Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “chaos”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

The Origin of the Terra Meridiani Sediments: Volatile Transport and the Formation of Sulfate Bearing Layered Deposits on Mars

The chemistry, sedimentology, and geology of the Meridiani sedimentary deposits are best explained by eolian reworking of the sublimation residue of a large scale ice/dust deposit. This large ice deposit was located in close proximity to Terra Meridiani and incorporated large amounts of dust, sand, and SO2 aerosols generated by impacts and volcanism during early martian history. Sulfate formation and chemical weathering of the initial igneous material is hypothesized to have occurred inside of the ice when the darker mineral grains were heated by solar radiant energy. This created conditions in which small films of liquid water were created in and around the mineral grains. This water dissolved the SO2 and reacted with the mineral grains forming an acidic environment under low water/rock conditions. Subsequent sublimation of this ice deposit left behind large amounts of weathered sublimation residue which became the source material for the eolian process that deposited the Terra Meridiani deposit. The following features of the Meridiani sediments are best explained by this model: The large scale of the deposit, its mineralogic similarity across large distances, the cation-conservative nature of the weathering processes, the presence of acidic groundwaters on a basaltic planet, the accumulation of a thick sedimentary sequence outside of a topographic basin, and the low water/rock ratio needed to explain the presence of very soluble minerals and elements in the deposit. Remote sensing studies have linked the Meridiani deposits to a number of other martian surface features through mineralogic similarities, geomorphic similarities, and regional associations. These include layered deposits in Arabia Terra, interior layered deposits in the Valles Marineris system, southern Elysium/Aeolis, Amazonis Planitia, and the Hellas basin, Aram Chaos, Aureum Chaos, and Ioni Chaos. The common properties shared by these deposits suggest that all of these deposits share a common formation process which must have acted over a large area of Mars. The results of this study suggest a mechanism for volatile transport on Mars without invoking an early greenhouse. They also imply a common formation mechanism for most of the sulfate minerals and layered deposits on Mars, which explains their common occurrence.

Niles, P.B.↗
NASA NTRS2009

Extending Newtonian Dynamics to Include Stochastic Processes

A paper presents further results of continuing research reported in several previous NASA Tech Briefs articles, the two most recent being Stochastic Representations of Chaos Using Terminal Attractors (NPO-41519), [Vol. 30, No. 5 (May 2006), page 57] and Physical Principle for Generation of Randomness (NPO-43822) [Vol. 33, No. 5 (May 2009), page 56]. This research focuses upon a mathematical formalism for describing post-instability motions of a dynamical system characterized by exponential divergences of trajectories leading to chaos (including turbulence as a form of chaos). The formalism involves fictitious control forces that couple the equations of motion of the system with a Liouville equation that describes the evolution of the probability density of errors in initial conditions. These stabilizing forces create a powerful terminal attractor in probability space that corresponds to occurrence of a target trajectory with probability one. The effect in configuration space (ordinary three-dimensional space as commonly perceived) is to suppress exponential divergences of neighboring trajectories without affecting the target trajectory. As a result, the post-instability motion is represented by a set of functions describing the evolution of such statistical quantities as expectations and higher moments, and this representation is stable.

Zak, Michail↗
NASA NTRS2018

Multifidelity Uncertainty Quantification of a Commercial Supersonic Transport

The objective of this work was to develop a multifidelity uncertainty quantification approach for efficient analysis of a commercial supersonic transport. An approach based on non-intrusive polynomial chaos was formulated in which a low-fidelity model could be corrected by any number of high-fidelity models. The formulation and methodology also allows for the addition of uncertainty sources not present in the lower fidelity models. To demonstrate the applicability of the multifidelity polynomial chaos approach, two model problems were explored. The first was supersonic airfoil with three levels of modeling fidelity, each capturing an additional level of physics. The second problem was a commercial supersonic transport. This model had three levels of fidelity that included two different modeling approaches and the addition of physics between the fidelity levels. Both problems illustrate the applicability and significant computational savings of the multifidelity polynomial chaos method.

