques to reason about compatibility, and (4) developing AI/ML models to fill gaps in our understanding of software compatibility. The project has developed several key technologies that help developers—by accelerating development workflows, removing the need for rebuilds, and enabling faster, automatic, and less error-prone code sharing. These technologies are used in LLNL codes and will be ready for the new El Capitan Exascale system in Livermore Computing. Many of these technologies have been hardened and integrated with LLNL’s Spack package manager, and they are already in use by production code teams. Results of BUILD have laid the groundwork for future advances in software integration—the ML and binary modification techniques developed in this project still need to be operationalized but have great potential to further speed up software integration.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

Study of Abnormal Vertical Emittance Growth in ATF Extraction Line

Since several years, the vertical beam emittance measured in the Extraction Line (EXT) of the Accelerator Test Facility (ATF) at KEK, that will transport the electron beam from the ATF Damping Ring (DR) to the future ATF2 Final Focus beam line, is significantly larger than the emittance measured in the DR itself, and there are indications that it grows rapidly with increasing beam intensity. This longstanding problem has motivated studies of possible sources of this anomalous emittance growth. One possible contribution is non-linear magnetic fields in the extraction region experimented by the beam while passing off-axis through magnets of the DR during the extraction process. In this paper, simulations of the emittance growth are presented and compared to observations. These simulations include the effects of predicted non-linear field errors in the shared DR magnets and orbit displacements from the reference orbit in the extraction region. Results of recent measurements using closed orbit bumps to probe the relation between the extraction trajectory and the anomalous emittance growth are also presented.

43 PARTICLE ACCELERATORS↗
DOE OSTI2011

Scripting in Radiation Therapy: An Automatic 3D Beam-Naming System

Scripts can be executed within the radiation treatment planning software framework to reduce human error, increase treatment planning efficiency, reduce confusion, and promote consistency within an institution or even among institutions. Scripting is versatile, and one application is an automatic 3D beam-naming system that describes the position of the beam relative to the patient in 3D space. The naming system meets the need for nomenclature that is conducive for clear and accurate communication of beam entry relative to patient anatomy. In radiation oncology in particular, where miscommunication can cause significant harm to patients, a system that minimizes error is essential. Frequent sharing of radiation treatment information occurs not only among members within a department but also between different treatment centers. Descriptions of treatment beams are perhaps the most commonly shared information about a patient's course of treatment in radiation oncology. Automating the naming system by the use of a script reduces the potential for human error, improves efficiency, enforces consistency, and would allow an institution to convert to a new naming system with greater ease. This script has been implemented in the Department of Radiation Oncology at the University of Washington Medical Center since December 2009. It is currently part of the dosimetry protocol and is accessible by medical dosimetrists, radiation oncologists, and medical physicists. This paper highlights the advantages of using an automatic 3D beam-naming script to flawlessly and quickly identify treatment beams with unique names. Scripting in radiation treatment planning software has many uses and great potential for improving clinical care.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2013

Design and Quasi-Equilibrium Analysis of a Distributed Frequency-Restoration Controller for Inverter-Based Microgrids

This paper discusses a proposed frequency restoration controller which operates as an outer loop to frequency droop for voltage-source inverters. By quasi-equilibrium analysis, we show that the proposed controller is able to provide arbitrarily small steady-state frequency error while maintaing power sharing between inverters without need for communication or centralized control. We derive rate of convergence, discuss design considerations (including a fundamental trade-off that must be made in design), present a design procedure to meet a maximum frequency error requirement, and show simulation results verifying our analysis and design method. The proposed controller will allow flexible plug-and-play inverter-based networks to meet a specified maximum frequency error requirement.

Distributed control↗
DOE OSTI2016

Technical Note: Evaluation of the systematic accuracy of a frameless, multiple image modality guided, linear accelerator based stereotactic radiosurgery system

Purpose: To evaluate the total systematic accuracy of a frameless, image guided stereotactic radiosurgery system. Methods: The localization accuracy and intermodality difference was determined by delivering radiation to an end-to-end prototype phantom, in which the targets were localized using optical surface monitoring system (OSMS), electromagnetic beacon-based tracking (Calypso®), cone-beam CT, “snap-shot” planar x-ray imaging, and a robotic couch. Six IMRT plans with jaw tracking and a flattening filter free beam were used to study the dosimetric accuracy for intracranial and spinal stereotactic radiosurgery treatment. Results: End-to-end localization accuracy of the system evaluated with the end-to-end phantom was 0.5 ± 0.2 mm with a maximum deviation of 0.9 mm over 90 measurements (including jaw, MLC, and cone measurements for both auto and manual fusion) for single isocenter, single target treatment, 0.6 ± 0.4 mm for multitarget treatment with shared isocenter. Residual setup errors were within 0.1 mm for OSMS, and 0.3 mm for Calypso. Dosimetric evaluation based on absolute film dosimetry showed greater than 90% pass rate for all cases using a gamma criteria of 3%/1 mm. Conclusions: The authors’ experience demonstrates that the localization accuracy of the frameless image-guided system is comparable to robotic or invasive frame based radiosurgery systems.

60 APPLIED LIFE SCIENCES↗