Synergistic enhancement of tribological performance and thermal stability in R-1234yf refrigeration systems using graphene oxide -silver hybrid nanolubricants Yamini S, Sekar S, Sivakami Sundari M, Senthil Kumar K Case Studies in Thermal Engineering, 2025 This study investigates the synergistic enhancement of tribological performance and thermal stability in R-1234yf refrigeration systems through the incorporation of graphene oxide -silver (GO-AgNPs) hybrid nano additives in polyol ester (POE) lubricants. GO-AgNPs hybrids were synthesized and dispersed in POE at concentrations of 0.1, 0.3, and 0.5 wt%. The nanolubricants were characterized using X-Ray Diffraction (XRD), scanning electron microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS), Fourier Transform Infrared Spectroscopy (FTIR) and TGA/DTA techniques. XRD analysis confirmed the presence of GO (2θ = 10.8°) and face-centered cubic AgNPs (AgNPs) with crystallite sizes of 18.5-20.1 nm. SEM imaging revealed well-dispersed graphene oxide (GO) sheets (2-5 μm) decorated with spherical silver nanoparticles (AgNPs) (20-25 nm), with some agglomeration observed at higher concentrations. FTIR spectroscopy indicated weak hydrogen bonding between GO functional groups and POE carbonyl groups without chemical modification of the base lubricant. Thermal analysis demonstrated significant stability enhancement, with decomposition onset temperature increasing from 298°C (pure POE) to 318°C (0.5 wt% GO-AgNPs). The hybrid nanolubricants exhibited superior tribological performance in R-1234yf systems, reducing friction coefficients by up to 42% and wear rates by 53% compared to pure POE. This was attributed to the formation of self-healing tribofilms facilitated by the thermally activated AgNPs and the laminar structure of GO. The 0.3 wt% GO-AgNPs concentration provided the optimal balance between enhanced performance and dispersion stability. These findings demonstrate the potential of GO-AgNPs hybrid nanolubricants for improving the efficiency and reliability of environmentally friendly R-1234yf refrigeration systems through synergistic enhancement of lubricant properties.
Chromatic Intelligence in Complete Graphs Through Polynomial Decomposition and Stability Optimization Umadevi. G, Yamini. S, Sivakami Sundari. M, Senthil Kumar. K Proceedings of 2025 10th International Conference on Science Technology Engineering and Mathematics Iconstem 2025, 2025 Graph coloring is a basic idea of graph theory, which consists of the process of assigning colors to the vertices in such a manner that there is no color overlap on the adjacent vertices. The chromatic properties of complete graphs are analysed through the development of mathematical models and equations to explain how color is allocated, redundant, and behaves about stability. The model makes use of the chromatic polynomial <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P(\text{kn}, \mathrm{k})$</tex>,the redundancy coefficient of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R \mathrm{c}(\mathrm{G})$</tex>, and the stability factor of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sigma \mathrm{c}(\mathrm{G})$</tex>. Python and MATLAB simulations were done on complete graphs with 5 to 500 vertices. The model that was proposed had a 100% chromatic quality, zero instability of color assignment, and a reduction of redundancy was 17.3% in the case of weighted optimization. The findings confirm the mathematical coherence of the framework proposed and prove the model to be efficient in analyzing large-scale colorability. It provides a good base to generalize chromatic modeling to the incomplete and dynamically evolving graphs.
Investigation of Nano Refrigerant Lubrication System in Vapor Compression Refrigeration System Using Environmental Friendly Refrigerant Yamini S, Divya S, Sivakami Sundari. M, Senthil Kumar. K Proceedings of 2025 10th International Conference on Science Technology Engineering and Mathematics Iconstem 2025, 2025 Compression of vaporized refrigerant is the essential process of the refrigeration cycle which is performed by using a compressor. The amount of power consumed by a refrigeration system is governed by the work input given to its compressor, which also determines the COP of the system. By reducing the work input given to the compressor, the power consumption of refrigerator is reduced along with the improvement in its COP. Nowadays, nanoparticles have emerged as the new generation additives in various working fluids because of their remarkable ability to improve the heat transfer, tribological and other thermo-physical properties of the base fluid. In such a vein, we propose a compressor oil-based nanofluid prepared by dispersing nanoparticles into the conventional compressor oil. In the present study, four mixtures of nanoadditive compressor oil were prepared by dispersing the nanoparticles like <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{Al} 2 \mathrm{O} 3, \text{TiO} 2$</tex> and ZnO into the conventional mineral oil as a lubricant. The thermophysical and tribological properties of these four mixtures were studied, out of which Mixture 4 was chosen because of its better lubrication and heat transfer properties which are considered as one of the key parameters for reducing work input to the compressor. This can result in reduced power consumption, with enhancement of COP. These results are analysed experimentally by carrying out performance and exergy analysis in a Vapour Compression Refrigeration System (VCRS), using R600a as a refrigerant. The experimental results show that there is an improvement of COP by 14.6% and exergy efficiency by 7.51%. Also, the efficiency defect in the major components of VCRS has been reduced effectively. The results of the performance tests done as per BIS show an improvement in the energy conservation by 27.5%.
