9,335 research outputs found
Team organization may help swarms of flies to become invisible in closed waveguides
We are interested in a time harmonic acoustic problem in a waveguide
containing flies. The flies are modelled by small sound soft obstacles. We
explain how they should arrange to become invisible to an observer sending
waves from and measuring the resulting scattered field at the same
position. We assume that the flies can control their position and/or their
size. Both monomodal and multimodal regimes are considered. On the other hand,
we show that any sound soft obstacle (non necessarily small) embedded in the
waveguide always produces some non exponentially decaying scattered field at
for wavenumbers smaller than a constant that we explicit. As a
consequence, for such wavenumbers, the flies cannot be made completely
invisible to an observer equipped with a measurement device located at
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νμλ
Όλ¬Έ (λ°μ¬) -- μμΈλνκ΅ λνμ : 곡과λν κΈ°κ³κ³΅νκ³Ό, 2020. 8. μ‘°λ§Ήν¨.Model updating methods for structural systems have been introduced in various numerical processes. To improve the updating method, the process must require an accurate analysis and minimized experimental uncertainties. Finite element model was employed to describe structural system. Structural vibration behavior of a plate model is expressed as a combination of the initial state behavior of the structure and its associated perturbations. The dynamic behavior obtained from a limited number of accessible nodes and their associated degrees of freedom is employed to detect structural changes that are consistent with the perturbations. The equilibrium model is described in terms of the measured and unmeasured modal data. Unmeasured information is estimated using an iterated improved reduction scheme. Because the identification problem depends on the measured information, the quality of the measured data determines the accuracy of the identified model and the convergence of the identification problem. The accuracy of the identification depends on the measurement/sensor location. We propose a more accurate identification method using the optimal sensor location selection method. Experimental examples are adopted to examine the convergence and accuracy of the proposed method applied to an inverse problem of system identification. Model updating methods for structural systems have been introduced in various fields. Model updating processes are important for improving a models accuracy by considering experimental data. Structural system identification was achieved here by applying the degree of freedom-based reduction method and the inverse perturbation method. Experimental data were obtained using the specific sensor location selection method. Experimental vibration data were restored to a full finite element model using the reduction method to compare and update the numerical model. Applied iteratively, the improved reduced system method boosts model accuracy
during full model restoration; however, iterative processes are time-consuming. The calculation efficiency was improved using the system equivalent reduction-expansion process in concert with the proper orthogonal
decomposition. A convolutional neural network was trained and applied to the updating process. We propose the use of an efficient model updating method using a convolutional neural network to reduce calculation time. Experimental and numerical examples were adopted to examine the efficiency and accuracy of the model updating method using a convolutional neural network. A more complex model is applied for model updating method and validated with proposed methods. A bolt assembly modeling is introduced and simplified with verified methodologies.ꡬ쑰 μμ€ν
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νκ³ μ€νμ ν΅ν λͺ¨λΈ κ°±μ μΌλ‘ λμ± λ¨μνλ λͺ¨λΈλ§μ μ μν©λλ€.Chapter 1. Introduction 1
1.1 Frequency model updating method . 1
1.2 Reduction methods . 3
1.2.1 Degree of freedom-based reduction method 3
1.2.2 Iterated improved reduced system 4
1.2.3 Proper orthogonal decomposition 8
1.2.4 System equivalent reduction-expansion process 9
1.3 Structural system identification . 11
1.3.1 Balance equation for system identification . 15
1.3.2 Inverse perturbation method . 16
1.4 Machine learning in identification process . 20
Chapter 2. Sensor location selection method 21
2.1 Vibration test setup . 21
2.1.1 Vibration test setup for system identification 21
2.1.2 Vibration data rebuilt for in-house code . 22
2.2 Nodal point consideration . 26
2.2.1 Sequential elimination method 26
2.2.2 Energy method 27
2.2.3 Nodal point consideration 28
