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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 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
NASA NTRS1975

Refinement and validation of two digital Microwave Landing System (MLS) theoretical models

Two digital microwave landing system theoretical models are considered which are generic models for the Doppler and scanning-beam frequency reference versions of the MLS. These models represent errors resulting from both system noise and discrete multipath. The data used for the validation effort were obtained from the Texas Instrument conventional scanning beam and the Hazeltine Doppler feasibility hardware versions of the MLS. Topics discussed include tape read software, time history plots, computation of power spectral density, smoothed power spectra, best-fit models, different equations for digital simulation, and discrete multipath errors.

Duff, W. G.↗
NASA NTRS1988

The dynamics of error growth in a quasigeostrophic channel model

The objective of the paper is to determine the extent to which baroclinic instability contributes to the growth of errors in simple, yet realistic models of atmospheric flow. The model used here is a two-level quasi-geostrophic channel model. Results of two predictability experiments are reported. In one experiment, the initial condition perturbation was confined to the highest wavenumbers and had an energy of 1 percent of the climatological energy of the model for these scales. In the other experiment, perturbations were put only in the planetary wave and had the same strength relative to climatology as in the first experiment, leading to much larger absolute errors.

Straus, David M.↗
NASA NTRS1978

Rotorcraft system identification techniques for handling qualities and stability and control evaluation

An integrated approach to rotorcraft system identification is described. This approach consists of sequential application of (1) data filtering to estimate states of the system and sensor errors, (2) model structure estimation to isolate significant model effects, and (3) parameter identification to quantify the coefficient of the model. An input design algorithm is described which can be used to design control inputs which maximize parameter estimation accuracy. Details of each aspect of the rotorcraft identification approach are given. Examples of both simulated and actual flight data processing are given to illustrate each phase of processing. The procedure is shown to provide means of calibrating sensor errors in flight data, quantifying high order state variable models from the flight data, and consequently computing related stability and control design models.

Hall, W. E., Jr.↗
NASA NTRS2005

The Terrestrial Planet Finder coronagraph dynamics error budget

The Terrestrial Planet Finder Coronagraph (TPF-C) demands extreme wave front control and stability to achieve its goal of detecting earth-like planets around nearby stars. We describe the performance models and error budget used to evaluate image plane contrast and derive engineering requirements for this challenging optical system.

coronagraph↗
DOE OSTI2024

Theoretical modeling of a bottom-raised oscillating surge wave energy converter structural loadings and power performances

Here, this study presents theoretical formulations to evaluate the fundamental parameters and performance characteristics of a bottom-raised oscillating surge wave energy converter (OSWEC) device. Employing a flat plate assumption and potential flow formulation in elliptical coordinates, closed-form equations for the added mass, radiation damping, and excitation forces/torques in the relevant pitch-pitch and surge-pitch directions of motion are developed and used to calculate the system's response amplitude operator and the forces and moments acting on the foundation. The model is benchmarked against numerical simulations using WAMIT and WEC-Sim, showcasing excellent agreement. The sensitivity of plate thickness on the analytical hydrodynamic solutions is investigated over several thickness-to-width ratios ranging from 1:80 to 1:10. The results show that as the thickness of the benchmark OSWEC increases, the deviation of the analytical hydrodynamic coefficients from the numerical solutions grows from 3% to 25%. Differences in the excitation forces and torques, however, are contained within 12%. While the flat plate assumption is a limitation of the proposed analytical model, the error is within a reasonable margin for use in the design space exploration phase before a higher-fidelity (and thus more computationally expensive) model is employed. A parametric study demonstrates the ability of the analytical model to quickly sweep over a domain of OSWEC dimensions, illustrating the analytical model's utility in the early phases of design.

