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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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NASA NTRS1988

Modeling surface deformations at complex strike-slip plate boundaries

Two new versions of the physical base-traction model of Li and Rice (1987) have been developed to study the loading processes and surface deformation near plate boundaries with geometric complexities, including a shallow creeping fault segment and two subparallel faults. Predictions of surface velocity and surface slip rate for the region between the San Andreas fault and the Pacific coast at the latitude of the Parkfield-Cholame segment using model parameters based on geologic, geodetic, and seismic considerations were found to be in good agreement with contemporary geodetic field data (not used in constrainning model parameters). Also, predicted surface velocity for the Salton Sea-Coachella Valley area were fit to recently derived contemporary geodetic data and very large baseline interferometry data, althoug here the model parameters are not well constrained.

Li, Victor C.↗

NASA NTRS1982

Atmospheric turbulence parameters for modeling wind turbine dynamics

A model which can be used to predict the response of wind turbines to atmospheric turbulence is given. The model was developed using linearized aerodynamics for a three-bladed rotor and accounts for three turbulent velocity components as well as velocity gradients across the rotor disk. Typical response power spectral densities are shown. The system response depends critically on three wind and turbulence parameters, and models are presented to predict desired response statistics. An equation error method, which can be used to estimate the required parameters from field data, is also presented.

Holley, W. E.↗

NASA NTRSDate not supplied

A Gaussian Process Enhancement to Linear Parameter Varying Models

Simulation and analysis for modern engineering systems now routinely requires the merging of multiple disciplines, physical-domains, time-scales, and data sets — all at ever increasing levels. These capabilities are especially needed in the domain of Advanced Air Mobility, where rapidly emerging vehicle designs are significantly more complex, while having to be both cost-effective and safe. To meet these engineering challenges, machine learning methods are an attractive option for merging models and data across multiple areas while providing uncertainty quantification and maintaining computational efficiency. This paper examines the use of Gaussian process machine learning to generalize and enhance the commonly used class of quasi-Linear Parameter Varying models for fast full-envelope simulation while also supporting control system design and analysis with model uncertainty. Gaussian process machine learning is selected because it: can fuse multiple data sets, enables an easy trade-off between data fitting and smoothing, provides model uncertainty quantification, scales well with increasing complexity, and does not generally require starting from a large training data set. To demonstrate the benefits of the approach, a robust stability analysis with Gaussian process uncertainty is shown for a NASA reference design of an electric quad-rotor air-taxi concept vehicle with motor parameter uncertainty.

Gaussian Process↗

NASA NTRS1980

Analysis of pressure spectra measurements in a ducted combustion system

Combustion noise propagation in an operating ducted liquid fuel combustion system is studied in relation to the development of combustion noise prediction and suppression techniques. The presence of combustor emissions in the duct is proposed as the primary mechanism producing the attenuation and dispersion of combustion noise propagating in an operating liquid fuel combustion system. First, a complex mathematical model for calculating attenuation and dispersion taking into account mass transfer, heat transfer, and viscosity effects due to the presence of liquid fuel droplets or solid soot particles is discussed. Next, a simpler single parameter model for calculating pressure auto-spectra and cross-spectra which takes into account dispersion and attenuation due to heat transfer between solid soot particles and air is developed. Then, auto-spectra and cross-spectra obtained from internal pressure measurements in a combustion system consisting of a J-47 combustor can, a spool piece, and a long duct are presented. Last, analytical results obtained with the single parameter model are compared with the experimental measurements. The single parameter model results are shown to be in excellent agreement with the measurements.

Miles, J. H.↗

NASA NTRS2000

Viscoelastic flow modeling in the extrusion of a dough-like fluid

This work attempts to investigate the effect of viscoelasticity and three-dimensional geometry in screw channels. The Phan-Thien Tanner (PTT) constitutive equation with simplified model parameters was solved in conjunction with the flow equations. Polyflow, a commercially available finite element code was used to solve the resulting nonlinear partial differential equations. The PTT model predicted one log scale lower pressure buildup compared to the equivalent Newtonian results. However, the velocity profile did not show significant changes for the chosen PTT model parameters. Past Researchers neglected viscoelastic effects and also the three dimensional nature of the flow in extruder channels. The results of this paper provide a starting point for further simulations using more realistic model parameters, which may enable the food engineer to more accurately scale-up and design extrusion processes.

