scholarly journals Precomputation of Critical State Soil Plastic Models

Processes ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 2142
Author(s):  
Vicente Navarro ◽  
Virginia Cabrera ◽  
Gema De la Morena ◽  
Daniel González ◽  
Laura Asensio ◽  
...  
Keyword(s):  
Yield Surface ◽  
Critical State ◽  
Large Scale ◽  
Optimization Problems ◽  
Practical Interest ◽  
Computational Time ◽  
Explicit Integration ◽  
Integration Algorithm ◽  
Modified Cam Clay ◽  
State Models

In this paper, a simple precomputing procedure is proposed to improve the numerical performance of the technological application of critical state soil models. In these models, if associated plasticity is assumed, the normalization of the stress space allows both the yield surface and the plastic components of the elastoplastic matrix to be defined as a function of a single variable. This approach facilitates their parameterization and precomputation, preventing the repetition of calculations when the boundary value problems appear at the yield surface with the calculation of plastic strain. To illustrate the scope of the procedure, its application on a modified Cam Clay model is analysed, which shows that the method allows a significant reduction of about 50% (as compared with the conventional explicit integration algorithm) in the computational time without reducing the precision. Although it is intended for critical state models in soils, the approach can be applied to other materials and types of constitutive models provided that parameterization is possible. It is therefore a methodology of practical interest, especially when a large volume of calculations is required, for example when studying large-scale engineering systems, performing sensitivity analysis, or solving optimization problems.

Fast sparse image reconstruction method in through-the-wall radars using limited memory Broyden–Fletcher–Goldfarb–Shanno algorithm

2021 ◽  
pp. 1-11
Author(s):  
Candida Mwisomba ◽  
Abdi T. Abdalla ◽  
Idrissa Amour ◽  
Florian Mkemwa ◽  
Baraka Maiseli
Keyword(s):  
Image Reconstruction ◽  
Large Scale ◽  
Optimization Problems ◽  
Classical Method ◽  
Computational Cost ◽  
Least Square ◽  
Computational Time ◽  
Reconstruction Method ◽  
Nonlinear Methods ◽  
Limited Memory

Abstract Compressed sensing allows recovery of image signals using a portion of data – a technique that has drastically revolutionized the field of through-the-wall radar imaging (TWRI). This technique can be accomplished through nonlinear methods, including convex programming and greedy iterative algorithms. However, such (nonlinear) methods increase the computational cost at the sensing and reconstruction stages, thus limiting the application of TWRI in delicate practical tasks (e.g. military operations and rescue missions) that demand fast response times. Motivated by this limitation, the current work introduces the use of a numerical optimization algorithm, called Limited Memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS), to the TWRI framework to lower image reconstruction time. LBFGS, a well-known Quasi-Newton algorithm, has traditionally been applied to solve large scale optimization problems. Despite its potential applications, this algorithm has not been extensively applied in TWRI. Therefore, guided by LBFGS and using the Euclidean norm, we employed the regularized least square method to solve the cost function of the TWRI problem. Simulation results show that our method reduces the computational time by 87% relative to the classical method, even under situations of increased number of targets or large data volume. Moreover, the results show that the proposed method remains robust when applied to noisy environment.

Combined equivalent & multi-scale simulation method for 3-D seismic analysis of large-scale shield tunnel

2014 ◽  
Vol 31 (3) ◽  
pp. 584-620 ◽  
Author(s):  
Weiwei Zhang ◽  
Xianlong Jin ◽  
Zhihao Yang
Keyword(s):  
Large Scale ◽  
Simulation Method ◽  
Equivalent Model ◽  
Scale Model ◽  
Shield Tunnel ◽  
Fe Model ◽  
Explicit Integration ◽  
Integration Algorithm ◽  
Content Type ◽  
Multi Scale

Purpose – The great magnitude differences between the integral tunnel and its structure details make it impossible to numerically model and analyze the global and local seismic behavior of large-scale shield tunnels using a unified spatial scale, even with the help of supercomputers. The paper aims to present a combined equivalent & multi-scale simulation method, by which the tunnel's major mechanical properties under seismic loads can be represented by the equivalent model, and the seismic responses of the interested details can be studied efficiently by the coupled multi-scale model. Design/methodology/approach – The nominal orthotropic material constants of the equivalent tunnel model are inversely determined by fitting the modal characteristics of the equivalent model with the corresponding segmental lining model. The critical sections are selected by comprehensive analyzing of the integral compression/extension and bending loads in the equivalent lining under the seismic shaking and the coupled multi-scale model containing the details of interest is solved by the mixed time explicit integration algorithm. Findings – The combined equivalent & multi-scale simulation method is an effective and efficient way for seismic analyses of large-scale tunnels. The response of each flexible joint is related to its polar location on the lining ring, and the mixed time integration method can speed-up the calculation process for hybrid FE model with great differences in element sizes. Originality/value – The orthotropic equivalent assumption is, to the best of the authors’ knowledge, for the first time, used in the 3D simulation of the shield tunnel lining, representing the rigidity discrepancies caused by the structural property.

