scholarly journals Design optimization of perforated plate matrix heat exchangers for cryogenic applications using Teaching Learning Based Optimization (TLBO) method

2019 ◽  
Vol 502 ◽  
pp. 012053
Author(s):  
L Ratna Raju ◽  
T K Nandi
Keyword(s):  
Design Optimization ◽  
Heat Exchangers ◽  
Perforated Plate ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Matrix Heat

Comparison of stationary and rotary matrix heat exchangers using teaching-learning-based optimization algorithm

2017 ◽  
Vol 232 (4) ◽  
pp. 493-502 ◽  
Author(s):  
Hassan Hajabdollahi
Keyword(s):  
Heat Exchanger ◽  
Pressure Drop ◽  
Optimization Algorithm ◽  
Heat Exchangers ◽  
Design Parameters ◽  
Matrix Heat Exchanger ◽  
Teaching Learning Based Optimization ◽  
Mass Flow Rates ◽  
Teaching Learning ◽  
Matrix Heat

In this paper, two kinds of compact heat exchanger including plate fin heat exchanger and rotary regenerator, respectively the stationary and rotary matrix heat exchanger, are compared. For this purpose, both heat exchangers are optimized by considering three simultaneous objective functions including effectiveness, heat exchanger volume, and total pressure drop using multi-objective teaching learning based optimization algorithm. Six different design parameters are considered for the both plate fin heat exchanger and rotary regenerator. Optimization is performed for the same and different hot and cold side mass flow rates. The optimum results reveal 13.26% growth in the effectiveness, 475.17% increase in the volume, and 95.45% reduction in the pressure drop in RR as compared with plate fin heat exchanger and for the final optimum point. As a result, rotary regenerator is more suitable in the case of high effectiveness and low pressure drop while plate fin heat exchanger is more suitable in the case of space limitation (lower heat exchanger volume).

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