Enormous Amount Of Computation Time Research Articles

Multilevel Monte Carlo method for the Brownian configuration field of polymer fluids

Stochastic Brownian dynamics is an extremely powerful way to simulate the polymer dynamics in solutions and melts. Mathematically, these models are described by stochastic differential equations. The most challenging problems are the Monte Carlo algorithm, which simulates the motion of a large number of model particles and hence requires an enormous amount of computer time. It is therefore necessary to develop an efficient numerical method in operational emergency response applications. In this paper, we give an improved multilevel Monte Carlo (improved-MLMC) method based on equilibrium control variables at each level to calculate the propagation of polymers. The improved-MLMC method can be shown to result in asymptotically optimal random errors and reduce total cost when compared to the standard Monte Carlo and MLMC methods. Finally, the effect of the Wi number (dimensionless parameter) on the total cost of the presented MLMC method is also discussed in detail.

Acceleration of the NVT Flash Calculation for Multicomponent Mixtures Using Deep Neural Network Models

Phase equilibrium calculation, also known as flash calculation, has been extensively applied in petroleum engineering, not only as a standalone application for a separation process but also as an integral component of compositional reservoir simulation. Previous research devoted numerous efforts to improve the accuracy of phase equilibrium calculations, which place more importance on safety than speed. However, the equation-of-state-based flash calculation consumes an enormous amount of computational time in compositional simulation and thus becomes a bottleneck to the broad application of compositional simulators. Therefore, it is of vital importance to accelerate flash calculation without much compromise in accuracy and reliability, turning it into an active research topic in the past two decades. With the rapid development of computational techniques, machine learning brings another wave of technology innovation. As a subfield of machine learning, the deep neural network becomes a promising computation...

RETRIEVAL OF SURFACE STRUCTURAL PARAMETERS FROM MULTILAYER RELAXED SURFACES

Trial search method of determination of structure from LEED I–V curves is to compute the same including multiple scattering for plausible guess structures and finding the actual structure from the best match. This requires enormous amount of computer time and for some cases even may turn unsuccessful. Using change of computational techniques and different search algorithms tremendous advancement have been made, but still it is possible to get struck in a local minima rather than global one. Therefore a method for determination of surface structure from LEED I–V curves, avoiding a search in the parameter space, is always considered important not only for reducing computational time enormously but also for avoiding the trapping in local minima. To this end, we have investigated the possibilities of retrieval of surface structural parameters by reversing the process of layer doubling method of conventional LEED theory.

NoC-Based Implementation of Free Form Deformations in Medical Imaging Registration

These days, computer-based image registration techniques are increasingly being used in the area of medical imaging as they offer significant benefits for aligning different images together and for visualizing their combined images. However, these techniques require an enormous amount of computation time due to the high resolution and complex nature of the medical images. We propose to alleviate this problem by using a dedicated Network-on-Chip (NoC)-based hardware platform for image registration. This paper describes a novel technique for field-programmable gate array (FPGA) implementation of the B-Spline-based free form deformation (FFD) algorithm, i.e., a widely used algorithm for modeling geometric shapes in a computerized environment. For performance enhancement, we have utilized a lightweight circuit-switched NoC architecture, which is adaptable to most FPGAs. The design description is captured in the Verilog language and implemented using the Xilinx XC2v6000 device at 37[Formula: see text]MHz. The proposed design is parameterizable at the compile time and supports a wide range of the image resolutions and computational precisions. The experimental results have shown a significant improvement in performance when compared with the other existing hardware implementations of the B-spline-based FFD algorithm.

An improved deconvolution algorithm using B-splines for well-test data analysis in petroleum engineering