scholarly journals Hybrid inner-outer algorithm for solving real-world mechanical optimization problems

2021 ◽  
Vol 68 (1) ◽  
pp. 1-18
Author(s):  
Omnia Osman Fadel Abouhabaga ◽  
Mohamed Hassan Gadallah ◽  
Hanan Kamel Kouta ◽  
Mohamed Abass Zaghloul
Keyword(s):  
Real World ◽  
Large Scale ◽  
Optimization Problems ◽  
Critical Role ◽  
Computational Time ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Mechanical Optimization ◽  
Future Work ◽  
Search Domain

AbstractIn the real world, the problems mostly are complex; more precisely, the problems generally are nonlinear or large scale other than if it was mandatory to resolve it under certain constraints, and that is common in engineering design problems. Therefore, the complexity of problem plays a critical role in determining the computational time and cost. Accordingly, a novel algorithm called inner-outer array is proposed in this paper. It depends on the design of parameters and then tolerance design as one of design of experiment stages. In this work, the inner-outer algorithm is used to solve real-world optimization problems to choose the preferable feasible regions of the entire search domain. Numerical results are documented and compared based on four well-known constrained mechanical engineering issues. It can be concluded that the performance of inner-outer algorithm is good to optimize constrained engineering problems, but it still needs some enhancements in the future work.

scholarly journals Safe Sample Screening for Robust Support Vector Machine

2020 ◽  
Vol 34 (04) ◽  
pp. 6981-6988
Author(s):  
Zhou Zhai ◽  
Bin Gu ◽  
Xiang Li ◽  
Heng Huang
Keyword(s):  
Support Vector Machine ◽  
Convex Optimization ◽  
Large Scale ◽  
Optimization Problems ◽  
Dc Programming ◽  
Computational Time ◽  
Support Vector ◽  
Training Samples ◽  
Convex Problem ◽  
Benchmark Datasets

Robust support vector machine (RSVM) has been shown to perform remarkably well to improve the generalization performance of support vector machine under the noisy environment. Unfortunately, in order to handle the non-convexity induced by ramp loss in RSVM, existing RSVM solvers often adopt the DC programming framework which is computationally inefficient for running multiple outer loops. This hinders the application of RSVM to large-scale problems. Safe sample screening that allows for the exclusion of training samples prior to or early in the training process is an effective method to greatly reduce computational time. However, existing safe sample screening algorithms are limited to convex optimization problems while RSVM is a non-convex problem. To address this challenge, in this paper, we propose two safe sample screening rules for RSVM based on the framework of concave-convex procedure (CCCP). Specifically, we provide screening rule for the inner solver of CCCP and another rule for propagating screened samples between two successive solvers of CCCP. To the best of our knowledge, this is the first work of safe sample screening to a non-convex optimization problem. More importantly, we provide the security guarantee to our sample screening rules to RSVM. Experimental results on a variety of benchmark datasets verify that our safe sample screening rules can significantly reduce the computational time.

scholarly journals A Branch-and-Bound Algorithm Embedded with DCA for DC Programming

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Meihua Wang ◽  
Fengmin Xu ◽  
Chengxian Xu
Keyword(s):  
Branch And Bound ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Dc Programming ◽  
Computational Time ◽  
Special Importance ◽  
Branch Number ◽  
Dc Algorithm ◽  
Computational Performance

The special importance of Difference of Convex (DC) functions programming has been recognized in recent studies on nonconvex optimization problems. In this work, a class of DC programming derived from the portfolio selection problems is studied. The most popular method applied to solve the problem is the Branch-and-Bound (B&B) algorithm. However, “the curse of dimensionality” will affect the performance of the B&B algorithm. DC Algorithm (DCA) is an efficient method to get a local optimal solution. It has been applied to many practical problems, especially for large-scale problems. A B&B-DCA algorithm is proposed by embedding DCA into the B&B algorithms, the new algorithm improves the computational performance and obtains a global optimal solution. Computational results show that the proposed B&B-DCA algorithm has the superiority of the branch number and computational time than general B&B. The nice features of DCA (inexpensiveness, reliability, robustness, globality of computed solutions, etc.) provide crucial support to the combined B&B-DCA for accelerating the convergence of B&B.

