Design and multi-objective optimization of heat exchangers for refrigerators

This paper presents a method for optimizing wavy plate-fin heat exchangers accurately and efficiently. It combines CFD simulation, Radical Basis Functions (RBF) with multi-objective optimization to improve the performance. The optimization of the Colburn factor j and the friction coefficient f is regarded as a multi-objective optimization problem, due to the existence of two contradictory goals. The approximation model was obtained by Radical Basis Functions, and the shape of the heat exchanger was optimized by multi-objective genetic algorithm (MOGA). The optimization results showed that j increased by 17.62% and f decreased by 20.76%, indicating that the heat exchange efficiency was significantly enhanced and the fluid structure resistance reduced. Then, from the aspects of field synergy and tubulence energy, the performance advantage of the optimized structure was further confirmed.

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Multi-objective optimization of a recuperative gas turbine cycle using non-dominated sorting genetic algorithm

Proceedings of the Institution of Mechanical Engineers Part A Journal of Power and Energy ◽

10.1177/0957650911420930 ◽

2011 ◽

Vol 225 (8) ◽

pp. 1041-1051 ◽

Cited By ~ 8

Author(s):

H Sayyaadi ◽

H R Aminian

Keyword(s):

Genetic Algorithm ◽

Gas Turbine ◽

Heat Exchangers ◽

Mixed Integer ◽

Design Stage ◽

Tube Length ◽

Pareto Optimal ◽

Multi Objective Optimization ◽

Multi Objective ◽

Gas Turbine Cycle

A regenerative gas turbine cycle with two particular tubular recuperative heat exchangers in parallel is considered for multi-objective optimization. It is assumed that tubular recuperative heat exchangers and its corresponding gas cycle are in design stage simultaneously. Three objective functions including the purchased equipment cost of recuperators, the unit cost rate of the generated power, and the exergetic efficiency of the gas cycle are considered simultaneously. Geometric specifications of the recuperator including tube length, tube outside/inside diameters, tube pitch, inside shell diameter, outer and inner tube limits of the tube bundle and the total number of disc and doughnut baffles, and main operating parameters of the gas cycle including the compressor pressure ratio, exhaust temperature of the combustion chamber and the air mass flowrate are considered as decision variables. Combination of these objectives anddecision variables with suitable engineering and physical constraints (including NO x and CO emission limitations) comprises a set of mixed integer non-linear problems. Optimization programming in MATLAB is performed using one of the most powerful and robust multi-objective optimization algorithms, namely non-dominated sorting genetic algorithm. This approach is applied to find a set of Pareto optimal solutions. Pareto optimal frontier is obtained, and a final optimal solution is selected in a decision-making process.

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Thermohydraulic sensitivity analysis and multi-objective optimization of Fe3O4/H2O nanofluid flow inside U-bend heat exchangers with longitudinal strip inserts

Applied Thermal Engineering ◽

10.1016/j.applthermaleng.2019.114518 ◽

2020 ◽

Vol 164 ◽

pp. 114518 ◽

Cited By ~ 7

Author(s):

Amir Ebrahimi-Moghadam ◽

Soheil Kowsari ◽

Faezeh Farhadi ◽

Mahdi Deymi-Dashtebayaz

Keyword(s):

Sensitivity Analysis ◽

Heat Exchangers ◽

Multi Objective Optimization ◽

Nanofluid Flow ◽

Multi Objective ◽

Longitudinal Strip

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Multi-objective optimization of CuO based organic Rankine cycle operated using R245ca

E3S Web of Conferences ◽

10.1051/e3sconf/201911600062 ◽

2019 ◽

Vol 116 ◽

pp. 00062 ◽

Cited By ~ 1

Author(s):

Parth Prajapati ◽

Vivek Patel

Keyword(s):

