Computer CPU Time Research Articles

A multiscale parallel algorithm for dual-phase-lagging heat conduction equation in composite materials

In this paper, a new numerical scheme which combines the multiscale asymptotic method and the Laplace transformation, is presented for solving the 3-D dual-phase-lagging equation in composite materials. The convergence results of the truncated first-order and second-order multiscale approximate solutions are given rigorously. The numerical experiments are carried out to validate the theoretical results of this paper. It is pointed out that the proposed method allows us to choose a relative coarse grid and solve problems in parallel, and therefore it can greatly save computer memory storage and CPU time.

Comparison of Kane’s and Lagrange’s Methods in Analysis of Constrained Dynamical Systems

SUMMARYDynamic modeling is a fundamental step in analyzing the movement of any mechanical system. Methods for dynamical modeling of constrained systems have been widely developed to improve the accuracy and minimize computational cost during simulations. The necessity to satisfy constraint equations as well as the equations of motion makes it more critical to use numerical techniques that are successful in decreasing the number of computational operations and numerical errors for complex dynamical systems. In this study, performance of a variant of Kane’s method compared to six different techniques based on the Lagrange’s equations is shown. To evaluate the performance of the mentioned methods, snake-like robot dynamics is considered and different aspects such as the number of the most time-consuming computational operations, constraint error, energy error, and CPU time assigned to each method are compared. The simulation results demonstrate the superiority of the variant of Kane’s method concerning the other ones.

Accurate, efficient and reliable parameter extraction of PEM fuel cells using shuffled multi-simplexes search algorithm

Parameter extraction is significant for simulation analysis, evaluation, optimal control and fault detection of proton exchange membrane fuel cell (PEMFC) system. Although various techniques have been developed for parameter extraction of PEMFC, it is still challenging to quickly obtain accurate and reliable results. In this paper, a shuffled multi-simplexes search (SMS) algorithm, merging the strengths of three concepts: (a) periodic shuffling multiple simplexes for an effective global exploration, (b) monitoring and revitalization of degenerate simplex to maintain the population diversity for preventing premature convergence and (c) modified simplex search algorithm to conduct a robust and powerful local exploitation, is proposed to fast and accurate extract the model parameter of PEMFC. To be objective and reproducible, the reported experimental V–I datasets of four PEMFC stacks are chosen for testing the parameter extraction performance of proposed SMS algorithm and comparing it with some state-of-the-art algorithms. The comparison results indicate that for all the four test cases, the proposed SMS consistently gets the optimal results among all compared algorithms and exhibits faster convergence speed, lesser computational CPU time (less than 0.3 s for 5000 function evaluations to converge), preferable accuracy and higher statistical robustness (the smallest standard deviation is 1.90E−16) than other algorithms. Due to these superiorities, the proposed SMS is quite promising and envisaged to be an accurate, efficient and reliable alternative for solving the parameter extraction problem of PEMFC model.

No Load Simulation and Downscaled Experiment of UHV Single-Phase Autotransformer Under DC Bias

The time-domain field-circuit coupled finite element method (FEM) is widely used to analyze the DC effects on transformers. However, the long transient process of calculation will encounter difficulties due to the tremendous requirement of computer memory and CPU time, especially when it comes to ultra-high-voltage (UHV) autotransformers, which has a large time constant due to their less resistance and larger inductance. Besides, compared to the AC voltage, the DC voltage is much smaller, miscalculations usually occur as the component of DC bias can be easily neglected in the simulation. In this paper, a time-domain field-circuit coupling FEM combined with the series resistance and voltage compensation method (SRVCM) is proposed, a large resistance is in series in the circuit model to speed up the convergence of transient process and amplify DC component. Afterwards, the voltage supply is increased to compensate for the reduced voltage drop due to series resistance. The excitation waveform and its frequency characteristics of UHV autotransformer under various DC bias are discussed under no-load condition. Its advantages over conventional decrease the numerical miscalculation and the iterations calculation of the transient process. An experimental verification of the proposed method is carried out by a downscaled autotransformer.

