• Coupled systems mechanics
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• 제11권5호
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• pp.411-438
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• 2022

In this paper, we present the parameter identification for inelastic and multi-scale problems. First, the theoretical background of several fundamental methods used in the upscaling process is reviewed. Several key definitions including random field, Bayesian theorem, Polynomial chaos expansion (PCE), and Gauss-Markov-Kalman filter are briefly summarized. An illustrative example is given to assimilate fracture energy in a simple inelastic problem with linear hardening and softening phases. Second, the parameter identification using the Gauss-Markov-Kalman filter is employed for a multi-scale problem to identify bulk and shear moduli and other material properties in a macro-scale with the data from a micro-scale as quantities of interest (QoI). The problem can also be viewed as upscaling homogenization.

• Earthquakes and Structures
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• 제25권3호
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• pp.187-198
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• 2023

Model Predictive Control (MPC) is an advanced control approach that uses the current states of the system model to predict its future behavior. In this article, according to the seismic dynamics of structural systems, the Predictive Functional Control (PFC) method is used to solve the control problem. Although conventional PFC is an efficient control method, its performance may be impaired due to problems such as uncertainty in the structure of state sensors and process equations, as well as actuator saturation. Therefore, it requires the utilization of appropriate estimation algorithms in order to accurately evaluate responses and implement actuator saturation. Accordingly, an extended PFC is presented based on the H-ifinity (H∞) filter (HPFC) while considering simultaneously the saturation actuator. Accordingly, an extended PFC is presented based on the H-ifinity (H∞) filter (HPFC) while considering the saturation actuator. Thus, the structural responses are formulated by two estimation models using the H∞ filter. First, the H∞ filter estimates responses using a performance bound (𝜃). Second, the H∞ filter is converted into a Kalman filter in a special case by considering the 𝜃 equal to zero. Therefore, the scheme based on the Kalman filter (KPFC) is considered a comparative model. The proposed method is evaluated through numerical studies on a building equipped with an Active Tuned Mass Damper (ATMD) under near and far-field earthquakes. Finally, HPFC is compared with classical (CPFC) and comparative (KPFC) schemes. The results show that HPFC has an acceptable efficiency in boosting the accuracy of CPFC and KPFC approaches under earthquakes, as well as maintaining a descending trend in structural responses.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2000년도 추계학술대회논문집B
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• pp.526-531
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• 2000

Application of electrodewatering (EDW) to mechanical dewatering system was studied to decrease water content in the sludge generated from waste water treatment process. Experiments realized the reduction of water content in the sewage sludge. EDW enhancing the conventional filtration by an electric field is an emerging technology with the potential to improve dewatering. In this study, a piston filter press was constructed, the digested sludges were dewatered by EDW under conditions of DC electric field and constant pressure in the piston filter press. Constant electric field from $0{\sim}120\;V/cm$ and constant pressure $98.1{\sim}392.4\;kPa$ were used. The results showed that as electric field was increased the dewatering rates increased and as pressure was increased the dewatering rates decreased. Also as polymer was added the dewatering rates increased. This experiments produced final sludge cake with water content of 60 wt% using EDW, compared with 80 wt% using pressure filtration alone.

• 한국물환경학회지
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• 제33권1호
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• pp.63-69
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• 2017

Small size privately owned wastewater treatment plants have been difficult to treat their wasted sludge and maintain steady effluent quality compared with publicly owned wastewater treatment plants. Therefore, this study has focused on treatment efficiency enhancement, specially nitrogen removal efficiency by recycling dewatering filtrate as an alkalinity additive from filter press using $CaCO_3$. As the result, it was found that the optimal mixing ratio between the excess sludge and $CaCO_3$ was 1:2. The major operation parameters such as specific substrate utilization rate, specific nitrification rate, and specific denitrification rate were also improved 64% ($0.048-0.079mg\;BOD_5/mg\;MLVSS{\cdot}day$), 35% ($0.020-0.027mg\;NH_3-N/mg\;MLVSS{\cdot}day$) and 68% ($0.051-0.086mg\;NO_3{^-}-N/mg\;MLVSS{\cdot}day$), respectively, after the adoption of new methods. Therefore, both the problem of sludge treatment at small scale plants and the need for efficiency improvement could be solved.

• 한국물환경학회지
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• 제32권5호
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• pp.458-464
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• 2016

This study evaluated the dewatering efficiency of inorganic dehydrating agents that contain oyster shells. A filter press dehydrator was used for this study. The experiments were performed by the single injection of calcium-based chemicals, the single injection of oyster shells, and the mixed injection of oyster shells and calcium-based chemicals. The moisture content of the dewatered cake generated after dehydration confirmed that the best dewatering efficiency was found at the mixing ratios of CaCO₃ to sludge, oyster shell to sludge, and inorganic dehydrating agent to sludge of 3:1, 10:1, and 1.5:2.5:1, respectively. The moisture contents exhibited less than 58% when the injection mixing ratio of inorganic dehydrating agent to sludge was 1.5:2.5:1. From EPS, SEM, and EDX analysis, it was found that the calcium adsorbed on the sludge surface could reduce extracellular polymeric substances (EPSs) and enhanced the dewaterbility. Based on the above results, it is considered possible to apply inorganic dehydrating agents containing recycled oyster shells in sludge dewatering in order to reduce sludge.