West, Thomas K., IV↗
NASA NTRSDate not supplied

Efficient Parametric Uncertainty Analysis of an Earth Entry Vehicle Concept Using Least Angle Regression

The objective of this work was to outline and apply an efficient and accurate parametric un-certainty propagation approach to the analysis of convective heating on an Earth entry vehicle concept. The described approach was based on Least Angle Regression used to solve a sparse and underdetermined linear system in the point-collocation non-intrusive polynomial chaos surrogate method. This approach involved an iterative process to computing the non-zero terms of the underlying polynomial chaos model using only enough samples to converge uncertainty interval predictions and Sobol index values based global nonlinear sensitivity estimates. The Earth entry vehicle was analyzed at three points along a representative trajectory for a Mars return mission. 329 sources of uncertainty were identified in the computational fluid dynamics model used to predict the forebody convective heating. These included uncertainty in flow field chemical rates, collision integrals, heats of formation, surface finite rate char model reaction rates, wall roughness height, and the turbulent Schmidt number. Results from this study showed that convective heating uncertainty as high as 50% of the nominal was predicted with only about 50 evaluations of the computational model. This was far fewer than would be required for a sampling-based approach or a full basis polynomial chaos model, which would have required over 50,000 samples. Additionally, results showed that over 90% of the total convective heating uncertainty was due to uncertainty in the N2catalytic rate on the surface, while the remainder of the uncertainty was attributed to the turbulent Schmidt number and the wall roughness uncertainties.

Thomas K West IV↗
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↗
DOE OSTI2025

Krylov complexity in mixed phase space

We investigate the Krylov complexity of thermofield double states in systems with mixed phase space, uncovering a direct correlation with the Brody distribution, which interpolates between Poisson and Wigner statistics. Our analysis spans two-dimensional random matrix models featuring (I) GOE-Poisson and (II) GUE-Poisson transitions and extends to higher-dimensional cases, including a stringy matrix model (GOE-Poisson) and the mass-deformed SYK model (GUE-Poisson). Krylov complexity consistently emerges as a reliable marker of quantum chaos, displaying a characteristic peak in the chaotic regime that gradually diminishes as the Brody parameter approaches zero, signaling a shift toward integrability. These results establish Krylov complexity as a powerful diagnostic of quantum chaos and highlight its interplay with eigenvalue statistics in mixed phase systems.

chaos & nonlinear dynamics↗
DOE OSTI2022

GenMod: A generative modeling approach for spectral representation of PDEs with random inputs

Here, we propose a method for quantifying uncertainty in high-dimensional PDE systems with random parameters, where the number of solution evaluations is small. Parametric PDE solutions are often approximated using a spectral decomposition based on polynomial chaos expansions. For the class of systems we consider (i.e., high dimensional with limited solution evaluations) the coefficients are given by an underdetermined linear system in a regression formulation. This implies additional assumptions, such as sparsity of the coefficient vector, are needed to approximate the solution. Here, we present an approach where we assume the coefficients are close to the range of a generative model that maps from a low to a high dimensional space of coefficients. Our approach is inspired be recent work examining how generative models can be used for compressed sensing in systems with random Gaussian measurement matrices. Using results from PDE theory on coefficient decay rates, we construct an explicit generative model that predicts the polynomial chaos coefficient magnitudes. The algorithm we developed to find the coefficients, which we call GenMod, is composed of two main steps. First, we predict the coefficient signs using Orthogonal Matching Pursuit. Then, we assume the coefficients are within a sparse deviation from the range of a sign-adjusted generative model. This allows us to find the coefficients by solving a nonconvex optimization problem, over the input space of the generative model and the space of sparse vectors. We obtain theoretical recovery results for a Lipschitz continuous generative model and for a more specific generative model, based on coefficient decay rate bounds. We examine three high-dimensional problems and show that, for all three examples, the generative model approach outperforms sparsity promoting methods at small sample sizes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Chaotic Dynamics Driven by Particle-Core Interactions

