Prediction Of Heat Transfer Coefficient Research Articles

Simulation of turbulent convective heat transfer of γ-al2o3/water nanofluid in a tube by ann and anfis models

In order to modeling and predicting heat transfer coefficient in na nofluids, art ificial neural network (ANN) and Adaptive Neuro-fuzzy Inference system (ANFIS) were used in this study. In ANN and ANFIS, Input data are Reynolds number and nanoparticles volume fractions, and output data is heat transfer coefficient. Both of them could predict very well, and there is good agreement between experimental data and predicted data. In ANFIS coefficient of determination (R2), average relative error and mean square error for train data are 0.99, 8.9×10–5 and 6.5476×10–5, respectively, and for test data are one, zero and zero. According to results, by increasing the Reynolds number and volume fractions, the heat transfer coefficient increases. For base fluid in Re = 16300, heat transfer coefficient is 10961.38 W/m2K, and for volume fraction 0.135, heat transfer coefficient is 13947.72 W/m2K, therefore, heat transfer coefficient of nanofluids increased 1.27 time compared to that of base fluid. Results obtained from ANFIS are reliable, and can be used in prediction. Also, for ANN, ARE, MSE and R2 value are, –0.003, 6.38264×10–5 and 0.99, respectively. So, there is good agreement between experimental data and ANN results too. According to errors, can conclude ANFIS is slightly better than ANN.

PREDICTION OF BOILING HEAT TRANSFER COEFFICIENTS FOR MINI-CHANNELS

PREDICTION OF BOILING HEAT TRANSFER COEFFICIENTS FOR MINI-CHANNELS

Robust and general predictive models for condensation heat transfer inside conventional and mini/micro channel heat exchangers

To design, scale-up, and optimize the two-phase flow heat exchangers, accurate information about the heat transfer coefficient (HTC) is required. Here, an extensive database including 11,128 experimental data points from 80 sources, covering 37 condensing fluids over a wide range of operating conditions, were gathered for developing general and accurate models to forecast the HTC in mini/micro and conventional channel heat exchangers. The conventional intelligent approaches, namely, gaussian process regression (GPR), radial basis function (RBF), and hybrid radial basis function (HRBF) and also the least square fitting method (LSFM) were utilized for development of the new models. Firstly, the gathered data were used in evaluation of earlier two-phase HTC models, and these models showed average absolute relative error (AARE) values higher than 29%. Thus, more accurate models are necessary for these systems. Based on Pearson’s correlation analysis, eight dimensionless groups were selected as the most important input parameters. The GPR model showed the best results in estimation of Nu number for the test process with AARE of 4.50%. However, RBF and HRBF models estimated the HTC with AARE values 19.41% and 24.36% for testing data points, respectively. In addition, a general explicit correlation was developed by the least square fitting method (LSFM) for estimating the HTC, with acceptable results at an AARE of 23.40% for all analyzed data. The performance of the new general correlation, GPR and the previous models were assessed for different channel sizes, flow regimes, fluid types, and operating conditions. The new models and well-known earlier ones were examined with about 372 additional data samples from four new independent sources. The proposed models showed excellent results for prediction of HTC. Finally, a sensitivity analysis was studied based on the experimental data and the outcomes of the LSFM.

ДИНАМИЧЕСКИЙ КОНТРОЛЬ И ПРОГНОЗИРОВАНИЕ СОСТОЯНИЯ ТЕПЛООБМЕННОГО ОБОРУДОВАНИЯ С УЧЕТОМ ВЛИЯНИЯ УПРАВЛЯЮЩИХ ВОЗДЕЙСТВИЙ

In this work, an artificial neural network (ANN) model was developed with the aim of predicting heat transfer coefficient for shell and tube heat exchanger. Using the model, the influence of the control parameter on the accuracy of forecasting was studied. The ANN was designed with two input layers. The first input layer contains historical process data including temperatures and material stream flow rates. The second input layer was supplied with future values of the control parameter. The prediction accuracy was compared with the work of the ANN without the second input layer by two statistical indicators: the mean absolute percentage error (MAPE) and the coefficient of determination (R2). The increase in values is MAPE = 4,0%, R2 = 0,06. The use of this approach improves the results of forecasting and makes it possible to develop an algorithm for selecting the optimal change in the control parameter.

