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Soibam, J., Scheiff, V., Aslanidou, I., Kyprianidis, K. & Bel Fdhila, R. (2023). Application of deep learning for segmentation of bubble dynamics in subcooled boiling. International Journal of Multiphase Flow, 169, Article ID 104589.
Open this publication in new window or tab >>Application of deep learning for segmentation of bubble dynamics in subcooled boiling
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2023 (English)In: International Journal of Multiphase Flow, ISSN 0301-9322, E-ISSN 1879-3533, Vol. 169, article id 104589Article in journal (Refereed) Published
Abstract [en]

The present work focuses on designing a robust deep-learning model to track bubble dynamics in a vertical rectangular mini-channel. The rectangular mini-channel is heated from one side with a constant heat flux, resulting in the creation of bubbles. Images of the bubbles are recorded using a high-speed camera, which serve as the input data for the deep learning model. The raw image data acquired from the high-speed camera is inherently noisy due to the presence of shadows, reflections, background noise, and chaotic bubbles. The objective is to extract the mask of the bubble given all these challenging factors. Transfer learning is adopted to eliminate the need for a large dataset to train the deep learning model and also to reduce computational costs. The trained model is then validated against the validation datasets, demonstrating an accuracy of 98% while detecting the bubbles. The model is then evaluated on different experimental conditions, such as lighting, background, and blurry images with noise. The model demonstrates high robustness to different conditions and is able to detect the edges of the bubbles and classify them accurately. Moreover, the model achieves an average intersection over union of 85%, indicating a high level of accuracy in predicting the masks of the bubbles. The method enables accurate recognition and tracking of individual bubble dynamics, capturing their coalescence, oscillation, and collisions to estimate local parameters by proving the bubble masks. This allows for a comprehensive understanding of their spatial-temporal behaviour, including the estimation of local Reynolds numbers.

National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-64495 (URN)10.1016/j.ijmultiphaseflow.2023.104589 (DOI)001070346100001 ()2-s2.0-85172483742 (Scopus ID)
Available from: 2023-10-11 Created: 2023-10-11 Last updated: 2023-11-29Bibliographically approved
Scheiff, V., Aslanidou, I., Kyprianidis, K. & Bel Fdhila, R. (2023). Experimental Study Of Nucleate Boiling Dynamics In A Rectangular Mini-Channel Set-Up. In: 8th Thermal and Fluids Engineering Conference (TFEC); March, 2023 Partially Online Virtual and at University of Maryland, MD Conference: . Paper presented at 8th Thermal and Fluids Engineering Conference (TFEC).
Open this publication in new window or tab >>Experimental Study Of Nucleate Boiling Dynamics In A Rectangular Mini-Channel Set-Up
2023 (English)In: 8th Thermal and Fluids Engineering Conference (TFEC); March, 2023 Partially Online Virtual and at University of Maryland, MD Conference, 2023Conference paper, Published paper (Refereed)
Abstract [en]

Nowadays thermal management becomes a challenge as it implies high power density with high lossesconverted to large heat release. For low power levels, natural or forced single-phase convection could besufficient. For a much higher heat release nucleate boiling can be the alternative solution since it can dissipate the heat more efficiently, thanks to the latent heat effect present during the phase change. Its performance depends on many parameters that enable potential control and make system integration often very complex. The transition towards nucleate boiling, called Onset of Nucleate Boiling requires better estimation, and the mechanism still lacks understanding, especially in mini-channels. This study aims to characterize nucleate boiling in a rectangular mini-channel experimental set-up, built at Mälardalenuniversity, to better characterize the onset of nucleate boiling and the fully developed bubbly flow. The experiment allows full control of single-phase and two-phase regimes by varying the thermo-hydraulic and heat transfer conditions. With the use of a high-speed camera, bubble dynamics and their principal characteristics such as size, shape, propagation, and nucleation site location are determined with a digital image analysis technique developed within this work. The image processing has proved to be successful even on noisy images due to shadows or background changes. The reconstruction of segmented bubbles enabled flexible and automated bubble and path detection with a statistical approach, especially at the Onset of Nucleate Boiling. Local Reynolds numbers are then estimated to determine the drag coefficient in the flow during bubble growth, or their coalescence.

