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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Comparison of Test and Finite Element Analysis for Two Full-Scale Helicopter Crash Tests

Finite element analyses have been performed for two full-scale crash tests of an MD-500 helicopter. The first crash test was conducted to evaluate the performance of a composite deployable energy absorber under combined flight loads. In the second crash test, the energy absorber was removed to establish the baseline loads. The use of an energy absorbing device reduced the impact acceleration levels by a factor of three. Accelerations and kinematic data collected from the crash tests were compared to analytical results. Details of the full-scale crash tests and development of the system-integrated finite element model are briefly described along with direct comparisons of acceleration magnitudes and durations for the first full-scale crash test. Because load levels were significantly different between tests, models developed for the purposes of predicting the overall system response with external energy absorbers were not adequate under more severe conditions seen in the second crash test. Relative error comparisons were inadequate to guide model calibration. A newly developed model calibration approach that includes uncertainty estimation, parameter sensitivity, impact shape orthogonality, and numerical optimization was used for the second full-scale crash test. The calibrated parameter set reduced 2-norm prediction error by 51% but did not improve impact shape orthogonality.

Annett, Martin S.↗
NASA NTRSDate not supplied

Variational Asymptotic Homogenization of Finitely Deformed Viscoelastic-Viscoplastic Composites

The objective of this paper is to develop a constitutive model for finitely deformed viscoelastic-viscoplastic materials and a micromechanics approach to homogenizing composites consisting of such materials. The development of the constitutive model involves establishing a thermodynamic framework based on finite strain theory, developing a viscoelasticity and a viscoplasticity model based on the thermodynamic framework, developing a radial return algorithm based on a classic framework, and deriving a closed-from incremental constitutive relation in the spatial configuration. The development of the micromechanics approach involves pulling-back the above constitutive relation to the material configuration, formulating a variational statement with the resulting constitutive relation, discretizing variational statement in a finite-dimensional space, and solving the discretized variational statement using an Euler–Newton predictor–corrector method. The constitutive model is calibrated via monotonic uniaxial compression tests on a polymer, and the calibrated model is validated by comparing its predictions with the cyclic test data. It is shown capable of characterizing viscoelasticity, viscoplasticity, and complex loading paths. The micromechanics approach’s capabilities are demonstrated through homogenizing a unidirectional fiber-reinforced composite, subjected to uniaxial, biaxial, and shear loading, at different strain rates. It is demonstrated to be capable of handling rate dependence and complex loading paths. The present framework can be further improved by implementing more sophisticated viscoelasticity and viscoplasticity models in future work.

Finite element analysis↗
DOE OSTI2017

Performance Optimization of Alternative Lower Global Warming Potential Refrigerants in Mini-Split Room Air Conditioners

Oak Ridge National laboratory (ORNL) recently conducted extensive laboratory, drop-in investigations for lower Global Warming Potential (GWP) refrigerants to replace R-22 and R-410A. ORNL studied propane, DR-3, ARM-20B, N-20B and R-444B as lower GWP refrigerant replacement for R-22 in a mini-split room air conditioner (RAC) originally designed for R-22; and, R-32, DR-55, ARM-71A, and L41-2, in a mini-split RAC designed for R-410A. We obtained laboratory testing results with very good energy balance and nominal measurement uncertainty. Drop-in studies are not enough to judge the overall performance of the alternative refrigerants since their thermodynamic and transport properties might favor different heat exchanger configurations, e.g. cross-flow, counter flow, etc. This study compares optimized performances of individual refrigerants using a physics-based system model tools. The DOE/ORNL Heat Pump Design Model (HPDM) was used to model the mini-split RACs by inputting detailed heat exchangers geometries, compressor displacement and efficiencies as well as other relevant system components. The RAC models were calibrated against the lab data for each individual refrigerant. The calibrated models were then used to conduct a design optimization for the cooling performance by varying the compressor displacement to match the required capacity, and changing the number of circuits, refrigerant flow direction, tube diameters, air flow rates in the condenser and evaporator at 100% and 50% cooling capacities. This paper compares the optimized performance results for all alternative refrigerants and highlights best candidates for R-22 and R-410A replacement.

