• Title/Summary/Keyword: Gradient model

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Buckling analysis of linearly tapered micro-columns based on strain gradient elasticity

  • Akgoz, Bekir;Civalek, Omer
    • Structural Engineering and Mechanics
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    • v.48 no.2
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    • pp.195-205
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    • 2013
  • The buckling problem of linearly tapered micro-columns is investigated on the basis of modified strain gradient elasticity theory. Bernoulli-Euler beam theory is used to model the non-uniform micro column. Rayleigh-Ritz solution method is utilized to obtain the critical buckling loads of the tapered cantilever micro-columns for different taper ratios. Some comparative results for the cases of rectangular and circular cross-sections are presented in graphical and tabular form to show the differences between the results obtained by modified strain gradient elasticity theory and those achieved by modified couple stress and classical theories. From the results, it is observed that the differences between critical buckling loads achieved by classical and those predicted by non-classical theories are considerable for smaller values of the ratio of the micro-column thickness (or diameter) at its bottom end to the additional material length scale parameters and the differences also increase due to increasing of the taper ratio.

AN AFFINE SCALING INTERIOR ALGORITHM VIA CONJUGATE GRADIENT AND LANCZOS METHODS FOR BOUND-CONSTRAINED NONLINEAR OPTIMIZATION

  • Jia, Chunxia;Zhu, Detong
    • Journal of applied mathematics & informatics
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    • v.29 no.1_2
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    • pp.173-190
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    • 2011
  • In this paper, we construct a new approach of affine scaling interior algorithm using the affine scaling conjugate gradient and Lanczos methods for bound constrained nonlinear optimization. We get the iterative direction by solving quadratic model via affine scaling conjugate gradient and Lanczos methods. By using the line search backtracking technique, we will find an acceptable trial step length along this direction which makes the iterate point strictly feasible and the objective function nonmonotonically decreasing. Global convergence and local superlinear convergence rate of the proposed algorithm are established under some reasonable conditions. Finally, we present some numerical results to illustrate the effectiveness of the proposed algorithm.

Mechanical-hygro-thermal vibrations of functionally graded porous plates with nonlocal and strain gradient effects

  • Fenjan, Raad M.;Hamad, Luay Badr;Faleh, Nadhim M.
    • Advances in aircraft and spacecraft science
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    • v.7 no.2
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    • pp.169-186
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    • 2020
  • Based upon differential quadrature method (DQM) and nonlocal strain gradient theory (NSGT), mechanical-hygro-thermal vibrational analyzes of shear deformable porous functionally graded (FG) nanoplate on visco-elastic medium has been performed. The presented formulation incorporates two scale factors for examining vibrational behaviors of nano-dimension plates more accurately. The material properties for FG plate are porosity-dependent and defined employing a modified power-law form. It is supposed that the nano-size plate is exposed to hygro-thermal and variable compressive mechanical loadings. The governing equations achieved by Hamilton's principle are solved implementing DQM. Presented results indicate the prominence of moisture/temperature variation, damping factor, material gradient index, nonlocal coefficient, strain gradient coefficient and porosities on vibrational frequencies of FG nano-size plate.

Nonlinear resonance of porous functionally graded nanoshells with geometrical imperfection

  • Wu-Bin Shan;Gui-Lin She
    • Structural Engineering and Mechanics
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    • v.88 no.4
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    • pp.355-368
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    • 2023
  • Employing the non-local strain gradient theory (NSGT), this paper investigates the nonlinear resonance characteristics of functionally graded material (FGM) nanoshells with initial geometric imperfection for the first time. The effective material properties of the porous FGM nanoshells with even distribution of porosities are estimated by a modified power-law model. With the guidance of Love's thin shell theory and considering initial geometric imperfection, the strain equations of the shells are obtained. In order to characterize the small-scale effect of the nanoshells, the nonlocal parameter and strain gradient parameter are introduced. Subsequently, the Euler-Lagrange principle was used to derive the motion equations. Considering three boundary conditions, the Galerkin principle combined with the modified Lindstedt Poincare (MLP) method are employed to discretize and solve the motion equations. Finally, the effects of initial geometric imperfection, functional gradient index, strain gradient parameters, non-local parameters and porosity volume fraction on the nonlinear resonance of the porous FGM nanoshells are examined.

Size dependent torsional vibration of a rotationally restrained circular FG nanorod via strain gradient nonlocal elasticity

  • Busra Uzun;Omer Civalek;M. Ozgur Yayli
    • Advances in nano research
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    • v.16 no.2
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    • pp.175-186
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    • 2024
  • Dynamical behaviors of one-dimensional (1D) nano-sized structures are of great importance in nanotechnology applications. Therefore, the torsional dynamic response of functionally graded nanorods which could be used to model the nano electromechanical systems or micro electromechanical systems with torsional motion about the center of twist is examined based on the theory of strain gradient nonlocal elasticity in this work. The mathematical background is constructed based on both strain gradient theory and Eringen's nonlocal elasticity theory. The equation of motions and boundary conditions of radially functionally graded nanorods are derived using Hamilton's principle and then transformed into the eigenvalue analysis by using Fourier sine series. A general coefficient matrix is obtained to assemble the Stokes' transformation. The case of a restrained functionally graded nanorod embedded in two elastic springs against torsional rotation is then deeply investigated. The effect of changing the functionally graded index, the stiffness of elastic boundary conditions, the length scale parameter and nonlocal parameter are investigated in detail.

