Prediction Of Heat Transfer Research Articles

EXPERIMENTAL STUDY ON THE HEAT TRANSFER CHARACTERISTICS OF SUPERCRITICAL CARBON DIOXIDE IN THE STRAIGHTLY-RIBBED TUBES

The supercritical carbon dioxide (S-CO<sub>2</sub>) Brayton cycle shows great advantages in structural compactness and thermal efficiency. To ensure the safety of the heating surface under high load, the flow and heat transfer characteristics in straightly-ribbed tubes is studied to explore the improvement on the convective heat transfer ability of S-CO<sub>2</sub> and inhibition on the deteriorated heat transfer. An experimental system of S-CO<sub>2</sub> is established, and the heat transfer characteristics of S-CO<sub>2</sub> in both vertically upward smooth and straightly-ribbed tubes are studied within a wide operating parameter range (pressure <i>P</i> = 7.5-9 MPa, mass flow rate <i>G</i> = 400-1000 kg·m<sup>-2</sup>·s<sup>-1</sup>, heat flux <i>q</i> = 40-600 kW·m<sup>-2</sup>). The rib width and rib height of tubes are 1 mm, with an inner diameter of 6 mm. The effect of flow parameters on the heat transfer performance is analyzed. The results show the convective heat transfer coefficient of straightly-ribbed tubes is about 1.5-2.3 times that of smooth tubes at supercritical pressure. The rib structure can effectively prevent the occurrence of deteriorated heat transfer of SCO<sub>2</sub>. A new heat transfer prediction model in the vertically straightly-ribbed tube is proposed based on the result of this experiment.

Towards the accurate prediction of axial flow and heat transfer in a tightly spaced bare rod bundle configuration

The understanding of flow behaviour and temperature distribution in rod bundles during operation is of great importance for the safety and economical design of existing nuclear reactors and as well as new nuclear technologies. In order to study the thermal-hydraulics phenomena in the fuel assembly geometries, researchers all over the world are increasingly using Computational Fluid Dynamics (CFD) as a reliable research tool. Nevertheless, the prediction capabilities of the most commonly used Reynolds Average Navier-Stokes (RANS) models must be assessed. In this regard, in the present research work, an extensive effort has been put forward and is described in three main steps. As a first step, a wide range of RANS and unsteady RANS computations have been performed to design a numerical experiment for a closely-spaced bare rod bundle in order to perform a direct numerical simulation (DNS), which will serve as a reference for the validation purpose. As a second step, the DNS of this bare rod bundle has been performed for three different Prandtl number (Pr) fluids, i.e. Pr = 2, 1 and 0.025. This DNS has been performed using a higher-order spectral element code and consists of 660 million grid points. Accordingly, an extensive database has been generated for validation purposes. Finally, this database is used to assess the prediction capabilities of different linear and non-linear RANS models. This article will provide a good overview of all three steps and will also highlight the main lessons learned from this research.

A mechanistic model in annular flow in microchannel tube for predicting heat transfer coefficient and pressure gradient

A mechanistic model that focuses on annular flow is proposed in this work. The new model predicts pressure gradient, void fraction, and heat transfer coefficient in annular flow in a microchannel tube. Six fluids, namely R32, R1234yf, R134a, R1234ze(E), R1233zd(E), and R1336mzz(Z) were tested in previous studies. The tested fluids have a wide range of properties. The results of these six fluids are reviewed and compared to models in the literature. The existing heat transfer models fail to predict the measurements accurately. The proposed model starts from the shear balance at the interface of liquid film and vapor core. The new model uses film thickness to calculate the film Reynolds number. The Film Nusselt number is then calculated. A database approach is adopted to enhance prediction accuracy. Based on the measurements in the database, the model has an MAE of 11.7%, and ME of 0.5% when predicting the heat transfer coefficient. Li and Hrnjak (2022), a flow pattern map based on the same fluids and the same facility, is used to determine the flow pattern. The model is compared to measurements from varied sources in the literature, and it also shows good accuracy. The model can also be extended to other flow patterns in a microchannel evaporator.

