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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Sudden stratospheric warmings as catastrophes

The sudden stratospheric warming (SSW) process is qualitatively studied using a conceptual and numerical approach guided by catastrophe theory. A simple example of a catastrophe taken from nonlinear dynamics is given, and results from previous modelling studies of SSW are interpreted in light of catastrophe theory. Properties of this theory such as hysteresis, cusp, and triggering essential to SSW are numerically demonstrated using the truncated quasi-geostrophic beta-plane model of Holton and Mass (1976). A qualitative explanation of the transition from the steady regime to the vacillation regime is given for the Holton and Mass model in terms of the topographically induced barotropic Rossby wave instability. Some implications for the simulation and prediction of SSW are discussed.

Chao, W. C.↗
NASA NTRS2000

Oceanic Excitation of Polar Motion During 1992-1994

Two global ocean general circulation models have been used to compute the angular momentum of nontidal oceanic current and sea level height variations: (1) the Princeton Modular Ocean Model (MOM) having 22 vertical layers and a rigid lid, and (2) the Miami Isopycnic-Coordinate Ocean Model (MICOM) having 11 vertical layers with a mixed layer and a free surface. Both models were run on the same 2 degree longitude by 1 degree latitude grid spanning 80 S to 80 N latitude. Following a 10-year spin-up with climatological air-sea fluxes, both models were forced during 1992-1994 with daily wind and heat flux from the NCEP operational analysis and sea surface salinity restoring to Levitus climatology. After correcting for the effects of mass non-conservation in these models, they are shown to predict similar effects on polar motion excitation of sea level height variations, but the effect of currents predicted by MOM is much larger than that predicted by MICOM.

Gross, R.↗
NASA NTRS1997

Optimization of a Parallel Ocean General Circulation Model

Global climate modeling is one of the grand chalenges of computational science, and ocean modeling plays an important role in both understanding the current climatic conditions and predicting the future climate change.

Optimization Ocean Modeling Parallel Computations↗
NASA NTRS1991

Evidence of chaotic pattern in solar flux through a reproducible sequence of period-doubling-type bifurcations

Presented here is a preliminary study of the limits to solar flux intensity prediction, and of whether the general lack of predictability in the solar flux arises from the nonlinear chaotic nature of the Sun's physical activity. Statistical analysis of a chaotic signal can extract only its most gross features, and detailed physical models fail, since even the simplest equations of motion for a nonlinear system can exhibit chaotic behavior. A recent theory by Feigenbaum suggests that nonlinear systems that can be led into chaotic behavior through a sequence of period-doubling bifurcations will exhibit a universal behavior. As the control parameter is increased, the bifurcation points occur in such a way that a proper ratio of these will approach the universal Feigenbaum number. Experimental evidence supporting the applicability of the Feigenbaum scenario to solar flux data is sparse. However, given the hypothesis that the Sun's convection zones are similar to a Rayleigh-Bernard mechanism, we can learn a great deal from the remarkable agreement observed between the prediction by theory (period doubling - a universal route to chaos) and the amplitude decrease of the signal's regular subharmonics. The authors show that period-doubling-type bifurcation is a possible route to a chaotic pattern of solar flux that is distinguishable from the logarithm of its power spectral density. This conclusion is the first positive step toward a reformulation of solar flux by a nonlinear chaotic approach. The ultimate goal of this research is to be able to predict an estimate of the upper and lower bounds for solar flux within its predictable zones. Naturally, it is an important task to identify the time horizons beyond which predictability becomes incompatible with computability.

Ashrafi, S.↗
NASA NTRS1991

Evidence of chaotic pattern in solar flux through a reproducible sequence of period-doubling-type bifurcations