13 HYDRO ENERGY↗
DOE OSTI2023

Characterizing Performance of Freshwater Wetland Methane Models Across Time Scales at FLUXNET-CH 4 Sites Using Wavelet Analyses

Process-based land surface models are important tools for estimating global wetland methane (CH 4 ) emissions and projecting their behavior across space and time. So far there are no performance assessments of model responses to drivers at multiple time scales. In this study, we apply wavelet analysis to identify the dominant time scales contributing to model uncertainty in the frequency domain. We evaluate seven wetland models at 23 eddy covariance tower sites. Our study first characterizes site-level patterns of freshwater wetland CH 4 fluxes (FCH 4 ) at different time scales. A Monte Carlo approach was developed to incorporate flux observation error to avoid misidentification of the time scales that dominate model error. Our results suggest that (a) significant model-observation disagreements are mainly at multi-day time scales (<15 days); (b) most of the models can capture the CH 4 variability at monthly and seasonal time scales (>32 days) for the boreal and Arctic tundra wetland sites but have significant bias in variability at seasonal time scales for temperate and tropical/subtropical sites; (c) model errors exhibit increasing power spectrum as time scale increases, indicating that biases at time scales <5 days could contribute to persistent systematic biases on longer time scales; and (d) differences in error pattern are related to model structure (e.g., proxy of CH 4 production). Our evaluation suggests the need to accurately replicate FCH 4 variability, especially at short time scales, in future wetland CH 4 model developments.

58 GEOSCIENCES↗
DOE OSTI2022

Impact of point spread function higher moments error on weak gravitational lensing – II. A comprehensive study

ABSTRACT Weak lensing is one of the most powerful probes for dark matter and dark energy science, although it faces increasing challenges in controlling systematic uncertainties as the statistical errors become smaller. The point spread function (PSF) needs to be precisely modeled to avoid systematic error on the weak lensing measurements. The weak lensing biases induced by errors in the PSF model second moments, i.e. its size and shape, are well-studied. However, Zhang et al. showed that errors in the higher moments of the PSF may also be a significant source of systematics for upcoming weak lensing surveys. Therefore, this work comprehensively investigate the modelling quality of PSF moments from the 3rd to 6th order, and propagate the PSFEx higher moments modelling error in the HSC survey data set to the weak lensing shear–shear correlation functions and their cosmological analyses. The overall multiplicative shear bias associated with errors in PSF higher moments can cause an ∼0.1σ shift on the cosmological parameters for LSST Y10, while the associated additive biases can induce 1σ uncertainties in cosmology parameter inference for LSST Y10, if not accounted. We compare the PSFEx model with PSF in Full FOV, and find similar performance in modelling the PSF higher moments. We conclude that PSF higher moment errors of the future PSF models should be reduced from those in current methods, otherwise needed to be explicitly modeled in the weak lensing analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Embedded Error Bayesian Calibration of Thermal Decomposition of Organic Materials

Organic materials are an attractive choice for structural components due to their light weight and versatility. However, because they decompose at low temperatures relative to tradiational materials they pose a safety risk due to fire and loss of structural integrity. To quantify this risk, analysts use chemical kinetics models to describe the material pyrolysis and oxidation using thermogravimetric analysis. This process requires the calibration of many model parameters to closely match experimental data. Previous efforts in this field have largely been limited to finding a single best-fit set of parameters even though the experimental data may be very noisy. Furthermore the chemical kinetics models are often simplified representations of the true de- composition process. The simplification induces model-form errors that the fitting process cannot capture. In this work we propose a methodology for calibrating decomposition models to thermogravimetric analysis data that accounts for uncertainty in the model-form and experimental data simultaneously. The methodology is applied to the decomposition of a carbon fiber epoxy composite with a three-stage reaction network and Arrhenius kinetics. The results show a good overlap between the model predictions and thermogravimetric analysis data. Uncertainty bounds capture devia- tions of the model from the data. The calibrated parameter distributions are also presented. In conclusion, the distributions may be used in forward propagation of uncertainty in models that leverage this material.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Methods for Incorporating Model Uncertainty into Exoplanet Atmospheric Analysis

