Mehar and Keerat method for solving system of fuzzy complex linear equations

2016 ◽  
Vol 31 (3) ◽  
pp. 1955-1965
Author(s):  
Jeevan Jot Kaur ◽  
Amit Kumar
Keyword(s):  
Linear Equations ◽  
Complex Linear

A Two-Phase Algorithm for the Chebyshev Solution of Complex Linear Equations

1994 ◽  
Vol 15 (6) ◽  
pp. 1440-1451
Author(s):  
Dirk P. Laurie ◽  
Lucas M. Venter
Keyword(s):  
Linear Equations ◽  
Two Phase ◽  
Complex Linear

Advanced Numerical Methods for the Prediction of Tonal Noise in Turbomachinery—Part II: Time-Linearized Methods

2013 ◽  
Vol 136 (2) ◽  
Author(s):  
Christian Frey ◽  
Graham Ashcroft ◽  
Hans-Peter Kersken ◽  
Christian Weckmüller
Keyword(s):  
Boundary Condition ◽  
Linear System ◽  
Near Field ◽  
Linear Equations ◽  
Time Integration ◽  
Aerodynamic Noise ◽  
Fan Noise ◽  
Rotor Stator Interaction ◽  
Complex Linear ◽  
Computational Fluid Dynamics Cfd

This is the second part of a series of two papers on unsteady computational fluid dynamics (CFD) methods for the numerical simulation of aerodynamic noise generation and propagation. It focuses on the application of linearized RANS methods to turbomachinery noise problems. The convective and viscous fluxes of an existing URANS solver are linearized and the resulting unsteady linear equations are transferred into the frequency domain, thereby simplifying the solution problem from unsteady time-integration to a complex linear system. The linear system is solved using a parallel, preconditioned general minimized residual (GMRES) method with restarts. In order to prescribe disturbances due to rotor stator interaction, a so-called gust boundary condition is implemented. Using this inhomogeneous boundary condition, one can compute the generation of the acoustic modes and their near field propagation. The application of the time-linearized methods to a modern high-bypass ratio fan is investigated. The tonal fan noise predicted by the time-linearized solver is compared to numerical results presented in the first part and to measurements.

FUZZY CENTRE BASED SOLUTION OF FUZZY COMPLEX LINEAR SYSTEM OF EQUATIONS

2013 ◽  
Vol 21 (04) ◽  
pp. 629-642 ◽  
Author(s):  
DIPTIRANJAN BEHERA ◽  
S. CHAKRAVERTY
Keyword(s):  
Linear Time ◽  
Linear Equations ◽  
Electric Circuit ◽  
System Of Linear Equations ◽  
New Approach ◽  
Linear System Of Equations ◽  
Linear Time Invariant ◽  
Complex Linear ◽  
Time Invariant ◽  
Good Agreement

A new approach to solve Fuzzy Complex System of Linear Equations (FCSLE) based on fuzzy complex centre procedure is presented here. Few theorems related to the investigation are stated and proved. Finally the presented procedure is used to analyze an example problem of linear time invariant electric circuit with complex crisp coefficient and fuzzy complex sources. The results obtained are also compared with the known solutions and are found to be in good agreement.

Comprehensive study on complex-valued ZNN models activated by novel nonlinear functions for dynamic complex linear equations

2021 ◽  
Vol 561 ◽  
pp. 101-114
Author(s):  
Jianhua Dai ◽  
Yiwei Li ◽  
Lin Xiao ◽  
Lei Jia ◽  
Qing Liao ◽  
...  
Keyword(s):  
Linear Equations ◽  
Nonlinear Functions ◽  
Complex Linear ◽  
Complex Valued ◽  
Comprehensive Study

scholarly journals Exciting Fixed Point Results on a Novel Space with Supportive Applications

