The Tailored CFD Package ‘containmentFOAM’ for Analysis of Containment Atmosphere Mixing, H2/CO Mitigation and Aerosol Transport

Kelm, Stephan; Kampili, Manohar; Liu, Xiongguo; George, A.; Schumacher, Dominik; Druska, Claudia; Struth, Stephan; Kuhr, Astrid; Ramacher, Lucian; Allelein, Hans-Josef; Prakash, K. Arul; Kumar, G. Vijaya; Cammiade, Liam M. F.; Ji, Ruiyun

doi:10.3390/fluids6030100

Cited by 30 publications

(7 citation statements)

References 34 publications

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“…Kim et al [158] conducted an analysis of the hydrogen distribution within an OPR1000 containment; however, details regarding the equations used or the validation of the code were not included. On the other hand, Kelm et al [159] described the development and validation strategy of their CFD code, containmentFOAM, which can implement wall condensation through either a volume source term or wall fluxes. In addition to handling turbulent mixing and water steam condensation, containmentFOAM also considers radiative heat exchange and aerosol transport.…”

Section: Authors Empirical Correlationsmentioning

confidence: 99%

Steam Condensation Heat Transfer in the Presence of Noncondensable Gases (NCGs) in Nuclear Power Plants (NPPs): A Comprehensive Review of Fundamentals, Current Status, and Prospects for Future Research

Albdour,

Addad,

Alyammahi

et al. 2024

International Journal of Energy Research

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Efficient steam condensation is crucial for safe nuclear power plant (NPP) operations, preventing pressure buildup, overheating, and the release of radioactive materials. However, the presence of noncondensable gases (NCGs), such as air, nitrogen, hydrogen, or helium, can hinder the condensation process by creating a thermal resistance layer that impedes steam diffusion and condensation on the system’s surface. Maximizing the efficiency of steam condensation requires a thorough grasp of the fundamental processes, theories, advancements, and technical hurdles. Therefore, this work thoroughly addresses these needs, with a particular emphasis on addressing the challenges posed by NCGs by dividing the work into four thematic areas. The first theme relates to a comprehensive examination of pure steam condensation phenomena, which includes an exploration of familiar condensation scenarios and various film condensation types. The second theme examines condensation in the presence of NCGs, their mixture properties, and related theories and modelling of heat and mass transfer. The third theme investigates condensation in NPP by exploring passive cooling systems and condensation phenomena under both natural and forced convection conditions during nuclear accidents, the origin of NCGs in NPP and their transportation aspects. This is followed by experimental work related to condensation scenarios and scale. Finally, the last theme looks upon the recent advancements in computational fluid dynamics (CFD) modelling of wall condensation, system analysis codes coupling with CFD, and the implementation of machine learning (ML) for predicting the condensation HTC. By bridging the gap between fundamental knowledge and practical applications, the four thematic areas presented in this work are aimed at providing a comprehensive foundation for researchers and experts in the field of steam condensation when NCGs are involved. The ultimate objective is to bolster the safety and efficacy of NPP operations by understanding the heat and mass transfer mechanisms while mitigating the risk of catastrophic events.

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Section: Authors Empirical Correlationsmentioning

confidence: 99%

Steam Condensation Heat Transfer in the Presence of Noncondensable Gases (NCGs) in Nuclear Power Plants (NPPs): A Comprehensive Review of Fundamentals, Current Status, and Prospects for Future Research

Albdour,

Addad,

Alyammahi

et al. 2024

International Journal of Energy Research

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“…The set of equations, which govern the buoyancy-driven mixing process within the containment, is solved using the containmentFOAM solver [12], a tailored solver and model library based on OpenFOAM ® . The pressure-velocity coupling was addressed by using the PIMPLE algorithm.…”

Section: Framework and Discretizationmentioning

confidence: 99%

Uncertainty Quantification of a Industrial Scale CFD Application Using Stochastic Spectral Methods

Wenig,

Kelm,

Klein

2023

5th International Conference on Uncertainty Quantification in Computational Sciences and Engineering