Heat Transfer ◽

Thermal Efficiency ◽

Heat Exchangers ◽

Waste Heat ◽

Organic Rankine Cycle ◽

Rankine Cycle ◽

Working Fluid ◽

Multi Objective Optimization ◽

Multi Objective ◽

Source Fluid

The present work deals with multi objective optimization of nanofluid based Organic Rankine Cycle (ORC) to utilise waste heat energy. Working fluid considered for the study is R245ca for its good thermodynamic properties and lower Global Warming Potential (GWP) compared to the conventional fluids used in the waste heat recovery system. Heat Transfer Search (HTS) algorithm is used to optimize the objective functions which tends to maximize thermal efficiency and minimize Levelised Energy Cost (LEC). To enhance heat transfer between the working fluid and source fluid, nanoparticles are added to the source fluid. Application of nanofluids in the heat transfer system helps in maximizing recovery of the waste heat in the heat exchangers. Based on the availability and cost, CuO nanoparticles are considered for the study. Effect of Pinch Point Temperature Difference (PPTD) and concentration of nanoparticles in heat exchangers is studied and discussed. Results showed that nanofluids based ORC gives maximum thermal efficiency of 18.50% at LEC of 2.59 $/kWh. Total reduction of 7.11% in LEC can be achieved using nanofluids.

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Multi-objective optimization and decision making of endoreversible combined cycles with consideration of different heat exchangers by finite time thermodynamics

Energy Conversion and Management ◽

10.1016/j.enconman.2018.06.046 ◽

2018 ◽

Vol 171 ◽

pp. 1052-1062 ◽

Cited By ~ 4

Author(s):

Amir Ghasemkhani ◽

Said Farahat ◽

Mohammad Mahdi Naserian

Keyword(s):

Decision Making ◽

Finite Time ◽

Heat Exchangers ◽

Finite Time Thermodynamics ◽

Multi Objective Optimization ◽

Multi Objective ◽

Combined Cycles

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Multi-Objective Design Optimization of Multi-Floor, Counterflow Micro Heat Exchangers

Volume 3: Gas Turbine Heat Transfer; Transport Phenomena in Materials Processing and Manufacturing; Heat Transfer in Electronic Equipment; Symposium in Honor of Professor Richard Goldstein; Symposium in Honor of Prof. Spalding; Symposium in Honor of Prof. Arthur E. Bergles ◽

10.1115/ht2013-17738 ◽

2013 ◽

Cited By ~ 1

Author(s):

Abas Abdoli ◽

George S. Dulikravich

Keyword(s):

Pressure Drop ◽

Heat Exchangers ◽

Flow Direction ◽

Heat Removal ◽

Heat Transfer Analysis ◽

Multi Objective Optimization ◽

Multi Objective ◽

Removal Capacity ◽

Branching Networks ◽

Analysis Package

Heat removal capacity, coolant pumping pressure drop and surface temperature non-uniformity are three major challenges facing single-phase flow microchannel compact heat exchangers. In this paper multi-objective optimization has been performed to increase heat removal capacity, and decrease pressure drop and temperature non-uniformity in single-flow microchannels. Three-dimensional (3D) 4-floor branching networks have been applied to increase heat removal capacity of a microchannel from silicon substrate (15×15×2 mm). Each floor has four different branching sub-networks with opposite flow direction with respect to the next one. Each branching network has four inlets and one outlet. However, branching patterns of each of these sub-networks could be different from the others. Conjugate heat transfer analysis has been performed by developing a software package which uses quasi-1D thermo-fluid analysis and a 3D steady heat conduction analysis. These two solvers are coupled through their common boundaries representing surfaces of the cooling microchannels. Using quasi-1D solver significantly decreases computing time and its results are in good agreement with 3D Navier-Stokes equations solver for these types of application. The analysis package is capable of generating 3D branching networks with random topologies. 1341 random cooling networks were simulated using this analysis package. Multi-objective optimization using modeFrontier software was performed using response surface approximation and genetic algorithm. Diameters and branching pattern of each sub-network and coolant flow direction on each floor were design variables of multi-objective optimization. Maximizing heat removal capacity, minimizing pressure drop and temperature non-uniformity on the hot surface were three simultaneous objectives of the optimization. Pareto-optimal solutions demonstrate that thermal loads of up to 500 W/cm2 can be managed with 3D 4-floor microchannel cooling networks.

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