Modeling and experimental verification of a squeeze mode magnetorheological damper using a novel hysteresis model

2019 ◽  
Vol 233 (15) ◽  
pp. 5253-5263 ◽  
Author(s):  
Fanxu Meng ◽  
Jin Zhou ◽  
Chaowu Jin ◽  
Wentao Ji
Keyword(s):  
Differential Evolution Algorithm ◽  
Magnetorheological Fluid ◽  
Magnetorheological Damper ◽  
Small Displacement ◽  
Hyperbolic Model ◽  
Model Parameters ◽  
Proposed Model ◽  
Strengthen Effect ◽  
Novel Model ◽  
Better Than

The squeeze mode of the magnetorheological damper can be used to stabilize precision instruments (balances, optical devices, etc.) to eliminate interference from external vibrational noise, due to the small displacement and large damping offered by the magnetorheological fluid. The squeeze-strengthen effect observed experimentally in the magnetorheological fluid with squeeze mode can lead to the strain stiffening phenomenon, which is similar to that of the magnetorheological elastomer. In this study, a novel model is developed to characterize the dynamics of the squeeze mode magnetorheological damper considering the strain stiffening hysteresis behavior. An experimentally derived differential evolution algorithm is used to identify the model parameters. Simulation results show that the proposed model can accurately describe the dynamics of the squeeze mode magnetorheological damper including the strain stiffening phenomenon. Furthermore, the identified results obtained by the proposed model appear to be better than those obtained by the hyperbolic model.

scholarly journals Latent Semantic Analysis using a Dennis Coefficient for English Sentiment Classification in a Parallel System

2018 ◽  
Vol 13 (3) ◽  
pp. 408-428 ◽  
Author(s):  
Phu Vo Ngoc
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Sentiment Classification ◽  
Training Data ◽  
The Novel ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Novel Model ◽  
Better Than

We have already survey many significant approaches for many years because there are many crucial contributions of the sentiment classification which can be applied in everyday life, such as in political activities, commodity production, and commercial activities. We have proposed a novel model using a Latent Semantic Analysis (LSA) and a Dennis Coefficient (DNC) for big data sentiment classification in English. Many LSA vectors (LSAV) have successfully been reformed by using the DNC. We use the DNC and the LSAVs to classify 11,000,000 documents of our testing data set to 5,000,000 documents of our training data set in English. This novel model uses many sentiment lexicons of our basis English sentiment dictionary (bESD). We have tested the proposed model in both a sequential environment and a distributed network system. The results of the sequential system are not as good as that of the parallel environment. We have achieved 88.76% accuracy of the testing data set, and this is better than the accuracies of many previous models of the semantic analysis. Besides, we have also compared the novel model with the previous models, and the experiments and the results of our proposed model are better than that of the previous model. Many different fields can widely use the results of the novel model in many commercial applications and surveys of the sentiment classification.

A New Rheological Model of Magnetorheological Fluids for CFD: Comparison and Validation

2018 ◽  
Author(s):  
Muaz Kemerli ◽  
Tahsin Engin ◽  
Zekeriya Parlak
Keyword(s):  
Experimental Data ◽  
Rheological Model ◽  
Numerical Models ◽  
Fluid Motion ◽  
Magnetorheological Fluid ◽  
Magnetorheological Damper ◽  
Control Approach ◽  
Sensitive Structure ◽  
Fluid Behaviour ◽  
Better Than

Magnetorheological fluid is a special smart fluid which can show different rheological properties under different magnetic flux densities due to its magnetically sensitive structure. This makes the fluid able to be manipulated and semi-actively controlled for various applications such as dampers, clutches and brakes. To provide an effective damping it is necessary to create an appropriate control algorithm. In order to design a structure with magnetorheological fluid and to get an idea for a control approach, the physics of the fluid motion has to be modelled. Computational Fluid Dynamics is an effective tool to model any fluid behaviour or any fluid involved structure. For magnetorheological devices, despite number of numerical models available in the literature, a befitting model is not yet presented. In this study a mapped rheological model is proposed and used in a magnetorheological damper simulation. The results are compared with other models and experimental data. It is shown that the new mapped model is effective and better than old approaches. It also showed a good agreement with the experimental data.