2.2.4 Numerical examples . 28
2.3 Sensor location selection method 32
Chapter 3. Residual error equation for identificataion process 36
3.1 Parameter optimizing equation setup 36
3.2 Convergence criterion . 38
3.3 Weighting factor for parameter evaluation 39
3.4 Identification examples 42
Chapter 4. Convolutional neural networks-based system identification method 54
4.1 Introduction . 54
4.2 The balance equation of the model updating method . 57
4.2.1 The IPM method 58
4.2.2 The DOF-based reduction method 59
4.2.3 Experimental data for the model updating method 63
4.3 Convolutional neural network-based identification 67
4.3.1 The SEREP and POD . 67
4.3.2 The 2D-CNN 72
4.4 Experimental examples 77
Chapter 5. A model updating of complex models 94
5.1 The model updating and digital twin . 94
5.2 A complex model example 95
5.2.1 The tank bracket model 95
5.2.2 The sensor location selection 98
5.3 The bolt joint assembly simplification . 102
Chapter 6. Conclusion 109
Appendix A. Structural design of soft robotics using a joint structure of photo responsive polymers 113
A.1 Overview 113
A.2 Structural desing of soft robotics . 114
A.3 Experimental setup 117
A.3.1 Systhesis process 117
A.3.2 Sample preparation 118
A.3.3 Spectrometer characterization 118
A.4 Structural modeling . 121
A.4.1 Multiscale mechanincs 121
A.4.2 Nonlinear FEM with a co-rotational formulation 123
A.5 Results and discussion 128
A.6 Summary of Appendix A 142
Bibliography 145
Abstract in Korean 158Docto
Model of Thermal Wavefront Distortion in Interferometric Gravitational-Wave Detectors I: Thermal Focusing
We develop a steady-state analytical and numerical model of the optical
response of power-recycled Fabry-Perot Michelson laser gravitational-wave
detectors to thermal focusing in optical substrates. We assume that the thermal
distortions are small enough that we can represent the unperturbed intracavity
field anywhere in the detector as a linear combination of basis functions
related to the eigenmodes of one of the Fabry-Perot arm cavities, and we take
great care to preserve numerically the nearly ideal longitudinal phase
resonance conditions that would otherwise be provided by an external
servo-locking control system. We have included the effects of nonlinear thermal
focusing due to power absorption in both the substrates and coatings of the
mirrors and beamsplitter, the effects of a finite mismatch between the
curvatures of the laser wavefront and the mirror surface, and the diffraction
by the mirror aperture at each instance of reflection and transmission. We
demonstrate a detailed numerical example of this model using the MATLAB program
Melody for the initial LIGO detector in the Hermite-Gauss basis, and compare
the resulting computations of intracavity fields in two special cases with
those of a fast Fourier transform field propagation model. Additional
systematic perturbations (e.g., mirror tilt, thermoelastic surface
deformations, and other optical imperfections) can be included easily by
incorporating the appropriate operators into the transfer matrices describing
reflection and transmission for the mirrors and beamsplitter.Comment: 24 pages, 22 figures. Submitted to JOSA
Dynamic graphics for experimental design
When designing an experiment, it is often assumed that the response to be measured can be modelled as a linear function of a vector of parameters plus an error term, y = X[beta] + [epsilon]. Using this model several properties of the design can be defined in terms of the matrix X[superscript]\u27 X, including A-, D-, E- and G-optimality. In this dissertation we review some common design properties and develop new graphical methods for displaying them using dynamic graphics techniques, including interactive updating, linking, animation and rotation. The effects of perturbations to the design on these properties are also displayed, and a new graphical search technique for improving designs is introduced. Our results indicate that these graphs can help to verify the stability of standard experimental designs, highlight weaknesses present in non-standard designs, and suggest possible remedies;In addition, an adaptation of Cook\u27s method for assessing local influence is developed to examine the effects of local perturbations to the model and to the design on selected design properties. Perturbations are made to case weights, design variables, and added variables not included in the assumed model. The design properties examined are D-optimality and the mean squared error of estimating the response at selected points in the design region. Graphical displays are used to interpret the results
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