Simulating Maximum and Residual Displacements of RC Structures: I. Accuracy

2011 ◽  
Vol 27 (4) ◽  
pp. 1187-1202 ◽  
Author(s):  
Ufuk Yazgan ◽  
Alessandro Dazio
Keyword(s):  
Companion Paper ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Seismic Performance Assessment ◽  
Shake Table Tests ◽  
Rc Structures ◽  
Flexural Response ◽  
Bilinear Models ◽  
Residual Displacements ◽  
Global And Local

Estimation of likely global and local response measures plays an important role in seismic performance assessment. The capabilities and limitations of beam-column element modeling strategies in predicting the dynamic nonlinear flexural response of RC models are investigated in this study. For this purpose, 12 shake table tests are numerically reproduced. Correlations of the predicted deformations with the measured ones are evaluated. The results show that maximum displacements can be estimated with sufficient accuracy if the adopted hysteresis model takes into account stiffness degradation. However, accurate estimation of the residual displacements is found to be difficult to achieve. The results suggest that the assumed small-cycle behavior has a strong influence on the estimated residual displacements. Fiber-section models are found to provide relatively more accurate estimates of the residual displacements than modified Takeda hysteretic and bilinear models. A companion paper, Part II: Sensitivity, presents the sensitivity of the simulated displacements to a set of the model parameters and idealizations.

Simulating Maximum and Residual Displacements of RC Structures: II. Sensitivity

2011 ◽  
Vol 27 (4) ◽  
pp. 1203-1218 ◽  
Author(s):  
Ufuk Yazgan ◽  
Alessandro Dazio
Keyword(s):  
Damping Ratio ◽  
Time Integration ◽  
Integration Step ◽  
Discretization Scheme ◽  
Hysteretic Model ◽  
Step Size ◽  
Shake Table Tests ◽  
Rc Structures ◽  
Residual Displacements ◽  
Response History Analysis

The simulated response of a structure subjected to seismic excitation is sensitive to the idealizations made to model its response. This paper examines critical idealizations and assumptions that have a strong influence on the accuracy of the maximum and residual displacements predicted by response-history analysis. A set of shake table tests are numerically reproduced for this purpose. The investigated idealizations include the discretization scheme, the axial load, the steel hysteretic model, the viscous damping ratio, and the time-integration step size. The results indicate that the simulated residual displacements are significantly more sensitive to the model idealizations than the maximum displacements. It is found that the adopted discretization scheme and the utilized steel hysteresis model have very large influences on simulated residual displacements.

scholarly journals Adaptive State-of-Charge Estimation for Lithium-Ion Batteries by Considering Capacity Degradation

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 122
Author(s):  
Peipei Xu ◽  
Junqiu Li ◽  
Chao Sun ◽  
Guodong Yang ◽  
Fengchun Sun
Keyword(s):  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Maximum Error ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Soc Estimation ◽  
Capacity Degradation ◽  
Battery Capacity

The accurate estimation of a lithium-ion battery’s state of charge (SOC) plays an important role in the operational safety and driving mileage improvement of electrical vehicles (EVs). The Adaptive Extended Kalman filter (AEKF) estimator is commonly used to estimate SOC; however, this method relies on the precise estimation of the battery’s model parameters and capacity. Furthermore, the actual capacity and battery parameters change in real time with the aging of the batteries. Therefore, to eliminate the influence of above-mentioned factors on SOC estimation, the main contributions of this paper are as follows: (1) the equivalent circuit model (ECM) is presented, and the parameter identification of ECM is performed by using the forgetting-factor recursive-least-squares (FFRLS) method; (2) the sensitivity of battery SOC estimation to capacity degradation is analyzed to prove the importance of considering capacity degradation in SOC estimation; and (3) the capacity degradation model is proposed to perform the battery capacity prediction online. Furthermore, an online adaptive SOC estimator based on capacity degradation is proposed to improve the robustness of the AEKF algorithm. Experimental results show that the maximum error of SOC estimation is less than 1.3%.

