Multiaxial Cyclic Ratcheting in Coiled Tubing—Part II: Experimental Program and Model Evaluation

1999 ◽  
Vol 122 (2) ◽  
pp. 162-167 ◽  
Author(s):  
Radovan Rolovic ◽  
Steven M. Tipton
Keyword(s):  
Internal Pressure ◽  
Constant Pressure ◽  
Material Behavior ◽  
Experimental Program ◽  
Test Machine ◽  
Loading History ◽  
Coiled Tubing ◽  
Proposed Model ◽  
Model Block ◽  
The Mean

An experimental program was conducted to evaluate the plasticity model proposed in a separate paper (Part I). Constant pressure, cyclic bend-straighten tests were performed to identify material parameters required by the analytical model. Block pressure, bend-straighten tests were conducted to evaluate the proposed model. Experiments were performed on full-size coiled tubing samples using a specialized test machine. Two commonly used coiled tubing materials and four specimen sizes were subjected to load histories consisting of bending-straightening cycles with varying levels of internal pressure. It was observed that cyclic ratcheting rates can be reversed without reversing the mean stress, i.e., diametral growth of coiled tubing can be followed by diametral shrinkage even when the internal pressure is kept positive, depending on the loading history. This material behavior is explained in the context of the new theory. The correlation between the predictions and the test data is very good. [S0094-4289(00)00502-8]

Constitutive Description of Temperature-Dependent Nonproportional Cyclic Viscoplasticity

1997 ◽  
Vol 119 (1) ◽  
pp. 12-19 ◽  
Author(s):  
Xian Jie Yang
Keyword(s):  
Evolution Equations ◽  
Kinematic Hardening ◽  
Complex Loading ◽  
Deformation Resistance ◽  
Material Behavior ◽  
Temperature History ◽  
Loading History ◽  
Dependent Behavior ◽  
Proposed Model ◽  
Hardening Rules

This paper is concerned with the constitutive modeling of the temperature history dependent behavior of metallic materials under uniaxial and nonproportional cyclic loadings. In the study, a class of kinematic hardening rules characterized by a decomposition of the total kinematic hardening variable is discussed. A new nonproportionality is defined. In order to consider the influence of complex cyclic loading and temperature histories on materials behavior, an apparent isotropic deformation resistance parameter Qasm is proposed and the evolution equations of the isotropic deformation resistance Q are offered to correlate the memory effect of previous loading history on material behavior. The proposed model is applied to the description of complex cyclic deformation behavior of 1Cr18Ni9Ti stainless steel, and this model gives good results for the prediction of complex tests under complex loading history and at stepwise temperature changes.

Simple explicit model of liquid mean flow in region above axial impeller

1985 ◽  
Vol 50 (11) ◽  
pp. 2396-2410
Author(s):  
Miloslav Hošťálek ◽  
Ivan Fořt
Keyword(s):  
Vortex Flow ◽  
Flow Model ◽  
Quantitative Agreement ◽  
Mean Flow ◽  
Flat Bottom ◽  
Proposed Model ◽  
Minimum Number ◽  
The Mean ◽  
Model Equations ◽  
Radial Circulation

The study describes a method of modelling axial-radial circulation in a tank with an axial impeller and radial baffles. The proposed model is based on the analytical solution of the equation for vortex transport in the mean flow of turbulent liquid. The obtained vortex flow model is tested by the results of experiments carried out in a tank of diameter 1 m and with the bottom in the shape of truncated cone as well as by the data published for the vessel of diameter 0.29 m with flat bottom. Though the model equations are expressed in a simple form, good qualitative and even quantitative agreement of the model with reality is stated. Apart from its simplicity, the model has other advantages: minimum number of experimental data necessary for the completion of boundary conditions and integral nature of these data.

scholarly journals Modeling the Excess Velocity of Low-Viscous Taylor Droplets in Square Microchannels

Fluids ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 162 ◽  
Author(s):  
Thorben Helmers ◽  
Philip Kemper ◽  
Jorg Thöming ◽  
Ulrich Mießner
Keyword(s):  
High Speed ◽  
Process Intensification ◽  
Continuous Phase ◽  
Channel Cross Section ◽  
Optimization Approach ◽  
Mean Flow ◽  
Bypass Flow ◽  
Wall Film ◽  
Proposed Model ◽  
The Mean