Ilk et al.’s deconvolution algorithm using B-splines involves the Laplace transformation of the convolution equation with respect to production rate and wellbore pressure based on Duhamel principle. However, for common cases, the production rate function has “discontinuity” with respect to production time; it does not satisfy the precondition that the function to be transformed by Laplace transformation should be continuous. This inherent defect may directly cause enormous amount of computational time or even the failure of the numerical Laplace inversion in the deconvolution process. Based on these concerns, a fundamentally improved deconvolution algorithm using B-splines is presented here. In the convolution equation, the wellbore pressure derivative corresponding to constant unit production rate as the target of deconvolution is still represented by weighted summation of second-order B-splines; however, the computation process of the deconvolution is kept in the level of integral in the real time space instead of the Laplace space, for the reason that there will be no continuity requirement for the production rate function in the application of Duhamel principle for the deconvolution computation problem. According to the real production rate history, a technique of piecewise analytical integration is adopted for obtaining the elements of sensitivity matrix of a linear system with respect to weight coefficients; the linear system is generated by substituting the measured wellbore pressure data and corresponding variable production rate data into the convolution equation containing B-splines. The proposed direct analytical solution method of the integration for calculating the elements of the sensitivity matrix can not only guarantee the success of the deconvolution computation, but also can largely enhance the deconvolution computation speed. Moreover, in order to further improve the computation speed, a binary search method is also applied to find which production segments (with constant production rate) the measured wellbore pressure data points locate at in the deconvolution computation process. Another linear system with respect to weight coefficients for the regularization from Ilk et al.’s deconvolution algorithm is appended in order to overcome the effect of data errors. The two linear systems are combined together as an over-determined linear system, which can be solved by the least square method. Eventually, the reconstructed wellbore pressure and its derivative by B-splines corresponding to the constant unit production rate can be obtained.Numerical experimental tests demonstrate that the improved deconvolution algorithm exhibits good accuracy, computation speed and stability of data error tolerance. And the statement on how to perform the regularization when data error exists is also made in order to deconvolve the correct wellbore pressure derivative. The improved deconvolution algorithm is also applied into an actual field example. It is found that the deconvolution results by the improved deconvolution algorithm have good agreement with the ones by Von Schroeter et al.’s deconvolution algorithm and by Levitan et al.’s deconvolution algorithm as a whole; and the feature of typical log-log curves of the wellbore pressure drop and the wellbore pressure derivative corresponding to the improved algorithm is very close to the one of typical log-log curves calculated directly from the wellbore pressure data in the well shut-in period. In addition, through many numerical experimental tests, it is also concluded that as the quantity of data largely increases, the improved Ilk et al.'s deconvolution algorithm exhibits the big advantage in fast computational speed over von Schroeter et al.’s algorithm and Levitan et al.’s algorithm.

A fast minimum variance beamforming method using principal component analysis.

Minimum variance (MV) beamforming has been studied for improving the performance of a diagnostic ultrasound imaging system. However, it is not easy for the MV beamforming to be implemented in a real-time ultrasound imaging system because of the enormous amount of computation time associated with the covariance matrix inversion. In this paper, to address this problem, we propose a new fast MV beamforming method that almost optimally approximates the MV beamforming while reducing the computational complexity greatly through dimensionality reduction using principal component analysis (PCA). The principal components are estimated offline from pre-calculated conventional MV weights. Thus, the proposed method does not directly calculate the MV weights but approximates them by a linear combination of a few selected dominant principal components. The combinational weights are calculated in almost the same way as in MV beamforming, but in the transformed domain of beamformer input signal by the PCA, where the dimension of the transformed covariance matrix is identical to the number of some selected principal component vectors. Both computer simulation and experiment were carried out to verify the effectiveness of the proposed method with echo signals from simulation as well as phantom and in vivo experiments. It is confirmed that our method can reduce the dimension of the covariance matrix down to as low as 2 × 2 while maintaining the good image quality of MV beamforming.

Design of FIR Filters with Discrete Coefficients Using Ant Colony Optimization

SUMMARYIn this paper, we propose a new design method for linear phase finite impulse response (FIR) filters with discrete coefficients. In a hardware implementation, filter coefficients must be represented as discrete values. The design problem of digital filters with discrete coefficients is formulated as an integer programming problem. But an enormous amount of computational time is required to solve the problem in a strict solver. Recently, ant colony optimization (ACO), a heuristic approach, has been used widely for solving combinational problems such as the traveling salesman problem. In our method, we formulate the design problem as a 0‐1 integer programming problem and solve it by using ACO. Several design examples are presented to show the effectiveness of the proposed method.

