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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Framework for Model Validation and Calibration of Microgrid Components Using PMU Data

This paper presents a framework for phasor measurement-based component model validation and calibration islanded microgrids, with emphasis on grid-forming inverters. Real measurements from the Plum Island microgrid facility are used to calibrate the model of a commercial inverter in the open-source phasor-based distribution system simulator GridLAB-D.Limitations of current distribution system simulators in replicating the behavior of commercial inverters are also identified.

microgrid, Synchrophasor data, model validation↗
DOE OSTI2020

Static Strain Modelling, Calibration, and Measurements for High-Temperature Wireless SAW Resonator Operation

Static strain measurements are relevant for Integrated System Health Monitoring of civil structures, aircrafts, power plants and advanced manufacturing equipment, with implications for safety, process efficiency, and maintenance costs. Wireless strain sensing is highly desirable where the presence of wires poses significant safety concerns, increases maintenance, and thus overall costs, or where they are not feasible, such as in applications which contain moving parts. This paper presents wireless interrogation of surface acoustic wave resonators (SAWRs) fabricated on langasite (LGS) along Euler angles (0°, 138.5°, 32.9°) and aimed at the detection of static strain at hightemperature (HT, above 100°C). Both commercial HT strain gauges and finite element analysis (FEA Abaqus software) were used and compared for SAWR strain calibration from room temperature (RT) to HT. In addition, this work investigated a compromise between high temperature operation vs. sensitivity based on the thickness of ceramic adhesive used. For instance, a reduction in the adhesive thickness from about 150 μm to 135 μm, resulted in a 39% increase in sensitivity at RT. It was also observed that the sensitivity dropped up to 34% from RT to 200°C. Furthermore, wireless operation, calibration, and increase in sensitivity constitute important advances in the use of LGS SAW devices to monitor static stress in high-temperature harsh environments.

42 ENGINEERING↗
DOE OSTI2022

Calibration of Distributed Temperature Sensors Using Commercially Available SMF-28 Optical Fiber From 22 °C to 1000 °C

Optical frequency domain reflectometry (OFDR) is a family of optical techniques which can be used to produce distributed temperature measurements from the spectral shift of an interference pattern based on the Rayleigh backscatter signature of an optical fiber. Adaptive signal processing techniques have recently been used with OFDR to record meaningful spectral shift data from commercially available SMF-28 optical fibers heated beyond 950 °C. Furthermore, a correlation between the measured spectral shift and temperature has not yet been developed at these high temperatures. To extend the measurable temperature range of OFDR in SMF-28, this work describes the development of such a correlation from room temperature (22 °C) to 1000 °C. The relationship between spectral shift and temperature change over this range was found to be best characterized by the fourth-order polynomial ΔT=(-4.241 * 10 -11 )S 4 + (-2.017 * 10 -7 )S 3 + (-3.677 * 10 -4 )S 2 + (-0.8057)S, where ΔT represents the temperature difference compared to the reference temperature, and S represents the spectral shift measured by the fibers. The calibration developed in this work assumes that the fiber has been fully annealed by heating the fiber to 1000 °C for a few hours. This paper is the first to demonstrate the calibration and use of SMF-28 distributed optical fiber sensors up to 1000 °C, enabled using adaptive OFDR-based signal processing.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Realization and Calibration of Continuously Parameterized Two-Qubit Gates on a Trapped-Ion Quantum Processor

Continuously parameterized two-qubit gates are a key feature of state-of-the-art trapped-ion quantum processors, as they have favorable error scalings and show distinct improvements in circuit performance over more restricted maximally entangling gatesets. In this work, we provide a comprehensive and pedagogical discussion on how to practically implement these continuously parameterized Mølmer–Sørensen gates on the Quantum Scientific Computing Open User Testbed, a low-level trapped-ion processor. To generate the arbitrary entangling angles, θ, we simply scale the amplitude of light used to generate the entanglement. However, doing so requires careful consideration of amplifier saturation as well as the variable light shifts that result. As such, we describe a method to calibrate and cancel the dominant fourth-order effects, followed by a dynamic virtual phase advance during the gate to cancel any residual light shifts, and find a linear scaling between θ and the residual light shift. Once we have considered and calibrated these effects, we demonstrate performance improvement with decreasing θ. Finally, we describe nuances of hardware control to transform the XX-type interaction of the arbitrary-angle Mølmer–Sørensen gate into a phase-agnostic and crosstalk-mitigating ZZ interaction.

