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At least 865 records · Page 48
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NASA NTRS2024

Interim Measures Pilot Study Completion Report, South Repeater Building, Solid Waste Management Unit 121, Hydraulic Containment and Groundwater Treatment System for Per- and Polyfluoroalkyl Substances

This Per- and Polyfluoroalkyl Substances (PFAS) Interim Measures Pilot Study Completion Report was prepared for the National Aeronautics and Space Administration by AECOM Technical Services, Inc. under Contract 80KSC019D0010, Task Order 80KSC021F0096. The purpose of this report is to document the pilot study activities at the South Repeater Building, Solid Waste Management Unit (SWMU) 121. This report details the additional assessment activities completed from June 2023 through November 2023 and the subsequent pilot study completed at the South Repeater Building conducted in December 2023 through February 2024. Initial activities were conducted in accordance with the Pilot Study Work Plan (NASA 2023), submitted to the KSC Remediation Team and accepted by the team on October 26, 2023. The objectives of the pilot study were to: • Provide information on aquifer characteristics and to aid in future modeling and remedial design activities to mitigate off-KSC migration of PFAS compounds. • Develop information on characteristics of the surficial aquifer system, specifically transmissivity, storage coefficient, hydraulic conductivity, and vertical hydraulic conductivity. • Obtain data to support construction and calibration of a groundwater flow model. • Acquire design parameters necessary for future remedial design activities, specifically radius of influence, drawdown, flow rates, and pump settings. The following activities were completed to meet the pilot study objectives: • Groundwater and surface water sampling • Geophysical investigation via Hydraulic Profiling Tool (HPT) and soil sampling • Installation of observation and extraction wells via rotosonic techniques • Slug, step, and pump testing activities • Initial modeling efforts for a groundwater hydraulic containment system. Results of the pilot study and modeling activities are being used in the development of a groundwater containment and treatment system for the site.

PFAS↗
DOE OSTI2022

Survey of Surveys: I. The largest compilation of radial velocities for the Galaxy

In the present-day panorama of large spectroscopic surveys, the amount, diversity, and complexity of the available data continuously increase. The overarching goal of studying the formation and evolution of our Galaxy is hampered by the heterogeneity of instruments, selection functions, analysis methods, and measured quantities. Here, we present a comprehensive catalogue, the Survey of Surveys (SoS), built by homogeneously merging the radial velocity (RV) determinations of the largest ground-based spectroscopic surveys to date, such as APOGEE, GALAH, Gaia-ESO, RAVE, and LAMOST, using Gaia as a reference. This pilot study serves to prove the concept and to test the methodology that we plan to apply in the future to the stellar parameters and abundance ratios as well. We have devised a multi-staged procedure that includes: (i) the cross match between Gaia and the spectroscopic surveys using the official Gaia cross-match algorithm, (ii) the normalisation of uncertainties using repeated measurements or the three-cornered hat method, (iii) the cross calibration of the RVs as a function of the main parameters on which depend (magnitude, effective temperature, surface gravity, metallicity, and signal-to-noise ratio) to remove trends and zero point offsets, and (iv) the comparison with external high-resolution samples, such as the Gaia RV standards and the Geneva-Copenhagen survey, to validate the homogenisation procedure and to calibrate the RV zero-point of the SoS catalogue. We provide the largest homogenised RV catalogue to date, containing almost 11 million stars, of which about half come exclusively from Gaia and half in combination with the ground-based surveys. We estimate the accuracy of the RV zero-point to be about 0.16–0.31 km s –1 and the RV precision to be in the range 0.05–1.50 km s –1 depending on the type of star and on its survey provenance. We validate the SoS RVs with open clusters from a high resolution homogeneous samples and provide the systemic velocity of 55 individual open clusters. Additionally, we provide median RVs for 532 clusters recently discovered by Gaia data. The SoS is publicly available and ready to be applied to various research projects, such as the study of star clusters, Galactic archaeology, stellar streams, or the characterisation of planet-hosting stars, to name a few. We also plan to include survey updates and more data sources in future versions of the SoS.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Autocalibration of the E3SM Version 2 Atmosphere Model Using a PCA-Based Surrogate for Spatial Fields