A key goal of exoplanet spectroscopy is to measure atmospheric properties, such as abundances of chemical species, in order to connect them to our understanding of atmospheric physics and planet formation. In this new era of high-quality JWST data, it is paramount that these measurement methods are robust. When comparing atmospheric models to observations, multiple candidate models may produce reasonable fits to the data. Typically, conclusions are reached by selecting the best-performing model according to some metric. This ignores model uncertainty in favor of specific model assumptions, potentially leading to measured atmospheric properties that are overconfident and/or incorrect. In this paper, we compare three ensemble methods for addressing model uncertainty by combining posterior distributions from multiple analyses: Bayesian model averaging, a variant of Bayesian model averaging using leave-one-out predictive densities, and stacking of predictive distributions. We demonstrate these methods by fitting the Hubble Space Telescope (HST) + Spitzer transmission spectrum of the hot Jupiter HD 209458b using models with different cloud and haze prescriptions. All of our ensemble methods lead to uncertainties on retrieved parameters that are larger but more realistic and consistent with physical and chemical expectations. Since they have not typically accounted for model uncertainty, uncertainties of retrieved parameters from HST spectra have likely been underreported. We recommend stacking as the most robust model combination method. Our methods can be used to combine results from independent retrieval codes and from different models within one code. They are also widely applicable to other exoplanet analysis processes, such as combining results from different data reductions.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2012

Simulation of Oxygen Disintegration and Mixing With Hydrogen or Helium at Supercritical Pressure

The simulation of high-pressure turbulent flows, where the pressure, p, is larger than the critical value, p(sub c), for the species under consideration, is relevant to a wide array of propulsion systems, e.g. gas turbine, diesel, and liquid rocket engines. Most turbulence models, however, have been developed for atmospheric-p turbulent flows. The difference between atmospheric-p and supercritical-p turbulence is that, in the former situation, the coupling between dynamics and thermodynamics is moderate to negligible, but for the latter it is very significant, and can dominate the flow characteristics. The reason for this stems from the mathematical form of the equation of state (EOS), which is the perfect-gas EOS in the former case, and the real-gas EOS in the latter case. For flows at supercritical pressure, p, the large eddy simulation (LES) equations consist of the differential conservation equations coupled with a real-gas EOS. The equations use transport properties that depend on the thermodynamic variables. Compared to previous LES models, the differential equations contain not only the subgrid scale (SGS) fluxes, but also new SGS terms, each denoted as a correction. These additional terms, typically assumed null for atmospheric pressure flows, stem from filtering the differential governing equations, and represent differences between a filtered term and the same term computed as a function of the filtered flow field. In particular, the energy equation contains a heat-flux correction (q-correction) that is the difference between the filtered divergence of the heat flux and the divergence of the heat flux computed as a function of the filtered flow field. In a previous study, there was only partial success in modeling the q-correction term, but in this innovation, success has been achieved by using a different modeling approach. This analysis, based on a temporal mixing layer Direct Numerical Simulation database, shows that the focus in modeling the q-correction should be on reconstructing the primitive variable gradients rather than their coefficients, and proposes the approximate deconvolution model (ADM) as an effective means of flow field reconstruction for LES heat flux calculation. Further, results for a study conducted for temporal mixing layers initially containing oxygen in the lower stream, and hydrogen or helium in the upper stream, show that, for any LES, including SGS-flux models (constant-coefficient Gradient or Scale-Similarity models, dynamic-coefficient Smagorinsky/Yoshizawa or mixed Smagorinsky/Yoshizawa/Gradient models), the inclusion of the q-correction in the LES leads to the theoretical maximum reduction of the SGS heat-flux difference. The remaining error in modeling this new subgrid term is thus irreducible.

Bellan, Josette↗
NASA NTRS1990

TOPEX/POSEIDON orbit maintenance maneuver design

The Ocean Topography Experiment (TOPEX/POSEIDON) mission orbit requirements are outlined, as well as its control and maneuver spacing requirements including longitude and time targeting. A ground-track prediction model dealing with geopotential, luni-solar gravity, and atmospheric-drag perturbations is considered. Targeting with all modeled perturbations is discussed, and such ground-track prediction errors as initial semimajor axis, orbit-determination, maneuver-execution, and atmospheric-density modeling errors are assessed. A longitude targeting strategy for two extreme situations is investigated employing all modeled perturbations and prediction errors. It is concluded that atmospheric-drag modeling errors are the prevailing ground-track prediction error source early in the mission during high solar flux, and that low solar-flux levels expected late in the experiment stipulate smaller maneuver magnitudes.