Finite Element Method for Fluid Structure Interaction with hp-Adaptivity

 Abstract—Recent advances in computational studies of fluid flows with structural interactions suggest that significant contributions have been made towards reliable solutions to the problem. Achieving accurate solutions however remains a considerable task since enormous amount of computer time and memory are usually needed. This applies particularly to a partitioned approach in which structure and fluid are solved separately. This paper attempts to solve fluid-structure interaction (FSI) problems with an hp-adaptive finite element method (hp-FEM). The FSI problem is formulated based on a partitioned approach and Arbitrary Lagrangian-Eulerian (ALE) descriptions for the incompressible fluid and structure domains. The hp-adaptivity is implemented with an a posteriori error estimator and adaptation to minimize error in energy norm. The automatic mesh adaptation over the triangular mesh is achieved with red-green-blue refinement technique. A strategy for mesh refinement to occur at prescribed key points is used for effective mesh adaptivity. The hp-adaptive approach is assessed with traditional uniform mesh refinement and also an h-adaptive method on a benchmark test case. From the error convergence, the hp-adaptive method is shown to be a viable approach in acquiring accurate solution of a partitioned-based FSI analysis without significant compromise in computational time and memory. It is also found that the convergence of solution in fluid and structure domains is considerably sensitive to the aspect ratios of triangular elements.

Design of FIR Filters with Discrete Coefficients using Ant Colony Optimization

SUMMARY In this paper, we propose a new design method for linear phase finite impulse response (FIR) filters with discrete coefficients. In a hardware implementation, filter coefficients must be represented as discrete values. The design problem of digital filters with discrete coefficients is formulated as an integer programming problem. But an enormous amount of computational time is required to solve the problem in a strict solver. Recently, ant colony optimization (ACO), a heuristic approach, has been used widely for solving combinational problems such as the traveling salesman problem. In our method, we formulate the design problem as a 0-1 integer programming problem and solve it by using ACO. Several design examples are presented to show the effectiveness of the proposed method.

Multi-Time Step Modeling of Plume Dynamics in Nanosecond-Scale Carbon Ablation

The time span of plume dynamics in laser ablation of carbon ranges from nanoseconds to milliseconds. Multi-time step approach is developed to study the plume dynamics over this entire range with minimum requirements of numerical computational resources. This approach is applied to study one of the important aspects of nanosecond-scale laser ablation, namely the shielding of incident laser beam with previously ejected plumes. Capturing the shielding effect requires smaller than nanosecond-scale time step because of large velocity and pressure gradients in plume. Use of this time step over the entire domain needs enormous amount of computer time to cover the whole time span of plume dynamics. Multi-time step modeling for such an application is therefore useful. In general, for nanosecond-scale laser ablation this shielding is caused by ionized particles and by gas molecules. It is shown for carbon plume resulting from the nanosecond-scale lasers that the degree of ionization is small. Ionization of ablated carbon is estimated by Saha equation for the given initial plume conditions. The shielding of incident laser beam is therefore calculated by normal molecular absorption. The laser-light intensity that reaches the target for subsequent pulses is evaluated.

High-speed evaluation of track-structure Monte Carlo electron transport simulations

There are many instances where Monte Carlo simulation using the track-structure method for electron transport is necessary for the accurate analytical computation and estimation of dose and other tally data. Because of the large electron interaction cross-sections and highly anisotropic scattering behavior, the track-structure method requires an enormous amount of computation time. For microdosimetry, radiation biology and other applications involving small site and tally sizes, low electron energies or high-Z/low-Z material interfaces where the track-structure method is preferred, a computational device called a field-programmable gate array (FPGA) is capable of executing track-structure Monte Carlo electron-transport simulations as fast as or faster than a standard computer can complete an identical simulation using the condensed history (CH) technique. In this paper, data from FPGA-based track-structure electron-transport computations are presented for five test cases, from simple slab-style geometries to radiation biology applications involving electrons incident on endosteal bone surface cells. For the most complex test case presented, an FPGA is capable of evaluating track-structure electron-transport problems more than 500 times faster than a standard computer can perform the same track-structure simulation and with comparable accuracy.

Elastic-Plastic Constitutive Equation Accounting for Microstructure

In this study, we focus on the modeling of solid structures that include microstructures observed in particle-dispersed composites. The finite element modeling can be used to clarify how the macroscopic behaviors of solid structures are influenced by the microstructures. In such a case, if the whole structure including the microstructures is modeled by the finite elements, an enormous number of finite elements and enormous amount of computational time are required. To overcome such difficulties, we propose a new method for modeling microstructures. In this method, an explicit form of the stress-strain relation covering both elastic and elastic-plastic regions is derived from the equivalent inclusion method proposed by Eshelby that provides mathematical solutions for stress and strain at an arbitrary point inside and outside the inclusion. The derived elastic-plastic constitutive equation takes account of the microstructures, so that the effect of microstructures on the macroscopic behaviors can be obtained from the conventional finite element method by using such a constitutive equation without modeling microstructures in the finite element analysis. The effectiveness of the proposed constitutive equation is verified for a simple problem by comparing the results of the one-element finite element analyses using the proposed constitutive equation with those of the detailed finite element analyses using multi-element finite element modeling.

