Pavel Kudinov

Fission Products (FPs) release into environment is the main consequence of a nuclear severe accident. Transport and retention of volatile FPs in the containment is influenced by various mechanisms, including pool scrubbing (retention of FPs in a water pool). A series of THAI facility tests has been carried out in OECD/NEA THEMIS project with focus on aerosol behavior in case of injection of a mixture of steam, non-condensable gases and aerosols into a water pool. The goal of this work is to develop models for THAI facility to simulate the aerosol transport under pool scrubbing condition using containment thermal-hydraulics code GOTHIC and to provide analysis for THAI experiments relevant to Nordic plant designs and conditions. The guidelines for modeling of the relevant phenomena and quantification of different aerosol particle deposition mechanisms are provided. The simulations highlight the complexity of aerosol transport from the submerged vent to vessel exhaust line involving several mechanisms such as pool scrubbing, drop deposition in gas space, re-entrainment, and agglomeration. Model validation against the experiments demonstrates a reasonable agreement on particle concentration in gas space. Nevertheless, there is a possibility for the compensation of errors given that the concentration is determined by the interplay between various mechanisms.

Thermal-Hydraulic (TH) experiments provide valuable insight into the physics of heat and mass transfer and qualified data for code development, calibration and validation. However, measurements are typically collected from sparsely distributed sensors, offering limited coverage over the domain of interest and phenomena of interest. Determination of the spatial configuration of these sensors is crucial and challenging during the pre-test design stage. This paper develops a data-driven framework for optimizing sensor placement in TH experiments, including (i) a sensitivity analysis to construct datasets, (ii) Proper Orthogonal Decomposition (POD) for dimensionality reduction, and (iii) QR factorization with column pivoting to determine optimal sensor configuration under spatial constraints. The framework is demonstrated on a test conducted in the TALL-3D Lead-bismuth eutectic (LBE) loop. In this case, the utilization of optical techniques, such as Particle Image Velocimetry (PIV), are impractical. Thereby the quantification of momentum and energy transport relies heavily on readings from Thermocouples (TCs). The test section was previously instrumented with many TCs determined through a manual process combining simulation results with expert judgement. The proposed framework provides a systematic and automated approach for sensor placement. The resulting TCs exhibit high sensitivity to the variation of uncertain input parameters and enable accurate full field reconstruction while maintaining robustness against measurement noise.

Measurement of the velocity field in thermal-hydraulic experiments is of great importance for phenomena interpretation and code validation. Direct measurement employing Particle Image Velocimetry (PIV) is challenging in some multiphase scenarios where the measurement system would be strongly affected by the phase interaction. In such cases, measurement can only be achieved via sparsely distributed sensors, such as Thermocouples (TCs) and pressure transducers. An example can refer to steam injection into a water pool where the rapid collapse of bubbles and significant temperature gradient make it impossible to obtain the main flow velocity at a large steam flux by PIV. This work investigates the feasibility and capability of utilization of data-driven modeling for flow reconstruction from sparse temperature data. The framework applies (i) a Proper Orthogonal Decomposition (POD) to encode variables from full space to latent space and (ii) a Fully connected Neural Network (FNN) to approximate sparse measurements to coefficients of latent space. Sensor positioning aiming to identify the optimal sensor location is also discussed. The proposed framework has been tested on a single-phase planar jet and steam condensing jets issued through a multi-hole sparger.

In engineering systems operating under high Schmidt (Sc) or Prandtl (Pr) number flow conditions, the demand for near-wall mesh refinement increases significantly, underscoring the need for cost-effective modeling approaches that avoid additional computational overhead. Existing models, which are predominantly designed for low-Sc flows, overlook temporal filtering effects, resulting in inaccuracies in theoretical description and mass transfer predictions. This paper addresses the impact of high Sc or Pr by refining the single-layer scalar diffusivity model. It introduces a switch between scalar filtering and eddy viscosity-dominated regions, leveraging two parameters: κSc, accounting for temporal filtering effects, and κRe, addressing variations in Reynolds number. In addition, we adopted a complementary outer layer term to model the upwarding trend in low frictional Reynolds number condition. Using the two-layer model with unity Sc and/or Pr, a close agreement with the von-Kármán constant in the velocity boundary layer was observed. The modified model demonstrated strong agreement with scalar profiles across a broad range of Sc and friction Reynolds numbers (Reτ) in direct numerical simulation and large eddy simulation data, demonstrating its accuracy at low Reτ and predictive performance at high Reτ. The two-layer model improves the prediction of turbulent mass transfer, providing better alignment with high Sc engineering correlations than existing wall model approach. This study provides valuable insight for modeling the mass and heat transfer processes under high Sc or Pr conditions.