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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

Is there a single origin of life?

The emergence of the first life on the earth is now established as an early event, closely related to the evolving earth. Lack of knowledge of the primitive terrestrial conditions contemporary with the evolving prebolic organic chemistry limits reconstruction techniques. The primitive earth's aqueous history is essential to unraveling this problem. Based on current knowledge of other planets of the solar system, no close analog to the early earth can be expected. It is still not known if there was a second origin or if only earth has life. This may depend upon the question of the survival of information-bearing chemical systems in a dynamic or chaotic environment and the chemical protection afforded within such a system. Water is the central molecule of controversy: the blessing and the curse of the chemist. New and novel chemical mechanisms and systems abound.

Soffen, Gerald A.↗
NASA NTRS1996

Chaos in a Fractional Order Chua System

This report studies the effects of fractional dynamics in chaotic systems. In particular, Chua's system is modified to include fractional order elements. Varying the total system order incrementally from 2.6 to 3.7 demonstrates that systems of 'order' less than three can exhibit chaos as well as other nonlinear behavior. This effectively forces a clarification of the definition of order which can no longer be considered only by the total number of differentiations or by the highest power of the Laplace variable.

Lorenzo, Carl F.↗
NASA NTRS2008

Jet Propulsion Laboratory: Annual Report 2007

Many milestones are celebrated in the business of space exploration, but one of them that arrived this year has particular meaning for us. Half a century ago, on January 31, 1958, the Jet Propulsion Laboratory was responsible for creating America's first satellite, Explorer 1, and joined with the Army to launch it into orbit. That makes 2007 the 50th year we have been sending robotic craft from Earth to explore space. No other event before or since has had such a profound effect on JPL's basic identity, setting it on the path to become the world's leader in robotic solar system exploration. It is not lost on historians that Explorer 1, besides being America's first satellite, was also the first spacecraft from any country to deliver scientific results in its case, the discovery of the Van Allen Radiation Belts that surround Earth. Science, of course, has been the prime motivator for all the dozens of missions that we have lofted into space in the half-century since then. JPL has sent spacecraft to every planet in the solar system from Mercury to Neptune, some of them very sophisticated machines. But in one way or another, they all owe their heritage to the 31-pound bullet-shaped probe JPL shot into space in 1958. Although we have ranged far and wide across the solar system, we have a very strong contingent of satellites and instruments dedicated, like Explorer, to the environment of our home planet. JPL missions have been providing much of the data to establish the facts of global warming - most especially, the melting of ice sheets in Greenland and Antarctica. During the past year, JPL and our parent organization, the California Institute of Technology, have created a task force to focus the special capabilities of the Laboratory and campus on ways to better understand the physics of global change. While Earth is a chaotic and dynamic system capable of large natural variations, evidence is mounting that human activities are playing an increasingly important role. A central piece of this effort is a search for novel energy sources to replace fossil fuels, the combustion of which adds carbon dioxide to our atmosphere. All of this is supported by our worldwide Deep Space Network, which provides the communication link between spacecraft and the ground. In addition, missions are infused with technologies developed by researchers working on projects for non-NASA sponsors as well as on pure research.

Jet Propulsion Laboratory (JPL)↗
NASA NTRSDate not supplied

Ocean-Land Teleconnections and Chaotic Atmospheric Variability in Climate Models, with Implications for Real-World Behavior

This study introduces a unique approach to estimating a fundamental but unmeasurable property of Nature: ρo 2 , the fraction of the variance of a given meteorological variable that is determined by boundary forcing (specifically, SSTs) as opposed to chaotic atmospheric dynamics. Here, we focus on continental monthly-averaged near-surface air temperature, or T2M. Rather than determining the fractions of SST-explained T2M variance found for a specific atmospheric general circulation model (AGCM) and then assuming that these fractions also represent Nature, we perform an analysis that entails: (i) evaluating a specific AGCM’s ability to capture observed variability with an infinite number of ensemble members, (ii) quantifying the degree to which different modeling systems agree on SST-land teleconnections, and (iii) combining these results into our novel estimates of ρo 2 . The derived ρo 2 fields suggest, with caveats, that AGCMs tend to overestimate the fraction of explained T2M variance (the signal-to-total variance ratio) in the Tropics.