Effects of wide-range copper surface wettability on spray cooling heat transfer

This study conducted a spray cooling experiment to investigate the effects of varying surface wettability on the heat transfer performance of modified copper surfaces in both single- and two-phase regimes. The contact angles of the test surfaces ranged from 0° to 150°. The spray cooling experiment was performed using deionized water. The results indicated that surface wettability influenced heat transfer performance during spray cooling. In the nonboiling regime, the surface with higher hydrophilicity exhibited higher heat transfer performance owing to the larger wetting area available for heat transfer. The surface temperature of the superhydrophobic surface reached saturation at a relatively low heat flux; therefore, nucleation occurred rapidly, and the increase in the heat transfer coefficient intensified as the heat flux increased. Consequently, the heat transfer performance of the hydrophobic surface exceeded that of the plain surface in the two-phase regime. Evaporation fraction values were derived for the various test surfaces. Finally, a dimensionless surface wettability number, defined as the ratio of the droplet contact diameter to the droplet height, was used to establish heat transfer prediction models considering surface wettability in the single-phase and two-phase regimes. The heat transfer performance in the single-phase and two-phase regimes could be predicted within error bands of 18 % and 33.3 %, respectively.

Investigation of a temperature-sensitive ferrofluid to predict heat transfer and irreversibilities in LS-3 solar collector under line dipole magnetic field and a rotary twisted tape

This article numerically studies the performance of the LS-3 parabolic trough collector. The parabolic trough solar system is equipped with an innovative rotary twisted tape. The absorber tube and twisted tape have the same length. Moreover, line dipole magnetic field is applied to improve the performance of solar collector. In addition, the characteristics of the irreversibilities are investigated. Commercial temperature-sensitive ferrofluid is selected for working fluid, and the nanoparticles along with the base fluid assumed to be uniform. The effect of rotational speed, twisted ratio, and line dipole magnetic field are compared with plain tube. The heat flux profile on absorber tube is ascertained by the Monte Carlo Ray Tracing method. Curve fitting method is used to obtain the mathematical function of the heat flux profile. To solve the equations including continuity, momentum, energy, and SST κ−ω model, the finite volume procedure is selected. The more convective heat transfer coefficient occurs by dipole magnetic field, the maximum enhancement reaches over 97% at B = 100 G. In addition, the twisted tape can increase heat transfer by 305% at twisted ratio = 2.5 and rotational speed = 200 rpm. Furthermore, if the twisted tape has rotational speed, the thermal entropy generation decreases.

Surrogate modeling of heat transfers of nanofluids in absorbent tubes with fins based on deep convolutional neural network

In this paper, we propose and investigate a deep convolutional neural network-based surrogate model for fast prediction of heat transfer of nanofluid in absorbent tubes with fins. Inspired by Unet, the proposed model consists of a contracting path and an expanding path, and skip connections are replaced by residual blocks to enhance prediction performance. The model can predict temperature field of tubes’ specified cross-sections in any quantity. The nanofluid-filled absorbent tubes with two types of fins (rectangular and semiepllise) are investigated, where the shapes of the fins are variable. The results show that the proposed model can predict the temperature field with very high accuracy and extremely fast speed: the average prediction error is less than 0.15% for all studied cases, and the prediction speed is more than 4 orders faster than numerical simulation with OpenFOAM. In addition, we analyze the main factors affecting the network model, including the learning rate, data size, network's layer, activation function and input presentation. It is found that the influence of activation function is significant, where ReLU6 is able to reduce the max prediction error to 2.7%, while the Tanh and Sigmoid functions have two and four times larger errors, respectively. The results of our current work show great application potential of the deep neural network-based surrogate model for rapid 3D geometry optimization of the nanofluid-filled fined absorbent tube of heat exchangers and solar collectors.

Prediction of heat and mass transfer in radiative hybrid nanofluid with chemical reaction using the least square method: A stability analysis of dual solution

This analysis aims to investigate the stability of dual solutions for the heat and mass transfer flow of hybrid nanofluid over a stretching/shrinking surface with a uniform shear flow. The impacts of thermal radiation, transpiration, and chemical reaction are also considered in the flow. Gold (Au) and zinc oxide (ZnO) are selected as nanomaterials while engine oil (EO) is chosen to be the base fluid. By applying the similarity transformation technique, the nondimensional differential equations are changed into dimensionless coupled differential equations. The least square method (LSM) is applied to solve the system analytically and obtained the dual solutions in a particular range of stretching/shrinking parameter Due to this, the stability analysis is implemented to ensure that only the first solution is stable. The smallest eigenvalues are computed by utilizing the bvp4c function in Matlab software, where positive eigenvalues are linked with the stable solution while negative eigenvalues are linked with the unstable solution. The impacts of several physical parameters on the governing problem are discussed in detail and displayed in graphical form.

