Flow Boiling Instabilities and Single-Phase Pressure Drop in Rectangular Microchannels with Different Inlet Restrictions

Schepperle, Mark; Mandal, Aum; Woias, Peter

doi:10.11159/icmfht23.135

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…The same experimental apparatus has been used in a set of studies reported in Refs. [20][21][22][23]. The heat sink housing is specifically designed for mechanical fixation of the microchannel, RTDs, and cartridge heaters, for hermetic sealing of the microchannel, and for thermal insulation from the environment.…”

Section: Experimental Methodologymentioning

confidence: 99%

Understanding Inconsistencies in Thermohydraulic Characteristics Between Experimental and Numerical Data for DI Water Flow Through a Rectangular Microchannel

Schepperle,

Samkhaniani,

Magnini

et al. 2024

ASME Journal of Heat and Mass Transfer

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Facing discrepancies between numerical simulation, experimental measurement and theory is common in studies of fluid flow and heat transfer in microchannels. The cause of these discrepancies is often linked to the transition from the macro-scale to the micro-scale, where the flow dynamics might be expected to deviate due to possible change in dominant forces. In this work, an attempt is made to achieve agreement between experiment, numerical simulation and theoretical description within the usual framework of laminar flow theory. For this purpose, the pressure drop, friction factor, and Poiseuille number under isothermal conditions and the temperature profile, heat transfer coefficient, Nusselt number, and thermal performance index under diabatic conditions (heating power of 10 W) in a heat sink with a stainless steel microchannel with a hydraulic diameter of 850 µm were investigated numerically and experimentally for mass flow rates between 1 and 68 g/min. The source of inconsistencies in pressure drop characteristics is found to be linked to the geometrical details of the utilized microchannel, e.g. the design of inlet/outlet manifolds, the artefacts of manufacturing technique and other features of the experimental test rig. For the heat transfer characteristics, it is identified, that an appropriate estimation of the outer boundary condition for the numerical simulation remains the crucial challenge to obtain a reasonable agreement. The manuscript presents a detailed overview on how to consider these details to mitigate the discrepancies and to establish a handshake between experiments, numerical simulations and theory.

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Section: Experimental Methodologymentioning

confidence: 99%

Understanding Inconsistencies in Thermohydraulic Characteristics Between Experimental and Numerical Data for DI Water Flow Through a Rectangular Microchannel

Schepperle,

Samkhaniani,

Magnini

et al. 2024

ASME Journal of Heat and Mass Transfer

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Identification of Microchannel Flow Boiling Regimes With Pt Thin-Film Impedance Sensors

Schepperle,

Woias

2024

IEEE Sensors J.

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Computer-Vision- and Deep-Learning-Based Determination of Flow Regimes, Void Fraction, and Resistance Sensor Data in Microchannel Flow Boiling

Schepperle,

Junaid,

Woias

2024

Sensors

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The aim of this article is to introduce a novel approach to identifying flow regimes and void fractions in microchannel flow boiling, which is based on binary image segmentation using digital image processing and deep learning. The proposed image processing pipeline uses adaptive thresholding, blurring, gamma correction, contour detection, and histogram comparison to separate vapor from liquid areas, while the deep learning method uses a customized version of a convolutional neural network (CNN) called U-net to extract meaningful features from video frames. Both approaches enabled the automatic detection of flow boiling conditions, such as bubbly, slug, and annular flow, as well as automatic void fraction calculation. Especially CNN demonstrated its ability to deliver fast and dependable results, presenting an appealing substitute to manual feature extraction. The U-net-based CNN was able to segment flow boiling images with a Dice score of 99.1% and classify the above flow regimes with an overall classification accuracy of 91%. In addition, the neural network was able to predict resistance sensor readings from image data and assign them to a flow state with a mean squared error (MSE) < 10−6.

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Flow Boiling Instabilities and Single-Phase Pressure Drop in Rectangular Microchannels with Different Inlet Restrictions

Cited by 3 publications

References 11 publications

Understanding Inconsistencies in Thermohydraulic Characteristics Between Experimental and Numerical Data for DI Water Flow Through a Rectangular Microchannel

Understanding Inconsistencies in Thermohydraulic Characteristics Between Experimental and Numerical Data for DI Water Flow Through a Rectangular Microchannel

Identification of Microchannel Flow Boiling Regimes With Pt Thin-Film Impedance Sensors

Computer-Vision- and Deep-Learning-Based Determination of Flow Regimes, Void Fraction, and Resistance Sensor Data in Microchannel Flow Boiling

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