Error growth and phase lag analysis for high Courant numbers

Sharma, Deewakar; Amiroudine, Sakir; Erriguible, Arnaud; Sengupta, Tapan K.

doi:10.1016/j.amc.2018.10.035

Cited by 2 publications

(1 citation statement)

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“…Therefore, an acceptable grid-independent solution was selected with about 2.7 million cells, which were used in the present computations. Reference [47] studied the effect of a Courant number of 0.11, 1, and 10 on the numerical stability and accuracy. It was indicated that stable and accurate numerical results were obtained at higher Courant numbers.…”

Section: The Numerical Modelmentioning

confidence: 99%

Wind Flow Characterisation over a PV Module through URANS Simulations and Wind Tunnel Optical Flow Methods

et al. 2021

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Despite their simplicity, photovoltaic (PV) modules are often arranged in structures that can be affected by severe and complex wind loads: in this context, the wind flow and the dynamic excitation induced by vortex shedding can introduce unexpected aeroelastic responses. This work introduces a novel wind tunnel application of experimental techniques to address this issue by the use of flow visualisation and video postprocessing, through the optical flow algorithm. Numerical simulations based on unsteady Reynolds-averaged Navier–Stokes (RANS) models are performed and compared against the experimental wind tunnel tests on a PV panel that was also instrumented with pressure taps. A setup with a 65∘ tilt angle was examined because, based on preliminary analyses, it was considered interesting for the free flow–wake transition associated with the dynamic response of the PV panel. The comparison of the experimental and numerical average wind fields supported that the proposed optical flow method was appropriate for characterising the wake of the panel, because there was enough seeding to perform the video postprocessing. Experiments and numerical predictions were compared as regards the average pressure distribution on the panel surfaces, and the average percentage was in the error of 7%; this supports that the URANS method was capable of reproducing the average behaviour of the panel, as well as for the selected configuration, which is particularly challenging. Furthermore, the simulated and measured power spectral densities of the wind speed were compared, and this resulted in the numerical model quite faithfully reproducing the frequency of the peak at 5 m/s, while the error was in the order of 20% for the 10 m/s case; this supports that, despite the URANS approach being affected by well-known critical points regarding the simulation of instantaneous quantities, it can be employed to elaborate information that can be particularly useful for the structural design of the panel. This kind of result can be considered as a first step, obtained with simplified and affordable methods, towards a characterisation of the dynamic behaviour of a PV panel in a real-world setup.

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Section: The Numerical Modelmentioning

confidence: 99%