• Smart Structures and Systems
• /
• v.3 no.4
• /
• pp.423-437
• /
• 2007

The empirical mode decomposition (EMD) method is well-known for its ability to decompose a multi-component signal into a set of intrinsic mode functions (IMFs). The method uses a sifting process in which local extrema of a signal are identified and followed by a spline fitting approximation for decomposition. This method provides an effective and robust approach for decomposing nonlinear and non-stationary signals. On the other hand, the IMF components do not automatically guarantee a well-defined physical meaning hence it is necessary to validate the IMF components carefully prior to any further processing and interpretation. In this paper, an attempt to use the EMD method to identify properties of nonlinear elastic multi-degree-of-freedom structures is explored. It is first shown that the IMF components of the displacement and velocity responses of a nonlinear elastic structure are numerically close to the nonlinear normal mode (NNM) responses obtained from two-dimensional invariant manifolds. The IMF components can then be used in the context of the NNM method to estimate the properties of the nonlinear elastic structure. A two-degree-of-freedom shear-beam building model is used as an example to illustrate the proposed technique. Numerical results show that combining the EMD and the NNM method provides a possible means for obtaining nonlinear properties in a structure.

• Earthquakes and Structures
• /
• v.18 no.1
• /
• pp.45-56
• /
• 2020

The application of critical excitation method with displacement-based objective function for multi degree of freedom (MDOF) systems is investigated. To this end, a new critical excitation method is developed to find the critical input motion of a MDOF system as a synthetic accelerogram. The upper bound of earthquake input energy per unit mass is considered as a new constraint for the problem, and its advantages are discussed. Considering this constraint, the critical excitation method is then used to generate synthetic accelerograms for MDOF models corresponding to three shear buildings of 10, 16, and 22 stories. In order to demonstrate the reliability of generated accelerograms to estimate dynamic response of the structures, three target ground motions with considerable level of energy contents are selected to represent "real critical excitation" of each model, and the method is used to re-generate these ground motions. Afterwards, linear dynamic analyses are conducted using these accelerograms along with the generated critical excitations, to investigate the key parameters of response including maximum displacement, maximum interstory drift, and maximum absolute acceleration of stories. The results show that the generated critical excitations can make an acceptable estimate of the structural behavior compared to the target ground motions. Therefore, the method can be reliably implemented to generate critical excitation of the structure when real one is not available.

• Structural Engineering and Mechanics
• /
• v.37 no.6
• /
• pp.575-592
• /
• 2011

For seismic resistant design of critical structures, a dynamic analysis, either response spectrum or time history is frequently required. Owing to the lack of recorded data and the randomness of earthquake ground motion that may be experienced by structure in the future, usually it is difficult to obtain recorded data which fit the requirements (site type, epicenteral distance, etc.) well. Therefore, the artificial seismic records are widely used in seismic designs, verification of seismic capacity and seismic assessment of structures. The purpose of this paper is to develop a numerical method using Artificial Neural Network (ANN) and wavelet packet transform in best basis method which is presented for the decomposition of artificial earthquake records consistent with any arbitrarily specified target response spectra requirements. The ground motion has been modeled as a non-stationary process using wavelet packet. This study shows that the procedure using ANN-based models and wavelet packets in best-basis method are applicable to generate artificial earthquakes compatible with any response spectra. Several numerical examples are given to verify the developed model.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2005.05a
• /
• pp.342-348
• /
• 2005

This study was carried out fusion-welded butt joints in SWS 490A high strength steel subjected to tensile test that load-deflection curve. The windowed or short-time Fourier transform (WFT or SIFT) makes possible for the analysis of non-stationary or transient signals into a joint time-frequency domain and the wavelet transform (WT) is used to decompose the acoustic emission (AE) signal into various discrete series of sequences over different frequency bands. In this paper, for acoustic emission signal analysis to use a continuous wavelet transform, in which the Gabor wavelet base on a Gaussian window function is applied to the time-frequency domain. A wavelet transform is demonstrated and the plots are very powerful in the recognition of the acoustic emission features. As a result, the technique of acoustic emission is ideally suited to study variables which control time and stress dependent fracture or damage process in metallic materials.

• Earthquakes and Structures
• /
• v.2 no.2
• /
• pp.149-169
• /
• 2011

Dynamic response of two adjacent single degree-of-freedom (SDOF) structures connected with friction damper under base excitation is investigated. The base excitation is modeled as a stationary white-noise random process. As the force-deformation behavior of friction damper is non linear, the dynamic response of connected structures is obtained using the equivalent linearization technique. It is observed that there exists an optimum value of the limiting frictional force of the damper for which the mean square displacement and the mean square absolute acceleration responses of the connected structures attains the minimum value. The close form expressions for the optimum value of damper frictional force and corresponding mean square responses of the coupled undamped structures are derived. These expressions can be used for initial optimal design of the friction damper for connected structures. A parametric study is also carried out to investigate the influence of system parameters such as frequency ratio and mass ratio on the response of the coupled structures. It has been observed that the frequency ratio has significant effect on the performance of the friction damper, whereas the effects of mass ratio are marginal. Finally, the verification of the derived close from expressions is made by correlating the response of connected structures under real earthquake excitations.

