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Multi-Agent Metaheuristic Framework for Thermal Design Optimization of a Shell and Tube Evaporator Operated with \(\hbox {R134a/Al }_{2}\hbox {O}_{3}\) Nanorefrigerant

  • Research Article - Mechanical Engineering
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Abstract

This study proposes a brand new practical multi-agent optimization framework based on an intelligent collaborative interaction between some prevalent metaheuristic algorithms available in the literature. Proposed optimization architecture is built on widely known and reputed master–slave model assisted with some useful and promising modifications. Conventional stochastic-based optimization algorithms including Particle Swarm Optimization, Crow Search Algorithm, Differential Evolution, and Global Best Algorithm are structurally coordinated to form the slave populations, while the best solutions obtained from these slave subpopulations are forming the master individuals. Contrary to the traditional master–slave approach, master individuals in this proposed framework becomes more functional by performing an extensive local search over numerical results of the best slave individuals. Main aim in constructing such a highly devised multi-agent algorithm is to maintain an effective communication domain between slave individuals (agents) as well as to enhance the capabilities of the cooperative search mechanism through the systematic combination of metaheuristics. Optimization performance of the proposed framework is tested on a suite of 29 optimization benchmark functions. Proposed optimization method surpasses the compared optimization algorithms in 27 out of 29 problems and proves its solution efficiency in multidimensional optimization problems. Then, proposed strategy is applied on single and multi-objective thermal design of a shell and tube evaporator operated with \(\hbox {R134a/Al}_{2}\hbox {O}_{3}\) nanorefrigerant. It is seen that maximum overall heat transfer coefficient is increased by 18.1% and minimum total cost of heat exchanger is reduced by 5.1% in the case of incorporating \(\hbox {Al}_{2}\hbox {O}_{3}\) nanoparticles into R134a.

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Abbreviations

\(A_\mathrm{s}\) :

Heat exchange area (\(\hbox {m}^{2}\))

\(a_\mathrm{s}\) :

Cross-sectional area normal to flow (\(\hbox {m}^{2}\))

B :

Baffle spacing (m)

\(C_\mathrm{p}\) :

Specific heat (kJ/kgK)

\(C_\mathrm{e}\) :

Energy cost (€/kWh)

\(C_\mathrm{l}\) :

Shell side clearance (m)

\(C_\mathrm{i}\) :

Capital investment cost (€)

\(C_\mathrm{np}\) :

Cost of nanoparticle (€)

\(C_\mathrm{o}\) :

Annual operation cost (€/year)

\(C_\mathrm{od}\) :

Total operating cost (€)

D :

Problem dimension

\(D_\mathrm{e}\) :

Hydraulic shell diameter (m)

\(D_\mathrm{s}\) :

Shell inside diameter (m)

d :

Tube diameter (m)

\(d_\mathrm{p}\) :

Nanoparticle diameter (m)

\(f_\mathrm{s}\) :

Shell side friction factor

F :

Correction factor

\(F_\mathrm{PD}\) :

Nanoparticle enhancement factor

G :

Mass velocity (\(\hbox {kg/m}^{2}\hbox {s}\))

H :

Annual operating time (h/year)

h :

Heat transfer coefficient (\(\hbox {W/m}^{2}\hbox {K}\))

i :

Annual discount rate (%)

k :

Heat conductivity (W/mK)

L :

Tube length (m)

\(\dot{m}\) :

Mass flow rate (kg/s)

N :

Population size

ny:

Equipment life (year)

\(N_\mathrm{p}\) :

Number of tube pass

\(N_\mathrm{t}\) :

Total number of tubes

P :

Pumping power (W)

Pr :

Prandtl number

\(P_\mathrm{t}\) :

Tube pitch (m)

\(\Delta P\) :

Pressure drop (Pa)

Re :

Reynolds number

Q :

Imposed heat load (W)

\(R_\mathrm{f}\) :

Fouling resistance (\(\hbox {m}^{2}\hbox {K/W}\))

T :

Temperature (\(\hbox {K-}^{\circ }\hbox {C}\))

\(\Delta T_\mathrm{LMTD}\) :

Logarithmic mean temperature difference

v :

Working fluid velocity (m/s)

x :

Vapour quality

U :

Overall heat transfer coefficient (\(\hbox {W/m}^{2}\hbox {K}\))

\(\mu \) :

Dynamic viscosity (Pa s)

\(\rho \) :

Density (\(\hbox {kg/m}^{3}\))

\(\eta \) :

Pumping efficiency

\(\phi \) :

Nanoparticle volume concentration (%), Chaotic random number

i:

Inlet

L:

Liquid

n:

Nanoparticle

o:

Outlet

r:

Refrigerant

s:

Shell side

t:

Tube side

TP:

Two phase

w:

Wall

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  1. Mechanical Engineering Department, Ege University, Izmir, Turkey

    Oguz Emrah Turgut

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Turgut, O.E. Multi-Agent Metaheuristic Framework for Thermal Design Optimization of a Shell and Tube Evaporator Operated with \(\hbox {R134a/Al }_{2}\hbox {O}_{3}\) Nanorefrigerant. Arab J Sci Eng 44, 777–801 (2019). https://doi.org/10.1007/s13369-018-3279-y

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