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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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A hybrid computer Monte Carlo technique for the simulation and optimization of systems with random parameters is presented. The method is applied to the simultaneous optimization of the means and variances of two parameters in the radar-homing missile problem treated by McGhee and Levine.
In this report we present our findings and outcomes of the NNRDS (analysis of Neural Networks as Random Dynamical Systems) project. The work is largely motivated by the analogy of a large class of neural networks (NNs) with a discretized ordinary differential equation (ODE) schemes. Namely, residual NNs, or ResNets, can be viewed as a discretization of neural ODEs (NODEs) where the NN depth plays the role of the time evolution. We employ several legacy tools from ODE theory, such as stiffness, nonlocality, autonomicity, to enable regularization of ResNets thus improving their generalization capabilities. Furthermore, armed with NN analysis tools borrowed from the ODE theory, we are able to efficiently augment NN predictions with uncertainty overcoming wellknown dimensionality challenges and adding a degree of trust towards NN predictions. Finally, we have developed a Python library QUiNN (Quantification of Uncertainties in Neural Networks) that incorporates improved-architecture ResNets, besides classical feed-forward NNs, and contains wrappers to PyTorch NN models enabling several major classes of uncertainty quantification methods for NNs. Besides synthetic problems, we demonstrate the methods on datasets from climate modeling and materials science.
The various methods that have been studied in the past to allow probabilistic analysis of dynamic response for systems with random parameters are reviewed. Dynamic response may have been obtained deterministically if the variations about the nominal values were small; however, for space structures which require precise pointing, the variations about the nominal values of the structural details and of the environmental conditions are too large to be considered as negligible. These uncertainties are accounted for in terms of probability distributions about their nominal values. The quantities of concern for describing the response of the structure includes displacements, velocities, and the distributions of natural frequencies. The exact statistical characterization of the response would yield joint probability distributions for the response variables. Since the random quantities will appear as coefficients, determining the exact distributions will be difficult at best. Thus, certain approximations will have to be made. A number of techniques that are available are discussed, even in the nonlinear case. The methods that are described were: (1) Liouville's equation; (2) perturbation methods; (3) mean square approximate systems; and (4) nonlinear systems with approximation by linear systems.
This paper discusses the merits of employing the new computer-controlled random environment control systems for fast and efficient large-scale data analysis when they are not being used for test control. Hardware and software requirements are stated. Several of the more familiar frequency functions are defined from the Fourier coefficients, the main product of the Fourier processors used in these control systems. Another function, the probability density function, is defined and suggestions for its application are made. An analysis example is presented. Some programming tips are listed, and the accuracy and reliability of frequency analysis measurements are considered.
Nonlinear system under random excitation, discussing analytic solution method and statistical properties
A method is described for minimizing the sensitivity of multivariable systems to parameter variations. The variable parameters are considered as random variables and their effect is included in a quadratic performance index. The performance index is a weighted sum of the state and control covariances that stem from both the random system disturbances and the parameter uncertainties. The numerical solution of the problem is described and application of the method to several initially sensitive tracking systems is discussed. The sensitivity factor of reduction was typically 2 or 3 over a system based on random system noise only, and yet resulted in state RMS increases of only about a factor of two.
