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Parametric oscillator

About: Parametric oscillator is a research topic. Over the lifetime, 5836 publications have been published within this topic receiving 95631 citations. The topic is also known as: Parametric excitation.

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Journal ArticleDOI
TL;DR: In this paper, the authors investigated a simple model of a massive inflaton field coupled to another scalar field with the interaction term, and developed the theory of preheating taking into account the expansion of the universe and back reaction of produced particles, including the effects of rescattering.
Abstract: Reheating after inflation occurs due to particle production by the oscillating inflaton field. In this paper we briefly describe the perturbative approach to reheating, and then concentrate on effects beyond the perturbation theory. They are related to the stage of parametric resonance, which we call preheating. It may occur in an expanding universe if the initial amplitude of oscillations of the inflaton field is large enough. We investigate a simple model of a massive inflaton field $\ensuremath{\varphi}$ coupled to another scalar field $\ensuremath{\chi}$ with the interaction term ${g}^{2}{\ensuremath{\varphi}}^{2}{\ensuremath{\chi}}^{2}$. Parametric resonance in this model is very broad. It occurs in a very unusual stochastic manner, which is quite different from parametric resonance in the case when the expansion of the universe is neglected. Quantum fields interacting with the oscillating inflaton field experience a series of kicks which, because of the rapid expansion of the universe, occur with phases uncorrelated to each other. Despite the stochastic nature of the process, it leads to exponential growth of fluctuations of the field $\ensuremath{\chi}$. We call this process stochastic resonance. We develop the theory of preheating taking into account the expansion of the universe and back reaction of produced particles, including the effects of rescattering. This investigation extends our previous study of reheating after inflation. We show that the contribution of the produced particles to the effective potential $V(\ensuremath{\varphi})$ is proportional not to ${\ensuremath{\varphi}}^{2}$, as is usually the case, but to $|\ensuremath{\varphi}|$. The process of preheating can be divided into several distinct stages. In the first stage the back reaction of created particles is not important. In the second stage back reaction increases the frequency of oscillations of the inflaton field, which makes the process even more efficient than before. Then the effects related to scattering of $\ensuremath{\chi}$ particles on the oscillating inflaton field terminate the resonance. We calculate the number density of particles ${n}_{\ensuremath{\chi}}$ produced during preheating and their quantum fluctuations $〈{\ensuremath{\chi}}^{2}〉$ with all back reaction effects taken into account. This allows us to find the range of masses and coupling constants for which one can have efficient preheating. In particular, under certain conditions this process may produce particles with a mass much greater than the mass of the inflaton field.

1,827 citations

Journal Article
TL;DR: In this article, a microwave cavity optomechanical system was realized by coupling the motion of an aluminum membrane to the resonance frequency of a superconducting circuit, and damping and cooling the membrane motion with radiation pressure forces.
Abstract: Accessing the full quantum nature of a macroscopic mechanical oscillator first requires elimination of its classical, thermal motion. The flourishing field of cavity optomechanics provides a nearly ideal architecture for both preparation and detection of mechanical motion at the quantum level. We realize a microwave cavity optomechanical system by coupling the motion of an aluminum membrane to the resonance frequency of a superconducting circuit [1]. By exciting the microwave circuit below its resonance frequency, we damp and cool the membrane motion with radiation pressure forces, analogous to laser cooling of the motion of trapped ions. The microwave excitation serves not only to cool, but also to monitor the displacement of the membrane. A nearly shot-noise limited, Josephson parametric amplifier is used to detect the mechanical sidebands of this microwave excitation and quantify the thermal motion as it is cooled with radiation pressure forces to its quantum ground state [2].

