CFD Uncertainty Quantification using stochastic spectral methods—Exemplary application to a buoyancy-driven mixing process

Wenig, Philipp J.; Kelm, Stephan; Klein, Markus

doi:10.1016/j.nucengdes.2023.112317

Cited by 6 publications

(1 citation statement)

References 33 publications

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“…However it is beyond the scope of the present paper to perform all the necessary calculations for an uncertainty quantification and to perform the relevant post-treatment of them in this view. It is an area of ongoing research [51]- [54] which is not mature enough to be used in the context of the present study. As a logical consequence, no uncertainty quantification are added to the numerical results in this paper.…”

Section: Numerical Versus Experimental Resultsmentioning

confidence: 99%

Roughness Effect on Thermal-Hydraulic Performances of Additively Manufactured Meandering Mini-Channels

Richermoz,

Gloriod,

Baffie

et al. 2023

Heat Transfer Engineering

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Additive manufacturing has made possible new geometry's design. The promise of these technologies is of prime importance in the heat exchanger's performances. Nevertheless, this fabrication process creates new more or less rough topologies, thus, different thermal-hydraulic behaviors, which are not fully known, controlled and predictable in the case of mini-channels (2 mm diameter in this study). The aim of this work is to test and compare the experimental thermal-hydraulic performances of different meandering mini-channels. They are obtained by a Laser-Powder Bed Fusion process with a circular or square cross-section. The geometry and the roughness of the channels are also characterized. Tests were conducted over a Reynolds number range of 200-6000. These experimental data are then used to build and validate a numerical model of the mock-up. The experimental and numerical results are consistent without requiring the use of tuning parameters. be compensated by the increase of the Nusselt number but to some limited extent. Indeed, several authors [4-8], [13], [18], show that the Nusselt number is not a linear function of the friction factor.The research about roughness modeling is not new but in the case of AM, the roughness features are very different from all other kind of roughness studied in past decades. Some authors [6], [16] mentioned these specific AM different roughness features, such as balling, hatch spacing, keyhole, dross… In the case of mini-channels, the flow is even more sensitive to these features and to high relative roughness. To study the AM roughness, there are mainly two numerical approaches.The first approach is roughness simulation, where "simulation" is to be heard in the sense that it consists in reproducing, i.e. meshing, the precise topology of the surfaces. It is not exclusively linked to AM but can be applied to it. Some authors create surfaces with: cones [19], cylinders [20], pavements [21], [22], half spheres [23] or ribs [24]. Some others created more complex structures as a mix of these previous elements [25] or a porous layer [26]. According to the present state of the art [27], the surface roughness consists on a fractal arrangement and mathematical functions are considered as the most accurate way to reproduce rough surfaces. The authors [28] use the Weierstraß-Mandelbrot function; the authors [29] combine a bi-cubic Coons patch; the authors [28], [30] use a parallel Lattice Boltzmann method; the authors [31] use the Johnson translator system [32][33][34]. This approach reproduces well the thermal-hydraulic performances of surfaces but needs an accurate surface characterization and a high simulation power. In fact, such surface meshing requires about 30 cells for each peak or between peaks. In other words, the flow around roughness peaks and through roughness valleys has to be captured by the grid. The technologies to characterize surfaces topologies are available at a laboratory scale but it requires open surfaces or else destructive measurements. 4The second approach is roug...

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Section: Numerical Versus Experimental Resultsmentioning

confidence: 99%

Roughness Effect on Thermal-Hydraulic Performances of Additively Manufactured Meandering Mini-Channels

Richermoz,

Gloriod,

Baffie

et al. 2023

Heat Transfer Engineering

View full text Add to dashboard Cite

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Uncertainty Quantification of LES for Buoyancy-Driven Mixing Processes Using PCE-HDMR

Wenig,

Kelm,

Klein

2023

ERCOFTAC Series

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

Wenig,

Kelm,

Klein

2023

Flow Turbulence Combust

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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 of stochastic results using a reasonable number of calculations. Therefore, the PCE–HDMR approach (Polynomial Chaos Expansion–High-Dimensional Model Representation) is utilized to qualify appropriate UQ methods for large-scale computations in the field of Computational Fluid Dynamics. This technique is a combination of Cut-HDMR, a hierarchical decomposition modeling approach, with 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 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 using PCE–HDMR and the Sparse Grid Method, 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 reference results. 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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CFD Uncertainty Quantification using stochastic spectral methods—Exemplary application to a buoyancy-driven mixing process

Cited by 6 publications

References 33 publications

Roughness Effect on Thermal-Hydraulic Performances of Additively Manufactured Meandering Mini-Channels

Roughness Effect on Thermal-Hydraulic Performances of Additively Manufactured Meandering Mini-Channels

Uncertainty Quantification of LES for Buoyancy-Driven Mixing Processes Using PCE-HDMR

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

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