An efficient wavelet-based optimization algorithm for the solutions of reaction-diffusion equations in biomedicine

In this paper, a mathematical model of nonlinear reaction-diffusion equation with Michaelis–Menten kinetics in a solid of planar and spherical shape is discussed. The proposed model is based on non-stationary diffusion equation containing a non-linear term related to Michaelis–Menten kinetics of the enzymatic reaction. An efficient wavelet-based spectral method has been developed for the analytical expressions pertaining to substrate concentration for all parameter values. The efficiency of the proposed wavelet method is confirmed by mean of the computational CPU time. The proposed wavelet-based results are compared with Adomian Decomposition Method (ADM). Satisfactory agreement with ADM results is observed. Moreover, the use of the wavelet method is found to be simple, efficient, flexible, and straight forward. Also, it requires less computation costs.

Design of a Broadband Solar Thermal Absorber Using a Deep Neural Network and Experimental Demonstration of Its Performance

In using nanostructures to design solar thermal absorbers, computational methods, such as rigorous coupled-wave analysis and the finite-difference time-domain method, are often employed to simulate light-structure interactions in the solar spectrum. However, those methods require heavy computational resources and CPU time. In this study, using a state-of-the-art modeling technique, i.e., deep learning, we demonstrate significant reduction of computational costs during the optimization processes. To minimize the number of samples obtained by actual simulation, only regulated amounts are prepared and used as a data set to train the deep neural network (DNN) model. Convergence of the constructed DNN model is carefully examined. Moreover, several analyses utilizing an evolutionary algorithm, which require a remarkable number of performance calculations, are performed using the trained DNN model. We show that deep learning effectively reduces the actual simulation counts compared to the case of a design process without a neural network model. Finally, the proposed solar thermal absorber is fabricated and its absorption performance is characterized.

Flow shop scheduling with blocking using modified harmony search algorithm with neighboring heuristics methods

The flow shop scheduling with blocking is considered an important scheduling problem which has many real-world applications. This paper proposes a new algorithm which applies heuristic techniques in harmony search algorithm (HSA) to minimize the total flow time. The proposed method is called modified harmony search algorithm with neighboring heuristics methods (MHSNH). To improve the initial harmony memory, we apply two heuristic techniques: nearest neighbor (NN) and constructive modified NEH (MNEH). A modified version of harmony search algorithm evolves to explore and generates a new solution. The newly generated solution is then enhanced by using neighboring heuristics. Lastly, another neighboring heuristic is applied to improve the obtained solution. The proposed algorithm is evaluated using 12 real-world problem instances each with 10 samples. The experimental evaluation is accomplished using two factors: CPU computational time and the number of iterations. For the first factor, comparative evaluation against six well-established methods shows that the proposed method achieves almost the best overall results in six problem instances out of the twelve and yields fruitful results for others. For the second factor, comparative evaluation against twelve well-regarded methods shows that the proposed method achieves the best overall results in three problem instances and obtains very good results in other instances. In a nutshell, the proposed MHSNH is an effective strategy for solving the job shop scheduling problem.

Fast 3-D Volume Integral Equation Domain Decomposition Method for Electromagnetic Scattering by Complex Inhomogeneous Objects Traversing Multiple Layers