Bhat, R. S.↗
NASA NTRS1983

Stratospheric wind errors, initial states and forecast skill in the GLAS general circulation model

Relations between stratospheric wind errors, initial states and 500 mb skill are investigated using the GLAS general circulation model initialized with FGGE data. Erroneous stratospheric winds are seen in all current general circulation models, appearing also as weak shear above the subtropical jet and as cold polar stratospheres. In this study it is shown that the more anticyclonic large-scale flows are correlated with large forecast stratospheric winds. In addition, it is found that for North America the resulting errors are correlated with initial state jet stream accelerations while for East Asia the forecast winds are correlated with initial state jet strength. Using 500 mb skill scores over Europe at day 5 to measure forecast performance, it is found that both poor forecast skill and excessive stratospheric winds are correlated with more anticyclonic large-scale flows over North America. It is hypothesized that the resulting erroneous kinetic energy contributes to the poor forecast skill, and that the problem is caused by a failure in the modeling of the stratospheric energy cycle in current general circulation models independent of vertical resolution.

Tenenbaum, J.↗
DOE OSTI2022

Imperfect slope measurements drive overestimation in a geometric cone model of lake and reservoir depth

Lake and reservoir (waterbody) depth is a critical characteristic that influences many important ecological processes. Unfortunately, depth measurements are labor-intensive to gather and are only available for a small fraction of waterbodies globally. Therefore, scientists have tried to predict depth from characteristics easily obtained for all waterbodies, such as surface area or the slope of the surrounding land. One approach for predicting waterbody depth simulates basins using a geometric cone model where the nearshore land slope and distance to the center of the waterbody are assumed to be representative proxies for in-lake slope and distance to the deepest point respectively. We tested these assumptions using bathymetry data from ~5000 lakes and reservoirs to examine whether differences in waterbody type or shape influenced depth prediction error. Here, we found that nearshore land slope was not representative of in-lake slope, and using it for prediction increases error substantially relative to models using true in-lake slope for all waterbody types and shapes. Predictions were biased toward overprediction in concave waterbodies (i.e., bowl-shaped; up to 18% of the study population) and reservoir waterbodies (up to 30% of the study population). Despite this systematic overprediction, model errors were fewer (in absolute and relative terms, irrespective of any specific slope covariate) for concave than convex waterbodies, suggesting the geometric cone model is an adequate representation of depth for these waterbodies. But because convex waterbodies are far more common (>72% of our study population), minimizing overall depth prediction error remains a challenge.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Spatio-temporal distribution of Oklahoma earthquakes: Exploring relationships using a nearest-neighbor approach: Nearest-neighbor analysis of Oklahoma

Determining the spatiotemporal characteristics of natural and induced seismic events holds the opportunity to gain new insights into why these events occur. Linking the seismicity characteristics with other geologic, geographic, natural, or anthropogenic factors could help to identify the causes and suggest mitigation strategies that reduce the risk associated with such events. The nearest-neighbor approach utilized in this work represents a practical first step toward identifying statistically correlated clusters of recorded earthquake events. Detailed study of the Oklahoma earthquake catalog’s inherent errors, empirical model parameters, and model assumptions is presented. We found that the cluster analysis results are stable with respect to empirical parameters (e.g., fractal dimension) but were sensitive to epicenter location errors and seismicity rates. Most critically, we show that the patterns in the distribution of earthquake clusters in Oklahoma are primarily defined by spatial relationships between events. This observation is a stark contrast to California (also known for induced seismicity) where a comparable cluster distribution is defined by both spatial and temporal interactions between events. These results highlight the difficulty in understanding the mechanisms and behavior of induced seismicity but provide insights for future work.

58 GEOSCIENCES↗
DOE OSTI2022

Adaptive experimental design for multi-fidelity surrogate modeling of multi-disciplinary systems

We present an adaptive algorithm for constructing surrogate models of multi-disciplinary systems composed of a set of coupled components. With this goal we introduce “coupling” variables with a priori unknown distributions that allow surrogates of each component to be built independently. Once built, the surrogates of the components are combined to form an integrated-surrogate that can be used to predict system-level quantities of interest at a fraction of the cost of the original model. The error in the integrated-surrogate is greedily minimized using an experimental design procedure that allocates the amount of training data, used to construct each component-surrogate, based on the contribution of those surrogates to the error of the integrated-surrogate. Here, the multi-fidelity procedure presented is a generalization of multi-index stochastic collocation that can leverage ensembles of models of varying cost and accuracy, for one or more components, to reduce the computational cost of constructing the integrated-surrogate. Extensive numerical results demonstrate that, for a fixed computational budget, our algorithm is able to produce surrogates that are orders of magnitude more accurate than methods that treat the integrated system as a black-box.

