Search results

1 – 10 of 203
Article
Publication date: 5 September 2023

Nikesh Chowrasia, Subramani S.N., Harish Pothukuchi and B.S.V. Patnaik

Subcooled flow boiling phenomenon is characterized by coolant phase change in the vicinity of the heated wall. Although coolant phase change from liquid to vapour phase…

Abstract

Purpose

Subcooled flow boiling phenomenon is characterized by coolant phase change in the vicinity of the heated wall. Although coolant phase change from liquid to vapour phase significantly enhances the heat transfer coefficient due to latent heat of vaporization, eventually the formed vapor bubbles may coalesce and deteriorate the heat transfer from the heated wall to the liquid phase. Due to the poor heat transfer characteristics of the vapour phase, the heat transfer rate drastically reduces when it reaches a specific value of wall heat flux. Such a threshold value is identified as critical heat flux (CHF), and the phenomenon is known as departure from nucleate boiling (DNB). An accurate prediction of CHF and its location is critical to the safe operation of nuclear reactors. Therefore, the present study aims at the prediction of DNB type CHF in a hexagonal sub-assembly.

Design/methodology/approach

Computational fluid dynamics (CFD) simulations are performed to predict DNB in a hexagonal sub-assembly. The methodology uses an Eulerian–Eulerian multiphase flow (EEMF) model in conjunction with multiple size group (MuSiG) model. The breakup and coalescence of vapour bubbles are accounted using a population balance approach.

Findings

Bubble departure diameter parameters in EEMF framework are recalibrated to simulate the near atmospheric pressure conditions. The predictions from the modified correlation for bubble departure diameter are found to be in good agreement against the experimental data. The simulations are further extended to investigate the influence of blockage (b) on DNB type CHF at low operating pressure conditions. Larger size vapour bubbles are observed to move away from the corner sub-channel region due to the presence of blockage. Corner sub-channels were found to be more prone to experience DNB type CHF compared to the interior and edge sub-channels.

Practical implications

An accurate prediction of CHF and its location is critical to the safe operation of nuclear reactors. Moreover, a wide spectrum of heat transfer equipment of engineering interest will be benefited by an accurate prediction of wall characteristics using breakup and coalescence-based models as described in the present study.

Originality/value

Simulations are performed to predict DNB type CHF. The EEMF and wall heat flux partition model framework coupled with the MuSiG model is novel, and a detailed variation of the coolant velocity, temperature and vapour volume fraction in a hexagonal sub-assembly was obtained. The present CFD model framework was observed to predict the onset of vapour volume fraction and DNB type CHF. Simulations are further extended to predict CHF in a hexagonal sub-assembly under the influence of blockage. For all the values of blockage, the vapour volume fraction is found to be higher in the corner region, and thus the corner sub-channel experiences CHF. Although DNB type CHF is observed in corner sub-channel, it is noticed that the presence of blockage in the interior sub-channel promotes the coolant mixing and results in higher values of CHF in the corner sub-channel.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 33 no. 12
Type: Research Article
ISSN: 0961-5539

Keywords

Article
Publication date: 9 January 2024

Sumant Kumar, B.V. Rathish Kumar, S.V.S.S.N.V.G. Krishna Murthy and Deepika Parmar

Thermo-magnetic convective flow analysis under the impact of thermal radiation for heat and entropy generation phenomena is an active research field for understanding the…

Abstract

Purpose

Thermo-magnetic convective flow analysis under the impact of thermal radiation for heat and entropy generation phenomena is an active research field for understanding the efficiency of thermodynamic systems in various engineering sectors. This study aims to examine the characteristics of convective heat transport and entropy generation within an inverted T-shaped porous enclosure saturated with a hybrid nanofluid under the influence of thermal radiation and magnetic field.