Experimental analysis of high temperature flow boiling of zeotropic mixture R134a/R245fa in a plate heat exchanger

The potential to improve the performance of organic Rankine cycle systems by using zeotropic mixtures as working fluids has been demonstrated in the open literature. However, there are very few research works in the open literature studying the mixture flow boiling in plate heat exchangers, and none of the existing works address high temperature boiling, which is the prevailing working condition in the evaporator of organic Rankine cycle systems. This paper presents an experimental study of high temperature flow boiling of zeotropic mixtures of R134a/R245fa in a plate heat exchanger. Five compositions were tested at the reduced pressures 0.45, 0.55 and 0.65 and various heat fluxes and mass fluxes. In addition, the predictive performance of several existing prediction methods for the heat transfer coefficient and pressure drop were evaluated, and based on the results, new heat transfer and pressure drop prediction methods were developed. The experimental results suggest that the heat transfer is dominated by the nucleate boiling. The mixture having a 0.427/0.573 mass composition presents the largest heat transfer degradation, up to 42 %, compared with the pure fluids. As far as the pressure drop is concerned, the results suggest that the zeotropic mixtures have the same pressure drop characteristics as the pure fluids, and the five mixtures have almost the same frictional pressure drop due to their similar thermo-physical properties. Existing methods provide a good prediction of the heat transfer data. The new heat transfer correlations enable an even better predictive performance than those available in the literature, however, it remains to evaluate the predictive performance of the new correlation for other working fluids, plate heat exchanger geometries and working conditions than those considered in the paper.

A new moving-boundary algorithm to predict heat transfer rate of counter-flow water-cooled transcritical CO2 gas cooler

A new moving-boundary model was proposed for predicting steady-state heat transfer rate of water-cooled transcritical CO2 gas cooler. The new model aims to separate the CO2 gas cooler into up to three zones depending on the local thermal capacitance rate, which is significantly different from the moving-boundary models used in subcritical region, which is based on fluid phase. The experimental data from the literature were used for model validation, and the results show that the prediction accuracy of the new moving-boundary model is comparable to that of the finite volume method. The deviations between predicted and measured heating capacities are within ±5%, and the predicted CO2 outlet temperatures are within ±4°C. The averaged computation time of the model is 22% of that of the finite volume model for the 45 data points in the present study. The proposed new model is suitable for steady-state simulation of transcritical CO2 water-cooled gas cooler, either standalone or integrated into overall CO2 heat pump water heater systems.

Turbulent convective heat transfer enhancement modeling of water-Al2O3 nanofluid using CFD mixture model and adaptive neural fuzzy inference system

ANFIS and multiphase Mixture model, two different methods from two very distinct engineering domains: Machine Intelligence and Computational Mechanics, have been put into a good amount of use over the past few years for modeling of nanofluids heat transfer enhancement. However, not only the previous investigations using both of these approaches suffer from the use of narrow range of nanofluid and flow properties, but also never have these two particular approaches been juxtaposed to point at a superior approach for specific flow regimes for predicting nanofluids heat transfer enhancement. In this study, water-Al2O3 nanofluid has been simulated using CFD multiphase Mixture model and ANFIS in order to assess the precision of both approaches to predict heat transfer enhancement of water-Al2O3 nanofluid for a very wide range of nanofluid configurations and flow properties, and to suggest the better approach for prediction of heat transfer enhancement for each specific flow regime. The results suggest that almost in every single case ANFIS is able to predict the heat transfer enhancement of nanofluids very efficiently with a maximum error of 0.35%, but the Mixture models’ predictions deviate significantly from the experimental correlation in some cases, though for intermediate nanofluid configurations, yielded results by Mixture model could be reliable with error around 1%.