Global Climate Model tuning (calibration) is a tedious and time-consuming process, with high-dimensional input and output fields. Experts typically tune by iteratively running climate simulations with hand-picked values of tuning parameters. Many, in both the statistical and climate literature, have proposed alternative calibration methods, but most are impractical or difficult to implement. We present a practical, robust, and rigorous calibration approach on the atmosphere-only model of the Department of Energy's Energy Exascale Earth System Model (E3SM) version 2. Our approach can be summarized into two main parts: (a) the training of a surrogate that predicts E3SM output in a fraction of the time compared to running E3SM, and (b) gradient-based parameter optimization. To train the surrogate, we generate a set of designed ensemble runs that span our input parameter space and use polynomial chaos expansions on a reduced output space to fit the E3SM output. We use this surrogate in an optimization scheme to identify values of the input parameters for which our model best matches gridded spatial fields of climate observations. To validate our choice of parameters, we run E3SMv2 with the optimal parameter values and compare prediction results to expertly-tuned simulations across 45 different output fields. This flexible, robust, and automated approach is straightforward to implement, and we demonstrate that the resulting model output matches present day climate observations as well or better than the corresponding output from expert tuned parameter values, while considering high-dimensional output and operating in a fraction of the time.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

AVHRR and VISSR satellite instrument calibration results for both Cirrus and marine stratocumulus IFO periods

Accurate characterizations of some cloud parameters are dependent upon the absolute accuracy of satellite radiance measurements. Visible wavelength measurements from both the AVHRR and VISSR instruments are often used to study cloud characteristics. Both of these instruments were radiometrically calibrated prior to launch, but neither has an onboard device to monitor degradation after launch. During the FIRE/SRB cirrus Intensive Field Operation (IFO), a special effort was made to monitor calibration of these two instruments onboard the NOAA-9 and GOES-6 spacecraft. In addition, several research groups have combined their efforts to assess the long-term performance of both instruments. These results are presented, and a limited comparison is made with the ERBE calibration standard.

Whitlock, C. H.↗
DOE OSTI2021

Accounting for uncertainty in complex alluvial aquifer modeling by Bayesian multi-model approach

Alluvial aquifers by nature are complex caused by varied depositional environments. Developing a reliable groundwater model to represent an alluvial aquifer is non-trivial. Also, relying on a single best calibrated model may not be sufficient because of an inadequate choice of model parameter values. To better understand groundwater dynamics and improve model prediction reliability, this study presents a Bayesian multi-model uncertainty quantification (BMMUQ) framework to account for model parameter uncertainty in complex alluvial groundwater modeling. The methodology was applied to the agriculturally intensive Mississippi River alluvial aquifer (MRAA), Northeast Louisiana. An aquifer architecture was first constructed using 7,259 well logs in the MRAA area which covers three fluvial deposits (alluvium, braided-stream terrace, and braided-stream terrace-loess). A 12-layer MODFLOW model was then developed to address the alluvial aquifer complexity and well calibrated through a genetic algorithm. This study quantified model parameter uncertainty in hydraulic conductivity and specific storage of sand facies. Bayesian model averaging (BMA) with the Expectation Maximization (EM) algorithm was adopted to derive posterior model weights and head variances of 50 alternative conceptual groundwater flow models, and thereby obtains BMA ensemble model predictions instead of only relying on the best calibrated conceptual model. Overall, results show that an estimated around 950 million m3 of groundwater storage loss occurs in 2015 with respect to the beginning of 2004, due to high groundwater demand for irrigation in the MRAA area. Explicitly quantifying model uncertainty can produce more reliable groundwater level predictions from BMA ensemble model. The presented groundwater modeling framework improves our understanding of the MRAA and provides a valuable tool to assist agricultural water management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations

