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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Retrieval of ocean surface parameters from the scanning multifrequency microwave radiometer (SMMR) on the Nimbus-7 satellite

Sea-surface temperature retrievals have been tested on 2 months of Nimbus-7 scanning multichannel microwave radiometer data. Using the prelaunch versions of the instrument calibration and geophysical parameter retrieval algorithms the initial results were poor. Improved algorithms produced substantially better results. It appears that at least for the night-Southern Hemisphere portion of the Nimbus-7 orbit, a rms measurement accuracy of 1.45 C has been achieved. Similar tests with wind speed retrievals yield an accuracy of 2.7 m/s rms with no substantial differences between day and night measurements but limited by availability of surface observations to the Northern Hemisphere. Moreover, it appears that the retrieved wind speed is more nearly related to the square of the wind observed at the surface than to the wind itself.

Wilheit, T. T., Jr.↗
DOE OSTI2021

Validation of the Community Land Model Version 5 over the Contiguous United States (CONUS) using in situ and remote sensing data sets

The Community Land Model (CLM) is an effective tool to simulate the biophysical and biogeochemical processes and their interactions with the atmosphere. Although CLM Version 5 (CLM5) constitutes various updates in these processes, its performance in simulating energy, water and carbon cycles over the Contiguous United States (CONUS) at scales which land surface changes and hydrometeorological and hydroclimatological applications are more locally relevant is yet to be assessed. In this study, we conducted three simulations at 0.125? during 1979-2018 over the CONUS using different configurations of CLM, namely CLM5-biogeochemistry (CLM5BGC), CLM4.5BGC, and CLM5-satellite phenology (CLM5SP). We validated and compared their simulations against multiple remote-sensed and in-situ datasets. Overall, the parametric and structural updates (e.g., carbon cost for nitrogen uptake, variable soil thickness, dry surface layer) in CLM5 improve its ability in capturing terrestrial biogeochemical dynamics. The low evapotranspiration in CLM5BGC is associated with biases in simulating vegetation phenological characteristics rather than soil water limitations. The mismatch between CLM5BGC-simulated peak leaf area index and reference data can be attributed to CLM5BGC's inability in simulating phenology of trees and grasses. The differences between CLM-simulated irrigation and reference estimates can be attributed to differences between processes represented in models and in reality, and uncertainties in input and validation datasets. Evaluation against observations at small catchments suggest that hydrologic parameters needed to be calibrated to improve simulations of runoff, especially subsurface runoff. Additional efforts are needed to incorporate spatially-distributed plant phenology and physiology parameters and regional-specific agricultural management practices (e.g., planting, harvest).

Cheng, Yanyan↗
DOE OSTI2023

Bayesian optimal experimental design for constitutive model calibration

Computational simulation is increasingly relied upon for high/consequence engineering decisions, which necessitates a high confidence in the calibration of and predictions from complex material models. However, the calibration and validation of material models is often a discrete, multi-stage process that is decoupled from material characterization activities, which means the data collected does not always align with the data that is needed. To address this issue, an integrated workflow for delivering an enhanced characterization and calibration procedure—Interlaced Characterization and Calibration (ICC)—is introduced and demonstrated. Further, this framework leverages Bayesian optimal experimental design (BOED), which creates a line of communication between model calibration needs and data collection capabilities in order to optimize the information content gathered from the experiments for model calibration. Eventually, the ICC framework will be used in quasi real-time to actively control experiments of complex specimens for the calibration of a high-fidelity material model. This work presents the critical first piece of algorithm development and a demonstration in determining the optimal load path of a cruciform specimen with simulated data. Calibration results, obtained via Bayesian inference, from the integrated ICC approach are compared to calibrations performed by choosing the load path a priori based on human intuition, as is traditionally done. The calibration results are communicated through parameter uncertainties which are propagated to the model output space (i.e. stress–strain). In these exemplar problems, data generated within the ICC framework resulted in calibrated model parameters with reduced measures of uncertainty compared to the traditional approaches.

