Numerical investigation of nanofluid flow using CFD and fuzzy-based particle swarm optimization
Authors: Vadim V Ponkratov
Mariya Yurievna Kuznetsova
Marischa Elveny
Rahmad Syah
Andrey Leonidovich Poltarykhin
Mahyuddin K M Nasution
Meisam Babanezhad
Journal: Scientific Reports (2021)
Discipline: Multidisciplinary
Keywords: artificial intelligence; numerical modeling; nanofluid flows; computational fluid dynamics; machine learning
Affiliations: Data Science & Computational Intelligence Research Group, Universitas Medan Area, Medan, Indonesia., Data Science & Computational Intelligence Research Group, Universitas Sumatera Utara, Medan, Indonesia., Financial University Under the Government of the Russian Federation, Moscow, Russian Federation., Sechenov First Moscow State Medical University, Moscow, Russia., Plekhanov Russian University of Economics, 117997, Moscow, Russia., Department of Energy, Faculty of Mechanical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran. .

Abstract:  This paper is focused on the application and performance of artificial intelligence in the numerical modeling of nanofluid flows. Suspension of metallic nanoparticles in the fluids has shown potential in heat transfer enhancement of the based fluids. There are many numerical studies for the investigation of thermal and hydrodynamic characteristics of nanofluids. However, the optimization of the computational fluid dynamics (CFD) modeling by an artificial intelligence (AI) algorithm is not considered in any study. The CFD is a powerful technique from an accuracy point of view. However, it could be time and cost-consuming, especially in large-scale and complicated problems. It is expected that the machine learning technique of the AI algorithms could improve such CFD drawbacks by patterning the CFD data. Once the AI finds the CFD pattern intelligently, there is no need for CFD calculations. The particle swarm optimization-based fuzzy inference system (PSOFIS) is considered in this study to predict the velocity profile of Al2O3/water turbulent flow in a heated pipe. One of the challenging problems in CFD modeling is the lost data for a specific boundary condition. For example, the CFD data are available for wall heat fluxes of 75, 85, 105, and 125 w/m2, but there is no data for the wall heat flux of 95 w/m2. So, the PSOFIS learns the available CFD data, and it predicts the velocity profile for where the data is not available (i.e., wall heat flux of 95 w/m2). The intelligence of PSOFIS is checked by the coefficient of determination (R2 pattern) for different values of accept ratio (AR) and inertia weight damping ratio (IWDR). The best intelligence is obtained for the AR and IWDR of 0.7 and 0.99, respectively. At this condition, the velocity profile predicted by both CFD and PSOFIS is compatible. As the performance of the PSOFIS, for learning time of 268 s, the prediction of the CFD data lost was negligible (~ 1 s). In contrast, the CFD calculation takes around 600 s for each simulation. 

Retracted: No

PubPeer comments:
 **There are two fundamental questions for the authors to be clarified:** Referring to the boxed statements in the text below (METHODOLOGY - CFD approach): a) ** As a result, many nonlinear partial differential equations were made. ** Q-a) Where are those many non-linear PDEs in the article? Under what circumstances they were made? The three gov. eqns. (1-3) are not made by the authors! b) ** the governing equations are converted to algebraic equations ** Q-b) What the authors mean by the *conversion* of the governing equations to *algebraic equations*, and where are those equations in the text? ... **Examples of unsupported statements by the authors!** ***Have the affiliations been also checked for their relevance to the claimed field of the research work?***   **https: pubpeer.com search?q=RAHMAD+SYAH** **https: pubpeer.com search?q=MARISCHA+ELVENY** **https: pubpeer.com search?q=Mahyuddin+K.+M.+Nasution** **https: www.nature.com articles s41598-021-96501-6** , 
 [Expression of Concern] , July 14, 2022. --- Editorial Expression of Concern to: Scientific Reports https: doi.org 10.1038 s41598-021-00279-6, published online 25 October 2021 The Editors are issuing an editorial expression of concern to alert readers that this article shows indication of irregularities in authorship. Concerns have been raised about the expertise of some of the authors not matching the subject matter of this article. Authors provided explanations regarding addition of authors during submission process, but they did not sufficiently document contributions of all authors. Additionally, the code used to generate the results had not been previously made available. It is now deposited in the zenodo repository and can be found at: https: zenodo.org record 6791248 --- [DOI link] to Zenodo entry. , 
 However, I have concerns about the data deposited as  supplementary - below, a comparison:   (The code is identical, except that comments are present in the thesis and absent in the supplementary material. The boxes are added to simplify navigation, i.e., to help the reader match identical blocks of code.) The similarity between two versions of code is much more extensive than the fragment shown. **Shouldn t the author of the original code have been credited?** , 
 More potential problems in the  supplementary data.  The  abalone dataset is available on Kaggle, described as The age of abalone is determined by cutting the shell through the cone, staining it, and counting the number of rings through a microscope -- a boring and time-consuming task. Other measurements, which are easier to obtain, are used to predict the age. Further information, such as weather patterns and location (hence food availability) may be required to solve the problem. Random assignment of train test is in contradiction with Fig. 6, whatever that figure was supposed to mean.  ,