scholarly journals Rumor Detection Based on Attention CNN and Time Series of Context Information

2021 ◽  
Vol 13 (11) ◽  
pp. 267
Author(s):  
Yun Peng ◽  
Jianmei Wang
Keyword(s):  
Time Series ◽  
Life Cycles ◽  
Attention Mechanism ◽  
Model Parameters ◽  
Proposed Model ◽  
Svm Algorithm ◽  
Model Training ◽  
Rumor Detection ◽  
Whole Life Cycle ◽  
Better Than

This study aims to explore the time series context and sentiment polarity features of rumors’ life cycles, and how to use them to optimize the CNN model parameters and improve the classification effect. The proposed model is a convolutional neural network embedded with an attention mechanism of sentiment polarity and time series information. Firstly, the whole life cycle of rumors is divided into 20 groups by the time series algorithm and each group of texts is trained by Doc2Vec to obtain the text vector. Secondly, the SVM algorithm is used to obtain the sentiment polarity features of each group. Lastly, the CNN model with the spatial attention mechanism is used to obtain the rumors’ classification. The experiment results show that the proposed model introduced with features of time series and sentiment polarity is very effective for rumor detection, and can greatly reduce the number of iterations for model training as well. The accuracy, precision, recall and F1 of the attention CNN are better than the latest benchmark model.

scholarly journals GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder

2021 ◽  
Vol 17 (12) ◽  
pp. e1009655
Author(s):  
Lei Li ◽  
Yu-Tian Wang ◽  
Cun-Mei Ji ◽  
Chun-Hou Zheng ◽  
Jian-Cheng Ni ◽  
...  
Keyword(s):  
Cross Validation ◽  
State Of The Art ◽  
Human Diseases ◽  
Biological Processes ◽  
Proposed Model ◽  
Disease Associations ◽  
Convolutional Autoencoder ◽  
Non Coding Rnas ◽  
Novel Model ◽  
Better Than

microRNAs (miRNAs) are small non-coding RNAs related to a number of complicated biological processes. A growing body of studies have suggested that miRNAs are closely associated with many human diseases. It is meaningful to consider disease-related miRNAs as potential biomarkers, which could greatly contribute to understanding the mechanisms of complex diseases and benefit the prevention, detection, diagnosis and treatment of extraordinary diseases. In this study, we presented a novel model named Graph Convolutional Autoencoder for miRNA-Disease Association Prediction (GCAEMDA). In the proposed model, we utilized miRNA-miRNA similarities, disease-disease similarities and verified miRNA-disease associations to construct a heterogeneous network, which is applied to learn the embeddings of miRNAs and diseases. In addition, we separately constructed miRNA-based and disease-based sub-networks. Combining the embeddings of miRNAs and diseases, graph convolution autoencoder (GCAE) is utilized to calculate association scores of miRNA-disease on two sub-networks, respectively. Furthermore, we obtained final prediction scores between miRNAs and diseases by adopting an average ensemble way to integrate the prediction scores from two types of subnetworks. To indicate the accuracy of GCAEMDA, we applied different cross validation methods to evaluate our model whose performance were better than the state-of-the-art models. Case studies on a common human diseases were also implemented to prove the effectiveness of GCAEMDA. The results demonstrated that GCAEMDA were beneficial to infer potential associations of miRNA-disease.

Parametric Identification of Carbon Nanotube Nanocomposites Constitutive Response

2019 ◽  
Vol 86 (4) ◽  
Author(s):  
Giovanni Formica ◽  
Michela Taló ◽  
Giulia Lanzara ◽  
Walter Lacarbonara
Keyword(s):  
Carbon Nanotube ◽  
Mesoscale Model ◽  
Differential Evolution Algorithm ◽  
Material Design ◽  
Fine Tuning ◽  
Model Parameters ◽  
Restoring Force ◽  
Stick Slip ◽  
Proposed Model ◽  
Constitutive Parameters

