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Necessary and sufficient conditions for resonant mixing of plane waves in elastic solids with quadratic nonlinearity
This paper studies the interactions of two plane waves in elastic solids with quadratic nonlinearity. In particular, the necessary and sufficient conditions for resonant mixing of two plane waves are derived. It is shown that the conventional resonance condition for resonant mixing of plane waves is only a necessary condition, not sufficient. Based on the newly derived necessary and sufficient conditions, resonant mixing of various types of plane waves are investigated and specific conditions for generating a resonant mixed wave are obtained for each case. These results are useful for developing nonlinear ultrasonic nondestructive evaluation techniques using the wave mixing method.
Nonlinear post-compression in multi-pass cells in the mid-IR region using bulk materials
We numerically investigate the regime of nonlinear pulse compression at mid-IR wavelengths in a multi-pass cell (MPC) containing a dielectric plate. This post-compression setup allows for ionization-free spectral broadening and self-compression while mitigating self-focusing effects. We find that self-compression occurs for a wide range of MPC and pulse parameters and derive scaling rules that enable its optimization. We also reveal the solitonic dynamics of the pulse propagation in the MPC and its limitations and show that spatiotemporal/spectral couplings can be mitigated for appropriately chosen parameters. In addition, we reveal the formation of spectral features akin to quasi-phase matched degenerate four-wave mixing. Finally, we present two case studies of self-compression at 3-μm and 6-μm wavelengths using pulse parameters compatible with driving high-field physics experiments. The simulations presented in this paper set a framework for future experimental work using few-cycle pulses at mid-IR wavelengths.
Nonlinear programming methods for solving nonlinear eigenvalue problems
Existence and extendability of continuous eigenvector branches and application of nonlinear programming algorithms to nonlinear heat generation and rotating string problems
Analysis of nonlinear panel flutter and response under random excitation or nonlinear aerodynamic loading
Nonlinear panel flutter analysis and response under random excitation or nonlinear aerodynamic loading, using Rayleigh-Ritz approximation to Hamilton variational principle
Determination of the effects of nozzle nonlinearities upon nonlinear stability of liquid propellant rocket motors
The research is reported concerning the development of a three-dimensional nonlinear nozzle admittance relation to be used as a boundary condition in the nonlinear combustion instability theories for liquid propellant rocket engines. The derivation of the nozzle wave equation and the application of the Galerkin method are discussed along with the nozzle response.
Nonlinear state estimation and feedback control of nonlinear and bilinear distributed parameter systems
This paper presents a theory of nonlinear state observers for nonlinear and bilinear distributed parameter systems. Convergence results are proved for these observers. Linear feedback control derived from such state observers is applied to the distributed parameter system and conditions are presented for closed-loop stability. The emphasis is on finite dimensional state observers and controllers (which can be implemented with on-line computers) and conditions for their successful operation with infinite dimensional distributed parameter systems.
The Design of Stable Nonlinear Controllers for a Class of Nonlinear Plants Based on Neutral Networks
Extensive empirical evidence has been published in the literature to demonstrate the enormous potential of multilayer neural network controllers. In spite of these results, practical implementation of these control schemes has been held back by the lack of an analytical proof of the stability of the controlled system. The objective of this paper is to present a nonlinear control scheme based on multilayer neural networks for the control of a class of nonlinear plants. The stability of the closed loop system will be rigorously established.
Nonlinear Observers for Gyro Calibration Coupled with a Nonlinear Control Algorithm
Nonlinear observers for gyro calibration are presented. The first observer estimates a constant gyro bias. The second observer estimates scale factor errors. The third observer estimates the gyro alignment for three orthogonal gyros. The observers are then combined. The convergence properties of all three observers, and the combined observers, are discussed. Additionally, all three observers are coupled with a nonlinear control algorithm. The stability of each of the resulting closed loop systems is analyzed. Simulated test results are presented for each system.
Nonlinear dynamics, bifurcations, and multi-stability in a vibro-impact system with geometric and multi-segmented freeplay nonlinearities
Freeplay is a common type of piecewise-smooth nonlinearity in dynamical systems, and it can cause discontinuity-induced bifurcations and other behaviors that may bring about undesirable and potentially damaging responses. Prior research has focused on piecewise-smooth systems with two or three distinct regions, but less attention is devoted to systems with more regions (i.e., multi-segmented systems). In this work, numerical analysis is performed on a dynamical system with multi-segmented freeplay, in which there are four stiffness transitions and five distinct regions in the phase space. Here, the effects of the multi-segmented parameters are studied through bifurcation diagram evolution along with induced multi-stable behavior and different bifurcations. These phenomena are interrogated through various tools, such as harmonic balance, basins of attraction, phase planes, and Poincaré section analysis. Results show that among the three multi-segmented parameters, the asymmetry has the strongest effect on the response of the system.