In many applications, electromagnetic scattering from inhomogeneous objects embedded in multiple layers needs to be simulated numerically. The straightforward solution by the method of moments (MoM) for the volume integral equation method is computationally expensive. Due to the shift-invariance and correlation properties of the layered-medium Green’s functions, the stabilized-biconjugate gradient fast Fourier transform (BCGS-FFT) has been developed to greatly reduce the computational complexity of the MoM, but so far this method is limited to objects located in a homogeneous background or in the same layer of a layered medium background. For those problems with objects located in different layers, FFT cannot be applied directly in the direction normal to the layer interfaces, thus the MoM solution requires huge computer memory and CPU time. To overcome these difficulties, the BCGS-FFT method combined with the domain decomposition method (DDM) is proposed in this article. With the BCGS-FFT-DDM, the objects or different parts of an object are first treated separately in several subdomains, each of which satisfies the 3-D shift-invariance and correlation properties; the couplings among the different objects/parts are then taken into account, where the coupling matrices can be built to satisfy the 2-D shift-invariance property if the objects/subdomains have the same mesh size on the $xy$ plane. Hence, 3-D FFT and 2-D FFT can respectively be applied to accelerate the self- and mutual-coupling matrix-vector multiplications. By doing so, the impedance matrix is explicitly formed as one including both the self- and mutual-coupling parts, and the solver converges well for problems with considerable conductivity contrasts. The computational complexity in memory and CPU time for self-coupling matrix-vector multiplication are $O(N_{{z}}^{q} N_{x} N_{y})$ and $O(N_{{z}} N_{x} N_{y} \log (N_{{z}} N_{x} N_{y}))$ respectively, and for mutual-coupling matrix-vector multiplication are $O(N_{{z}}^{p} N_{{z}}^{q} N_{x} N_{y})$ and $O(N_{{z}}^{p} N_{{z}}^{q} N_{x} N_{y} \log (N_{x} N_{y}))$ , respectively, for the proposed method, where $N_{x}$ and $N_{y}$ are the cell numbers of all the subdomains in the $x$ - and $y$ -directions, and $N_{{z}}^{p}$ and $N_{{z}}^{q}$ the cell numbers of different subdomains in the ${z}$ -direction. Several results of different subsurface sensing scenarios are presented to show the capabilities of this method.

A hybrid adaptive finite element phase‐field method for quasi‐static and dynamic brittle fracture

SummaryThe phase‐field approach has unique advantages in describing fracture phenomena, which has received extensive attention in the past decade. Nevertheless, the phase‐field modeling of fracture is computationally demanding, due to the high temporal‐spatial resolution required for crack tracking. In this contribution, a novel hybrid adaptive finite element phase‐field method (ha‐PFM) is developed to solve brittle fracture problems under quasi‐static and dynamic loading. ha‐PFM can dynamically track the propagation of the cracks and adaptively refine the meshes based on a novel crack tip identification strategy. Afterward, the refined meshes in the noncrack progression region are reconverted into coarse meshes. This scheme prominently reduces the computational cost, eg, CPU time and memory usage. Unlike the previous adaptive phase‐field method, multilevel hybrid triangular and quadrilateral elements were developed to discretize the computational domain, which eliminates hanging nodes and ensures that the meshes in the vicinity of the crack tip are highly isotropic. Several representative benchmarks containing quasi‐static and dynamic fracture were reinvestigated with ha‐PFM, and its excellent performance is substantiated by comparison with the standard phase‐field method and literature results.

Hybrid pore-network and lattice-Boltzmann permeability modelling accelerated by machine learning

In this paper, a permeability calculation workflow is presented that couples pore network modeling (PNM) with a Lattice Boltzmann Method (LBM) to benefit from the strengths of both approaches. Pore network extraction is implemented using a watershed segmentation algorithm on 12 three-dimensional porous rock images. The permeabilities of all throats are calculated using the LBM and substituted in the pore network model instead of using the cylindrical formulation for throat’s permeability based on the Hagen–Poiseuille equation. Solving the LBM for every throat results in an accurate representation of flow but the algorithm is computationally expensive. In order to minimize the computational costs, LBM is used to model the steady-state incompressible fluid flow through 9333 different throat images and an Artificial Neural Network (ANN) is trained to mimic the trend of throat’s permeabilities based on the cross–sectional images. To this end, we extract several morphological features of the throats cross–sectional images and search for the best describing feature. It is found that the averaged distance map of the throat images is highly correlated with the LBM-based permeability of throats to the extent that even a simple empirical correlation can reasonably describe the relationship between these two parameters. Finally, we compare the absolute permeability of samples obtained by full LBM with the presented hybrid method. Results show that the proposed method provides an accurate estimation of permeability with a considerable reduction in the computational CPU time.