1,126 citations

01 Jan 2003
TL;DR: The author explains the design process and some concepts in structural dynamics, including Hamilton's principle, which guided the development of the piezoelectric beam actuator.
Abstract: Preface to the third edition.- Preface to the second edition.- Preface to the first edition.- 1 Introduction.- 1.1 Active versus passive.- 1.2 Vibration suppression.- 1.3 Smart materials and structures.- 1.4 Control strategies.- 1.4.1 Feedback.- 1.4.2 Feedforward.- 1.5 The various steps of the design.- 1.6 Plant description, error and control budget.- 1.7 Readership and Organization of the book.- 1.8 References.- 1.9 Problems.- 2 Some concepts in structural dynamics.- 2.1 Introduction.- 2.2 Equation of motion of a discrete system.- 2.3 Vibration modes.- 2.4 Modal decomposition.- 2.4.1 Structure without rigid body modes.- 2.4.2 Dynamic flexibility matrix.- 2.4.3 Structure with rigid body modes.- 2.4.4 Example.- 2.5 Collocated control system.- 2.5.1 Transmission zeros and constrained system.- 2.6 Continuous structures.- 2.7 Guyan reduction.- 2.8 Craig-Bampton reduction.- 2.9 References.- 2.10 Problems.- 3 Electromagnetic and piezoelectric transducers.- 3.1 Introduction.- 3.2 Voice coil transducer.- 3.2.1 Proof-mass actuator.- 3.2.2 Geophone.- 3.3 General electromechanical transducer.- 3.3.1 Constitutive equations.- 3.3.2 Self-sensing.- 3.4 Reaction wheels and gyrostabilizers.- 3.5 Smart materials.- 3.6 Piezoelectric transducer.- 3.6.1 Constitutive relations of a discrete transducer.- 3.6.2 Interpretation of k2.- 3.6.3 Admittance of the piezoelectric transducer.- 3.7 References.- 3.8 Problems.- 4 Piezoelectric beam, plate and truss.- 4.1 Piezoelectric material.- 4.1.1 Constitutive relations.- 4.1.2 Coenergy density function.- 4.2 Hamilton's principle.- 4.3 Piezoelectric beam actuator.- 4.3.1 Hamilton's principle.- 4.3.2 Piezoelectric loads.- 4.4 Laminar sensor.- 4.4.1 Current and charge amplifiers.- 4.4.2 Distributed sensor output.- 4.4.3 Charge amplifier dynamics.- 4.5 Spatial modalfilters.- 4.5.1 Modal actuator.- 4.5.2 Modal sensor.- 4.6 Active beam with collocated actuator-sensor.- 4.6.1 Frequency response function.- 4.6.2 Pole-zero pattern.- 4.6.3 Modal truncation.- 4.7 Admittance of a beam with a piezoelectric patch.- 4.8 Piezoelectric laminate.- 4.8.1 Two dimensional constitutive equations.- 4.8.2 Kirchhoff theory.- 4.8.3 Stiffness matrix of a multi-layer elastic laminate.- 4.8.4 Multi-layer laminate with a piezoelectric layer.- 4.8.5 Equivalent piezoelectric loads.- 4.8.6 Sensor output.- 4.8.7 Beam model vs. plate model.- 4.8.8 Additional remarks.- 4.9 Active truss.- 4.9.1 Open-loop transfer function.- 4.9.2 Admittance function.- 4.10 Finite element formulation.- 4.11 References.- 4.12 Problems.- 5 Passive damping with piezoelectric transducers.- 5.1 Introduction.- 5.2 Resistive shunting.- 5.3 Inductive shunting.- 5.4 Switched shunt.- 5.4.1 Equivalent damping ratio.- 5.5 References.- 5.6 Problems.- 6 Collocated versus non-collocated control.- 6.1 Introduction.- 6.2 Pole-zero flipping.- 6.3 The two-mass problem.- 6.3.1 Collocated control.- 6.3.2 Non-collocated control.- 6.4 Notch filter.- 6.5 Effect of pole-zero flipping on the Bode plots.- 6.6 Nearly collocated control system.- 6.7 Non-collocated control systems.- 6.8 The role of damping.- 6.9 References.- 6.10 Problems ..- 7 Active damping with collocated system.- 7.1 Introduction.- 7.2 Lead control.- 7.3 Direct velocity feedback (DVF).- 7.4 Positive Position Feedback (PPF).- 7.5 Integral Force Feedback(IFF).- 7.6 Duality between the Lead and the IFF controllers.- 7.6.1 Root-locus of a single mode.- 7.6.2 Open-loop poles and zeros.- 7.7 Actuator and sensor dynamics.- 7.8 Decentralized control with collocated pairs.- 7.8.1 Cross talk.- 7.8.2 Force actuator and displacement sensor.- 7.8.3 Displacement actuator and force sensor.- 7.9 References.- 7.10 Problems.- 8 Vibration isolation.- 8.1 Introduction.- 8.2 Relaxation isolator.- 8.2.1 Electromagnetic realization.- 8.3 Active isolation.- 8.3.1 Sky-hook damper.- 8.3.2 Integral Force Feedback.- 8.4 Flexible body.- 8.4.1 Free-free beam with isolator.- 8.5 Payload isolation in spacecraft.- 8.5.1 Interaction isolator/attitude control.- 8.5.2 Gough-Stewart platform.- 8.6 Six-axis isolator.- 8.6.1 Relaxation isolator.- 8.6.2 Integral Force Feedback.- 8.6.3 Spherical joints, modal spread.- 8.7 Active vs. passive.- 8.8 Car suspension.- 8.9 References.- 8.10 Problems.- 9 State space approach.- 9.1 Introduction.- 9.2 State space description.- 9.2.1 Single degree of freedom oscillator.- 9.2.2 Flexible structure.- 9.2.3 Inverted pendulum.- 9.3 System transfer function.- 9.3.1 Poles and zeros.- 9.4 Pole placement by state feedback.- 9.4.1 Example: oscillator.- 9.5 Linear Quadratic Regulator.- 9.5.1 Symmetric root locus.- 9.5.2 Inverted pendulum.- 9.6 Observer design.- 9.7 Kalman Filter.- 9.7.1 Inverted pendulum.- 9.8 Reduced order observer.- 9.8.1 Oscillator.