A preliminary study of the limits to solar flux intensity prediction, and of whether the general lack of predictability in the solar flux arises from the nonlinear chaotic nature of the Sun's physical activity is presented. Statistical analysis of a chaotic signal can extract only its most gross features, and detailed physical models fail, since even the simplest equations of motion for a nonlinear system can exhibit chaotic behavior. A recent theory by Feigenbaum suggests that nonlinear systems that can be led into chaotic behavior through a sequence of period-doubling bifurcations will exhibit a universal behavior. As the control parameter is increased, the bifurcation points occur in such a way that a proper ratio of these will approach the universal Feigenbaum number. Experimental evidence supporting the applicability of the Feigenbaum scenario to solar flux data is sparse. However, given the hypothesis that the Sun's convection zones are similar to a Rayleigh-Bernard mechanism, we can learn a great deal from the remarkable agreement observed between the prediction by theory (period doubling - a universal route to chaos) and the amplitude decrease of the signal's regular subharmonics. It is shown that period-doubling-type bifurcation is a possible route to a chaotic pattern of solar flux that is distinguishable from the logarithm of its power spectral density. This conclusion is the first positive step toward a reformulation of solar flux by a nonlinear chaotic approach. The ultimate goal of this research is to be able to predict an estimate of the upper and lower bounds for solar flux within its predictable zones. Naturally, it is an important task to identify the time horizons beyond which predictability becomes incompatible with computability.

Ashrafi, S.↗
NASA NTRS2003

Extension of Liouville Formalism to Postinstability Dynamics

A mathematical formalism has been developed for predicting the postinstability motions of a dynamic system governed by a system of nonlinear equations and subject to initial conditions. Previously, there was no general method for prediction and mathematical modeling of postinstability behaviors (e.g., chaos and turbulence) in such a system. The formalism of nonlinear dynamics does not afford means to discriminate between stable and unstable motions: an additional stability analysis is necessary for such discrimination. However, an additional stability analysis does not suggest any modifications of a mathematical model that would enable the model to describe postinstability motions efficiently. The most important type of instability that necessitates a postinstability description is associated with positive Lyapunov exponents. Such an instability leads to exponential growth of small errors in initial conditions or, equivalently, exponential divergence of neighboring trajectories. The development of the present formalism was undertaken in an effort to remove positive Lyapunov exponents. The means chosen to accomplish this is coupling of the governing dynamical equations with the corresponding Liouville equation that describes the evolution of the flow of error probability. The underlying idea is to suppress the divergences of different trajectories that correspond to different initial conditions, without affecting a target trajectory, which is one that starts with prescribed initial conditions.

Zak, Michail↗
NASA NTRS2005

Thermophysical and Optical Properties of Semiconducting Ga2Te3 Melt

The majority of bulk semiconductor single crystals are presently grown from their melts. The thermophysical and optical properties of the melts provide a fundamental understanding of the melt structure and can be used to optimize the growth conditions to obtain higher quality crystals. In this paper, we report several thermophysical and optical properties for Ga2Te3 melts, such as electrical conductivity, viscosity, and optical transmission for temperatures ranging from the melting point up to approximately 990 C. The conductivity and viscosity of the melts are determined using the transient torque technique. The optical transmission of the melts is measured between the wavelengths of 300 and 2000 nm by an dual beam reversed-optics spectrophotometer. The measured properties are in good agreement with the published data. The conductivities indicate that the Ga2Te3 melt is semiconductor-like. The anomalous behavior in the measured properties are used as an indication of a structural transformation in the Ga2Te3 melt and discussed in terms of Eyring's and Bachinskii's predicted behaviors for homogeneous melts.

Li, Chao↗
NASA NTRS1997

The Influence of Planetary Mass on the Dynamical Lifetime of Planetary Systems

Recent numerical and analytic studies of planetary orbits have demonstrated the importance of resonances and chaos in destabilizing planetary systems. Newton's "clockwork" description of regular, predictable planetary orbits has been replaced by a view in which many systems can have long but finite lifetimes. This new knowledge has altered our perceptions of the later stages of planetary growth and of the stability of planetary systems. Stability criteria are inexact and time dependent. Most previous studies have focused on the effects in initial planetary orbits on the stability of the system. We are conducting an investigation which focuses on the dependence of stability criteria on planetary mass. Synthetic systems are created by increasing the masses of the planets in our Solar System or of the moons of a particular planet; these systems are then integrated until orbit crossing occurs. We have found that over some ranges, the time until orbit crossing varies to a good approximation as a power clothe factor by which the masses of the secondaries arc increased; some scatter occurs as a consequence of vie chaotic nature of orbital evolution. The slope of this power law varies substantially from system to system, and for moons it is mildly dependent on the inclusion of the planet's quadrupole moment in the gravitational potential.