Randal D Koster↗
DOE OSTI2024

Chaotic roots of the modular multiplication dynamical system in Shor's algorithm

Shor's factoring algorithm, believed to provide an exponential speedup over classical computation, relies on finding the period of an exactly periodic quantum modular multiplication operator. This exact periodicity is the hallmark of an integrable system, which is paradoxical from the viewpoint of quantum chaos, given that the classical limit of the modular multiplication operator is a highly chaotic system that occupies the “maximally random” Bernoulli level of the classical ergodic hierarchy. In this work, we approach this apparent paradox from a quantum dynamical systems viewpoint, and consider whether signatures of ergodicity and chaos may indeed be encoded in such an “integrable” quantization of a chaotic system. We show that Shor's modular multiplication operator, in specific cases, can be written as a superposition of quantized 𝐴-baker's maps exhibiting more typical signatures of quantum chaos and ergodicity. This work suggests that the integrability of Shor's modular multiplication operator may stem from the interference of other “chaotic” quantizations of the same family of maps, and paves the way for deeper studies on the interplay of integrability, ergodicity, and chaos in and via quantum algorithms.

Dynamical systems↗
DOE OSTI2022

Space-Split Algorithm for Sensitivity Analysis of Discrete Chaotic Systems With Multidimensional Unstable Manifolds

Accurate approximations of the change of a system's output and its statistics with respect to the input are highly desired in computational dynamics. Ruelle's linear response theory provides breakthrough mathematical machinery for computing the linear response of chaotic dynamical systems. In this paper, we propose an algorithm for sensitivity analysis of discrete chaos with an arbitrary number of positive Lyapunov exponents. We combine the concept of perturbation space-splitting, which regularizes Ruelle's original expression, together with measure-based parameterization of the expanding subspace. We use these tools to rigorously derive trajectory-following recursive relations that converge exponentially fast, and construct a memory-efficient Monte Carlo scheme for derivatives of the output statistics. Thanks to the regularization and lack of simplifying assumptions on the system's behavior, our method is immune to the common problems of other popular methods such as the exploding tangent solutions and unphysical shadowing directions. Here, we provide a ready-to-use algorithm, analyze its complexity, and demonstrate several numerical examples of sensitivity computation using physically-inspired low-dimensional systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Distributed deep reinforcement learning for simulation control

Abstract Several applications in the scientific simulation of physical systems can be formulated as control/optimization problems. The computational models for such systems generally contain hyperparameters, which control solution fidelity and computational expense. The tuning of these parameters is non-trivial and the general approach is to manually ‘spot-check’ for good combinations. This is because optimal hyperparameter configuration search becomes intractable when the parameter space is large and when they may vary dynamically. To address this issue, we present a framework based on deep reinforcement learning (RL) to train a deep neural network agent that controls a model solve by varying parameters dynamically. First, we validate our RL framework for the problem of controlling chaos in chaotic systems by dynamically changing the parameters of the system. Subsequently, we illustrate the capabilities of our framework for accelerating the convergence of a steady-state computational fluid dynamics solver by automatically adjusting the relaxation factors of the discretized Navier–Stokes equations during run-time. The results indicate that the run-time control of the relaxation factors by the learned policy leads to a significant reduction in the number of iterations for convergence compared to the random selection of the relaxation factors. Our results point to potential benefits for learning adaptive hyperparameter learning strategies across different geometries and boundary conditions with implications for reduced computational campaign expenses 4 4 Data and codes available at https://github.com/Romit-Maulik/PAR-RL . .

42 ENGINEERING↗
NASA NTRS1988

A physical model for earthquakes. I - Fluctuations and interactions. II - Application to southern California

The idea that earthquakes represent a fluctuation about the long-term motion of plates is expressed mathematically through the fluctuation hypothesis, under which all physical quantities which pertain to the occurance of earthquakes are required to depend on the difference between the present state of slip on the fault and its long-term average. It is shown that under certain circumstances the model fault dynamics undergo a sudden transition from a spatially ordered, temporally disordered state to a spatially disordered, temporally ordered state, and that the latter stages are stable for long intervals of time. For long enough faults, the dynamics are evidently chaotic. The methods developed are then used to construct a detailed model for earthquake dynamics in southern California. The result is a set of slip-time histories for all the major faults, which are similar to data obtained by geological trenching studies. Although there is an element of periodicity to the events, the patterns shift, change and evolve with time. Time scales for pattern evolution seem to be of the order of a thousand years for average recurring intervals of about a hundred years.