National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-62214 (URN)2-s2.0-85171268187 (Scopus ID)
Conference
8th Thermal and Fluids Engineering Conference (TFEC)
Available from: 2023-04-12 Created: 2023-04-12 Last updated: 2023-10-11Bibliographically approved
Rabhi, A., Aslanidou, I., Kyprianidis, K. & Bel Fdhila, R. (2022). A One-Dimensional Thermo-Hydraulic Steady-State Modelling Approach For Two-Phase Loop Thermosyphons. In: 16th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics: . Paper presented at 16th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics.
Open this publication in new window or tab >>A One-Dimensional Thermo-Hydraulic Steady-State Modelling Approach For Two-Phase Loop Thermosyphons
2022 (English)In: 16th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, 2022Conference paper, Published paper (Refereed)
Abstract [en]

The interest in using Two-Phase Loop Thermosyphons(TPLT) for heat recovery and energy saving within different in-dustrial processes has been in rise on the last few decades. Thesedevices are characterized by geometrical flexibility, as well asenhanced heat exchange rates. However, TPLT operation in-volves complex physical mechanisms, where different flow andheat transfer regimes are encountered. These regimes are crucialto be assessed and understood, in order to successfully predictand optimize the TPLT operation.

In this paper, a comprehensive one-dimensional thermo-hydraulic modelling approach is developed and presented in or-der to simulate the TPLT operation. The novelty of this modellies in the exhibition of the different experienced complex flowpatterns, heat transfer regimes and physical mechanisms, includ-ing the dry-out prediction and reporting. This modelling frame-work is based on the separated two-fluid model coupled withmass, momentum and energy balances as well as relevant ther-modynamic constraints. The obtained results are compared to theavailable experimental measurements from literature, and a goodagreement is found with a maximum prediction error of 7%.

Furthermore, a sensitivity analysis is performed aiming todetermine the effect of the operating saturation temperature, andtherefore the filling ratio, on the average heat transfer coefficientof the TPLT’s evaporator. Optimal values leading to enhance theheat removal are proposed and discussed at the end of this paper.

Keywords
Two-phase loop thermosyphons, Two-phase cooling, 1D thermo-hydraulic modelling, Critical heat flux
National Category
Energy Systems
Identifiers
urn:nbn:se:mdh:diva-59731 (URN)
Conference
16th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics
Available from: 2022-08-11 Created: 2022-08-11 Last updated: 2022-12-06Bibliographically approved
Rabhi, A., Aslanidou, I., Kyprianidis, K. & Bel Fdhila, R. (2021). Onset of Nucleate Boiling Model for Rectangular Upward Narrow Channel: CFD Based Approach. International Journal of Heat and Mass Transfer, 165, Article ID 120715.
Open this publication in new window or tab >>Onset of Nucleate Boiling Model for Rectangular Upward Narrow Channel: CFD Based Approach
2021 (English)In: International Journal of Heat and Mass Transfer, ISSN 0017-9310, E-ISSN 1879-2189, Vol. 165, article id 120715Article in journal (Refereed) Published
Abstract [en]

Despite that mechanistic and accurate correlations predicting the Onset of Nucleate Boiling (ONB) for pool boiling are widely presented in the literature, models for forced convective boiling remain few. These models do not provide the desired quality, principally because they do not consider important features of convective boiling. In this work, numerical investigations of the ONB for water boiling flow at atmospheric pressure upward a narrow rectangular channel (3 mm × 100 mm × 400 mm) are carried out based on Computational Fluid Dynamics (CFD) simulations. The predictions of the CFD calculations are validated with the available experimental data. A new ONB model incorporating the convective boiling features is developed and proposed. This model is derived based on several CFD simulation data, covering wide operating conditions. The flow Reynolds number ranges from 959 to 13500, inlet subcooling from 2.5 to 30 K and applied heat flux from 5 to 90 kW/m2. The new model predictions have a standard deviation of 2.7% where its performance is better than ±0.3 K when compared with additional simulation data that are provided for validation. © 2020 Elsevier Ltd

