Adaptive neuro-fuzzy inference system based data interpolation for particle image velocimetry in fluid flow applications. (March 2023)

• Record Type: Journal Article
• Authors: Kazemi, Mohammad Amin; Pa, Mary; Uddin, Mohammad Nasir; Rezakazemi, Mashallah
• Is Part Of: Engineering applications of artificial intelligence. Volume 119(2023)
• Publication Date: 2023-03
• Subjects: Artificial intelligence -- Neuro-fuzzy system -- Data interpolation -- Gappy data -- Particle image velocimetry -- Fluid flow; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105723
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: This paper presents an adaptive neuro-fuzzy inference system (ANFIS) approach for recovering the missing velocity vectors that commonly occur during fluid flow measurements in fluid mechanics. The capability of ANFIS in refilling the missing data is demonstrated with two case studies. First, the ANFIS is applied to estimate the velocity field data within the masked region of an available particle image velocimetry (PIV) experiment. Then, the ANFIS is trained using the data from outside the masked region and learns the relationship between the velocity vectors. Thus, it predicts the fluid patterns within the gappy area based on its understanding. ANFIS is also applied in another study to capture the small feature in the fluid flow. The vortices within a small area behind an obstacle are removed, and the rest of the data is introduced into the ANFIS model. The efficacy of the proposed ANFIS algorithm in predicting the missing velocity vectors is compared to both artificial neural network (ANN) and 2D cubic interpolation algorithms. It is found that intelligent algorithms (ANFIS and ANN) can predict the presence of vortices in the fluid flow even when there is no information about a circulating flow in the training dataset. The performance of ANFIS (R 2 = 0.91) in accurately predicting the velocity vectors is superior to the conventional ANN (R 2 = 0.86).