42 ENGINEERING↗
DOE OSTI2020

Multiscale-Informed Modeling of High Temperature Component Response with Uncertainty Quantification

This report summarizes a joint effort between Argonne National Laboratory, Idaho National Laboratory, and Los Alamos National Laboratory to develop and deploy constitutive models targeted at predicting the life of Grade 91 alloy components subjected to high temperature environments typical of those that structural components in advanced nuclear reactors would experience. Two distinct, but complementary constitutive modeling approaches have been taken here. The first employs a phenomenological viscoplastic model for which parameters have been calibrated based on experimental data for a wide range of Grade 91 alloy that has undergone a variety of processing. A Bayesian approach was used to derive distributions of uncertain parameters for this model based on this data set. The second approach is a reduced order model suitable for engineering-scale analysis that is based on the results of a large set of mesoscale simulations. Mesoscale models allow for the microstructure and composition of a particular alloy to be directly taken into account in the computation of the viscoplastic response, but are computationally expensive, which makes it impractical to directly call those models for the material constitutive response in an engineering-scale simulation. The reduced-order representation of the response of the underlying model used here allows for an engineering-scale model to take into account the characteristics of the underlying microstructure, while only incurring a reasonable computational expense. Both of these approaches have different strengths, and are applicable for different parts of the design/analysis process. The phenomenological models can be readily parameterized based on a set of experimental data for a given class of materials and used for scoping calculations. Once a specific material is chosen and adequately characterized, the reduced order models can accurately predict the response of that specific alloy, and because the models are based on predictive models of the underlying microstructure, they can be used to more confidently predict the response under conditions in regions where there is limited experimental data. Both of these models have been integrated in the Grizzly code, which is used here to perform proof-of-concept uncertainty quantification analyses of a simple component under prototypical conditions. The built- in stochastic analysis capabilities in the MOOSE framework that Grizzly is built on are used here to run large sets of simulations for this uncertainty quantification analysis. As would be expected, because the reduced order models are developed for a much more tightly defined alloy, they predict tighter distributions of the time to failure than the phenomenological models, which are calibrated to a broader set of data. Also important is that these simulations demonstrate that a reduced order modeling approach can be successfully deployed to propagate uncertainties from the material scale to practical engineering-scale component simulations.

42 ENGINEERING↗
DOE OSTI2023

Sequential Bayesian Experimental Design for Calibration of Expensive Simulation Models

Simulation models of critical systems often have parameters that need to be calibrated using observed data. For expensive simulation models, calibration is done using an emulator of the simulation model built on simulation output at different parameter settings. Using intelligent and adaptive selection of parameters to build the emulator can drastically improve the efficiency of the calibration process. The article proposes a sequential framework with a novel criterion for parameter selection that targets learning the posterior density of the parameters. The emergent behavior from this criterion is that exploration happens by selecting parameters in uncertain posterior regions while simultaneously exploitation happens by selecting parameters in regions of high posterior density. Furthermore, the advantages of the proposed method are illustrated using several simulation experiments and a nuclear physics reaction model.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2018

On-Ground Calibration and Optical Alignment for the Orion Optical Navigation Camera

The Orion Multi-Purpose Crew Vehicle on-board Navigation System will utilize the Optical Navigation measurements of the Moon and Earth during cis-lunar operations. Misalignment or an un-calibrated optical navigation camera may cause large measurement residuals in any on-board attitude determination and navigation system. Therefore, a novel estimation technique to calibrate the internal camera parameters, and a high accuracy optical alignment procedure to estimate the external camera alignment are introduced in this paper. The intrinsic camera parameters such as the focal length, the principle point offsets, and the camera lens distortion parameters will be estimated and evaluated using images of star fields. This calibration estimation technique can be used either on-ground or in flight. The proposed technique in this paper is using the discrepancy between imaged star vectors attained from the OpNav camera, and the matched star vectors from the star catalog to determine the changes in internal camera parameters. This gave rise to the two basic types of calibration the attitude dependent and attitude independent methods. The former utilizes the errors in imaged and cataloged vectors themselves, and the latter using the discrepancy in angles between pairs of vectors from the camera and catalog. The alignment procedure is carried out using Theodolite autocollimator measurements taken off alignment cubes mounted on the Orion frame and also the measurements from the OpNav focal plane. It is assumed that the alignment cubes and OpNav camera are rigidly mounted to the frame so that flexing effects do not significantly alter the orientation of the cubes relative to the OpNav camera.