Experimental and theoretical study of vapor/air mixture condensation inside an inclined blind-end pipe in natural convection with considering fog formation

This study investigated the vapor condensation characteristics in an inclined pipe under natural convection conditions experimentally and numerically. The inclined blind-end concentric pipe had an inner diameter of 134 mm and a length of 680 mm, which is cooled by cooling water. The experimental pressure range was 0.2–0.6 MPa, and the dimensionless mass number range was 0.44 to 202. The fog formation phenomenon was observed in the experiment. An empirical correlation with a broad range of dimensionless mass numbers was developed based on the experimental results to predict the heat transfer coefficient (HTC). When the air mass fraction was large and the HTC was low, the effect of fog formation on the HTC had to be considered. In the numerical simulation, this study aimed to develop an HTC prediction model with broader applicability. Based on the diffusion boundary layer theory, this study improved Peterson's model by adding fog effect and wave effect. The model showed high adaptability to this experiment and other experiments in the natural convection condensation database. The improved model performed significantly better, with 96% of the data falling within an error zone of 30%. The wide range of dimensionless mass number test data might complement the natural convection condensation experimental database.

Distillate Flux Enhancement of Direct Contact Membrane Distillation Modules with Inserting Cross-Diagonal Carbon-Fiber Spacers.

A new design of direct-contact membrane distillation (DCMD) modules with cross-diagonal carbon-fiber spacers of various hydrodynamic angles in flow channels to promote turbulence intensity was proposed to enhance pure water productivity. Attempts to reduce the temperature polarization coefficient were achieved by inserting cross-diagonal carbon-fiber spacers in channels, which create wakes and eddies in both heat and mass transfer behaviors to enhance the permeate flux enhancement. A simplified equation was formulated to obtain the theoretical predictions of heat transfer coefficients in the current DCMD device. The permeate fluxes and temperature distributions of both hot and cold feed streams are represented graphically with the inlet volumetric flow rate and inlet temperature of the hot saline feed stream as parameters. The higher distillate flux of countercurrent-flow operations for saline water desalination was accomplished as compared to the concurrent-flow operations of various hydrodynamic angles. The results show that the agreement between the theoretical predictions and experimental results is reasonably good. The effects of countercurrent-flow operations and inserting carbon fiber spacers have confirmed technical feasibility and device performance enhancement of up to 45%. The influences of operating and design parameters on the pure water productivity with the expense of energy consumption are also discussed.

Turbulent Heat Transfer Characteristics of Supercritical Carbon Dioxide for a Vertically Upward Flow in a Pipe Using Computational Fluid Dynamics and Artificial Neural Network

Abstract Modeling of turbulence heat transfer for supercritical fluids, using computational fluid dynamics (CFD) software, is always challenging due to the drastic property variations near the critical point. The use of artificial neural networks (ANNs) along with numerical methods has shown promising results in predicting heat transfer coefficients of heat exchangers. In this study, the accuracy of four different turbulent models available in the commercial CFD software—ansysfluent is investigated against the available experimental results. The k–ε Re-normalization group (RNG) model, with enhanced wall treatment, is found to be the best-suited turbulence model. Further, K–ε RNG turbulence model is used in CFD for parametric analysis to generate the data for ANN studies. A total of 1,34,698 data samples was generated and fed into the ANN program to develop an equation that can predict the heat transfer coefficient. It was found that, for the considered range of values, the absolute average relative deviation is 3.49%.