GWP↗
NASA NTRS2014

Absolute Calibration of Optical Satellite Sensors Using Libya 4 Pseudo Invariant Calibration Site

The objective of this paper is to report the improvements in an empirical absolute calibration model developed at South Dakota State University using Libya 4 (+28.55 deg, +23.39 deg) pseudo invariant calibration site (PICS). The approach was based on use of the Terra MODIS as the radiometer to develop an absolute calibration model for the spectral channels covered by this instrument from visible to shortwave infrared. Earth Observing One (EO-1) Hyperion, with a spectral resolution of 10 nm, was used to extend the model to cover visible and near-infrared regions. A simple Bidirectional Reflectance Distribution function (BRDF) model was generated using Terra Moderate Resolution Imaging Spectroradiometer (MODIS) observations over Libya 4 and the resulting model was validated with nadir data acquired from satellite sensors such as Aqua MODIS and Landsat 7 (L7) Enhanced Thematic Mapper (ETM+). The improvements in the absolute calibration model to account for the BRDF due to off-nadir measurements and annual variations in the atmosphere are summarized. BRDF models due to off-nadir viewing angles have been derived using the measurements from EO-1 Hyperion. In addition to L7 ETM+, measurements from other sensors such as Aqua MODIS, UK-2 Disaster Monitoring Constellation (DMC), ENVISAT Medium Resolution Imaging Spectrometer (MERIS) and Operational Land Imager (OLI) onboard Landsat 8 (L8), which was launched in February 2013, were employed to validate the model. These satellite sensors differ in terms of the width of their spectral bandpasses, overpass time, off-nadir-viewing capabilities, spatial resolution and temporal revisit time, etc. The results demonstrate that the proposed empirical calibration model has accuracy of the order of 3% with an uncertainty of about 2% for the sensors used in the study.

radiometric calibration↗
DOE OSTI2012

U.S. Department of Energy Office of Legacy Management Calibration Facilities - 12103

This paper describes radiometric calibration facilities located in Grand Junction, Colorado, and at three secondary calibration sites. These facilities are available to the public for the calibration of radiometric field instrumentation for in-situ measurements of radium (uranium), thorium, and potassium. Both borehole and hand-held instruments may be calibrated at the facilities. Aircraft or vehicle mounted systems for large area surveys may be calibrated at the Grand Junction Regional Airport facility. These calibration models are recognized internationally as stable, well-characterized radiation sources for calibration. Calibration models built in other countries are referenced to the DOE models, which are also widely used as a standard for calibration within the U.S. Calibration models are used to calibrate radiation detectors used in uranium exploration, remediation, and homeland security. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

THE EFFECT OF METALLICITY-DEPENDENT T-τ RELATIONS ON CALIBRATED STELLAR MODELS

Mixing length theory is the predominant treatment of convection in stellar models today. Usually described by a single free parameter, α, the common practice is to calibrate it using the properties of the Sun, and apply it to all other stellar models as well. Asteroseismic data from Kepler and CoRoT provide precise properties of other stars which can be used to determine α as well, and a recent study of stars in the Kepler field of view found α to vary with metallicity. Interpreting α obtained from calibrated stellar models, however, is complicated by the fact that the value for α depends on the surface boundary condition of the stellar model, or T-τ relation. Calibrated models that use typical T-τ relations, which are static and insensitive to chemical composition, do not include the complete effect of metallicity on α. We use three-dimensional radiation-hydrodynamic simulations to extract metallicity-dependent T-τ relations and use them in calibrated stellar models. We find the previously reported α-metallicity trend to be robust, and not significantly affected by the surface boundary condition of the stellar models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Application of Genetic Algorithm (GA) Assisted Partial Least Square (PLS) Analysis on Trilinear and Non-trilinear Fluorescence Data Sets to Quantify the Fluorophores in Multifluorophoric Mixtures: Improving Quantification Accuracy of Fluorimetric Estimations of Dilute Aqueous Mixtures

