Plasma fluid and Maxwell equations are used to derive an analytical expression for the high-gain of a Cherenkov Free-Electron Laser (FEL). The evolution of the gain from the linear to the non-linear regime is investigated. The gain expression is then generalized to predict the linear and non-linear effects at any stage of interaction, permitting us to identify the criteria of optimal design of Cherenkov FELs. In this study, the spatial dependence of the characterizing parameters of electron beam velocity and density, such as the average value and modulation amplitude, are considered. Also, the phase shift between the bunched electrons and the radiation field is taken into account. In the linear limit, it is shown that our gain expression agrees satisfactorily with those obtained in previous studies.

Magnetohydrodynamic Mixed Convection and Entropy Analysis of Nanofluid in Gamma-Shaped Porous
Cavity.

Inclined magneto: convection, internal heat, and entropy generation of nanofluid in an I-shaped cavity saturated with porous media

Effect Of The Fractional Derivatives On Unsteady Natural Convection Of A Nanofluid In An Enclosure.

Effects of the Caputo fractional derivatives on convective flow in wavy vented enclosures filled with a porous medium using Al2O3Cu hybrid nanofluids.

MHD mixed convection and entropy generation of nanofluid in a lid-driven U-shaped cavity

Entropy generation and nanofluid mixed convection in a C-shaped cavity

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Real-life problems are usually time-consuming since they require solving large instances of NP-hard problems. Exact search methods in most of the cases cannot afford practical solutions for such problems. Metaheuristics arise as promising solvers for these problems, by obtaining acceptable solutions in terms of quality and computational cost in a reasonable time bound. Nowadays, energy efficiency is also taken into consideration during the design of new algorithms because of the million times that algorithms run on labs and computation centers. This work presents two novel experiments for investigating the numerical performance and energy efficiency of the sequential and parallel metaheuristics. The main aim of this study is to analyze the energy consumption of three well-known and commonly used metaheuristics (Genetic Algorithm, Variable Neighborhood Search, and Simulated Annealing) and their parallel versions. The discussions reveal the differences/similarities between the different sequential/parallel algorithms, which include trajectory-based and population-based metaheuristics so that this study is useful for the future design of energy-aware algorithms.