Frequency Domain I: Bode Plots and Transfer Functions

2021 ◽  
pp. 139-175
Author(s):  
John Milton ◽  
Toru Ohira
Keyword(s):  
Frequency Domain ◽  
Transfer Functions ◽  
Bode Plots ◽  
Domain I

Error analyses of a Multi-Input-Multi-Output cantilever beam test

2021 ◽  
pp. 107754632110337
Author(s):  
Arup Maji ◽  
Fernando Moreu ◽  
James Woodall ◽  
Maimuna Hossain
Keyword(s):  
Transfer Function ◽  
Frequency Domain ◽  
Cantilever Beam ◽  
Transfer Functions ◽  
Linear Superposition ◽  
Transfer Function Matrix ◽  
Error Analyses ◽  
Pseudo Inverse ◽  
Function Matrix

Multi-Input-Multi-Output vibration testing typically requires the determination of inputs to achieve desired response at multiple locations. First, the responses due to each input are quantified in terms of complex transfer functions in the frequency domain. In this study, two Inputs and five Responses were used leading to a 5 × 2 transfer function matrix. Inputs corresponding to the desired Responses are then computed by inversion of the rectangular matrix using Pseudo-Inverse techniques that involve least-squared solutions. It is important to understand and quantify the various sources of errors in this process toward improved implementation of Multi-Input-Multi-Output testing. In this article, tests on a cantilever beam with two actuators (input controlled smart shakers) were used as Inputs while acceleration Responses were measured at five locations including the two input locations. Variation among tests was quantified including its impact on transfer functions across the relevant frequency domain. Accuracy of linear superposition of the influence of two actuators was quantified to investigate the influence of relative phase information. Finally, the accuracy of the Multi-Input-Multi-Output inversion process was investigated while varying the number of Responses from 2 (square transfer function matrix) to 5 (full-rectangular transfer function matrix). Results were examined in the context of the resonances and anti-resonances of the system as well as the ability of the actuators to provide actuation energy across the domain. Improved understanding of the sources of uncertainty from this study can be used for more complex Multi-Input-Multi-Output experiments.

Asymptotic magnitude Bode plots of fractional-order transfer functions

2019 ◽  
Vol 6 (4) ◽  
pp. 1019-1026 ◽  
Author(s):  
Ameya Anil Kesarkar ◽  
Selvaganesan Narayanasamy
Keyword(s):  
Fractional Order ◽  
Transfer Functions ◽  
Bode Plots

Integrated and frequency-band magnitude, two alternative measures of the size of an earthquake

1970 ◽  
Vol 60 (3) ◽  
pp. 917-937 ◽  
Author(s):  
B. F. Howell ◽  
G. M. Lundquist ◽  
S. K. Yiu
Keyword(s):  
Transfer Function ◽  
Frequency Domain ◽  
Frequency Band ◽  
Transfer Functions ◽  
Body Wave ◽  
Average Amplitude ◽  
Equivalent Quantity ◽  
Short Period ◽  
Alternative Measures ◽  
Body Wave Magnitude

Abstract Integrated magnitude substitutes the r.m.s. average amplitude over a pre-selected interval for the peak amplitude in the conventional body-wave magnitude formula. Frequency-band magnitude uses an equivalent quantity in the frequency domain. Integrated magnitude exhibits less scatter than conventional body-wave magnitude for short-period seismograms. Frequency-band magnitude exhibits less scatter than body-wave magnitude or integrated magnitude for both long- and short-period seismograms. The scatter of frequency-band magnitude is probably due to real azimuthal effects, crustal-transfer-function variations, errors in compensation for seismograph response, microseismic moise and uncertainties in the compensation for attenuation with distance. To observe azimuthal variations clearly, the crustal-transfer functions and seismograph response need to be known more precisely than was the case in this experiment, because these two sources of scatter can be large enough to explain all of the observed variations.