• Structural Engineering and Mechanics
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• 제63권6호
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• pp.779-788
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• 2017

System identification and damage detection for structural health monitoring have received considerable attention. Various time domain analysis methodologies based on measured vibration data of structures have been proposed. Among them, recursive least-squares estimation of structural parameters which is also known as parametric Kalman filter (PKF) approach has been studied. However, the conventional PKF requires that all the external excitations (inputs) be available. On the other hand, structural uncertainties are inevitable for civil infrastructures, it is necessary to develop approaches for probabilistic damage detection of structures. In this paper, a parametric Kalman filter with unknown inputs (PKF-UI) is proposed for the simultaneous identification of structural parameters and the unmeasured external inputs. Analytical recursive formulations of the proposed PKF-UI are derived based on the conventional PKF. Two scenarios of linear observation equations and nonlinear observation equations are discussed, respectively. Such a straightforward derivation of PKF-UI is not available in the literature. Then, the proposed PKF-UI is utilized for probabilistic damage detection of structures by considering the uncertainties of structural parameters. Structural damage index and the damage probability are derived from the statistical values of the identified structural parameters of intact and damaged structure. Some numerical examples are used to validate the proposed method.

• Smart Structures and Systems
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• 제21권6호
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• pp.741-749
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• 2018

Structural health monitoring (SHM) systems are necessary to achieve smart predictive maintenance and repair planning as well as they lead to a safe operation of mechanical structures. In the context of vibration-based SHM the measured structural responses are employed to draw conclusions about the structural integrity. This usually leads to a mathematically illposed inverse problem which needs regularization. The restriction of the solution set of this inverse problem by using prior information about the damage properties is advisable to obtain meaningful solutions. Compared to the undamaged state typically only a few local stiffness changes occur while the other areas remain unchanged. This change can be described by a sparse damage parameter vector. Such a sparse vector can be identified by employing $L_1$-regularization techniques. This paper presents a novel framework for damage parameter identification by combining sparse solution techniques with an Extended Kalman Filter. In order to ensure sparsity of the damage parameter vector the measurement equation is expanded by an additional nonlinear $L_1$-minimizing observation. This fictive measurement equation accomplishes stability of the Extended Kalman Filter and leads to a sparse estimation. For verification, a proof-of-concept example on a quadratic aluminum plate is presented.

• Smart Structures and Systems
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• 제15권2호
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• pp.409-424
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• 2015

Structural nonlinearity is a common phenomenon encountered in engineering structures under severe dynamic loading. It is necessary to localize and identify structural nonlinearities using structural dynamic measurements for damage detection and performance evaluation of structures. However, identification of nonlinear structural systems is a difficult task, especially when proper mathematical models for structural nonlinear behaviors are not available. In prior studies on nonparametric identification of nonlinear structures, the locations of structural nonlinearities are usually assumed known and all structural responses are measured. In this paper, an identification algorithm is proposed for locating and identifying model-free structural nonlinearities and systems using incomplete measurements of structural responses. First, equivalent linear structural systems are established and identified by the extended Kalman filter (EKF). The locations of structural nonlinearities are identified. Then, the model-free structural nonlinear restoring forces are approximated by power series polynomial models. The unscented Kalman filter (UKF) is utilized to identify structural nonlinear restoring forces and structural systems. Both numerical simulation examples and experimental test of a multi-story shear building with a MR damper are used to validate the proposed algorithm.

• Smart Structures and Systems
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• 제14권6호
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• pp.1105-1129
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• 2014

The unscented Kalman filter (UKF) has been developed for nonlinear model parametric identification, and it assumes that the model parameters are symmetrically distributed about their mean values without any constrains. However, the parameters in many applications are confined within certain ranges to make sense physically. In this paper, a constrained unscented Kalman filter (CUKF) algorithm is proposed to improve accuracy of numerical substructure modeling in hybrid testing. During hybrid testing, the numerical models of numerical substructures which are assumed identical to the physical substructures are updated online with the CUKF approach based on the measurement data from physical substructures. The CUKF method adopts sigma points (i.e., sample points) projecting strategy, with which the positions and weights of sigma points violating constraints are modified. The effectiveness of the proposed hybrid testing method is verified by pure numerical simulation and real-time as well as slower hybrid tests with nonlinear specimens. The results show that the new method has better accuracy compared to conventional hybrid testing with fixed numerical model and hybrid testing based on model updating with UKF.

• Advances in nano research
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• 제9권1호
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• pp.59-67
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• 2020

Filter cake formation during rotary drilling operation is an unavoidable scenario, hence there is need for constant improvement in the approaches used in monitoring the cake thickness growth in order to prevent drill-string sticking. This study proposes an improved model that predicts the growth of mud cake thickness overtime with the consideration of the addition of nanoparticles in the formulated drilling fluid system. Ferric oxide, titanium dioxide and copper oxide nanoparticles were used in varying amounts (2 g, 4 g and 6 g), and filtration data were obtained from the HPHT filtration test. The filter cakes formed were further analyzed with scanning electron microscope to obtain the morphological characteristics. The data obtained was used to validate the new filtrate loss model. This model specifically presents the concept of time variation in filter cake formation as against the previous works of constant and definite time. Regression coefficient which is a statistical measure was used to validate the new model and the predicted results were compared with the API model. The new model showed R² values of 99.9%, and the predictions from the proposed filtration model can be said to be more closely related to the experimental data than that predicted from the API model from the SSE and RMSE results.