On the input-output stability of time-varying nonlinear feedback systems. II - Conditions involving circles in the frequency plane and sector nonlinearities.
Stability theory based on functional methods, examining feedback system with linear time invariant and nonlinear elements
Dynamic Performance Enhancement for Nonlinear Stochastic Systems Using RBF Driven Nonlinear Compensation with Extended Kalman Filter
In this paper, a novel hybrid control method is proposed to enhance the control performance of the PI based control system for a class of nonlinear and non-Gaussian stochastic dynamic processes with unmeasurable states. Firstly, to enhance the tracking performance of the PI controller where the PI parameters are fixed in many actual control systems, the compensative signal is formed using the extended Kalman filter(EKF) based state estimator and driven by the radial basis function (RBF) neural network based compensator. In addition, the weights of RBF is trained to minimize the entropy criterion of tracking error as the process is subjected to non-Gaussian disturbances. Meanwhile, since the precise statistical property of noises is hard to obtain for many industrial processes, the kernel density estimation (KDE) technique is employed in this paper to estimate the entropy. The convergence of RBF network is discussed and the stability of the resulted closed-loop hybrid control system is analyzed in mean square sense. Finally, a numerical example and a practical system testing are given to illustrate the effectiveness of the proposed control method.
Modeling Nonperturbative Field-Driven Vibronic Dynamics: Selective State Preparation and Nonlinear Spectroscopy
The partially linearized density matrix formalism for nonadiabatic dynamics is adapted to incorporate a classical external electromagentic field into the system Hamiltonian. This advancement encompasses the possibility of describing field-driven dynamics and computing a variety of linear and nonlinear spectroscopic signals beyond the perturbative limit. Here, the capabilities of the developed approach are demonstrated on a simple two-state vibronic model coupled to a bath, for which we (a) perform an exhaustive search in the field parameter space for optimal state preparation and (b) compute time-resolved transient absorption spectroscopy to monitor the effect of different pulse shapes on measurable experimental signals. While no restrictions on the form of the field have to be assumed, we focus here on Gaussian shaped (linearly) chirped pulses.
Pseudodiagonalization Method for Accelerating Nonlinear Subspace Diagonalization in Density Functional Theory
In density functional theory, each self-consistent field (SCF) nonlinear step updates the discretized Kohn-Sham orbitals by solving a linear eigenvalue problem. The concept of pseudodiagonalization is to solve this linear eigenvalue problem approximately, and specifically utilizing a method involving a small number of Jacobi rotations that takes advantage of the good initial guess to the solution given by the approximation to the orbitals from the previous SCF iteration. The approximate solution to the linear eigenvalue problem can be very rapid, particularly for those steps near SCF convergence. Here, we adapt pseudodiagonalization to finite-temperature and metallic systems, where partially-occupied orbitals must be individually resolved with some accuracy. We apply pseudodiagonalization to the subspace eigenvalue problem that arises in Chebyshev-filtered subspace iteration. In tests on metallic and other systems for a range of temperatures, we show that pseudodiagonalization achieves similar rates of SCF convergence to exact diagonalization.
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
It is widely known that neural networks (NNs) are universal approximators of continuous functions. However, a less known but powerful result is that a NN with a single hidden layer can accurately approximate any nonlinear continuous operator. This universal approximation theorem of operators is suggestive of the structure and potential of deep neural networks (DNNs) in learning continuous operators or complex systems from streams of scattered data. Here, in this work, we thus extend this theorem to DNNs. We design a new network with small generalization error, the deep operator network (DeepONet), which consists of a DNN for encoding the discrete input function space (branch net) and another DNN for encoding the domain of the output functions (trunk net). We demonstrate that DeepONet can learn various explicit operators, such as integrals and fractional Laplacians, as well as implicit operators that represent deterministic and stochastic differential equations. We study different formulations of the input function space and its effect on the generalization error for 16 different diverse applications.
The analysis of nonlinear panel flutter and response under random excitation or nonlinear aerodynamic loading
Nonlinear panel flutter for random excitation and linear/nonlinear aerodynamic loading, using Rayleigh-Ritz approximation to Hamilton variational principle
Nonlinear dynamic analysis of structures. Volume 1 - Nonlinear damping in structures Final report
Analytical and experimental study of dynamic responses of structures with nonlinear and nonproportional damping
Parameterizing the nonlinear feedback on ENSO from tropical instability waves (TIWs) by nonlinear eddy thermal diffusivity
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