| 1. |
- Rabhi, Achref, 1991-, et al.
(författare)
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A One-Dimensional Thermo-Hydraulic Steady-State Modelling Approach For Two-Phase Loop Thermosyphons
- 2022
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Ingår i: 16th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics.
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 2. |
- Rabhi, Achref, 1991-, et al.
(författare)
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CFD Investigations of Subcooled Nucleate Boiling Flows and Acting Interfacial Forces in Concentric Pipes
- 2020
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Ingår i: Proceedings of The 61st SIMS Conference on Simulation and Modelling SIMS 2020, September 22-24, Virtual Conference, Finland. - : Linköping University Electronic Press. - 9789179297312 ; , s. 385-392
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 3. |
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| 4. |
- Rabhi, Achref, 1991-
(författare)
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Numerical Modelling of Subcooled Nucleate Boiling for Thermal Management Solutions Using OpenFOAM
- 2021
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Two-phase cooling solutions employing subcooled nucleate boiling flows e.g. thermosyphons, have gained a special interest during the last few decades. This interest stems from their enhanced ability to remove extremely high heat fluxes, while keeping a uniform surface temperature. Consequently, modelling and predicting boiling flows is very important, in order to optimise the two-phase cooling operation and to increase the involved heat transfer coefficients. In this work, a subcooled boiling model is implemented in the open-source code OpenFOAM to improve and extend its existing solver reactingTwoPhaseEulerFoam dedicated to model boiling flows. These flows are modelled using Computational Fluid Dynamics (CFD) following the Eulerian two-fluid approach. The simulations are used to evaluate and analyse the existing Active Nucleation Site Density (ANSD) models in the literature. Based on this evaluation, the accuracy of the CFD simulations using existing boiling sub-models is determined, and features leading to improve this accuracy are highlighted. In addition, the CFD simulations are used to perform a sensitivity analysis of the interfacial forces acting on bubbles during boiling flows. Finally, CFD simulation data is employed to study the Onset of Nucleate Boiling (ONB) and to propose a new model for this boiling sub-model, with an improved prediction accuracy and extended validity range.It is shown in this work that predictions associated with existing boiling sub-models are not accurate, and such sub-models need to take into account several convective boiling quantities to improve their accuracy. These quantities are the thermophysical properties of the involved materials, liquid and vapour thermodynamic properties and the heated surface micro-structure properties. Regarding the interfacial momentum transfer, it is shown that all the interfacial forces have considerable effects on boiling, except the lift force, which can be neglected without influencing the simulations' output. The new proposed ONB model takes into account convective boiling features, and it able to predict the ONB with a very good accuracy with a standard deviation of 2.7% or 0.1 K. This new ONB model is valid for a wide range of inlet Reynolds numbers, covering both regimes, laminar and turbulent, and a wide range of inlet subcoolings and applied heat fluxes.
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| 5. |
- Rabhi, Achref, 1991-, et al.
(författare)
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Onset of Nucleate Boiling Model for Rectangular Upward Narrow Channel : CFD Based Approach
- 2021
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Ingår i: International Journal of Heat and Mass Transfer. - : Elsevier Ltd. - 0017-9310 .- 1879-2189. ; 165
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Tidskriftsartikel (refereegranskat)abstract
- 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
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| 6. |
- Soibam, Jerol, et al.
(författare)
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Derivation and Uncertainty Quantification of a Data-Driven Subcooled Boiling Model
- 2020
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Ingår i: Energies. - : MDPI. - 1996-1073. ; 13:22
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 7. |
- Soibam, Jerol, 1992-, et al.
(författare)
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PREDICTION OF THE CRITICAL HEAT FLUX USING PARAMETRIC GAUSSIAN PROCESS REGRESSION
- 2021
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Ingår i: Proceedings of the 15th International Conference on Heat Transfer, Fluid Mechanics andThermodynamics (HEFAT2021). - : HEFAT. - 9781775922162 ; , s. 1865-1870
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Konferensbidrag (refereegranskat)abstract
- 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.
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