Prediction Of Heat Transfer Coefficient Research Articles

Assessing the effects of fluids flow on heat transfer performance in direct contact heat transfer process through EMD-LSSVM model: An experimental study

Abstract The problem that the exact nonlinear connection between fluids flow and heat transfer in direct contact heat evaporator was studied. The direct contact heat evaporator structure can be optimized and direct contact heat transfer process can be improved with the help of the optimum matching relationship between the complex flow and heat transfer. The methods used are the signal processing technology (i.e., empirical mode decomposition) and the machine learning algorithm (i.e., least squares support vector machine) for calculating experimental heat transfer coefficient here. The important specific and quantitative results are that the volumetric heat transfer coefficient prediction accuracy can be improved by the proposed the hybrid model. When empirical mode decomposition is combined with least squares support vector machine, the accuracy of the hybrid model can be improved by 20% without and 62% with the influencing factors. The conclusions that can be drawn from the results are two-fold. The optimal matching relationship between internal flow characteristics and heat transfer performance can be understood in direct contact heat transfer system for waste heat utilization. The hybrid model can improve the prediction accuracy and efficiency, reduce the number of experiments and costs, save raw materials and shorten the design cycle. Hence, the novelty of the work is that a new hybrid model is proposed for predicting VHTC. The gap it filled in the literature is that the prediction of volumetric heat transfer performance during direct contact heat transfer process is considered. In addition, it is carried out in a highly accurate and highly efficient way using this proposed model.

A theoretical analysis and parametric study of filmwise condensation on three-dimensional pin fins

In this paper, a theoretical model of filmwise condensation of steam on three-dimensional pin fins fabricated by selective laser melting (SLM), an additive manufacturing (AM) technique, is developed. The model considers the effects of surface tension and gravity on the liquid film flow over the pin fin surface. The three-dimensional nature of the liquid film flow over the fin flank and the unique features of the fin structures as a result of the laser melting process are modeled. Visualization studies are performed to verify the assumptions made in the model. From the modeling results, the local heat transfer coefficient and length-averaged heat transfer coefficient are obtained. The liquid film thickness at various locations of the pin fin is analyzed. The effects of fin tip dimensions, fin stem radius and fin pitch on the liquid film characteristics and heat transfer coefficient are systematically investigated. Our results showed that a thin film region exists in the flat and circular segments of the fin tip which cover approximately 25 – 30% of the fin surface. For a fixed fin diameter, it is found that the length-averaged heat transfer coefficient can be optimized by varying the dimensions of the flat and circular segments. A locally thin film region resulting from the suction effect is observed near the fin base for small fin spacings. However, the suction effect reduces with increasing fin spacing. Due to the three-dimensional nature of the pin fins which induces surface tension in the circumferential direction, the liquid film distribution is uniform. The differences in the length-averaged heat transfer coefficient at different circumferential locations are smaller than 5%. Finally, a comparison with existing experimental results demonstrates that a relatively accurate prediction of the average heat transfer coefficient can be achieved by our model with a maximum deviation of 8.3%.

Correlations for predicting heat transfer coefficients in bubble columns

Knowledge of the heat transfer coefficients in bubble columns can offer valuable references for the design and scale-up of reactors. This work aims to develop reliable correlations that can be applied in precisely predicting the global and local heat transfer coefficients in air-water or air-water-glass beads bubble columns. The influence of superficial gas velocities, column dimensions, volume fraction of particles, internals and radial positions on the heat transfer coefficients is considered in the developed correlations. In addition, the effect of the function forms on the accuracy of calculated local heat transfer coefficients is investigated. The Levenberg-Marquardt Algorithm is used to determine the values of the parameters in the empirical correlations. The recommended correlations can be applied in accurately predicting the heat transfer coefficients in bubble columns.

Numerical Study on Performance Optimization of an Energy-Saving Insulated Window

Window energy consumption has become a key factor in designing buildings with optimal energy efficiency. To that end, herein, the use of an energy-saving insulated window (ESIW) is proposed, particularly for winter heat conservation. DeST software was used to evaluate the energy consumption properties of a house with an ESIW-structure window, as well as that of six other window structures currently on the market. The results were subsequently compared. Furthermore, a series of numerical simulations were carried out using Airpak software to investigate the insulation performance of four ESIW models (A, B, C, and D) under different influencing factors. Finally, the response surface method (RSM) was used to obtain the optimal ESIW structure installation conditions and the weight of each factor. The data shows that houses with ESIW-structure windows exhibit a more suitable indoor natural temperature; less heating load, cooling load, and cumulative annual load; and a more feasible price–load ratio than other energy-saving windows. Furthermore, the average temperature gradually decreased in response to decreasing the electric heater power and energy-saving standard, and increasing the heat transfer coefficient (HTC) and window-to-wall ratio (WWR). Thus, as the energy-saving standard (ESS) increases, the importance of the WWR increases in parallel. This study puts forward an HTC prediction formula that is applicable to different conditions. The optimal thermal efficiency conditions consisted of HTC = 1.07 W/m2 × K, WWR = 0.26, and an ESS of 75%. This study demonstrates that the ESIW system has optimal energy-saving properties and broad adaptability and operability, which can be applied in building insulation as a key insulation component.