Photosynthesis plays an important role in carbon, nitrogen, and water cycles. Ecosystem models for photosynthesis are characterized by many parameters that are obtained from limited in situ measurements and applied to the same plant types. Previous site-by-site calibration approaches could not leverage big data and faced issues like overfitting or parameter non-uniqueness. Here we developed an end-to-end programmatically differentiable (meaning gradients of outputs to variables used in the model can be obtained efficiently and accurately) version of the photosynthesis process representation within the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) model. As a genre of physics-informed machine learning (ML), differentiable models couple physics-based formulations to neural networks (NNs) that learn parameterizations (and potentially processes) from observations, here photosynthesis rates. We first demonstrated that the framework was able to correctly recover multiple assumed parameter values concurrently using synthetic training data. Then, using a real-world dataset consisting of many different plant functional types (PFTs), we learned parameters that performed substantially better and greatly reduced biases compared to literature values. Further, the framework allowed us to gain insights at a large scale. Our results showed that the carboxylation rate at 25 °C (V c,max25 ) was more impactful than a factor representing water limitation, although tuning both was helpful in addressing biases with the default values. This framework could potentially enable substantial improvement in our capability to learn parameters and reduce biases for ecosystem modeling at large scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Reweighting Underlying Event and Colour Reconnection parameter variations in Sherpa

We propose and validate a new method to trace the impact of parameter variations in the simulation of multi-parton interactions and colour reconnections in the Sherpa event generator. They are reflected, at an event-by-event basis, through relative weights with respect to the central production parameters that give rise to the generated events and distributions. Our method facilitates the tuning of the Monte Carlo event generator at a dramatically reduced computational cost, alleviates parameter sensitivity studies, and enables robust quantification of parametric uncertainties on-the-fly, one of the missing ingredients for future simulations of high-energy particle collisions. The method can easily be adapted to and implemented in other event generators. To illustrate its potential, we here consider combined tunes of the multi-parton-interaction and colour-reconnection models in Sherpa using LHC proton-proton collision data at $\sqrt{s}=7\,\text{TeV}$. We furthermore calibrate the energy-scaling behaviour of dimensionful model parameters based on $\sqrt{s}=13\,\text{TeV}$ LHC data and Tevatron data taken at $\sqrt{s}=1.96\,\text{TeV}$.

Pabst, Moritz [Gottingen U.]↗
NASA NTRS2023

JPSS-3 / 4 VIIRS Response Versus Scan Angle Characterization and Performance

Scientific studies of the Earth’s climate increasingly rely on high-quality satellite observations. The Visible Infrared Imaging Radiometer Suite (VIIRS) is a key sensor onboard a series of satellites [Suomi National Polar-orbiting Partnership (SNPP) and Joint Polar-orbiting Satellite System 1–4 (JPSS-1–JPSS-4)] that generate scientific data from land, ocean, and atmosphere used in these climate models. Providing quality scientific data from space-borne sensors requires the instruments to be well-calibrated. While much of the calibration can be maintained on-orbit, some aspects of the calibration can best be measured prior to launch. One VIIRS parameter that needs to be measured pre-launch is the response versus scan angle (RVS). The RVS measures the relative change in the reflectance of the scanning optics as a function of the angle of incidence. With the RVS, the gain calibration measured on-orbit can be transferred to any scan angle. The JPSS-3 and JPSS-4 instruments have undergone ground testing including the RVS measurements, which is the subject of this work. Results indicate that the measurements are comparable to previous VIIRS builds and are expected to contribute to the generation of high-quality science data once JPSS-3 and JPSS-4 are on-orbit.

