Review of correlations for subcooled flow boiling heat transfer and assessment of their applicability to water

Fang, Xiande; Yuan, Yuliang; Xu, Anyi; Tian, Le; Wu, Qi

doi:10.1016/j.fusengdes.2017.09.008

Cited by 41 publications

(3 citation statements)

References 34 publications

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“…The experimental data were compared with Butterworth's correlation for subcooled flow boiling heat transfer [25,29,30]. Butterworth et al extended Chen's correlation [31] and applied it to subcooled flow boiling [32].…”

Section: Comparison Of Butterworth's Correlation For the Bare Surfacementioning

confidence: 99%

Enhancement of Subcooled Flow Boiling Heat Transfer with High Porosity Sintered Fiber Metal

2021

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We conducted experimental research using high-porosity sintered fiber attached on the surface, as a passive method to increase the heat flux for subcooled flow boiling. Two different porous thicknesses (1 and 0.5 mm) and one bare surface (0 mm) were compared under three different inlet subcooling temperatures (30, 50 and 70 K) and low mass flux (150–600 kg·m−2·s−1) using deionized water as the working fluid under atmospheric pressure. The test section was a rectangular channel, and the hydraulic diameter was 10 mm. The results showed that the heat flux on porous surfaces with a thickness of 1 and 0.5 mm increased by 60% and 40%, respectively, compared to bare surfaces at ΔTsat = 40 K at a subcooled temperature of 50 K and mass flux of 300 kg·m−2·s−1. An abrupt increase in the wall superheat was avoided, and critical heat flux (CHF) was not reached on the porous surfaces. The flow pattern and bubble were recorded with a high-speed camera, and the bubble dynamics are discussed.

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Section: Comparison Of Butterworth's Correlation For the Bare Surfacementioning

confidence: 99%

Enhancement of Subcooled Flow Boiling Heat Transfer with High Porosity Sintered Fiber Metal

2021

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“…So far, many experimental works have focused on developing empirical correlations based on a high number of experimental data using conventional channels with various geometrical characteristics and a wide range of working fluids, materials and operating conditions. These empirical correlations are either subsequently utilised by other researchers to confirm the accuracy of their measurements or they are used in numerical models as sub-models to predict important flow boiling characteristics usually measured on the radial axis (e.g., radial profiles) in the axial direction (e.g., axial profiles) [4][5][6][7]. To numerically predict flow boiling characteristics, it is necessary that an appropriate CFD modelling approach is employed by selecting suitable boiling sub-models.…”

Section: Introductionmentioning

confidence: 99%

Validation of the Eulerian–Eulerian Two-Fluid Method and the RPI Wall Partitioning Model Predictions in OpenFOAM with Respect to the Flow Boiling Characteristics within Conventional Tubes and Micro-Channels

Vontas,

Pavarani,

Miché

et al. 2023

Energies

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Flow boiling within conventional, mini and micro-scale channels is encountered in a wide range of engineering applications such as nuclear reactors, steam engines and cooling of electronic devices. Due to the high complexity and importance of the boiling process, several numerical and experimental investigations have been conducted for the better understanding of the underpinned physics and heat transfer characteristics. One of the most widely used numerical approaches that can analyse such phenomena is the Eulerian–Eulerian two-fluid method in conjunction with the RPI model. However, according to the current state-of-the-art methods this modelling approach heavily relies on empirical closure relationships derived for conventional channels, limiting its applicability to mini- and micro-scale channels. The present paper aims to give further insights into the applicability of this modelling approach for non-conventional channels. For this purpose, a numerical investigation utilising the Eulerian–Eulerian two-fluid model and the RPI wall heat flux partitioning model in OpenFOAM 8.0 is conducted. Initially the parameters comprising the empirical closure relationships used in the RPI sub-models are tuned against the DEBORA experiments on conventional channels, through an extensive sensitivity analysis. In the second part of the investigation, numerical simulations against flow boiling experiments within micro-channels are performed, utilising the previously optimised and validated model setup. Furthermore the importance of including a bubble coalescence and break-up sub-model to capture parameters such as the radial velocity profiles, is also illustrated. However, when the optimal model setup, in conventional tubes, is used against micro-channel experiments, the need to develop new correlations from data obtained from mini and micro-scale channel studies, not from experimental data on conventional channels, is revealed.

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“…Above this temperature the flow is in the nucleate boiling regime, and must be modelled accordingly. A good review of the nucleate boiling correlations available in the literature can be found in [202].…”

Section: N U =0023rementioning

confidence: 99%

Simulation driven machine learning methods to optimise design of physical experiments and enhance data analysis for testing of fusion energy heat exchanger components

Lewis

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Plasma facing components (PFCs) must be designed to routinely withstand the harsh environment of a fusion device, where temperatures at the core of the plasma exceed 150,000,000 °C. The heat by induction to verify extremes (HIVE) experimental facility was established to replicate the thermal loads a PFC is subjected to during normal operation of a fusion device.To maximise its impact on the design of PFCs, HIVE must deliver smarter testing and improved component insight. Currently, the experimental parameters required to deliver a certain response to the component are decided at the point of testing through a combination of previous experience, intuition, and trial & error, which is both time-consuming and unreliable. To assess a PFC’s suitability, knowledge of its mechanical performance while operating at high temperatures is desirable, however HIVE only records pointwise temperature measurements on the component’s surface using thermocouples. Currently, HIVE has no method of inferring a component’s mechanical response using the temperature measure-ments.Both the challenges of smarter testing and improved component insight can be achieved through the identification of inverse solutions. A popular approach to solving engineering inverse problems is surrogate assisted optimisation, where a machine learning model is trained using finite element (FE) simulation data. Much of the work in literature use single value surrogate models on quite simplistic problems, however HIVE is a real-world, multi-physics problem which requires full field (FF) surrogate models to solve its multitude of inverse problems.The development of a method which can easily construct FE data driven FF surrogates would be invaluable for a variety of tasks in engineering, as well as solving inverse problems. In this work, it demonstrates that it can provide a much more robust and comprehensive method of characterising a PFC’s strengths and limitations, enabling more informed decisions to be made during its design cycle.

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Review of correlations for subcooled flow boiling heat transfer and assessment of their applicability to water

Cited by 41 publications

References 34 publications

Enhancement of Subcooled Flow Boiling Heat Transfer with High Porosity Sintered Fiber Metal

Enhancement of Subcooled Flow Boiling Heat Transfer with High Porosity Sintered Fiber Metal

Validation of the Eulerian–Eulerian Two-Fluid Method and the RPI Wall Partitioning Model Predictions in OpenFOAM with Respect to the Flow Boiling Characteristics within Conventional Tubes and Micro-Channels

Simulation driven machine learning methods to optimise design of physical experiments and enhance data analysis for testing of fusion energy heat exchanger components

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