Faculty Publications
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Item Numerical investigations of free and forced convection with various features using mesoscopic Lattice Boltzmann method(Elsevier Ltd, 2022) Bhatt, T.; Arumuga Perumal, D.; Anbalagan, A.This paper is concerned with the free and forced convection of heat transfer characteristics by Lattice Boltzmann method (LBM). The LBM is used as the alternative method for the prediction of fluid flow and heat transfer characteristics for the past few years. The fluid flow equation was combined with energy equation in order to get the temperature field in the flow. In natural convection problem, the motion of the fluid inside a cavity domain is considered. The motion takes place because of change in density and due to the gravitational force. The problem of natural convection and forced convection is solved using internal energy density distribution function model for Rayleigh number ranging from 103 to 106. And for the forced convection problem the Reynolds number is varied. In all the problems streamline patterns are plotted, where the intricate details of the flow such as primary and secondary vortices are captured. Thus, it is shown that Lattice Boltzmann Method can be used to solve fluid flow and heat transfer characteristics. © 2022Item AntLion Optimization Algorithm based Type II Diabetes Mellitus Prediction(Institute of Electrical and Electronics Engineers Inc., 2022) Anbalagan, A.; Baskar, C.; Deekshetha, H.R.; Reshma, S.; Vijayalakshmi, M.; Arumuga Perumal, D.Diabetes Mellitus is one of the common diseases prevailing in most developed and developing countries. In recent decades, there has been a huge rise in diabetes patients in India. Based on recent statistics, nearly 72.96 million young people are suffering from diabetes. Thus, it is essential to diagnose diabetes at an early stage. In this work, the PIMA dataset is used to design an optimized and super-vised learning model based on K-nearest neighbor classification. The optimization algorithm used to generate useful features to predict diabetes mellitus is the Antlion optimization algorithm. The proposed work yields an accuracy of 80% for the selected features like Pregnancy, BMI, BP, Age, Glucose, and Diabetes Pedigree Function. © 2022 IEEE.Item Advanced thermal vision techniques for enhanced fault diagnosis in electrical equipment: a review(Springer, 2025) Anbalagan, A.; Persiya, J.; Mohamed Mansoor Roomi, S.; Arumuga Perumal, D.A.; Poornachari, P.; Vijayalakshmi, M.; Ebenezer, L.Ensuring the reliability and safety of electrical equipment is essential for industrial and residential applications. Traditional fault diagnosis methods involving physical inspections are time-consuming and ineffective for early fault detection. Infrared (IR) thermography offers a non-invasive and efficient solution by identifying anomalies in temperature profiles. This review explores thermal vision-based fault diagnosis techniques, including region of interest (ROI) segmentation, image pre-processing, and fault diagnosis algorithms, with a focus on deep learning approaches. The study highlights the effectiveness of machine learning models in enhancing fault detection accuracy while identifying challenges such as environmental variations, data inconsistencies, and system integration issues. The review discusses the role of real-time applications, wireless technologies, and AI-based automation in improving fault detection. Research gaps are identified, and future directions are proposed to enhance efficiency, reliability, and industrial adoption. © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2025.Item Simulation of fluid flow in a lid-driven cavity with different wave lengths corrugated walls using Lattice Boltzmann method(Taiwan Institute of Chemical Engineers, 2023) Fatima, N.; Rajan, I.; Arumuga Perumal, D.A.; Anbalagan, A.; Ahmed, S.A.A.; Gorji, M.R.; Ahmad, Z.Background: The Lid-driven cavity (LDC) flow is an interesting problem in fluid mechanics. The lattice Boltzmann Method (LBM) is used to simulate fluid flow in a LDC with different wave lengths corrugated walls. Methods: The D2Q9 model is used for the 2D bounded domain where the analysis of bottom-bounded wall corrugations on the flow features is analyzed. For validation, a square corrugation along the bottom wall with a driven top wall is considered. A lattice size independence study is performed and the LBM code is substantiated with published results for different values of Reynolds number. The code is then modified by using sinusoidal corrugated walls with different wavelengths along the bottom surface. Significant finding: The streamline patterns, vorticity contours and kinetic energy contours are studied for different Reynolds number. Results shown that the position, number and size of vortices depend on the number of corrugations and value of Reynolds number used. The secondary vortices tend to increase in size as the Reynolds number increase. The kinetic energy contours show maximum energy near the top wall which reduces inside the cavity. © 2023Item Deep dual domain joint discriminant feature framework for emotion based music player(Springer, 2024) Anbalagan, A.; Challa, R.T.; Saketh, S.; Chakka, S.; Arumuga Perumal, D.; Poornachari, P.Emotion based music player is an interdisciplinary study of computer vision and psychology. As music enhances the positive vibes it plays a significant role in soothing people’s emotion. Emotions can be predicted through facial expression analysis using vision-based methods. However, challenges like environment and expression complexity have become hindrance to attain a good recognition rate. Therefore, we put forward a deep dual domain joint feature framework based on linear discriminant analysis for facial emotion recognition. First, we detect the human face and learn the emotion pattern using the popular complementary deep domain networks called EfficientNet and ResNet50. The learned deep dual domain space is projected onto linear discriminant space to achieve a joint discriminant feature space. The recognition rate of the proposed joint discriminant feature framework is analyzed using support vector machine. To prove the efficacy of the proposed framework, we validated it on two Benchmarks namely FER2013 and CK48+ datasets. The proposed framework achieved a good recognition rate of 99% and 98.6% on FER2013 and CK48+ respectively. Experimental analysis on our EmDe dataset showed an accuracy of 99% and proves that the deep dual domain joint discriminant framework as a promising pipeline for emotion-based music player system. © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2024.Item Infrared Perspectives: Computing laptop energy dissipation via thermal imaging and the Stefan-Boltzmann equation(Elsevier Ltd, 2024) Anbalagan, A.; Arumuga Perumal, D.; Persiya, J.Energy conservation is crucial for reducing greenhouse gas emissions and addressing climate change. Laptops contribute significantly to energy consumption, emphasizing the need for improved energy efficiency. This paper explores the application of thermal imaging technology to enhance energy conservation in laptops. Thermal imaging provides valuable insights into heat distribution on the laptop's surface, aiding in identifying areas of excessive energy consumption. By identifying areas of a laptop that generate excessive heat and implementing energy-efficient measures, energy consumption can be reduced, and the device's lifespan can be extended. The study leverages computer vision and artificial intelligence techniques to analyze thermal images. We collected the thermal images for the dataset using the FLIR E75 Thermal camera. Two methods of Region of Interest (ROI) extraction, contour-based thresholding, and Detectron2-based extraction are employed. Feature extraction includes statistical, texture, spatial, and energy features, and Principal Component Analysis (PCA) is used to reduce dimensionality. K-means clustering categorizes data points based on reduced features, and performance metrics validate the effectiveness of the clustering methods. The study also computes energy dissipation from thermal images using the Stefan-Boltzmann Law. Results indicate that thermal imaging, coupled with advanced analysis techniques, holds promise for improving energy conservation in laptops. © 2024 Elsevier Ltd