An accurate and efficient Chebyshev expansion method for large-scale transient heat conduction problems

Efficient and accurate method for 2D periodic structures based on the physical features of the transient heat conduction

A new efficient simulation method based on Bayes' theorem and importance sampling Markov chain simulation to estimate the failure-probability-based global sensitivity measure

<p>Permeability is a key parameter for characterizing fluid flow in digital rocks. It depends on pore geometry and topology and can be computed numerically via solving the Stokes equation. However, the associated computational effort can be enormous for large-scale models, even using the efficient Lattice-Boltzmann method. In this study, an efficient method is developed for computing the equivalent permeability of digital rocks by simplifying the Stokes equation to Darcy equation. The method is based on the idea that a 3D digital core can be approximated by the combination of multiple 2D slices/layers, and the property of each layer is governed by the Stokes equation. Specifically, to mimic the 2D fine-scale velocity solved from the Stokes equation, a local permeability is assigned according to the velocity for each voxel. In addition, the nearest distance from each voxel to the solid wall is used to approximate the 2D fine-scale velocity, without the need of solving the Stokes equation in 2D for each layer. By this means, the 3D Stokes equation can be simplified to multiple 2D cases that provide the local permeability distribution for the 3D Darcy equation, and thus the computational cost can be significantly reduced. Case studies have been conducted on various samples in different scales. The results demonstrate that the computed 3D permeabilities using finite difference method (based on the Darcy equation) agree well with those using Lattice-Boltzmann method (based on the Stokes equation), and a speedup factor of about O(10) is achieved. The method can be applied to both sandstone and carbonate rocks for fast estimation of block permeability.</p>

With a great capability of dealing with epistemic uncertainty, evidence theory has been utilised to conduct reliability analysis for engineering systems recently. Unfortunately, the discontinuous nature of uncertainty quantification using evidence theory incurs a huge computational cost. This paper proposes an efficient method to improve the computational efficiency of evidence theory for reliability analysis. In this method, evidence variables are transformed into random variables. Support vector regression is used to construct the approximation model of the limit-state function. The most probable point (MPP) of the approximate reliability problem with only random variables is searched out. Based on the MPP, the most probable focal element (MPFE) of the original problem with evidence variables is identified. According to the MPFE and the monotonicity of the limit-state function, contributions of some focal elements to belief and plausibility in evidence theory can be judged directly. Hence, the number of focal elements involved in the calculation of extreme values of the limit-state function is reduced. Four numerical examples are utilised to test the performance of the proposed method. Results indicate that the proposed method can reduce the computational cost on reliability analysis under epistemic uncertainty while ensuring the high accuracy of reliability analysis results.

Systems for retrieving and managing content-based medical images are becoming more important, especially as medical imaging technology advances and the medical image database grows. In addition, these systems can also use medical images to better grasp and gain a deeper understanding of the causes and treatments of different diseases, not just for diagnostic purposes. For achieving all these purposes, there is a critical need for an efficient and accurate content-based medical image retrieval (CBMIR) method. This paper proposes an efficient method (RbQE) for the retrieval of computed tomography (CT) and magnetic resonance (MR) images. RbQE is based on expanding the features of querying and exploiting the pre-trained learning models AlexNet and VGG-19 to extract compact, deep, and high-level features from medical images. There are two searching procedures in RbQE: a rapid search and a final search. In the rapid search, the original query is expanded by retrieving the top-ranked images from each class and is used to reformulate the query by calculating the mean values for deep features of the top-ranked images, resulting in a new query for each class. In the final search, the new query that is most similar to the original query will be used for retrieval from the database. The performance of the proposed method has been compared to state-of-the-art methods on four publicly available standard databases, namely, TCIA-CT, EXACT09-CT, NEMA-CT, and OASIS-MRI. Experimental results show that the proposed method exceeds the compared methods by 0.84%, 4.86%, 1.24%, and 14.34% in average retrieval precision (ARP) for the TCIA-CT, EXACT09-CT, NEMA-CT, and OASIS-MRI databases, respectively.