NASA Discipline Life Support Systems↗

NASA NTRS2016

Developing Atmospheric Retrieval Methods for Direct Imaging Spectroscopy of Gas Giants in Reflected Light I: Methane Abundances and Basic Cloud Properties

Reflected light spectroscopy and photometry of cool, directly imaged extrasolar giant planets are expected to be performed in the next decade by space-based telescopes equipped with optical wavelength coronagraphs and integral field spectrographs, such as the Wide-Field Infrared Survey Telescope (WFIRST). We are developing a new atmospheric retrieval methodology to help assess the science return and inform the instrument design for such future missions, and ultimately interpret the resulting observations. Our retrieval technique employs an albedo model coupled with both a Markov chain Monte Carlo Ensemble Sampler (emcee) and a multimodal nested sampling algorithm (MultiNest) to map the posterior distribution. This combination makes the global evidence calculation more robust for any given model, and highlights possible discrepancies in the likelihood maps. Here we apply this methodology to simulated spectra of cool giant planets. As a proof-of-concept, our current atmospheric model contains 1 or 2 cloud layers, methane as a major absorber, and a H2-He background gas. This 6-to-9 parameter model is appropriate for Jupiter-like planets and can be easily expanded in the future. In addition to deriving the marginal likelihood distribution and confidence intervals for the model parameters, we perform model selection to determine the significance of methane and cloud detection as a function of expected signal-to-noise, in the presence of spectral noise correlations. After internal validation, the method is applied to realistic reflected-light spectra of Jupiter, Saturn, and HD 99492 c, a likely observing target. We find that the presence or absence of clouds and methane can be determined with high accuracy, while parameters uncertainties are model-dependent.

spectroscopy↗

NASA NTRS1997

Transient Finite Element Analyses Developed to Model Fan Containment Impact Events

Research is underway to establish an increased level of confidence in existing numerical techniques for predicting transient behavior when the fan of a jet engine is released and impacts the fan containment system. To evaluate the predictive accuracy that can currently be obtained, researchers at the NASA Lewis Research Center used the DYNA 3D computer code to simulate large-scale subcomponent impact tests that were conducted at the University of Dayton Research Institute (UDRI) Impact Physics Lab. In these tests, 20- by 40-in. flat metal panels, contoured to the shape of a typical fan case, were impacted by the root section of a fan blade. The panels were oriented at an angle to the path of the projectile that would simulate the conditions in an actual blade-out event. The metal panels were modeled in DYNA 3D using a kinematic hardening model with the strain rate dependence of the yield stress governed by the Cowper-Simons rule. Failure was governed by the effective plastic strain criterion. The model of the fan blade and case just after impact is shown. By varying the maximum effective plastic strain, we obtained good qualitative agreement between the model and the experiments. Both the velocity required to penetrate the case and the deflection during impact compared well. This indicates that the failure criterion and constitutive model may be appropriate, but for DYNA 3D to be useful as a predictive tool, methods to determine accurate model parameters must be established. Simple methods for measuring model parameters are currently being developed. In addition, alternative constitutive models and failure criteria are being investigated.

Pereira, J. Michael↗

NASA NTRS1988

On the problem of modeling for parameter identification in distributed structures

Structures are often characterized by parameters, such as mass and stiffness, that are spatially distributed. Parameter identification of distributed structures is subject to many of the difficulties involved in the modeling problem, and the choice of the model can greatly affect the results of the parameter identification process. Analogously to control spillover in the control of distributed-parameter systems, identification spillover is shown to exist as well and its effect is to degrade the parameter estimates. Moreover, as in modeling by the Rayleigh-Ritz method, it is shown that, for a Rayleigh-Ritz type identification algorithm, an inclusion principle exists in the identification of distributed-parameter systems as well, so that the identified natural frequencies approach the actual natural frequencies monotonically from above.

Norris, Mark A.↗

NASA NTRS1989

On the problem of modeling for parameter identification in distributed structures

Norris, Mark A.↗

NASA NTRS1999

Real-Time Parameter Estimation in the Frequency Domain

A method for real-time estimation of parameters in a linear dynamic state space model was developed and studied. The application is aircraft dynamic model parameter estimation from measured data in flight for indirect adaptive or reconfigurable control. Equation error in the frequency domain was used with a recursive Fourier transform for the real-time data analysis. Linear and nonlinear simulation examples and flight test data from the F-18 High Alpha Research Vehicle HARV) were used to demonstrate that the technique produces accurate model parameter estimates with appropriate error bounds. Parameter estimates converged in less than 1 cycle of the dominant dynamic mode natural frequencies, using control surface inputs measured in flight during ordinary piloted maneuvers. The real-time parameter estimation method has low computational requirements, and could be implemented aboard an aircraft in real time.