Samaan, Malak↗
DOE OSTI2026

AWSD Reactive Burn Model for the HMX‐Based High Explosive LX‐04

An Arrhenius–Wescott–Stewart–Davis (AWSD) reactive burn model is applied to describe shock initiation and detonation properties of the HMX-based high explosive LX-04. The parameters in the model are calibrated to data from multiple sources. The thermodynamic equations of state used in the model are calibrated to a combination of thermochemical calculations for HMX and LX-04 as well as experimentally-measured cylinder expansion results for LX-04. The kinetic parameters are calibrated to velocity data from gas gun experiments performed using EDC-32—a high explosive with the same chemical composition as LX-04 but different structural properties, and scaled rate stick data for PBX 9012. The AWSD model is shown to accurately describe the shock initiation and propagation of LX-04. Very good agreement is observed between the available experimental data and the AWSD model output. The presented results constitute an accurate LX-04 reactive burn model for use in engineering-scale models and simulations.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

Computer Model Calibration with Time Series Data Using Deep Learning and Quantile Regression

Computer models play a key role in many scientific and engineering problems. One major source of uncertainty in computer model experiments is input parameter uncertainty. Computer model calibration is a formal statistical procedure to infer input parameters by combining information from model runs and observational data. The existing standard calibration framework suffers from inferential issues when the model output and observational data are high-dimensional dependent data, such as large time series, due to the difficulty in building an emulator and the nonidentifiability between effects from input parameters and data-model discrepancy. To overcome these challenges, we, in this work, propose a new calibration framework based on a deep neural network (DNN) with long short-term memory layers that directly emulates the inverse relationship between the model output and input parameters. Adopting the “learning with noise” idea, we train our DNN model to filter out the effects from data-model discrepancy on input parameter inference. We also formulate a new way to construct interval predictions for DNN using quantile regression to quantify the uncertainty in input parameter estimates. Through a simulation study and real data application with the Weather Research and Forecasting Model Hydrological modeling system (WRF-Hydro), we show our approach can yield accurate point estimates and well-calibrated interval estimates for input parameters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Knowledge-informed deep learning for hydrological model calibration: an application to Coal Creek Watershed in Colorado

Abstract. Deep learning (DL)-assisted inverse mapping has shown promise in hydrological model calibration by directly estimating parameters from observations. However, the increasing computational demand for running the state-of-the-art hydrological model limits sufficient ensemble runs for its calibration. In this work, we present a novel knowledge-informed deep learning method that can efficiently conduct the calibration using a few hundred realizations. The method involves two steps. First, we determine decisive model parameters from a complete parameter set based on the mutual information (MI) between model responses and each parameter computed by a limited number of realizations (∼50). Second, we perform more ensemble runs (e.g., several hundred) to generate the training sets for the inverse mapping, which selects informative model responses for estimating each parameter using MI-based parameter sensitivity. We applied this new DL-based method to calibrate a process-based integrated hydrological model, the Advanced Terrestrial Simulator (ATS), at Coal Creek Watershed, CO. The calibration is performed against observed stream discharge (Q) and remotely sensed evapotranspiration (ET) from the water year 2017 to 2019. Preliminary MI analysis on 50 realizations resulted in a down-selection of 7 out of 14 ATS model parameters. Then, we performed a complete MI analysis on 396 realizations and constructed the inverse mapping from informative responses to each of the selected parameters using a deep neural network. Compared with calibration using observations covering all time steps, the new inverse mapping improves parameter estimations, thus enhancing the performance of ATS forward model runs. The Nash–Sutcliffe efficiency (NSE) of streamflow predictions increases from 0.53 to 0.8 when calibrating against Q alone. Using ET observations, on the other hand, does not show much improvement on the performance of ATS modeling mainly due to both the uncertainty of the remotely sensed product and the insufficient coverage of the model ET ensemble in capturing the observation. By using observed Q only, we further performed a multiyear analysis and show that Q is best simulated (NSE > 0.8) by including in the calibration the dry-year flow dynamics that show more sensitivity to subsurface characteristics than the other wet years. Moreover, when continuing the forward runs till the end of 2021, the calibrated models show similar simulation performances during this evaluation period as the calibration period, demonstrating the ability of the estimated parameters in capturing climate sensitivity. Our success highlights the importance of leveraging data-driven knowledge in DL-assisted hydrological model calibration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Machine learning assisted modeling of mixing timescale for LES/PDF of high-Karlovitz turbulent premixed combustion

