Dmitry Grishchenko

ORCID: 0000-0002-3066-3492
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About
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Research Areas
  • Nuclear Engineering Thermal-Hydraulics
  • Nuclear reactor physics and engineering
  • Nuclear Materials and Properties
  • Risk and Safety Analysis
  • Combustion and Detonation Processes
  • Stochastic Gradient Optimization Techniques
  • Sparse and Compressive Sensing Techniques
  • Spacecraft and Cryogenic Technologies
  • Metallurgical Processes and Thermodynamics
  • Heat transfer and supercritical fluids
  • Heat Transfer and Boiling Studies
  • Fluid Dynamics and Mixing
  • Nuclear and radioactivity studies
  • Distributed Control Multi-Agent Systems
  • Radioactive element chemistry and processing
  • Complexity and Algorithms in Graphs
  • Risk and Portfolio Optimization
  • Face and Expression Recognition
  • Nuclear Physics and Applications
  • Cyclone Separators and Fluid Dynamics
  • Graphite, nuclear technology, radiation studies
  • Solidification and crystal growth phenomena
  • Thermodynamic and Structural Properties of Metals and Alloys
  • Machine Learning and ELM
  • Optimization and Variational Analysis

KTH Royal Institute of Technology
2016-2025

Université Grenoble Alpes
2019-2021

Laboratoire Jean Kuntzmann
2021

Laboratoire d'Informatique de Grenoble
2020-2021

Discovery Air (Canada)
2020

Oak Ridge National Laboratory
2018

AlbaNova
2013

St. Petersburg State Technological Institute
2008

Peter the Great St. Petersburg Polytechnic University
2006

In engineering systems operating under high Schmidt (Sc) or Prandtl (Pr) number flow conditions, the demand for near-wall mesh refinement increases significantly, underscoring need cost-effective modeling approaches that avoid additional computational overhead. Existing models, which are predominantly designed low-Sc flows, overlook temporal filtering effects, resulting in inaccuracies theoretical description and mass transfer predictions. This paper addresses impact of Sc Pr by refining...

10.1063/5.0255551 article EN cc-by Physics of Fluids 2025-02-01

Direct contact condensation (DCC) of steam in the pressure suppression pool (PSP) is used to control containment Boiling Water Reactors (BWRs) and Advanced Pressurized (APWRs). The competition between momentum heat sources induced by injection through multi-hole spargers blowdown pipes determines whether thermally stratified or mixed. Development thermal stratification affects capacity PSP condense steam. To enable computationally efficient modeling transients, Effective Heat Source (EHS)...

10.1016/j.nucengdes.2023.112222 article EN cc-by Nuclear Engineering and Design 2023-02-22

Design and safety analysis of the currently developed pool type liquid metal cooled fast nuclear reactors is impaired by limited operational experience for such systems insufficient confidence in predictive capabilities applied modelling. Understanding pool-reactor thermal-hydraulics crucial assessment reactor performance passive reliability. Credibility tools can be established process code validation, which includes open blind benchmarks against integral experiments. TALL-3D a lead-bismuth...

10.1016/j.nucengdes.2019.110386 article EN cc-by-nc-nd Nuclear Engineering and Design 2019-11-15

This paper describes the main objectives, technical content, and status of H2020 project entitled “High-performance advanced methods experimental investigations for safety evaluation generic Small Modular Reactors (McSAFER)”. The pillars this are combination safety-relevant thermal hydraulic experiments numerical simulations different approaches evaluations light water-cooled (SMR). It goals, consortium, involved test facilities, e.g., COSMOS-H (KIT), HWAT (KTH), MOTEL (LUT), including...

10.3390/en14196348 article EN cc-by Energies 2021-10-04

In this work we present three different randomized gossip algorithms for solving the average consensus problem while at same time protecting information about initial private values stored nodes. We give iteration complexity bounds all methods, and perform extensive numerical experiments.

10.48550/arxiv.1706.07636 preprint EN other-oa arXiv (Cornell University) 2017-01-01

Boiling Water Reactors (BWR) and Advanced Pressurized (APWR) often use spargers to release steam from the primary coolant system into a pool with subcooled water prevent containment overpressure. Direct contact Condensation (DCC) creates sources of mass, heat, momentum determined by condensation regimes in pool. Thermal stratification can develop if buoyancy forces created heat source dominate source. Only part stratified volume be used as sink which is safety concern. Modeling injection its...

10.1016/j.ijheatmasstransfer.2024.125969 article EN cc-by International Journal of Heat and Mass Transfer 2024-07-19

Boiling Water Reactor (BWR) employs the Pressure Suppression Pool (PSP) as a heat sink to prevent overpressure of reactor vessel and containment. Steam can be injected into PSP through spargers in normal accident conditions blowdown pipes case loss coolant (LOCA). There is safety limit on maximum temperature at which such steam injection might cause dynamic loads containment structures. The performance pool affected if thermal stratification developed when hot layer grows rapidly while cold...

10.1016/j.net.2024.07.045 article EN cc-by-nc-nd Nuclear Engineering and Technology 2024-07-23

RELAP5 is a system thermal-hydraulic code that used to perform safety analysis on nuclear reactors. Since the based steady state, two-phase flow regime maps, there concern may provide significant errors for rapid transient conditions. In this work, capability of predict oscillatory behavior natural circulation driven, at low pressure investigated. The simulations are compared with series experiments were performed in CIRCUS-IV facility Delft University Technology. For purpose, we developed...

10.1155/2015/130741 article EN cc-by Science and Technology of Nuclear Installations 2015-01-01

Many applications in machine learning or signal processing involve nonsmooth optimization problems. This nonsmoothness brings a low-dimensional structure to the optimal solutions. In this paper, we propose randomized proximal gradient method harnessing underlying structure. We introduce two key components: (i) random subspace algorithm; and (ii) an identification-based sampling of subspaces. Their interplay significant performance improvement on typical problems terms dimensions explored.

10.1287/moor.2020.1092 article EN Mathematics of Operations Research 2021-02-23
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