Browsing by Author "Ravi, R."

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Comparative study of ASTM A262 practices A, E and G108 tests for the evaluation of sensitization in AISI type 316 stainless steel
(2003) Parvathavarthini, N.; Dayal, R.K.; Ravi, R.; Khatak, H.S.
In this paper the results obtained in a comparative study of ASTM A 262 Practices A (metallography), E (immersion test) and G108 (Electrochemical Potentiokinetic Reactivation technique) tests for the evaluation of sensitization in AISI type 316 stainless steel are reported. Different degrees of sensitization (DOS) were obtained by heat treating the material at 948K for durations ranging from 3h to 2000h. Depending upon the thermal history, the material may be either in sensitized, non-sensitized or desensitized condition. ASTM A262 Practices A, E and G108 tests were performed on each specimen. Prolonged aging beyond desensitization resulted in the precipitation of embrittling secondary phases which were separated by electrolytic method and were identified by X ray diffraction technique. The need to carry out both ASTM A262 A and G108 to differentiate between sensitization and desensitization is brought out in this investigation. Both act as complementary to each other and when performed separately yields results which are inconclusive. A method has been suggested to distinguish between various microstructural states possible in a fabricated or service-exposed component.
Deep learning architecture for big data analytics in detecting intrusions and malicious URL
(Institution of Engineering and Technology, 2019) Harikrishnan, N.B.; Ravi, R.; Padannayil, K.P.; Poornachandran, P.; Annappa, A.; Alazab, M.
Security attacks are one of the major threats in today’s world. These attacks exploit the vulnerabilities in a system or online sites for financial gain. By doing so, there arises a huge loss in revenue and reputation for both government and private firms. These attacks are generally carried out through malware interception, intrusions, phishing uniform resource locator (URL). There are techniques like signature-based detection, anomaly detection, state full protocol to detect intrusions, blacklisting for detecting phishing URL. Even though these techniques claim to thwart cyberattacks, they often fail to detect new attacks or variants of existing attacks. The second reason why these techniques fail is the dynamic nature of attacks and lack of annotated data. In such a situation, we need to propose a system which can capture the changing trends of cyberattacks to some extent. For this, we used supervised and unsupervised learning techniques. The growing problem of intrusions and phishing URLs generates a need for a reliable architectural-based solution that can efficiently identify intrusions and phishing URLs. This chapter aims to provide a comprehensive survey of intrusion and phishing URL detection techniques and deep learning. It presents and evaluates a highly effective deep learning architecture to automat intrusion and phishing URL Detection. The proposed method is an artificial intelligence (AI)-based hybrid architecture for an organization which provides supervised and unsupervised-based solutions to tackle intrusions, and phishing URL detection. The prototype model uses various classical machine learning (ML) classifiers and deep learning architectures. The research specifically focuses on detecting and classifying intrusions and phishing URL detection. © The Institution of Engineering and Technology 2020.
In this paper the results obtained in a comparative study of ASTM A 262 Practices A (metallography), E (immersion test) and G108 (Electrochemical Potentiokinetic Reactivation technique) tests for the evaluation of sensitization in AISI type 316 stainless steel are reported. Different degrees of sensitization (DOS) were obtained by heat treating the material at 948K for durations ranging from 3h to 2000h. Depending upon the thermal history, the material may be either in sensitized, non-sensitized or desensitized condition. ASTM A262 Practices A, E and G108 tests were performed on each specimen. Prolonged aging beyond desensitization resulted in the precipitation of embrittling secondary phases which were separated by electrolytic method and were identified by X ray diffraction technique. The need to carry out both ASTM A262 A and G108 to differentiate between sensitization and desensitization is brought out in this investigation. Both act as complementary to each other and when performed separately yields results which are inconclusive. A method has been suggested to distinguish between various microstructural states possible in a fabricated or service-exposed component.
(Comparative study of ASTM A262 practices A, E and G108 tests for the evaluation of sensitization in AISI type 316 stainless steel) Parvathavarthini, N.; Dayal, R.K.; Ravi, R.; Khatak, H.S.
2003
Influence of water-methanol injection and turbocharging on the performance of a hydrogen-fueled spark ignition engine
(John Wiley and Sons Inc, 2024) Chitragar, P.R.; Shivaprasad, K.V.; Ichchangi, M.; Ravi, R.; Yadav, M.S.; Kumar, K.
This article presents a study that compares the performance and emission characteristics of a four-stroke, four-cylinder spark ignition (SI) engine fueled by gasoline and neat hydrogen. The engine was equipped with turbocharging to optimize ignition timing for power boosting and vaporized water–methanol injection to reduce emissions. Engine tests were conducted at speeds ranging from 2000 to 6000 rpm, with a fixed intake pressure and varying quantities of hydrogen and spark advance timings. The study compared the results of non-turbocharged and turbocharged engines with water–methanol injection in terms of combustion, performance, and emissions. The findings showed that the turbocharged water–methanol hydrogen operation had a higher brake thermal efficiency (BTE) than its counterpart, while the brake power of the hydrogen engine operation increased with turbocharging but slightly decreased with water–methanol injection. Additionally, volumetric efficiency improved by 7% for turbocharged and 4% for water-injected hydrogen engine operation compared to the counterpart. The cylinder pressure for turbocharging with water–methanol operation yielded 16.32% higher compared with counterpart gasoline engine operation. Finally, nitrogen oxides (NOx) emissions were reduced with turbocharging and water–methanol injection compared to the counterpart non-turbocharged hydrogen engine operation. © 2023 Wiley Periodicals LLC.
Investigation of forced convective and subcooled flow boiling heat transfer coefficients of water-ethanol mixture: numerical study
(International Information and Engineering Technology Association, 2021) Suhas, S.; Ravi, R.; Sathyabhama, A.
The subcooled flow boiling is related to the operation of electronic devices, Hybrid electric vehicle (HEV) Battery module and small catalytic reactors. It is well known that the operational temperature must be maintained to avoid any malfunction of these heat dissipative devices. In this paper the forced convective and subcooled flow boiling heat transfer coefficients of water-ethanol mixture is determined numerically by Volume of fluid analysis (VOF). The interaction between liquid and local vapour is analysed by solving the bubble volume of fraction in the numerical study. Crank Nicolson implicit scheme is used for discretizing the scalar convection equation for bubble void fraction and transforming into algebraic equation. Thomas Algorithm is used to solve the algebraic equations of bubble void fraction. The corrector predictor equation method is used to solve for bubble void fraction when the value obtained is less than 0 or exceeds 1. The thermodynamic and Thermophysical properties are substituted in the x-momentum and energy equation to determine the values of pressure drop, velocity and temperature of the fluid. From the temperature values, the subcooled flow boiling heat transfer coefficient is obtained. It is found that the addition of ethanol to water decreases the forced convective and subcooled flow boiling heat transfer coefficient of the water-ethanol mixture. The numerically determined heat transfer coefficient of water ethanol mixture is compared with that of the experimental results. The average deviation between the experimentally determined and numerically determined subcooled flow boiling heat transfer coefficient of water ethanol-mixture is found to be 24.13%. © 2021 International Information and Engineering Technology Association. All rights reserved.