Hysteresis due to stick-slip energy dissipation in carbon nanotube (CNT) nanocomposites is experimentally observed, measured, and identified through a one-dimensional (1D) phenomenological model obtained via reduction of a three-dimensional (3D) mesoscale model. The proposed model is shown to describe the nanocomposite hysteretic response, which features the transition from the purely elastic to the post-stick-slip behavior characterized by the interfacial frictional sliding motion between the polymer chains and the CNTs. Parametric analyses shed light onto the physical meaning of each model parameter and the influence on the material response. The model parameters are determined by fitting the experimentally acquired force–displacement curves of CNT/polymer nanocomposites using a differential evolution algorithm. Nanocomposite beam-like samples made of a high performance engineering polymer and high-aspect-ratio CNTs are fabricated and tested in a bending mode at increasing deflection amplitudes. The entire time histories of the restoring force are fitted by the model through a unique set of parameters. The parameter identification is carried out for nanocomposites with various CNT weight fractions, so as to highlight the model capability to identify a wide variety of nanocomposite hysteretic behaviors through a fine tuning of its constitutive parameters. By exploiting the proposed model, a nanostructured material design and its optimization are made possible toward the exploitation of these promising materials for engineering applications.

A grey hysteresis model of magnetorheological damper

2021 ◽  
pp. 1045389X2110571
Author(s):  
Xiong Deng ◽  
Xiaomin Dong ◽  
Wenfeng Li ◽  
Jun Xi
Keyword(s):  
Predictive Ability ◽  
Mr Damper ◽  
Inverse Model ◽  
Magnetorheological Damper ◽  
Grey Model ◽  
Small Samples ◽  
Model Parameters ◽  
Grey Theory ◽  
Hysteresis Model ◽  
Novel Model

Owing to the complex nonlinear hysteresis of magnetorheological (MR) damper, the modeling of an MR damper is an issue. This paper examines a novel MR damper hysteresis model based on the grey theory, which can fully mine the internal laws for the data with small samples and poor information. To validate the model, the experiment is conducted in the MTS platform, and then the experimental results are compiled to identify the model parameters. Considering the complexity of the grey model and its inverse model solution, the grey model is simplified in two ways based on the grey relational analysis method. Furthermore, the simplified grey model compares to other models to prove the superiority of the grey model. The analysis suggests the fitting results correspond to the measured results, and the mean relative error (MRE) of grey model is within 2.04%. After the grey model is simplified, its accuracy is slightly reduced, while its inverse model is easier to solve and makes a unique solution. Finally, compared with the polynomial and Bouc-Wen model, the novel model with fewer identification parameters has high accuracy and predictive ability. This novel model has fabulous potential in designing the control strategy of MR damper.

In-Plane Flexible Ring Tire Model—Part 1: Modeling and Parameter Identification

2018 ◽  
Vol 46 (3) ◽  
pp. 174-219 ◽  
Author(s):  
Bin Li ◽  
Xiaobo Yang ◽  
James Yang ◽  
Yunqing Zhang ◽  
Zeyu Ma
Keyword(s):  
Static Load ◽  
Model Parameters ◽  
Tire Model ◽  
Test Results ◽  
Lumped Mass ◽  
Virtual Test ◽  
Proposed Model ◽  
Particle Swarm Method ◽  
Vehicle Dynamic Simulation ◽  
Flexible Ring

ABSTRACT The tire model is essential for accurate and efficient vehicle dynamic simulation. In this article, an in-plane flexible ring tire model is proposed, in which the tire is composed of a rigid rim, a number of discretized lumped mass belt points, and numerous massless tread blocks attached on the belt. One set of tire model parameters is identified by approaching the predicted results with ADAMS® FTire virtual test results for one particular cleat test through the particle swarm method using MATLAB®. Based on the identified parameters, the tire model is further validated by comparing the predicted results with FTire for the static load-deflection tests and other cleat tests. Finally, several important aspects regarding the proposed model are discussed.

