scholarly journals Reducing Computational Costs in the Basic Perturbation Lemma

2006 ◽  
pp. 33-48
Author(s):  
Ainhoa Berciano ◽  
María José Jiménez ◽  
Pedro Real
Keyword(s):  
Computational Costs

THE NEW ALGORITHM OF SOLVING HARMONIC BALANCE EQUATIONS

2019 ◽  
Author(s):  
Vladimir Lantsov ◽  
A. Papulina
Keyword(s):  
Harmonic Balance ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Balance Equations ◽  
Model Order ◽  
Cad Systems ◽  
Computational Costs ◽  
Reduction Methods ◽  
Model Equations

The new algorithm of solving harmonic balance equations which used in electronic CAD systems is presented. The new algorithm is based on implementation to harmonic balance equations the ideas of model order reduction methods. This algorithm allows significantly reduce the size of memory for storing of model equations and reduce of computational costs.

Identification, Prediction and Data Analysis of Noncoding RNAs: A Review

2019 ◽  
Vol 15 (3) ◽  
pp. 216-230 ◽  
Author(s):  
Abbasali Emamjomeh ◽  
Javad Zahiri ◽  
Mehrdad Asadian ◽  
Mehrdad Behmanesh ◽  
Barat A. Fakheri ◽  
...  
Keyword(s):  
Computational Methods ◽  
State Of The Art ◽  
Noncoding Rnas ◽  
Research Field ◽  
Design Strategies ◽  
Structural Prediction ◽  
Computational Costs ◽  
Cellular Processes ◽  
Laboratory Techniques ◽  
Fast Prediction

Background:Noncoding RNAs (ncRNAs) which play an important role in various cellular processes are important in medicine as well as in drug design strategies. Different studies have shown that ncRNAs are dis-regulated in cancer cells and play an important role in human tumorigenesis. Therefore, it is important to identify and predict such molecules by experimental and computational methods, respectively. However, to avoid expensive experimental methods, computational algorithms have been developed for accurately and fast prediction of ncRNAs.Objective:The aim of this review was to introduce the experimental and computational methods to identify and predict ncRNAs structure. Also, we explained the ncRNA’s roles in cellular processes and drugs design, briefly.Method:In this survey, we will introduce ncRNAs and their roles in biological and medicinal processes. Then, some important laboratory techniques will be studied to identify ncRNAs. Finally, the state-of-the-art models and algorithms will be introduced along with important tools and databases.Results:The results showed that the integration of experimental and computational approaches improves to identify ncRNAs. Moreover, the high accurate databases, algorithms and tools were compared to predict the ncRNAs.Conclusion:ncRNAs prediction is an exciting research field, but there are different difficulties. It requires accurate and reliable algorithms and tools. Also, it should be mentioned that computational costs of such algorithm including running time and usage memory are very important. Finally, some suggestions were presented to improve computational methods of ncRNAs gene and structural prediction.

scholarly journals Performance of Adaptive Unstructured Mesh Modelling in Idealized Advection Cases over Steep Terrains

Atmosphere ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 444 ◽  
Author(s):  
Jinxi Li ◽  
Jie Zheng ◽  
Jiang Zhu ◽  
Fangxin Fang ◽  
Christopher. Pain ◽  
...  
Keyword(s):  
Unstructured Mesh ◽  
Computational Cost ◽  
Adaptive Mesh ◽  
Galerkin Finite Element Method ◽  
Adaptive Meshes ◽  
Galerkin Finite Element ◽  
Computational Costs ◽  
Cut Cell ◽  
Discontinuous Galerkin Finite Element ◽  
Terrain Following

Advection errors are common in basic terrain-following (TF) coordinates. Numerous methods, including the hybrid TF coordinate and smoothing vertical layers, have been proposed to reduce the advection errors. Advection errors are affected by the directions of velocity fields and the complexity of the terrain. In this study, an unstructured adaptive mesh together with the discontinuous Galerkin finite element method is employed to reduce advection errors over steep terrains. To test the capability of adaptive meshes, five two-dimensional (2D) idealized tests are conducted. Then, the results of adaptive meshes are compared with those of cut-cell and TF meshes. The results show that using adaptive meshes reduces the advection errors by one to two orders of magnitude compared to the cut-cell and TF meshes regardless of variations in velocity directions or terrain complexity. Furthermore, adaptive meshes can reduce the advection errors when the tracer moves tangentially along the terrain surface and allows the terrain to be represented without incurring in severe dispersion. Finally, the computational cost is analyzed. To achieve a given tagging criterion level, the adaptive mesh requires fewer nodes, smaller minimum mesh sizes, less runtime and lower proportion between the node numbers used for resolving the tracer and each wavelength than cut-cell and TF meshes, thus reducing the computational costs.