High-intensity beams in modern linacs are frequently encircled by diffuse halos, which drive sustained particle losses and result in gradual degradation of accelerating structures. In large part, the growth of halos is facilitated by internal space-charge forces within the beams, and detailed characterization of this process constitutes an active area of ongoing research. A partial understanding of dynamics that ensue within space-charge dominated beams is presented by the particle-core interaction paradigm – a mathematical model wherein single particle dynamics, subject to the collective potential of the core, are treated as a proxy for the broader behavior of the beam. In this work, we investigate the conditions for the onset of large-scale chaos within the framework of this model, and demonstrate that the propensity towards stochastic evolution is strongly dependent upon the charge distribution of the beam. In particular, we show that while particle motion within a uniformly charged beam is dominantly regular, rapid deterministic chaos readily arises within space-charge dominated Gaussian beams. Importantly, we find that for sufficiently high values of the beam’s space charge and beam pulsation amplitude, enhanced chaotic mixing between the core and the halo can lead to an enhanced radial diffusion of charged particles. We explain our results from analytic grounds by demonstrating that chaotic motion is driven by the intersection of two principal resonances of the system, and derive the relevant overlap conditions. Additionally, our analysis illuminates a close connection between the mathematical formulation of the particle-core interaction model and the Andoyer family of integrable Hamiltonians

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

The quantum p -spin glass model: a user manual for holographers

We study a large-N bosonic quantum mechanical sigma-model with a spherical target space subject to disordered interactions, more colloquially known as the p-spin spherical model. Replica symmetry is broken at low temperatures and for sufficiently weak quantum fluctuations, which drives the system into a spin glass (SG) phase. The first half of this paper is dedicated to a discussion of this model's thermodynamics, with particular emphasis on the marginally stable SG. This phase exhibits an emergent conformal symmetry in the strong coupling regime, which dictates its thermodynamic properties. It is associated with an extensive number of nearby states in the free energy landscape. We discuss in detail an elegant approximate solution to the SG equations, which interpolates between the conformal regime and an ultraviolet-complete short distance solution. In the second half of this paper we explore the real-time dynamics of the model and uncover quantum chaos as measured by out-of-time-order four-point functions, both numerically and analytically. We find exponential Lyapunov growth, which intricately depends on the model's couplings and becomes strongest in the quantum critical regime. We emphasize that the SG phase also exhibits quantum chaos, albeit with parametrically smaller Lyapunov exponent than in the replica symmetric phase. An analytical calculation in the marginal SG phase suggests that this Lyapunov exponent vanishes in a particular infinite coupling limit. Finally, we comment on the potential meaning of these observations from the perspective of holography.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Scrambling and operator entanglement in local non-Hermitian quantum systems

The breakdown of Lieb-Robinson bounds in local, non-Hermitian quantum systems opens up the possibility for a rich landscape of quantum many-body phenomenology. We elucidate this by studying information scrambling and quantum chaos in non-Hermitian variants of paradigmatic local quantum spin-chain models. We utilize a mixture of exact diagonalization and tensor network techniques for our numerical results and focus on three dynamical quantities: (i) out-of-time-ordered correlators (OTOCs), (ii) operator entanglement of the dynamics, and (iii) entanglement growth following a quench from product initial states. We show that while OTOCs fail to capture information scrambling in a simple, local, non-Hermitian transverse-field Ising model, the closely related operator entanglement is a robust measure of dynamical properties of interest. Moreover, we show that the short-time growth of operator entanglement can generically detect entanglement phase transitions in these systems while its long-time average is shown to be a reliable indicator of quantum chaos and entanglement phases. This allows us to extend operator entanglement-based diagnostics from previous works on closed and open quantum systems, to the new arena of monitored quantum dynamics. Finally, we remark on the efficacy of these dynamical quantities in detecting integrability/chaos in the presence of continuous monitoring.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Parametric model-order-reduction development for unsteady convection

