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Four-Objective Optimization of an Irreversible Magnetohydrodynamic Cycle.

Qingkun Wu1,2,3, Lingen Chen1,2,3, Yanlin Ge1,2,3

  • 1Institute of Thermal Science and Power Engineering, Wuhan Institute of Technology, Wuhan 430205, China.

Entropy (Basel, Switzerland)
|July 8, 2023
PubMed
Summary

This study optimizes irreversible magnetohydrodynamic cycles using finite time thermodynamics and a genetic algorithm. Multi-objective optimization yields better results than single-objective approaches for power output and efficiency.

Keywords:
NSGA-II algorithmdeviation indexfinite time thermodynamicsirreversible MHD cyclemulti-objective optimizationperformance comparison

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Area of Science:

  • Thermodynamics
  • Magnetohydrodynamics
  • Computational Engineering

Background:

  • Existing models of irreversible magnetohydrodynamic (MHD) cycles provide a basis for thermodynamic analysis.
  • Finite time thermodynamics offers a framework for optimizing processes within finite time constraints.
  • Multi-objective optimization is crucial for balancing competing performance metrics in complex systems.

Purpose of the Study:

  • To perform multi-objective optimization of an irreversible magnetohydrodynamic cycle.
  • To evaluate the performance of different objective function combinations and decision-making methods.
  • To compare multi-objective optimization results against single-objective optimizations.

Main Methods:

  • Utilized finite time thermodynamic theory and the Non-dominated Sorting Genetic Algorithm II (NSGA-II).
  • Introduced heat exchanger thermal conductance distribution and working fluid isentropic temperature ratio as optimization variables.
  • Defined power output, efficiency, ecological function, and power density as objective functions for optimization.

Main Results:

  • Multi-objective optimization using LINMAP and TOPSIS decision-making approaches yielded lower deviation indexes (0.1764 at constant gas velocity, 0.1767 at constant Mach number) compared to Shannon Entropy (0.1940, 0.1950).
  • These multi-objective results were superior to those obtained from any single-objective optimization for power output, efficiency, ecological function, or power density.
  • Deviation indexes for multi-objective optimization were significantly lower than for single-objective optimizations across various performance metrics.

Conclusions:

  • Multi-objective optimization provides a more effective approach for enhancing magnetohydrodynamic cycle performance compared to single-objective strategies.
  • The combination of finite time thermodynamics and NSGA-II effectively balances multiple performance criteria.
  • LINMAP and TOPSIS decision-making methods are suitable for selecting optimal parameters in complex thermodynamic systems.