Chaotic Mountain Gazelle Optimizer Improved by Multiple Oppositional-Based Learning Variants for Theoretical Thermal...

Area of Science:

• Engineering Optimization
• Computational Fluid Dynamics
• Materials Science

Background:

• Metaheuristic algorithms, like the Mountain Gazelle Optimizer (MGO), are used for complex engineering designs but can suffer from premature convergence.
• Optimizing shell-and-tube heat exchangers for thermal and economic performance is crucial in many industrial applications.
• Nanofluids offer potential for improved heat transfer efficiency compared to conventional fluids.

Purpose of the Study:

• To propose a novel hybrid metaheuristic algorithm for the thermo-economic design of shell-and-tube heat exchangers.
• To enhance the Mountain Gazelle Optimization method by integrating chaotic sequences and an improved quasi-dynamical oppositional learning mutation scheme.
• To evaluate the performance of the proposed algorithm in optimizing heat exchanger design using nanofluids.

Main Methods:

• A hybrid algorithm was developed by augmenting the Mountain Gazelle Optimization method with chaotic sequences and a novel quasi-dynamical oppositional learning mutation scheme.
• The enhanced algorithm incorporates an adaptive switch mechanism to balance exploration and exploitation.
• The algorithm's efficiency was validated using benchmark functions before application to heat exchanger design.

Main Results:

• The proposed hybrid algorithm demonstrated improved search efficiency and solution quality compared to the original MGO.
• The thermo-economic design of a shell-and-tube heat exchanger using water + SiO2 nanofluid resulted in a 16.3% reduction in total cost compared to ordinary water.
• Optimal design configurations were achieved for various nanoparticle-based nanofluids.

Conclusions:

• The enhanced metaheuristic algorithm effectively addresses the premature convergence issue in MGO.
• The integration of nanofluids, particularly water + SiO2, offers significant cost savings in shell-and-tube heat exchanger design.
• Biologically inspired optimization algorithms are viable tools for solving complex engineering design problems.