SEARCH · Engineering Papers

Results for “uncertainty calibration”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2

All sources NASA NTRS DOE OSTI

NASA NTRS2011

MODIS Radiometric Calibration and Uncertainty Assessment

Since launch, Terra and Aqua MODIS have collected more than II and 9 years of datasets for comprehensive studies of the Earth's land, ocean, and atmospheric properties. MODIS observations are made in 36 spectral bands: 20 reflective solar bands (RSB) and 16 thermal emissive bands (TEB). Compared to its heritage sensors, MODIS was developed with very stringent calibration and uncertainty requirements. As a result, MODIS was designed and built with a set of state of the art on-board calibrators (OBC), which allow key sensor performance parameters and on-orbit calibration coefficients to be monitored and updated if necessary. In terms of its calibration traceability, MODIS RSB calibration is reflectance based using an on-board solar diffuser (SD) and the TEB calibration is radiance based using an on-board blackbody (BB). In addition to on-orbit calibration coefficients derived from its OBC, calibration parameters determined from sensor pre-launch calibration and characterization are used in both the RSB and TEB calibration and retrieval algorithms. This paper provides a brief description of MODIS calibration methodologies and discusses details of its on-orbit calibration uncertainties. It assesses uncertainty contributions from individual components and differences between Terra and Aqua MODIS due to their design characteristics and on-orbit periormance. Also discussed in this paper is the use of MODIS LIB uncertainty index CUI) product.

Xiong, Xiaoxiong↗

NASA NTRS1999

Uncertainty Analysis of Inertial Model Attitude Sensor Calibration and Application with a Recommended New Calibration Method

Statistical tools, previously developed for nonlinear least-squares estimation of multivariate sensor calibration parameters and the associated calibration uncertainty analysis, have been applied to single- and multiple-axis inertial model attitude sensors used in wind tunnel testing to measure angle of attack and roll angle. The analysis provides confidence and prediction intervals of calibrated sensor measurement uncertainty as functions of applied input pitch and roll angles. A comparative performance study of various experimental designs for inertial sensor calibration is presented along with corroborating experimental data. The importance of replicated calibrations over extended time periods has been emphasized; replication provides independent estimates of calibration precision and bias uncertainties, statistical tests for calibration or modeling bias uncertainty, and statistical tests for sensor parameter drift over time. A set of recommendations for a new standardized model attitude sensor calibration method and usage procedures is included. The statistical information provided by these procedures is necessary for the uncertainty analysis of aerospace test results now required by users of industrial wind tunnel test facilities.

Tripp, John S.↗

NASA NTRS2014

Method for Ground-to-Space Laser Calibration System

The present invention comprises an approach for calibrating the sensitivity to polarization, optics degradation, spectral and stray light response functions of instruments on orbit. The concept is based on using an accurate ground-based laser system, Ground-to-Space Laser Calibration (GSLC), transmitting laser light to instrument on orbit during nighttime substantially clear-sky conditions. To minimize atmospheric contribution to the calibration uncertainty the calibration cycles should be performed in short time intervals, and all required measurements are designed to be relative. The calibration cycles involve ground operations with laser beam polarization and wavelength changes.

Lukashin, Constantine↗

NASA NTRS2015

Method for Ground-to-Satellite Laser Calibration System

Lukashin, Constantine↗

NASA NTRS2020

Pre-launch Radiometric Calibration and Uncertainty Analysis of Landsat Thermal Infrared Sensor 2

The Thermal Infrared Sensor-2 that will be on-board Landsat 9 has undergone a pre-launch testing campaign to characterize its radiometric, spectral, and spatial performance and demonstrate compliance to its requirements. This work reviews key elements of the instrument-level radiometric testing using an SI traceable source to derive its uncertainties. Those arising from on-orbit calibration using the TIRS-2 on-board blackbody are also discussed. We use a Monte Carlo approach to propagate the uncertainties through a non-linear calibration equation and address both random and systematic uncertainty terms. Achieving the required performance demonstrates the instrument’s potential for enhancing our understanding of the Earth’s environment.

Landsat↗

NASA NTRS2020

Prelaunch Radiometric Calibration and Uncertainty Analysis of Landsat Thermal Infrared Sensor 2

The Thermal Infrared Sensor-2 (TIRS-2) that will be onboard Landsat 9 has undergone a prelaunch testing campaign to characterize its radiometric, spectral, and spatial performances and demonstrate compliance to its requirements. This work reviews the key elements of the instrument-level radiometric testing using an SI-traceable source to derive its uncertainties. Those arising from on-orbit calibration using the TIRS-2 onboard blackbody are also discussed. We use a Monte Carlo approach to propagate the uncertainties through a nonlinear calibration equation and address both random and systematic uncertainty terms. Achieving the required performance demonstrates the instrument's potential for enhancing our understanding of the Earth's environment.