Excitation-emission matrix fluorescence (EEMF) and total synchronous fluorescence spectroscopy (TSFS) are the 2 fluorescence techniques that are commonly used for the analysis of multifluorophoric mixtures. These 2 fluorescence techniques are conceptually different and provide certain advantages over each other. The manual analysis of such highly correlated large volume of EEMF and TSFS towards developing a calibration model is difficult. Partial least square (PLS) analysis can analyze the large volume of EEMF and TSFS data sets by finding important factors that maximize the correlation between the spectral and concentration information for each fluorophore. However, often the application of PLS analysis on entire data sets does not provide a robust calibration model and requires application of suitable pre-processing step. The present work evaluates the application of genetic algorithm (GA) analysis prior to PLS analysis on EEMF and TSFS data sets towards improving the precision and accuracy of the calibration model. The GA algorithm essentially combines the advantages provided by stochastic methods with those provided by deterministic approaches and can find the set of EEMF and TSFS variables that perfectly correlate well with the concentration of each of the fluorophores present in the multifluorophoric mixtures. The utility of the GA assisted PLS analysis is successfully validated using (i) EEMF data sets acquired for dilute aqueous mixture of four biomolecules and (ii) TSFS data sets acquired for dilute aqueous mixtures of four carcinogenic polycyclic aromatic hydrocarbons (PAHs) mixtures. In the present work, it is shown that by using the GA it is possible to significantly improve the accuracy and precision of the PLS calibration model developed for both EEMF and TSFS data set. Hence, GA must be considered as a useful pre-processing technique while developing an EEMF and TSFS calibration model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1988

Effects of switch leakages upon Nimbus-7 SMMR calibration

A calibration model for the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) is studied. This model not only removes major drawbacks of the current calibration model but also helps us understand the performance degradation of the aging instrument. The current Nimbus-7 SMMR calibration algorithm was derived without considering the interference effect between the two orthogonally polarized signals merging at a ferrite polarization selector switch. The resulting calibrated brightness temperatures, considered as a function of scan angle phi, are not symmetric around phi=0. However, neither the origin of the asymmetry nor the manner in which the two orthogonal components are mixed is fully understood. The proposed calibration model incorporates all the leakage factors associated with the ferrite switches along the signal paths. The resulting calibration equations clarify how the orthogonal components of surface brightness are coupled at radiometers. As a consequence, the origin of the asymmetry is clearly identified and explained. In addition, the feasibility of absolute calibration using in-orbit data is discussed.

Han, Daesoo↗
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.↗
NASA NTRS1988

Effects of switch leakages upon Nimbus-7 SMMR calibration

A calibration model for the Nimbus 7 Scanning Multichannel Microwave Radiometer (SMMR) is studied. This model not only removes major drawbacks of the current calibration model but also helps in the understanding of the performance degradation of the aging instrument. The current Nimbus 7 SMMR calibration algorithm was derived without considering the interference effect between the two orthogonally polarized signals merging at a ferrite polarization selector switch. The resulting calibrated brightness temperatures, considered as a function of scan angle, are not symmetric around scan angle = 0. However, neither the origin of the asymmetry nor the manner in which the two orthogonal components are mixed has been fully understood. The new calibration model proposed incorporates all the leakage factors associated with the ferrite switches along the signal paths. The resulting calibration equations clarify how the orthogonal components of surface brightness are coupled at radiometers. As a consequence, the origin of the asymmetry is clearly identified and explained. In addition, the feasibility of absolute calibration using in-orbit data is discussed.