Morelli, Eugene A.↗

NASA NTRS2022

Exploration of an Adaptive Routine for Battery Modeling

The purpose of this document is to explore the use of adaptive routines in battery modeling. The adaptive routines consist of real-time state estimators combined with battery parameter model components that are adjusted in real-time as battery data becomes available. Several aspects are explored. It is shown that model parameter identification is possible for simple battery models using available input/output data measurements. The online system identification used is recursive least squares. Model identification may be combined with a state observer such as the extended Kalman filter or the unscented Kalman filter to form an adaptive model combined with state estimation. However, such a combination is found to be problematic due to uncertainty, observability and stability issues. This paper is organized as follows. Section 1 introduces adaptive routines and possible roles they play in battery modeling. In Section 2 real-time parameter identification is described with results based on battery data. Section 3 reviews various state estimators and results using a simple battery model. In Section 4 parameter identification and state estimation are combined to form an adaptive routine. Finally, in Section 5 conclusions are drawn and future work is suggested.

Adaptive↗

NASA NTRS1992

A piecewise continuous Timoshenko beam model for the dynamic analysis of tapered beam-like structures

Distributed parameter modeling offers a viable alternative to the finite element approach for modeling large flexible space structures. The introduction of the transfer matrix method into the continuum modeling process provides a very useful tool to facilitate the distributed parameter model applied to some more complex configurations. A uniform Timoshenko beam model for the estimation of the dynamic properties of beam-like structures has given comparable results. But many aeronautical and aerospace structures are comprised of non-uniform sections or sectional properties, such as aircraft wings and satellite antennas. This paper proposes a piecewise continuous Timoshenko beam model which is used for the dynamic analysis of tapered beam-like structures. A tapered beam is divided into several segments of uniform beam elements. Instead of arbitrarily assumed shape functions used in finite element analysis, the closed-form solution of the Timoshenko beam equation is used. Application of the transfer matrix method relates all the elements as a whole. By corresponding boundary conditions and compatible conditions a characteristic equation for the global tapered beam has been developed, from which natural frequencies can be derived. A computer simulation is shown in this paper, and compared with the results obtained from the finite element analysis. While piecewise continuous Timoshenko beam model decreases the number of elements significantly; comparable results to the finite element method are obtained.

Shen, Ji Yao↗

NASA NTRS2022

Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters using a Deep Neural Network

Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) series, have been observing severe convection at 15–60-minute intervals for over 40 years. When properly assessed, such a data record can be valuable in efforts of estimating severe storm risk throughout the diurnal cycle based on automated detection of patterns consistently found atop severe storms. Furthermore, environmental conditions favorable for severe weather are well-known and are thought to be represented well by modern reanalysis products. Promoting resilience against such hazards on local and global scales is a chief goal the NASA Disasters program, which seeks to encourage use of satellite observations to mitigate risk. For instance, hail is the costliest severe weather hazard across the globe in terms of insured loss, but reporting inconsistencies for hail events globally make it difficult to develop models that can quantify the risk. Satellite observation and model reanalysis taken together have the potential to, with reasonable skill and specificity, characterize environmental conditions that are favorable for hazardous weather, and thereby enable creation of hazard climatologie. Such climatologies are particularly useful over regions without extensive radar networks or storm reporting. By mapping the multivariate combination of observed cloud features and reanalysis environmental parameters/indices to United States Next Generation Weather Radar (NEXRAD) radar-estimated Maximum Expected Size of Hail (MESH) by way of a deep neural network (DNN), estimates of likelihood for potentially severe hail can be produced. Such estimates are of greater complexity and efficiency than could be performed with previous multivariate or logistic regression analyses for observed points within convective systems. Statistical distributions of convective parameters from satellite and reanalysis are shown to highlight non-severe/severe class separation for well-known hailstorm predictors, e.g., overshooting cloud top characteristics, deep-layer wind shear, mid-level stability, helicity, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN, which can efficiently produce a quantitative hail risk metric with better than 70% detection rate and under 30% false alarms. These hail classifications can then be aggregated across the satellite record to yield a hazard climatology for hail frequency and severity – knowledge of which is of particular interest to those who manage risk (e.g., insurers) and are seeking opportunities to identify hail-prone regions, particularly in developing nations. This NASA study uses satellite observations and model parameters in a DNN to perform climatological hailstorm analysis in support of catastrophe model development, with the hope of promoting risk resilience particularly in regions without adequate weather radar coverage.

Passive Remote Sensing↗

NASA NTRS1994

Advances in parameter estimation techniques applied to flexible structures

In this work, various parameter estimation techniques are investigated in the context of structural system identification utilizing distributed parameter models and 'measured' time-domain data. Distributed parameter models are formulated using the PDEMOD software developed by Taylor. Enhancements made to PDEMOD for this work include the following: (1) a Wittrick-Williams based root solving algorithm; (2) a time simulation capability; and (3) various parameter estimation algorithms. The parameter estimations schemes will be contrasted using the NASA Mini-Mast as the focus structure.

