Estimation of forced heat convection in a rectangular channel with curved-winglet vortex generator: A machine learning approach

Berber, Adnan; Gürdal, Mehmet

doi:10.1016/j.tsep.2022.101563

Cited by 6 publications

(1 citation statement)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other research carried out by Berber et al [37] related to the current study focused on heat convection in a rectangular channel with curved-winglet vortex generator inserts using machine learning. They investigated the impact of the proposed novel fin geometry and attack angle in the range of 30 • to 90 • with varying Reynolds numbers and plate temperatures on convection heat transfer with the help of ANN algorithms.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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

Machine Learning Approach for Flow Fields Over a Circular Cylinder Based on Particle Image Velocimetry Measurements

Aksoy,

Goktepeli,

Ispir

et al. 2023

Measurement

View full text Add to dashboard Cite

Reconstruction of flow field with missing experimental data of a circular cylinder via machine learning algorithm

Aksoy,

Goktepeli,

Ispir

et al. 2023

Physics of Fluids

View full text Add to dashboard Cite

In this study, artificial neural networks (ANNs) have been implemented to recover missing data from the particle image velocimetry (PIV), providing quantitative measurements of velocity fields. Due to laser reflection or lower intensity of particles in the interrogation area, the reconstruction of erroneous velocity vectors is required. Therefore, the distribution of time-averaged and normalized flow characteristics around a circular cylinder has been demonstrated as streamwise and cross-stream velocities at Re = 8000. These velocity components have been given for different regions at x/D = 0.5, x/D = 1.25, x/D = 2, and y/D = 0. These stations have been chosen to estimate missing data for near-wake, mid-wake, far-wake, and symmetry regions. The missing data ratios (A*) for 0.5 ≤ x/D ≤ 2 are A* = 3.5%, 7%, and 10%. In addition, these values are A* = 4%, 8%, and 12% for y/D = 0, while A* = 7.5% for the shaded region. The increment of area positively affects the estimation results for near-wake and mid-wake regions. Moreover, the errors tend to decrease by moving away from the body. At y/D = 0, increasing the area negatively influences the prediction of the results. The mean velocity profiles of predicted and experimental data have also been compared. The missing data have been predicted with a maximum percentage error of 3.63% for horizontal stations. As a result, the ANN model has been recommended to reconstruct PIV data.

show abstract

Estimation of forced heat convection in a rectangular channel with curved-winglet vortex generator: A machine learning approach

Cited by 6 publications

References 69 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

Machine Learning Approach for Flow Fields Over a Circular Cylinder Based on Particle Image Velocimetry Measurements

Reconstruction of flow field with missing experimental data of a circular cylinder via machine learning algorithm

Contact Info

Product

Resources

About