Usage of machine learning algorithms in crack propagation prediction in various materials
Applications Invited
27
Scopus Publications
Scopus Publications
Influence of antioxidants and nanoparticles with Ceiba pentandra biodiesel/diesel blends on performance and emission characteristics of diesel engine Mohan Gift, Booma Devi P, Suresh Kumar P, Loganathan T Indian Journal of Chemical Technology, 2026 The demand for renewable fuel sources has increased as a result of declining fossil fuel supplies and declining air quality. Plant-based biodiesel appears to be a desirable alternative to fossil diesel; however, the usage of biodiesel is restricted due to its low heating value, poor atomization, lower thermal efficiency, and higher nitrogen oxides (NOx) emissions. In particular, reducing NOx emissions from engines is crucial for environmental protection and public health. The addition of nanoparticles and antioxidant additives to biodiesel plays a crucial role in overcoming its limitations. Antioxidants help reduce NOx emissions by removing decomposing peroxides and free radicals, as well as by disrupting the chain reactions of free radicals. This study looks at the influence of incorporating butylated hydroxytoluene (BH) antioxidant and aluminium oxide (Al2O3) nanoparticles into a Ceiba pentandra biodiesel blend (CPB) on engine performance and emission characteristics. The experimental work has been carried out on a direct injection (DI) diesel engine by blending 250, 500, 750, and 1000 ppm of BH and 25, 50, and 75 ppm of Al2O3 with 20% CPB. A total of eight different test blends were prepared and utilized for engine operation, and the results were compared with baseline diesel fuel. The experimental results expose that adding BH and Al2O3 significantly reduced NOx emissions. Compared to diesel, the addition of 1000 ppm BH and 50 ppm Al2O3 reduced the emission of NOx by 16.1%, carbon monoxide (CO) by 52.38%, and unburned hydrocarbon (HC) by 25.93%. However, there was a slight increase of 2.27 % in brake thermal efficiency (BTE) and a decrease of 7.14% in brake specific fuel consumption (BSFC).
Enhancing power systems with AI: Design to emission reduction N. Karpagam, M. L. Sworna Kokila, Bibin Christopher V., Nidhi Kunar, M. D. Mohan Gift, Sampath Boopathi Innovations in Power Systems and Applications, 2025 Artificial intelligence integration into power systems has been the revolution that transformed how energy is generated, distributed, and consumed. In this regard, this chapter discusses AI-driven methodologies for power system design, optimization, and operation with regards to their potential to reduce carbon emissions. Some of the key applications in this regard include predictive maintenance, smart grid management, and energy demand forecasting, all of which work towards improving system reliability and minimizing waste energy. Advanced AI models, including machine learning and deep learning, allow for real-time decision-making, optimization of renewable energy integration, and dynamic load balancing. They support the installation of distributed energy resources, including solar and wind, which promotes the shift towards cleaner energy systems. The chapter advances how AI can spur transformative reductions in greenhouse gas emissions while paving the way to resilient, intelligent, and sustainable power systems by addressing challenges such as system stability and scalability.
Harnessing Pedagogical Content Knowledge for Cross-Disciplinary Innovation in Engineering H. Kareemullah, M. D. Mohan Gift, R. Bhaskaran, T. Santhana Krishnan, S. Senthil kumar Current Trends and Best Practices of Pedagogical Content Knowledge Pck, 2025 The chapter will present the integration of PCK toward the fostering of cross-disciplinary innovation within engineering. Being itself the composite of content expertise with effective teaching strategies, PCK acts as the main ingredient in developing holistic approaches to education that transcend traditional boundaries. On the basis of PCK, educators will be able to design curricula that enhance not only disciplinary-based understanding but also interdisciplinary collaboration. The chapter shares cases on how PCK-driven approaches to engineering education will let learners solve complex problems creatively and think innovatively about a wide range of contexts. It also provides strategies for embedding PCK in engineering programs through collaborative projects, interdisciplinary workshops, and experiential learning opportunities.
