The simulation of multidimensional multiphase flows

Film Boiling Conjugate Heat Transfer during Immersion Quenching

Boiling conjugate heat transfer is an active field of research encountered in several industries, including metallurgy, power generation and electronics. This paper presents a computational fluid dynamics approach capable of accurately modelling the heat transfer and flow phenomena during immersion quenching: a process in which a hot solid is immersed into a liquid, leading to sudden boiling at the solid–liquid interface. The adopted methodology allows us to couple solid and fluid regions with very different physics, using partitioned coupling. The energy equation describes the solid, while the Eulerian two-fluid modelling approach governs the fluid’s behaviour. We focus on a film boiling heat transfer regime, yet also consider natural convection, nucleate and transition boiling. A detailed overview of the methodology is given, including an analytical description of the conjugate heat transfer between all three phases. The latter leads to the derivation of a fluid temperature and Biot number, considering both fluid phases. These are then employed to assess the solver’s behaviour. In comparison with previous research, additional heat transfer regimes, extra interfacial forces and separate energy equations for each fluid phase, including phase change at their interface, are employed. Finally, the validation of the computational approach is conducted against published experimental and numerical results.

Multiphase flow detection with photonic crystals and deep learning

Multiphase flows are ubiquitous in industrial settings. It is often necessary to characterize these fluid mixtures in support of process optimization. Unfortunately, existing commercial technologies often fail to provide frequent, accurate, and cost-efficient data necessary to enable process optimization. Here we show a new physics-based concept and testing with lab and field prototypes leveraging photonic crystals for real-time characterization of multiphase flows. In particular, low power (~1 mW) microwave transmission through photonic crystals filled with fluid mixtures may be interrogated by deep learning analysis techniques to provide a fast and accurate characterization of phase fraction and flow morphology. Moreover when these flow characteristics are known, the flow rate is accurately inferred from the differential pressure necessary for the flow to pass through the photonic crystal. This insight provides a basis to develop a unique class of inexpensive, accurate, and convenient techniques to characterize multiphase flows.

Effects of flow velocity and bubble size distribution on oxygen mass transfer in bubble column reactors-A critical evaluation of the computational fluid dynamics-population balance model

Computational fluid dynamics (CFD) is used to simulate a bubble column reactor operating in the bubbly (homogenous) regime. The Euler-Euler two-fluid model, integrated with the population balance model (PBM), is adopted to compute the flow and bubble size distribution (BSD). The CFD-PBM model is validated against published experimental data for BSD, global gas holdup, and oxygen mass transfer coefficient. The sensitivity of the model with respect to the specification of boundary conditions and the bubble coalescence/breakup models is assessed. The coalescence model of Prince and Blanch (1990) provides the best results, whereas the output is shown to be insensitive to the breakup model. The CFD-PBM study demonstrates the importance of considering the BSD in order to correctly model mass transfer. Results show that the constant bubble size assumption results in a large error in the oxygen mass transfer coefficient, while giving acceptable results for gas holdup. PRACTITIONER POINTS: Constant bubble size (CBS) and population balance model (PBM) are compared for a bubble column reactor. Both PBM and CBS can predict gas holdup; however, PBM can correctly predict gas-liquid mass transfer whereas CBS cannot. Best practices for selecting coalescence, breakup, and drag models are determined.

Investigation on Bubble Diameter Distribution in Upward Flow by the Two-Fluid and Multi-Fluid Models

Bubble flow can be simulated by the two-fluid model and the multi-fluid model based on the Eulerian method. In this paper, the gas phase was further divided into several groups of dispersed phases according to the diameter by using the Eulerian-Eulerian (E-E) multi-fluid model. The diameters of bubbles in each group were considered to be the same, and their distributions were reorganized according to a specific probability density function. The experimental data of two kinds of bubble flow with different characteristics were used to verify the model. With the help of the open-source CFD software, OpenFOAM-7.x (OpenFOAM-7.0, produced by OpenFOAM foundation, Reading, England), the influences of the group number, the probability distribution function, and the parameters of different bubble diameters on the calculation results were studied. Meanwhile, the numerical simulation results were compared with the two-fluid model and the experimental data. The results show that for the bubble flow with the unimodal distribution, both the multi-fluid model and the two-fluid model can obtain the distribution of gas volume fraction along the pipe radius. The calculation results of the multi-fluid model agree with the experimental data, while those of the two-fluid model differ greatly from the experimental data, which verifies the advantage of the multi-fluid model in calculating the distribution of gas volume fraction in the polydisperse bubble flow. Meanwhile, the multi-fluid model can be used to accurately predict the distribution of the parameters of each phase of the bubble flow if the reasonable bubble diameter distribution is provided and the appropriate interphase force calculation model is determined.