Application of shannon's entropy to classify emergent behaviors in a simulation of laser dynamics

Guisado, J. L.; Morales, Francisco J.; Guerra, José Antonio Iglesias

doi:10.1016/j.mcm.2005.09.012

Cited by 43 publications

(22 citation statements)

References 8 publications

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“…It is λ τ τ possible to classify the occurrence of each type of behavior in the parametric space by calculating the Shannon's entropy S of the distribution of values achieved by n or N , after running the simulation for a time interval [19]. In order to calculate S, the whole range of values taken by n or N is divided into 10 3 equally spaced bins and the frequency (f i ) at which n or N has a value which is inside every particular non-void bin i is computed.…”

Section: Simulation Resultsmentioning

confidence: 99%

Cellular Automata and Cluster Computing: An Application to the Simulation of Laser Dynamics

Guisado

Morales

Vega

2007

Advs. Complex Syst.

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Firstly, the application of a cellular automata (CA) model to simulate the dynamics of lasers is reviewed. With this kind of model, the macroscopic properties of the laser system emerge as a cooperative phenomenon from elementary components locally interacting under simple rules. Secondly, a parallel implementation of this kind of model for distributed-memory parallel computers is presented. Performance and scalability of this parallel implementation running on a computer cluster are analyzed, giving very satisfactory results. This confirms the feasibility of running large 3D simulations -unaffordable on an individual machine -on computer clusters, in order to simulate specific real laser systems.

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Section: Simulation Resultsmentioning

confidence: 99%

Cellular Automata and Cluster Computing: An Application to the Simulation of Laser Dynamics

Guisado

Morales

Vega

2007

Advs. Complex Syst.

Self Cite

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“…(22), (23) and (26). Seven decision variables such as effectiveness of source-side heat exchanger (e H Þ, effectiveness of sink-side heat exchanger (e L Þ, effectiveness of regeneratorside heat exchanger (e R Þ, pressure recovery co-efficients ða 1 ; a 2 Þ, source temperature (T H1 ) and temperature of the working fluid (T 4 ) are considered.…”

Section: Objective Function Decision Variables and Constraintsmentioning

confidence: 99%

“…Shannon's entropy method is a valuable means to get weights of alternatives [26]. Considering L ij in decision matrix F ij with 'n' number of alternatives and 'm' objectives, the element of this matrix for jth objective is calculated as…”

Section: Shannon Entropy Approachmentioning

confidence: 99%

See 1 more Smart Citation

Multi-objective thermodynamic optimization of an irreversible regenerative Brayton cycle using evolutionary algorithm and decision making

Kumar

Kaushik

Kumar

et al. 2016

Ain Shams Engineering Journal

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Brayton heat engine model is developed in MATLAB simulink environment and thermodynamic optimization based on finite time thermodynamic analysis along with multiple criteria is implemented. The proposed work investigates optimal values of various decision variables that simultaneously optimize power output, thermal efficiency and ecological function using evolutionary algorithm based on NSGA-II. Pareto optimal frontier between triple and dual objectives is obtained and best optimal value is selected using Fuzzy, TOPSIS, LINMAP and Shannon's entropy decision making methods. Triple objective evolutionary approach applied to the proposed model gives power output, thermal efficiency, ecological function as (53.89 kW, 0.1611, À142 kW) which are 29.78%, 25.86% and 21.13% lower in comparison with reversible system. Furthermore, the present study reflects the effect of various heat capacitance rates and component efficiencies on triple objectives in graphical custom. Finally, with the aim of error investigation, average and maximum errors of obtained results are computed. Ó 2015 Faculty of Engineering, Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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“…Guisado et al [23] emphasized on Shannon entropy technique as one of the important tool to get hold of weights of alternatives. This method gives a matrix L ij with a decision matrix F ij considering n different alternatives with m objectives in the system.…”

Section: Shannon Entropy Approachmentioning

confidence: 99%

Multi-objective thermo-economic optimization of solar parabolic dish Stirling heat engine with regenerative losses using NSGA-II and decision making

Arora

Kaushik

Kumar

2016

International Journal of Electrical Power & Energy Systems

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Application of shannon's entropy to classify emergent behaviors in a simulation of laser dynamics

Cited by 43 publications

References 8 publications

Cellular Automata and Cluster Computing: An Application to the Simulation of Laser Dynamics

Cellular Automata and Cluster Computing: An Application to the Simulation of Laser Dynamics

Multi-objective thermodynamic optimization of an irreversible regenerative Brayton cycle using evolutionary algorithm and decision making

Multi-objective thermo-economic optimization of solar parabolic dish Stirling heat engine with regenerative losses using NSGA-II and decision making

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