Optimizing Hybrid Active–Passive Thermal Management of Prismatic Li-Ion Batteries Using Phase Change Materials and Porous-Filled Mini-Channels R. J. Venkatesh, Vara Prasad Bhemuni, Dilip Shyam Prakash Chinnam, M. D. Mohan Gift Energy Storage, 2024 Tackling climate change is crucial, and electrifying the vehicular transportation sector is essential to reduce greenhouse gas emissions. Lithium‐ion (Li‐ion) batteries are highly efficient for electric vehicles (EVs) but face challenges such as thermal management, risk of thermal runaway, and high costs of lithium and cobalt. Overcoming these challenges is vital for the widespread adoption of hybrid and EVs. To overcome this drawback, this article proposed a large‐kernel attention graph convolutional network (LKAGCN) with leaf in wind optimization algorithm (LWOA) named as LKAGCN‐LWOA technique, which enhances the thermal management of prismatic Li‐ion batteries by integrating both active and passive cooling techniques. The system incorporates phase change materials (PCMs) with porous‐filled mini‐channels to regulate battery temperature effectively. The LKAGCN analyze thermal properties, battery conditions, and PCM characteristics to predict and optimize the thermal behavior of the battery pack using LWOA. The proposed methods tune the parameters of the hybrid thermal management system, ensuring efficient thermal regulation and improved performance. The proposed method is compared to various existing methods such as convolutional neural network (CNN), Taguchi method, and Finite element model (FEM).
Additive manufacturing and 3D printing innovations: Revolutionizing industry 5.0 M. D. Mohan Gift, T. S. Senthil, Dler Salih Hasan, K. Alagarraja, P. Jayaseelan, Sampath Boopathi Technological Advancements in Data Processing for Next Generation Intelligent Systems, 2024 Additive manufacturing (AM), commonly known as 3D printing, has emerged as a transformative technology with profound implications for multiple industries. The convergence of AM with Industry 4.0 principles and advanced technologies has given rise to Industry 5.0, a new era of manufacturing characterized by enhanced integration and digitalization. This chapter explores the dynamic landscape of AM within the context of Industry 5.0, highlighting research trends, innovations, and challenges. Key developments include materials advancements, multi-material printing, digital twins, bioprinting, AI-driven design, and sustainability initiatives. Industry 5.0's impact is felt globally, with applications spanning aerospace, healthcare, fashion, and beyond. Collaboration between academia and industry, regulatory frameworks, and the pursuit of sustainable practices are driving forces shaping the future of AM in Industry 5.0.
Biomining method to extract metal components using computer-printed circuit board e-waste Sudheer Hanumanthakari, M. D. Mohan Gift, K. V. Kanimozhi, Murapaka Dhanalakshmi Bhavani, Kalyan Devappa Bamane, Sampath Boopathi Handbook of Research on Safe Disposal Methods of Municipal Solid Wastes for A Sustainable Environment, 2023 In the present scenario, the e-waste from the various electronic sectors has been increasing due to increased utilization of electric components. In this chapter, the bioleaching(biomining) process of a computer printed circuit board (CPCB) is illustrated to extract the metal components. Basic concepts for e-waste management, their impacts, and various e-waste treatment methods have been explained. The various existing conventional metal extraction methods for the wasted CPCB have also been explored. Definitions, types, cryogenic bioleaching (biomining), influencing factors, and procedures of the bioleaching process have been illustrated. The microbiological methods for the processing of e-waste, the selection of process parameters, and the optimization or maximization of metal extraction processes were demonstrated to promote the e-waste management processes.
Study of Numerous Resins Used in Polymer Matrix Composite Materials T. Ramakrishnan, M. D. Mohan Gift, S. Chitradevi, R. Jegan, P. Subha Hency Jose, H.N. Nagaraja, Rajneesh Sharma, P. Selvakumar, Sintayehu Mekuria Hailegiorgis Advances in Materials Science and Engineering, 2022
