Meshless numerical simulation of (full) potential flows in a nozzle by genetic algorithms

2003 ◽  
Vol 43 (10-11) ◽  
pp. 1167-1176 ◽  
Author(s):  
G. Winter ◽  
J. C. Abderramán ◽  
J. A. Jiménez ◽  
B. González ◽  
E. Benitez ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Genetic Algorithms ◽  
Full Potential ◽  
Potential Flows

scholarly journals Parameter characterization of HTPEMFC using numerical simulation and genetic algorithms

2021 ◽  
Author(s):  
Raúl Losantos ◽  
Manuel Montiel ◽  
Radu Mustata ◽  
Fernando Zorrilla ◽  
Luis Valiño
Keyword(s):  
Numerical Simulation ◽  
Genetic Algorithms

Artificial Neural Network Trained, Genetic Algorithms Optimized Thermal Energy Storage Heatsinks for Electronics Cooling

2005 ◽  
Author(s):  
Jeevan Kanesan ◽  
Parthiban Arunasalam ◽  
Kankanhalli N. Seetharamu ◽  
Ishak A. Azid
Keyword(s):  
Numerical Simulation ◽  
Genetic Algorithms ◽  
Transition Temperature ◽  
Energy Storage ◽  
Thermal Energy ◽  
Temperature Difference ◽  
Thermal Energy Storage ◽  
Operating Temperature ◽  
Electronics Cooling ◽  
Stabilization Time

A thermal response model for designing thermal energy storage heatsink utilized for electronics cooling is developed in this paper. In this study, thermal energy storage (TES) heatsink made out of aluminum with paraffin as the phase change material (PCM) is considered. By using numerical simulation, stabilization time and maximum operating temperature to transition temperature difference is obtained for varying fin thicknesses, fin height, number of fins and PCM volume. The numerical simulation results were then compared with existing experimental work. The numerical results matched the melting temperature variation obtained by the experimental work. The validated numerical results are then used to train the artificial neural networks (ANN) to predict stabilization time and maximum operating temperature to transition temperature difference for new fin thicknesses, fin height, number of fins and PCM volume. Finally the optimization of the fin thickness, fin height, number of fins and PCM volume of the thermal energy storage heatsink is obtained by embedding the trained ANN as a fitness function into genetic algorithms (GA). The objective of optimization is to maximize stabilization time and to minimize maximum operating temperature to transition temperature difference. Finally the optimized results for the TES heatsink is used to build a new computer model for numerical analysis. The final optimized model results and the validated preliminary model results are then compared. The final results will show a significant improvement from the validated model. Further the study will show that by combining ANN and GA, a superior tool for optimization is realized.

Shock fitting in conical supersonic full potential flows

1985 ◽  
Vol 13 (3) ◽  
pp. 319-335 ◽  
Author(s):  
M.J. Siclari ◽  
M. Visich
Keyword(s):  
Full Potential ◽  
Potential Flows ◽  
Fitting In ◽  
Shock Fitting

scholarly journals Dynamics of an Array Mechanical Arms Coupled Each to a Fitzhugh-Nagumo Neuron

2021 ◽  
Author(s):  
KOUAMI Nadine ◽  
NANA Bonaventure ◽  
WOAFO Paul
Keyword(s):  
Numerical Simulation ◽  
Action Potential ◽  
Resting State ◽  
Electrical Signal ◽  
Pulse Generation ◽  
Electromechanical System ◽  
Transient Chaos ◽  
System Parameters ◽  
Potential Flows ◽  
Dynamical Behaviors

Abstract In this work, an array of electromechanical systems driven by an electrical line of Fitzhugh-Nagumo neuron is analyzed. It is shown that a single electromechanical system can display different dynamical behaviors such as single and multiple pulse generation, transient chaos, permanent chaos, and antimonotonicity according to the system parameters. In the case of an array of the electromechanical system constituted of a series of coupled discrete Fitzhugh-Nagumo neuron, the numerical simulation shows that as the action potential flows in the discrete array, each electromechanical system executes a pulse-like motion coming at each resting state as the electrical signal passes the node. The electromechanical system analyzed can be seen as a model for multi-periodic actuation processes or a leg model in a millipede system. Furthermore, this line can also carry an envelope of action potential and can be useful for various kinds of information processing systems.

Unconditionally Stable Calculation of Transonic Potential Flow Through Cascades Using an Adaptive Mesh for Shock Capture

1983 ◽  
Vol 105 (3) ◽  
pp. 504-513 ◽  
Author(s):  
J. R. Caspar
Keyword(s):  
Physical Space ◽  
Adaptive Mesh ◽  
Control Area ◽  
Problem Analysis ◽  
Full Potential ◽  
Unconditionally Stable ◽  
Potential Flows ◽  
Flow Through ◽  
Iterative Procedures ◽  
Coarse Meshes

This paper describes an unconditionally stable procedure for calculating practical transonic potential flows through two-dimensional cascades. The full potential equation is cast in conservation form and approximated in physical space with centered control area techniques. Discrete analogs of nonphysical weak solutions are eliminated by the introduction of artificial viscosity in supersonic regions using artificial compressibility in which the density is evaluated a certain distance upstream from where it is to be applied. An explicit treatment of the density causes standard iterative procedures to diverge, except on relatively coarse meshes on which shocks are poorly captured. Instead, the insight of a model problem analysis is used to treat the density partially and implicitly.

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