Arsen Shamilevich Iskhakov

Also known as: Iskhakov A.S.; Arsen S. Iskhakov

Nuclear Engineering

Works (19)

Updated: April 4th, 2024 16:00

2023 article

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.

By: A. Iskhakov n, V. Leite*, E. Merzari* & N. Dinh n

Contributors: A. Iskhakov n, V. Leite*, E. Merzari* & N. Dinh n

author keywords: Coarse grid computational fluid dynamics; data-driven high-to-low; machine learning; mixing; turbulence modeling
UN Sustainable Development Goal Categories
Sources: Web Of Science, NC State University Libraries, ORCID
Added: May 15, 2023

2023 article

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.

By: A. Iskhakov n, C. Tai n, I. Bolotnov n, T. Nguyen*, E. Merzari*, D. Shaver*, N. Dinh n

Contributors: A. Iskhakov n, C. Tai n, I. Bolotnov n, T. Nguyen*, E. Merzari*, D. Shaver*, N. Dinh n

author keywords: Machine learning; turbulence modeling; forced convection; low- and high-Prandtl fluids; data-driven modeling
Sources: Web Of Science, NC State University Libraries, ORCID
Added: March 16, 2023

2023 article

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.

By: C. Tai n, T. Nguyen*, A. Iskhakov n, E. Merzari*, N. Dinh n & I. Bolotnov n

author keywords: Direct numerical simulation; vertical mixed convection; low-Prandtl-number fluids; downcomer
Sources: Web Of Science, NC State University Libraries
Added: July 3, 2023

2023 journal article

Machine learning from RANS and LES to inform coarse grid simulations

PROGRESS IN NUCLEAR ENERGY, 163.

By: A. Iskhakov n, N. Dinh n, V. Leite* & E. Merzari*

author keywords: Machine learning; Coarse grid RANS; Mixing in upper plenum; Turbulence modeling; Error correction
UN Sustainable Development Goal Categories
7. Affordable and Clean Energy (OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: August 28, 2023

2022 article

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.

By: A. Iskhakov n, C. Tai n, I. Bolotnov n & N. Dinh n

Contributors: A. Iskhakov n

author keywords: Data-driven methods; coarse grid modeling; system thermal hydraulics; machine learning; multiscale bridging
TL;DR: An overview of recent applications of DD methods in the areas of fluid dynamics and TH is provided, demonstrating that they are being widely applied for engineering-scale analysis and outlining of potential frameworks for further developments in DD CG modeling of system-level TH. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Sources: Web Of Science, NC State University Libraries, ORCID
Added: September 9, 2022

2022 report

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

Source: ORCID
Added: September 5, 2023

2022 article

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

By: A. Iskhakov, N. Dinh, V. Leite & E. Merzari

Contributors: A. Iskhakov, N. Dinh, V. Leite & E. Merzari

Source: ORCID
Added: October 16, 2022

2022 article

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

By: C. Tai, T. Nguyen, A. Iskhakov, E. Merzari, N. Dinh & I. Bolotnov

Contributors: C. Tai, T. Nguyen, A. Iskhakov, E. Merzari, N. Dinh & I. Bolotnov

Source: ORCID
Added: October 16, 2022

2021 report

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

By: I. Bolotnov n, N. Dihn n, A. Iskhakov n, C. Tai n, E. Merzari*, T. Nguyen*, E. Baglietto*, R. Wiser* ...

Source: ORCID
Added: September 5, 2023

2021 journal article

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

PHYSICS LETTERS A, 406.

By: A. Iskhakov n, N. Dinh n & E. Chen n

Contributors: A. Iskhakov n, N. Dinh n & E. Chen n

author keywords: Physics-informed machine learning; PDE-integrated neural network; Closure model
TL;DR: Despite its complexity and computational cost, the proposed physics-integrated ML shows a potential to develop a "PDE-Integrated" closure relations for turbulent models and offers principal advantages, namely: the target outputs for a DFNN might be unknown and can be recovered using the knowledge base (PDEs). (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: June 10, 2021

2021 journal article

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/

Contributors: A. Iskhakov & N. Dinh

Source: ORCID
Added: September 5, 2023

2021 article

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

By: A. Iskhakov & N. Dinh

Contributors: A. Iskhakov & N. Dinh

Source: ORCID
Added: October 16, 2022

2020 article

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

By: A. Iskhakov & N. Dinh

Contributors: A. Iskhakov & N. Dinh

Source: ORCID
Added: October 16, 2022

2020 journal article

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

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

Contributors: A. S. Iskhakov & N. T. Dinh

Source: ORCID
Added: September 5, 2023

2019 journal article

Hugoniot analysis of energetic molten lead-water interaction

Annals of Nuclear Energy, 129, 437–449.

By: A. Iskhakov*, V. Melikhov & O. Melikhov

Contributors: A. Iskhakov*

UN Sustainable Development Goal Categories
6. Clean Water and Sanitation (OpenAlex)
Source: ORCID
Added: September 5, 2023

2019 journal article

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

Nuclear Engineering and Design, 347, 151–157.

By: A. Iskhakov*, V. Melikhov, O. Melikhov, S. Yakush* & L. Chung*

Contributors: A. Iskhakov*

Source: ORCID
Added: September 5, 2023

2019 journal article

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

Nuclear Engineering and Design, 341, 198–208.

By: A. Iskhakov*, V. Melikhov, O. Melikhov & S. Yakush*

Contributors: A. Iskhakov*

Source: ORCID
Added: September 5, 2023

2018 journal article

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

Science and Technology of Nuclear Installations, 2018, 10.

By: S. Yakush*, A. Iskhakov*, V. Melikhov & O. Melikhov

Contributors: S. Yakush*, A. Iskhakov*, V. Melikhov & O. Melikhov

UN Sustainable Development Goal Categories
6. Clean Water and Sanitation (OpenAlex)
Source: ORCID
Added: October 16, 2022

2017 journal article

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.

Contributors: A. Iskhakov*

UN Sustainable Development Goal Categories
7. Affordable and Clean Energy (OpenAlex)
Source: ORCID
Added: September 5, 2023

Employment

Updated: September 11th, 2023 10:32

2023 - present

North Carolina State University Raleigh, NC, US
Postdoctoral Research Scholar Mathematics

Citation Index includes data from a number of different sources. If you have questions about the sources of data in the Citation Index or need a set of data which is free to re-distribute, please contact us.

Certain data included herein are derived from the Web of Science© and InCites© (2024) of Clarivate Analytics. All rights reserved. You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.