Microspinning: Local Surface Mixing via Rotation of Magnetic Microparticles for Efficient Small-Volume Bioassays

The need for high-throughput screening has led to the miniaturization of the reaction volume of the chamber in bioassays. As the reactor gets smaller, surface tension dominates the gravitational or inertial force, and mixing efficiency decreases in small-scale reactions. Because passive mixing by simple diffusion in tens of microliter-scale volumes takes a long time, active mixing is needed. Here, we report an efficient micromixing method using magnetically rotating microparticles with patterned magnetization induced by magnetic nanoparticle chains. Because the microparticles have magnetization patterning due to fabrication with magnetic nanoparticle chains, the microparticles can rotate along the external rotating magnetic field, causing micromixing. We validated the reaction efficiency by comparing this micromixing method with other mixing methods such as simple diffusion and the use of a rocking shaker at various working volumes. This method has the potential to be widely utilized in suspension assay technology as an efficient mixing strategy

Modal-Energy-Based Neuro-Controller for Seismic Response Reduction of a Nonlinear Building Structure

This study presents a neuro-control algorithm based on structural modal energy that outputs an optimal control signal to reduce vibration during earthquakes. The modal energy of a structure is used in the objective function during the training process of a neural network. The modal energy and control signal are then minimized by the proposed neuro-control technique. A three-story nonlinear building was installed with an active mass damper, which was used to verify the applicability of the proposed control algorithm. The El Centro earthquake was adopted to train the modal-energy-based neuro-controller. The six recorded earthquakes were employed to consider unknown earthquake effects after training. The results obtained from the proposed control algorithm were compared with those obtained from a non-controlled response and a multilayer perceptron. The numerical results show that the proposed control algorithm is quite effective in reducing the structural response and modal energy. While nonlinear hysteretic behaviors appear in the non-controlled responses, these nonlinear behaviors almost entirely disappear with control

An Intelligent Process to Estimate the Nonlinear Behaviors of an Elasto-Plastic Steel Coil Damper Using Artificial Neural Networks

This study developed a nonlinear behavior prediction model for elasto-plastic steel coil dampers (SCDs) using artificial neural networks (ANN). To train the ANN, first, the input and output data of the behavior of the elasto-plastic SCD was prepared. This study utilized the design parameters and load–displacement curves of the SCD to train the ANN. The elasto-plastic load–displacement curve of the SCD was obtained from simulation results using an ANSYS workbench. The design parameters (wire diameter, internal diameter, number of active windings, yield strength) of the SCD were defined as the input patterns, while the yield deformation, first stiffness, and second stiffness were output patterns. During learning of the neural network model, 60 datasets of the SCD were used as the learning pattern, and the remaining 21 were used to verify the model. Although this study used a small number of learning patterns, the ANN predicted accurate results for yield displacement, first stiffness, and second stiffness. In this study, the ANN was found to perform very well, predicting the nonlinear response of the SCD, compared with the values obtained from a finite element analysis program

An Intelligent Process to Estimate the Nonlinear Behaviors of an Elasto-Plastic Steel Coil Damper Using Artificial Neural Networks

This study developed a nonlinear behavior prediction model for elasto-plastic steel coil dampers (SCDs) using artificial neural networks (ANN). To train the ANN, first, the input and output data of the behavior of the elasto-plastic SCD was prepared. This study utilized the design parameters and load–displacement curves of the SCD to train the ANN. The elasto-plastic load–displacement curve of the SCD was obtained from simulation results using an ANSYS workbench. The design parameters (wire diameter, internal diameter, number of active windings, yield strength) of the SCD were defined as the input patterns, while the yield deformation, first stiffness, and second stiffness were output patterns. During learning of the neural network model, 60 datasets of the SCD were used as the learning pattern, and the remaining 21 were used to verify the model. Although this study used a small number of learning patterns, the ANN predicted accurate results for yield displacement, first stiffness, and second stiffness. In this study, the ANN was found to perform very well, predicting the nonlinear response of the SCD, compared with the values obtained from a finite element analysis program

Effects of vertical stiffness of rail fastening system on the behavior of the end regions of railway bridges with slab tracks

A railway bridge with slab track is subjected to end rotations because of the deflection of the girder during train operation. At the ends of a slab track, the end rotation of the bridge girder causes uplift deformation of the slab track, and leads to compressive stresses in the rail fasteners. In this study, a prototype bridge consisting of one span of a girder and one span of an abutment along with a slab track was constructed, and the uplift and compressive forces generated in the rail fastening system were experimentally analyzed. To effectively analyze the experimental results using a numerical method, a series of finite element analyses were performed considering the nonlinear nature of the rail fastening system. A comparison between the experimental and analytical results indicated that the higher the stiffness of the rail fastening system, the greater the uplift and compressive forces. In addition, a nonlinear model provided better correlation with the experimental results than a linear model. Therefore, when reviewing the serviceability of the rail fastening system at railway bridge ends, an adequate finite element model considering the uplift and compressive forces in the rail fastening system should be used