Rundle, John B.↗
NASA NTRS1994

Terminal Model Of Newtonian Dynamics

Paper presents study of theory of Newtonian dynamics of terminal attractors and repellers, focusing on issues of reversibility vs. irreversibility and deterministic evolution vs. probabilistic or chaotic evolution of dynamic systems. Theory developed called "terminal dynamics" emphasizes difference between it and classical Newtonian dynamics. Also holds promise for explaining irreversibility, unpredictability, probabilistic behavior, and chaos in turbulent flows, in thermodynamic phenomena, and in other dynamic phenomena and systems.

Zak, Michail↗
NASA NTRS1991

Fractal dimension in nonhyperbolic chaotic scattering

In chaotic scattering there is a Cantor set of input-variable values of zero Lebesgue measure (i.e., zero total length) on which the scattering function is singular. For cases where the dynamics leading to chaotic scattering is nonhyperbolic (e.g., there are Kolmogorov-Arnol'd-Moser tori), the nature of this singular set is fundamentally different from that in the hyperbolic case. In particular, for the nonhyperbolic case, although the singular set has zero total length, strong evidence is presented to show that its fractal dimension is 1.

Lau, Yun-Tung↗
NASA NTRS1993

Modelling chaotic vibrations using NASTRAN

Due to the unavailability and, later, prohibitive cost of the computational power required, many phenomena in nonlinear dynamic systems have in the past been addressed in terms of linear systems. Linear systems respond to periodic inputs with periodic outputs, and may be characterized in the time domain or in the frequency domain as convenient. Reduction to the frequency domain is frequently desireable to reduce the amount of computation required for solution. Nonlinear systems are only soluble in the time domain, and may exhibit a time history which is extremely sensitive to initial conditions. Such systems are termed chaotic. Dynamic buckling, aeroelasticity, fatigue analysis, control systems and electromechanical actuators are among the areas where chaotic vibrations have been observed. Direct transient analysis over a long time period presents a ready means of simulating the behavior of self-excited or externally excited nonlinear systems for a range of experimental parameters, either to characterize chaotic behavior for development of load spectra, or to define its envelope and preclude its occurrence.

Sheerer, T. J.↗
NASA NTRS1995

Lyapunov exponents from CHUA's circuit time series using artificial neural networks

In this paper we present the general problem of identifying if a nonlinear dynamic system has a chaotic behavior. If the answer is positive the system will be sensitive to small perturbations in the initial conditions which will imply that there is a chaotic attractor in its state space. A particular problem would be that of identifying a chaotic oscillator. We present an example of three well known different chaotic oscillators where we have knowledge of the equations that govern the dynamical systems and from there we can obtain the corresponding time series. In a similar example we assume that we only know the time series and, finally, in another example we have to take measurements in the Chua's circuit to obtain sample points of the time series. With the knowledge about the time series the phase plane portraits are plotted and from them, by visual inspection, it is concluded whether or not the system is chaotic. This method has the problem of uncertainty and subjectivity and for that reason a different approach is needed. A quantitative approach is the computation of the Lyapunov exponents. We describe several methods for obtaining them and apply a little known method of artificial neural networks to the different examples mentioned above. We end the paper discussing the importance of the Lyapunov exponents in the interpretation of the dynamic behavior of biological neurons and biological neural networks.

Gonzalez, J. Jesus↗
DOE OSTI2024

Exact enforcement of temporal continuity in sequential physics-informed neural networks

