Flow optimization in a microchannel with vortex generators using genetic algorithm

Gönül, Alişan; Okbaz, Abdülkerim; Kayacı, Nurullah; Dalkılıç, Ahmet Selim

doi:10.1016/j.applthermaleng.2021.117738

Cited by 35 publications

(9 citation statements)

References 81 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the optimum attack angle was suggested to be 30° for best thermo-hydraulic performance, considering both the Nusselt number and pressure penalty. Similar findings representing fluctuating Nusselt numbers by continuous increments in attack angle have also been reported by related studies by Deshmukh et al [64] and Gonul et al [65], who proposed optimum attack angles ranging from 30° to 60° to achieve the highest Nusselt numbers.…”

Section: Shap Analysissupporting

confidence: 88%

Machine Learning Analysis of Thermal Performance Indicator of Heat Exchangers with Delta Wing Vortex Generators

Aksöz,

Günay,

Aziz

et al. 2024

Energies

View full text Add to dashboard Cite

In this work, the design features of delta wing vortex generators (DWVGs) on the thermo-hydraulic performance of heat exchangers are investigated using machine learning. Reynolds numbers, attack angle, length, wing-to-width ratio, and relative pitch ratio of DWVGs were used as descriptor variables, with Nusselt numbers, friction factors, and performance evaluation criterion (PEC) serving as target variables. Decision tree classification revealed the pathways leading to high or low values of the performance variables. Among many of those pathways, it was found that high Reynolds numbers (between 8160 and 9800) and high attack angles (greater than or equal to 47.5°) lead to high Nusselt numbers. On the other hand, an attack angle between 41° and 60°, a Reynolds number less than 8510, and a wing-to-width ratio greater than or equal to 0.4 causes a high friction factor. Finally, the PEC is likely to enhance when the Reynolds number is higher than or equal to 10,300 and the attack angle is between 47.5° and 60°. In addition to the decision tree analysis, SHapley Additive exPlanations (SHAP) analysis (a part of explainable machine learning) was also applied to reveal the importance of design features and their positive and negative effects on the target variables. For example, for a Nusselt number as the target variable, the Reynolds number was found to be the most influential variable, followed by the attack angle and the relative pitch ratio, all of which had a positive impact on the target. It was then concluded that machine learning methods could help provide strong insights into the configuration design features of heat exchangers in DWVGs to improve their efficiency and save energy.

show abstract

Section: Shap Analysissupporting

confidence: 88%

Machine Learning Analysis of Thermal Performance Indicator of Heat Exchangers with Delta Wing Vortex Generators

Aksöz,

Günay,

Aziz

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…It has been shown [10][11][12] that the heat transfer performance of complex structured microchannel heat sinks is excellent because the continuous variation of the channel cross section can interrupt the thermal boundary layer development and keep the flow and heat transfer in an underdeveloped state, which serves to enhance the heat transfer; moreover, the setting of rough elements inside the channel enhances the internal disturbance, which in turn has an important impact on the flow and heat transfer characteristics [13,14].…”

Section: Introductionmentioning

confidence: 99%

“…A series of microchannel units with the same structure interconnect and further enhance the material process. Benefiting from the combination of multiple microchannel units, the synthesis rate in SAR microchannels is significantly greater than that of straight microchannels with only a single microchannel unit [11,12].…”

Section: Introductionmentioning

confidence: 99%

NSGA-II-Based Microchannel Structure Optimization Problem Study

Wang

2022

Scientific Programming

View full text Add to dashboard Cite

A multiobjective genetic algorithm is used to optimize the structure of circular concave cavities and microchannels. The objective functions of thermal resistance and pumping power are constructed by the response plane approximation method according to the simulation results, and then a mathematical model of multiobjective genetic optimization with the structural parameters of microchannels as variables is established. The Pareto optimized solution sets of thermal resistance and pumping power are calculated by the nondominated ranking genetic algorithm NSGA-II, and the comprehensive heat transfer performance is evaluated by the enhanced heat transfer factor. The results show that the multivariate statistical coefficients R 2 of the thermal resistance and pumping power objective functions are 0.932 9 and 0.996 6, respectively, indicating the high accuracy of the fitted functions. The optimized channel structure ( e 1 = 0.036.8 mm , e 2 = 0.019.3 mm ) was used to achieve a more uniform temperature field distribution and better integrated heat transfer performance (enhanced heat transfer factor η = 1.23 ). When the thermal resistance is larger or the pump work is larger, the comprehensive heat transfer effect is not as good as the working condition when the thermal resistance and pump work are more uniform.

show abstract

“…While ACEO can produce stronger mixing at a lower voltage, DCEO's proportional relationship between the applied potential and velocity, combined with its inherent flow physics, can yield results superior to those of ACEO under certain conditions. Indeed, efficient and effective mixing relies on the generation of powerful vortices [13,14]. Three-dimensional vortices, such as those generated by spiral, helical, or toroidal structures, have the potential to be far more efficient than their planar counterparts owing to the complexity they introduce into the flow field [15,16].…”

Section: Introductionmentioning

confidence: 99%

A Novel DC Electroosmotic Micromixer Based on Helical Vortices

Saravanakumar,

Jamshidi Seresht,

Izquierdo

et al. 2024

Actuators

View full text Add to dashboard Cite

This work introduces a novel direct current electroosmosis (DCEO) micromixer designed for rapid and efficient fluid mixing. This micromixer demonstrates excellent capability, achieving approximately 98.5% mixing efficiency within a one-second timespan and 99.8% efficiency within two seconds, all within a simple channel of only 1000 µm in length. A distinctive feature of this micromixer is its ability to generate robust and stable helical vortices by applying a controlled DC electric field. Unlike complex, intricate microfluidic designs, this work proposes a simple yet effective approach to fluid mixing, making it a versatile tool suitable for various applications. In addition, through simple modifications to the driving signal configuration and channel geometry, the mixing efficiency can be further enhanced to 99.3% in one second.

show abstract

Flow optimization in a microchannel with vortex generators using genetic algorithm

Cited by 35 publications

References 81 publications

Machine Learning Analysis of Thermal Performance Indicator of Heat Exchangers with Delta Wing Vortex Generators

Machine Learning Analysis of Thermal Performance Indicator of Heat Exchangers with Delta Wing Vortex Generators

NSGA-II-Based Microchannel Structure Optimization Problem Study

A Novel DC Electroosmotic Micromixer Based on Helical Vortices

Contact Info

Product

Resources

About