42 ENGINEERING↗
NASA NTRS1985

Observing the earth radiation budget from satellites - Past, present, and a look to the future

Satellite measurements of the radiative exchange between the planet earth and space have been the objective of many experiments since the beginning of the space age in the late 1950's. The on-going mission of the Earth Radiation Budget (ERB) experiments has been and will be to consider flight hardware, data handling and scientific analysis methods in a single design strategy. Research and development on observational data has produced an analysis model of errors associated with ERB measurement systems on polar satellites. Results show that the variability of reflected solar radiation from changing meteorology dominates measurement uncertainties. As an application, model calculations demonstrate that measurement requirements for the verification of climate models may be satisfied with observations from one polar satellite, provided there is information on diurnal variations of the radiation budget from the ERBE mission.

House, F. B.↗
NASA NTRS2019

Estimation of coarse dead wood stocks in intact and degraded forests in the Brazilian Amazon using airborne lidar

Coarse dead wood is an important component of forest carbon stocks, but it is rarely measured in Amazon forests and is typically excluded from regional forest carbon budgets. Our study is based on line intercept sampling for fallen coarse dead wood conducted along 103 transects with a total length of 48 km matched with forest inventory plots where standing coarse dead wood was measured in the footprints of larger areas of airborne lidar acquisitions. We developed models to relate lidar metrics and Landsat time series variables to coarse dead wood stocks for intact, logged, burned, or logged and burned forests. Canopy characteristics such as gap area produced significant individual relations for logged forests. For total fallen plus standing coarse dead wood (hereafter defined as total coarse dead wood), the relative root mean square error for models with only lidar metrics ranged from 33 % in logged forest to up to 36 % in burned forests. The addition of historical information improved model performance slightly for intact forests (31 % against 35 % relative root mean square error), not justifying the use of a number of disturbance events from historical satellite images (Landsat) with airborne lidar data. Lidar-derived estimates of total coarse dead wood compared favorably with independent ground-based sampling for areas up to several hundred hectares. The relations found between total coarse dead wood and variables quantifying forest structure derived from airborne lidar highlight the opportunity to quantify this important but rarely measured component of forest carbon over large areas in tropical forests.

Brazilian Amazon↗
DOE OSTI2019

Hierarchical Modeling and Statistical Calibration for Photometric Redshifts

The cosmological exploitation of modern photometric galaxy surveys requires both accurate (unbiased) and precise (narrow) redshift probability distributions derived from broadband photometry. Existing methodologies do not meet these requirements. Standard template fitting delivers interpretable models and errors, but lacks the flexibility to learn inaccuracies in the observed photometry or spectral templates. Machine learning addresses those issues, but requires representative training data, and the resulting models and uncertainties cannot be interpreted in the context of a physical model or outside of the training data. We present a hierarchical modeling approach simultaneously addressing the issues of flexibility, interpretability, and generalization. It combines template fitting with flexible (machine-learning-like) models to correct the spectral templates, model their redshift distributions, and recalibrate the photometric observations. By optimizing the full posterior distribution of the model and solving for its (thousands of) parameters, one can perform a global statistical calibration of the data and the spectral energy distribution (SED) model. We apply this approach to the public Dark Energy Survey Science Verification data and show that it provides more accurate and compact redshift posterior distributions than existing methods, as well as insights into residual photometric and SED systematics. Here, the model is causal and makes predictions for future data (e.g., additional photometric bandpasses), and its internal parameters and components are interpretable. This approach does not formally require the training data to be complete or representative; in principle, it can even work in regimes in which few or no spectroscopic redshifts are available.

79 ASTRONOMY AND ASTROPHYSICS↗