Stochastic Multi-Objective Generation Dispatch

This article explores the use of genetic algorithm to search for the assigned weightage pattern of objectives to obtain the best compromised thermal power generation schedule in the multi-objective framework. The multi-objective problem is formulated considering non-commensurable objectives viz. operating cost, NOx emission, and variance of real, as well as reactive, power generation mismatch with explicit recognition of statistical uncertainties in the thermal power generation cost curves, emission curves, and power demands, which are considered random variables. The solution set of such formulated problems is non-inferior due to contradictions among the objectives taken. The generation of non-inferior solutions requires an enormous amount of computation time when the objectives are more than two. In this article, the weighting method is used to convert the multi-objective optimization problem into a scalar optimization problem. Scalar optimization problem is solved many times for a different set of weight pattern to generate non-inferior solutions. The weighting patterns are either presumed on the basis of the decision maker's intuition or simulated with suitable step size. Such a simulation procedure may fail to provide the decision maker with the non-inferior solution that actually corresponds to the best compromised solution by virtue of the step size chosen. Thus, scalar weights are searched by genetic algorithms in the non-inferior domain. Among the generated population of scalar weights that generate non-inferior solutions, the system operator chooses the population of weighting pattern that provides maximum satisfaction level from the membership function of participating objectives and is termed as fitness function. The goals/objectives of fuzzy nature can be quantified by defining their membership functions. The validity of the proposed method has been demonstrated on an 11 node IEEE system comprising of five generators. The result of the proposed method is compared with the interactive method in which weighting patterns are simulated by giving suitable variation to weights in a specific manner.

Heuristic algorithms for a turbine-blade-balancing problem

In the turbine-blade manufacturing industry, turbine-blades are machined and then are assembled to form a cylindrical roll of blades. The roll of blades should be balanced as much as possible, since otherwise the efficiency of the turbine generator might degrade. The turbine-blade balancing problem is known to be NP-hard, thus finding an optimal solution is likely to be computationally intractable for large problems. Several heuristic methods for turbine-blade balancing appeared in the literature. We propose heuristic methods for balancing blades based on the number partitioning algorithm. The proposed methods outperformed existing methods remarkably in terms of the accuracy with a negligible increase in the running time. Scope and purpose Turbine-blade balancing is an important task in turbine-blade manufacturing and in the regular overhaul of a turbine. Manual balancing is very time consuming and tedious, and often sophisticated machines are used on the shop floor for balancing blades. Exact solution procedures for solving the turbine-blade balancing problem would not be appropriate, since an enormous amount of computer time would be required. However, balancing blades has to be solved in real time on the shop floor. Therefore heuristic procedures that can solve the balancing problem with reasonable computational effort are desirable. In this paper, we propose several heuristic methods for balancing blades.

Ab Initio QM/MM Simulation with Proper Sampling: “First Principle” Calculations of the Free Energy of the Autodissociation of Water in Aqueous Solution

Quantum mechanical calculations of activation free energies of chemical reactions in condensed phases present a major challenge for computational chemistry. On one hand, it is important to use high-level ab initio methods to obtain reliable results. On the other hand, it is essential to perform sufficient configurational sampling to obtain meaningful free energies. Although the advance of quantum mechanical/molecular mechanics (QM/MM) approaches has made this problem tractable, it still requires an enormous amount of computer time. The present work advances several strategies that allow one to perform practical ab initio QM/MM calculations of free energy profiles in solutions and proteins. The basic idea is the use of a simple reference potential for the ab initio calculations (e.g., Bentzien; et al. J. Phys. Chem. B 1998, 102, 2293). One version of this approach evaluates the free energy of transfer from the reference potential to the ab initio potential by a single step free energy perturbation (FEP) approach. A new version evaluates this free energy by the linear response approximation (LRA), which involves running trajectories on both the reference and the ab initio potentials. The performance of both approaches is examined by calculating the potential of mean force for the autodissociation reaction of water in solution. It is found that the LRA approach allows one to obtain reasonable results even in cases where the ab initio and reference potentials are significantly different. The present work also explores options for increasing the size of the quantum mechanical region. Here it is shown that the constrained DFT (CDFT) method provides a promising strategy. Finally, the general issue of modeling the autodissociation reaction by quantum mechanical approaches is briefly considered. It is pointed out that the use of the empirical valence bond (EVB) approach in the sampling process should provide a way for evaluating the elusive nonequilibrium solvation effect.