Residual displacements of RC structures as SDOF systems

2014 ◽  
Vol 44 (5) ◽  
pp. 713-734 ◽  
Author(s):  
Eftychia Liossatou ◽  
Michael N. Fardis
Keyword(s):  
Rc Structures ◽  
Residual Displacements

scholarly journals Battery state of charge estimation and online wheel slip ratio control for autonomous wheeled mobile robot

2021 ◽  
Author(s):  
Maral Partovibakhsh
Keyword(s):  
Kalman Filter ◽  
Power Consumption ◽  
Mobile Robot ◽  
Unscented Kalman Filter ◽  
Longitudinal Velocity ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Slip Ratio ◽  
Soc Estimation ◽  
Increasing Function

For autonomous mobile robots moving in unknown environment, accurate estimation of available power along with the robot power demand for each mission is paramount to successful completion of that mission. Regarding the power consumption, the control unit deals with two tasks simultaneously: 1) it has to monitor the power supply (batteries) state of charge (SoC) constantly. This leads to estimation of robot current available power. Besides, batteries are sensitive to deep discharge or overcharge. The battery SoC is an essential factor in power management of a mobile robot. Accurate estimation of the battery SoC can improve power management, optimize the performance, extend the lifetime, and prevent permanent damage to the batteries. 2) The dynamic characteristics of the terrain the robot traverse requires rapid online modifications in its behaviour. The power required for driving a wheel is an increasing function of its slip ratio. For a wheeled robot moving for driving a wheel is an increasing function of its slip ratio. For a wheeled robot moving on different terrains, slip of the wheels should be checked and compensated for to keep the robot moving with less power consumption. To reduce the power consumption, the target robot moving with less power consumption. To reduce the power consumption, the target of the control system is to keep the slip ratio of the driving wheels around the desired value of the control system is to keep the slip ratio of the driving wheels around the desired value. To fulfill the above mentioned tasks, in this thesis, to increase model validity of lithium-ion battery in various charge/discharge scenarios during the mobile robot operation, the battery capacity fade and internal resistance change are modeled by adding them as state variables to a state space model. Using the output measured data, adaptive unscented Kalman Filter (AUKF) is employed for online model parameters identification of the equivalent circuit model at each sampling time. Subsequently, based on the updated model parameters, SoC estimation is conducted using AUKF. The effectiveness of the proposed method is verified through experiments under different power duties in the lab environment through experiments under different power duties in the lab environment. Better results are obtained both in battery model parameters estimation and the battery SoC estimation in comparison with other Kalman filter extensions. Furthermore, for effective control of the slip ratio, a model-based approach to estimating the longitudinal velocity of the mobile robot is presented. The AUKF is developed to estimate the vehicle longitudinal velocity and the wheel angular velocity using measurements from wheel encoders. Based on the estimated slip ratio, a sliding mode controller is designed for slip control of the uncertain nonlinear dynamical system in the presence of model uncertainties, parameter variations, and disturbances. Experiments are carried out in real time on a four-wheel mobile robot to verify the effectiveness of the estimation algorithm and the controller. It is shown that the controller is able to control the slip ratio of the mobile robot on different terrains while adaptive concept of AUKF leads to better results than the unscented Kalman filter in estimating the vehicle velocity which is difficult to measure in actual practice.

Model-based evaluation of nitrogen removal in a tannery wastewater treatment plant

2004 ◽  
Vol 50 (6) ◽  
pp. 251-260 ◽  
Author(s):  
M.S. Moussa ◽  
A.R. Rojas ◽  
C.M. Hooijmans ◽  
H.J. Gijzen ◽  
M.C.M. van Loosdrecht
Keyword(s):  
Wastewater Treatment ◽  
Activated Sludge ◽  
Wastewater Treatment Plants ◽  
Computer Modelling ◽  
Treatment Plant ◽  
Domestic Wastewater ◽  
Plant Performance ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Biological Tests

Computer modelling has been used in the last 15 years as a powerful tool for understanding the behaviour of activated sludge wastewater treatment systems. However, computer models are mainly applied for domestic wastewater treatment plants (WWTPs). Application of these types of models to industrial wastewater treatment plants requires a different model structure and an accurate estimation of the kinetics and stoichiometry of the model parameters, which may be different from the ones used for domestic wastewater. Most of these parameters are strongly dependent on the wastewater composition. In this study a modified version of the activated sludge model No. 1 (ASM 1) was used to describe a tannery WWTP. Several biological tests and complementary physical-chemical analyses were performed to characterise the wastewater and sludge composition in the context of activated sludge modelling. The proposed model was calibrated under steady-state conditions and validated under dynamic flow conditions. The model was successfully used to obtain insight into the existing plant performance, possible extension and options for process optimisation. The model illustrated the potential capacity of the plant to achieve full denitrification and to handle a higher hydraulic load. Moreover, the use of a mathematical model as an effective tool in decision making was demonstrated.