A Novel Pothole Detection Model Based on YOLO Algorithm for VANET International Journal of Intelligent Systems and Applications in Engineering, 2024
Artificial Neural Network Prediction of Tribological Properties of Nano Lubricants with Eco-Friendly Refrigerant Yamini. S, Sivakami Sundari M, Sekar. S, Senthil Kumar. K, Divya. S Proceedings of 9th International Conference on Science Technology Engineering and Mathematics the Role of Emerging Technologies in Digital Transformation Iconstem 2024, 2024 With the emergence of the Montreal and Kyoto Protocols, there's been a prohibition on refrigerants in vapor compression systems that harm the ozone layer and contribute to global warming. While alternative refrigerants have been suggested, they tend to be more energy-intensive than the conventional ones they replace. A promising approach to mitigate this increased energy consumption lies in the use of innovative lubricants that can enhance system efficiency. Nano-lubricants have recently gained attention for their superior performance compared to traditional lubricants in refrigeration systems. However, formulating nano-lubricants and determining their composition with the desired thermophysical and tribological properties pose significant challenges. The development of nano-lubricants requires extensive experimentation, and predicting the properties of these lubricants, especially those that offer optimal performance, demands even more rigorous experimentation. Recently, Artificial Neural Networks (ANNs) have been employed to forecast system performance based on existing experimental data. In this study, we leverage literature data on nano-lubricants of varying compositions to design an ANN architecture. The model is then trained, assessed, and validated for predicting the properties of nano-lubricants with unknown compositions. The ANN's predictions are subsequently compared with actual results to gauge its accuracy and reliability.
A Comprehensive Evaluation of Driver Drowsiness Identification System using Camera based Improved Deep Learning Methodology P. Malathi, Sivakami Sundari M, S. Karkuzhali, Umamaheswari. B, A Thenmozhi, Velu Aiyyasamy IEEE 9th International Conference on Smart Structures and Systems Icsss 2023, 2023 Particularly on highways, drowsy driving causes a large number of collisions while driving. With the goal to identify driver sleepiness while enhancing roadway security, it is now vital to grasp the situation and take early remedial steps. Using a proposed Image based Learning Strategy for Drowsiness Identification (ILSDI) and cross-validation with the conventional model called Convolutional Neural Network (CNN), the suggested framework offers an approach to assess the degree of fatigue among drivers according to modifications in a driver eyeballs motion. This will assist in deal with the problem concerning roadway protection. In addition, four types of expressions on the face were identified and categorized—open, closed, blinking, and no gazing using ILSDI and CNN models, indicating levels of tiredness. Finding, following, and analyzing the driver’s face and eyes in real-time to calculate a sleepiness index is the goal of this technology, which operates in different lighting circumstances. Avoiding these kinds of accidents is possible with the help of a driver sleepiness monitoring structure, which uses a digital camera and accompanying software to measure the rate of blinking as well as the dimension of the driver’s eyes. The driver sleepiness recognition system can identify when the driver is getting sleepy and sound an alarm if necessary. It is based on an offline implementation of a deep learning algorithm that uses ILSDI and CNN.
Artificial Neural Network simulation for Markovian Queuing Models in a Busy airport Sivakami Sundari M, Yamini S, Kalicharan Rath, Senthil Kumar K, Palaniammal S 2020 International Conference on Computer Science Engineering and Applications Iccsea 2020, 2020 Successful takeoff of a flight in a busy airport depends on how effectively various queues in the airports are managed. Due to globalization, the number of passengers flying is ever-increasing and due to this, the number of flights handled by busy airports is increasing exponentially. Queuing theory is generally used to design the number of servers in the existing and new systems to optimize the waiting cost. Accordingly, service providers decide whether to increase the number of service stations or not. However in the case of airport operations unlike other queuing systems number of runways cannot be increased to minimize the flight queue length on runways to reduce waiting cost. Flights waiting for takeoff and landing are resulting in high operating costs. Fuel costs during flight waiting on queues to take off and flight flying due to runway congestion for which flight waiting on the sky for landing are phenomenal. In most of the airports only a single runway is available and all the flights depend on that single runway. In this work flights waiting for the runway are considered as a single server queuing system and are modeled using ANN to predict the queue behavior and thereby maximize the use of runways effectively and minimize waiting cost.