Microscopic multiphase flows have gained broad interest due to their capability to transfer processes into new operational windows and achieving significant process intensification. However, the hydrodynamic behavior of Taylor droplets is not yet entirely understood. In this work, we introduce a model to determine the excess velocity of Taylor droplets in square microchannels. This velocity difference between the droplet and the total superficial velocity of the flow has a direct influence on the droplet residence time and is linked to the pressure drop. Since the droplet does not occupy the entire channel cross-section, it enables the continuous phase to bypass the droplet through the corners. A consideration of the continuity equation generally relates the excess velocity to the mean flow velocity. We base the quantification of the bypass flow on a correlation for the droplet cap deformation from its static shape. The cap deformation reveals the forces of the flowing liquids exerted onto the interface and allows estimating the local driving pressure gradient for the bypass flow. The characterizing parameters are identified as the bypass length, the wall film thickness, the viscosity ratio between both phases and the C a number. The proposed model is adapted with a stochastic, metaheuristic optimization approach based on genetic algorithms. In addition, our model was successfully verified with high-speed camera measurements and published empirical data.

scholarly journals A Deep Learning Approach to Predict Autism Spectrum Disorder Using Multisite Resting-State fMRI

2021 ◽  
Vol 11 (8) ◽  
pp. 3636
Author(s):  
Faria Zarin Subah ◽  
Kaushik Deb ◽  
Pranab Kumar Dhar ◽  
Takeshi Koshiba
Keyword(s):  
Autism Spectrum Disorder ◽  
Resting State ◽  
State Of The Art ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Bootstrap Analysis ◽  
Proposed Model ◽  
Art Methods ◽  
The Mean

Autism spectrum disorder (ASD) is a complex and degenerative neuro-developmental disorder. Most of the existing methods utilize functional magnetic resonance imaging (fMRI) to detect ASD with a very limited dataset which provides high accuracy but results in poor generalization. To overcome this limitation and to enhance the performance of the automated autism diagnosis model, in this paper, we propose an ASD detection model using functional connectivity features of resting-state fMRI data. Our proposed model utilizes two commonly used brain atlases, Craddock 200 (CC200) and Automated Anatomical Labelling (AAL), and two rarely used atlases Bootstrap Analysis of Stable Clusters (BASC) and Power. A deep neural network (DNN) classifier is used to perform the classification task. Simulation results indicate that the proposed model outperforms state-of-the-art methods in terms of accuracy. The mean accuracy of the proposed model was 88%, whereas the mean accuracy of the state-of-the-art methods ranged from 67% to 85%. The sensitivity, F1-score, and area under receiver operating characteristic curve (AUC) score of the proposed model were 90%, 87%, and 96%, respectively. Comparative analysis on various scoring strategies show the superiority of BASC atlas over other aforementioned atlases in classifying ASD and control.

Shakedown Limit Load Determination for a Kinematically Hardening 90-Degree Pipe Bend Subjected to Constant Internal Pressure and Cyclic Bending

2007 ◽  
Author(s):  
Hany F. Abdalla ◽  
Mohammad M. Megahed ◽  
Maher Y. A. Younan
Keyword(s):  
Finite Element ◽  
Internal Pressure ◽  
Plastic Material ◽  
Kinematic Hardening ◽  
Limit Load ◽  
Material Behavior ◽  
Pipe Bend ◽  
Cyclic Bending ◽  
Perfectly Plastic ◽  
Shakedown Limit