Design/methodology/approach

The mathematical model incorporates the Darcy-Forchheimer-Brinkmann model and considers thermal radiation in the energy balance equation. The complete mathematical model has been numerically simulated through the penalty finite element approach at varying values of flow parameters, such as Rayleigh number (Ra), Hartmann number (Ha), Darcy number (Da), radiation parameter (Rd) and porosity value (e). Furthermore, the graphical results for energy variation have been monitored through the energy-flux vector, whereas the entropy generation along with its individual components, namely, entropy generation due to heat transfer, fluid friction and magnetic field, are also presented. Furthermore, the results of the Bejan number for each component are also discussed in detail. Additionally, the concept of ecological coefficient of performance (ECOP) has also been included to analyse the thermal efficiency of the model.

Findings

The graphical analysis of results indicates that higher values of Ra, Da, e and Rd enhance the convective heat transport and entropy generation phenomena more rapidly. However, increasing Ha values have a detrimental effect due to the increasing impact of magnetic forces. Furthermore, the ECOP result suggests that the rising value of Da, e and Rd at smaller Ra show a maximum thermal efficiency of the mathematical model, which further declines as the Ra increases. Conversely, the thermal efficiency of the model improves with increasing Ha value, showing an opposite trend in ECOP.

Practical implications

Such complex porous enclosures have practical applications in engineering and science, including areas like solar power collectors, heat exchangers and electronic equipment. Furthermore, the present study of entropy generation would play a vital role in optimizing system performance, improving energy efficiency and promoting sustainable engineering practices during the natural convection process.

Originality/value

To the best of the authors’ knowledge, this study is the first ever attempted detailed investigation of heat transfer and entropy generation phenomena flow parameter ranges in an inverted T-shaped porous enclosure under a uniform magnetic field and thermal radiation.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 34 no. 2
Type: Research Article
ISSN: 0961-5539

Keywords

Open Access
Article
Publication date: 22 June 2022

Serena Summa, Alex Mircoli, Domenico Potena, Giulia Ulpiani, Claudia Diamantini and Costanzo Di Perna

Nearly 75% of EU buildings are not energy-efficient enough to meet the international climate goals, which triggers the need to develop sustainable construction techniques with…

1064

Abstract

Purpose

Nearly 75% of EU buildings are not energy-efficient enough to meet the international climate goals, which triggers the need to develop sustainable construction techniques with high degree of resilience against climate change. In this context, a promising construction technique is represented by ventilated façades (VFs). This paper aims to propose three different VFs and the authors define a novel machine learning-based approach to evaluate and predict their energy performance under different boundary conditions, without the need for expensive on-site experimentations

Design/methodology/approach

The approach is based on the use of machine learning algorithms for the evaluation of different VF configurations and allows for the prediction of the temperatures in the cavities and of the heat fluxes. The authors trained different regression algorithms and obtained low prediction errors, in particular for temperatures. The authors used such models to simulate the thermo-physical behavior of the VFs and determined the most energy-efficient design variant.

Findings

The authors found that regression trees allow for an accurate simulation of the thermal behavior of VFs. The authors also studied feature weights to determine the most relevant thermo-physical parameters. Finally, the authors determined the best design variant and the optimal air velocity in the cavity.

Originality/value

This study is unique in four main aspects: the thermo-dynamic analysis is performed under different thermal masses, positions of the cavity and geometries; the VFs are mated with a controlled ventilation system, used to parameterize the thermodynamic behavior under stepwise variations of the air inflow; temperatures and heat fluxes are predicted through machine learning models; the best configuration is determined through simulations, with no onerous in situ experimentations needed.

Details

Construction Innovation , vol. 24 no. 7
Type: Research Article
ISSN: 1471-4175

Keywords

Article
Publication date: 12 February 2024

Boyi Li, Miao Tian, Xiaohan Liu, Jun Li, Yun Su and Jiaming Ni

The purpose of this study is to predict the thermal protective performance (TPP) of flame-retardant fabric more economically using machine learning and analyze the factors…

Abstract

Purpose

The purpose of this study is to predict the thermal protective performance (TPP) of flame-retardant fabric more economically using machine learning and analyze the factors affecting the TPP using model visualization.

Design/methodology/approach

A total of 13 machine learning models were trained by collecting 414 datasets of typical flame-retardant fabric from current literature. The optimal performance model was used for feature importance ranking and correlation variable analysis through model visualization.