scholarly journals Very Fast and Accurate Procedure for the Characterization of Photovoltaic Panels from Datasheet Information

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Antonino Laudani ◽  
Francesco Riganti Fulginei ◽  
Alessandro Salvini ◽  
Gabriele Maria Lozito ◽  
Salvatore Coco
Keyword(s):  
Numerical Methods ◽  
Search Space ◽  
Extraction Process ◽  
Reduced Form ◽  
Photovoltaic Panels ◽  
Approximation Techniques ◽  
Computational Costs ◽  
Minimization Technique ◽  
Accurate Procedure

In recent years several numerical methods have been proposed to identify the five-parameter model of photovoltaic panels from manufacturer datasheets also by introducing simplification or approximation techniques. In this paper we present a fast and accurate procedure for obtaining the parameters of the five-parameter model by starting from its reduced form. The procedure allows characterizing, in few seconds, thousands of photovoltaic panels present on the standard databases. It introduces and takes advantage of further important mathematical considerations without any model simplifications or data approximations. In particular the five parameters are divided in two groups, independent and dependent parameters, in order to reduce the dimensions of the search space. The partitioning of the parameters provides a strong advantage in terms of convergence, computational costs, and execution time of the present approach. Validations on thousands of photovoltaic panels are presented that show how it is possible to make easy and efficient the extraction process of the five parameters, without taking care of choosing a specific solver algorithm but simply by using any deterministic optimization/minimization technique.

Optimization of Operation Parameters for Direct Spring Loaded Pressure Relief Valve in a Pipeline System

2018 ◽  
Vol 140 (5) ◽  
Author(s):  
Hyunjun Kim ◽  
Sanghyun Kim ◽  
Youngman Kim ◽  
Jonghwan Kim
Keyword(s):  
Pressure Relief Valve ◽  
Pipeline System ◽  
Relief Valve ◽  
Pressure Relief ◽  
Computational Costs ◽  
Constant Degree ◽  
System A ◽  
Disk Mass ◽  
Analysis Scheme ◽  
Surge Control

A direct spring loaded pressure relief valve (DSLPRV) is an efficient hydraulic structure used to control a potential water hammer in pipeline systems. The optimization of a DSLPRV was explored to consider the instability issue of a valve disk and the surge control for a pipeline system. A surge analysis scheme, named the method of characteristics, was implemented into a multiple-objective genetic algorithm to determine the adjustable factors in the operation of the DSLPRV. The forward transient analysis and multi-objective optimization of adjustable factors, such as the spring constant, degree of precompression, and disk mass, showed substantial relaxation in the surge pressure and oscillation of valve disk in a hypothetical pipeline system. The results of the regression analysis of surge were compared with the optimization results to demonstrate the potential of the developed method to substantially reduce computational costs.

scholarly journals Efficient Searchable Symmetric Encryption Supporting Dynamic Multikeyword Ranked Search

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Yu Zhang ◽  
Yin Li ◽  
Yifan Wang
Keyword(s):  
Binary Tree ◽  
Search Time ◽  
Symmetric Encryption ◽  
Time Cost ◽  
Computational Costs ◽  
Related Space ◽  
Insertion And Deletion ◽  
Searchable Symmetric Encryption ◽  
Ranked Search ◽  
Binary Tree Structure

Searchable symmetric encryption that supports dynamic multikeyword ranked search (SSE-DMKRS) has been intensively studied during recent years. Such a scheme allows data users to dynamically update documents and retrieve the most wanted documents efficiently. Previous schemes suffer from high computational costs since the time and space complexities of these schemes are linear with the size of the dictionary generated from the dataset. In this paper, by utilizing a shallow neural network model called “Word2vec” together with a balanced binary tree structure, we propose a highly efficient SSE-DMKRS scheme. The “Word2vec” tool can effectively convert the documents and queries into a group of vectors whose dimensions are much smaller than the size of the dictionary. As a result, we can significantly reduce the related space and time cost. Moreover, with the use of the tree-based index, our scheme can achieve a sublinear search time and support dynamic operations like insertion and deletion. Both theoretical and experimental analyses demonstrate that the efficiency of our scheme surpasses any other schemes of the same kind, so that it has a wide application prospect in the real world.