Application of Tuned Mass Damper to Mitigation of the Seismic Responses of Electrical Equipment in Nuclear Power Plants

A tuned mass damper (TMD) was developed for mitigating the seismic responses of electrical equipment inside nuclear power plants (NPPs), in particular, the response of an electrical cabinet. A shaking table test was performed, and the frequency and damping ratio were extracted, to confirm the dynamics of the cabinet. Electrical cabinets with and without TMDs were modeled while using SAP2000 software (Version 20, Computers and Structures, NY, USA) that was based on the results. TMDs were designed while using an optimization method and the equations of Den Hartog, Warburton, and Sadek. The numerical models were verified while using the shaking table test results. A sinusoidal sweep wave was applied as input to identify the vibration characteristics of the electrical cabinet over a wide frequency range. Applying various seismic loads that were adjusted to meet the RG 1.60 design response spectrum of 0.3 g then validated the control performance of the TMD. The minimum and maximum response spectrum reduction rates of the designed TMDs were 44.7% and 62.9%, respectively. Further, the amplification factor of the electrical cabinet with the TMD was decreased by 53%, on average, with the proposed optimization method. In conclusion, TMDs can be considered to be an effective way of enhancing the seismic performance of the electrical equipment inside NPPs

Application of machine learning algorithm for the estimation of time-dependent strength of basic oxygen furnace slag-treated soil

The main purpose of this study is to predict the time-dependent strength of BOF slag-treated dredged soil using four machine learning (ML) algorithms (random forests, multi-layer perceptron, support vector regression, k-nearest neighbors). These models were trained using a dataset developed from the published literature. The slag type, slag content, water content, and curing time were used as input values. Here, the curing time was divided into three stages according to the magnitude of strength development. Among the algorithms, the multi-layer perceptron (MLP) was selected as the optimal model, and its predicted strength was compared with that of BOF slag-treated soil calculated by the previous empirical equation. In addition, MLP accurately predicted the strength of BOF slag-treated soil compared with that of the empirical equation. Consequentially, ML algorithms had higher applicability for estimating of the time-dependent strength of BOF slag-treated soils

Seismic Vulnerability of Cabinet Facility with Tuned Mass Dampers Subjected to High- and Low-Frequency Earthquakes

The study investigates the collapse probability of a cabinet facility with a tuned mass damper (TMD) subjected to high- and low-frequency earthquakes. For this aim, a prototype of the cabinet in Korea is utilized for the numeric simulation. The accuracy of the finite element model is evaluated via the impact hammer tests. To mitigate the seismic response of the structure, a TMD system is developed whose properties are designed based on the outcomes from the modal analysis (i.e., modal frequencies and mode shapes). Furthermore, the influences of earthquake frequency contents on the seismic response are evaluated. The numeric analyses are conducted using a series of eighty earthquakes that are classified into two groups corresponding to low- and high-frequency motions. Finally, fragility curves are developed for the cabinet subjected to different ground motion sets. The results quantify the seismic vulnerability of the structure and demonstrate the influences of earthquake frequency contents and the vibration control system on the seismic response of the cabinet

Vibration Control of Nuclear Power Plant Piping System Using Stockbridge Damper under Earthquakes

Generally the piping system of a nuclear power plant (NPP) has to be designed for normal loads such as dead weight, internal pressure, temperature, and accidental loads such as earthquake. In the proposed paper, effect of Stockbridge damper to mitigate the response of piping system of NPP subjected to earthquake is studied. Finite element analysis of piping system with and without Stockbridge damper using commercial software SAP2000 is performed. Vertical and horizontal components of earthquakes such as El Centro, California, and Northridge are used in the piping analysis. A sine sweep wave is also used to investigate the control effects on the piping system under wide frequency range. It is found that the proposed Stockbridge damper can reduce the seismic response of piping system subjected to earthquake loading

Aerosol Properties within and above the Planetary Boundary Layer across the Korean Peninsula during December 2016

During December 2016, airborne aerosol measurements were taken at multiple heights across the Korean Peninsula to examine the vertical properties of aerosols. This study showed that aerosols above the planetary boundary layer (PBL) show similar concentrations and particle size distributions (PSDs), regardless of the relative locations in Korea. On the other hand, aerosols within the PBL differ depending on the geographical location, origin and path of the air mass. The concentrations are the highest in Seoul, followed by Gangneung, East Sea and the Yellow Sea. The known east–west aerosol gradient did not appear and the reasons are discussed in this paper. The study further shows that the aerosols of upwind regions affect the aerosols above the PBL, whereas aerosols in the PBL are affected by local sources and atmospheric conditions in addition to aerosols of upwind areas