- 9.8.2 Inverted pendulum.- 9.9 Separation principle.- 9.10 Transfer function of the compensator.- 9.10.1 The two-mass problem.- 9.11 References.- 9.12 Problems.- 10 Analysis and synthesis in the frequency domain.- 10.1 Gain and phase margins.- 10.2 Nyquist criterion.- 10.2.1 Cauchy's principle.- 10.2.2 Nyquist stability criterion.- 10.3 Nichols chart.- 10.4 Feedback specification for SISO systems.- 10.4.1 Sensitivity.- 10.4.2 Tracking error.- 10.4.3 Performance specification.- 10.4.4 Unstructured uncertainty.- 10.4.5 Robust performance and robust stability.- 10.5 Bode gain-phase relationships.- 10.6 The Bode Ideal Cutoff.- 10.7 Non-minimum phase systems.- 10.8 Usual compensators.- 10.8.1 System type.- 10.8.2 Lead compensator.- 10.8.3 PI compensator.- 10.8.4 Lag compensator.- 10.8.5 PID compensator.- 10.9 Multivariable systems.- 10.9.1 Performance specification.- 10.9.2 Small gain theorem.- 10.9.3 Stability robustness tests.- 10.9.4 Residual dynamics.- 10.10References.- 10.11Problems.- 11 Optimal control.- 11.1 Introduction.- 11.2 Quadratic integral.- 11.3 Deterministic LQR.- 11.4 Stochastic response to a white noise.- 11.4.1 Remark.- 11.5 Stochastic LQR.- 11.6 Asymptotic behavior of the closed-loop.- 11.7 Prescribed degree of stability.- 11.8 Gain and phase margins of the LQR.- 11.9 Full state observer.- 11.9.1 Covariance of the reconstruction error.- 11.10Kalman-Bucy Filter (KBF).- 11.11Linear Quadratic Gaussian (LQG).- 11.12Duality.- 11.13Spillover.- 11.13.1Spillover reduction.- 11.14Loop Transfer Recovery (LTR).- 11.15Integral control with state feedback.- 11.16Frequency shaping.- 11.16.1Frequency-shaped cost functionals.- 11.16.2Noise model ..- 11.17References.- 11.18Problems.- 12 Controllability and Observability.- 12.1 Introduction.- 12.1.1 Definitions.- 12.2 Controllability and observability matrices.- 12.3 Examples.- 12.3.1 Cart with two inverted pendulums.- 12.3.2 Double inverted pendulum.- 12.3.3 Two d.o.f. oscillator.- 12.4 State transformation.- 12.4.1 Control canonical form.- 12.4.2 Left and right eigenvectors.- 12.4.3 Diagonal form.- 12.5 PBH test.- 12.6 Residues.- 12.7 Example.- 12.8 Sensitivity.- 12.9 Controllability and observability Gramians.- 12.10Internally balanced coordinates.- 12.11Model reduction.- 12.11.1Transfer equivalent realization.- 12.11.2Internally balanced realization.- 12.11.3Example.- 12.12References.- 12.13Problems.- 13 Stability.- 13.1 Introduction.- 13.1.1 Phase portrait.- 13.2 Linear systems.- 13.2.1 Routh-Hurwitz criterion.- 13.3 Lyapunov's direct method.- 13.3.1 Introductory example.- 13.3.2 Stability theorem.- 13.3.3 Asymptotic stability theorem.- 13.3.4 Lasalle's theorem.- 13.3.5 Geometric interpretation.- 13.3.6 Instability theorem.- 13.4 Lyapunov functions for linear systems.- 13.5 Lyapunov's indirect method ..- 13.6 An application to controller design.- 13.7 Energy absorbing controls.- 13.8 References.- 13.9 Problems.- 14 Applications.- 14.1 Digital implementation.- 14.1.1 Sampling, aliasing and prefiltering.- 14.1.2 Zero-order hold, computational delay.- 14.1.3 Quantization.- 14.1.4 Discretization of a continuous controller.- 14.2 Active damping of a truss structure.- 14.2.1 Actuator placement.- 14.2.2 Implementation, experimental results.- 14.3 Active damping generic interface.- 14.3.1 Active damping.- 14.3.2 Experiment.- 14.3.3 Pointing and position control.- 14.4 Active damping of a plate.- 14.4.1 Control design.- 14.5 Active damping of a stiff beam.- 14.5.1 System design.- 14.6 The HAC/LAC strategy.- 14.6.1 Wide-band position control.- 14.6.2 Compensator design.- 14.6.3 Results.- 14.7 Vibroacoustics: Volume displacement sensors.- 14.7.1 QWSIS sensor.- 14.7.2 Discrete array sensor.- 14.7.3 Spatial aliasing.- 14.7.4 Distributed sensor.- 14.8 References.- 14.9 Problems.- 5 Tendon Control of Cable Structures.- 15.1 Introduction.- 15.2 Tendon control of strings and cables.- 15.3 Active damping strategy.- 15.4 Basic Experiment.- 15.5 Linear theory of decentralized active damping.- 15.6 Guyed truss experiment.- 15.7 Micro Precision Interferometer testbed.- 15.8 Free floating truss experiment.- 15.9 Application to cable-stayed bridges.- 15.10Laboratory experiment.- 15.11Control of parametric resonance.- 15.12Large scale experiment.- 15.13 References.- 16 Active Control of Large Telescopes.- 16.1 Introduction.- 16.2 Adaptive optics.- 16.3 Active optics.- 16.3.1 Monolithic primary mirror.- 16.3.2 Segmented primary mirror.- 16.4 SVD controller.- 16.4.1 Loop shaping of the SVD controller.- 16.5 Dynamics of a segmented mirror.- 16.6 Control-structure interaction.- 16.6.1 Multiplicative uncertainty.- 16.6.2 Additive uncertainty.- 16.6.3 Discussion.- 16.7 References.- 17 Semi-active control.- 17.1 Introduction.- 17.2 Magneto-rheological fluids.- 17.3 MR devices.- 17.4 Semi-active suspension.- 17.4.1 Semi-active devices.- 17.5 Narrow-band disturbance.- 17.5.1 Quarter-car semi-active suspension.- 17.6 References.- 17.7 Problems.- Bibliography.- Index.