A time-averaged error indicator with POD- h Greedy is developed to drive parametric model order reduction (pMOR) for 2D unsteady natural convection in a high-aspect ratio slot parameterized with the Prandtl number, Rayleigh number, and slot angle with respect to the gravity. The error indicator is extended to accommodate the energy equation and Leray regularization. Despite being two-dimensional and laminar, the target flow regime presents several challenges: 1) there is a bifurcation in the angle parameter space; 2) the solution can be multivalued, even at steady state; and 3) the solution exhibits spatio-temporal chaos at several points in the parameter space. The authors explore several reduced-order models (ROMs) and demonstrate that Leray-regularized Galerkin ROMs provide a robust solution approach for this class of flows. They further demonstrate that error-indicated pMOR can efficiently predict several QOIs, such as mean flow, mean Nusselt number and mean turbulent kinetic energy, even in the presence of a bifurcation. Finally, they show that spatio-temporal chaos can lead to lack of reproducibility in both the full-order model and the reduced-order model and that the variance in the full-order model provides a lower bound on the pMOR error in these cases.

leray regularization↗
NASA NTRS1983

On error detection in the dynamics part of primitive equation models

Rossby-Haurwitz waves have been commonly employed as initial conditions for testing the dynamics part of global atmospheric models. Difficulties arise in connection with the primitive equation (PE) model. Chao and Geller (1982) have suggested that it would be better if normal mode solutions of the linearized primitive equations would be used as initial conditions. However, tests involving normal mode conditions are not adequate for the detection of errors in the nonlinear terms. The present investigation has the objective to review the study conducted by Chao and Geller and to provide tests for the nonlinear terms.

Chao, W. C.↗
NASA NTRS1996

Seismic Excitation of the Polar Motion

The mass redistribution in the earth as a result of an earthquake faulting changes the earth's inertia tensor, and hence its rotation. Using the complete formulae developed by Chao and Gross (1987) based on the normal mode theory, we calculated the earthquake-induced polar motion excitation for the largest 11,015 earthquakes that occurred during 1977.0-1993.6. The seismic excitations in this period are found to be two orders of magnitude below the detection threshold even with today's high precision earth rotation measurements. However, it was calculated that an earthquake of only one tenth the size of the great 1960 Chile event, if happened today, could be comfortably detected in polar motion observations. Furthermore, collectively these seismic excitations have a strong statistical tendency to nudge the pole towards approx. 140 deg E, away from the actually observed polar drift direction. This non-random behavior, similarly found in other earthquake-induced changes in earth rotation and low-degree gravitational field by Chao and Gross (1987), manifests some geodynamic behavior yet to be explored.

Chao, Benjamin Fong↗
NASA NTRS1996

Seismic Excitation of the Polar Motion, 1977-1993

The mass redistribution in the earth as a result of an earthquake faulting changes the earth's inertia tensor, and hence its rotation. Using the complete formulae developed by CHAO and GROSS (1987) based on the normal mode theory, we calculated the earthquake-induced polar motion excitation for the largest 11,015 earthquakes that occurred during 1977.0-1993.6. The seismic excitations in this period are found to be two orders of magnitude below the detection threshold even with today's high precision earth rotation measurements. However, it was calculated that an earthquake of only one tenth the size of the great 1960 Chile event, if happened today, could be comfortably detected in polar motion observations. Furthermore, collectively these seismic excitations have a strong statistical tendency to nudge the pole towards approximately 140deg E, away from the actual observed polar drift direction. This non-random behavior, similarly found in other earthquake-induced changes in earth rotation and low-degree gravitational field by CHAO and GROSS (1987), manifests some geodynamic behavior yet to be explored.