Development of a mathematical model for prediction of heat transfer coefficient in micro-channel heat sink

The advancement of miniaturization and high-density manufacturing of electronic devices necessitates lightweight and high-performance heat dissipation components. As a result, traditional coolants and cooling technologies are rapidly unable to satisfy the ever conditioning problems and requirements of specific high temperature generated electronic devices. A microchannel heat sink (MCHS) has been extensively researched and commonly used in electronics cooling as a novel system that is highly suitable for applications with high power and small heat dissipation space. This research presents a mathematical model focused on dimension analysis to determine the heat transfer coefficient of a microchannel heat sink in electronics cooling. Buckingham Pi (π)theorem with dimensional analysis was used to determine a functional correlation between the heat transfer coefficient and independent variables. Independent variables comprise; thermal conductivity (copper MCHS), hydraulic diameter (MCHS), length (MCHS), a mass flow rate of nanofluid, the density of nanofluid, inlet temperature of nanofluid, heat flux, % by volume concentration of nanoparticle and thermal conductivity of nanofluid. The established relationship between dependent and independent variables concerning various operating conditions is helpful for analyzing behaviour.

The Heat Transfer Coefficient Predictions in Engineering Applications

Most engineering applications have boundary layers; the convective transport of mass, momentum and heat normally occurs through a thin boundary layer close to the wall. It is essential to predict the boundary layer heat transfer phenomenon on the surface of various engineering machines through calculations. The experimental, analogy and numerical methods are the three main methods used to obtain convective heat transfer coefficient. The Reynolds analogy provides a useful method to estimate the heat transfer rate with known surface friction. In the Reynolds analogy, the heat transfer coefficient is independent of the temperature ratio between the wall and the fluid. Other methods also ignore the effect of the temperature ratio. This paper summarizes the methods of predicting heat transfer coefficients in engineering applications. The effects of the temperature ratio between the wall and the fluid on the heat transfer coefficient predictions are studied by summarizing the researches. Through the summary, it can be found that the heat transfer coefficients do show a dependence on the temperature ratio. And these effects are more obvious in turbulent flow and pointing out that the inaccuracy in the determination of the heat transfer coefficient and proposing that the conjugate heat transfer analysis is the future direction of development.

Status, Challenges, and Potential for Machine Learning in Understanding and Applying Heat Transfer Phenomena

AbstractMachine learning (ML) offers a variety of techniques to understand many complex problems in different fields. The field of heat transfer, and thermal systems in general, are governed by complicated sets of physics that can be made tractable by reduced-order modeling and by extracting simple trends from measured data. Therefore, ML algorithms can yield computationally efficient models for more accurate predictions or to generate robust optimization frameworks. This study reviews past and present efforts that use ML techniques in heat transfer from the fundamental level to full-scale applications, including the use of ML to build reduced-order models, predict heat transfer coefficients and pressure drop, perform real-time analysis of complex experimental data, and optimize large-scale thermal systems in a variety of applications. The appropriateness of different data-driven ML models in heat transfer problems is discussed. Finally, some of the imminent opportunities and challenges that the heat transfer community faces in this exciting and rapidly growing field are identified.

RANS 3D CFD simulations to enhance the thermal prediction of tyre thermodynamic model: A hierarchical approach

In this work, a combined numerical/experimental analysis is performed for an automotive tyre. A preliminary experimental activity is realized on examined tyre to measure the temperatures of its layers under various operating conditions. In a second stage, a 3D CFD model of tyre is developed in a commercial code and steady RANS simulations are performed in the full range of angular velocity with the aim to refine the prediction of convective thermal power and heat transfer coefficient. CFD simulation results are passed to a user-defined 3D thermodynamic model to furnish a detailed and reliable tyre thermal output with the advantage of a low computational time. Tyre thermodynamic model, enhanced by CFD-related thermal characteristics, demonstrates the capability to properly forecast the measured temperature of tyre layers in a wide range of investigated operating conditions. The proposed numerical approach represents a valuable tool supporting the optimization of tyre behavior and the development of advanced control rules for optimal tyre management.