Findings

Five models with better performance were screened, all of which showed R2 greater than 0.96 and root mean squared error less than 3.0. Heat map results revealed that the TPP of fabrics differed significantly under different types of thermal exposure. The effect of fabric weight was more apparent in the flame or low thermal radiation environment. The increase in fabric weight, fabric thickness, air gap width and relative humidity of the air gap improved the TPP of the fabric.

Practical implications

The findings suggested that the visual analysis method of machine learning can intuitively understand the change trend and range of second-degree burn time under the influence of multiple variables. The established models can be used to predict the TPP of fabrics, providing a reference for researchers to carry out relevant research.

Originality/value

The findings of this study contribute directional insights for optimizing the structure of thermal protective clothing, and introduce innovative perspectives and methodologies for advancing heat transfer modeling in thermal protective clothing.

Details

International Journal of Clothing Science and Technology, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0955-6222

Keywords

Article
Publication date: 12 December 2023

T.M. Jeyashree and P.R. Kannan Rajkumar

This study focused on identifying critical factors governing the fire response of prestressed hollow-core slabs. The hollow-core slabs used as flooring units can be subjected to…

Abstract

Purpose

This study focused on identifying critical factors governing the fire response of prestressed hollow-core slabs. The hollow-core slabs used as flooring units can be subjected to elevated temperatures during a fire. The fire response of prestressed hollow-core slabs is required to develop slabs with greater fire endurance. The present study aims to determine the extent to which the hollow-core slab can sustain load during a fire without undergoing progressive collapse under extreme fire and heating scenarios.

Design/methodology/approach

A finite element model was generated to predict the fire response of prestressed hollow core slabs under elevated temperatures. The accuracy of the model was predicted by examining thermal and structural responses through coupled temperature displacement analysis. A sensitivity analysis was performed to study the effects of concrete properties on prediction of system response. A parametric study was conducted by varying the thickness of the slab, fire and heating scenarios.

Findings

Thermal conductivity and specific heat of concrete were determined as sensitive parameters. The thickness of the slab was identified as a critical factor at a higher load level. Asymmetric heating of the slab resulted in higher fire resistance compared with symmetric heating.

Originality/value

This is the first study focused on studying the effect of modeling uncertainties on the system response by sensitivity analysis under elevated temperatures. The developed model with a parametric study helps in identifying critical factors for design purposes.

Details

Journal of Structural Fire Engineering, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2040-2317

Keywords

Article
Publication date: 16 February 2024

Sergejs Pavlovs, Andris Jakovičs and Alexander Chudnovsky

The purpose of this paper is the study of the electro-vortex flow (EVF) as well as heating and melting processes for mini industrial direct current electric arc furnace (DC EAF).

Abstract

Purpose

The purpose of this paper is the study of the electro-vortex flow (EVF) as well as heating and melting processes for mini industrial direct current electric arc furnace (DC EAF).

Design/methodology/approach

A mini DC EAF was designed, manufactured and installed to study the industrial processes of heating and melting a small amount of melt, being 4.6 kg of steel in the case under study. Numerical modelling of metal melting was performed using the enthalpy and porosity approach at equal values and non-equal values of the solidus and liquidus temperatures of the metal. The EVF of the liquid phase of metal was computed using the large eddy simulation model of turbulence. Melt temperature measurements were made using an infrared camera and a probe with a thermocouple sensor. The melt speed was estimated by observing the movement of particles at the top surface of melt.

Findings

The thermal flux for metal heating and melting, which is supplied through an arc spot at the top surface of metal, is estimated using the thermal balance of the furnace at melting point. The melting time was estimated using numerical modelling of heating and melting of metal. The process started at room temperature and finished once whole volume of metal was molten. The evolution of the solid/melt phase boundary as well as evolution of EVF patterns of the melt was studied.