JPSS↗
NASA NTRS2006

Calibration Issues and Operating System Requirements for Electron-Probe Microanalysis

Instrument purchase requirements and dialogue with manufacturers have established hardware parameters for alignment, stability, and reproducibility, which have helped improve the precision and accuracy of electron microprobe analysis (EPMA). The development of correction algorithms and the accurate solution to quantitative analysis problems requires the minimization of systematic errors and relies on internally consistent data sets. Improved hardware and computer systems have resulted in better automation of vacuum systems, stage and wavelength-dispersive spectrometer (WDS) mechanisms, and x-ray detector systems which have improved instrument stability and precision. Improved software now allows extended automated runs involving diverse setups and better integrates digital imaging and quantitative analysis. However, instrumental performance is not regularly maintained, as WDS are aligned and calibrated during installation but few laboratories appear to check and maintain this calibration. In particular, detector deadtime (DT) data is typically assumed rather than measured, due primarily to the difficulty and inconvenience of the measurement process. This is a source of fundamental systematic error in many microprobe laboratories and is unknown to the analyst, as the magnitude of DT correction is not listed in output by microprobe operating systems. The analyst must remain vigilant to deviations in instrumental alignment and calibration, and microprobe system software must conveniently verify the necessary parameters. Microanalysis of mission critical materials requires an ongoing demonstration of instrumental calibration. Possible approaches to improvements in instrument calibration, quality control, and accuracy will be discussed. Development of a set of core requirements based on discussions with users, researchers, and manufacturers can yield documents that improve and unify the methods by which instruments can be calibrated. These results can be used to continue improvements of EPMA.

Carpenter, P.↗
NASA NTRS1991

Thermal calibration and analysis of a Bridgman flight furnace

A combination of computer modeling and experimental measurements is proposed which permits the calibration of the temperature profile for a spacecraft flight furnace. The Fluent code based on the finite difference method uses the furnace set-point temperatures as boundary conditions to model a Bridgman directional solidification furnace. Experimental measurements are employed as a comparison for the numerical analysis, and the radial gradients and wall temperatures are selected when the calculated values are sufficiently close to those from measurements. The calculations are found to agree with experimental data, and some results of the modeling are given which relate to specific furnace parameters. The CFD approach facilitates the transposition of furnace calibration to microgravitational operation in addition to providing precise initial radial temperature gradients and furnace-wall temperatures.

Knuteson, D. J.↗
NASA NTRS2014

Optimal Design of Calibration Signals in Space Borne Gravitational Wave Detectors

Future space borne gravitational wave detectors will require a precise definition of calibration signals to ensure the achievement of their design sensitivity. The careful design of the test signals plays a key role in the correct understanding and characterization of these instruments. In that sense, methods achieving optimal experiment designs must be considered as complementary to the parameter estimation methods being used to determine the parameters describing the system. The relevance of experiment design is particularly significant for the LISA Pathfinder mission, which will spend most of its operation time performing experiments to characterize key technologies for future space borne gravitational wave observatories. Here we propose a framework to derive the optimal signals in terms of minimum parameter uncertainty to be injected to these instruments during its calibration phase. We compare our results with an alternative numerical algorithm which achieves an optimal input signal by iteratively improving an initial guess. We show agreement of both approaches when applied to the LISA Pathfinder case.

LISA Path_nder mission↗
DOE OSTI2022

Machine Learning Based Network Parameter Estimation Using AMI Data

The expansion of distribution power system and the growing penetration of distributed energy resources present new challenges for situational awareness. Calibrating the extended system model with sensor measurements and maintaining the usability is critical for utilities. This paper presents a distribution network parameter estimation (DNPE) approach using machine learning (ML) and metering data that improve the quality of extended distribution power system modeling. The reliability model can improve the ability of endpoint data to be translated into network-level situational awareness in real time and help distribution system operators (DSOs) solve branch flow and voltage problems. In addition, a data analytic and automate processing scheme is proposed to improve the sensor data quality and prevent misleading information. The effectiveness of the proposed method is verified with actual advanced metering infrastructure (AMI) data on a real utility feeder model, while considering the higher penetration of photovoltaic power generation. The test of DNPE and study results are demonstrated in this paper.