A 3-D High-Order Reverse-Time Migration Method for High-Resolution Subsurface Imaging With a Multistation Ultra-Wideband Radar System

A three-dimensional high-order reverse-time migration (3-D HO-RTM) method is proposed to perform subsurface electromagnetic imaging with an ultra-wideband radar (UWBR) system consisting of a multi-input and multi-output antenna array. By using a UWBR system to collect temporal scattering signals, subsurface targets can be detected, and the image of targets can be obtained by imaging methods such as the back-propagation method, frequency-wavenumber migration technique, time-reversal mirror, and reverse-time migration method. The proposed HO-RTM method is based on the high-order finite-difference time-domain (HO-FDTD) method to significantly reduce the computational cost in the conventional RTM method. The measured data from an experimental lunar exploration system Chang'E-5 have been collected on a 7 m × 2.5 m × 2.5 m laboratory model with volcanic ash and validated by the 3-D HO-RTM method. Results show that all buried objects can be effectively identified by the HO-RTM, and its computer memory and CPU time are only 3.87% and 0.7128% of the conventional RTM method, respectively.

Financial fraud detection of the Egyptian companies annual reports using artificial bee colony algorithm

Due to the increasing fraud cases, especially in the financial statements and its huge negative impact on the economy as a whole, along with the absence of clear codes among the Egyptian companies to detect fraud. This paper examines the use of artificial bee colony (ABC) algorithm for extracting the most suitable feature subset of accounting ratios to detect fraud on a sample of 83 Egyptian companies from 2010-2017 combined with several classifiers. Results show that the application of ABC has a significant effect on reducing the accounting ratios with higher accuracy along with decreasing incorrectly classified cases and reducing the required CPU computational time across six different classifiers.

Financial fraud detection of the Egyptian companies annual reports using artificial bee colony algorithm

Due to the increasing fraud cases, especially in the financial statements and its huge negative impact on the economy as a whole, along with the absence of clear codes among the Egyptian companies to detect fraud. This paper examines the use of artificial bee colony (ABC) algorithm for extracting the most suitable feature subset of accounting ratios to detect fraud on a sample of 83 Egyptian companies from 2010-2017 combined with several classifiers. Results show that the application of ABC has a significant effect on reducing the accounting ratios with higher accuracy along with decreasing incorrectly classified cases and reducing the required CPU computational time across six different classifiers.

SLDP: A secure and lightweight link discovery protocol for software defined networking

In Software Defined Networks (SDNs), the global view of the underlying network topology is created and maintained at the logically centralized controller. SDN achieves it by decoupling the data plane from the control plane. The up-to-date global view at SDN controller enables the applications (running on top of it) to innovate through dynamic network programmability. To establish a global view, a controller needs to discover a physical topology of the underlying SDN network infrastructure, which is challenging due to various reasons such as the lack of SDN protocols standardization and authentication mechanisms, use of sub-optimal link discovery protocols (e.g., OFDP and LLDP), dynamic topology due to movement of virtualized data centers, switches, and multi-tenant cloud networks, and lack of integration of security schemes for the topology discovery.In this paper, we propose a SDN Link Discovery Protocol (SLDP) for efficient discovery and extraction of topology information in SDN networks. The design of SLDP is motivated from the need of a secure, lightweight, and efficient link discovery protocol in SDN. SLDP aims to prevent, detect, and mitigate various security threats such as poison, replay, and flooding attacks, which are due to lack of source authentication, lack of packet integrity checks, and reuse of static packets. SLDP creates and maintains the global network topology at SDN controller by using smaller size and lower number of SLDP packets during the topology discovery process. Thus, it significantly minimizes the topology discovery overhead in the network. We implemented SLDP on Mininet emulator, and the results show the effectiveness and correctness of SLDP concerning topology discovery time, CPU computational time, and bandwidth overheads, when compared with the traditional OpenFlow Link Discovery Protocol (OFDP). Additionally, SLDP successfully prevent, detect, and mitigate various attacks (e.g., poison, replay, and flooding) in different SDN scenarios.

