Optimization of drilling performance using various metaheuristics

et al. 2023

Pollack

The paper compares different metaheuristics for using heat exchangers as a benchmark to estimate the best design parameter values using optimization efficient algorithms. Many MATLAB algorithms are used in this study. Also, an engineering equation solver, which is commercial software, is used to solve the issue. The design calculates three variables, which are the length, and inner and outer pipe diameter of the heat exchanger. The results showed that the best algorithms are particle swarm optimization, and when using this algorithm, the optimal design of the double pipe heat exchanger is as follows: the pipe length is 5.6734·10−1 m, the pipe inner diameter is 8.0203·10−3 m, and the pipe outer diameter is 2.2439·10−2 m.

Section: Introductionmentioning

confidence: 99%

Comparative study of different meta-heuristics on optimal design of a heat exchanger

Jalghaf

et al. 2023

Pollack

“…Optimization algorithms (Ghafil and Jármai, 2020a) are powerful techniques to find the best possible solution among many other feasible or unfeasible solutions. Artificial bee colony (Ghafil and Jármai, 2018) and particle swarm optimization (Alsamia et al, 2021) are famous examples of metaheuristics (Almufti, 2019) which can be inspired by natural or human-made phneomina (Ghafil et al, 2021). One of the critical applications for optimization is curve fitting (Chen et al, 2005) which is a traditional engineering concept.…”

Section: Introductionmentioning

confidence: 99%

Interpolated spline method for a thermal distribution of a pipe with a turbulent heat flow

Jármai

et al. 2021

MDT

This work presents an interpolated spline method to mathematically represent experimental data of a thermal distribution on a tube with heat flux. Linear regression was compared with the double linear interpolation process with an optimization algorithm and cubic spline curve method with the proposed problem. The results show that the interpolated experimental data can highly improve the efficiency of the cubic spline curves and lead to a smooth empirical equation for the experiments. The optimization algorithm chooses the interpolated points in a way that provides more minor errors.

“…The type of metaheuristic algorithm that should be used for curve fitting is depending on the fitting problem itself. Some simple problems may need a single algorithm, and other complicated fitting problems can only be solved by multiple metaheuristics working in parallel to have a good degree of fitting (Alsamia et al, 2021). This paper, we have found a new regression method for optimal mathematical representation of experimental data called interpolated spline curve.…”

Section: Introductionmentioning

confidence: 99%

A new method to predict temperature distribution on a tube at constant heat flux

Kovács

et al. 2021

MDT

Surface temperature distribution on a tube is one of the main factors affecting the calculation of the heat transfer coefficient calculation. When an electric heater heats the tube, a magnetic flux is generated that affects the thermocouples readings; therefore, an efficient fitting technique is needed to represent these readings. This work proposes an interpolated spline method to mathematically represent experimental data of a thermal distribution on a tube with heat flux. Linear regression was compared with a double linear interpolation process with an optimization algorithm and cubic spline curve method on the proposed problem. The results show that the interpolated experimental data can highly improve the regression of the spline curve. Consequently, an interpolated spline curve gives better surface temperature distribution and better estimation for the average temperature. The interpolated points on spline segments are chosen by an optimization algorithm, which is particle swarm optimization, in a way that provides more minor errors.