Chao, Benjamin Fong↗
NASA NTRS2008

The Role of Continental-scale Landmass in Monsoons-A GCM Investigation

It was argued by Chao and Chen (2001) that land-sea thermal contrast on the continental scale is not a necessary condition for monsoons and that a monsoon is an ITCZ that have moved into the subtropics in its annual cycle of latitudinal movement. Chao and Chen supported their contention by GCM experiments in which they replaced landmass by ocean and were able to generate monsoons. However, land-sea thermal contrast does exist and must play a role in monsoonal rainfall distribution. Land-sea thermal contrast is one facet of continental-scale landmass. The other important characteristic of landmass is its topography. In this article the roles of landmass in monsoonal rainfall distribution and in middle latitude storm tracks are examined through GCM experiments. Comparison of a set of two GCM experiments in which the sea surface temperature (SST) from observations is prescribed from observations with and without a six-month delay reveals the role of Land-sea thermal contrast. Another set of experiments, which repeats the first set but with topography of all landmass reduced to zero, reveals the role of topography of landmass. These experiments confirm that land-sea thermal contrast is not a necessary condition for monsoons and that a monsoon should be viewed as an ITCZ displaced into the subtropics, instead of a continent-sized giant sea breeze. However, land-sea thermal contrast does have influence on the distribution of monsoonal rainfall. The temperature rise over south Asia as the season moves into summer helps the Asian monsoon to start early. However, this role is not the same as that of the land-sea thermal contrast as in the conventional explanation for the cause of monsoon. The heated landmass in summer contributes to the displacement of ITCZ into the subtropics. Also, the heated landmass in summer, by drawing moisture toward itself, limits the range of the summer storm tracks in the middle latitude oceans. On the ether hand, in winter the landmass does not present a competition for rainfall and thus allow middle latitude storm tracks to expand over the ocean.

Chao, Winston↗
NASA NTRS2008

The Role of Continental-scale Landmass in Monsoonal and Global Precipitation Distribution

It was argued by Chao and Chen (20011) that land-sea thermal contrast on the continental scale is not a necessary condition for monsoons and that a monsoon is an ITCZ that have moved into the subtropics in its annual cycle of latitudinal movement. Chao and Chen supported their contention by GCM experiments in which they replaced landmass by ocean and were able to generate monsoons. However, land-sea thermal contrast does exist and must play a role in monsoonal rainfall distribution. Land-sea thermal contrast is one facet of continental-scale landmass. in this article the roles of land-sea thermal contrast in monsoonal rainfall distribution and in middle latitude storm tracks are examined through GCM experiments. Comparison of a set of two GCM experiments in which the sea surface temperature (SST) from observations is prescribed from observations with and without a six-month delay reveals the role of land-sea thermal contrast. These experiments confirm that land-sea thermal contrast is not a necessary condition for monsoons and that a monsoon should be viewed as an ITCZ displaced into the subtropics, instead of a continent-sized giant sea breeze. However, land-sea thermal contrast does have influence on the distribution of monsoonal rainfall. The temperature rise over south Asia as the season moves into summer helps the Asian monsoon to start early. However, this role is not the same as that of the land-sea thermal contrast as in the conventional explanation for the cause of monsoon. The heated Landmass in summer contributes to the displacement of 1TCZ into the subtropics Also, the heated landmass in summer, by drawing moisture toward itself, limits the range of the summer storm tracks in the middle latitude oceans. On the other band, in winter the landmass does not present a competition for rainfall and thus allow middle latitude storm tracks to expand over the ocean.

Chao, Winston C.↗
NASA NTRS2010

The Origin of Monsoons: The Role of Continental-Scale Landmass

In a challenge to the traditional explanation for the cause of monsoons Chao and Chen (JAS 2001) argued that land-sea thermal contrast is not necessary for the existence of monsoons. However, the question of whether land-sea thermal contrast plays any modifying role still exists. This study tries to answer that question. The result is a more complete theory of the origin of monsoons than that proposed by Chao and Chen. Two criticisms of the traditional explanation for the cause of monsoons do not apply to this theory. They are: 1) no explanation for monsoon onset and retreat, let alone the fact that monsoon onset is much faster than monsoon retreat, and 2) for the South Asian monsoon, land-sea thermal contrast is greatest just prior to monsoon onset, not at the height of the monsoon season. Land-sea thermal contrast acts as a facilitator for monsoon onset. If it does not exist, monsoon onset can still occur but at a later time. Our results are supported by GCM experiments.

Chao, Winston↗