experimental investigation and modeling of the heat transfer coefficient in the pool boiling: bubble dynamic and artificial intelligence

In this work, the heat transfer coefficient in the pool boiling process was investigated for different alcoholic solutions. to exact evaluation, the bubble dynamic including bubble departure diameter, bubble departure frequency, and active nucleation sites’ density were studied. the results showed that with increasing isopropanol concentration (20 v.% - 80 v.%), bubble departure frequency and active nucleation sites increased while bubble departure diameter decreased. the bubble dynamic cannot be effective in any amount and must be optimized to reach an optimum heat transfer coefficient. isopropanol concentration of 20 v.% was reported as an optimum state and lower decrease versus deionized water (11.892%). this result confirmed that the bubble departure diameter played a significant role in promoting the heat transfer coefficient. finally, to predict the experimental data, a genetic algorithm (ga) based correlation (power-law function) was developed. the optimization procedure revealed that the ga model had a good agreement with the experimental data (r²=0.968, aad= 0.0288). in addition, this approach was compared with conventional models (palen, stephan, unal, fujita, and inoue). the ga and the stephan models presented the best and worst performance, respectively.