• International Journal of Automotive Technology
• /
• v.7 no.3
• /
• pp.289-294
• /
• 2006

Better fundamental understanding of the interactions between the in-cylinder flows and combustion process is an important requirement for further improvement in the fuel economy and emissions of internal combustion(IC) engines. Flow near a spark plug at the time of ignition plays an important role for early flame kernel development(EFKD). Velocity data measurements in this study were made with a two-component laser Doppler velocimetry(LDV) near a spark plug in a single cylinder optical spark ignition(SI) engine with a heart-shaped combustion chamber. LDV velocity data were collected on an individual cycle basis under wide-open motored conditions with an engine speed of 1,000rpm. This study examines and compares the flow fields as interpreted through ensemble, cyclic and discrete wavelet transformation(DWT) analysis. The energy distributions in the non-stationary engine flows are also investigated over crank angle phase and frequency through continuous wavelet transformation(CWT) for a position near a spark plug. Wavelet analysis is appropriate for analyzing the flow fields in engines because it gives information about the transient events in a time and frequency plane. The results of CWT analysis are provided and compared with the mean flows of DWT first decomposition level for all cycles at a position. Low frequency high energy found with CWT corresponds well with the peak locations of the mean velocity. The high frequency flows caused by the intake jet gradually decay as the piston approaches the bottom dead center(BDC).

• Nuclear Engineering and Technology
• /
• v.53 no.4
• /
• pp.1088-1099
• /
• 2021

Time-frequency analysis technique is an effective analysis tool for non-stationary processes. In the field of reactor neutron noise, the time-frequency analysis method has not been thoroughly researched and widely used. This work has studied the time-frequency analysis of the reactor neutron noise experimental signals under bubble disturbance and control rod vibration. First, an experimental platform was established, and it could be employed to reactor neutron noise experiment and data acquisition. Secondly, two types of reactor neutron noise experiments were performed, and valid experimental data was obtained. Finally, time-frequency analysis was conducted on the experimental data, and effective analysis results were obtained in the low-frequency part. Through this work, it can be concluded that the time-frequency analysis technique can effectively investigate the core dynamics behavior and deepen the identification of the unstable core process.

• Nuclear Engineering and Technology
• /
• v.55 no.11
• /
• pp.4213-4227
• /
• 2023

Computer Automated Radioactive Particle Tracking (CARPT) technique has been successfully utilized to measure the velocity profiles and mixing parameters in different multiphase flow systems where a single radioactive tracer is used to track the tagged phase. However, many industrial processes use a wide range of particles with different physical properties where solid particles could vary in size, shape and density. For application in such systems, the capability of current single tracer CARPT can be advanced to track more than one particle simultaneously. Tracking multiple particles will thus enable to track the motion of particles of different size shape and density, determine segregation of particles and probing particle interactions. In this work, a newly developed Multiple Radioactive Particle Tracking technique (M-RPT) used to track two different radioactive tracers is demonstrated. The M-RPT electronics was developed that can differentiate between gamma counts obtained from the different radioactive tracers on the basis of their gamma energy peak. The M-RPT technique was validated by tracking two stationary and moving particles (Sc-46 and Co-60) simultaneously. Finally, M-RPT was successfully implemented to track two phases, solid and liquid, simultaneously in three phase slurry bubble column reactors.

• Journal of Institute of Control, Robotics and Systems
• /
• v.10 no.12
• /
• pp.1295-1304
• /
• 2004

In this paper, the Bayesian recurrent neural network is proposed to predict time series data. A neural network predictor requests proper learning strategy to adjust the network weights, and one needs to prepare for non-linear and non-stationary evolution of network weights. The Bayesian neural network in this paper estimates not the single set of weights but the probability distributions of weights. In other words, the weights vector is set as a state vector of state space method, and its probability distributions are estimated in accordance with the particle filtering process. This approach makes it possible to obtain more exact estimation of the weights. In the aspect of network architecture, it is known that the recurrent feedback structure is superior to the feedforward structure for the problem of time series prediction. Therefore, the recurrent neural network with Bayesian inference, what we call Bayesian recurrent neural network (BRNN), is expected to show higher performance than the normal neural network. To verify the proposed method, the time series data are numerically generated and various kinds of neural network predictor are applied on it in order to be compared. As a result, feedback structure and Bayesian learning are better than feedforward structure and backpropagation learning, respectively. Consequently, it is verified that the Bayesian reccurent neural network shows better a prediction result than the common Bayesian neural network.

• Wind and Structures
• /
• v.19 no.3
• /
• pp.339-352
• /
• 2014

This paper discusses the appropriate duration for basic gust wind speeds in wind loading codes and standards, and in wind engineering generally. Although various proposed definitions are discussed, the 'moving average' gust duration has been widely accepted internationally. The commonly-specified gust duration of 3-seconds, however, is shown to have a significant effect on the high-frequency end of the spectrum of turbulence, and may not be ideally suited for wind engineering purposes. The effective gust durations measured by commonly-used anemometer types are discussed; these are typically considerably shorter than the 'standard' duration of 3 seconds. Using stationary random process theory, the paper gives expected peak factors, $g_u$, as a function of the non-dimensional parameter ($T/{\tau}$), where T is the sample, or reference, time, and ${\tau}$ is the gust duration, and a non-dimensional mean wind speed, $\bar{U}.T/L_u$, where $\bar{U}$ is a mean wind speed, and $L_u$ is the integral length scale of turbulence. The commonly-used Durst relationship, relating gusts of various durations, is shown to correspond to a particular value of turbulence intensity $I_u$, of 16.5%, and is therefore applicable to particular terrain and height situations, and hence should not be applied universally. The effective frontal areas associated with peak gusts of various durations are discussed; this indicates that a gust of 3 seconds has an equivalent frontal area equal to that of a tall building. Finally a generalized gust response factor format, accounting for fluctuating and resonant along-wind loading of structures, applicable to any code is presented.