The Experts below are selected from a list of 223383 Experts worldwide ranked by ideXlab platform
V. V. Bychkov - One of the best experts on this subject based on the ideXlab platform.
-
The Nonlinear Equation for curved flames applied to the problem of flames in cylindrical tubes
Physics of Fluids, 1999Co-Authors: V. V. Bychkov, A.i. KleevAbstract:The Nonlinear Equation for curved stationary flames of realistic expansion coefficients is solved numerically for the problem of flame propagation in cylindrical tubes. Two different configurations of a flame front corresponding to convex and concave flames are obtained. The convex and concave flames propagate with different velocities that depend on the tube radius and on the expansion coefficient of the burning matter. For tubes of a moderate radius the velocity amplification for convex flames exceeds the respective velocity amplification of two-dimensional flames almost twice. For tubes of a large radius unlimited increase of the curved flame velocity with increase of the tube width takes place. The obtained theoretical results are in good quantitative agreement with the results of numerical experiments on flame dynamics in cylindrical tubes.
-
Nonlinear Equation for a curved stationary flame and the flame velocity
Physics of Fluids, 1998Co-Authors: V. V. BychkovAbstract:A Nonlinear Equation for a curved stationary flame subject to the Darrieus–Landau instability is obtained for an arbitrary ratio of the fuel density and the density of the burnt matter under the assumptions of a thin flame front and weak Nonlinearity. On the basis of the Nonlinear Equation the velocity of a two-dimensional curved stationary flame is calculated. The obtained velocity is in a good agreement with the results of two-dimensional simulations of flame dynamics in tubes.
-
THREE-DIMENSIONAL CURVED FLAMES : STATIONARY FLAMES IN CYLINDRICAL TUBES
Physical Review E, 1997Co-Authors: V. V. Bychkov, A.i. Kleev, Michael A. Liberman, S. M. GolbergAbstract:Curved axisymmetric stationary flames in cylindrical tubes are investigated on the basis of a model Nonlinear Equation for a flame front subject to the Landau-Darrieus instability. It is found that the increase of the flame velocity due to a curved shape
H A Talebi - One of the best experts on this subject based on the ideXlab platform.
-
generalized three phase robust load flow for radial and meshed power systems with and without uncertainty in energy resources using dynamic radial basis functions neural networks
Journal of Cleaner Production, 2018Co-Authors: Hamid Reza Baghaee, Mojtaba Mirsalim, G B Gharehpetian, H A TalebiAbstract:Abstract This paper presents a new approach for robust load-flow in radial and meshed electric power systems. In the presented method, with an acceptable level of accuracy, and even exact, the ability of radial basis function (RBF) artificial neural networks (ANNs) for Nonlinear mapping is exploited to solve Nonlinear Equation set of load flow analysis that can be applied to a wide range of Nonlinear Equation sets. Unlike Newton Raphson (NR) method, the proposed method does not need to calculate partial derivatives and inverse Jacobian matrix and so has less computation time. Moreover, it is suitable for the radial and ill-conditioned networks that have higher values of R/X ratio. The method includes all types of buses, i.e. PQ, PV and Slack buses. The proposed method is a general method which is applicable to all types of power system networks, including radial, meshed, and open-loop. The proposed method is applied to different power and distribution test systems and compared with the other load-flow methods and the results validate its authenticity, robustness, efficiency and accuracy.
Hamid Reza Baghaee - One of the best experts on this subject based on the ideXlab platform.
-
generalized three phase robust load flow for radial and meshed power systems with and without uncertainty in energy resources using dynamic radial basis functions neural networks
Journal of Cleaner Production, 2018Co-Authors: Hamid Reza Baghaee, Mojtaba Mirsalim, G B Gharehpetian, H A TalebiAbstract:Abstract This paper presents a new approach for robust load-flow in radial and meshed electric power systems. In the presented method, with an acceptable level of accuracy, and even exact, the ability of radial basis function (RBF) artificial neural networks (ANNs) for Nonlinear mapping is exploited to solve Nonlinear Equation set of load flow analysis that can be applied to a wide range of Nonlinear Equation sets. Unlike Newton Raphson (NR) method, the proposed method does not need to calculate partial derivatives and inverse Jacobian matrix and so has less computation time. Moreover, it is suitable for the radial and ill-conditioned networks that have higher values of R/X ratio. The method includes all types of buses, i.e. PQ, PV and Slack buses. The proposed method is a general method which is applicable to all types of power system networks, including radial, meshed, and open-loop. The proposed method is applied to different power and distribution test systems and compared with the other load-flow methods and the results validate its authenticity, robustness, efficiency and accuracy.
G B Gharehpetian - One of the best experts on this subject based on the ideXlab platform.
-
generalized three phase robust load flow for radial and meshed power systems with and without uncertainty in energy resources using dynamic radial basis functions neural networks
Journal of Cleaner Production, 2018Co-Authors: Hamid Reza Baghaee, Mojtaba Mirsalim, G B Gharehpetian, H A TalebiAbstract:Abstract This paper presents a new approach for robust load-flow in radial and meshed electric power systems. In the presented method, with an acceptable level of accuracy, and even exact, the ability of radial basis function (RBF) artificial neural networks (ANNs) for Nonlinear mapping is exploited to solve Nonlinear Equation set of load flow analysis that can be applied to a wide range of Nonlinear Equation sets. Unlike Newton Raphson (NR) method, the proposed method does not need to calculate partial derivatives and inverse Jacobian matrix and so has less computation time. Moreover, it is suitable for the radial and ill-conditioned networks that have higher values of R/X ratio. The method includes all types of buses, i.e. PQ, PV and Slack buses. The proposed method is a general method which is applicable to all types of power system networks, including radial, meshed, and open-loop. The proposed method is applied to different power and distribution test systems and compared with the other load-flow methods and the results validate its authenticity, robustness, efficiency and accuracy.
Mojtaba Mirsalim - One of the best experts on this subject based on the ideXlab platform.
-
generalized three phase robust load flow for radial and meshed power systems with and without uncertainty in energy resources using dynamic radial basis functions neural networks
Journal of Cleaner Production, 2018Co-Authors: Hamid Reza Baghaee, Mojtaba Mirsalim, G B Gharehpetian, H A TalebiAbstract:Abstract This paper presents a new approach for robust load-flow in radial and meshed electric power systems. In the presented method, with an acceptable level of accuracy, and even exact, the ability of radial basis function (RBF) artificial neural networks (ANNs) for Nonlinear mapping is exploited to solve Nonlinear Equation set of load flow analysis that can be applied to a wide range of Nonlinear Equation sets. Unlike Newton Raphson (NR) method, the proposed method does not need to calculate partial derivatives and inverse Jacobian matrix and so has less computation time. Moreover, it is suitable for the radial and ill-conditioned networks that have higher values of R/X ratio. The method includes all types of buses, i.e. PQ, PV and Slack buses. The proposed method is a general method which is applicable to all types of power system networks, including radial, meshed, and open-loop. The proposed method is applied to different power and distribution test systems and compared with the other load-flow methods and the results validate its authenticity, robustness, efficiency and accuracy.