An Advanced Frequency-Domain Code for BWR Stability Analysis and Design

2013 ◽  
Author(s):  
Behrooz Askari ◽  
George Yadigaroglu
Keyword(s):  
Steady State ◽  
Frequency Domain ◽  
Transfer Functions ◽  
Density Wave ◽  
System Stability ◽  
Reactor Kinetics ◽  
Analysis And Design ◽  
Drift Flux ◽  
Reactor Transfer ◽  
Phase Instabilities

Density Wave Oscillations in BWRs are coupled with the reactor kinetics. A new analytical, frequency-domain tool that uses the best available models and methods for modeling BWRs and analyzing their stability is described. The steady state of the core is obtained first in 3D with two-group diffusion equations and spatial expansion of the neutron fluxes in Legendre polynomials. The time-dependent neutronics equations are written in terms of flux harmonics (nodal-modal equations) for the study of “out-of-phase” instabilities. Considering separately all fuel assemblies divided into a number of axial segments, the thermal-hydraulic conservation equations are solved (drift-flux, non-equilibrium model). The thermal-hydraulics are iteratively fully coupled to the neutronics. The code takes all necessary information from plant files via an interface. The results of the steady state are used for the calculation of the transfer functions and system transfer matrices using extensively symbolic manipulation software (MATLAB). The resulting very large matrices are manipulated and inverted by special procedures developed within the MATLAB environment to obtain the reactor transfer functions that enable the study of system stability. Applications to BWRs show good agreement with measured stability data.

Controller Design of a Distributed Slewing Flexible Structure—A Frequency Domain Approach

1997 ◽  
Vol 119 (4) ◽  
pp. 809-814 ◽  
Author(s):  
S. M. Yang ◽  
J. A. Jeng ◽  
Y. C. Liu
Keyword(s):  
Frequency Domain ◽  
Transfer Functions ◽  
Controller Design ◽  
System Stability ◽  
Feedback Gain ◽  
Flexible Structure ◽  
Control Law ◽  
Discrete Parameter ◽  
Locus Method ◽  
Beam Experiment

The vibration control of a slewing flexible structure by collocated and noncollocated feedback is presented in this paper. A stability criterion derived from the root locus method in frequency domain is applied to predict the closed-loop system stability of the distributed parameter model whose analytical transfer functions are formulated. It is shown that the control law design requires neither distributed state sensing/estimation nor functional feedback gain; moreover, the spillover problem associated with discrete parameter model can be prevented. Implementation of the noncollocated feedback in a slewing beam experiment validates that the control law is effective in pointing accuracy while suppressing the tip vibration.

scholarly journals Bond graph based frequency domain sensitivity analysis of multidisciplinary systems

2002 ◽  
Vol 216 (1) ◽  
pp. 85-99 ◽  
Author(s):  
W Borutzky ◽  
J Granda
Keyword(s):  
Frequency Domain ◽  
Transfer Functions ◽  
Linear Time ◽  
Graph Model ◽  
Bond Graph ◽  
Sensitivity Matrix ◽  
Symbolic Form ◽  
Multidisciplinary Systems ◽  
Set Up ◽  
The Time Domain

Multidisciplinary systems are described most suitably by bond graphs. In order to determine unnormalized frequency domain sensitivities in symbolic form, this paper proposes to construct in a systematic manner a bond graph from another bond graph, which is called the associated incremental bond graph in this paper. Contrary to other approaches reported in the literature the variables at the bonds of the incremental bond graph are not sensitivities but variations (incremental changes) in the power variables from their nominal values due to parameter changes. Thus their product is power. For linear elements their corresponding model in the incremental bond graph also has a linear characteristic. By deriving the system equations in symbolic state space form from the incremental bond graph in the same way as they are derived from the initial bond graph, the sensitivity matrix of the system can be set up in symbolic form. Its entries are transfer functions depending on the nominal parameter values and on the nominal states and the inputs of the original model. The sensitivities can be determined automatically by the bond graph preprocessor CAMP-G and the widely used program MATLAB together with the Symbolic Toolbox for symbolic mathematical calculation. No particular program is needed for the approach proposed. The initial bond graph model may be non-linear and may contain controlled sources and multiport elements. In that case the sensitivity model is linear time variant and must be solved in the time domain. The rationale and the generality of the proposed approach are presented. For illustration purposes a mechatronic example system, a load positioned by a constant-excitation d.c. motor, is presented and sensitivities are determined in symbolic form by means of CAMP-G/MATLAB.

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