Pressure influence on saturated boiling of R141b over Cu and Si-coated surfaces

Pool boiling has been a research topic of great interest over the decades due to its inherent capabilities of large heat transfer rates with narrow temperature gaps and it advocates its suitability in a large number of industrial applications. The present paper describes the effect of operating pressure on pool boiling of R141b over a plain Cu surface as well as Si-coated surface prepared by a direct current (DC) sputtering technique. The working fluid R141b undergoes saturated pool boiling under pressure ranging from −20 kPa(g) to + 30 kPa(g) with the acquired experimental data and trends compared with the existing correlations and theories. Within the pressure range considered, the surface superheat variation was insignificant at lower heat fluxes; however, at higher heat fluxes, the maximum reduction was found to be by 9.5°C and 14.8°C for the plain Cu surface and Si-coated surface, respectively, regarding the corresponding values of −20 kPa(g) pressure. With respect to the results under atmospheric conditions, at the pressure of + 30 kPa(g), a corresponding increase in heat transfer coefficient of 12.1% for the plain Cu surface and of 17.8% for a Si-coated surface was observed at a heat flux of 225 kW/m2 and 272 kW/m2, respectively. In comparison to the results under atmospheric pressure conditions, the accompanying augmentation in the critical heat flux was observed as 13.3% for the plain Cu and 21.2% for the Si-coated surfaces at a pressure of + 30 kPa(g). Based on the experimental data, a correlation is developed for predicting heat transfer coefficients within the given pressure range.

Investigation of Heat Transfer in a Large-Scale External Heat Exchanger with Horizontal Smooth Tube Bundle

In the research work, energy transport between a dense fluidized bed and submerged horizontal tube bundle is analyzed in the commercial external heat exchanger (EHE). In order to investigate the heat transfer behavior, the authors carried out eight performance tests in a fluidized bed heat exchange chamber with a cross-section of 2.7 × 2.3 m in depth and width and a height of 1.3 m. The authors have been developing a mechanistic model for the prediction of the average heat transfer coefficient, which includes the effect of the geometric structure of the tube bundle and the location of the heat transfer surface on the heat transfer rate. The computational results depict that the average heat transfer coefficient is essentially affected by superficial gas velocity and suspension density rather than bed particle size. The empirical correlations have been proposed for predicting heat transfer data since the existing literature data is not sufficient for industrial fluidized bed heat exchangers. On the basis of the evaluated operating conditions of an external heat exchanger, the optimal conditions where heat transfer occurs could be deduced. The developed mechanistic heat transfer model is validated by experimental data under the examined conditions.

Experimental analysis of high temperature flow boiling heat transfer and pressure drop in a plate heat exchanger

• High temperature flow boiling of seven working fluids is experimentally analyzed. • Two heat transfer mechanisms are identified among different working fluids. • A superposition model is developed for the heat transfer coefficient prediction. • The heat transfer coefficient of propane is up to 152% higher than that of R236fa. • Isobutane has the lowest frictional pressure drop among the seven working fluids. Organic Rankine cycle technology has gained worldwide acceptance as an efficient way to utilize low-grade heat sources. Plate heat exchangers are the most common type of heat exchanger employed as evaporators in small-scale organic Rankine cycle units, in which a high saturation temperature is the prevailing working condition. However, there is a lack of research on high temperature flow boiling in plate heat exchangers. This paper presents an experimental analysis on flow boiling heat transfer and pressure drop characteristics in a plate heat exchanger, and the development of prediction methods for the heat transfer coefficient and frictional pressure drop. Seven working fluids, R134a, R236fa, R245fa, R1234ze(E), R1233zd(E), propane and isobutane, were tested at the reduced pressures of 0.45, 0.55 and 0.65, corresponding to saturation temperatures ranging from 55 °C to 141 °C, and various mass fluxes. Two heat transfer mechanisms, nucleate boiling and thin-film evaporation, were identified in the heat transfer processes of the different working fluids, due to the diversity in their thermo-physical properties. Moreover, the results indicate that propane and isobutane have higher heat transfer coefficients than the other working fluids, while R236fa has the lowest heat transfer coefficient. The frictional pressure drops show the same characteristics for all the working fluids, increasing with the increase of the vapor quality and mass flux and the decrease of the saturation temperature. A superposition model presented in the paper achieves a good prediction for the heat transfer data, with a 12.8% mean absolute percentage deviation. A correlation developed in a previous work by the authors enables a prediction with an 11.1% mean absolute percentage deviation for the pressure drop data. The prediction methods presented in the paper will facilitate the modelling and design of plate heat exchanger evaporators in organic Rankine cycle units.