Han, Daesoo↗
DOE OSTI2017

Modeling Soot Oxidation and Gasification with Bayesian Statistics

This paper presents a statistical method for model calibration using data collected from literature. The method is used to calibrate parameters for global models of soot consumption in combustion systems. This consumption is broken into two different submodels: first for oxidation where soot particles are attacked by certain oxidizing agents; second for gasification where soot particles are attacked by H 2 O or CO 2 molecules. Rate data were collected from 19 studies in the literature and evaluated using Bayesian statistics to calibrate the model parameters. Bayesian statistics are valued in their ability to quantify uncertainty in modeling. The calibrated consumption model with quantified uncertainty is presented here along with a discussion of associated implications. The oxidation results are found to be consistent with previous studies. Significant variation is found in the CO 2 gasification rates.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2015

Microbial models with data‐driven parameters predict stronger soil carbon responses to climate change

Abstract Long‐term carbon (C) cycle feedbacks to climate depend on the future dynamics of soil organic carbon ( SOC ). Current models show low predictive accuracy at simulating contemporary SOC pools, which can be improved through parameter estimation. However, major uncertainty remains in global soil responses to climate change, particularly uncertainty in how the activity of soil microbial communities will respond. To date, the role of microbes in SOC dynamics has been implicitly described by decay rate constants in most conventional global carbon cycle models. Explicitly including microbial biomass dynamics into C cycle model formulations has shown potential to improve model predictive performance when assessed against global SOC databases. This study aimed to data‐constrained parameters of two soil microbial models, evaluate the improvements in performance of those calibrated models in predicting contemporary carbon stocks, and compare the SOC responses to climate change and their uncertainties between microbial and conventional models. Microbial models with calibrated parameters explained 51% of variability in the observed total SOC , whereas a calibrated conventional model explained 41%. The microbial models, when forced with climate and soil carbon input predictions from the 5th Coupled Model Intercomparison Project ( CMIP 5), produced stronger soil C responses to 95 years of climate change than any of the 11 CMIP 5 models. The calibrated microbial models predicted between 8% (2‐pool model) and 11% (4‐pool model) soil C losses compared with CMIP 5 model projections which ranged from a 7% loss to a 22.6% gain. Lastly, we observed unrealistic oscillatory SOC dynamics in the 2‐pool microbial model. The 4‐pool model also produced oscillations, but they were less prominent and could be avoided, depending on the parameter values.

Hararuk, Oleksandra↗
DOE OSTI2024

Using a 1D Radially Symmetric Coda Envelope Model for Robust Moment Magnitude ( M w ) Estimation in Iraq’s Tectonically Diverse Zones

Robust estimation of moment magnitude (M w ) can be challenging for Iraq due to the strong lateral heterogeneity across diverse tectonic zones. We aim to improve moment magnitude estimation by investigating the reliability of using a 1D coda envelope model in diverse tectonic zones of different lateral effects and offer a way forward for reliable estimates of M w for small events that are difficult to waveform model. Iraq comprises two main tectonic zones: (1) the Outer platform, consisting of the northwestern Zagros fold-thrust belt and the Mesopotamian foredeep, and (2) the Inner Arabian platform which is overlain by the Iraqi desert. A simple 1D coda envelope model was used because coda waves have a low sensitivity to the source and path heterogeneity. Three separate coda calibrations were conducted to investigate the robustness of a single 1D calibration to fit the country: Whole-region calibration, Zagros calibration, and Mesopotamia calibration. In the whole-region calibration, we used stations from both the Zagros and Mesopotamia zones. In the two other calibration models, we used only stations that were in those particular zones. Ground-truth reference spectra derived from the coda spectral ratio method were used to constrain high-frequency site terms. There was no drastic difference when comparing the moment magnitudes calculated from the waveform modeling and the three calibration models. The results show that the 1D coda envelope model is a reliable method even for a region with diverse tectonic zones. Therefore, we recommend using the whole-region calibration model for moment magnitude estimation that provides more complete path coverage and avoids biases introduced by path correction failures. The proposed calibration is a fundamental step in updating the comprehensive earthquake catalog and probabilistic seismic hazard assessments for Iraq.

