• Wind and Structures
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• v.27 no.6
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• pp.381-397
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• 2018

Nonlinear behavior in fluid-structure interaction (FSI) of bridge decks becomes increasingly significant for modern bridges with increasing spans, larger flexibility and new aerodynamic deck configurations. Better understanding of the nonlinear aeroelasticity of bridge decks and further development of reduced-order nonlinear models for the aeroelastic forces become necessary. In this paper, the amplitude-dependent and neutral angle dependent nonlinearities of the motion-induced loads are further highlighted by series of computational fluid dynamics (CFD) simulations. An effort has been made to investigate a semi-analytical time-domain model of the nonlinear motion induced loads on the deck, which enables nonlinear time domain simulations of the aeroelastic responses of the bridge deck. First, the computational schemes used here are validated through theoretically well-known cases. Then, static aerodynamic coefficients of the Great Belt East Bridge (GBEB) cross section are evaluated at various angles of attack, leading to the so-called nonlinear backbone curves. Flutter derivatives of the bridge are identified by CFD simulations using forced harmonic motion of the cross-section with various frequencies. By varying the amplitude of the forced motion, it is observed that the identified flutter derivatives are amplitude-dependent, especially for $A^*_2$ and $H^*_2$ parameters. Another nonlinear feature is observed from the change of hysteresis loop (between angle of attack and lift/moment) when the neutral angles of the cross-section are changed. Based on the CFD results, a semi-analytical time-domain model for describing the nonlinear motion-induced loads is proposed and calibrated. This model is based on accounting for the delay effect with respect to the nonlinear backbone curve and is established in the state-space form. Reasonable agreement between the results from the semi-analytical model and CFD demonstrates the potential application of the proposed model for nonlinear aeroelastic analysis of bridge decks.

• Nuclear Engineering and Technology
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• v.51 no.6
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• pp.1540-1553
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• 2019

A nuclear power plant (NPP) is a highly complex system-of-systems as manifested through its internal systems interdependence. The negative impact of such interdependence was demonstrated through the 2011 Fukushima Daiichi nuclear disaster. As such, there is a critical need for new strategies to overcome the limitations of current risk assessment techniques (e.g. the use of static event and fault tree schemes), particularly through simulation of the nonlinear dynamic feedback mechanisms between the different NPP systems/components. As the first and key step towards developing an integrated NPP dynamic probabilistic risk assessment platform that can account for such feedback mechanisms, the current study adopts a system dynamics simulation approach to model the thermal dynamic processes in: the reactor core; the secondary coolant system; and the pressurized water reactor. The reactor core and secondary coolant system parameters used to develop system dynamics models are based on those of the Palo Verde Nuclear Generating Station. These three system dynamics models are subsequently validated, using results from published work, under different system perturbations including the change in reactivity, the steam valve coefficient, the primary coolant flow, and others. Moving forward, the developed system dynamics models can be integrated with other interacting processes within a NPP to form the basis of a dynamic system-level (systemic) risk assessment tool.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.792-800
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• 2021

This paper presents a new structure for a linear active magnetic bearing using a Halbach magnet array. The proposed magnetic bearing consisted of a Halbach magnet array, center magnet, and single coil. The proposed linear active magnetic bearing has a high dynamic force compared to the previous study. The high dynamic force could be obtained by varying the thickness of a horizontally magnetized magnet. The new structure of Halbach linear active magnetic bearing has a high dynamic force. Therefore, the proposed linear active magnetic bearing increased the bandwidth of the system. Magnetic modeling and optimal design of the new structure of the Halbach linear active magnetic bearing were performed. The optimal design was executed on the geometric parameters of the proposed linear active magnetic bearing using Sequential Quadratic Programming. The proposed linear active magnetic bearing had a static force of 45.06 N and a Lorentz force constant of 19.54 N/A, which is higher than previous research.

• Steel and Composite Structures
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• v.38 no.1
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• pp.87-102
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• 2021

To investigate the failure form, bending stiffness, and residual bearing capacity of monolithic composite beams with laminated slab throughout the fire process, fire tests of four monolithic composite beams with laminated slab were performed under constant load and temperature increase. Different factors such as post-pouring layer thickness, lap length of the prefabricated bottom slab, and stud spacing were considered in the fire test. The test results demonstrate that, under the same fire time and external load, the post-pouring layer thickness and stud spacing are important parameters that affect the fire resistance of monolithic composite beams with laminated slab. Similarly, the post-pouring layer thickness and stud spacing are the predominant factors affecting the bending stiffness of monolithic composite beams with laminated slab after fire exposure. The failure forms of monolithic composite beams with laminated slab after the fire are approximately the same as those at room temperature. In both cases, the beams underwent bending failure. However, after exposure to the high-temperature fire, cracks appeared earlier in the monolithic composite beams with laminated slab, and both the residual bearing capacity and bending stiffness were reduced by varying degrees. In this test, the bending bearing capacity and ductility of monolithic composite beams with laminated slab after fire exposure were reduced by 23.3% and 55.4%, respectively, compared with those tested at room temperature. Calculation methods for the residual bearing capacity and bending stiffness of monolithic composite beams with laminated slab in and after the fire are proposed, which demonstrated good accuracy.

