Intensity and regimes changing of hydrodynamic cavitation considering temperature effects

Ge, Mingming; Zhang, Guangjian; Petkovšek, Martin; Long, Kunpeng; Coutier-Delgosha, Olivier

doi:10.1016/j.jclepro.2022.130470

Cited by 54 publications

(26 citation statements)

References 37 publications

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“…The pneumatic tubes linking the pressure gauges (0–20 bars with 0.25% uncertainty, Rosemount 3051T) are connected to the sidewalls locating 50 mm upstream and 90 mm downstream until the throat is reached. The details of test sections can be referred from previous works [47] .…”

Section: Experimental Setup and Methodsmentioning

confidence: 99%

Combined suppression effects on hydrodynamic cavitation performance in Venturi-type reactor for process intensification

Sun

Zhang

et al. 2022

Ultrasonics Sonochemistry

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Section: Experimental Setup and Methodsmentioning

confidence: 99%

Combined suppression effects on hydrodynamic cavitation performance in Venturi-type reactor for process intensification

Sun

Zhang

et al. 2022

Ultrasonics Sonochemistry

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“…The PIV technology could capture the spatially and temporally resolved velocity measurements on the flow plane [22]. The subsurface vortices of Type 2 were generated by the flow distributor along the left wall of the channel.…”

Section: Dye Test and Piv Post-processmentioning

confidence: 99%

A Study Comparing the Subsurface Vortex Characteristics in Pump Sumps

Kim

Kim²,

Kim³

et al. 2022

Energies

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The vortex generated around the suction region of the pump sump causes problems such as damage to the pump, increased maintenance costs, and failure to supply coolant smoothly. Therefore, analyzing vortices is essential in pump sump design. However, the CFD analysis alone is insufficient in pump sumps vortex analysis since the reliability of the results is doubtful in scaled model tests. This study conducted the model test to validate a suitable CFD simulation method by identifying the Type 2 vortex among the three types of subsurface vortices. The dye test and PIV technology were used to visualize the Type 2 subsurface vortices, whereas the PIV vorticity results were then compared to the CFD results. The average vorticity of 60.2 (1/s) was identified as the reference level of Type 2 subsurface vortices formation by mapping the dye test results with the PIV vorticity results. Furthermore, the average vorticities of 84.63 (1/s) and 85.15 (1/s) were recorded in the presence of Type 2 subsurface vortices in PIV and CFD, respectively, and these values can be applied to the designing of pump sumps.

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“…It is always combined with the Galerkin projection method to construct the POD reduced-order model that can predict the physical field distribution [13]. The spectrum coefficients system with lower degrees of freedom is solved in the POD reduced-order model [14], rather than all discretized grids, as in direct numerical simulation, and thus the POD reduced-order model can significantly improve the simulation speed. Researchers have paid extensive attention to the POD reduced-order model, and it is widely applied to various engineering projects, including turbulent flow [15,16], and pipe flow [17].…”

Section: Introductionmentioning

confidence: 99%

Fast Prediction of the Temperature Field Surrounding a Hot Oil Pipe Using the POD-BP Model

Yan,

Jiao,

Nie

et al. 2023

Processes

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The heat transfer assessment of a buried hot oil pipe is essential for the economical and safe transportation of the pipeline, where the basis is to determine the temperature field surrounding the pipe quickly. This work proposes a novel method to efficiently predict the temperature field surrounding a hot oil pipe, which combines the proper orthogonal decomposition (POD) method and the backpropagation (BP) neural network, named the POD-BP model. Specifically, the BP neural network is used to establish the mapping relationship between spectrum coefficients and the preset parameters of the sample. Compared with the classical POD reduced-order model, the POD-BP model avoids solving the system of reduced-order governing equations with spectrum coefficients as variables, thus improving the prediction speed. Another advantage is that it is easy to implement and does not require tremendous mathematical derivation of reduced-order governing equations. The POD-BP model is then used to predict the temperature field surrounding the hot oil pipe, and the sample matrix is obtained from the numerical results using the finite volume method (FVM). In validation cases, both steady and unsteady states are investigated, and multiple boundary conditions, thermal properties, and even geometry parameters (different buried depths and pipe diameters) are tested. The mean errors of steady and unsteady cases are 0.845~3.052% and 0.133~1.439%, respectively. Appealingly, almost no time, around 0.008 s, is consumed in predicting unsteady situations using the proposed POD-BP model, while the FVM requires a computational time of 70 s.

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Intensity and regimes changing of hydrodynamic cavitation considering temperature effects

Cited by 54 publications

References 37 publications

Combined suppression effects on hydrodynamic cavitation performance in Venturi-type reactor for process intensification

Combined suppression effects on hydrodynamic cavitation performance in Venturi-type reactor for process intensification

A Study Comparing the Subsurface Vortex Characteristics in Pump Sumps

Fast Prediction of the Temperature Field Surrounding a Hot Oil Pipe Using the POD-BP Model

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