Yale, Christopher Gordon [Sandia National Laborato↗
DOE OSTI2022

Correction and calibration of atmospheric impact observations in GOES GLM data

The Earth's atmosphere is impacted daily by both meteoroids and artificial objects. Calibrated observations of the emitted light at sufficiently high sampling rates can enable or improve the estimation of impactor attributes such as size, cohesion, trajectory, and composition, but are difficult to obtain owing to the unpredictability, brevity, and high dynamic (brightness) range of impacts. Ground-based camera systems have successfully monitored small regions of the atmosphere at video frame rates and with limited radiometric capabilities, but most impacts occur over the 70% of the Earth's surface covered by water and are therefore missed by these networks. The Geostationary Lightning Mapper (GLM) instruments aboard Geostationary Operational Environmental Satellites 16 and 17 provide near-hemispherical coverage at 500 frames per second. These data have been shown to contain the signatures of many independently confirmed impacts, often from both viewing angles simultaneously, and constitute an observational resource that is currently unparalleled in the public domain. NASA's Asteroid Threat Assessment Project has implemented an automated impact detection pipeline that processes data from GLM daily. Given a detected impact, the GLM data contain a wealth of information for use in quantitative follow-up analyses. However, impact events differ from lightning in ways that violate key assumptions built into GLM's design. The result is that GLM's onboard processing introduces errors into pixel observations of impact events and the calibrated energies near the periphery of the detector may be substantially overestimated. We present methods for mitigating these and other issues to produce a data product more suitable for impact analyses than the existing GLM lightning product.

58 GEOSCIENCES↗
DOE OSTI2022

Calibrating constitutive models with full‐field data via physics informed neural networks

Abstract The calibration of solid constitutive models with full‐field experimental data is a long‐standing challenge, especially in materials that undergo large deformations. In this paper, we propose a physics‐informed deep‐learning framework for the discovery of hyperelastic constitutive model parameterizations given full‐field surface displacement data and global force‐displacement data. Contrary to the majority of recent literature in this field, we work with the weak form of the governing equations rather than the strong form to impose physical constraints upon the neural network predictions. The approach presented in this paper is computationally efficient, suitable for irregular geometric domains, and readily ingests displacement data without the need for interpolation onto a computational grid. A selection of canonical hyperelastic material models suitable for different material classes is considered including the Neo–Hookean, Gent, and Blatz–Ko constitutive models as exemplars for general non‐linear elastic behaviour, elastomer behaviour with finite strain lock‐up, and compressible foam behaviour, respectively. We demonstrate that physics informed machine learning is an enabling technology and may shift the paradigm of how full‐field experimental data are utilized to calibrate constitutive models under finite deformations.

Hamel, Craig M.↗
DOE OSTI2024

Binary pseudo-random array for calibration of interferometers with transmission spheres and cylinders

Binary pseudo-random array (BPRA) “white noise” artifacts are highly effective for characterizing the instrument transfer function (ITF) of surface topography metrology tools and wavefront measurement instruments. These BPRA artifacts feature all spatial frequencies within the instrument bandpass equally, resulting in a power spectral density with a white-noise-like character. This characteristic allows for direct ITF determination with uniform sensitivity across the entire spatial frequency range. We have developed a novel BPRA calibration standard that combines the diffractive pattern of a reflection computer-generated hologram (CGH) with the white noise generating BPRA pattern. By integrating these technologies using the same lithographic techniques, the resulting calibration sample enables ITF characterization of a Fizeau interferometer with a transmission sphere, or any nulling optic.

