Jinyong Feng joined the Baglietto CFD research group in the NSE department as a Research Scientist from February 2019. Prior to the appointment of research scientist, he worked as a Postdoctoral Associate in the NSE department since August 2017. At MIT, he conducted modeling and simulation research in the area of nuclear thermal hydraulics with the emphasis on the development and implementation of advance single and multiphase flow models in CFD. He received his Ph.D. from the department of nuclear engineering at NC State University in 2017 and his thesis focused on the evaluation of interfacial forces and bubble-induced turbulence using DNS coupled with level-set interface tracking method. His ultimate research goal is to promote nuclear energy by developing more physics-based models and simulation methods.
PhD, Nuclear Engineering, North Carolina State University, 2017
MEng, Nuclear Engineering, Texas A&M University, 2013
BS, Nuclear Science and Technology, University of Science and Technology of China, 2012
My research interests include multi-phase flow and heat transfer, advanced experimental diagnostics, reactor thermal-hydraulics, materials and safety. I work in cooperation with Profs. Baglietto, Bucci and Buongiorno. My current research is focused in three areas:
As first-principle based approach, DNS directly solves the Navier-Stokes equations without any closures. The advancement of numerical algorithms and computational facilities in the past 20 years makes DNS computationally feasible for the verification and validation of physical models. Based on single fluid assumption, interface tracking method (ITM) extends the DNS capabilities from single phase to multiphase. DNS coupled with ITM approaches will contribute to the development of next generation physical models for the successful application of CFD methods in nuclear reactor thermal hydraulics analysis
RANS-based turbulence models have been widely used in the industries due to its robustness and computationally efficiency. However, they fail to provide good predictions in some challenging flow scenarios involving large scale flow separation and flow mixing. A second-generation RANS-based turbulence model is developed in our group providing adaptive scale resolution of turbulence and producing enhanced predictions. The success of this turbulence model are demonstrated in various applications, like flow mixing in T-junction, grid-to-rod fretting, flow around helical tube bundles, etc.
Leveraging new physical understanding from the state-of-art experiment and first-principle based numerical simulations, the next-generation multiphase wall boiling closures are being developed for the simulation of wall boiling phenomena. The ultimate goal of the new models is to introduce and demonstrate all necessary mechanisms required to accurately predict the temperature and heat flux for the flow boiling from atmospherical pressure to PWR operational pressure, from the flow regime of onsite of nucleate boiling to departure from nucleate boiling, from vertical channel flow to inclined channel flow, etc.