We develop an excited-state real-space renormalization group (RSRG-X) formalism to describe the dynamics of conserved densities in randomly interacting spin-12 systems. Our formalism is suitable for systems with U(1) and Z2 symmetries, and we apply it to chains of randomly positioned spins with dipolar XX+YY interactions, as arise in Rydberg quantum simulators and other platforms. The formalism generates a sequence of effective Hamiltonians that provide approximate descriptions for dynamics on successively smaller energy scales. These effective Hamiltonians involve “superspins”: two-level collective degrees of freedom constructed from (anti)aligned microscopic spins. Conserved densities can then be understood as relaxing via coherent collective spin flips. For the well-studied simpler case of randomly interacting nearest-neighbor XX+YY chains, the superspins reduce to single spins. Our formalism also leads to a numerical method capable of simulating the dynamics up to an otherwise inaccessible combination of large system size and late time. Focusing on disorder-averaged infinite-temperature autocorrelation functions, in particular the spin survival probability Sp¯(t), we demonstrate quantitative agreement between our algorithm and exact diagonalization (ED) at low but nonzero frequencies. Such agreement holds for chains with nearest-neighbor, next-nearest-neighbor, and long-range dipolar interactions. Our results indicate decay of Sp¯(t) slower than any power law and feature no significant deviation from the ∼1/ln2(t) asymptote expected from the infinite-randomness fixed-point of the nearest-neighbor model. We also apply the RSRG-X formalism to two-dimensional long-range systems of moderate size and find slow late-time decay of Sp¯(t).
Effects of turbulence-induced pathlength fluctuations on the accuracy of single-color laser ranging systems are examined. Correlation and structure functions for the path deviations are derived using several proposed models for the variation of the turbulence structure parameter with altitude. For single-color systems, random pathlength fluctuations can limit the accuracy of a range measurement to a few centimeters when the turbulence is strong and the effective propagation path is long (greater than 10 km). Two-color systems can partially correct for the random path fluctuations so that in most cases their accuracy is limited to a few millimeters. However, at low elevation angles for satellite ranging (less than 20 deg) and over long horizontal paths, two-color systems can also have errors approaching a few centimeters.
It is shown how the availability and MTBF (Mean Time Between Failures) of a redundant system with subsystems maintenanced at the points of so-called stationary renewal processes can be determined from the distributions of the intervals between maintenance actions and of the failure-free operating intervals of the subsystems. The results make it possible, for example, to determine the frequency and duration of hidden failure states in computers which are incidentally corrected during the repair of observed failures.
Stability problems in randomly excited dynamic systems, discussing partial differential equations governing evolution of conditional probabilities and expectations
Continuous structural systems stability under random load excitation from linear partial differential equations of motion
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This paper explores some of the boundaries in performance of slotted ALOHA systems by analyzing a simple and almost optimal centrally supervised control. The control results in a very simple Markov chain model and allows an examination of stability, conditional waiting time distribution of transmitting terminals, and many other system measures. The key to the simplicity is to have a probability of successful packet transmission that is independent of the number of transmitting terminals. In considering waiting time, we calculate the mean and other moments of the waiting time of a terminal when it enters the system to find (n - 1) other terminals already there competing for the channel. Under this control, the average time is proportional to n. The control requires exact knowledge of the number of terminals contending for the channel, and hence is not implementable, except as an approximation.
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In 1974, the Random Access Measurement System (RAMS) will be launched aboard the Nimbus F satellite as part of the Tropical Wind, Energy Conversion, and Reference Level Experiment (TWERLE). This paper describes operation and performance of the RAMS instrument, which will provide a means of tracking and collecting data from a large number of instrumented platforms. In operation, the RAMS will perform satellite onboard processing of up to eight simultaneous platform transmissions, following search and detection of the randomly received platform transmissions in a compressed-time expanded-frequency domain. The processed data is stored aboard the satellite for readout every 108 minutes (orbital period), and platform locational coordinates and/or velocity components are determined in a central ground data processing facility.
Tumbling motion system for object demagnetization
A multi-input/output random vibration control algorithm was developed based on system identification concepts derived from random vibration spectral analysis theory. The unique features of the algorithm are: (1) the number of input excitors and the number of output control responses need not be identical; (2) the system inverse response matrix is obtained directly from the input/output spectral matrix; and (3) the system inverse response matrix is updated every control loop cycle to accommodate system amplitude nonlinearities. A laboratory demonstration case of two imputs with three outputs is presented to demonstrate the system capabilities.
Optimization of relay control system disturbed by Gaussian noise of known spectral density