Modified heat transfer correction function for modeling multiphase condensing flows in transonic regime

Water droplet formation and growth in the nucleation process are crucial aspects of steam condensation in moist air flow, and they are accompanied by intensive heat exchange between phases. This study proposes a mathematical model for predicting heat transfer coefficients in the condensation process, which is applicable for single-phase and multiphase flows. The proposed model based on the correction function forms a continuous model for the low Knudsen number regime, and in the region of large Knudsen numbers, it employs a simplified droplet growth model based on Hertz and Knudsen's approach derived from the kinetic theorem. The proposed model was compared with existing models based on the continuous approach, that is, models derived by Gyarmathy, Fuchs-Sutigin, and Young, as well as models based on the kinetic theorem. Furthermore, the proposed model was implemented into a numerical CFD tool, and numerical studies were conducted for two representative cases of single-species and multispecies flows, that is, steam and humid-air expansion in a converging-diverging nozzle. For steam studies, the proposed model showed a similar convergence with the experiment as Young's model however, its agreement with the experimental results for multispecies condensation flow was better. The proposed model is promising for the study of condensation when there is rapid droplet growth, which occurs in multispecies flows, even if further study is needed to find a correlation between the condensation coefficient and fluid properties.

Dynamic analysis and investigation of the thermal transient effects in a CSTR reactor producing biogas

In the field of primary energy saving, biomethane is becoming more and more attractive. The need to manage urban waste, combined with the increasing use of biofuels, makes this resource crucial for sustainability objectives. Therefore, a detailed estimation of biogas production from an anaerobic digester is an important aspect for the related research activity. This work presents a thermal transient model developed in MATLAB® that can accurately predict heat transfer and biological phenomena occurring within the digester. The model developed in this paper can conveniently consider the thermal inertia of the treated biomass during the retention time. This model is subsequently linked to the TRNSYS environment, where the produced biogas is upgraded to biomethane. For this purpose, a membrane permeation model is considered. A concentrating photovoltaic/thermal collector is coupled to the digester to meet the plant electricity and thermal energy demands. In particular, the electricity demand is mainly due to the upgrading process while the thermal energy is necessary for the anaerobic digestion process. The results of the dynamic analysis show that neglecting the thermal inertia of the digester leads to an overestimation of the total biogas production by 20%. Furthermore, the integration of renewables with the anaerobic digestion process shows remarkable results. In this case study, the Primary Energy Saving is roughly 5% and the Simple-Pay Back is less than 10 years, despite the small scale selected for solar collectors.

Numerical predictions of flow topology and heat transfer in a square duct with staggered V-ribs

Performance augmentation of a square duct heat exchanger by a passive technique is numerically investigated. V-pattern ribs are used as vortex turbulators to enhance heat exchanger performance. The V-rib arrangement is designed with two important factors: 1. to increase the heat transfer coefficient by disturbing thermal boundary layers and to expedite fluid mixing and 2. to remain or decline the friction loss across the ribbed duct when compared with a typical in-line V-rib arrangement. The effects of rib heights (b/H = 0.05–0.20), rib pitch (P/H = 1–2) and flow paths (+x and -x) on fluid structure and thermal characteristics are studied within a laminar flow regime. Numerical analysis using the finite volume method (FVM) is chosen to predict the fluid structure and thermal mechanisms within the ribbed duct. Validating topics: smooth duct validation and grid independence, are firstly investigated. Simulation results are analyzed in forms of fluid structure and thermal behaviors. Performance assessment (thermal enhancement factor, friction factor ratio and Nusselt number ratio) within the tested section are also concluded. The simulation results show that the best TEF is found to be around 4.5, while the maximum Nusselt number ratio is around 19.39.

Analysis of conjugate heat transfer in a roots blower and validation with infrared thermography

• Conjugate heat transfer in a Roots blower. • Gap size calibration achieved deviation of 7.5% on flow and 4.3% on power. • Discharge air temperature deviation was within 4 °C. • High speed infrared thermography based surface temperature deviation was within 6 °C. Oil-free Roots blower is a type of Positive Displacement Machine used for low pressure ratio applications. Its volumetric efficiency is dependent on the leakage of compressed gas through the clearance gaps. In the absence of internal cooling, prediction of temperature distribution and conjugate heat transfer becomes important for reliable design and operation. The interaction of gas flow and structural deformation in the blower is highly transient and one of the most challenging problems which need to be addressed for accurate performance prediction. To achieve this, the technique which includes moving and transforming the mesh is employed. The performance test data used for validation included discharge air temperature, mass flow rate, pressure, and power. The comparison between numerical and experimental results has shown good agreement of 7.5% on flow, 4.3% on power while the discharge temperature deviation was within 4°C. High speed infrared thermography of the rotor lobe and housing surface temperatures was used to evaluate the accuracy of the Conjugate Heat Transfer model. The difference between the experiment and simulation was within 6°C. The aim of this study was to develop and validate the numerical model for analysis of heat transfer between pressurized air and compressor elements and to provide temperature distribution in structural elements. By this means, the operational gap sizes can be reliably minimised during the design of a machine.