Accurate modeling of mixing in the transported probability density function (PDF) method remains a great challenge, especially for turbulent premixed combustion under extreme conditions such as high Karlovitz number Ka. Recently, a power-law based mixing timescale model was developed for the large-eddy simulations (LES)/PDF modeling of high-Ka number turbulent premixed flames. It is found in this work that the power-law mixing timescale model is highly sensitive to the model parameters. It is thus critically needed to develop accurate calibration of these model parameters. The empirical specification of the model parameters developed in Zhang et. al. is found to be inadequate for accurate modeling of the mixing timescale. Here, machine learning is introduced as an attractive alternative in this work for the specification of the model parameters. A high-Ka number DNS jet flame is used as the training and validation of the machine learning models. The choices of the input parameters are discussed and compared for the machine learning models. The effect of differential molecular diffusion on mixing is examined by including the effect of the Lewis number in the training of the machine learning models. The performance of different machine learning algorithms is compared for the specification of the mixing model parameters. Overall, excellent performance of the machine learning models is observed for assisting the mixing modeling. The feasibility, interpretability, applicability, generality, and portability of using machine learning are discussed in general to provide a perspective on applying data-driven machine learning for turbulent combustion modeling studies.

42 ENGINEERING↗
DOE OSTI2021

Binning is Sinning (Supernova Version): The Impact of Self-calibration in Cosmological Analyses with Type Ia Supernovae

Recent cosmological analyses (e.g., JLA, Pantheon) of Type Ia supernovae (SNe Ia) have propagated systematic uncertainties into a covariance matrix and either binned or smoothed the systematic uncertainty vectors in redshift space. We demonstrate that systematic error budgets of these analyses can be improved by a factor of ~ 1.5 × with the use of unbinned and unsmoothed covariance matrices. To understand this, we employ a separate approach that simultaneously fits for cosmological parameters and additional self-calibrating scale parameters that constrain the size of each systematic. We show that the covariance-matrix approach and scale-parameter approach indeed yield equivalent results, implying that in both cases the data can self-calibrate certain systematic uncertainties, but that this ability is hindered when information is binned or smoothed in redshift space. We review the top systematic uncertainties in current analyses and find that the reduction of systematic uncertainties in the unbinned case depends on whether a systematic is solely degenerate with the cosmological model in redshift space or whether it can be described by additional correlations between supernova properties and luminosity. Furthermore, we show that the power of self-calibration increases with the size of the data set, which presents a tremendous opportunity for upcoming analyses of photometrically classified samples, like those of Legacy Survey of Space and Time (LSST) and the Nancy Grace Roman Telescope (NGRST). However, to take advantage of self-calibration in large, photometrically classified samples, we must first address the issue that binning is required in currently used photometric analysis methods.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2020