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Hasanen A. Hammad ◽  
Hassen Aydi ◽  
Yaé Ulrich Gaba
Keyword(s):  
Fixed Point ◽  
Metric Space ◽  
Metric Spaces ◽  
Linear Equations ◽  
Topological Properties ◽  
Rlc Circuit ◽  
Complex Linear ◽  
New Space ◽  
Complex Valued ◽  
Theoretical Results

The goal of this paper is to present a new space, a complex valued controlled rectangular b -metric space (for short, υ ℂ -metric space). Some examples and topological properties of υ ℂ -metric spaces are given. Also, some related common fixed point results are discussed. Our results generalize a lot of works in this direction. Moreover, we apply the theoretical results to find a unique solution of a complex valued Atangana-Baleanu fractional integral operator and a system of complex linear equations. Finally, a numerical example to find the current that passes through the RLC circuit is illustrated.

Advanced Numerical Methods for the Prediction of Tonal Noise in Turbomachinery: Part II—Time-Linearized Methods

2012 ◽  
Author(s):  
Christian Frey ◽  
Graham Ashcroft ◽  
Hans-Peter Kersken ◽  
Christian Weckmüller
Keyword(s):  
Boundary Condition ◽  
Linear System ◽  
Near Field ◽  
Linear Equations ◽  
Time Integration ◽  
Aerodynamic Noise ◽  
Fan Noise ◽  
Rotor Stator Interaction ◽  
Complex Linear ◽  
Preconditioned Gmres

This is the second part of a series of two papers on unsteady CFD methods for the numerical simulation of aerodynamic noise generation and propagation. It focuses on the application of linearized RANS methods to turbomachinery noise problems. The convective and viscous fluxes of an existing URANS solver are linearized and the resulting unsteady linear equations are transfered into the frequency domain, thereby simplifying the solution problem from unsteady time-integration to a complex linear system. The linear system is solved using a parallel, preconditioned GMRES method with restarts. In order to prescribe disturbances due to rotor stator interaction a so-called gust boundary condition is implemented. Using this inhomogeneous boundary condition one can compute the generation of the acoustic modes and their near field progagation. The application of the time-linearized methods to a modern high-bypass ratio fan is investigated. The tonal fan noise predicted by the time-linearized solver is compared to numerical results presented in the first part and to measurements.

Design and analysis of new complex zeroing neural network for a set of dynamic complex linear equations

2019 ◽  
Vol 363 ◽  
pp. 171-181 ◽  
Author(s):  
Lin Xiao ◽  
Qian Yi ◽  
Jianhua Dai ◽  
Kenli Li ◽  
Zeshan Hu
Keyword(s):  
Neural Network ◽  
Linear Equations ◽  
Complex Linear

Seismic trace interpolation using half‐step prediction filters

Geophysics ◽  
1999 ◽  
Vol 64 (5) ◽  
pp. 1461-1467 ◽  
Author(s):  
Milton J. Porsani
Keyword(s):  
Input Data ◽  
Interpolation Method ◽  
Linear Equations ◽  
Seismic Section ◽  
Linear System Of Equations ◽  
Unit Step ◽  
Systems Of Linear Equations ◽  
Complex Linear ◽  
Marine Seismic ◽  
Prediction Filter

A method to perform seismic trace interpolation known as the Spitz method handles spatially aliased events. The Spitz method uses the unit‐step prediction filter to estimate data spaced at Δx/2. The missing data are obtained by solving a complex linear system of equations whose unknowns are the coefficients at the interpolated location. We attack this problem by introducing a half‐step prediction filter that makes trace interpolation significantly more efficient and easier for implementation. A complex half‐step prediction filter at frequency f/2 is computed in the least‐squares sense to predict odd data components from even ones. At the frequency f, the prediction operator is shrunk and convolved with the input data spaced at Δx to predict data at Δx/2 directly. Instead of solving two systems of linear equations, as proposed by Spitz, only a system for the half‐step prediction filter has to be solved. Numerical examples using a marine seismic common‐midpoint (CMP) gather and a poststack seismic section were used to illustrate the new interpolation method.

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