Numerical simulation and artificial neural network prediction of hydrodynamic and heat transfer in a geothermal heat exchanger to obtain the optimal diameter of tubes with the lowest entropy using water and Al2O3/water nanofluid

Since the world is facing an energy crisis, and the emission of carbon-based fuel combustion is causing global warming, more research has been devoted to studying renewable energies. Due to the broad use of geothermal heat exchangers in central air-conditioning systems and also as a direct source of energy, in the present study, we have conducted a simulation of a geothermal heat exchanger and investigated the optimal diameter and nanoparticle concentration in order to minimize entropy generation. The fluid flow is turbulent, and SST K−ε is utilized as the turbulence model. The results show that as the thermal resistance of the inner wall increases, the outlet temperature rises. The effects of thermal resistance and diameter are thoroughly investigated, and the results have been used for selecting the least entropy generation. The results showed that the diameter in which the entropy is minimized increases by increasing the inner wall's thermal resistance. The best case is high thermal resistance in the outer walls and meager resistance in the inner walls. Using inner walls with high thermal resistance can decrease the adverse effects of entropy generation in large diameters. Also, the addition of nanoparticles is investigated, and the average Nusselt number and entropy generation, respectively, increased and decreased by almost 10%. Artificial neural network models are proposed to predict Nusselt number and entropy generation based on numerical results. The models are able to achieve MAE of lower than 3% and R2 higher than 0.95.

Comparison of GRNN and RF algorithms for predicting heat transfer coefficient in heat exchange channels with bulges

In the research of heat transfer, heat exchanger plays an important role in highly integrated and high-precision thermal management to ensure the thermal balance. In previous studies, traditional experiments and CFD simulations consume lots of time and computational resources, while the heat transfer correlations have large errors. Hence, this research aims to establish a reliable method to predict the Heat Transfer Coefficient (HTC) of heat exchange channels more quickly and accurately. In this paper, General Regression Neural Network (GRNN) and Random Forests (RF) models, which are trained by hundreds of CFD simulation results, are adopted to predict the heat-exchange performances of channels with different height bulges. The prediction results show that the HTC of channels with different bulge arrangements are accurately predicted, supported by R2 > 0.97 in both training and validation sets. Also, it shows that GRNN is more applicable to heat exchange channels than RF. Besides, it can be inferred from the prediction results that the front-end bulge has a significant impact on the overall HTC, as does the uniformity of the bulge height. In conclusion, machine learning algorithms have great potential in predicting the HTC of channels, and the GRNN algorithm may perforce better when calculate other complex heat transfer problems.

Air side performance characterization of wavy Fin-and-tube heat exchangers having elliptic tubes with large waffle heights

Demand of less energy consumption and higher energy efficiency suggests increasing heat transfer performance of a fin-and-tube heat exchanger. This paper experimentally and numerically examines the air side performance of wavy fin-and-tube heat exchangers (FTHXs) with elliptic tubes. In a properly regulated and evenly distributed airflow supply loop, the inlet/outlet temperatures and pressure drops across the test section are obtained using two measuring meshes and a differential pressure transducer to resolve thermal and frictional outcomes in terms of the heat transfer coefficient and pressure drop. The theoretical formulation considers the thermo-fluid analysis of crossing airflows over the wavy finned tube heat exchanger. In this research, the test heat exchanger contains elliptic tubes incorporated with 4-row wavy fins, while the proposed design adopts a large setting of 3.24-mm waffle height and 3.02-mm fin pitch, respectively. The predicted heat transfer coefficients and pressure drops are in good agreement with the measured data at varied inlet air velocities to validate the CFD model. Numerical simulations are then extended to investigate the influences of waffle height, fin pitch, wave length and inlet air velocity on thermofluid characteristics of FTHXs. Six common correlations of Colburn factors (j) and friction factor (f) factors are compared with predictions to evaluate their applicabilities for reasonable estimates of thermal and frictional performance of FTHXs. It is noticed that the correlation by Wang et al. can achieve the most accurate results in calculating the j and f factors.