A simplified technique for determining the shakedown limit load of a structure employing an elastic-perfectly-plastic material behavior was previously developed and successfully applied to a long radius 90-degree pipe bend. The pipe bend is subjected to constant internal pressure and cyclic bending. The cyclic bending includes three different loading patterns namely; in-plane closing, in-plane opening, and out-of-plane bending moment loadings. The simplified technique utilizes the finite element method and employs small displacement formulation to determine the shakedown limit load without performing lengthy time consuming full cyclic loading finite element simulations or conventional iterative elastic techniques. In the present paper, the simplified technique is further modified to handle structures employing elastic-plastic material behavior following the kinematic hardening rule. The shakedown limit load is determined through the calculation of residual stresses developed within the pipe bend structure accounting for the back stresses, determined from the kinematic hardening shift tensor, responsible for the translation of the yield surface. The outcomes of the simplified technique showed very good correlation with the results of full elastic-plastic cyclic loading finite element simulations. The shakedown limit moments output by the simplified technique are used to generate shakedown diagrams of the pipe bend for a spectrum of constant internal pressure magnitudes. The generated shakedown diagrams are compared with the ones previously generated employing an elastic-perfectly-plastic material behavior. These indicated conservative shakedown limit moments compared to the ones employing the kinematic hardening rule.

Fatigue Behaviour of Dented Pipes Under Internal Pressure: A Numerical-Experimental Approach

2018 ◽  
Author(s):  
Mario A. Polanco-Loria ◽  
Håvar Ilstad
Keyword(s):  
Fatigue Life ◽  
Internal Pressure ◽  
Fatigue Behavior ◽  
Crack Depth ◽  
Fatigue Behaviour ◽  
Experimental Methodology ◽  
Experimental Program ◽  
Metal Loss ◽  
Life Predictions ◽  
Dent Depth

This work presents a numerical-experimental methodology to study the fatigue behavior of dented pipes under internal pressure. A full-scale experimental program on dented pipes containing gouges were achieved. Two types of defects were studied: metal loss (plain dent) and sharp notch. Both defects acting independently reduce the fatigue life performance but their combination is highly detrimental and must be avoided. We did not find a severity threshold (e.g. dent depth or crack depth) where these defects could coexist. In addition, based on numerical analyses we proposed a new expression for stress concentration factor (SCF) in line with transversal indentation. This information was successfully integrated into a simple fatigue model where the fatigue life predictions were practically inside the window of experimental results.

scholarly journals Recommending Mobile Microblog Users via a Tensor Factorization Based on User Cluster Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Xiangwen Liao ◽  
Lingying Zhang ◽  
Jingjing Wei ◽  
Dingda Yang ◽  
Guolong Chen
Keyword(s):  
Network Centrality ◽  
Network Clustering ◽  
Tensor Factorization ◽  
Latent Factors ◽  
Cp Decomposition ◽  
Temporal Features ◽  
User Influence ◽  
Proposed Model ◽  
The Mean ◽  
Content Similarity

User influence is a very important factor for microblog user recommendation in mobile social network. However, most existing user influence analysis works ignore user’s temporal features and fail to filter the marketing users with low influence, which limits the performance of recommendation methods. In this paper, a Tensor Factorization based User Cluster (TFUC) model is proposed. We firstly identify latent influential users by neural network clustering. Then, we construct a features tensor according to latent influential user’s opinion, activity, and network centrality information. Furthermore, user influences are predicted by the latent factors resulting from the temporal restrained CP decomposition. Finally, we recommend microblog users considering both user influence and content similarity. Our experimental results show that the proposed model significantly improves recommendation performance. Meanwhile, the mean average precision of TFUC outperforms the baselines with 3.4% at least.

scholarly journals Development of Macroscopic Cell-Based Logistic Lane Change Prediction Model

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Christina Ng ◽  
Susilawati Susilawati ◽  
Md Abdus Samad Kamal ◽  
Irene Mei Leng Chew
Keyword(s):  
Prediction Model ◽  
Transmission Model ◽  
Lane Change ◽  
Traffic Data ◽  
Cell Transmission Model ◽  
Traffic State ◽  
Proposed Model ◽  
Significant Difference ◽  
The Mean ◽  
Change Prediction

