Department of Mechanical engineering
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Item Development of keyless biometric authenticated vehicles ignition system(Elsevier, 2023) Srinivasan, P.The vehicles are started or ignited using manual keys or sensor based keys. In two wheelers after placing keys the bike is started using either button or kicking method. The other vehicles are started using key cranking or key is placed in specific sensor control area and start button is pressed. The existing method makes any one can able to operate the vehicle if they have keys. The proposed method used to operate the vehicles only they are authorized. The authorization key is available in the controller memory. The proposed method uses dactylogram scanner to access the vehicles. The scanner scans the finger, if the person is authenticated immediately it allows the person to start the vehicles. The controller is attached with the relay electronic board which controls the ignition part. The proposed method uses ATMEGA 162. The device is able to access the vehicle by 3 persons. Only the specific 3 person finger prints are enrolled in the controller. The system is enabled with GSM access. If unauthenticated person access the finger print scanner, the vehicle is not ignited. The GSM module communicates the unauthenticated access attempt to the owner of the vehicles in the form of SMS. If the device is accessed with authenticated person and the vehicle is successfully started. In the meantime unauthorized person starts access with scanner, immediately message was received in the stored GSM mobile number. The average authentication time to start the vehicle is 3.81 s.Item IoT based air quality measurement and alert system for steel, material and copper processing industries(Elsevier, 2023) Srinivasan, P.The air quality of industry gets affected due to the industrial processing like heating of metals such as steel, copper. The burning and processing alters the level of NitrogenDioxide (NO2), Carbon Monoxide (CO), Carbon Dioxide (C02), methane (CH4), and Liquid Petroleum Gas (LPG). The each parameter in the air has to be maintained in safe level. If safe level is not maintained makes discomfort to the human health. The existing methods are measuring the air quality and display it at same station which may handle with less care. The proposed method consists of MQ2, MQ3, MQ6, and MQ9 sensors used to measure CO, CO2, NO2, and CH4 &LPG levels. The sensor gives the analog output. The proposed method uses the Arduino Nano - ATMEGA 168 based controlled to handle the analog sensors. The analog quantity is converted into digital value and which compared with safe limit value by the controller. The measured value is updated in cloud server- web based and Mobile application. The emergency alarm is fixed in the instrument in sound and light form to alert the workers. The device is tested in air polluted industry. The device showed the CO level as 525 PPM, NO2 level as 56 PBP, CO2 level as 164PPM, and mixed gas as 154PSI. The device notified CO level is high. The output is observed in cloud and mobile application.Item Implementation of battery degradation on lithiumion batteries using PYNQ-FPGA(IEEE, 2024) Srinivasan, P.Predicting the remaining usable life (RUL) of a lithium-ion battery properly is vital for appropriate maintenance and overall health evaluation, which is particularly pertinent in the burgeoning electric vehicle industry, where optimising battery performance is essential. Determining the rate of battery deterioration is a complex task because of the wide variety of internal and external elements that could affect it. Our study addresses this challenge by using datasets on battery ageing sourced from NASA's Prognostic Center of Excellence (PCoE) to introduce a data-driven approach for State of Health (SOH) estimation. In our pursuit of RUL prediction, we have devised a machine-learning model employing the ADAM optimiser for optimisation. Consequently, our proposed model utilises software programming on PYNQ FPGA to discern battery degradation. The findings of these innovative approaches are thoroughly analysed and assessed, showcasing the effectiveness of our approach in navigating the complexities associated with predicting battery RUL.Item Numerical simulation and experimental investigation on Phase Change Materials based energy storage system for cooling the water in process industries towards water conservation and environmental sustainability(Elsevier, 2024-04) Srinivasan, P.Water conservation is one of the significant concerns in the industrial sector in cooling processes. Water is lost predominantly in the cooling towers by evaporation, drift, and blowdown. A new Phase Change Material (PCM) based cooling system is proposed as a replacement for cooling towers to eliminate water losses completely. In the current research, the temperature drop of water known as range in the cooling tower is investigated in the proposed system by varying the process parameters such as water velocity, tube materials and tube lenght. In the present study, OM50, an organic PCM with a melting temperature of 500 C is chosen for experimental and numerical analysis as the normal operating temperature of water in the cooling towers is in this range. Based on the experiment, it is observed that the temperature range of water almost remains constant for the melt fraction values of PCM from about 0.1 to 0.8 and taking about 35 % of the total time required for completely melting the PCM. However the cooling range steeply decreases for the melt fraction variation from 0.8 to 1. Hence the designing of PCM based cooling system will be effective for the melting of the PCM from 0.1 to 0.8 leading to constant cooling range of temperature. The experimental results obtained are also validated by CFD simulation using ANSYS 18.1 and the results obtained are in close agreement with each other. The range obtained in the present research is almost in the same range of cooling tower used in the chiller plant presented in the case study. The proposed system may be able to replace cooling towers towards conservation of water and environmental sustainability.Item Smart city air quality management with IOT and Bayesian optimization for pollution monitoring(IEEE, 2025-02) Srinivasan, P.The rapid urbanization happening around the globe is having a huge effect on the environment. Cities in the poor world are particularly vulnerable to air pollution. In light of this issue, several nations are mandating that cities implement plans to enhance air quality, and the new global air quality recommendations from the World Health Organization (WHO) are adding fuel to the fire. Inadequate outreach, few observations, disconnected city operations, and inconsistent protocols are some of the issues that hinder these deployments, as does the absence of collaborative UAQM governance. The proposed approach consists of three phases, which are data preparation, feature selection, and training. When it comes to categorical preprocessing, there are two typical ways to deal with missing values are after removing rows with missing values, use KNN Imputer to fill in the blanks. One goal of feature selection methods is to make analysis easier and faster by reducing the target dataset's dimensionality. Training the model was done using BO. With an average accuracy rate of 93.14 percent, the proposed model beats out the alternatives, including SVM and RBF.