Terra MODIS: 20 years of on-orbit calibration and performance

Since its launch in December 1999, Terra Moderate Resolution Imaging Spectroradiometer (MODIS) has successfully operated for more than 20 years, with its observations generating a broad range of science data products that have greatly enabled the remote sensing community and users worldwide in their studies of many key geophysical parameters of the Earth’s systems. MODIS collects data in 36 spectral bands, covering wavelengths from 0.41 to 14.4 μm, which are calibrated by a set of onboard calibrators (OBCs). Also contributing to the sensor’s mission-long on-orbit calibration and characterization are near-monthly scheduled lunar observations and multiple time series of the sensor’s responses over select ground targets at a variety of scan angles. To a large extent, the quality of MODIS data products relies strongly on the dedicated efforts to operate and calibrate the instrument, to derive and update calibration parameters, and to develop and implement new calibration strategies and algorithms in response to on-orbit changes of the sensor’s characteristics and its OBC functions. We provide an overview of the Terra MODIS on-orbit operation and calibration activities over the last 20 years, including changes made to extend and preserve the instrument and OBC functions and their operation strategies. It also illustrates the sensor’s on-orbit performance with results derived from its OBC, lunar observations, and select ground targets and discusses major changes in sensor characteristics and corrections applied to the L1B algorithms as well as calibration lookup table updates. To date, the Terra MODIS instrument and its OBCs continue to operate and function normally. Except for those identified prelaunch, most spectral bands and detectors continue to meet their specified calibration requirements. Also discussed in our paper are lessons learned from Terra MODIS operation and calibration, as well as future efforts to further extend and maintain the quality of its long-term data records.

Terra↗
NASA NTRS1982

LANDSAT-D conical scanner evaluation plan

The planned activities involved in the inflight sensor calibration and performance evaluation are discussed and the supporting software requirements are specified. The possible sensor error sources and their effects on sensor measurements are summarized. The methods by which the inflight sensor performance will be analyzed and the sensor modeling parameters will be calibrated are presented. In addition, a brief discussion on the data requirement for the study is provided.

Bilanow, S.↗
DOE OSTI2022

Coupling localized Noah-MP-Crop model with the WRF model improved dynamic crop growth simulation across Northeast China

Croplands play a critical role in regulating the energy and moisture exchanges between the land surface and atmosphere. However, the interactions between cropland and climate are usually poorly represented due to a lack of detailed representation in crop types and field management. Here, we coupled the Noah-MP-Crop model with the state-of-the-art Weather Research and Forecasting (WRF) model to explore and evaluate the crop growth dynamics in response to climate variations across Northeast China. The default parameters of the crop model were not exactly suitable for the agricultural ecosystems in Northeast China. The detailed cropland distribution, and crop phenology parameters including growing degree days (GDD) and planting (harvesting) date were first created using multi-source remote sensing products and reanalysis data, and was then successfully used to simulate the growth and yield for corn and soybean and associated energy exchanges. We also optimized and calibrated other crop parameters using the time-series of the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface products. The modified crop model substantially improved the simulation of crop growth, plant physiology, and biomass accumulation for both corn and soybean. Coupling the localized dynamic crop model into the WRF led to considerable decreases in the simulated mean-absolute-errors (MAEs) and biases of the leaf area index, evapotranspiration, and gross primary production compared with the MODIS observed values. Compared with the statistical yield from each province, the modified crop model underestimated the corn yield from 11.1% to 48.6%, whereas overestimated the soybean yield from 16.5% to 162.6%.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

In-Flight Calibration Processes for the MMS Fluxgate Magnetometers

The calibration effort for the Magnetospheric Multiscale Mission (MMS) Analog Fluxgate (AFG) and DigitalFluxgate (DFG) magnetometers is a coordinated effort between three primary institutions: University of California, LosAngeles (UCLA); Space Research Institute, Graz, Austria (IWF); and Goddard Space Flight Center (GSFC). Since thesuccessful deployment of all 8 magnetometers on 17 March 2015, the effort to confirm and update the groundcalibrations has been underway during the MMS commissioning phase. The in-flight calibration processes evaluatetwelve parameters that determine the alignment, orthogonalization, offsets, and gains for all 8 magnetometers usingalgorithms originally developed by UCLA and the Technical University of Braunschweig and tailored to MMS by IWF,UCLA, and GSFC. We focus on the processes run at GSFC to determine the eight parameters associated with spin tonesand harmonics. We will also discuss the processing flow and interchange of parameters between GSFC, IWF, and UCLA.IWF determines the low range spin axis offsets using the Electron Drift Instrument (EDI). UCLA determines the absolutegains and sensor azimuth orientation using Earth field comparisons. We evaluate the performance achieved for MMS andgive examples of the quality of the resulting calibrations.