The use of deep learning methods in scientific computing represents a potential paradigm shift in engineering problem solving. One of the most prominent developments is Physics-Informed Neural Networks (PINNs), in which neural networks are trained to satisfy partial differential equations (PDEs). While this method shows promise, the standard version has been shown to struggle in accurately predicting the dynamic behavior of time-dependent problems. To address this challenge, methods have been proposed that decompose the time domain into multiple segments, employing a distinct neural network in each segment and directly incorporating continuity between them in the loss function of the minimization problem. In this work we introduce a method to exactly enforce continuity between successive time segments via a solution ansatz. This hard constrained sequential PINN (HCS-PINN) method is simple to implement and eliminates the need for any loss terms associated with temporal continuity. The method is tested for a number of benchmark problems involving both linear and non-linear PDEs. Examples include various first order time dependent problems in which traditional PINNs struggle, namely advection, Allen–Cahn, and Korteweg–de Vries equations. Furthermore, second and third order time-dependent problems are demonstrated via wave and Jerky dynamics examples, respectively. Notably, the Jerky dynamics problem is chaotic, making the problem especially sensitive to temporal accuracy. Finally, the numerical experiments conducted with the proposed method demonstrated superior convergence and accuracy over both traditional PINNs and the soft-constrained counterparts.

42 ENGINEERING↗
NASA NTRS2002

Parametric Identification of Nonlinear Dynamical Systems

In this project, we looked at the application of harmonic balancing as a tool for identifying parameters (HBID) in a nonlinear dynamical systems with chaotic responses. The main idea is to balance the harmonics of periodic orbits extracted from measurements of each coordinate during a chaotic response. The periodic orbits are taken to be approximate solutions to the differential equations that model the system, the form of the differential equations being known, but with unknown parameters to be identified. Below we summarize the main points addressed in this work. The details of the work are attached as drafts of papers, and a thesis, in the appendix. Our study involved the following three parts: (1) Application of the harmonic balance to a simulation case in which the differential equation model has known form for its nonlinear terms, in contrast to a differential equation model which has either power series or interpolating functions to represent the nonlinear terms. We chose a pendulum, which has sinusoidal nonlinearities; (2) Application of the harmonic balance to an experimental system with known nonlinear forms. We chose a double pendulum, for which chaotic response were easily generated. Thus we confronted a two-degree-of-freedom system, which brought forth challenging issues; (3) A study of alternative reconstruction methods. The reconstruction of the phase space is necessary for the extraction of periodic orbits from the chaotic responses, which is needed in this work. Also, characterization of a nonlinear system is done in the reconstructed phase space. Such characterizations are needed to compare models with experiments. Finally, some nonlinear prediction methods can be applied in the reconstructed phase space. We developed two reconstruction methods that may be considered if the common method (method of delays) is not applicable.

Feeny, Brian↗
NASA NTRS2003

A Time Integration Algorithm Based on the State Transition Matrix for Structures with Time Varying and Nonlinear Properties

A variable order method of integrating the structural dynamics equations that is based on the state transition matrix has been developed. The method has been evaluated for linear time variant and nonlinear systems of equations. When the time variation of the system can be modeled exactly by a polynomial it produces nearly exact solutions for a wide range of time step sizes. Solutions of a model nonlinear dynamic response exhibiting chaotic behavior have been computed. Accuracy of the method has been demonstrated by comparison with solutions obtained by established methods.

Bartels, Robert E.↗
DOE OSTI2021

Multiscale Microtubule Dynamics in Active Nematics

In microtubule-based active nematics, motor-driven extensile motion of microtubule bundles powers chaotic large-scale dynamics. We quantify the interfilament sliding motion both in isolated bundles and in a dense active nematic. The extension speed of an isolated microtubule pair is comparable to the molecular motor stepping speed. In contrast, the net extension in dense 2D active nematics is significantly slower; the interfilament sliding speeds are widely distributed about the average and the filaments exhibit both contractile and extensile relative motion. These measurements highlight the challenge of connecting the extension rate of isolated bundles to the multimotor and multifilament interactions present in a dense 2D active nematic. Furthermore, they also provide quantitative data that is essential for building multiscale models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1988

Dynamics of the Uranian and Saturnian satellite systems - A chaotic route to melting Miranda?

Miranda's anomalously large inclination, in conjunction with the postaccretional resurfacing of both Miranda and Ariel and anomalously large eccentricities characterizing the inner Uranian satellites, are presently held to suggest the disruption of resonant configurations that once existed in this satellite system. Classical analytical methods for the dynamics of resonance are here used to demonstrate how temporary capture into a second- or higher-order resonance can generate large increases in eccentricity and inclination on comparatively short time-scales. Such capture into resonance may result in chaotic motion.

Dermott, Stanley F.↗