Munechika, K↗
DOE OSTI2022

Comprehensive compartmental model and calibration algorithm for the study of clinical implications of the population-level spread of COVID-19: a study protocol

The complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease progression difficult. Simple mechanistic models with deterministic parameters are useful for short-term predictions but have ultimately been unsuccessful in extrapolating the trajectory of the pandemic because of unmodelled dynamics and the unrealistic level of certainty that is assumed in the predictions. We propose a 22-compartment epidemiological model that includes compartments not previously considered concurrently, to account for the effects of vaccination, asymptomatic individuals, inadequate access to hospital care, post-acute COVID-19 and recovery with long-term health complications. Additionally, new connections between compartments introduce new dynamics to the system and provide a framework to study the sensitivity of model outputs to several concurrent effects, including temporary immunity, vaccination rate and vaccine effectiveness. Subject to data availability for a given region, we discuss a means by which population demographics (age, comorbidity, socioeconomic status, sex and geographical location) and clinically relevant information (different variants, different vaccines) can be incorporated within the 22-compartment framework. Considering a probabilistic interpretation of the parameters allows the model's predictions to reflect the current state of uncertainty about the model parameters and model states. We propose the use of a sparse Bayesian learning algorithm for parameter calibration and model selection. This methodology considers a combination of prescribed parameter prior distributions for parameters that are known to be essential to the modelled dynamics and automatic relevance determination priors for parameters whose relevance is questionable. This is useful as it helps prevent overfitting the available epidemiological data when calibrating the parameters of the proposed model. Population-level administrative health data will serve as partial observations of the model states.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Artificial to Spiking Neural Networks Conversion with Calibration in Scientific Machine Learning

Here, we introduce a method to convert physics-informed neural networks (PINNs), commonly used in scientific machine learning, to spiking neural networks (SNNs), which are expected to have higher energy efficiency compared to traditional artificial neural networks (ANNs). We first extend the calibration technique of SNNs to arbitrary activation functions beyond ReLU, making it more versatile, and we prove a theorem that ensures the effectiveness of the calibration. We successfully convert PINNs to SNNs, enabling computational efficiency for diverse regression tasks in solving multiple differential equations, including the unsteady Navier–Stokes equations. We demonstrate great gains in terms of overall efficiency, including separable PINNs (SPINNs), which accelerate the training process. Overall, this is the first work of this kind and the proposed method achieves relatively good accuracy with low spike rates.

PINN↗
DOE OSTI2023

Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors

This software offers methods and functions for training calibrated out-of-distribution detectors for medical image classification tasks. It includes functionalities for training, synthesizing data augmentations, calibration, and out-of-distribution detection. Developed using PyTorch, this software is compatible with standard neural network architectures used for imaging data. Additionally, it provides capabilities to compute evaluation metrics for assessing the performance and quality of the detectors.

Narayanaswamy, VivekSivaraman↗
DOE OSTI2025

Calibration of hydrogen atoms measurement using femtosecond two-photon laser-induced fluorescence

What we believe to be a new calibration method for H-fs-TALIF is proposed, and the ratio of two-photon absorption cross-sections, σ (2) , for atomic hydrogen (H) and krypton (Kr) is determined using the broadband emission of a femtosecond laser system. The estimated ratio of the two-photon absorption cross-sections for H and Kr is σ (2) (Kr)/σ (2) (H) = 0.027 ± 20%, which is nearly twenty times lower than values previously obtained with narrowband nanosecond lasers. This discrepancy highlights the need for independent calibration of TALIF measurements for a specific laser pulsewidth range.

Starikovskiy, Andrey [Princeton Univ., NJ (United ↗
DOE OSTI2024

Two-step retrace error calibration removing tilt ambiguity in coherence scanning interferometry

In interferometry measurement, the retrace error often limits its high-precision metrology applications. Retrace error calibration with tilted flats can give a relation between the retrace error and the introduced tilt angles, but there is still an ambiguity between the introduced tilt angles and the tilt terms in the created retrace error. Here, we propose a novel, to the best of our knowledge, two-step calibration method to resolve this tilt ambiguity. It involves additional measurements of spherical mirror(s) with known curvature(s). The experiment shows that the curvature deviation due to the tilt ambiguity can be significantly reduced after applying the proposed method.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Meteoric 10Be Flux Calibration Data for the East River Watershed, Colorado, USA