Parameter estimation, machine learning, power dist↗
DOE OSTI2023

A Model Calibration Method for Grid-Forming Inverters Using Iterative Bayesian Optimization

As inverter-based resources (IBRs) are rapidly deployed, especially at the distribution and microgrid levels, the need to include their accurate representations in power systems models increases. With well-calibrated models, IBR-interactions can be examined, preventing any stability and operational issues, especially in islanded systems. This paper proposes a method to calibrate generic inverter models using Bayesian optimization, but with a parameter-grouping approach to improve speed and accuracy. This approach is illustrated for a grid-forming inverter using synthetic data and field measurements from a simple IBRbased microgrid. Tests show that the calibration algorithm has modest computation cost and is robust to noisy measurements..

Biswas, Shuchismita↗
DOE OSTI2022

Decoding Golden Eagle Movement Behavior from High-Resolution, Variable-Rate Telemetry Data Through Bayesian Filtering

The recent advances in animal tracking technology have enabled the collection of a vast amount of in situ data regarding the movement of wildlife at high spatiotemporal resolution. These data are usually available at variable time resolutions and contains noise (error) originating from GPS fixes. Decoding movement characteristics, particularly of flying animals, from telemetry data while handling these factors is a challenging yet important task for conservation purposes. Typically, this task is broken into two subtasks: resampling, and model calibration. The resampling subtask converts the variable rate positional data into a constant time interval data, while the model calibration subtask uses the resampled data to tune time-invariant parameters of the proposed models. For telemetry data at high temporal resolutions (order of 1 second), it is very challenging to decouple noise from actual movements using interpolation-based resampling techniques. Any errors introduced during resampling can significantly alter the the calibration and prediction attributes of the movement model. We address this problem through a unified Bayesian state-space framework that can handle both the resampling and calibration tasks in a single step. In addition, we use the speed and heading of the bird from telemetry data to regularize the position information of the bird. We use a Kalman filtering approach to include these nonlinearly related motion parameters within the state space framework. We cross-validated to quantify how this inclusion affects the model performance in estimating true bird movements. The relationship between the true state of the bird and environmental and topographical covariates is then represented parametrically. These parameters are then tuned using stochastic sampling strategies like Markov Chain Monte Carlo (MCMC). We use the telemetry data collected from golden eagles in the western USA to demonstrate the applicability of this approach to build a predictive, probabilistic movement model. Our preliminary results show that this approach provides improved predictive performance in terms of capturing higher-order motion parameters such as angular and horizontal accelerations, which may have simpler and more direct relationships with environmental covariates than corresponding speeds. In this talk, we will demonstrate how this state-space approach benefits the prediction capabilities of a movement model in simulating golden eagle paths through a wind power plant in Wyoming given certain atmospheric conditions. The model outcomes are aimed at informing mitigation strategies that can minimize the potential for collisions of golden eagles with wind turbines.

Bayesian methods↗
NASA NTRS2012

Advanced Fire Detector for Space Applications

A document discusses an optical carbon monoxide sensor for early fire detection. During the sensor development, a concept was implemented to allow reliable carbon monoxide detection in the presence of interfering absorption signals. Methane interference is present in the operating wavelength range of the developed prototype sensor for carbon monoxide detection. The operating parameters of the prototype sensor have been optimized so that interference with methane is minimized. In addition, simultaneous measurement of methane is implemented, and the instrument automatically corrects the carbon monoxide signal at high methane concentrations. This is possible because VCSELs (vertical cavity surface emitting lasers) with extended current tuning capabilities are implemented in the optical device. The tuning capabilities of these new laser sources are sufficient to cover the wavelength range of several absorption lines. The delivered carbon monoxide sensor (COMA 1) reliably measures low carbon monoxide levels even in the presence of high methane signals. The signal bleed-over is determined during system calibration and is then accounted for in the system parameters. The sensor reports carbon monoxide concentrations reliably for (interfering) methane concentrations up to several thousand parts per million.