This paper aims at developing a macroscopic cell-based lane change prediction model in a complex urban environment and integrating it into cell transmission model (CTM) to improve the accuracy of macroscopic traffic state estimation. To achieve these objectives, first, based on the observed traffic data, the binary logistic lane change model is developed to formulate the lane change occurrence. Second, the binary logistic lane change is integrated into CTM by refining CTM formulations on how the vehicles in the cell are moving from one cell to another in a longitudinal manner and how cell occupancy is updated after lane change occurrences. The performance of the proposed model is evaluated by comparing the simulated cell occupancy of the proposed model with cell occupancy of US-101 next generation simulation (NGSIM) data. The results indicated no significant difference between the mean of the cell occupancies of the proposed model and the mean of cell occupancies of actual data with a root-mean-square-error (RMSE) of 0.04. Similar results are found when the proposed model was further tested with I80 highway data. It is suggested that the mean of cell occupancies of I80 highway data was not different from the mean of cell occupancies of the proposed model with 0.074 RMSE (0.3 on average).

scholarly journals Portfolio Optimization with a Mean–Absolute Deviation–Entropy Multi-Objective Model

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1266
Author(s):  
Weng Siew Lam ◽  
Weng Hoe Lam ◽  
Saiful Hafizah Jaaman
Keyword(s):  
Portfolio Optimization ◽  
Performance Ratio ◽  
Programming Approach ◽  
Mean Absolute Deviation ◽  
Diversification Strategy ◽  
Absolute Deviation ◽  
Multi Objective ◽  
Proposed Model ◽  
The Mean ◽  
Naïve Diversification

Investors wish to obtain the best trade-off between the return and risk. In portfolio optimization, the mean-absolute deviation model has been used to achieve the target rate of return and minimize the risk. However, the maximization of entropy is not considered in the mean-absolute deviation model according to past studies. In fact, higher entropy values give higher portfolio diversifications, which can reduce portfolio risk. Therefore, this paper aims to propose a multi-objective optimization model, namely a mean-absolute deviation-entropy model for portfolio optimization by incorporating the maximization of entropy. In addition, the proposed model incorporates the optimal value of each objective function using a goal-programming approach. The objective functions of the proposed model are to maximize the mean return, minimize the absolute deviation and maximize the entropy of the portfolio. The proposed model is illustrated using returns of stocks of the Dow Jones Industrial Average that are listed in the New York Stock Exchange. This study will be of significant impact to investors because the results show that the proposed model outperforms the mean-absolute deviation model and the naive diversification strategy by giving higher a performance ratio. Furthermore, the proposed model generates higher portfolio mean returns than the MAD model and the naive diversification strategy. Investors will be able to generate a well-diversified portfolio in order to minimize unsystematic risk with the proposed model.

scholarly journals Improvements on Learning Kernel Extended Dictionary for Face Recognition

2020 ◽  
Vol 34 (4) ◽  
pp. 387-394
Author(s):  
Soodabeh Amanzadeh ◽  
Yahya Forghani ◽  
Javad Mahdavi Chabok
Keyword(s):  
Sparse Representation ◽  
Face Image ◽  
Collaborative Representation ◽  
Training Images ◽  
Input Face ◽  
Proposed Model ◽  
Eigen Values ◽  
New Type ◽  
The Mean ◽  
Better Than

Kernel extended dictionary learning model (KED) is a new type of Sparse Representation for Classification (SRC), which represents the input face image as a linear combination of dictionary set and extended dictionary set to determine the input face image class label. Extended dictionary is created based on the differences between the occluded images and non-occluded training images. There are four defaults to make about KED: (1) Similar weights are assigned to the principle components of occlusion variations in KED model, while the principle components of the occlusion variations have different weights, which are proportional to the principle components Eigen-values. (2) Reconstruction of an occluded image is not possible by combining only non-occluded images and the principle components (or the directions) of occlusion variations, but it requires the mean of occlusion variations. (3) The importance and capability of main dictionary and extended dictionary in reconstructing the input face image is not the same, necessarily. (4) KED Runtime is high. To address these problems or challenges, a novel mathematical model is proposed in this paper. In the proposed model, different weights are assigned to the principle components of occlusion variations; different weights are assigned to the main dictionary and extended dictionary; an occluded image is reconstructed by non-occluded images and the principle components of occlusion variations, and also the mean of occlusion variations; and collaborative representation is used instead of sparse representation to enhance the runtime. Experimental results on CAS-PEAL subsets showed that the runtime and accuracy of the proposed model is about 1% better than that of KED.

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