Maben, Egbert↗

NASA NTRS1994

Microphysical modeling of cirrus. 2: Sensitivity studies

The one-dimensional cirrus model described in part 1 of this issue has been used to study the sensitivity of simulated cirrus microphysical and radiative properties to poorly known model parameters, poorly understood physical processes, and environmental conditions. Model parameters and physical processes investigated include nucleation rate, mode of nucleation (e.g., homogeneous freezing of aerosols and liquid droplets or heterogeneous deposition), ice crystal shape, and coagulation. These studies suggest that the leading sources of uncertainty in the model are the phase change (liquid-solid) energy barrier and the ice-water surface energy which dominate the homogeneous freezing nucleation rate and the coagulation sticking efficiency at low temperatures which controls the production of large ice crystals (radii greater than 100 mcirons). Environmental conditions considered in sensitivity tests were CN size distribution, vertical wind speed, and cloud height. We found that (unlike stratus clouds) variations in the total number of condensation nuclei (NC) have little effect on cirrus microphysical and radiative properties, since nucleation occurs only on the largest CN at the tail of the size distribution. The total number of ice crystals which nucleate has little or no relationship to the number of CN present and depends primarily on the temperature and the cooling rate. Stronger updrafts (more rapid cooling) generate higher ice number densities, ice water content, cloud optical depth, and net radiative forcing. Increasing the height of the clouds in the model leads to an increase in ice number density, a decrease in effective radius, and a decrease in ice water content. The most prominent effect of increasing cloud height was a rapid increase in the net cloud radiative forcing which can be attributed to the change in cloud temperature as well as change in cloud ice size distributions. It has long been recognized that changes in cloud height or cloud area have the greatest potential for causing feedbacks on climate change. Our results suggest that variations in vertical velocity or cloud microphysical changes associatd with cloud height changes may also be important.

Jensen, Eric J.↗

NASA NTRS2008

Uncertainty Analysis of Air Radiation for Lunar Return Shock Layers

By leveraging a new uncertainty markup technique, two risk analysis methods are used to compute the uncertainty of lunar-return shock layer radiation predicted by the High temperature Aerothermodynamic Radiation Algorithm (HARA). The effects of epistemic uncertainty, or uncertainty due to a lack of knowledge, is considered for the following modeling parameters: atomic line oscillator strengths, atomic line Stark broadening widths, atomic photoionization cross sections, negative ion photodetachment cross sections, molecular bands oscillator strengths, and electron impact excitation rates. First, a simplified shock layer problem consisting of two constant-property equilibrium layers is considered. The results of this simplified problem show that the atomic nitrogen oscillator strengths and Stark broadening widths in both the vacuum ultraviolet and infrared spectral regions, along with the negative ion continuum, are the dominant uncertainty contributors. Next, three variable property stagnation-line shock layer cases are analyzed: a typical lunar return case and two Fire II cases. For the near-equilibrium lunar return and Fire 1643-second cases, the resulting uncertainties are very similar to the simplified case. Conversely, the relatively nonequilibrium 1636-second case shows significantly larger influence from electron impact excitation rates of both atoms and molecules. For all cases, the total uncertainty in radiative heat flux to the wall due to epistemic uncertainty in modeling parameters is 30% as opposed to the erroneously-small uncertainty levels (plus or minus 6%) found when treating model parameter uncertainties as aleatory (due to chance) instead of epistemic (due to lack of knowledge).

Kleb, Bil↗

NASA NTRS2000

Real-Time Parameter Estimation in the Frequency Domain

A method for real-time estimation of parameters in a linear dynamic state-space model was developed and studied. The application is aircraft dynamic model parameter estimation from measured data in flight. Equation error in the frequency domain was used with a recursive Fourier transform for the real-time data analysis. Linear and nonlinear simulation examples and flight test data from the F-18 High Alpha Research Vehicle were used to demonstrate that the technique produces accurate model parameter estimates with appropriate error bounds. Parameter estimates converged in less than one cycle of the dominant dynamic mode, using no a priori information, with control surface inputs measured in flight during ordinary piloted maneuvers. The real-time parameter estimation method has low computational requirements and could be implemented

Morelli, Eugene A.↗

NASA NTRS1995

The Singularity Mystery Associated with a Radially Continuous Maxwell Viscoelastic Structure

The singularity problem associated with a radially continuous Maxwell viscoclastic structure is investigated. A special tool called the isolation function is developed. Results calculated using the isolation function show that the discrete model assumption is no longer valid when the viscoelastic parameter becomes a continuous function of radius. Continuous variations in the upper mantle viscoelastic parameter are especially powerful in destroying the mode-like structures. The contribution to the load Love numbers of the singularities is sensitive to the convexity of the viscoelastic parameter models. The difference between the vertical response and the horizontal response found in layered viscoelastic parameter models remains with continuous models.

Fang, Ming↗