EXPONENTIATED HALF-LOGISTIC LOMAX DISTRIBUTION WITH PROPERTIES AND APPLICATION

2019 ◽  
Vol XVI (2) ◽  
pp. 1-11
Author(s):  
Farrukh Jamal ◽  
Hesham Mohammed Reyad ◽  
Soha Othman Ahmed ◽  
Muhammad Akbar Ali Shah ◽  
Emrah Altun
Keyword(s):  
Real Data ◽  
Continuous Model ◽  
Model Parameters ◽  
Data Set ◽  
Lomax Distribution ◽  
Mathematical Properties ◽  
Proposed Model ◽  
Probability Weighted Moment ◽  
Record Statistics ◽  
Maximum Likelihood Criterion

A new three-parameter continuous model called the exponentiated half-logistic Lomax distribution is introduced in this paper. Basic mathematical properties for the proposed model were investigated which include raw and incomplete moments, skewness, kurtosis, generating functions, Rényi entropy, Lorenz, Bonferroni and Zenga curves, probability weighted moment, stress strength model, order statistics, and record statistics. The model parameters were estimated by using the maximum likelihood criterion and the behaviours of these estimates were examined by conducting a simulation study. The applicability of the new model is illustrated by applying it on a real data set.

scholarly journals A General Temperature-Dependent Stress–Strain Constitutive Model for Polymer-Bonded Composite Materials

Polymers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1393
Author(s):  
Xiaochang Duan ◽  
Hongwei Yuan ◽  
Wei Tang ◽  
Jingjing He ◽  
Xuefei Guan
Keyword(s):  
Composite Materials ◽  
Constitutive Model ◽  
Model Parameters ◽  
Temperature Dependent ◽  
Stress Strain ◽  
Proposed Model ◽  
Single Temperature ◽  
Testing Data ◽  
Bonded Composite ◽  
Tension And Compression

This study develops a general temperature-dependent stress–strain constitutive model for polymer-bonded composite materials, allowing for the prediction of deformation behaviors under tension and compression in the testing temperature range. Laboratory testing of the material specimens in uniaxial tension and compression at multiple temperatures ranging from −40 ∘C to 75 ∘C is performed. The testing data reveal that the stress–strain response can be divided into two general regimes, namely, a short elastic part followed by the plastic part; therefore, the Ramberg–Osgood relationship is proposed to build the stress–strain constitutive model at a single temperature. By correlating the model parameters with the corresponding temperature using a response surface, a general temperature-dependent stress–strain constitutive model is established. The effectiveness and accuracy of the proposed model are validated using several independent sets of testing data and third-party data. The performance of the proposed model is compared with an existing reference model. The validation and comparison results show that the proposed model has a lower number of parameters and yields smaller relative errors. The proposed constitutive model is further implemented as a user material routine in a finite element package. A simple structural example using the developed user material is presented and its accuracy is verified.

scholarly journals Flexural behavior of composite structural insulated panels with magnesium oxide board facings

2020 ◽  
Vol 20 (4) ◽  
Author(s):  
Łukasz Smakosz ◽  
Ireneusz Kreja ◽  
Zbigniew Pozorski
Keyword(s):  
Magnesium Oxide ◽  
Material Model ◽  
Expanded Polystyrene ◽  
Flexural Behavior ◽  
Joint Analysis ◽  
Model Parameters ◽  
Fe Model ◽  
Computational Results ◽  
Four Point Bending ◽  
Proposed Model

Abstract The current report is devoted to the flexural analysis of a composite structural insulated panel (CSIP) with magnesium oxide board facings and expanded polystyrene (EPS) core, that was recently introduced to the building industry. An advanced nonlinear FE model was created in the ABAQUS environment, able to simulate the CSIP’s flexural behavior in great detail. An original custom code procedure was developed, which allowed to include material bimodularity to significantly improve the accuracy of computational results and failure mode predictions. Material model parameters describing the nonlinear range were identified in a joint analysis of laboratory tests and their numerical simulations performed on CSIP beams of three different lengths subjected to three- and four-point bending. The model was validated by confronting computational results with experimental results for natural scale panels; a good correlation between the two results proved that the proposed model could effectively support the CSIP design process.

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