Place, publisher, year, edition, pages
Elsevier Ltd, 2021
Keywords
Computational Fluid Dynamics, Mini- and microchannels, Onset of Nucleate Boiling, Subcooled nucleate boiling flows, Atmospheric movements, Atmospheric pressure, Forecasting, Heat flux, Nucleate boiling, Reynolds number, Computational fluid dynamics simulations, Convective boiling, Important features, Narrow rectangular channel, Numerical investigations, Operating condition, Standard deviation
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-52882 (URN)10.1016/j.ijheatmasstransfer.2020.120715 (DOI)000596070000026 ()2-s2.0-85096857683 (Scopus ID)
Note

Export Date: 21 December 2020; Article; CODEN: IJHMA; Correspondence Address: Rabhi, A.; Mälardalen Univeristy, Högskoleplan 1, 722 20 Västerås, Sweden; email: achref.rabhi@mdh.se; Funding details: Stiftelsen för Kunskaps- och Kompetensutveckling, KKS; Funding details: ABB; Funding text 1: The authors gratefully acknowledge ABB AB, Westinghouse Electric Sweden AB, HITACHI ABB Power Grids Sweden and the Swedish Knowledge Foundation (KKS) for their support and would like to particularly thank ABB AB for providing the HPC platform.

Available from: 2020-12-21 Created: 2020-12-21 Last updated: 2022-11-08Bibliographically approved
Soibam, J., Rabhi, A., Aslanidou, I., Kyprianidis, K. & Bel Fdhila, R. (2021). PREDICTION OF THE CRITICAL HEAT FLUX USING PARAMETRIC GAUSSIAN PROCESS REGRESSION. In: Proceedings of the 15th International Conference on Heat Transfer, Fluid Mechanics andThermodynamics (HEFAT2021): . Paper presented at THE 15th INTERNATIONAL CONFERENCE ON HEAT TRANSFER, FLUID MECHANICS AND THERMODYNAMICS, HEFAT, Virtual Conference, 26-28 July 2021 (pp. 1865-1870). HEFAT
Open this publication in new window or tab >>PREDICTION OF THE CRITICAL HEAT FLUX USING PARAMETRIC GAUSSIAN PROCESS REGRESSION
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2021 (English)In: Proceedings of the 15th International Conference on Heat Transfer, Fluid Mechanics andThermodynamics (HEFAT2021), HEFAT , 2021, p. 1865-1870Conference paper, Published paper (Refereed)
Abstract [en]

A sound understanding of the critical heat flux is of prime importance for any industrial boiling system design and safety. From the literature, the majority of the critical heat flux studies are based on empirical knowledge, often supported by ex- perimental investigations which are performed under specific conditions difficult to be generalized. Consequently, most of the available correlations have ±30% predictive error when com- pared to measurement data. Hence, accurate prediction of this quantity remains an open challenge for the thermal engineering community. The present study aims to investigate the hidden features that exist in experimental data using a machine learning technique. Firstly, a literature survey is carried out to collect experimental data for boiling flows in tubes under low pressure and low flow conditions. These experimental data consist of the following parameters: system pressure, mass flux, characteristic dimensions, thermodynamic quality, inlet subcooling, and critical heat flux. A parametric Gaussian process regression model is used to predict the critical heat flux. The prediction obtained from the model is then compared with experimental measurements and the values obtained from the critical heat flux look-up table. The model used in this study is capable of predicting the critical heat flux with better accuracy along with the information of prediction uncertainty. Moreover, it provides insights on the relevance of the different input parameters to the prediction of the critical heat flux and aligns well with the underlying physics. The model used in this study shows a good level of robustness which can be further extended for other geometries, datasets, and operating conditions. 