Lissauer, J. J.↗
NASA NTRS2015

Subharmonic Oscillations and Chaos in Dynamic Atomic Force Microscopy

The increasing use of dynamic atomic force microscopy (d-AFM) for nanoscale materials characterization calls for a deeper understanding of the cantilever dynamics influencing scan stability, predictability, and image quality. Model development is critical to such understanding. Renormalization of the equations governing d- AFM provides a simple interpretation of cantilever dynamics as a single spring and mass system with frequency dependent cantilever stiffness and damping parameters. The renormalized model is sufficiently robust to predict the experimentally observed splitting of the free-space cantilever resonance into multiple resonances upon cantilever-sample contact. Central to the model is the representation of the cantilever sample interaction force as a polynomial expansion with coefficients F(sub ij) (i,j = 0, 1, 2) that account for the effective interaction stiffness parameter, the cantilever-to-sample energy transfer, and the amplitude of cantilever oscillation. Application of the Melnikov method to the model equation is shown to predict a homoclinic bifurcation of the Smale horseshoe type leading to a cascade of period doublings with increasing drive displacement amplitude culminating in chaos and loss of image quality. The threshold value of the drive displacement amplitude necessary to initiate subharmonic generation depends on the acoustic drive frequency, the effective damping coefficient, and the nonlinearity of the cantilever-sample interaction force. For parameter values leading to displacement amplitudes below threshold for homoclinic bifurcation other bifurcation scenarios can occur, some of which lead to chaos.

Cantrell, John H.↗
NASA NTRS2016

Light Diffraction by Large Amplitude Ultrasonic Waves in Liquids

Light diffraction from ultrasound, which can be used to investigate nonlinear acoustic phenomena in liquids, is reported for wave amplitudes larger than that typically reported in the literature. Large amplitude waves result in waveform distortion due to the nonlinearity of the medium that generates harmonics and produces asymmetries in the light diffraction pattern. For standing waves with amplitudes above a threshold value, subharmonics are generated in addition to the harmonics and produce additional diffraction orders of the incident light. With increasing drive amplitude above the threshold a cascade of period-doubling subharmonics are generated, terminating in a region characterized by a random, incoherent (chaotic) diffraction pattern. To explain the experimental results a toy model is introduced, which is derived from traveling wave solutions of the nonlinear wave equation corresponding to the fundamental and second harmonic standing waves. The toy model reduces the nonlinear partial differential equation to a mathematically more tractable nonlinear ordinary differential equation. The model predicts the experimentally observed cascade of period-doubling subharmonics terminating in chaos that occurs with increasing drive amplitudes above the threshold value. The calculated threshold amplitude is consistent with the value estimated from the experimental data.

Adler, Laszlo↗
NASA NTRSDate not supplied

End-To-End Uncertainty Quantification with Analytical Derivatives for Design Under Uncertainty

Uncertainty quantification (UQ) is a rapidly growing and evolving discipline, especially within the aerospace community. Performing analysis with UQ can provide decision makers with a wealth of information about a candidate design. However, the value of UQ is fully realized when the information gained during UQ analysis is leveraged in a feedback loop of a design optimization process, often referred to as design under uncertainty. Although design under uncertainty can be a powerful risk mitigation technique, there are a number of roadblocks that prevent its implementation. Two primary factors are computational costs and added complexity of the analysis. High fidelity simulations on the order tens of uncertain variables quickly become computationally infeasible. Also, implementing UQ into an existing multidisciplinary design and optimization (MDO) process often requires extensive knowledge of the UQ methods and careful treatment of the problem formulation. The objective of this work is to address these two primary roadblocks and enable practitioners to efficiently perform design under uncertainty with limited knowledge of the UQ discipline. Methods outlined in this paper demonstrate MDO incorporating UQ into the design process, leveraging an analytic derivative tool chain through the entire optimization. The proposed approach leverages machine learning techniques to generate a differentiable confidence interval output from polynomial chaos models. This technique, coupled with the incorporation of analytical derivatives through the Polynomial Chaos Expansion (PCE) process, eliminates the need to estimate derivatives which are usually obtained from finite difference, complex step, or similar methods. Developing a differentiable confidence interval allows mixed uncertainty problems (both epistemic and aleatory) to be modeled. Without such modeling, these problems cannot accurately predict objective functions containing statistical quantities such as mean and variance. The addition of analytic derivatives to a polynomial chaos-based UQ method decreases the computational costs of performing design under uncertainty by orders of magnitude in comparison with methods such as complex step. The method and codes developed are modular in nature and are a drop-in solution for design under uncertainty within existing MDO problems. A low-fidelity analytical multidisciplinary optimization under uncertainty for a wing design in OpenMDAO is detailed in this paper. This demonstration case will include both objective functions and constraints which are influenced by uncertain parameters.