58 GEOSCIENCES↗
DOE OSTI2019

Modeling Present and Future Permafrost Distribution at Seward Peninsula, Alaska: Modeling Archive

This dataset contains results from two GIPL runs for RCP4.5 and RCP8.5 across the Seward Peninsula. The model was run on 500 m by 500 m resolution for the period 1901-2100. Meteo variables include forcing air temperature and precipitation as well as modeled maxim and average snow height. This dataset also includes ground temperature at 10 different depths for every grid cell, active layer depth and talik thickness. Auxillary variables include ecotype map and projected latitude and longitude. Ground temperature borehole data that is used for model calibration and validation of GIPL2 for Seward Peninsula 500 m resolution model runs for 1901-2100 time period. Dataset also includes calibrated model parameters for future use. The main purpose of this dataset is the reproducibility of research. Data were produced for regional permafrost distribution assessment for present and future, in addition, model parameters can be used for future model runs on adjacent territories. This dataset contains *.pdf, *.txt, *.csv, and *.nc. files. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Quantifying model structural error: Efficient Bayesian calibration of a regional groundwater flow model using surrogates and a data-driven error model

Groundwater model structural error is ubiquitous, due to simplification and/or misrepresentation of real aquifer systems. During model calibration, the basic hydrogeological parameters may be adjusted to compensate for structural error. This may result in biased predictions when such calibrated models are used to forecast aquifer responses to new forcing. Here, we investigate the impact of model structural error on calibration and prediction of a real-world groundwater flow model, using a Bayesian method with a data-driven error model to explicitly account for model structural error. The error-explicit Bayesian method jointly infers model parameters and structural error and thereby reduces parameter compensation. In this study, Bayesian inference is facilitated using high performance computing and fast surrogate models (based on machine learning techniques) as a substitute for the computationally expensive groundwater model. We demonstrate that with explicit treatment of model structural error, the Bayesian method yields parameter posterior distributions that are substantially different from those derived using classical Bayesian calibration that does not account for model structural error. We also found that the error-explicit Bayesian method gives signficantly more accurate prediction along with reasonable credible intervals. Finally, through variance decomposition, we provide a comprehensive assessment of prediction uncertainty contributed from parameter, model structure, and measurement uncertainty. The results suggest that the error-explicit Bayesian approach provides a solution to real-world modeling applications for which data support the presence of model structural error, yet model deficiency cannot be specifically identified or corrected.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

A comparative survey of current and proposed tropospheric refraction-delay models for DSN radio metric data calibration

The standard tropospheric calibration model implemented in the operational Orbit Determination Program is the seasonal model developed by C. C. Chao in the early 1970's. The seasonal model has seen only slight modification since its release, particularly in the format and content of the zenith delay calibrations. Chao's most recent standard mapping tables, which are used to project the zenith delay calibrations along the station-to-spacecraft line of sight, have not been modified since they were first published in late 1972. This report focuses principally on proposed upgrades to the zenith delay mapping process, although modeling improvements to the zenith delay calibration process are also discussed. A number of candidate approximation models for the tropospheric mapping are evaluated, including the semi-analytic mapping function of Lanyi, and the semi-empirical mapping functions of Davis, et. al.('CfA-2.2'), of Ifadis (global solution model), of Herring ('MTT'), and of Niell ('NMF'). All of the candidate mapping functions are superior to the Chao standard mapping tables and approximation formulas when evaluated against the current Deep Space Network Mark 3 intercontinental very long baselines interferometry database.

Estefan, J. A.↗
NASA NTRS2010

Model Robust Calibration: Method and Application to Electronically-Scanned Pressure Transducers

This article presents the application of a recently developed statistical regression method to the controlled instrument calibration problem. The statistical method of Model Robust Regression (MRR), developed by Mays, Birch, and Starnes, is shown to improve instrument calibration by reducing the reliance of the calibration on a predetermined parametric (e.g. polynomial, exponential, logarithmic) model. This is accomplished by allowing fits from the predetermined parametric model to be augmented by a certain portion of a fit to the residuals from the initial regression using a nonparametric (locally parametric) regression technique. The method is demonstrated for the absolute scale calibration of silicon-based pressure transducers.

Walker, Eric L.↗