Dr. Jai Prakash is an Associate Professor in the Department of Mathematics at Mahindra University École Centrale School of Engineering, Hyderabad. He received his B.Sc. degree from Ewing Christian College, Prayagraj in 2002 and completed M.Sc. in Mathematics from Banaras Hindu University, Varanasi in 2004. He earned his Ph.D. from Indian Institute of Technology Kharagpur in the year 2010. Dr. Prakash's research interests lie broadly in the area of Fluid Mechanics in particular flow through porous media, Mathematical Modelling.
2018 - 2021, MTR/2017/000446, 12.07.2018, Hydrodynamics of Bubbles/ Drops/ Soft Particles in Porous Media- Volume Averaging Approach, INR 6,60,000, Science and Engineering Research Board (SERB), Govt. of India, funded under Mathematical Research Impact Centric Support (MATRICS), Jai Prakash, Department of Mathematics, Mahindra University.
- Motion of Bubbles and Drops:
The migration of small drops of one fluid through a second, immiscible fluid at small Reynolds numbers plays an important role in a variety of natural and industrial processes. Some of the examples are raindrop formation, the mechanics and rheology of emulsions, oil recovery. During the motion of a fluid drop in a second, immiscible fluid, the interfacial stresses acting at the drop surface tend to deform it. If the motion is sufficiently slow or the drop is sufficiently small, the drop will in the first approximation be spherical, but in case of dominant interfacial stress it is important to study the motion of deformed drop. It is general phenomena that rise in temperature reduces the surface tension which leads to migration of drops due to temperature, this phenomenon is termed as thermocapillary migration also the presence of any solute reduces the surface tension and this leads to migration of drops, this phenomenon is called as marangoni effect. It would be interesting to study the thermocappillary and marangoni effect on the deformed drops.
- Development of Efficient Filter to Estimate Radioactive Release from Nuclear Power Plant (NPP) :
It is absolutely important to estimate the radioactivity release following an accident in a nuclear power plant due to its short and long-term impacts on the surrounding population and the environment. In case of any accidental release, a quick and reliable estimate is required to plan a rapid and effective emergency response and furthermore to design an appropriate evacuation strategy. The accurate prediction of incurred dose rate during normal or accident scenario is another important aspect. The Kalman-Filters can reduce the uncertainties in plume gamma dose estimation and release rate calculation. Three non-linear estimation techniques based on Kalman filtering namely, extended Kalman filter (EKF), unscented Kalman filter (UKF), and cubature Kalman filter (CKF) are proposed in order to estimate release activity and to improve the prediction of dose rates.