Ben D Phillips↗
NASA NTRSDate not supplied

End-To-End Uncertainty Quantification with Analytical Derivatives for Design Under Uncertainty

Uncertainty quantification (UQ) is a rapidly growing and evolving discipline, especially within the aerospace community. Performing analysis with UQ can provide decision makers with a wealth of information about a candidate design. However, the value of UQ is fully realized when the information gained during UQ analysis is leveraged in a feedback loop of a design optimization process, often referred to as design under uncertainty. Although design under uncertainty can be a powerful risk mitigation technique, there are a number of roadblocks that prevent its implementation. Two primary factors are computational costs and added complexity of the analysis. High fidelity simulations on the order tens of uncertain variables quickly become computationally infeasible. Also, implementing UQ into an existing multidisciplinary design and optimization (MDO) process often requires extensive knowledge of the UQ methods and careful treatment of the problem formulation. The objective of this work is to address these two primary roadblocks and enable practitioners to efficiently perform design under uncertainty with limited knowledge of the UQ discipline. Methods outlined in this paper demonstrate MDO incorporating UQ into the design process, leveraging an analytic derivative tool chain through the entire optimization. The proposed approach leverages machine learning techniques to generate a differentiable confidence interval output from polynomial chaos models. This technique, coupled with the incorporation of analytical derivatives through the Polynomial Chaos Expansion (PCE) process, eliminates the need to estimate derivatives which are usually obtained from finite difference, complex step, or similar methods. Developing a differentiable confidence interval allows mixed uncertainty problems (both epistemic and aleatory) to be modeled. Without such modeling, these problems cannot accurately predict objective functions containing statistical quantities such as mean and variance. The addition of analytic derivatives to a polynomial chaos-based UQ method decreases the computational costs of performing design under uncertainty by orders of magnitude in comparison with methods such as complex step. The method and codes developed are modular in nature and are a drop-in solution for design under uncertainty within existing MDO problems. A low-fidelity analytical multidisciplinary optimization under uncertainty for a wing design in OpenMDAO is detailed in this paper. This demonstration case will include both objective functions and constraints which are influenced by uncertain parameters.

Ben Phillips↗
NASA NTRS2016

Assessment of Arctic and Antarctic Sea Ice Predictability in CMIP5 Decadal Hindcasts

This paper examines the ability of coupled global climate models to predict decadal variability of Arctic and Antarctic sea ice. We analyze decadal hindcasts/predictions of 11 Coupled Model Intercomparison Project Phase 5 (CMIP5) models. Decadal hindcasts exhibit a large multimodel spread in the simulated sea ice extent, with some models deviating significantly from the observations as the predicted ice extent quickly drifts away from the initial constraint. The anomaly correlation analysis between the decadal hindcast and observed sea ice suggests that in the Arctic, for most models, the areas showing significant predictive skill become broader associated with increasing lead times. This area expansion is largely because nearly all the models are capable of predicting the observed decreasing Arctic sea ice cover. Sea ice extent in the North Pacific has better predictive skill than that in the North Atlantic (particularly at a lead time of 3-7 years), but there is a reemerging predictive skill in the North Atlantic at a lead time of 6-8 years. In contrast to the Arctic, Antarctic sea ice decadal hindcasts do not show broad predictive skill at any timescales, and there is no obvious improvement linking the areal extent of significant predictive skill to lead time increase. This might be because nearly all the models predict a retreating Antarctic sea ice cover, opposite to the observations. For the Arctic, the predictive skill of the multi-model ensemble mean outperforms most models and the persistence prediction at longer timescales, which is not the case for the Antarctic. Overall, for the Arctic, initialized decadal hindcasts show improved predictive skill compared to uninitialized simulations, although this improvement is not present in the Antarctic.

sea ice↗
NASA NTRS1987

Instabilities in a three-level coherently pumped laser

A theory for a coherently pumped, homogeneously broadened laser is developed which predicts instability at excitations 1.6 times threshold. The system exhibits a period-doubling sequence, chaos, and a period-three window.