Aaron Pearlman↗

DOE OSTI2024

Bayesian calibration and uncertainty quantification of a rate-dependent cohesive zone model for polymer interfaces

In this work we present a rate-dependent cohesive zone model for the fracture of polymeric interfaces and performs a Bayesian calibration, an uncertainty quantification, and a sensitivity analysis for the model. The proposed cohesive zone model accounts for both reversible elastic and irreversible rate-dependent separation sliding deformation at the interface. The viscous dissipation due to the irreversible opening at the interface is modeled using elastic-viscoplastic kinematics that incorporates the effects of strain rate. Inverse calibration of parameters for such complex models through trial and error is challenging due to the large number of parameters of the model. Moreover, the calibrated parameter values are often non-unique and uncertain when the available experimental data is limited. To tackle this challenge, we employ a Bayesian calibration approach to identify parameters from experimental data, the resulting parameters significantly enhance the accuracy of the model. To quantify the uncertainty associated with the inverse parameter estimation, a modular Bayesian approach is employed to calibrate the unknown model parameters, accounting for the parameter uncertainty of the cohesive zone model. The advantages of the Bayesian calibration over a deterministic parameter fit are demonstrated. Further, to quantify the model uncertainties, such as incorrect assumptions or missing physics, a discrepancy function is introduced, which significantly improves the model’s prediction. Finally, the total uncertainty of the model is quantified in a predictive setting. A sensitivity analysis is performed to assess how changes in the input variables of the model affect the peak load, facilitating the identification of a concise set of highly influential parameters. The present approach can be used for calibration and uncertainty quantification for other complex computational mechanics models. It should also facilitate the designing of interface materials under uncertainty.

42 ENGINEERING↗

DOE OSTI2024

Calibration and uncertainty quantification for Davis Equation of State models for the High Explosive PBX 9501 products

Abstract This paper investigates the uncertainty in the parameters used in the calibration of an Davis Equation Of State (EOS) for the detonation products of the High Explosive PBX 9501. The procedure sought to make use of all available information about this HE to inform the best set of calibration parameters as well as the uncertainty in these parameters. The procedure made use of historical experimental data, the results from thermo‐chemical modeling as well as data on the best isentrope function fit to cylinder test experimental data. Combining all these heterogeneous data sources together in a Bayesian calibration, yielded a posterior mean and covariance. Sampling from the posterior distribution and evaluating an important Quantity Of Interest (QOI) in the EOS model, the detonation speed of a one‐inch rate stick, produced a distribution which showed variations which were in agreement with experiments. The uncertainty in the EOS was reported as eleven sets of model calibrations which spanned the range of this QOI.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2025

Requirements on the gain calibration for LiteBIRD polarisation data with blind component separation

The detection of primordial B modes of the cosmic microwave background (CMB) could provide information about the early stages of the Universe's evolution. The faintness of this signal requires exquisite calibration accuracy and control of instrumental systematic effects which otherwise could bias the measurements. In this work, we study the impact of an imperfect relative polarisation gain calibration on the recovered value of the tensor-to-scalar ratio r for the LiteBIRD experiment, through the application of the blind Needlet Internal Linear Combination (NILC) foreground-cleaning method. We derive requirements on the relative calibration accuracy of the overall polarisation gain (Δg ν ) for each LiteBIRD frequency channel. Our results show that minimum variance techniques, as NILC, are less sensitive to systematic gain calibration uncertainties compared to a parametric approach, if the latter is not equipped with a proper modelling of these instrumental effects. In this study, the most stringent requirements are found in the channels where the CMB signal is relatively brighter, with the tightest constraints at 166 GHz (Δg ν ≈ 0.16%). This differs from the outcome of an analogous analysis performed with a parametric method, where the tightest requirements are obtained for the foreground-dominated channels. Gain calibration uncertainties, corresponding to the derived requirements, are then simultaneously propagated into all frequency channels. By doing so, we find that the overall impact on estimated r is lower than the total gain systematic budget for LiteBIRD approximately by a factor 5, due to the correlations of the impacts of gain calibration uncertainties in different frequency channels. In order to decouple the systematic effect from the specific choice of the model, we derive the requirements assuming constant spectral parameters for the foreground emission. To assess the robustness of the obtained results against more realistic scenarios, we repeat the analysis assuming sky models of intermediate and high complexity. In these further cases, we adopt an optimised NILC pipeline, called the Multi-Clustering NILC (MC-NILC). We find that the impact of gain calibration uncertainties on r is lower than the LiteBIRD gain systematics budget for the intermediate-complexity sky model. For the high-complexity case, instead, it would be necessary to tighten the requirements by a factor 1.8.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Scintillation light calibrations, systematic uncertainties, and triggering efficiency in the MicroBooNE detector