• Earthquakes and Structures
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• v.22 no.6
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• pp.539-548
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• 2022

Steel plate shear walls (SPSWs) are one of the most important and widely used lateral load-bearing systems. The reason for this is easier execution than reinforced concrete (RC) shear walls, faster construction time, and lower final weight of the structure. However, the main drawback of SPSWs is premature buckling in low drift ratios, which affects the energy absorption capacity and global performance of the system. To address this problem, two groups of SPSWs under cyclic loading were investigated using the finite element method (FEM). In the first group, several series of circular rings have been used and in the second group, a new type of SPSW with concentric circular rings (CCRs) has been introduced. Numerous parameters include in yield stress of steel plate wall materials, steel panel thickness, and ring width were considered in nonlinear static analysis. At first, a three-dimensional (3D) numerical model was validated using three sets of laboratory SPSWs and the difference in results between numerical models and experimental specimens was less than 5% in all cases. The results of numerical models revealed that the full SPSW undergoes shear buckling at a drift ratio of 0.2% and its hysteresis behavior has a pinching in the middle part of load-drift ratio curve. Whereas, in the two categories of proposed SPSWs, the hysteresis behavior is complete and stable, and in most cases no capacity degradation of up to 6% drift ratio has been observed. Also, in most numerical models, the tangential stiffness remains almost constant in each cycle. Finally, for the innovative SPSW, a relationship was suggested to determine the shear capacity of the proposed steel wall relative to the wall slenderness coefficient.

• Steel and Composite Structures
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• v.43 no.2
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• pp.139-152
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• 2022

Steel-reinforced concrete (SRC) L-shaped column is the vertical load-bearing member with high spatial adaptability. The seismic behavior of SRC L-shaped column is complex because of their irregular cross sections. In this study, the hysteretic performance of six steel truss reinforced concrete L-shaped columns specimens under the combined loading of compression, bending, shear, and torsion was tested. There were two parameters, i.e., the moment ratio of torsion to bending (γ) and the aspect ratio (column length-to-depth ratio (φ)). The failure process, torsion-displacement hysteresis curves, and bending-displacement hysteresis curves of specimens were obtained, and the failure patterns, hysteresis curves, rigidity degradation, ductility, and energy dissipation were analyzed. The experimental research indicates that the failure mode of the specimen changes from bending failure to bending-shear failure and finally bending-torsion failure with the increase of γ. The torsion-displacement hysteresis curves were pinched in the middle, formed a slip platform, and the phenomenon of "load drop" occurred after the peak load. The bending-displacement hysteresis curves were plump, which shows that the bending capacity of the specimen is better than torsion capacity. The results show that the steel truss reinforced concrete L-shaped columns have good collapse resistance, and the ultimate interstory drift ratio more than that of the Chinese Code of Seismic Design of Building (GB50011-2014), which is sufficient. The average value of displacement ductility coefficient is larger than rotation angle ductility coefficient, indicating that the specimen has a better bending deformation resistance. The specimen that has a more regular section with a small φ has better potential to bear bending moment and torsion evenly and consume more energy under a combined action.

• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.47-57
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• 2021

Neuromorphic technology is proposed to complement the shortcomings of existing artificial intelligence technology by mimicking the human brain structure and computational process with hardware. NA-IDE has also been proposed for developing neuromorphic hardware-based IoT applications. To implement an SNN model in NA-IDE, commonly used input data must be transformed for use in the SNN model. In this paper, we implemented a neural coding method encoder component that converts image data into a spike train signal and uses it as an SNN input. The decoder component is implemented to convert the output back to image data when the SNN model generates a spike train signal. If the decoder component uses the same parameters as the encoding process, it can generate static data similar to the original data. It can be used in fields such as image-to-image and speech-to-speech to transform and regenerate input data using the proposed encoder and decoder.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.11
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• pp.92-99
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• 2021

Agricultural tractors drive and operate both off-road and on-road. Tire-road interaction significantly affects the tractive performance of a tractor, which is difficult to predict numerically. Many empirical models have been developed to predict the tractive performance of tractors using the cone index, which can be measured through simple tests. However, a magic formula model that can determine the tractive performance without a cone index can be used instead of traditional empirical models as the cone index cannot be measured on asphalt roads. The aim of this study was to predict the tractive performance of a tractor using the magic formula tire model. The traction force of the tires on an asphalt road was measured using an agricultural tractor. The dynamic wheel load was calculated to derive the coefficients of the traction-slip curve using the measured static wheel load and drawbar pull of the tractor. Curve fitting was performed to fit the experimental data using the magic formula. The parameters of the magic formula tire model were well identified, and the model successfully determined the coefficient of traction of the tractor.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.10
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• pp.154-160
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• 1996

The control of diamond turning is usually achieved through a laser-interferometer feedback of slide position. The limitation of this control scheme is that the feedback signal does not account for additional dynamics of the tool post and the material removal process. If the tool post is rigid and the material removal process is relatively static, then such a non-collocated position feedback control scheme may surfice. However, as the accuracy requirement gets tighter and desired surface cnotours become more complex, the need for a direct tool-tip sensing becomes inevitable. The physical constraints of the machining process prohibit any reasonable implementation of a tool-tip motion measurement. It is proposed that the measured force normal to the face of the workpiece can be filtered through an appropriate admittance transfer function to result in the estimated dapth of cut. This can be compared to the desired depth of cut to generate the adjustment control action in additn to position feedback control. In this work, the design methodology on the admittance model-based control with a conventional controller is presented. The recursive least-squares algorithm with forgetting factor is proposed to identify the parameters and update the cutting process in real time. The normal cutting forces are measured to identify the cutting dynamics in the real diamond turning process using the precision dynamoneter. Based on the parameter estimation of cutting dynamics and the admitance model-based nanodynamic control scheme, simulation results are shown.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.