Computationally efficient optimization of wavy surface roughness in cooling channels using simulated annealing

Munku Kang, Leslie K. Hwang, Beomjin Kwon

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


This article reports a computationally efficient optimization for wavy surface roughness in cooling channels based on simulated annealing. Our approach rapidly calculates the performance metrics of cooling channels using closed-form models and approximates an optimum design via a random optimization technique, simulated annealing. The proposed method optimizes the wall roughness of cooling channels for maximizing the heat transfer rate while satisfying the pressure drop constraint. The channel wall smoothly converges and diverges throughout to locally modulate the heat transfer rate and the pressure drop. For a given pressure constraint and heat load distribution, the optimizer is able to compare more than 10,000 possible channel designs within a minute and generate a distinct channel design that achieves the optimization objective. The channel temperature predicted by the optimizer differs up to 10.9% to the estimations by a finite volume model. Lastly, optimized wavy channel designs are introduced that exhibit up to 2.7 times greater performance factor than a conventional channel geometry. This work demonstrates the potential of algorithm-based optimization techniques for designing efficient thermodynamic systems.

Original languageEnglish (US)
Article number119300
JournalInternational Journal of Heat and Mass Transfer
StatePublished - Apr 2020


  • Cooling channel
  • Simulated annealing
  • Surface roughness
  • Thermal optimization

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Mechanical Engineering
  • Fluid Flow and Transfer Processes


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