Simulation benchmark based on THAI-experiment on dissolution of a steam stratification by natural convection

Freitag, M.; Schmidt, E.; Gupta, Sanjeev; Poß, G.

doi:10.1016/j.nucengdes.2015.07.001

Cited by 17 publications

(6 citation statements)

References 1 publication

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“…Experimental data obtained from the THAI test series is used to validate and improve computational models used for the prediction of thermal-hydraulic flows and phenomena. Therefore, CFD methods are applied to model the THAI TH32 experiment [6], which investigates buoyancy-driven mixing processes between two miscible fluids within a vessel of volume V ≈ 60 m 3 , while an underlying natural circulation flow is created by heating the lower and cooling the upper wall sections. The experiment comprises multiple consecutive phases, which are shown over time in Figure 2 and mimics the release and dispersion of hydrogen in a nuclear reactor containment.…”

Section: Application Setup and Uncertaintiesmentioning

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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Section: Application Setup and Uncertaintiesmentioning

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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“…For the sake of simplicity, the LES filtering operations were omitted. u is the velocity vector field, ρ is the density field, p is the static pressure field, g = (0, g, 0) is the gravitational acceleration vector, h is the enthalpy, K = 1 2 |u| 2 is the kinetic energy of the system, Y i is the mass fraction of the ith species from the set of gas species indices given by N = {1, 2}, and the rate of strain tensor is defined as τ (u) = 1 2 ∇u + (∇u) T .…”

Section: Governing Equationsmentioning

confidence: 99%

“…For the application of UQ to complex engineering applications with high-dimensional and computationally expensive character, it is important to employ reliable and efficient methods for uncertainty analysis. Therefore, as a basis for the application to a technical scale experiment [1], method development is conducted using a generic test case from literature. This test case reflects similar physics to those anticipated for the application case.…”

Section: Introductionmentioning

confidence: 99%

CFD Uncertainty Quantification using PCE-HDMR – Exemplary Application to a Buoyancy-Driven Mixing Process

Wenig

Kelm

Klein

2023

Preprint

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For the investigation of uncertainties in high dimensional spaces of computationally expensive engineering applications, reliable Uncertainty Quantification (UQ) methods are needed. These methods should provide accurate and efficient High-Dimensional Model Representations (HDMR) of stochastic results using a reasonable number of calculations. Therefore, the PCE-HDMR approach is utilized to qualify appropriate UQ methods for large-scale computations in the field of Computational Fluid Dynamics (CFD). This technique is a combination of Cut-HDMR, a hierarchical decomposition modeling approach, with Polynomial Chaos Expansion (PCE). To demonstrate its effectiveness, the PCE-HDMR methodology in conjunction with complementary modeling techniques is applied for the UQ analysis of a buoyancy-driven mixing process between two miscible fluids within the Differentially Heated Cavity (DHC) of aspect ratio 4. The results include a thorough probabilistic representation of time-dependent response quantities that comprehensively describe the mixing process. The stochastic models are derived from Large Eddy Simulations (LES) using PCE HDMR and the Sparse Grid Method (SGM), which serves as a reference for the results from PCE-HDMR. The results show that PCE-HDMR provides accurate statistics of the modeled time-dependent stochastic processes and shows good agreement with the SGM reference. Thus, PCE-HDMR indicates great potential for UQ of technical-scale computations due to its efficiency and flexibility in the construction of stochastic models.

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“…In the THAI facility (Becker Technologies, Germany), experiment tests TH21, TH22, and TH24 were performed to simulate the natural convection through differential heating and cooling of the vessel walls [6,7]. Later, the experimental data on TH21, TH22, and TH24 were used for numerical validation by means of computational fluid dynamics (CFD) and lumped parameter (LP) codes [8,9]. In the MISTRA facility (CEA, France), the natural convection experiment (NATHCO) in the framework OECD SETH-2 project was performed [10].…”

Section: Introductionmentioning

confidence: 99%

“…In the present study, the results obtained by the high Reynolds number and a low Reynolds number turbulence models were compared against the results of the experimental data. The test data on the previous publication (TH24 test) revealed that the wall temperature of the inner cylinder is governed by the thermal inertia of the structure itself [8]. In addition, flat bars that were used to fix the measurement instrumentations, such as the thermocouple and capillary tube, which were simulated in order to know their effect on the overall heat transfer.…”

Section: Introductionmentioning

confidence: 99%

Unsteady Natural Convection in a Cylindrical Containment Vessel (CIGMA) With External Wall Cooling: Numerical CFD Simulation

et al. 2020

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In the case of a severe accident, natural convection plays an important role in the atmosphere mixing of nuclear reactor containments. In this case, the natural convection might not in the steady-state condition. Hence, instead of steady-state simulation, the transient simulation should be performed to understand natural convection in the accident scenario within a nuclear reactor containment. The present study, therefore, was aimed at the transient 3-D numerical simulations of natural convection of air around a cylindrical containment with unsteady thermal boundary conditions (BCs) at the vessel wall. For this purpose, the experiment series was done in the CIGMA facility at Japan Atomic Energy Agency (JAEA). The upper vessel or both the upper vessel and the middle jacket was cooled by subcooled water, while the lower vessel was thermally insulated. A 3-D model was simulated with OpenFOAM®, applying the unsteady Reynolds-averaged Navier–Stokes equations (URANS) model. Different turbulence models were studied, such as the standard k-ε, standard k-ω, k-ω shear stress transport (SST), and low-Reynolds-k-ε Launder–Sharma. The results of the four turbulence models were compared versus the results of experimental data. The k-ω SST showed a better prediction compared to other turbulence models. Additionally, the accuracy of the predicted temperature and pressure were improved when the heat conduction on the internal structure, i.e., flat bar, was considered in the simulation. Otherwise, the predictions on both temperature and pressure were underestimated compared with the experimental results. Hence, the conjugate heat transfer in the internal structure inside the containment vessel must be modeled accurately.

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Simulation benchmark based on THAI-experiment on dissolution of a steam stratification by natural convection

Cited by 17 publications

References 1 publication

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

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

CFD Uncertainty Quantification using PCE-HDMR – Exemplary Application to a Buoyancy-Driven Mixing Process

Unsteady Natural Convection in a Cylindrical Containment Vessel (CIGMA) With External Wall Cooling: Numerical CFD Simulation

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