Prediction of boiler gas side effective heat transfer coefficients using mixture density networks and historic plant data

Machine learning has received increased recognition for applications in engineering such as the thermal engineering discipline due to its abilities to circumvent thermodynamic simulation approaches and capture complex inter-dependencies. There have been recent headways to couple deep learning models to process simulations, given the deeper insight they can provide. The present study entails the development of a mixture density network (MDN) capable of predicting effective heat transfer coefficients for the various heat exchanger components of a utility scale boiler. Large boilers are susceptible to dead zones and other anomalous phenomena that influence performance and manifest as multimodalities in the measured data, which system-level 1D process models struggle to capture. The overall water-side heat load calculation, as well as mass and energy balances around the components were done to determine the heat transfer coefficients at each stage of the boiler using historic sensor data. The measured data was then used to train a deep learning model capable of outputting predicted heat transfer coefficients and local model uncertainty. The predictive model can be coupled to a water circuit process model which can be used to study aspects such as metal temperatures and operating philosophies at the different operating loads of the plant.

Evaluation and prediction of overall heat transfer coefficient of Scraped surface heat exchanger during kheer making

Scraped surface heat exchanger (SSHE) is one of the adaptable equipments for products which are viscous, containing suspended constituents which tend to deposit and form layer on the heat transfer surface. The study was undertaken to evaluate and predict the overall heat transfer coefficient (U) of SSHE for making kheer. The performance of the developed SSHE was evaluated at three different steam pressures (1.0, 1.5 and 2.0 kg/cm2), scraper speeds (10, 20 and 30 rpm) and batch sizes (10, 15 and 20 kg) during kheer making. There was significant increase in the U-value with increase in operating steam pressures, batch sizes and scraper speeds. The quality of the kheer prepared in SSHE at optimized condition (i.e. 1 kg/cm2 steam pressure, 20 kg batch size and 30 rpm scraper speed) was found superior in terms of the sensory attributes, having U-value of 1197.40 W/m2K, steam side heat transfer coefficient (ho) was 8634.60 W/m2K and product side heat transfer coefficient (hi) was 2251.53 W/m2K. The regression equation was developed for predicting the U-value.

Prediction of Condensation Heat Transfer Coefficient based on the Flow Pattern inside 4 mm Microfin Small-Diameter Tubes

Prediction of Condensation Heat Transfer Coefficient based on the Flow Pattern inside 4 mm Microfin Small-Diameter Tubes

Application of support vector machines for accurate prediction of convection heat transfer coefficient of nanofluids through circular pipes

PurposeConvection is one of the main heat transfer mechanisms in both high to low temperature media. The accurate convection heat transfer coefficient (HTC) value is required for exact prediction of heat transfer. As convection HTC depends on many variables including fluid properties, flow hydrodynamics, surface geometry and operating and boundary conditions, among others, its accurate estimation is often too hard. Homogeneous dispersion of nanoparticles in a base fluid (nanofluids) that found high popularities during the past two decades has also increased the level of this complexity. Therefore, this study aims to show the application of least-square support vector machines (LS-SVM) for prediction of convection heat transfer coefficient of nanofluids through circular pipes as an accurate alternative way and draw a clear path for future researches in the field.Design/methodology/approachThe proposed LS-SVM model is developed using a relatively huge databank, including 253 experimental data sets. The predictive performance of this intelligent approach is validated using both experimental data and empirical correlations in the literature.FindingsThe results show that the LS-SVM paradigm with a radial basis kernel outperforms all other considered approaches. It presents an absolute average relative deviation of 2.47% and the regression coefficient (R2) of 0.99935 for the estimation of the experimental databank. The proposed smart paradigm expedites the procedure of estimation of convection HTC of nanofluid flow inside circular pipes.Originality/valueTherefore, the focus of the current study is concentrated on the estimation of convection HTC of nanofluid flow through circular pipes using the LS-SVM. Indeed, this estimation is done using operating conditions and some simply measured characteristics of nanoparticle, base fluid and nanofluid.

Recommendation for prediction of pool boiling heat transfer of various liquids on microstructured surfaces

Recommendations for prediction heat transfer coefficients and critical heat flux according to available in the literature experimental data of heat transfer and critical heat flux for boiling different liquids on microstructured surfaces made by deformed cutting method are obtained. In the work, on available in the literature sources of experimental data on heat transfer and critical heat flux at boiling of various liquids on the microstructured surfaces made by the deforming cutting method, recommendations for prediction of heat transfer coefficients and critical heat fluxes are received. Microstructured surfaces allow to intensify the heat transfer is 1.1 to 6 times. Due to the variable wettability of microstructured surfaces elements, critical heat flux increase before 4 times. The proposed criteria equations allow predicting heat transfer coefficients with an error of 30%, and critical heat fluxes with an error of 30-35%. In order to improve the accuracy of forecasting, the possibility of using an artificial neural network model for generalizing heat transfer coefficients is shown. Forecasting using an artificial neural network model allows us to determine the heat transfer coefficients with an error of ±20%.The equations are of interest for designing cooling systems for microelectronic devices, heat and mass transfer devices, boiling zones of heat pipes and thermosyphons, etc.