DEVELOPMENT OF INTELLECTUAL CONTROL ALGORITHMS FOR PLASTER MECHATRON COMPLEXES FOR AGRICULTURAL ENTERPRISES

2020 ◽  
Author(s):  
O.L. Cvetkova ◽  
◽  
A.R. Ajdinyan
Keyword(s):  
Low Cost ◽  
Inverse Kinematic ◽  
Control Algorithms ◽  
High Quality ◽  
Inverse Kinematic Problem ◽  
Intelligent Algorithms ◽  
Agricultural Enterprises ◽  
Computational Costs ◽  
Control Actions ◽  
Intellectual Control

Agricultural enterprises are interested in high-quality and low-cost plastering of technological and warehouse premises. It is proposed to solve the problem using mechatronic complexes intended for plastering surfaces characterized by different features of irregularities. The work considers intelligent algorithms for controlling the actions of a stucco robot based on the use of an artificial neural network. Intelligent algorithms will provide the formation of control actions for the robot when applying the mortar to the surface, with a rough leveling of the mortar layer, will allow solving the inverse kinematic problem of position for the plastering robot with less computational costs.

An Efficient Sparse Finite Element Solver for the Radiative Transfer Equation

2009 ◽  
Author(s):  
Gisela Widmer
Keyword(s):  
Linear System ◽  
Radiative Transfer ◽  
Transfer Equation ◽  
Degrees Of Freedom ◽  
Radiative Transfer Equation ◽  
Three Dimensional ◽  
Discrete Ordinates ◽  
Five Dimensions ◽  
Computational Costs ◽  
Dimensional Domain

The stationary monochromatic radiative transfer equation (RTE) is posed in five dimensions, with the intensity depending on both a position in a three-dimensional domain as well as a direction. For non-scattering radiative transfer, sparse finite elements [1, 2] have been shown to be an efficient discretization strategy if the intensity function is sufficiently smooth. Compared to the discrete ordinates method, they make it possible to significantly reduce the number of degrees of freedom N in the discretization with almost no loss of accuracy. However, using a direct solver to solve the resulting linear system requires O(N3) operations. In this paper, an efficient solver based on the conjugate gradient method (CG) with a subspace correction preconditioner is presented. Numerical experiments show that the linear system can be solved at computational costs that are nearly proportional to the number of degrees of freedom N in the discretization.

Confinement Effects on Added Mass of Cylindrical Structures in a Potential Flow

2017 ◽  
Author(s):  
R. Capanna ◽  
G. Ricciardi ◽  
C. Eloy ◽  
E. Sarrouy
Keyword(s):  
Mechanical Behaviour ◽  
Potential Flow ◽  
Nuclear Power ◽  
Stokes Equations ◽  
Reactor Core ◽  
Axial Flow ◽  
Added Mass ◽  
Seismic Load ◽  
Slender Body Theory ◽  
Computational Costs

Efficient modelling and accurate knowledge of the mechanical behaviour of the reactor core are needed to estimate the effects of seismic excitation on a nuclear power plant. The fuel assemblies (in the reactor core) are subjected to an axial water flow which modifies their dynamical behaviour. Several fluid-structure models simulating the response of the core to a seismic load has been developed in recent years; most of them require high computational costs. The work which is presented here is a first step in order to simplify the fluid forces modelling, and thus to be able to catch the main features of the mechanical behaviour of reactor core with low computational costs. The main assumption made in this work is to consider the fluid flow as an inviscid potential flow. Thus, the flow can be described only using one scalar function (velocity potential) instead of a vector field and strongly simplifies the fluid mechanics equations, avoiding the necessity to solve Navier-Stokes equations. The pressure distribution around a cylinder is first solved in Fourier space for different values of the parameters (wavenumber, confinement size). The method is applied to a simple geometry (cylinder in an axial flow with a variable confinement) in order to test its effectiveness. The empirical model is then compared to simulations and reference works in literature. The configuration with large confinement has been solved, and results were in agreement with Slender Body Theory. The dependency on the confinement size strongly depends on the wavenumber, but in any case added mass increases as the confinement size decreases. Finally, future perspectives to extend the model to a group of cylinders and to improve the model are discussed (i.e. add viscosity to the model).

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