Ryan, J. C.↗
NASA NTRS2001

Intercomparison of Satellite Derived Gravity Time Series with Inferred Gravity Time Series from TOPEX/POSEIDON Sea Surface Heights and Climatological Model Output

The upcoming GRACE mission promises to open a window on details of the global mass budget that will have remarkable clarity, but it will not directly answer the question of what the state of the Earth's mass budget is over the critical last quarter of the 20th century. To address that problem we must draw upon existing technologies such as SLR, DORIS, and GPS, and climate modeling runs in order to improve our understanding. Analysis of long-period geopotential changes based on SLR and DORIS tracking has shown that addition of post 1996 satellite tracking data has a significant impact on the recovered zonal rates and long-period tides. Interannual effects such as those causing the post 1996 anomalies must be better characterized before refined estimates of the decadal period changes in the geopotential can be derived from the historical database of satellite tracking. A possible cause of this anomaly is variations in ocean mass distribution, perhaps associated with the recent large El Nino/La Nina. In this study, a low-degree spherical harmonic gravity time series derived from satellite tracking is compared with a TOPEX/POSEIDON-derived sea surface height time series. Corrections for atmospheric mass effects, continental hydrology, snowfall accumulation, and ocean steric model predictions will be considered.

Cox, C.↗
NASA NTRS2021

Investigation of several proxies to estimate sulfuric acid concentration in volcanic plume conditions

Sulfuric acid (H2SO4) is commonly accepted as a key precursor for atmospheric new particle formation (NPF). However, direct measurements of [H2SO4] remain challenging, thus preventing the determination of this important quantity, and, consequently, a complete understanding of its contribution to the NPF process. Several proxies have been developed to bridge the gaps, but their ability to predict [H2SO4] in very specific conditions such as those encountered in volcanic plumes (including in particular high sulphur dioxide mixing ratios) has not been evaluated so far. In this context, the main objective of the present study was to develop new proxies for daytime [H2SO4] in volcanic plume conditions and compare their performance to that of the proxies available in the literature. In specific, the data collected at Maïdo during the OCTAVE 2018 campaign, in the volcanic eruption plume of the Piton de la Fournaise, were first used to derive seven proxies based on the knowledge of sulphur dioxide (SO2) mixing ratio, global radiation, condensation sink (CS) and relative humidity (RH). In three of the seven proxies (F1–F3), all variables were given equal weight in the prediction of [H2SO4], while adjusted powers were allowed for the different variables in the other four proxies (A1–A4). Proxies A1–A4 were overall found to perform better compared to F1–F3, with, in specific, improved predictive ability for [H2SO4] > 2 × 108 per cu.cm. The CS was observed to play an important role in regulating [H2SO4], while, in contrast, the inclusion of RH did not improve the predictions. A last expression accounting for an additional sink term related to cluster formation, S1, was also tested and showed a very good predictive ability over the whole range of measured [H2SO4]. The newly developed proxies were in a second step further evaluated using airborne measurements performed in the passive degassing plume of Etna during the STRAP 2016 campaign. Increased correlations between observed and predicted [H2SO4] were obtained when the dependence of predicted [H2SO4] over CS was the lowest, and when the dependence over [SO2] was concurrently the highest. The best predictions were finally retrieved by the simple formulation of F2 (in which [SO2] and radiation alone were assumed to explain the variations of [H2SO4] with equal contributions), with a pre factor adapted to the STRAP data. All in all, our results illustrate the fairly good capacity of the proxy available in the literature to describe [H2SO4] in volcanic plume conditions, but highlight at the same time the benefit of the newly developed proxies for the prediction of the highest concentrations ([H2SO4] > 2–3 × 108 per cu.cm). Also, the contrasting behaviours of the new proxies in the two investigated datasets indicate that in volcanic plumes like in other environments, the relevance of a proxy can be affected by changes in environmental conditions, and that location specific coefficients do logically improve the predictions.

new particle formation↗