This data package contains tabular and geospatial data used to quantify and model meteoric beryllium-10 fluxes in the East River watershed, Colorado, USA. The tabular component includes calibration-site data from five glacial moraine sites and includes environmental variables used to evaluate spatial controls on meteoric 10Be delivery, including elevation, mean annual precipitation (MAP), mean snow depth, and mean snow water equivalent (SWE). These site-level data were used to compare observed fluxes with environmental gradients across the watershed and to evaluate the effects of erosion correction on flux estimates. The package also includes supporting slope and curvature values used to assess topographic inputs to the erosion analysis. A second component of the data package contains updated manuscript tables and regression outputs used to summarize the relationships between meteoric 10Be flux and environmental predictors. These tables include meteoric 10Be sample information and AMS results, site-level environmental values, site-level meteoric 10Be inventory and flux values, watershed-averaged predicted fluxes, soil bulk density measurements, fine-fraction values, soil pH measurements, and regression statistics including slope, intercept, coefficient of determination, and p-value. The regression products include both standard linear regressions and regressions in which the intercept is constrained to pass through zero, and they support the analyses presented in the companion manuscript. Together, these tabular files provide the numerical basis for the manuscript tables and the regression-based interpretation of meteoric 10Be flux variability in a snow-dominated mountain watershed. The geospatial component of the package consists of GeoTIFF raster files used to generate the map products presented in Figures 2 and 6 of the companion manuscript. These rasters represent watershed-scale spatial layers for environmental variables and regression-based predictions of meteoric 10Be flux. This dataset contains comma-separated values files (.csv), Microsoft Excel files (.xlsx), GeoTIFF raster files (.tif), and upporting metadata files, including CSV data dictionaries and readme text files (.csv, .txt). The tabular files can be opened with standard spreadsheet software, and the raster files can be viewed and analyzed in GIS software such as ArcGIS Pro or QGIS. Together, these files document the numerical and spatial datasets used to calibrate and predict meteoric 10Be delivery in the East River watershed.

East River↗
DOE OSTI2021

Soil Water Potentials (1990-2018) from a calibrated ELM-FATES, and rooting depth analyses scripts, PA-BCI, Panama

This dataset has ELM-FATES simulation outputs and scripts that accompany and reproduce the manuscript, Chitra-Tarak et al. (2021) Hydraulically-vulnerable trees survive on deep-water access during droughts in a tropical forest. New Phytologist. https: //doi.org/10.1111/nph.17464”. This dataset is organized into four parts: (1) source code for the model, Energy Exascale Earth System Land Model-Functionally Assembled Advanced Terrestrial simulator (ELM-FATES) (2) R scripts associated with ELM-FATES simulations: to prepare files for parameter ensemble simulation runs, calibrate the model with observed hydrological fluxes observed at BCI, re-run the model with tuned parameters and obtain long-term soil matric potentials by depth (3) ELM-FATES hydrological simulation outputs at PA-BCI, Panama (1990-2018, daily), and (4) R scripts for inverse rooting depth modeling and post-processing: prepare datasets and inversely model tree rooting depths at BCI, test hypotheses about the relationship of rooting depths with plant hydraulic traits and drought-induced mortality, and reproduce the manuscript itself. n.b. 1. Data needed to run R scripts in (2) and (4) and thus to reproduce the manuscript are sourced from various public datasets. Refer to the data availability section of the accompanying manuscript. 2. This dataset version (prepared on May 12, 2021) matches the revised and published version of the accompanying manuscript (published May 16, 2021) replacing a previous version. In the publication, this dataset is cited as follows: Chitra-Tarak R, Xu C, Aguilar S, Anderson-Teixeira K, Chambers J, Detto M, Faybishenko B, Fisher R, Knox R, Koven C et al. 2020. Soil water potentials (1990–2018) from a calibrated ELM-FATES, and rooting depth analyses scripts, PA-BCI, Panama. 2.0. NGEE Tropics Data Collection. doi: 10.15486/ngt/1696806

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Long-Term Infrasound Sensor Calibration and Characterization