Multiobjective load dispatch by fuzzy logic based searching weightage pattern

Abstract A multiobjective thermal power dispatch problem minimizes number of objectives viz cost and emission together while allocating the electricity demand among the committed generating units subject to physical and technological constraints. Such problems are solved to generate non-inferior solutions using weighting method or e-constraint method. Afterwards the decision maker is provided with a set of simple but effective tools to choose the best alternative among non-inferior solutions. The generation of non-inferior solution requires an enormous amount of computation time when the number of objectives is more than two. In the paper, the multiobjective problem has been solved a using weighted technique. The Evolutionary optimization technique has been employed in which the ‘preferred’ weightage pattern has been searched to get the ‘best’ optimal solution in non-inferior domain. Decision making theories attempt to deal with the vagueness or fuzziness inherent in subjective or impressive determination of goals. So fuzzy set theory has been exploited to decide the ‘preferred’ optimal operating point. The non-inferior solution that attains maximum satisfaction level from the membership functions of the participating objectives has been adjudged the ‘best’ solution. The proposed method requires few search moves to get the optimal operating point in the non-inferior domain for any number of goals. The validity of the proposed method has been demonstrated on a 25 nodes IEEE system comprising five generators.

Improvement of Impact Force Estimation Exerted on Spacecraft and its Accuracy.

Damage detection and warning for space debris collision is one of the most serious problems for space systems operations. Estimation of impact force and its location is necessary for such systems, which can be realized by inverse analysis. However, this analysis requires an enormous amount of computation time, which may preclude on-board processing. To overcome this problem, this paper presents two improvement procedures based on an inverse analysis by using a priori knowledge on duration of impact force that is assumed much shorter than measurement time. Improvements are demonstrated in terms of calculation time and the effects of errors in coefficient matrix on estimation accuracy are analyzed based on the analytical solutions of the structure vibration model with acceleration data corrupted by white noise.

Predicting vapor pressures using neural networks

Calculating surface vapor pressures of volatile inorganic components, nitric acid, hydrochloric acid and ammonia, is essential for modeling condensation and evaporation processes occurring in atmospheric aerosols. The vapor pressure of these compounds depends on temperature, relative humidity, phase state, and particle composition, and their calculation consumes an enormous amount of computer time in Eulerian photochemical/aerosol models. Here we use a thermodynamic model to generate a large set of vapor pressure data as a function of aerosol composition, relative humidity, and temperature. These data are then used as a training set for neural networks. Once the networks memorize the data, interpolation of vapor pressures for intermediate compositions, temperatures and relative humidities is automatic. The neural network models are able to reproduce the values predicted by the thermodynamic models accurately and are 4–1200 times faster depending on atmospheric conditions and the assumptions employed in the thermodynamic calculations.

Fusion rule engineering

The determination of the fusion rules of WZW theories constitutes an important step in the program of classifying and solving conformal field theories. A drawback of the existing algorithms for such calculations is that, even at a modestly large number of primary fields, they require an enormous amount of computer time and memory. However, by breeding together two of these algorithms in an appropriate manner, it is possible to speed up the calculations and, therefore, greatly extend the accessible range of theories.

On the Simulation Run Length: A Step towards Real Time Control in an FMS

In any real time control system, its scheduling and control policy should be reassessed every time the state of the system changes. In large and complex systems, this could be a self‐defeating goal. Implementing real time control in such systems would require an enormous amount of computation time. Determination of discrete time interval (simulation window length) is the main objective of this study. To implement and demonstrate this methodology, we selected a Flexible Manufacturing System (FMS) which approximates a dynamic job shop. The Expert Control System (ECS) developed in this study integrated programmes for different functions and employed multi‐pass simulation to determine the best scheduling strategy in the system. The simulation output is then subjected to Analysis of Variance (ANOVA) and Newman‐Keuls′ range tests to obtain a “good” simulation window length for different performance criteria of optimisation.