Place, publisher, year, edition, pages
HEFAT, 2021
Keywords
Boiling flows, Critical Heat Flux, parametric Gaussian process, Machine Learning, Heat Transfer
National Category
Engineering and Technology Energy Engineering
Research subject
Energy- and Environmental Engineering; Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-55650 (URN)978-1-77592-216-2 (ISBN)
Conference
THE 15th INTERNATIONAL CONFERENCE ON HEAT TRANSFER, FLUID MECHANICS AND THERMODYNAMICS, HEFAT, Virtual Conference, 26-28 July 2021
Available from: 2021-08-26 Created: 2021-08-26 Last updated: 2023-11-29Bibliographically approved
Soibam, J., Aslanidou, I., Kyprianidis, K. & Bel Fdhila, R. (2020). A Data-Driven Approach for the Prediction of Subcooled Boiling Heat Transfer. In: Proceedings of The 61st SIMS Conference on Simulation and Modelling SIMS 2020: . Paper presented at 61st SIMS Conference on Simulation and Modelling SIMS 2020, September 22-24, Virtual Conference, Finland (pp. 435-442).
Open this publication in new window or tab >>A Data-Driven Approach for the Prediction of Subcooled Boiling Heat Transfer
2020 (English)In: Proceedings of The 61st SIMS Conference on Simulation and Modelling SIMS 2020, 2020, p. 435-442Conference paper, Published paper (Other academic)
Abstract [en]

In subcooled flow boiling, heat transfer mechanism involves phase change between liquid phase to the vapour phase. During this phase change, a large amount of energy is transferred, and it is one of the most effective heat transfer methods. Subcooled boiling heat transfer is an attractive trend for industrial applications such as cooling electronic components, supercomputers, nuclear industry, etc. Due to its wide variety of applications for thermal management, there is an increasing demand for a faster and more accurate way of modelling. 

In this work, a supervised deep neural network has been implemented to study the boiling heat transfer in subcooled flow boiling heat transfer. The proposed method considers the near local flow behaviour to predict wall temperature and void fraction of a sub-cooled mini-channel. The input of the network consists of pressure gradients, momentum convection, energy con- vection, turbulent viscosity, liquid and gas velocities, and surface information. The output of the model is based on the quantities of interest in a boiling system i.e. wall temperature and void fraction. The network is trained from the results obtained from numerical simulations, and the model is used to reproduce the quantities of interest for interpolation and extrapolation datasets. To create an agile and robust deep neural network model, state-of-the-art methods have been implemented in the network to avoid the overfitting issue of the model. The results obtained from the deep neural network model shows a good agreement with the numerical data, the model has a maximum relative error of 0.5 % while predicting the temperature field, and for void fraction, it has approximately 5 % relative error in interpolation data and a maximum 10 % relative error for the extrapolation datasets. 

Series
Linköping Electronic Conference Proceedings, ISSN 1650-3686 ; 176:62
National Category
Energy Systems Energy Engineering Fluid Mechanics and Acoustics Applied Mechanics
Identifiers
urn:nbn:se:mdh:diva-54529 (URN)10.3384/ecp20176435 (DOI)
Conference
61st SIMS Conference on Simulation and Modelling SIMS 2020, September 22-24, Virtual Conference, Finland
Available from: 2021-06-07 Created: 2021-06-07 Last updated: 2023-11-29Bibliographically approved
Rabhi, A., Aslanidou, I., Kyprianidis, K. & Bel Fdhila, R. (2020). CFD Investigations of Subcooled Nucleate Boiling Flows and Acting Interfacial Forces in Concentric Pipes. In: Proceedings of The 61st SIMS Conference on Simulation and Modelling SIMS 2020, September 22-24, Virtual Conference, Finland: . Paper presented at 61st SIMS Conference on Simulation and Modelling SIMS 2020 (pp. 385-392). , Article ID SIMS-36.
Open this publication in new window or tab >>CFD Investigations of Subcooled Nucleate Boiling Flows and Acting Interfacial Forces in Concentric Pipes
2020 (English)In: Proceedings of The 61st SIMS Conference on Simulation and Modelling SIMS 2020, September 22-24, Virtual Conference, Finland, 2020, p. 385-392, article id SIMS-36Conference paper, Published paper (Refereed)
Abstract [en]

Boiling flows are widely encountered in several engineering and industrial processes. They have a special interest in nuclear industry, where a Computational Fluid Dynamic(CFD) thermohydraulic investigation becomes very popular for design and safety. Many attempts to model numerically subcooled nucleate boiling flows can be found in the literature, where several interfacial forces acting on bubbles which are interacting on the bulk fluid were neglected, due to the hard convergence of the calculations, or to the bad accuracy of the obtained results. In this paper, a sensitivity analysis is carried out for the interfacial forces acting on bubbles during subcooled nucleate boiling flows. For this purpose, 2D CFD axisymmetric simulations based on an Eulerian approach are performed. The developed models aim to mimic the subcooled nucleate boiling flows in concentric pipes, operating at high pressure. The predicted spatial fields of boiling quantities of interest are presented and commented. The numerical results are compared against the available experimental data, where it is shown that neglecting some interfacial forces like the lift or the wall lubrication forces will yield to good predictions for some quantities but will fail the prediction for others. The models leading to the best predictions are highlighted and proposed as recommendations for future CFD simulations of subcooled nucleate boiling flows.