Electromagnetic field Measurements↗
DOE OSTI2024

Variational inference of effective range parameters for 3 He− 4 He scattering

We use two different methods, Monte Carlo sampling and variational inference (VI), to perform a Bayesian calibration of the effective-range parameters in 3 He– 4 He elastic scattering. The parameters are calibrated to data from a recent set of 3 He– 4 He elastic scattering differential cross section measurements. Analysis of these data for E lab ≤ 4.3 MeV yields a unimodal posterior for which both methods obtain the same structure. However, the effective-range expansion amplitude does not account for the 7/2 − state of 7 Be so, even after calibration, the description of data at the upper end of this energy range is poor. The data up to E lab = 2.6 MeV can be well described, but calibration to this lower-energy subset of the data yields a bimodal posterior. After adapting VI to treat such a multi-modal posterior we find good agreement between the VI results and those obtained with parallel-tempered Monte Carlo sampling.

effective field theory↗
NASA NTRS1997

The Age of the Surface of Venus

Impact craters on Venus appear to be uniformly and randomly scattered over a once, but no longer, geologically active planet. To first approximation, the planet shows a single surface of a single age. Here we use Monte Carlo cratering simulations to estimate the age of the surface of Venus. The simulations are based on the present populations of Earth-approaching asteroids, Jupiter-family, Halley-family, and long period comets; they use standard Schmidt-Housen crater scalings in the gravity regime; and they describe interaction with the atmosphere using a semi-analytic 'pancake' model that is calibrated to detailed numerical simulations of impactors striking Venus. The lunar and terrestrial cratering records are also simulated. Both of these records suffer from poor statistics. The Moon has few young large craters and fewer still whose ages are known, and the record is biased because small craters tend to look old and large craters tend to look young. The craters of the Earth provide the only reliable ages, but these craters are few, eroded, of uncertain diameter, and statistically incomplete. Together the three cratering records can be inverted to constrain the flux of impacting bodies, crater diameters given impact parameters, and the calibration of atmospheric interactions. The surface age of Venus that results is relatively young. Alternatively, we can use our best estimates for these three input parameters to derive a best estimate for the age of the surface of Venus. Our tentative conclusions are that comets are unimportant, that the lunar and terrestrial crater records are both subject to strong biases, that there is no strong evidence for an increasing cratering flux in recent years, and that that the nominal age of the surface of Venus is about 600 Ma, although the uncertainty is about a factor of two. The chief difference between our estimate and earlier, somewhat younger estimates is that we find that the venusian atmosphere is less permeable to impacting bodies than supposed by earlier studies. An older surface increases the likelihood that Venus is dead.

Zahnle, K. J.↗
NASA NTRS2001

A TRMM-Calibrated Infrared Rainfall Algorithm Over Brazil

An improved version of the Convective/Stratiform Technique (CST) and its application over Brazil are presented. Keeping the major components of traditional CST, we have modified the scheme for classing convective and stratiform rainfall and tested various schemes to eliminate non-raining cirrus clouds. The parameters of the technique are calibrated using Tropical Rainfall Measuring Mission (TRMM) multi-sensor observations, including TMI, PR and Visible Infrared Scanner (VIRS). This technique takes advantage of the high temporal sampling rate of geosynchronous infrared channel and the better instantaneous rain observation of TRMM Microwave Imager (TMI) and PR. Moreover, sparse TMI rain estimates are used to dynamically calibrate the parameters of the technique to improve its performance. The technique is applied to make rainfall estimates on various time scales and to study rainfall statistics such as the distributions of rain intensity and storm duration over a four-month period beginning January 1999. The study period coincides with the TRMM/Large-Scale Biosphere-Atmosphere experiment in Amazonia (LBA) ground validation experiment, and observations from the LBA radar are used to validate the technique. Results show the mean diurnal cycle of precipitation over the LBA area, and are compared to radar data from the Tropical Oceans and Global Atmosphere (TOGA) radar. When the larger geographic region is considered, the analysis of the mean hourly estimates revealed the pronounced effects of rivers, topography and local circulations on the rainfall.

Negri, Andrew J.↗