Originality/value

Numerical studies of heating and melting processes in metal were performed in the case of intensive liquid phase turbulent circulation due to the Lorentz force in the melt, which results from the interaction of electrical current with a self-magnetic field.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0332-1649

Keywords

Article
Publication date: 19 February 2024

Muhammad Sohail and Syed Tehseen Abbas

This study aims to analyze the Prandtl fluid flow in the presence of better mass diffusion and heat conduction models. By taking into account a linearly bidirectional stretchable…

Abstract

Purpose

This study aims to analyze the Prandtl fluid flow in the presence of better mass diffusion and heat conduction models. By taking into account a linearly bidirectional stretchable sheet, flow is produced. Heat generation effect, thermal radiation, variable thermal conductivity, variable diffusion coefficient and Cattaneo–Christov double diffusion models are used to evaluate thermal and concentration diffusions.

Design/methodology/approach

The governing partial differential equations (PDEs) have been made simpler using a boundary layer method. Strong nonlinear ordinary differential equations (ODEs) relate to appropriate non-dimensional similarity variables. The optimal homotopy analysis technique is used to develop solution.

Findings

Graphs analyze the impact of many relevant factors on temperature and concentration. The physical parameters, such as mass and heat transfer rates at the wall and surface drag coefficients, are also displayed and explained.

Originality/value

The reported work discusses the contribution of generalized flux models to note their impact on heat and mass transport.

Details

Multidiscipline Modeling in Materials and Structures, vol. 20 no. 2
Type: Research Article
ISSN: 1573-6105

Keywords

Article
Publication date: 29 March 2024

Pratheek Suresh and Balaji Chakravarthy

As data centres grow in size and complexity, traditional air-cooling methods are becoming less effective and more expensive. Immersion cooling, where servers are submerged in a…

Abstract

Purpose

As data centres grow in size and complexity, traditional air-cooling methods are becoming less effective and more expensive. Immersion cooling, where servers are submerged in a dielectric fluid, has emerged as a promising alternative. Ensuring reliable operations in data centre applications requires the development of an effective control framework for immersion cooling systems, which necessitates the prediction of server temperature. While deep learning-based temperature prediction models have shown effectiveness, further enhancement is needed to improve their prediction accuracy. This study aims to develop a temperature prediction model using Long Short-Term Memory (LSTM) Networks based on recursive encoder-decoder architecture.

Design/methodology/approach

This paper explores the use of deep learning algorithms to predict the temperature of a heater in a two-phase immersion-cooled system using NOVEC 7100. The performance of recursive-long short-term memory-encoder-decoder (R-LSTM-ED), recursive-convolutional neural network-LSTM (R-CNN-LSTM) and R-LSTM approaches are compared using mean absolute error, root mean square error, mean absolute percentage error and coefficient of determination (R2) as performance metrics. The impact of window size, sampling period and noise within training data on the performance of the model is investigated.

Findings

The R-LSTM-ED consistently outperforms the R-LSTM model by 6%, 15.8% and 12.5%, and R-CNN-LSTM model by 4%, 11% and 12.3% in all forecast ranges of 10, 30 and 60 s, respectively, averaged across all the workloads considered in the study. The optimum sampling period based on the study is found to be 2 s and the window size to be 60 s. The performance of the model deteriorates significantly as the noise level reaches 10%.

Research limitations/implications

The proposed models are currently trained on data collected from an experimental setup simulating data centre loads. Future research should seek to extend the applicability of the models by incorporating time series data from immersion-cooled servers.

Originality/value

The proposed multivariate-recursive-prediction models are trained and tested by using real Data Centre workload traces applied to the immersion-cooled system developed in the laboratory.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0961-5539

Keywords

Article
Publication date: 28 September 2023

Shafia Rana, M. Nawaz and Sayer Obaid Alharbi

The purpose of this study is to analyze the transportation of heat and mass in three-dimensional (3D) shear rate-dependent viscous fluid. Thermal enhancement plays a significant…

129

Abstract

Purpose

The purpose of this study is to analyze the transportation of heat and mass in three-dimensional (3D) shear rate-dependent viscous fluid. Thermal enhancement plays a significant role in industrial and engineering applications. For this, the authors dispersed trihybrid nanoparticles into the fluid to enhance the working fluid’s thermal enhancement.