Series
Linköping Electronic Conference Proceedings, ISSN 1650-3686, E-ISSN 1650-3740
Keywords
Subcooled nucleate boiling flows, Computational Fluid Dynamics, Interfacial forces, Sensitivity analysis
National Category
Engineering and Technology Energy Engineering
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-53298 (URN)10.3384/ecp20176385 (DOI)978-91-7929-731-2 (ISBN)
Conference
61st SIMS Conference on Simulation and Modelling SIMS 2020
Projects
DIGI-BOIL
Available from: 2021-02-02 Created: 2021-02-02 Last updated: 2022-11-08Bibliographically approved
Soibam, J., Rabhi, A., Aslanidou, I., Kyprianidis, K. & Bel Fdhila, R. (2020). Derivation and Uncertainty Quantification of a Data-Driven Subcooled Boiling Model. Energies, 13(22), Article ID 5987.
Open this publication in new window or tab >>Derivation and Uncertainty Quantification of a Data-Driven Subcooled Boiling Model
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2020 (English)In: Energies, E-ISSN 1996-1073, Vol. 13, no 22, article id 5987Article in journal (Refereed) Published
Abstract [en]

Subcooled flow boiling occurs in many industrial applications where enormous heat transfer is needed. Boiling is a complex physical process that involves phase change, two-phase flow, and interactions between heated surfaces and fluids. In general, boiling heat transfer is usually predicted by empirical or semiempirical models, which are horizontal to uncertainty. In this work, a data-driven method based on artificial neural networks has been implemented to study the heat transfer behavior of a subcooled boiling model. The proposed method considers the near local flow behavior to predict wall temperature and void fraction of a subcooled minichannel. The input of the network consists of pressure gradients, momentum convection, energy convection, turbulent viscosity, liquid and gas velocities, and surface information. The outputs of the models are based on the quantities of interest in a boiling system wall temperature and void fraction. To train the network, high-fidelity simulations based on the Eulerian two-fluid approach are carried out for varying heat flux and inlet velocity in the minichannel. Two classes of the deep learning model have been investigated for this work. The first one focuses on predicting the deterministic value of the quantities of interest. The second one focuses on predicting the uncertainty present in the deep learning model while estimating the quantities of interest. Deep ensemble and Monte Carlo Dropout methods are close representatives of maximum likelihood and Bayesian inference approach respectively, and they are used to derive the uncertainty present in the model. The results of this study prove that the models used here are capable of predicting the quantities of interest accurately and are capable of estimating the uncertainty present. The models are capable of accurately reproducing the physics on unseen data and show the degree of uncertainty when there is a shift of physics in the boiling regime.

Place, publisher, year, edition, pages
MDPI, 2020
Keywords
computational fluid dynamics (CFD), artificial neural network (ANN), subcooled boiling flows, uncertainty quantification (UQ), Monte Carlo dropout, deep ensemble, deep neural network (DNN)
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-52858 (URN)10.3390/en13225987 (DOI)000594195200001 ()2-s2.0-85106615320 (Scopus ID)
Available from: 2020-12-17 Created: 2020-12-17 Last updated: 2023-11-29Bibliographically approved
Rabhi, A. & Bel Fdhila, R. (2019). EVALUATION AND ANALYSIS OF ACTIVE NUCLEATION SITE DENSITY MODELS IN BOILING. In: : . Paper presented at Second Pacific Rim Thermal Engineering Conference, December 13-17, 2019, Maui, Hawaii, USA.
Open this publication in new window or tab >>EVALUATION AND ANALYSIS OF ACTIVE NUCLEATION SITE DENSITY MODELS IN BOILING
2019 (English)Conference paper, Published paper (Refereed)
Keywords
Active nucleation site density, Subcooled boiling, Minichannel, Computational fluid dynamics, Model evaluation
National Category
Fluid Mechanics and Acoustics
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-47975 (URN)
Conference
Second Pacific Rim Thermal Engineering Conference, December 13-17, 2019, Maui, Hawaii, USA
Projects
DigiBoil – Development of computational fluid dynamic boiling heat transfer models with applicability to high heat flux
Note