Robust data-driven turbulence closures for improved heat transfer prediction in complex geometries

Data-driven turbulence closures have been used to successfully improve the prediction of first order effects in flows where the test case is similar to the training conditions. In this work, we examine the ability of such closures to improve second order effects, such as heat transfer for complex geometries. First, an upper bound for the available performance of the data-driven model is shown by inserting the Reynolds stresses from the high fidelity training data in a stable manner to a modified Reynolds-Averaged Navier–Stokes (RANS) model. Data-driven closures are then developed from this data set with a focus on robustness and the improvement in the heat transfer effects analysed. The geometries considered are four topology optimised square ducts. Each duct is optimised with varying weights of a multi-objective function to maximise heat transfer and minimise pressure losses. One duct is used in training and the remaining three are set aside for testing the developed closure. For the training case, it is found that simple data-driven closures for the Reynolds stresses alone, are capable of improving prediction of the velocity and temperature fields by 38.2% and 34.8% respectively even in complex geometries. These improvements are largely retained in the testing cases demonstrating the robust generalisation of the developed model for this class of flow.

Non-gray gas and particle radiation in a pulverized coal jet flame

In pulverized coal combustion, the participating radiative media includes gas, soot, and coal/char particles; a fundamental understanding of their radiation behavior is essential for the prediction of radiation heat transfer and control of radiation in the furnace. In this study, the individual contributions of gas, soot, and coal particles to the total radiation were examined based on the large eddy simulation of the CRIEPI pulverized coal jet flame. The radiation transfer equation was solved using the discrete ordinate method with state-of-the-art non-gray radiative property models of multi-phase media. The full spectrum correlated k-distributions (FSCK) model was used to calculate the radiative property of the gas-soot mixture. A burnout-based particle radiative property model was applied for coal/char particles. The results showed that the predicted spatial distributions of coal particles, tar, and soot were in good agreement with the experimental data. The gray gas radiation model predicted temperatures approximately 50 K lower in this flame, and the peak soot volume fraction was predicted at approximately 22% lower. For the laboratory scale burner under examination, the individual radiative contributions of gas, soot, and coal/char were found to be 35%, 40%, and 25%, respectively, so that it was necessary to consider all components for accurate prediction. Moreover, these contribution ratios varied with the flame position. Because of the high particle concentration, the individual contribution of coal/char particles accounted for up to 65% in the devolatilization region. Soot radiation played an important role in the pulverized coal flame. In the region where the ensemble-averaged soot volume fraction reached the maximum of 2.7 ppm, the individual contribution of soot was 50% of the total radiation, which accounted for 70% of the total particles (soot and char) radiation.

Investigation on the radiation heat transfer in dilution zone of a 100 kWth oxy-fuel pressurized circulating fluidized bed

Due to high pressure, complicated flue gas components and high particle concentration in dilute phase region, radiation heat transfer in the oxy-fuel combustion pressurized circulating fluidized bed (PCFB) is complex. To indicate the radiation heat transfer, both experiments and numerical simulations were carried out, where the Computational Fluid Dynamics (CFD) modelling was based on the full spectrum correlated k-distribution model (FSCK) combined with Mie theory. The multiphase flow behaviors, combustion reactions and radiation heat transfer of the PCFB under oxy-fuel combustion at 0.6 MPa were revealed with good agreement between the simulated result and the experimental data. The predictions of radiation heat transfer were accomplished under different oxygen concentration and operating pressure. The results illustrated that the existence and accumulation of particles lead to the increase of flue gas optical thickness, which hinders more radiation energy transmitted to the wall. Various oxygen concentration leads to the different distribution of temperature and radiative medium concentration, which all have an impact on total radiation intensity. Compared with temperature, particle radiation and gas radiation have less effect on radiation heat transfer under different atmosphere. In addition, the operating pressure affected the flue gas temperature as well as particle concentration and radiative gas concentration in the dilution zone, and the combustion atmosphere also has an influence on the above characteristics. Complicated thermodynamic states lead to dissimilar thermal radiation characteristics under different operating condition.