Arsen Shamilevich Iskhakov

Data-Driven High-to-Low for Coarse Grid System Thermal Hydraulics

Iskhakov, A. S., Leite, V. C., Merzari, E., & Dinh, N. T. (2023, April 28). NUCLEAR SCIENCE AND ENGINEERING, Vol. 4.

Data-Driven RANS Turbulence Closures for Forced Convection Flow in Reactor Downcomer Geometry

Iskhakov, A. S., Tai, C.-K., Bolotnov, I. A., Nguyen, T., Merzari, E., Shaver, D. R., & Dinh, N. T. (2023, March 16). NUCLEAR TECHNOLOGY, Vol. 3.

Direct Numerical Simulation of Low and Unitary Prandtl Number Fluids in Reactor Downcomer Geometry

Tai, C.-K., Nguyen, T., Iskhakov, A. S., Merzari, E., Dinh, N. T., & Bolotnov, I. A. (2023, June 17). NUCLEAR TECHNOLOGY, Vol. 6.

Machine learning from RANS and LES to inform coarse grid simulations

PROGRESS IN NUCLEAR ENERGY, 163.

A Perspective on Data-Driven Coarse Grid Modeling for System Level Thermal Hydraulics

Iskhakov, A. S., Tai, C.-K., Bolotnov, I. A., & Dinh, N. T. (2022, September 10). NUCLEAR SCIENCE AND ENGINEERING, Vol. 9.

Challenge Problem 1: Preliminary Model Development and Assessment of Flexible Heat Transfer Modeling Approaches

Data-driven Hi2Lo for Coarse-grid System Thermal Hydraulic Modeling

ArXiv. http://www.scopus.com/inward/record.url?eid=2-s2.0-85126815096&partnerID=MN8TOARS

Direct Numerical Simulation of Low and Unitary Prandtl Number Fluids in Reactor Downcomer Geometry

ArXiv. http://www.scopus.com/inward/record.url?eid=2-s2.0-85128839982&partnerID=MN8TOARS

Challenge Problem 1: Benchmark Specifications for the Direct Numerical Simulation of Canonical Flows

Integration of neural networks with numerical solution of PDEs for closure models development

PHYSICS LETTERS A, 406.

REVIEW OF PHYSICS-BASED AND DATA-DRIVEN MULTISCALE SIMULATION METHODS FOR COMPUTATIONAL FLUID DYNAMICS AND NUCLEAR THERMAL HYDRAULICS

ArXiv. https://publons.com/wos-op/publon/58758834/

Review of physics-based and data-driven multiscale simulation methods for computational fluid dynamics and nuclear thermal hydraulics

ArXiv. http://www.scopus.com/inward/record.url?eid=2-s2.0-85102858814&partnerID=MN8TOARS

Physics-integrated machine learning: Embedding a neural network in the navier-stokes equations. Part II

ArXiv. http://www.scopus.com/inward/record.url?eid=2-s2.0-85108266968&partnerID=MN8TOARS

Physics-integrated machine learning: embedding a neural network in the Navier-Stokes equations. Part I

ArXiv. https://publons.com/wos-op/publon/58758828/

Hugoniot analysis of energetic molten lead-water interaction

Annals of Nuclear Energy, 129, 437–449.

Hugoniot analysis of experimental data on steam explosion in stratified melt-coolant configuration

Nuclear Engineering and Design, 347, 151–157.

Steam generator tube rupture in lead-cooled fast reactors: Estimation of impact on neighboring tubes

Nuclear Engineering and Design, 341, 198–208.

Pressure Waves due to Rapid Evaporation of Water Droplet in Liquid Lead Coolant

Science and Technology of Nuclear Installations, 2018, 10.

Numerical Modeling of the Hydrodynamic Loads Applied on the «BREST-300» Reactor Steam Generator Tubes during a Primary-to-Secondary Leak Accident

Vestnik MEI, (3), 33–40.