scholarly journals Online Parameter Identification and State of Charge Estimation of Battery Based on Multitimescale Adaptive Double Kalman Filter Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Wenxian Duan ◽  
Chuanxue Song ◽  
Yuan Chen ◽  
Feng Xiao ◽  
Silun Peng ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Unscented Kalman Filter ◽  
Least Square Method ◽  
State Of Charge ◽  
Least Square ◽  
Accurate Estimation ◽  
Battery Life ◽  
Model Parameters ◽  
Simulation Results

An accurate state of charge (SOC) can provide effective judgment for the BMS, which is conducive for prolonging battery life and protecting the working state of the entire battery pack. In this study, the first-order RC battery model is used as the research object and two parameter identification methods based on the least square method (RLS) are analyzed and discussed in detail. The simulation results show that the model parameters identified under the Federal Urban Driving Schedule (HPPC) condition are not suitable for the Federal Urban Driving Schedule (FUDS) condition. The parameters of the model are not universal through the HPPC condition. A multitimescale prediction model is also proposed to estimate the SOC of the battery. That is, the extended Kalman filter (EKF) is adopted to update the model parameters and the adaptive unscented Kalman filter (AUKF) is used to predict the battery SOC. The experimental results at different temperatures show that the EKF-AUKF method is superior to other methods. The algorithm is simulated and verified under different initial SOC errors. In the whole FUDS operating condition, the RSME of the SOC is within 1%, and that of the voltage is within 0.01 V. It indicates that the proposed algorithm can obtain accurate estimation results and has strong robustness. Moreover, the simulation results after adding noise errors to the current and voltage values reveal that the algorithm can eliminate the sensor accuracy effect to a certain extent.

scholarly journals The Bayesian Covariance Structure Model for Testlets

2020 ◽  
pp. 107699862094120
Author(s):  
Jean-Paul Fox ◽  
Jeremias Wenzel ◽  
Konrad Klotzke
Keyword(s):  
Random Effects ◽  
Random Effect ◽  
Covariance Structure ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Random Effects Model ◽  
Structure Model ◽  
Modeling Framework ◽  
Irt Models ◽  
Covariance Structure Model

Standard item response theory (IRT) models have been extended with testlet effects to account for the nesting of items; these are well known as (Bayesian) testlet models or random effect models for testlets. The testlet modeling framework has several disadvantages. A sufficient number of testlet items are needed to estimate testlet effects, and a sufficient number of individuals are needed to estimate testlet variance. The prior for the testlet variance parameter can only represent a positive association among testlet items. The inclusion of testlet parameters significantly increases the number of model parameters, which can lead to computational problems. To avoid these problems, a Bayesian covariance structure model (BCSM) for testlets is proposed, where standard IRT models are extended with a covariance structure model to account for dependences among testlet items. In the BCSM, the dependence among testlet items is modeled without using testlet effects. This approach does not imply any sample size restrictions and is very efficient in terms of the number of parameters needed to describe testlet dependences. The BCSM is compared to the well-known Bayesian random effects model for testlets using a simulation study. Specifically for testlets with a few items, a small number of test takers, or weak associations among testlet items, the BCSM shows more accurate estimation results than the random effects model.

scholarly journals Bayesian modelling of a hepatitis A outbreak in men who have sex with men in Sydney, Australia, 1991/1992

2019 ◽  
Vol 147 ◽  
Author(s):  
X.-S. Zhang ◽  
A. Charlett
Keyword(s):  
Least Squares ◽  
Hepatitis A ◽  
Hepatitis A Virus ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Posterior Distributions ◽  
Bayesian Modelling ◽  
Distributed Parameters ◽  
Sex With Men ◽  
Sydney Australia