Feature selection is an essential step in the preprocessing of data in pattern recognition and data mining. Nowadays, the feature selection problem as an optimization problem can be solved with nature-inspired algorithm. In this paper, we propose an efficient feature selection method based on the cuckoo search algorithm called CBCSEM. The proposed method avoids the premature convergence of traditional methods and the tendency to fall into local optima, and this efficient method is attributed to three aspects. Firstly, the chaotic map increases the diversity of the initialization of the algorithm and lays the foundation for its convergence. Then, the proposed two-population elite preservation strategy can find the attractive one of each generation and preserve it. Finally, Lévy flight is developed to update the position of a cuckoo, and the proposed uniform mutation strategy avoids the trouble that the search space is too large for the convergence of the algorithm due to Lévy flight and improves the algorithm exploitation ability. The experimental results on several real UCI datasets show that the proposed method is competitive in comparison with other feature selection algorithms.

Automobile navigation in tunnel environment is challenging. GPS sensors and ordinary cameras can't function effectively. For navigation, infrared cameras are installed on top of our experimental vehicle, and here we propose an efficient object detection method to detect emergency lights from the collected data in tunnel environment. The proposed method firstly detects keypoints by setting thresholds for intensity of uniformly sampled points. Each keypoint is then verified by the appearance of its surrounding sub-image. After clustering the keypoints which satisfy the verification, the method verifies the keypoint clusters by their appearance and temporal information. Though the later steps are time-consuming, they deal with very few instances. And this improves the efficiency of the method, while not losing effectiveness of the appearance and temporal information. Thus the method gives promising results in real time. Detection performance and efficiency are verified by experiments carried on challenging real data.

In industrial areas, the machining performance with linear G01 code is a crucial indicator to evaluate the computer numerical control (CNC) systems and many researchers have presented various methods to deal with the corner tracking issue. However, the axis jerk limitations are not satisfied well and the different axis kinematic limitations are not considered in most researches, which will reduce the machining efficiency and machining quality simultaneously. In this paper, a novel method including trajectory planning, feedrate scheduling, and interpolating is proposed to obtain better machining quality and higher machining efficiency. In trajectory planning, a B-spline curve is utilized to smooth the linear toolpath and obtain a curvature-smooth trajectory, which is third-order geometry continuous. Thereby, a time-optimal method for the geometric continuous trajectory is proposed based on linear programming algorithm in the feedrate scheduling and the bounded multi-constraints, including axis velocity, axis acceleration, axis jerk, and feedrate. Moreover, it can be seen that the proposed method is near Bang-bang control. To reduce the computation time of the optimal numerical method, an efficient method with a look-ahead window around the transition B-spline curve is applied. In the interpolation stage, a novel interpolation method about arc-length is proposed to improve computation efficiency. Finally, simulation and experiment are conducted to show superiorities of the proposed method to the already existing approaches. The results show that the cycling time of the proposed method is reduced by more than 7% than G2 method and 20% than G3 method with better contour performance.

Mesh deformation and data interpolation using radial basis functions (RBF) in combination with data reduction greedy algorithm has proven to be an efficient method, both in providing high quality deformed meshes and speed up computations. In the present work an in-house hybrid unstructured Reynolds-averaged Navier-Stokes solver (HUNS3D) has been extended to include dynamic mesh motion and aeroelastic behavior prediction. For present computational aeroelastic simulations, RBF interpolation serves as single subroutine and carries out the required data interpolation for both the surface loads and deformations. For mesh motion and displacement interpolation the already developed RBF interpolation methods works reasonably well. But for interpolation of aerodynamic loads the current procedures become expensive in terms of computational time and are greatly influenced by the parameters used in the interpolation. In this paper a more efficient and robust method is presented that localizes the interpolation. This method resembles in concept to the pointwise form of partition of unity method but somewhat differs in its implementation. It is efficient in terms of computational time and can be readily parallelized. Also it reduces the influence of the interpolation parameters on the coupling behavior. The proposed method has been tested by performing static aeroelastic computations in transonic flow over the AGARD 445.6 wing, HIRENASD wing/body configuration and a flexible wing with spar-rib-skin construction. The method has shown its effectiveness in aeroelastic behavior prediction for different aerodynamic configurations.

For large-scale finite element analysis, using a numerical method to reduce the problem size is a standard strategy to analyze a problem efficiently. For the analysis with reduction, the most time-consuming process is reducing the degrees of freedom, not solving the global sparse system in the standard analysis. Therefore, improving the efficiency of the reduction process is the key to further speedup the analyses of large-scale problems. Nonlinear analysis is much more computationally intensive than linear elastic analysis. There is a need to investigate a more efficient reduction method for nonlinear problems. This paper presents a new domain decomposition method for nonlinear substructures, which can greatly reduce the analyses time for large-scale nonlinear problems. In the proposed method, the nonlinear behavior of a substructure is updated by adding correcting modes, instead of totally repeating the whole reduction process. The efficiency can be further improved by cooperating with a parallel processing technique. The proposed method was implemented in a parallel object-oriented finite element analysis program, and its performance and accuracy were verified on various examples.