Experimental design of a new fast sensorless control of DFIG in complex domain

In this study, a new robust sensorless control of doubly-fed induction generator (DFIG) in the complex domain has been investigated. The proposed sensorless control is based on the extended complex Kalman filter (ECKF) and proportional–integral complex controller. The design of this sensorless control in the complex domain allowed a threefold objective: a decrease in the system's dimension by half, an optimisation in the implementation of the control strategy and a decrease of the CPU computational time. In fact, to obtain the latter, the dimension of the DFIG model involved in this control has been halved, which leads to a reduction in the control's schema. Moreover, all the matrices involved in the ECKF have smaller dimensions than those of the real extended Kalman filter and no matrix inversion is needed because the output of the system is a scalar variable. The proposed sensorless control has been tested experimentally for several operating points, namely: nominal operation, operation under asymmetrical voltage dip and parameter variations. Moreover, the observability of the complex state model has been analysed and the required conditions for the local weak observability have been defined. The experimental results confirm the theoretical study and show a good reliability and a high rotor speed estimation accuracy.

A multi-scale analysis for advection-diffusion equation with rapidly oscillatory coefficients

This paper presents a second-order and two-scale computation method of initial-boundary value problem for advection-diffusion equation with rapidly oscillating coefficients. The proposed method can greatly save computer memory and CPU time, and it possesses good precision at the same time. The numerical results demonstrate that the presented method is effective and reliable.

An accelerated nonlocal Poisson‐Boltzmann equation solver for electrostatics of biomolecule

The nonlocal modified Poisson-Boltzmann equation (NMPBE) is one important variant of a commonly used dielectric continuum model, the Poisson-Boltzmann equation (PBE), for computing electrostatics of biomolecules. In this paper, an accelerated NMPBE solver is constructed by finite element and finite difference hybrid techniques. It is then programmed as a software package for computing electrostatic solvation and binding free energies for a protein in a symmetric 1:1 ionic solvent. Numerical results validate the new solver and its numerical stability. They also demonstrate that the new solver has much better performance than the corresponding finite element solver in terms of computer CPU time. Furthermore, they show that the binding free energies produced by NMPBE can match chemical experiment data better than those by PBE.

Low-Cost Numerical Method for Solving a Coefficient Inverse Problem for the Wave Equation in Three-Dimensional Space

For the acoustic-sensing problem of determining the characteristics of a local inhomogeneity scattering a wave field in three-dimensional space, a numerical algorithm is proposed and justified that is efficient in terms of computational resources and CPU time. The algorithm is based on the fast Fourier transform, which is used under certain a priori assumptions on the character of the inhomogeneity and the observation domain of the scattered field. Typical numerical results obtained by solving this inverse problem with simulated data on a personal computer are presented, which demonstrate the capabilities of the algorithm.

A generalized element-free Galerkin method for Stokes problem

A generalized element-free Galerkin (GEFG) method is developed in this paper for solving Stokes problem in primitive variable form. To obtain stable numerical results for both velocity and pressure, extended terms are only introduced into the approximate space of velocity in a special way as that in the generalized finite element method. Theoretical analysis shows that the GEFG method implies a stabilized formulation similar to that in the variational multiscale element-free Galerkin (VMEFG) method. Numerical results show the efficiency of the present method and reveal that both computational errors and CPU times of the present method are less than those of the VMEFG and the finite element methods.

A highly efficient numerical approach: extended cohesive damage model for predicting multicrack propagation

A highly efficient numerical approach: extended cohesive damage model (ECDM) for predicting multicrack propagation is introduced in this paper. The ECDM is developed within the framework of the eXtended Finite Element Method (XFEM). Unlike XFEM the enriched degrees of freedom (DoFs) are eliminated from the final condensed equilibrium equations in the ECDM. To account for the cohesive crack effect, an equivalent damage scalar relating to a strain field is introduced in terms of energy dissipation. The ECDM is capable of characterizing discontinuities with conventional DoFs only, thus it is significantly efficient in modelling multicrack propagation. The basic formulations, numerical implementation and detailed investigation of the performance of the ECDM through modelling the selected benchmark specimens are given in this paper. This investigation shows the ECDM can effectively guarantee the convergent solutions in nonlinear fracture analysis and can efficiently reduce the computer CPU time in modelling selected fracture benchmark specimens by more than 60% compared to the XFEM in ABAQUS. Therefore, the ECDM is a robust computational approach for predicting multicrack failure mechanism in engineering materials and structures.