scholarly journals Spectral proximal method for solving large scale sparse optimization

2021 ◽  
Vol 36 ◽  
pp. 04007
Author(s):  
Gillian Yi Han Woo ◽  
Hong Seng Sim ◽  
Yong Kheng Goh ◽  
Wah June Leong
Keyword(s):  
Unconstrained Optimization ◽  
Gradient Method ◽  
Large Scale ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Sparse Optimization ◽  
Computational Time ◽  
Proximal Method ◽  
Unconstrained Optimization Problem ◽  
Spectral Gradient Method

In this paper, we propose to use spectral proximal method to solve sparse optimization problems. Sparse optimization refers to an optimization problem involving the ι0 -norm in objective or constraints. The previous research showed that the spectral gradient method is outperformed the other standard unconstrained optimization methods. This is due to spectral gradient method replaced the full rank matrix by a diagonal matrix and the memory decreased from Ο(n2) to Ο(n). Since ι0-norm term is nonconvex and non-smooth, it cannot be solved by standard optimization algorithm. We will solve the ι0 -norm problem with an underdetermined system as its constraint will be considered. Using Lagrange method, this problem is transformed into an unconstrained optimization problem. A new method called spectral proximal method is proposed, which is a combination of proximal method and spectral gradient method. The spectral proximal method is then applied to the ι0-norm unconstrained optimization problem. The programming code will be written in Python to compare the efficiency of the proposed method with some existing methods. The benchmarks of the comparison are based on number of iterations, number of functions call and the computational time. Theoretically, the proposed method requires less storage and less computational time.

scholarly journals An Improved Implementation of Time Domain Elastodynamic BIEM in 3D for Large Scale Problems and its Application to Ultrasonic NDE

2007 ◽  
Vol 1 (2) ◽  
Author(s):  
Hitoshi Yoshikawa ◽  
Naoshi Nishimura
Keyword(s):  
Time Domain ◽  
Large Scale ◽  
Scattering Problem ◽  
Time Integration ◽  
Simple Wave ◽  
Integral Equation Method ◽  
Boundary Integral ◽  
Computational Time ◽  
Integration Algorithm ◽  
Ultrasonic Nde

This paper discusses a three dimensional implementation of boundary integral equation method (BIEM) for large scale time domain elastodynamic problems and its application to ultrasonic nondestructive evaluation (NDE). We improve the time integration algorithm of the BIEM in order to reduce the required computational time. We show the e±ciency of the proposed method by applying it to a simple wave scattering problem and to a more realistic crack determination problem related to ultrasonic NDE.

Large Scale Plant Optimization Problems

1988 ◽  
Author(s):  
Paul Cronin ◽  
Harry Woerde ◽  
Rob Vasbinder
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Plant Optimization

Combustion Driven by Fragment-based Ab Initio Molecular Dynamics Simulation

2019 ◽  
Author(s):  
Liqun Cao ◽  
Jinzhe Zeng ◽  
Mingyuan Xu ◽  
Chih-Hao Chin ◽  
Tong Zhu ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Ab Initio ◽  
Large Scale ◽  
Methane Combustion ◽  
Combustion Method ◽  
Dynamics Simulation ◽  
Ab Initio Molecular Dynamics ◽  
Computational Time ◽  
Combustion Mechanism ◽  
Daily Lives

Combustion is a kind of important reaction that affects people's daily lives and the development of aerospace. Exploring the reaction mechanism contributes to the understanding of combustion and the more efficient use of fuels. Ab initio quantum mechanical (QM) calculation is precise but limited by its computational time for large-scale systems. In order to carry out reactive molecular dynamics (MD) simulation for combustion accurately and quickly, we develop the MFCC-combustion method in this study, which calculates the interaction between atoms using QM method at the level of MN15/6-31G(d). Each molecule in systems is treated as a fragment, and when the distance between any two atoms in different molecules is greater than 3.5 Å, a new fragment involved two molecules is produced in order to consider the two-body interaction. The deviations of MFCC-combustion from full system calculations are within a few kcal/mol, and the result clearly shows that the calculated energies of the different systems using MFCC-combustion are close to converging after the distance thresholds are larger than 3.5 Å for the two-body QM interactions. The methane combustion was studied with the MFCC-combustion method to explore the combustion mechanism of the methane-oxygen system.

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