Abstract To control hepatitis A spread by vaccination, accurate estimation of transmissibility is vital. Regan et al. (2016) proposed a model of hepatitis A virus (HAV) transmission and used least squares to calibrate model to the 1991/1992 HAV outbreak in men who have sex with men (MSM) in Sydney, Australia. Based on the estimate of R0, they obtained the critical immunity of 70% and showed that when the proportion immune <70%, there is a definite chance for outbreaks to take place. The immunity level from previous surveys ranges from 32% to 64% after 1996 while no outbreaks in Australian MSMs have been reported since 1996. Further noticing the ill-distributed parameters, we argue that their estimate of R0 is not accurate. In this study, we revisited their model by Bayesian inference, which has privilege over least squares. We obtained the appropriate posterior distributions of parameters and the estimate of R0 ranges from 1.38 to 2.89, indicating a critical immunity of 65%. The reduction in critical immunity and outbreak probabilities predicts the absence of outbreaks in Australian MSMs since 1996. Our study shows the importance of using appropriate methods to provide reliable and accurate estimates of the model parameters especially the transmissibility.

scholarly journals Multi-Collocation-Based Estimation of Wave Climate in a Non-Tidal Bay: The Case Study of Bagnoli-Coroglio Bay (Tyrrhenian Sea)

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1936 ◽  
Author(s):  
Pasquale Contestabile ◽  
Fabio Conversano ◽  
Luca Centurioni ◽  
Umberto Golia ◽  
Luigi Musco ◽  
...  
Keyword(s):  
Wave Propagation ◽  
Wave Climate ◽  
Propagation Model ◽  
Open Boundary ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Tyrrhenian Sea ◽  
Wave Direction ◽  
Wave Propagation Model

In this paper, the advantages of shaping a non-conventional triple collocation-based calibration of a wave propagation model is pointed out. Illustrated through a case study in the Bagnoli-Coroglio Bay (central Tyrrhenian Sea, Italy), a multi-comparison between numerical data and direct measurements have been carried out. The nearshore wave propagation model output has been compared with measurements from an acoustic Doppler current profiler (ADCP) and an innovative low-cost drifter-derived GPS-based wave buoy located outside the bay. The triple collocation—buoy, ADCP and virtual numerical point—make possible an implicit validation between instrumentations and between instrumentation and numerical model. The procedure presented here advocates for an alternative “two-step” strategy. Indeed, the triple collocation technique has been used solely to provide a first “rough” calibration of one numerical domain in which the input open boundary has been placed, so that the main wave direction is orthogonally aligned. The need for a fast and sufficiently accurate estimation of wave model parameters (first step) and then an ensemble of five different offshore boundary orientations have been considered, referencing for a more detailed calibration to a short time series of a GPS-buoy installed in the study area (second step). Such a stage involves the introduction of an enhancement factor for the European Centre for Medium-Range Weather Forecasts (ECMWF) dataset, used as input for the model. Finally, validation of the final model’s predictions has been carried out by comparing ADCP measurements in the bay. Despite some limitations, the results reveal that the approach is promising and an excellent correlation can be found, especially in terms of significant wave height.

scholarly journals Fast four-dimensional tensile test monitored via X-ray computed tomography: Elastoplastic identification from radiographs

2019 ◽  
Vol 54 (1) ◽  
pp. 44-53 ◽  
Author(s):  
Clément Jailin ◽  
Ante Buljac ◽  
Amine Bouterf ◽  
François Hild ◽  
Stéphane Roux
Keyword(s):  
Tensile Test ◽  
Correlation Method ◽  
Integrated Approach ◽  
Companion Paper ◽  
Constitutive Law ◽  
Free Form ◽  
Model Parameters ◽  
Displacement Fields ◽  
Integrated Method ◽  
Ductile Behavior

A projection-based digital volume correlation method (presented in a companion paper) is extended to an integrated approach for the calibration of an elastoplastic law based on a single radiograph per loading step. Instead of following a two-step sequential procedure (i.e. first, measurement of the displacement field; second, identification), the integrated method aims at identifying few model parameters directly from the gray-level projections. The analysis of an in situ tensile test composed of 127 loading steps performed in 6 min is presented. An isotropic elastoplastic constitutive law with free-form hardening behavior (i.e. controlled by only eight parameters) is identified and shows a ductile behavior (up to 6.3% strain before failure). A large improvement on the residual quality is shown and validates the proposed model and procedure. The obtained displacement fields are similar to those measured with no mechanical integration. A different parameterization of the constitutive law provides a very close result, thereby assessing the robustness of the procedure.

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