1,107 citations

Journal ArticleDOI
TL;DR: An applications-oriented review of optical parametric amplifiers in fiber communications is presented, focusing on the intriguing applications enabled by the parametric gain, such as all-optical signal sampling, time-demultiplexing, pulse generation, and wavelength conversion.
Abstract: An applications-oriented review of optical parametric amplifiers in fiber communications is presented. The emphasis is on parametric amplifiers in general and single pumped parametric amplifiers in particular. While a theoretical framework based on highly efficient four-photon mixing is provided, the focus is on the intriguing applications enabled by the parametric gain, such as all-optical signal sampling, time-demultiplexing, pulse generation, and wavelength conversion. As these amplifiers offer high gain and low noise at arbitrary wavelengths with proper fiber design and pump wavelength allocation, they are also candidate enablers to increase overall wavelength-division-multiplexing system capacities similar to the more well-known Raman amplifiers. Similarities and distinctions between Raman and parametric amplifiers are also addressed. Since the first fiber-based parametric amplifier experiments providing net continuous-wave gain in the for the optical fiber communication applications interesting 1.5-/spl mu/m region were only conducted about two years ago, there is reason to believe that substantial progress may be made in the future, perhaps involving "holey fibers" to further enhance the nonlinearity and thus the gain. This together with the emergence of practical and inexpensive high-power pump lasers may in many cases prove fiber-based parametric amplifiers to be a desired implementation in optical communication systems.

857 citations

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