Arsen Iskhakov
Postdoctoral Research Scholar
SAS Hall 4210
Education
Ph.D. Nuclear Engineering NC State 2023
Ph.D. Nuclear Power Installations Moscow Power Engineering Institute 2019
M.Eng. Nuclear Power Engineering and Thermophysics Moscow Power Engineering Institute 2015
B.Eng. Nuclear Power Plants and Installations Moscow Power Engineering Institute 2013
Area(s) of Expertise
Arsen Iskhakov conducts research in the realm of numerical modeling, with a primary emphasis on fluid dynamics and thermal hydraulics. Within these domains, his work is aimed at understanding complex fluid behavior and heat transfer phenomena.
Furthermore, Arsen's research portfolio extends beyond conventional methods, as he actively explores the integration of machine learning techniques to augment and refine traditional modeling approaches. By using machine learning, he seeks to enhance the accuracy and efficiency of simulations within fluid dynamics and thermal hydraulics, pushing the boundaries of scientific understanding and practical applications in these critical fields.
Publications
- Data-Driven High-to-Low for Coarse Grid System Thermal Hydraulics , NUCLEAR SCIENCE AND ENGINEERING (2023)
- Data-Driven RANS Turbulence Closures for Forced Convection Flow in Reactor Downcomer Geometry , NUCLEAR TECHNOLOGY (2023)
- Direct Numerical Simulation of Low and Unitary Prandtl Number Fluids in Reactor Downcomer Geometry , NUCLEAR TECHNOLOGY (2023)
- Machine learning from RANS and LES to inform coarse grid simulations , PROGRESS IN NUCLEAR ENERGY (2023)
- A Perspective on Data-Driven Coarse Grid Modeling for System Level Thermal Hydraulics , NUCLEAR SCIENCE AND ENGINEERING (2022)
- Challenge Problem 1: Preliminary Model Development and Assessment of Flexible Heat Transfer Modeling Approaches , (2022)
- Data-driven Hi2Lo for Coarse-grid System Thermal Hydraulic Modeling , arXiv (2022)
- Direct Numerical Simulation of Low and Unitary Prandtl Number Fluids in Reactor Downcomer Geometry , arXiv (2022)
- Challenge Problem 1: Benchmark Specifications for the Direct Numerical Simulation of Canonical Flows , (2021)
- Integration of neural networks with numerical solution of PDEs for closure models development , PHYSICS LETTERS A (2021)