Previous testing has shown that infrasound sensors deployed in the field can exhibit notable deviations from their nominal, lab-based calibrations. These variations may be due to changes in environmental conditions, long-term sensor drift, or other unresolved features. In early 2018, we installed two identical infrasound elements with five infrasound sensors at each element (Chaparral M50A, Chaparral M64LN, CEA/Martec MB2005, CEA/Seismowave MB3a, and Hyperion IFS-5113A). These sensors were accepted or under consideration for use in the International Monitoring System network of the Comprehensive Nuclear-Test-Ban Treaty. Each element had all sensors connected to a single digitizer and port to the atmosphere. We also recorded instrument enclosure air temperature and humidity and external air temperature. Using the MB2005 as the reference, we examine the relative sensor response (both magnitude and phase) as a function of time and frequency and compare it with quarterly laboratory calibrations and environmental conditions. Here, we find that the magnitude response for all sensors exhibits some variability in both the lab and field, with the amplitude variations often >5%. The field-based variations are more severe and occur on both long-term (months) and short-term (hours) timescales. Short-term variability correlates with changes in environmental conditions and is considerable (up to 25%) for the Chaparral M50A and noticeable (∼5%) for the French Alternative Energies and Atomic Energy Commission (CEA) MB3a. Long-term magnitude variability for the Chaparral M50A was up to 50% and does not clearly correlate with environmental conditions. The other sensors show some long-term magnitude offsets, but they have relatively stable responses in the conditions we examined. The MB3a also displays some frequency-dependent magnitude variability and shows a minor dependence on temperature. Phase estimates are stable and near zero for all sensors tested. These results strongly suggest sensor response and variability due to environmental conditions should be considered in future infrasound data interpretation and sensor selection and development.

Fee, David↗
DOE OSTI2020

Charge response and energy calibration of ProtoDUNE-

ProtoDUNE-SP is a test bed liquid argon time projection chamber (LArTPC) for the far detector of the Deep Underground Neutrino Experiment (DUNE). This LArTPC was calibrated using cosmic-ray cathode-crossing muons, electric field maps, and purity-monitor data to correct for nonuniformities in the detector response and cosmic-ray stopping muons to perform the absolute energy scale calibration for further physics analysis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

SURFplus model calibration for PBX 9012

PBX 9012 is a plastic bonded explosive composed of 90.2 wt % HMX (cyclo-tetramethylene-tetranitramine) and 9.8 % Viton A (vinylidene-hexafluoropropylene copolymer). It has nearly the same composition as LX-07 (90% HMX and 10% Viton A) but with a slightly lower density. Hence it has a higher porosity and is slightly more shock sensitive. Here we describe the SURFplus model calibration for PBX 9012 using data from five shock-to-detonation transition (SDT) gas gun experiments by Burns and Chiquete, detonation front shape data from rate stick experiments and cylinder test experiment reported in Anderson et al., Jackson et al. The SDT experiments provide Pop plot data points (run distance-to-detonation) which characterize shock initiation, Lagrangian velocity time histories from embedded magnetic velocity gauges which give information on the reaction behind the lead shock, and Hugoniot data points for the reactants EOS. The rate stick experiments provide data on the curvature effect which characterize propagating detonation waves. The cylinder test experiments provide data on the detonation release isentrope used to calibrate the products EOS. The HE model uses a Davis reactants EOS with parameters that Burns and Chiquete [2020] fit to Hugoniot data, and Davis products EOS with parameters that Anderson et al. [2020] fit to cylinder test data.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

Low-Activity Waste Glass Standards Preparation and Characterization for Calibration of Analytical Instruments

A matrix of seven low-activity waste (LAW) glasses was fabricated and characterized to be used by the Hanford Waste Treatment and Immobilization Plant (WTP) as analytical instrument calibration and matrix interference standards when measuring LAW glass compositions. The matrix of glasses was designed to represent the range of LAW glass compositions that will be generated by the WTP. Samples from each of the seven glasses were measured with electron probe microanalysis (EPMA), ion chromatography (IC), and bulk inductively coupled plasma mass spectroscopy (ICP-MS) to determine the variability and accuracy across batches and within individually poured glass bar samples. High relative percent differences and standard deviations were calculated for components with low concentrations due to instrument detection limits. IC and ICP-MS percent differences and standard deviations were higher at low component concentrations. Otherwise, variability in measured compositions from IC and ICP-MS was generally lower than measurements from EPMA, with the exclusion of Cl, F, Si, and B. Overall, the two methods proved comparable and complementary. From EPMA, IC, and ICP-MS data, volatile elements were underrepresented compared to batched compositions, which suggested loss of these elements during melting. EPMA data was statistically analyzed to evaluate homogeneity within single bars and whole compositions for each of the seven glasses. Most of the variability within glass compositions occurred at the bar-to-bar level; however, no clear systematic trends were observed. Based on these analyses, when using these glasses as reference material, it is recommended that the overall mean and variance of each composition be used for performing instrument calibrations and analytical corrections.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