Sivasankar G A
@kitcbe.com
Assistant Professor/ Aeronautical Engineering
KIT Kalaignar Karunanidhi Institute of Technology
EDUCATION
Master of Technology in Aircraft Maintenance Engineering
RESEARCH, TEACHING, or OTHER INTERESTS
Aerospace Engineering, Multidisciplinary
7
Scopus Publications
Scopus Publications
- Building heating by solar parabolic through collector with metallic fined PCM for net zero energy/emission buildings
T. Sathish, D.B. Sivakumar, G.A. Sivasankar, K.T. Thilagham, Seeniappan Kaliappan, R. Saravanan, Mohd Ubaidullah, Mohaseen S. Tamboli, and Manish Gupta
Elsevier BV - Sustainable nano-added biofuel production from borassus flabellifer oil for conventional internal combustion engines
G.A. Sivasankar, C. Balakrishna Moorthy, Seeniappan Kaliappan, Ravishankar Sathyamurthy, T. Sathish, R. Saravanan, and Ümit Ağbulut
Elsevier BV - Effects of CuO/Al<inf>2</inf>O<inf>3</inf>/Water-WO<inf>3</inf> nanomaterials on NETZERO energy/emission-based hydrogen generation
T. Sathish, R. Saravanan, T. Vijay Muni, G.A. Sivasankar, J. Prem Kumar, M. Rajasimman, Yasser Vasseghian, and Y.-K. Park
Elsevier BV - Turbulence Modeling Through Deep Learning: An In-Depth Study of Wasserstein GANs
Wajdi Alghamdi, S. Mayakannan, G A Sivasankar, Jagendra Singh, B. Ravi Naik, and Ch. Venkata Krishna Reddy
IEEE
This research study compares the accuracy of different techniques based on deep learning (DL) for predicting turbulent flows. Different types of Generative Adversarial Networks (GANs) are examined in terms of their applicability to the study and simulation of turbulence. Next, we select Wasserstein Gans (WGANs) to produce localized disturbances. Network features including the learning rate and loss function are examined as they pertain to the performance of the WGANs during training on turbulent data gleaned from high-resolution Direct Numerical Simulations (DNS). DNS input data and the generated turbulent structures are proven to agree qualitatively well. The projected turbulent fields are evaluated quantitatively and statistically. - Evaluation and comparison on mechanical properties of abaca and hemp fiber reinforced hybrid epoxy resin composites
G A Sivasankar, P Arun Karthick, C Boopathi, S Brindha, R J T Nirmalraj, and A Benham
Elsevier BV - Innovative Machine Learning method for the creation of Power-Maximizing Solar Trackers
Yuvaraj Mariappan, Mani P. K, Sivasankar G A, Srinivasan K, Pawan Kumar Inaniya, and Satyendra Vishwakarma
IEEE
Energy consumption is generally high in summer, today there are no homes without air conditioning. The Power consumption is naturally higher in summer. At the same time power generation is not going to increase. Solar power is a natural way for us to meet our home electricity needs, not just this summer. Inverters are generally fitted in many homes today. They manage to keep the inverter on when there is no electricity. But the electricity stored in this inverter is the electricity available to us through the power board. That is what they are storing and using. But this solar panel will also help to overcome the power shortage. In this paper, an innovative machine learning model was proposed to track solar panels to obtain maximum energy optimization. We can produce a portion of our electricity needs from sunlight if we do not buy only the solar panel and its charging controller. The proposed model regularly tracks the solar panel direction and identifies the maximum optimized location while the solar light was regularly fallen in the panel. The proposed model was compared with existing models in various aspects and the results were discussed. - Investigation of heat transfer for silver oxide (Ag<inf>2</inf>O) and iron oxide (Fe<inf>3</inf>O<inf>4</inf>) using nano fluid over a stretch sheet
M.R. Rashmi, Ramesh Kola, Manoj Kumar, Kumar Pratyush, Priya Dule, and G.A. Sivasankar
Elsevier BV