Multi-objective thermodynamic optimization of an irreversible regenerative Brayton cycle using evolutionary algorithm and decision making

Abstract

Brayton heat engine model is developed in MATLAB simulink environment and thermodynamic optimization based on finite time thermodynamic analysis along with multiple criteria is implemented. The proposed work investigates optimal values of various decision variables that simultaneously optimize power output, thermal efficiency and ecological function using evolutionary algorithm based on NSGA-II. Pareto optimal frontier between triple and dual objectives is obtained and best optimal value is selected using Fuzzy, TOPSIS, LINMAP and Shannon's entropy decision making methods. Triple objective evolutionary approach applied to the proposed model gives power output, thermal efficiency, ecological function as (53.89 kW, 0.1611, À142 kW) which are 29.78%, 25.86% and 21.13% lower in comparison with reversible system. Furthermore, the present study reflects the effect of various heat capacitance rates and component efficiencies on triple objectives in graphical custom. Finally, with the aim of error investigation, average and maximum errors of obtained results are computed. Ó 2015 Faculty of Engineering, Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Power, Efficiency, Power Density and Ecological Function Optimization for an Irreversible Modified Closed Variable-Temperature Reservoir Regenerative Brayton Cycle with One Isothermal Heating Process

“…Under the condition that the DPD is kept constant, the requirement of the heat exchangers corresponding to the high-temperature reservoirs can be reduced by increasing C L /C H or C L /C H1 and reducing C H /C w f or C H1 /C w f . Figure 16 shows the optimization flowchart for calculating the Pareto frontier using the NSGA-II algorithm [39][40][41][45][46][47][48][49][50][51][52][53][54]. With π and HCDs as the design variables and W , η , P and E as the optimization objectives, the cycle's double, triple or quadruple objective optimization is conducted.…”

Design optimization of a three-stage transmission using advanced optimization techniques

“…A posteriori articulation method begins with the generation of a Pareto frontier that is then investigated using decision methods such as Linear Programming for Multidimensional Analysis of Preference (LINMAP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Fuzzy [18,19] in order to identify the optimal result. This allows for trade-off studies [15] to be performed on the frontier to discover new insights on objectives, variable, and constraint assignments.…”

Thermodynamic Analysis of an Irreversible Maisotsenko Reciprocating Brayton Cycle

“…Since FTT [11][12][13][14][15][16][17][18][19][20][21][22][23][24] has been applied to the performance analyses and optimizations for gas turbine cycles, plentiful achievements in scientific research have been obtained. Amass of work about the performance analyses and optimizations using FTT for simple [25][26][27][28][29], regenerated [30][31][32][33][34], intercooled and regenerated [35,36], combined Brayton and inverse Brayton [37], multi-stage intercooled and regenerated [38] and reciprocating Brayton cycles [39,40], by selecting the P, η, and ecological function as optimization objectives, considering HTL and/or IILs has been published. Based on References [7,40], an irreversible model of the Maisotsenko reciprocating Brayton cycle (MRBC) will be established using the FTT theory with considerations of HTL, piston friction loss (PFL), and IILs in this paper.…”