Using Mechanical Learning Analysis of Determinants of Housing Sales and Establishment of Forecasting Model (기계학습을 활용한 주택매도 결정요인 분석 및 예측모델 구축)

  • Kim, Eun-mi;Kim, Sang-Bong;Cho, Eun-seo
    • Journal of Cadastre & Land InformatiX
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    • v.50 no.1
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    • pp.181-200
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    • 2020
  • This study used the OLS model to estimate the determinants affecting the tenure of a home and then compared the predictive power of each model with SVM, Decision Tree, Random Forest, Gradient Boosting, XGBooest and LightGBM. There is a difference from the preceding study in that the Stacking model, one of the ensemble models, can be used as a base model to establish a more predictable model to identify the volume of housing transactions in the housing market. OLS analysis showed that sales profits, housing prices, the number of household members, and the type of residential housing (detached housing, apartments) affected the period of housing ownership, and compared the predictability of the machine learning model with RMSE, the results showed that the machine learning model had higher predictability. Afterwards, the predictive power was compared by applying each machine learning after rebuilding the data with the influencing variables, and the analysis showed the best predictive power of Random Forest. In addition, the most predictable Random Forest, Decision Tree, Gradient Boosting, and XGBooost models were applied as individual models, and the Stacking model was constructed using Linear, Ridge, and Lasso models as meta models. As a result of the analysis, the RMSE value in the Ridge model was the lowest at 0.5181, thus building the highest predictive model.

A Gaussian Mixture Model for Binarization of Natural Scene Text

  • Tran, Anh Khoa;Lee, Gueesang
    • Smart Media Journal
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    • v.2 no.2
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    • pp.14-19
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    • 2013
  • Recently, due to the increase of the use of scanned images, the text segmentation techniques, which play critical role to optimize the quality of the scanned images, are required to be updated and advanced. In this study, an algorithm has been developed based on the modification of Gaussian mixture model (GMM) by integrating the calculation of Gaussian detection gradient and the estimation of the number clusters. The experimental results show an efficient method for text segmentation in natural scenes such as storefronts, street signs, scanned journals and newspapers at different size, shape or color of texts in condition of lighting changes and complex background. These indicate that our model algorithm and research approach can address various issues, which are still limitations of other senior algorithms and methods.

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A New Identification Method for a Fuzzy Model (퍼지모델의 새로운 설정 방법)

  • 박민기;지승환;박민용
    • Journal of the Korean Institute of Intelligent Systems
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    • v.5 no.2
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    • pp.70-78
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    • 1995
  • The identification of a fuzzy model using input-output data consists of two parts :Structure identification and parameter identification. In this paper an algorithm to identify those parameters and structures is suggested to solve the problems of the conventional methods. Given a set of input-output data, the consequent parameters are identified by the Hough transform and clustering method, each of which considers the linearity and continuity respectively. The gradient descent algorithm is used to fine-tune parameters of a fuzzy model. Finally, it is shown that this method is useful for the identification of a fuzzy model by simulation, where we only consider a single input and single output system.

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The effect of corner shape in the casting mould on thermal stresses distribution (金型의 모서리부 形狀이 熱應力分布에 미치는 影響)

  • 민수홍;구본권;김옥삼
    • Transactions of the Korean Society of Mechanical Engineers
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    • v.15 no.2
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    • pp.567-574
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    • 1991
  • In this study thermal stress generated in three ingot moulds(GC25) during the solidification process of aluminum were analyzed by the two-dimensional thermo-elasto-plastic theory. In temperature analysis, all of the three models are shown steep temperature rising each case in initial stage of cooling. In thermal stress analysis, all of three models took compressible stress on inside wall of the mould, and tensible along with on out side. Model 2 take place less compressible, tensible stress then model 1. But model 3. have similar as thermal stress as model 2. The analysis will made one possible to calculate an optimum mould shape whose thermal stress gradient becomes minimum.

Neural network based model for seismic assessment of existing RC buildings

  • Caglar, Naci;Garip, Zehra Sule
    • Computers and Concrete
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    • v.12 no.2
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    • pp.229-241
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    • 2013
  • The objective of this study is to reveal the sufficiency of neural networks (NN) as a securer, quicker, more robust and reliable method to be used in seismic assessment of existing reinforced concrete buildings. The NN based approach is applied as an alternative method to determine the seismic performance of each existing RC buildings, in terms of damage level. In the application of the NN, a multilayer perceptron (MLP) with a back-propagation (BP) algorithm is employed using a scaled conjugate gradient. NN based model wasd eveloped, trained and tested through a based MATLAB program. The database of this model was developed by using a statistical procedure called P25 method. The NN based model was also proved by verification set constituting of real existing RC buildings exposed to 2003 Bingol earthquake. It is demonstrated that the NN based approach is highly successful and can be used as an alternative method to determine the seismic performance of each existing RC buildings.