Scintillation light, produced alongside ionisation charge from particle interactions, plays a critical role in liquid argon time projection chamber (LArTPC) detectors. A detailed understanding of its production and detection mechanisms is essential for robust calibration, systematic uncertainty evaluation, and physics analysis. This article describes the MicroBooNE light simulation, light-based triggering schemes, photomultiplier tube gain calibration, light response stability, and light-based systematic uncertainties over the course of five years of data collection. In addition, we present a measurement of scintillation light triggering efficiency, focusing on the lowest-light regime relevant to rare-event searches and low-energy neutrino interactions. Finally, we discuss two notable observations in MicroBooNE's data, both reported here for the first time: an approximately 50% decline in MicroBooNE's light yield over time, concentrated in the first two years of running; and a higher than expected O(200 kHz) rate of single photoelectron noise. The results presented provide an important benchmark of long-term light detection performance in LArTPC neutrino detectors.

Abratenko, P. [Tufts U.]↗

NASA NTRS2020

Calibration Probe Uncertainty and Validation for the Hypersonic Material Environmental Test System

This paper presents an uncertainty analysis of the stagnation-point calibration probe surface predictions for conditions that span the performance envelope of the Hypersonic Materials Environmental Test System facility located at NASA Langley Research Center. A second-order stochastic expansion was constructed over 47 uncertain parameters to evaluate the sensitivities, identify the most significant uncertain variables, and quantify the uncertainty in the stagnation-point heat flux and pressure predictions of the calibration probe for a low- and high-enthalpy test condition. A sensitivity analysis showed that measurement bias uncertainty is the most significant contributor to the stagnation-point pressure and heat flux variance for the low-enthalpy condition. For the high-enthalpy condition, a paradigm shift in sensitivities revealed the computational fluid dynamics model input uncertainty as the main contributor. A comparison between the prediction and measurement of the stagnation-point conditions under uncertainty showed that there was evidence of statistical disagreement. A validation metric was proposed and applied to the prediction uncertainty to account for the statistical disagreement when compared to the possible stagnation-point heat flux and pressure measurements.

Brune, Andrew J.↗

DOE OSTI2020

Nondestructive measurements of residual 235 U mass of Israeli Research Reactor-1 fuel using the Advanced Experimental Fuel Counter

In 2018, a measurement campaign took place with participants from Los Alamos National Laboratory (LANL), the Nuclear Research Centre-Negev (NRCN) and Soreq Nuclear Research Center (SNRC) at the Israeli Research Reactor-1 (IRR-1) in which 14 of the reactor’s used fuel assemblies (FAs) with varied amount of depletion were measured with the nondestructive assay instrument Advanced Experimental Fuel Counter (AEFC). Designed for safeguards purposes, the AEFC measures both neutrons emitted from the FA (passive neutrons) and fission neutrons induced by an external neutron source (in this experiment, 252 Cf). Signals recorded with the AEFC include total neutron count rates (Singles), time-correlated neutron count rates (Doubles), and total gamma-ray count rates. The 235 U content of the FAs was previously assessed by two independent methods: (1) measurement of the transparency of the FA to low-energy gamma rays from an activated rhenium source (rhenium gamma transmission, or the RGT method) and (2) calculation of the 30-year burnup history of the core using detailed three-dimensional Monte-Carlo core depletion calculations. The results from the FAs that had been measured via the RGT method were used to construct the calibration curves, which translate the AEFC count rates to 235 U mass. Then, the calibration was evaluated using AEFC measurements of six additional FAs that were not measured via the RGT method. From the results, it was determined the Doubles calibration curve was more reliable than that of the Singles and follows a simple second-order polynomial fit for the whole range of residual 235 U mass content, albeit with larger statistical uncertainty. Detailed uncertainties quantification was conducted for both the AEFC Singles and Doubles. This includes the analysis of statistical uncertainties, calibration uncertainty, and random uncertainties due to the sensitivity of the AEFC to several sources of uncertainty, namely the FA position, FA orientation, interrogation source position, and ambient pool temperature. Finally, an overall total uncertainty of 6 g of 235 U is estimated for the Singles and Doubles, which is mainly due to calibration uncertainty (for the Singles) and statistical uncertainty (for the Doubles), and which constitutes 3%–6% of the 235 U total mass in the FAs, depending on their level of depletion.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Calculating Interval Uncertainties for Calibration Standards That Drift with Time