Artificial neural network simulation for markovian queuing models Sivakami Sundari M, Senthil Kumar K, Yamini S, Palaniammal S Indian Journal of Computer Science and Engineering, 2020 Successful ranking of a website by Google or electric charging of vehicle, congestion is pervasive in all domains. This implies that the presence of queues everywhere or in various places simultaneously. Under this environment, a good understanding of the relationship between queueing and delay is essential in the design of mathematical queuing models. However, uncertainty is an unavoidable phenomenon in any decision-making process. Good number of mathematical approaches has been presented in the literature to the analysis of queuing. Uncertainty is usually considered as unidimensional in nature that can be handled with probability theory. The objective of queuing analysis is to offer a reasonably satisfactory service to waiting customers. Queuing theory is not an optimization technique. Rather, it determines the measure of performance of waiting lines, such as the average waiting time in the queue and the productivity of the service facility, which can then be used to design the service installation. Assumed systems and systems that are too complicated to be disturbed are often difficult to study by analytical techniques. Simulation is one technique that can be seen successfully utilized for analyzing such systems. Artificial neural networks (ANN) form a branch of artificial intelligence. Neural networks represent a connection of simple processing elements capable of processing information in response to external inputs. In this work, such a Markovian queue is simulated using ANN and presented the result. The result shows that the ANN is capable of solving complex queuing problems.
An ann simulation of single server with infinite capacity queuing system Sivakami Sundari M*, , Palaniammal S, and International Journal of Innovative Technology and Exploring Engineering, 2019 Successful formulation of queuing models depends on arrival rate, nature of waiting in queues, type of service and customer leaving the system depends on type of arrival, nature of service, number of servers deputed, type of queues, number of customers approaching for service in the system and delay. Kendall notations are popularly used for designating the queuing models like M/M/C/E/D. Various mathematical models have been developed to solve the queuing problem analytically. However solving queuing models with power of computers is the new area of research and this work intends to develop single server infinite capacity queuing system using Artificial Neural Network(ANN). The results of simulation are compared with that of analytical method.
RECENT SCHOLAR PUBLICATIONS
Synergistic enhancement of tribological performance and thermal stability in R-1234yf refrigeration systems using graphene oxide-silver hybrid nanolubricants S Yamini, S Sekar, M Sivakami Sundari, K Senthil Kumar Case Studies in Thermal Engineering 72, 106430 , 2025 2025.0 Citations: 5
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Artificial Neural Network Prediction of Tribological Properties of Nano Lubricants with Eco-Friendly Refrigerant 1Yamini. S, 2Sivakami Sundari M, 3Sekar. S, 4Senthil Kumar. K …
ARTIFICIAL NEURAL NETWORK SIMULATION FOR MARKOVIAN QUEUING MODELS K Senthil Kumar, S Palaniammal
MOST CITED SCHOLAR PUBLICATIONS
Simulation of M/M/1 queuing system using ANN MS Sundari, S Palaniammal Malay J Math 1, 279-294 , 2015 2015.0 Citations: 23
A novel pothole detection model based on YOLO algorithm for VANET S Kamalakannan, S Navaneethan, YS Deshmukh, V Sujay, ... International Journal of Intelligent Systems and Applications in Engineering … , 2024 2024.0 Citations: 20
An ann simulation of single server with infinite capacity queuing systemInternational Journal of Innovative Technology and Exploring Engineering PS Sivakami Sundari M International Journal of Innovative Technology and Exploring Engineering 8 … , 2019 2019.0 Citations: 10
Artificial Neural Network simulation for Markovian Queuing models in a busy airport S Yamini, K Rath, S Palaniammal 2020 International Conference on Computer Science, Engineering and … , 2020 2020.0 Citations: 7
Synergistic enhancement of tribological performance and thermal stability in R-1234yf refrigeration systems using graphene oxide-silver hybrid nanolubricants S Yamini, S Sekar, M Sivakami Sundari, K Senthil Kumar Case Studies in Thermal Engineering 72, 106430 , 2025 2025.0 Citations: 5
A Comprehensive Evaluation of Driver Drowsiness Identification System using Camera based Improved Deep Learning Methodology P Malathi, S Karkuzhali, A Thenmozhi, V Aiyyasamy 2023 9th International Conference on Smart Structures and Systems (ICSSS), 1-6 , 2023 2023.0 Citations: 3
Simulation of Boarding Time Prediction using M/M/C/K Queuing Model for Airport Passengers PS Sivakami Sundari M International Journal of Recent Technology and Engineering 8 (4), 5863-5867 , 2019 2019.0
Artificial Neural Network Prediction of Tribological Properties of Nano Lubricants with Eco-Friendly Refrigerant 1Yamini. S, 2Sivakami Sundari M, 3Sekar. S, 4Senthil Kumar. K …
ARTIFICIAL NEURAL NETWORK SIMULATION FOR MARKOVIAN QUEUING MODELS K Senthil Kumar, S Palaniammal