Prediction of the Oscillatory Heat Transfer Coefficient in Thermoacoustic Refrigerators

Thermoacoustic refrigerators are emerging devices that make use of meaningful high-pressure sound waves to induce cooling. Despite the accelerated progress in the field of thermoacoustics, knowledge of the heat transfer process in the heat exchange of the devices is still developing. This work applies different soft computing techniques, namely, an artificial neural network trained by particle swarm optimisation (ANN-PSO), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural networks (ANNs) to predict the oscillatory heat transfer coefficient in the heat exchangers of a thermoacoustic device. This study provides the details of the parametric analysis of an artificial neural network model trained by particle swarm optimisation. The solution model considers the number of neurons, the swarm population, and the acceleration factors to develop and analyse the architecture of several models. The regression model (R2) and mean squared error (MSE) were used to evaluate the accuracy of the models. The result showed that the proposed soft computing techniques can potentially be used for the modelling and the analysis of the oscillatory heat transfer coefficient with a higher level of accuracy. The result reported in this study implies that the prediction of the OHTC can be considered for the enhancement of thermoacoustic refrigerators performances.

Prediction of heat transfer coefficient and friction factor of mini channel shell and tube heat exchanger using numerical analysis and experimental validation

Minichannels are a key technology that results in energy, space, material, and cost savings in shell and tube heat exchangers (STHEs) because they improve performance by increasing compactness and reducing the dimensions, volume, and mass, as well as the amount of working fluids used. This paper employed the Computational Fluid Dynamics (CFD) method to investigate the effect of minichannels on the thermal and hydrodynamic performance of a STHE. The reliability and accuracy of numerical results were corroborated with experimental results. Numerical solutions were performed under constant heat flux conditions at 2650 < Re < 9500, the range in which internal flows are considered transition and turbulent flows. The results showed that the higher the Reynolds (Re) number, the higher the convective heat transfer coefficient (CHTC) and pressure drop. The performance benchmark (ψ) is defined as the variations in CHTC per unit of pressure drop. The results showed that the higher the Re number, the lower the ψ. Based on numerical solutions regarding ψ, the flow in the minichannel tube reached optimal conditions both thermally and hydrodynamically at Re ≅ 5900. The deviation between the numerical and experimental CHTCs ranged from −15 % to +15 %, while that between the numerical and experimental friction factors ranged from −13 % to +13 %. These results can be used for engineering designs and applications.

Two-Dimensional Flow Boiling Characteristics With Wettability Surface in Microgap Heat Sink and Heat Transfer Prediction Using Artificial Neural Network