Calibrated values of many devices exhibit predictable drift over time. To provide an uncertainty statement valid over the entire calibration interval, one must account for drift. In this article, a method of accounting for drift is proposed based on guidance in the Guide to Expression of Uncertainty in Measurement. An additional uncertainty term is computed using a linear regression of historical measurement data, which is included along with the time-of-test uncertainty. This method is evaluated by analyzing its average out-of-tolerance (OOT) rate using a Monte Carlo simulation, which results in the desired 5% average OOT rate when the total uncertainty is expanded to a 95% confidence interval.

47 OTHER INSTRUMENTATION↗

NASA NTRS2007

Detector Noise Characterization and Performance of MODIS Thermal Emissive Bands

MODIS has 16 thermal emissive bands, a total of 160 individual detectors (10 for each spectral bands), located on the two cold focal plane assemblies (CFPA). MODIS TEB detectors were fully characterized pre-launch in a thermal vacuum (TV) environment using a NIST traceable blackbody calibration source (BCS) with temperatures ranging from 170 to 340K. On-orbit the TEB detectors are calibrated using an on-board blackbody (BB) on a scan-by-scan basis. For nominal on-orbit operation, the on-board BB temperature is typically controlled at 285K for Aqua MODIS and 290K for Terra MODIS. For the MODIS TEB calibration, each detector's noise equivalent temperature difference (NEdT) is often used to assess its performance and this parameter is a major contributor to the calibration uncertainty. Because of its impact on sensor calibration and data product quality, each MODIS TEB detector's NEdT is monitored on a daily basis at a fixed BB temperature and completely characterized on a regular basis at a number of BB temperatures. In this paper, we describe MODIS on-orbit TEB NEdT characterization activities, approaches, and results. We compare both pre-launch and on-orbit performance with sensor design specification and examine detector noise characterization impact on the calibration uncertainty. To date, 135 TEB detectors (out of a total of 160 detectors) in Terra MODIS (launched in December 1999) and 158 in Aqua MODIS (launched in May 2002) continue to perform with their NEdT below (or better than) their design specifications. A complete summary of all TEB noisy detectors, identified both pre-launch and on-orbit, is provided.

Xiong, X.↗

NASA NTRS2017

Revisiting Short-Wave-Infrared (SWIR) Bands for Atmospheric Correction in Coastal Waters

The shortwave infrared (SWIR) bands on the existing Earth Observing missions like MODIS have been designed to meet land and atmospheric science requirements. The future geostationary and polar-orbiting ocean color missions, however, require highly sensitive SWIR bands (greater than 1550nm) to allow for a precise removal of aerosol contributions. This will allow for reasonable retrievals of the remote sensing reflectance (R(sub rs)) using standard NASA atmospheric corrections over turbid coastal waters. Design, fabrication, and maintaining high-performance SWIR bands at very low signal levels bear significant costs on dedicated ocean color missions. This study aims at providing a full analysis of the utility of alternative SWIR bands within the 1600nm atmospheric window if the bands within the 2200nm window were to be excluded due to engineering/cost constraints. Following a series of sensitivity analyses for various spectral band configurations as a function of water vapor amount, we chose spectral bands centered at 1565 and 1675nm as suitable alternative bands within the 1600nm window for a future geostationary imager. The sensitivity of this band combination to different aerosol conditions, calibration uncertainties, and extreme water turbidity were studied and compared with that of all band combinations available on existing polar-orbiting missions. The combination of the alternative channels was shown to be as sensitive to test aerosol models as existing near-infrared (NIR) band combinations (e.g., 748 and 869nm) over clear open ocean waters. It was further demonstrated that while in extremely turbid waters the 1565/1675 band pair yields R(sub rs) retrievals as good as those derived from all other existing SWIR band pairs (greater than 1550nm), their total calibration uncertainties must be less than 1% to meet current science requirements for ocean color retrievals (i.e., delta R(sub rs) (443) less than 5%). We further show that the aerosol removal using the NIR and SWIR bands (available on the existing polar-orbiting missions) can be very sensitive to calibration uncertainties. This requires the need for monitoring the calibration of these bands to ensure consistent multi-mission ocean color products in coastal/inland waters.

atmospheric correction↗

NASA NTRS1998

Uncertainty in Calibration, Detection and Estimation of Metal Concentrations in Engine Plumes Using OPAD

Improvements in uncertainties in the values of radiant intensity (I) can be accomplished mainly by improvements in the calibration process and in minimizing the difference between the background and engine plume radiance. For engine tests in which the plume is extremely bright, the difference in luminance between the calibration lamp and the engine plume radiance can be so large as to cause relatively large uncertainties in the values of R. This is due to the small aperture necessary on the receiving optics to avoid saturating the instrument. However, this is not a problem with the SSME engine since the liquid oxygen/hydrogen combustion is not as bright as some other fuels. Applying the instrumentation to other type engine tests may require a much brighter calibration lamp.