A Signcryption proposed by Yuliang Zheng in 1997 is a hybrid public key primitive that combines a digital signature and an encryption. It provides more efficient method than a straightforward composition of a signature scheme with an encryption scheme. In a mobile communication environment, the authenticated key agreement protocol should be designed to have lower computational complexity and memory requirements. Therefore, in this paper, our proposed method that efficient authenticated key exchange protocol using Signcryption scheme and based on EC using Signcryption in mobile and wireless network. And we analyze computational cost and security requirements of proposed protocol.

Ill-posed inverse problems arise in many fields of science and engineering. These problems are usually very sensitive to the presence of noise in the measured data. Regularization methods aim at reducing this sensitivity. Among these methods Iterated Tikhonov (IT), in both its standard and general form, has been widely investigated due to its ease of implementation and high level of accuracy. One of the most crucial point for its implementation is the stopping criterion. Possibly the most popular choice is the Discrepancy Principle (DP). Unfortunately, the DP requires additional knowledge of the problem, namely the norm of the noise that corrupts the data. In this paper we present a stopping criterion based on the Generalized Cross Validation (GCV) that does not require any additional knowledge of the problem. We propose it for both Iterated Tikhonov in standard and general form. One of the main drawback of the GCV is its computational cost. Thanks to the projection into Krylov subspace of fairly small dimension we are able to formulate our proposal for both small and large scale problems. The proposed method still requires the estimate of some parameters. Even though our approach is fairly stable with respect of the choice of these parameters, we develop a completely automatic method that does not require any hand-tuning and can be run in a truly "plug and play" fashion. We show the performances of the proposed methods on some selected numerical examples.

This paper presents an efficient inverse analysis technique based on a sensitivity coefficient algorithm to estimate the unknown boundary conditions of multidimensional steady and transient heat conduction problems. Sensitivity coefficients were used to represent the temperature response of a system under unit loading conditions. The proposed method, coupled with the sensitivity analysis in the finite element formulation, is capable of estimating both the unknown temperature and heat flux on the surface provided that temperature data are given at discrete points in the interior of a solid body. Inverse heat conduction problems are referred to as ill-posed because minor inaccuracy or error in temperature measurements cause a drastic effect on the predicted surface temperature and heat flux. To verify the accuracy and validity of the new method, two-dimensional steady and transient problems are considered. Their surface temperature and heat flux are evaluated. From a comparison with the exact solution, the effects of measurement accuracy, number and location of measuring points, a time step, and regularization terms are discussed. © 1998 Scripta Technica. Heat Trans Jpn Res, 26(6): 345–359, 1997

<div class="section abstract"><div class="htmlview paragraph">In the past decades, automotive structure design has sought to minimize its mass while maintaining or improving structural performance. As such, topology optimization (TO) has become an increasingly popular tool during the conceptual design stage. While the designs produced by TO methods provide significant performance-to-mass ratio improvements, they require considerable computational resources when solving large-scale problems. An alternative for large-scale problems is to decompose the design domain into multiple scales that are coupled with homogenization. The problem can then be solved with hierarchical multiscale topology optimization (MSTO). The resulting optimal, homogenized macroscales are de-homogenized to obtain a high-fidelity, physically-realizable design. Even so MSTO methods are still computationally expensive due to the combined costs of solving nested optimization problems and performing de-homogenization. To address these issues, this paper presents an efficient de-homogenization method that can be applied to any macroscale topology in order to obtain a high-fidelity multiscale structure. In contrast to prior de-homogenization methods, an alternative representation of the rectangular hole microstructure is proposed so that it is only dependent on the local density and stress distributions. Consequently, MSTO methods are not needed for the de-homogenization method to be applied. This makes the method applicable to any conceptual design, including those from simple single-scale TO codes. Additionally, the proposed de-homogenization method avoids the expensive mapping optimization problem associated with most projection de-homogenization methods by clustering sub-domains of the structure into discrete orientation angles. Pre-computed microstructures for each of the discrete angles are then assembled into each sub-domain. The proposed method is showcased for the design of a simple bumper and hood structure. The material distribution of each component is optimized in a two-dimensional TO problem for maximum stiffness.</div></div>

Damage identification of structures using incomplete mode shape and improved TLBO-PSO with self-controlled multi-stage strategy