Abstract As technology becomes increasingly miniaturized, thermal management becomes challenging to keep devices away from overheating due to extremely localized heat dissipation. Two-phase cooling or flow boiling in microspaces utilizes the highly efficient thermal energy transport of phase change from liquid to vapor. However, the excessive consumption of liquid-phase by highly localized heat source causes the two-phase flow maldistribution, leading to a significantly reduced heat transfer coefficient, high-pressure loss, and limited flow rate. In this study, flow boiling in a two-dimensional (2D) microgap heat sink with a hydrophilic coating is investigated with bubble morphology, heat transfer, and pressure drop for conventional (nonhydrophilic) and hydrophilic heat sinks. The experiments are carried out on a stainless steel (SS) plate, having a microgap depth of 170 μm using de-ionized (DI) water at room temperature. Two different hydrophilic surfaces (partial and full channel shape) are fabricated on the heated surface to compare the thermal performance with the conventional surface. Vapor films and slugs are flushed quickly on the hydrophilic surfaces, resulting in heat transfer enhancement on the hydrophilic heat sink compared to the conventional heat sink. The channel hydrophilic heat sink shows better cooling performance and pressure stability as it provides a smooth route for the incoming water to cool the hot spot. Moreover, the artificial neural network (ANN) prediction of heat transfer coefficient shows a good agreement with the experimental results as data fit within ±5% average error.

Heat transfer characteristics of horizontal tubes in the dilute phase of the pressurized fluidized bed

When the membrane wall water tubes and immersed tubes heat exchanger cannot effectively control the furnace temperature, arranging the horizontal tubes in the dilute phase is a method to solve the limited layout of the heat exchanger area. In order to study the heat transfer characteristics of the horizontal tubes in the dilute phase, the local and average heat transfer coefficients (HTC) were measured based on the pressurized fluidized bed apparatus under different pressures, particle sizes and gas velocities. The experimental results showed the improved heat transfer at elevated pressure and high gas velocities. The heat transfer tube with a lower height position had higher heat transfer coefficients because of the particle concentration effect. Under the same system pressure, the circumferential HTC of the horizontal tube were slightly higher at 0° and 180° circumferential positions, but the overall distribution tended to be uniform. Based on the experimental results, an empirical model for the average heat transfer coefficient prediction was developed and can predict 97.4% of the experimental data from this study and other different sources within 25% error with the absolute average deviation of 8.18%. This work can provide reliable experimental data for the design of heat exchanger tubes in dilute phase zone of pressurized fluidized bed and existing model verification. Also the correlation proposed in the paper can be translated to CFD code for further application.

On The Turbulent Flow Past Cambered Bodies with Blowing.(Dept.M)

This paper investigates, both experimentally and analytically, the heat and fluid flow around the trailing edge of a film cooled gas turbine blade. In the analysis, steady state, two dimensional, incompressible, recirculating turbulent flow is considered. The momentum and energy equations are derived and transformed to the generalized format of Gosman and et al (2). The kinetic energy and dissipation term of turbulence (i.e. the k-E, model) is con sidered in the turbulence computational programs. Small number of empirical constants were obtained from both the mathematical model and the conducted experimental work. These mathematical constants provide the most realistic predictions of heat transfer coefficients, when compared with the available experimental data. The investigation considers varying Reynolds number in the range of 6.25 x 104 to 1.29 x 105. There is a satisfactory agreement between the present experimental and theoritecal results, for the blowing parameter (M = Uc/U ) in the range from zero to one.

A numerical investigation on the heat transfer and turbulence production characteristics induced by a swirl spacer in a single-tube geometry under single-phase flow condition

Heat transfer in the rod bundle is augmented by the mixing vanes on the spacer grid. We conducted a computational fluid dynamics (CFD) simulation with three isotropic turbulence models – standard k–ε, realizable k–ε, and SST k–ω models – to investigate the relationship between heat transfer and turbulence behaviors downstream a simulant spacer (with four vanes) in a single tube under single-phase flow conditions. Quantitatively, the predicted heat transfer coefficient (HTC), secondary flow intensity, and turbulence intensity with the SST k–ω model displayed a better agreement (than the other isotropic models) with the experimental data in previous studies. Moreover, the turbulence production was localized in the near-spacer region (z/D < 10, where D is the inner diameter), which corresponds with the HTC augmentation region. These results indicate that examining the turbulence production when discussing the HTC augmentation downstream the spacer is essential.