Kutzner, Joerg↗
NASA NTRS2010

Finite Element Model Calibration Approach for Area I-X

Ares I-X is a pathfinder vehicle concept under development by NASA to demonstrate a new class of launch vehicles. Although this vehicle is essentially a shell of what the Ares I vehicle will be, efforts are underway to model and calibrate the analytical models before its maiden flight. Work reported in this document will summarize the model calibration approach used including uncertainty quantification of vehicle responses and the use of non-conventional boundary conditions during component testing. Since finite element modeling is the primary modeling tool, the calibration process uses these models, often developed by different groups, to assess model deficiencies and to update parameters to reconcile test with predictions. Data for two major component tests and the flight vehicle are presented along with the calibration results. For calibration, sensitivity analysis is conducted using Analysis of Variance (ANOVA). To reduce the computational burden associated with ANOVA calculations, response surface models are used in lieu of computationally intensive finite element solutions. From the sensitivity studies, parameter importance is assessed as a function of frequency. In addition, the work presents an approach to evaluate the probability that a parameter set exists to reconcile test with analysis. Comparisons of pretest predictions of frequency response uncertainty bounds with measured data, results from the variance-based sensitivity analysis, and results from component test models with calibrated boundary stiffness models are all presented.

Horta, Lucas G.↗
NASA NTRS1994

Stellar-based calibration in the far infrared with application to IRAS Band 4

Because stars emit very small portions of their outputs in the far infrared, using them as calibrators requires precise measurement and correction for filter leaks at shorter wavelengths. Therefore, it is common to base far infrared calibrations on planetary objects such as asteriods. However, asteroids are complex geological bodies whose thermal properties depend on their evolutionary histories as well as on their gross parameters such as mass and composition, making them difficult to model as calibrators. We propose a new method for measuring filter leaks that can be carried out using the end-to-end detector system and therefore allows reliable use of stellar calibrators. We illustrate this method by showing that the Infrared Astronomy Satellite (IRAS) 100 micrometers (Band 4) filters had a short wavelength leak of 14.3% +/- 3.6% on stars similar to alpha Boo, but that there is no detectable leak in the 60 micrometers (Band 3) filters. We derive a calibration for Band 4 from stellar colors in a way that is closely analogous to the calibrations of Bands 1, 2, and 3. With correction for the leak, the stellar-based calibration is virtually identical to the original calibration based on asteroids; this result requires that the spectra of the asteriods for the original calibration differ from greybody behavior between 60 and 100 micrometers by about 10%.

Kirby, D. J.↗
NASA NTRS2020

Twenty Years of Terra MODIS Spectral Performance using the Spectro-Radiometric Calibration Assembly

The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on-board the Terra spacecraft has provided valuable Earth data to the science community for the last 20 years. Equipped with several on-board calibrators (OBCs), MODIS has continued to operate nominally since its launch in December 1999. The Spectro-Radiometric Calibration Assembly (SRCA) is one such OBC that is able to provide on-orbit measurements of the MODIS reflective solar bands (RSBs) in radiometric, spatial and spectral modes. While the SRCA is operating in spectral mode, it is able to monitor the center wavelength (CW), bandwidth (BW) and in-band relative spectral response (RSR) of most RSBs. Prelaunch measurements of the CWs, BWs and RSRs of the RSBs were performed at the system level using the Spectral measurement Assembly (SpMA). Using both the prelaunch measurements and the measurements obtained on-orbit using the SRCA, the changes in the spectral response of the MODIS reflective bands can be monitored throughout the mission. This paper will provide a brief description of the spectral calibration approach and report on-orbit changes in these spectral performance parameters and their uncertainties over the last 20 years. It will also address changes to the SRCA operation on-orbit and their impact on measured spectral calibration results. Despite two decades in orbit, the spectral responses for most Terra MODIS reflective bands continue to remain within their design specifications.

MODIS↗