Hopkins, Randall C.↗

DOE OSTI2024

Boundary-Aware Adversarial Learning Domain Adaption and Active Learning for Cross-Sensor Building Extraction

The use of convolutional neural networks (CNNs) for building extraction from remote sensing images has been widely studied and many public datasets have been made available for accelerating development of these CNN models. Yet adapting pretrained models at scale in real-world scenarios remains a challenging task. The main barrier is that certain new labels are still needed to compensate for domain shifting between the labeled data and new images that potentially cover new geographic locations or that are from a different sensor. In this article, we propose to add informatively labeled samples from a new image pool under the paradigm of active learning. To select the most useful samples based on model uncertainty, we first tackle the problem of uncalibrated uncertainty estimation due to distribution shifting by adapting feature extractors with boundary-based adversarial learning. Calibrated uncertainty is used as the query criterion in the active learning process, where the most uncertain samples are selected for annotation and included for model retraining. The proposed workflow was tested with three data pairs in which each workflow represents a scenario often encountered in real-world applications, including adapting pretrained models to new images collected with different sensors or to new geographic areas where appearances and types of buildings are very different. Compared to several baselines, including random sampling, temperature scaling (a well-known uncertainty calibration technique), different query strategies, and active domain adaptation methods, the proposed workflow shows that strategically querying a smaller set of samples for labeling achieves comparable or better building extraction performance. The proposed method reduces the number of labeled samples required to achieve sufficient model accuracy, thus significantly reducing hundreds of person-hours for labeled data creation. In addition, we include a few considerations when deploying this workflow in a GPU cluster that can be easily adapted to achieve operational building extraction model retraining.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Calibration and Uncertainty Estimation Using the Ensemble Kalman Filter with a Large Subsurface Flow and Transport Model - 20321

At routinely monitored groundwater contamination sites, periodically measured environmental conditions such as groundwater levels and contaminant concentrations are used to inform and confirm a conceptual site model (CSM) and guide the development and calibration of a numerical groundwater flow and transport model. The calibration of groundwater flow and transport models after each measurement (sampling) event can illuminate deficiencies in a CSM, identify areas where additional monitoring is warranted, and predict the behavior of the system to guide decision making. However, manual and automated (e.g. PEST) model calibration tools can be time-consuming and computationally expensive to implement after each sampling event. Perhaps as a result, such calibration tools generally utilize all available monitoring data simultaneously rather than sequentially assimilating monitoring data one sampling event at a time as the results from sampling become available. A more real-time data assimilation approach may reduce parameter uncertainty, quantify the value of additional monitoring data, and produce a usable model more quickly and with less effort. To mitigate the potential time-consuming aspects of manual and widely applied automated calibration techniques, a data assimilation algorithm called the ensemble Kalman filter (EnKF) was evaluated as a relatively efficient method of model calibration and uncertainty assessment via the sequential integration of monitoring data into a model. The EnKF was able to successfully and efficiently assimilate monitoring and modeling data to calibrate a complex flow and transport model at a real-world site with significant subsurface heterogeneity, uncertainty, and 12 years of monitoring data (over 4,000 individual measurements of groundwater levels and over 2,500 measurements of contaminant concentrations). Starting with an uncalibrated model data from annual sampling events were sequentially assimilated, and the resultant predication errors and estimated parameter uncertainties were tracked. After all monitoring data were assimilated, both flow and transport residuals at the end of the EnKF process were comparable to those produced via a concurrent PEST calibration effort but required fewer model simulations. Both uncertainty and prediction errors decreased over time. In a real-time application, the adequacy of the model could be assessed after each sampling event. The benefits of such a real-time approach to utilizing monitoring data include reduced costs (in the form of model updates or site characterization efforts), early flagging of possible errors in the CSM, and a reduced risk of overfitting and corresponding increased confidence in model predictions. This tool may be particularly useful compared to other calibration techniques (e.g. manual, PEST) when model runtimes are long, calibration parameters are many, or parameter uncertainty is large. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