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Accurate prediction of flow phenomena is crucial for a wide range of industrial and scientific applications. However, due to the complexity of many fluid systems, even state-of-theart Computational Fluid Dynamics (CFD) simulations involve various sources of uncertainty, which need to be quantified. Therefore, this work focuses on the Uncertainty Quantification (UQ) of a CFD simulation of a technical scale experiment, which investigates buoyancy-driven mixing processes between two miscible fluids. The CFD model is subject to uncertainties in initial and boundary conditions, as well as in thermo-physical properties. Since computational runs of the CFD model are computationally expensive, the uncertainty analysis requires the use of efficient methods. For this reason, stochastic spectral methods, such as Polynomial Chaos Expansion (PCE) and Karhunen-Loève Expansion (KLE), are applied along with novel approaches like Stochastic Model Composition (SMC) for the uncertainty quantification of responses. Fluctuations due to turbulence are identified as considerable source of uncertainty and are modeled with tailored stochastic models. Results include a comprehensive probabilistic representation of response quantities with probability density functions (PDFs), statistical moments, variance-based decomposition and supplementary error estimates of the underlying stochastic models. This enables well-founded result interpretation and informed decision making. The UQ results show that the uncertain input parameters considerably affect the duration of the mixing process. Through the investigations, the applied UQ methods were tested on an engineering application and demonstrate great potential as a viable technique for uncertainty quantification in technical-scale computations.

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“…Mixture properties ϕ m are computed from the individual specie properties ϕ i and species mass fractions Y i : ϕ m = ∑ i∈N Y i ϕ i . A comprehensive model description can be found in [24].…”

Section: Governing Equationsmentioning

confidence: 99%

“…Term-based indexing in Equation ( 23) instead of orderbased indexing simplifies the expression. Finally, a limited number of random variables n and order truncation leads to Equation (24) with P summation terms. According to [39], the orthogonal polynomials are generated numerically by using Gauss-Wigert [40], discretized Stieltjes [41], Chebyshev [41], or Gramm-Schmidt [42] approaches.…”

Section: Uncertainty Quantificationmentioning

confidence: 99%

Towards Uncertainty Quantification of LES and URANS for the Buoyancy-Driven Mixing Process between Two Miscible Fluids—Differentially Heated Cavity of Aspect Ratio 4

Wenig

Kelm

et al. 2021

Fluids

Self Cite

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Numerical simulations are subject to uncertainties due to the imprecise knowledge of physical properties, model parameters, as well as initial and boundary conditions. The assessment of these uncertainties is required for some applications. In the field of Computational Fluid Dynamics (CFD), the reliable prediction of hydrogen distribution and pressure build-up in nuclear reactor containment after a severe reactor accident is a representative application where the assessment of these uncertainties is of essential importance. The inital and boundary conditions that significantly influence the present buoyancy-driven flow are subject to uncertainties. Therefore, the aim is to investigate the propagation of uncertainties in input parameters to the results variables. As a basis for the examination of a representative reactor test containment, the investigations are initially carried out using the Differentially Heated Cavity (DHC) of aspect ratio 4 with Ra=2×109 as a test case from the literature. This allows for gradual method development for guidelines to quantify the uncertainty of natural convection flows in large-scale industrial applications. A dual approach is applied, in which Large Eddy Simulation (LES) is used as reference for the Unsteady Reynolds-Averaged Navier–Stokes (URANS) computations. A methodology for the uncertainty quantification in engineering applications with a preceding mesh convergence study and sensitivity analysis is presented. By taking the LES as a reference, the results indicate that URANS is able to predict the underlying mixing process at Ra=2×109 and the variability of the result variables due to parameter uncertainties.

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The Tailored CFD Package ‘containmentFOAM’ for Analysis of Containment Atmosphere Mixing, H2/CO Mitigation and Aerosol Transport

Cited by 30 publications

References 34 publications

Steam Condensation Heat Transfer in the Presence of Noncondensable Gases (NCGs) in Nuclear Power Plants (NPPs): A Comprehensive Review of Fundamentals, Current Status, and Prospects for Future Research

Steam Condensation Heat Transfer in the Presence of Noncondensable Gases (NCGs) in Nuclear Power Plants (NPPs): A Comprehensive Review of Fundamentals, Current Status, and Prospects for Future Research

Uncertainty Quantification of a Industrial Scale CFD Application Using Stochastic Spectral Methods

Towards Uncertainty Quantification of LES and URANS for the Buoyancy-Driven Mixing Process between Two Miscible Fluids—Differentially Heated Cavity of Aspect Ratio 4

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