The SCALEX facility – an apparatus for scaled fluid dynamical experiments

Ghazijahani, Mohammad Sharifi; Kästner, Christian; Valori, Valentina; Thieme, Alexander; Täschner, Kerstin; Schumacher, Jörg; Cierpka, Christian

doi:10.1515/teme-2022-0121

Cited by 7 publications

(10 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, experiments were conducted to study the spatio-temporal dynamics of the turbulent flow in Rayleigh-Bénard Convection (RBC). For this, a Rayleigh-Bénard cell (RBCell) with an aspect ratio of Γ = 10 was placed inside a pressure vessel (SCALEX 30 ) to conduct experiments in pressurized SF 6 . Three experiments in three different pressures of 1, 1.5, and 2.5 bar were conducted, resulting in Rayleigh numbers of Ra = 9.4 × 10 5 , 2.0 × 10 6 , and 5.5 × 10 6 .…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, experiments in pressurized fluids are necessary in order to reach high Rayleigh numbers. In this regard, the Scaled Convective Airflow Laboratory Experiment (SCALEX) at Technische Universität Ilmenau was designed to conduct experiments for pressurized gases 30 . The pressure vessel is cylindrical and has a diameter of 1100 mm and a length of 2200 mm.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, it is necessary to provide similar experimental measurements for the gases like air or, in the case of this study, SF 6 . In this regard, the SCALEX facility at Technische Universität Ilmenau was designed to investigate RBC in pressurized air and SF 6 at high Rayleigh numbers [30][31][32] . Recently, initial SPIV experiments in the SCALEX over an RBC with Γ = 10 were conducted by Valori et al 33 .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On the role of vortices in turbulent Rayleigh-Bénard convection

Ghazijahani,

Cierpka

2024

Preprint

Self Cite

View full text Add to dashboard Cite

Understanding turbulent thermal convection is essential for modeling many natural phenomena. This study investigates the spatio-temporal dynamics of the vortical structures in the mid-plane of turbulent Rayleigh-Bénard convection in SF6 via experiments. For this, a Rayleigh-Bénard cell of aspect ratio 10 is placed inside a pressure vessel and pressurized up to 1, 1.5, and 2.5 bar in order to reach Rayleigh numbers of Ra = 9.4×105,2.0×106 and 5.5×106. For all three cases, the Prandtl number is Pr = 0.79 and ΔT ≈ 7K. Then, stereoscopic particle image velocimetry is conducted to measure the three velocity components in the horizontal mid-plane for 5.78×103 free-fall times. For the given aspect ratio the flow is no longer dominated by the side walls of the cell and turbulent superstructures that show a 2D repetitive organization form. These superstructures show diverse shapes with faster dissipation rates as Ra increases. Out-of-plane vortices are the main feature of the flow. As Ra increases, the number of these vortices also increases, and their size shrinks. However, their total number is almost constant for each Ra through the measurement period. Furthermore, their occurrence is random and does not depend on whether the flow is upward-heated, downward-cooled, or horizontally-directed. Vortex tracking was applied to measure life time, displacement, and traveled distance of these structures. The relation between life time and traveled distance is rather linear. Interestingly, in the vortex centers, the out-of-plane momentum transport is larger in comparison to the bulk flow. Therefore, these vortices will play a major role in the heat transport in such flows.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On the role of vortices in turbulent Rayleigh-Bénard convection

Ghazijahani,

Cierpka

2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Finally, it could be very interesting to try to predict turbulent flow with a kind of forever-evolving dynamics that never repeats itself in the course of time. A very good example, perhaps, is the turbulent Rayleigh-Bénard convection, with its extremely complex and chaotic dynamics 35,36 .…”

Section: Discussionmentioning

confidence: 99%

Prediction of turbulent flows behind cylinder arrays via Echo State Networks

Ghazijahani,

Cierpka

2024

Preprint

Self Cite

View full text Add to dashboard Cite

This study aims at the prediction of the turbulent flow behind cylinder arrays by the application of Echo State Networks (ESN). Three different arrangements of arrays of seven cylinders are chosen for the current study. Three different wake flow regimes are represented by these arrays: single bluff body flow, transient flow, and co-shedding flow, which allows inherently similar, yet very different, turbulent flows to be studied. The data is reduced by Proper Orthogonal Decomposition (POD), and then the Time Coefficients of the POD Modes (TCPM) are predicted by the ESN. The ESN is optimized with respect to its three main hyperparameters, Input Scaling (INS), Spectral Radius (SR), and Leaking Rate (LR), in order to produce the best predictions in terms of Weighted Prediction Score (WPS). In general, the ESN is capable of imitating the complex dynamics of turbulent flows even for long periods. Furthermore, the mutual interdependencies of the TCPM are well preserved. However, the ESN’s performance in different hyperparameters depends on the flow characteristics. Generally, as flow dynamics become faster and more intermittent, larger LR and INS values make better predictions, whereas less clear trends in SR values tend to emerge.

show abstract

“…Furthermore, it is desirable to further extend the application of spatial prediction to more complex turbulent flows, such as flow behind arrays of multiple cylinders 74 or even turbulent Rayleigh B enard convection. 75 Another possible field of application is to complement the information in the case of a low number of measurement positions, for example, in weather or ocean flow data. Although, ground truth data are necessary to train the ESN networks, it might be applicable to extrapolate into a larger spatial domain.…”

Section: Discussionmentioning

confidence: 99%

Spatial prediction of the turbulent unsteady von Kármán vortex street using echo state networks

Sharifi Ghazijahani,

Heyder,

Schumacher

et al. 2023

Physics of Fluids

Self Cite

View full text Add to dashboard Cite

The spatial prediction of the turbulent flow of the unsteady von Kármán vortex street behind a cylinder at Re = 1000 is studied. For this, an echo state network (ESN) with 6000 neurons was trained on the raw, low-spatial resolution data from particle image velocimetry. During prediction, the ESN is provided one half of the spatial domain of the fluid flow. The task is to infer the missing other half. Four different decompositions termed forward, backward, forward–backward, and vertical were examined to show whether there exists a favorable region of the flow for which the ESN performs best. Also, it was checked whether the flow direction has an influence on the network's performance. In order to measure the quality of the predictions, we choose the vertical velocity prediction of direction (VVPD). Furthermore, the ESN's two main hyperparameters, leaking rate (LR) and spectral radius (SR), were optimized according to the VVPD values of the corresponding network output. Moreover, each hyperparameter combination was run for 24 random reservoir realizations. Our results show that VVPD values are highest for LR ≈ 0.6, and quite independent of SR values for all four prediction approaches. Furthermore, maximum VVPD values of ≈0.83 were achieved for backward, forward–backward, and vertical predictions while for the forward case VVPDmax=0.74 was achieved. We found that the predicted vertical velocity fields predominantly align with their respective ground truth. The best overall accordance was found for backward and forward–backward scenarios. In summary, we conclude that the stable quality of the reconstructed fields over a long period of time, along with the simplicity of the machine learning algorithm (ESN), which relied on coarse experimental data only, demonstrates the viability of spatial prediction as a suitable method for machine learning application in turbulence.

show abstract

The SCALEX facility – an apparatus for scaled fluid dynamical experiments

Cited by 7 publications

References 30 publications

On the role of vortices in turbulent Rayleigh-Bénard convection

On the role of vortices in turbulent Rayleigh-Bénard convection

Prediction of turbulent flows behind cylinder arrays via Echo State Networks

Spatial prediction of the turbulent unsteady von Kármán vortex street using echo state networks

Contact Info

Product

Resources

About