Best practice guidelines in numerical simulations and CFD benchmarking for hydrogen safety applications

Tolias, Ilias C.; Giannissi, S.G.; Venetsanos, A.G.; Keenan, James J.; Shentsov, Volodymyr; Makarov, Dmitriy; Coldrick, Simon; Kotchourko, A.; Ren, Kaijun; Jedicke, Olaf; Melideo, Daniele; Baraldi, Daniele; Slater, S.; Duclos, Audrey; Verbecke, F.; Molkov, Vladimir

doi:10.1016/j.ijhydene.2018.06.005

Cited by 59 publications

(22 citation statements)

References 48 publications

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“…The conservation equations of mass and momentum for the air-helium mixture and the conservation equation for helium mass fraction are solved. The key parameters of the modelling strategy follow the BPG [13] and are similar in all simulations:…”

Section: Simulation Setupmentioning

confidence: 99%

“…Based on the BPG that have been presented in [13] sensitivities studies are essential, in order to obtain accurate and numerical-effect-free results (as much as possible). As mentioned in Section Simulation setup, the domain is extended outside the garage in order to avoid applying boundary conditions directly to the vent.…”

Section: Domain Independence Studymentioning

confidence: 99%

“…As mentioned in Section Simulation setup, the domain is extended outside the garage in order to avoid applying boundary conditions directly to the vent. This practice is considered necessary (see also [13], [37]), because applying the boundary exactly at the vent opening can affect the results significantly as it is demonstrated in Figure 3. Without domain extension, the predicted concentrations are significantly under-predicted.…”

Section: Domain Independence Studymentioning

confidence: 99%

“…Moreover, different grid types (hexahedral, tetrahedral and hybrid) were used without investigating their effect on the results [11]. For all the above, the first aim of the current study is to demonstrate the use of BPG that have been developed [13] for hydrogen safety CFD simulations. These BPG focus on the practical needs of engineers and aim to deal with the fact that several different users can produce different results for the same problem, using the same models or even CFD code [14], as it is exhibited in some of the works referenced above.…”

Section: Introductionmentioning

confidence: 99%

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On the CFD modelling of hydrogen dispersion at low-Reynolds number release in closed facility

Giannissi

Tolias

Melideo

et al. 2021

International Journal of Hydrogen Energy

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Hydrogen release inside closed facilities could cause explosions with harmful consequences. Safety assessment should be performed in order to design prevention and mitigation measures in case of such an accident. A numerical study for helium (as hydrogen surrogate) accumulation inside a closed facility representative of a real-scale garage at low release rate is conducted. Due to the nature of the examined flow several turbulence modelling approaches (RANS and LES type) and the laminar approach were examined with the aim to evaluate their predictive capabilities in flows resulting from low-Reynolds number leaks. Best practice guidelines are followed in the simulations and the comparison of computational results with experimental data showed that RANS and LES approaches reproduced well the gas distribution inside the facility, while laminar approach predicted more enhanced stratification at the release phase.Statistical Performance Measures were used to evaluate the models and narrower acceptable ranges are suggested for releases in indoor configurations compared to open environments.

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Section: Simulation Setupmentioning

confidence: 99%

Section: Domain Independence Studymentioning

confidence: 99%

Section: Domain Independence Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On the CFD modelling of hydrogen dispersion at low-Reynolds number release in closed facility

Giannissi

Tolias

Melideo

et al. 2021

International Journal of Hydrogen Energy

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“…8(a)) due to flame-acoustic interactions (Bauwens et al, 2010) cannot be reproduced by FLACS (Hisken, 2018). As a matter of fact, so far there is no reliable CFD tool being able to accurately calculate the acoustically driven pressures (Tolias et al, 2018;Vyazmina et al, 2018).…”

Section: Different Vent Opening Designs Of 95 Vol % Gas Concentration Casementioning

confidence: 99%

Numerical and analytical prediction of pressure and impulse from vented gas explosion in large cylindrical tanks

Hao

2019

Process Safety and Environmental Protection

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This paper presents an extension of previous study on estimating the internal and external pressure and impulse from vented gas explosion in large cylindrical tanks. Unlike the smallmedium scale explosion from cylindrical tanks in previous work, explosion pressure and impulse from large-scale explosions are numerically and analytical investigated in this study.CFD simulations are conducted and validated by using experimental data. Sensitivity study on grid selection for large-scale explosion simulations is performed. By adopting two scale-up factors for the analytical correlations, the internal pressure and external pressure on adjacent tanks from vented gas explosion in a tank are accurately predicted. Parametric study of the effectiveness of separation gap between adjacent tanks on pressure and impulse mitigation is also carried out. Influences of the venting size, tank diameter and tank height on the effectiveness of separation distance for pressure and impulse mitigation from large-scale vented gas explosion in an adjacent tank are studied.

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Finite volume method network for the acceleration of unsteady computational fluid dynamics: Non‐reacting and reacting flows

Jeon

Lee

Kim

2022

Intl J of Energy Research

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Summary Despite rapid improvements in the performance of the central processing unit (CPU), the calculation cost of simulating chemically reacting flow using CFD remains infeasible in many cases. The application of the convolutional neural networks (CNNs) specialized in image processing in flow field prediction has been studied, but the need to develop a neural network design fitted for CFD has recently emerged. In this study, a neural network model introducing the finite volume method (FVM) with unique network architecture and physics‐informed loss function was developed to accelerate CFD simulations. The developed network model, considering the nature of the CFD flow field where the identical governing equations are applied to all grids, can predict the future fields with only two previous fields unlike the CNNs requiring many field images (>10 000). The performance of this baseline model was evaluated using CFD time series data from non‐reacting flow and reacting flow simulation; counterflow and hydrogen flame with 20 detailed chemistries. Consequently, we demonstrated that (a) the FVM‐based network architecture provided significantly improved accuracy of multistep time series prediction compared to the previous MLP model (b) the physic‐informed loss function prevented non‐physical overfitting problem and ultimately reduced the error in time series prediction (c) observing the calculated residuals in an unsupervised manner could monitor the network accuracy. Additionally, under the reacting flow dataset, the computational speed of this network model was measured to be about 10 times faster than that of the CFD solver.

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Best practice guidelines in numerical simulations and CFD benchmarking for hydrogen safety applications

Cited by 59 publications

References 48 publications

On the CFD modelling of hydrogen dispersion at low-Reynolds number release in closed facility

On the CFD modelling of hydrogen dispersion at low-Reynolds number release in closed facility

Numerical and analytical prediction of pressure and impulse from vented gas explosion in large cylindrical tanks

Finite volume method network for the acceleration of unsteady computational fluid dynamics: Non‐reacting and reacting flows

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