Computational Fluid Dynamic (CFD) models employed to simulate thermohydraulic flows where boiling is occurring, require the knowledge of several parameters that are crucial for the flow prediction. It has been shown in several publications that the active nucleation site density (ANSD) is one of the key parameters that has strong influence on the simulation accuracy. This work which aims to evaluate the main used ANSD models, takes place within a larger project frame dedicated to improving the prediction of subcooled boiling using both measurements and CFD simulations. Existing empirical and semi-empirical ANSD correlations are generally formulated and validated solely against a restricted range of experimental conditions. This limits their validity domain and justifies their re-evaluation undertaken in this publication. 3D CFD simulations are performed to investigate subcooled flow boiling heat transfer and evaluate ANSD models by comparing the flow results to our own published measurements in Kromer 2015 and Kromer et al. 2016. The selected measurements consist of temperature values at three axial positions situated on the heated wall center-line of a vertical mini-channel water model (400mm x 100mm x 3mm) at atmospheric pressure and heated from one side. The reviewed ANSD models are implemented in the open-source finite volume CFD code OpenFOAM in order to benchmark them versus temperature measurements and identify the parameters that are affecting most the accuracy of the predictions. The results show that the axial wall temperature is underestimated by all the tested models. However, the models based on a mechanistic or experimental approach, Benjamin & Balakrishnan 1997 and Hibiki & Ishii 2003, that take into account the heated wall surface and material characteristics e.g. density, thermal conductivity, specific heat and roughness, provide much better results with a temperature difference of 4°C compared to more than 10°C for the others

Available from: 2020-05-19 Created: 2020-05-19 Last updated: 2021-02-02Bibliographically approved
Hosain, M. L., Bel Fdhila, R. & Kyprianidis, K. (2019). Simulation and validation of flow and heat transfer in an infinite mini-channel using Smoothed Particle Hydrodynamics. In: Energy Procedia: . Paper presented at 10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, China (pp. 5907-5912). Elsevier, 158
Open this publication in new window or tab >>Simulation and validation of flow and heat transfer in an infinite mini-channel using Smoothed Particle Hydrodynamics
2019 (English)In: Energy Procedia, Elsevier, 2019, Vol. 158, p. 5907-5912Conference paper, Published paper (Refereed)
Abstract [en]

Fluid flow and heat transfer in small channels have a wide range of engineering and medical applications. It has always been a topic of numerous theoretical, numerical and experimental studies. Several numerical methods have been used to simulate such flows. The most common approaches are the finite volume method (FVM) and the direct numerical simulation (DNS), which are numerically expensive to solve cases involving complex engineering problems. The main purpose of this work is to investigate the usability of the mesh-free particle based Smoothed Particle Hydrodynamics (SPH) method to simulate convective heat transfer. To validate our approach, as a starting point, we choose to solve a simple well-established problem which is the laminar flow and heat transfer through an infinitely long mini-channel. The solution obtained from SPH method has been compared to the solution from FVM method and analytical solution with good accuracy. The results presented in this paper show that SPH is capable to solve laminar forced convection heat transfer, however, turbulent flow cases need to be considered to be able to utilize the SPH method for engineering thermal applications.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Poiseuille flow, mini-channel, CFD analysis, Heat transfer, SPH, FVM
National Category
Energy Engineering Fluid Mechanics and Acoustics
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-41275 (URN)10.1016/j.egypro.2019.01.533 (DOI)000471031706043 ()2-s2.0-85063895098 (Scopus ID)
Conference
10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, China
Projects
MR-OMDO
Available from: 2018-11-01 Created: 2018-11-01 Last updated: 2020-12-22Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-8849-7661

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