Design/methodology/approach

The finite element method is a numerical scheme and is powerful in achieving convergent and grid-independent solutions compared with other numerical techniques. This method was initially assigned to structural problems. However, it is equally successful for computational fluid dynamics problems.

Findings

Wall shear stress has shown an increasing behavior as the intensity of the magnetic field is increased. Simulations have predicted that Ohmic heat in the case of trihybrid nanofluid (MoS2–Al2O3–Cu/C2H6O2) has the greatest value in comparison with mono and hybrid nanofluids. The most significant influence of chemical reaction on the concentration in tri-nanofluid is noted. This observation is pointed out for both types of chemical reaction (destructive or generative) parameters.

Originality/value

Through a literature survey, the authors analyzed that no one has yet to work on a 3D magnetohydrodynamics Carreau–Yasuda trihybrid nanofluid over a stretched sheet for improving heat and mass transfer over hybrid nanofluids. Herein, molybdenum disulfide (MoS2), aluminum oxide (Al2O3) and copper (Cu) nanoparticles are mixed in ethylene glycol (C2H6O2) to study the thermal enhancement and mass transport of their corresponding resultant mono (Cu/C2H6O2), hybrid (Al2O3–Cu/C2H6O2) and trihybrid (MoS2–Al2O3–Cu/C2H6O2) nanofluids.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 33 no. 12
Type: Research Article
ISSN: 0961-5539

Keywords

Article
Publication date: 26 December 2023

Hamza Berrehal, Roshanak Karami, Saeed Dinarvand, Ioan Pop and Ali Chamkha

This paper aims to study numerically the flow, heat transfer, and entropy generation of aqueous copper oxide-silver hybrid nanofluid over a down-pointing rotating vertical cone…

Abstract

Purpose

This paper aims to study numerically the flow, heat transfer, and entropy generation of aqueous copper oxide-silver hybrid nanofluid over a down-pointing rotating vertical cone, with linear surface temperature (LST) and linear surface heat flux (LSHF), in the presence of a cross-magnetic field. In industrial applications, such as oil and gas plants, food industries, steel factories and nuclear packages, the real bodies may contain nonorthogonal walls and variable cross-section three-dimensional forms which this issue can clarify the importance of selective geometry in the present research.

Design/methodology/approach

The mass-based scheme is accomplished for the simulation, and the entropy generation and Bejan number will be analyzed in conjunction with the aforementioned model. It has been hypothesized that two types of boundary conditions (LST and LSHF) as well as five nanoparticle shapes (sphere, brick, cylinder, platelet and disk) present a collection of crucial results. The overseeing PDEs are changed over completely to the dimensionless ODEs, and these are solved by Runge–Kutta–Fehlberg approach combined with a shooting methodology for certain values of physical parameters.

Findings

Subsequent to the fantastic compromise of the computational outcomes with past reports, the outcomes are introduced to conduct the investigation of the hydrodynamics/thermal boundary layers, the skin friction and the Nusselt number, as well as entropy generation and Bejan number. A state of hybrid nanofluid, which exhibits a remarkable increase in heat transfer in comparison to the states of mono-nanofluid and regular fluid, has been found to have the highest Nusselt number; however, the skin friction values should always be taken into account and managed. The entropy generation improves with the mass of the second nanoparticle (silver), while the opposite pattern is exhibited for the Bejan number. Furthermore, the lowest value of entropy generation number belongs to the cylindrical shape of nanoparticles in the LST case. In final, a significant accomplishment of the current study is the accurate output of the mass-based scheme for an entropy analysis problem.

Originality/value

To the best of the authors’ knowledge, for the first time, in this study, a new development of natural convective flow of a hybrid nanofluid about the warmed (LST and LSHF) and down-pointing rotating vertical cone by the mass-based algorithm has been presented. The applied methodology considers the masses of base fluid (water) and nanoparticles (Ag and CuO) as an alternative to the first and second nanoparticles volume fraction. Indeed, the combination use of the Tiwari–Das nanofluid model and the mass-based hybridity algorithm for the entropy generation analysis can be the main novelty of this work.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 34 no. 2
Type: Research Article
ISSN: 0961-5539

Keywords

1 – 10 of 203