Numerical Development of Heat Transfer Coefficient Correlation for Spray Cooling in Continuous Casting

The desire to remain competitive and continuously produce high quality and high strength steel at the maximum production rate in continuous casting requires dynamic control over the spray cooling rate. Efficient and uniform heat removal without cracking or deforming the slab during spray cooling is critical. The challenge is to obtain accurate Heat Transfer Coefficient (HTC) on the slab surface as boundary condition for solidification calculations. Experiment based HTC correlations are limited to handful operating conditions and they might fail when changes occur. The current study presents a numerical model for spray cooling featuring the simulation of atomization and droplet impingement heat transfer in continuous casting. With the aid of high-performance computer, parametric studies were performed and the results were converted into mathematically simple HTC correlations as a function of essential operating parameters. Finally, a Graphic User Interface (GUI) was developed to facilitate future applications of the correlations. The HTC prediction is stored in the versatile comma-separated values (csv) format and it can be directly applied to solidification calculations. The proposed numerical methodology should benefit the steel industry by expediting the development process of HTC correlations and can further improve the accuracy of the existing casting control systems.

Heat Transfer Correlations for Secondary Cooling in Continuous Casting

The general term “spray cooling” is for the research presented here limited to the spray cooling of hot surfaces with film boiling, starting at temperatures of about 1200 °C and finishing at the Leidenfrost point where cooling intensity changes rapidly. This is typical area of secondary cooling in continuous casting. Herein, a correlation for Heat Transfer Coefficient (HTC). The most frequently used parameter of water impingement density is in the presented correlation used together with impact pressure to get good results. This study uses both water and mist nozzles. It is shown and experimentally verified why equations based only on the water impingement density cannot provide sufficiently precise predictions of HTC.

Predicting heat transfer coefficient of a shell-and-plate, moving packed-bed particle-to-sCO2 heat exchanger for concentrating solar power

The particle-to-sCO2 heat exchanger plays a significant role in coupling the high temperature particle receiver to supercritical carbon dioxide Brayton cycle in concentrating solar power plants. In this work, heat transfer characteristics of a shell-and-plate, moving packed-bed heat exchanger are numerically evaluated through continuum modeling. It is found that the local heat transfer coefficient h for different input parameters have a similar shape: h quickly drops at the thermal entry region and then remains as a constant. The asymptotic value of h increases with the particle flow velocity but decreases with particle channel width. A universal correlation between the local heat transfer coefficient and the particle flow properties in terms of the dimensionless Nusselt number is then proposed. Moreover, by calculating the overall heat transfer coefficient and performing a sensitivity analysis, we show that the heat exchanger has a two-regime behavior: in the regime with low particle flow rate, the performance mostly depends on the particle flow velocity and the channel width, and is restricted by the small amount of energy stored in the particle flow. In the high flow rate regime, the effective conductivity of the particle flow becomes the determining factor on the performance of the heat exchanger.

Visualization study on the condensation in a propylene loop heat pipe operating at condenser temperatures between 153 and 283 K

A cryogenic loop heat pipe (CLHP) is an effective means of temperature control at low temperatures, but the physical properties of cryogenic working fluids are quite different from those of room-temperature working fluids, and their phase change and heat transfer characteristics need to be further studied. In this paper, a test system for evaluating the loop heat pipe (LHP) heat transfer performance and visualizing the condensation phenomenon in the condenser was fabricated. The performance of a propylene LHP with 20 W of heat exchange was tested as the condenser temperature was varied from 283 to 153 K, and the flow patterns and heat transfer characteristics during condensation were obtained. With the decreasing condenser temperature, slug flow disappeared, and pseudo-slug flow occurred and transitioned into wavy-annular flow, which was mainly caused by the increase of density ratio and viscosity ratio of the liquid to vapor. These change of flow pattern further affected the two-phase heat transfer and the performance of the CLHP. Pseudo-slug flow was observed at a condenser temperature of 273 to 203 K and it had a notable influence on the condensation pressure drop. The heat transfer coefficient of the two-phase region ranged from 2500 to 5500 W/m2K. When the condenser temperature ranged from 203 to 173 K, the transition of flow pattern and the fluctuation in the system caused a large deviation between the obtained and predicted heat transfer coefficients, and at other condenser temperatures, the obtained values were consistent with the predicted values for the corresponding flow patterns.

Effect of steam velocity during dropwise condensation

Dropwise condensation (DWC) is a complex phenomenon involving droplets nucleation, coalescence and motion. Starting from the nanoscale up to the macroscale, DWC involves millions of droplets per square meter. The maximum dimension assumed by a droplet before sliding is characterized by the departing radius: it is the radius at which the droplet starts to sweep through the surface as a consequence of the acting forces (gravity force, adhesion force, drag force induced by the flowing vapor). In the literature, very few works investigate the effect of vapor velocity on the heat transfer coefficient (HTC) and on the droplet departing radius during DWC. Furthermore, the effect of vapor velocity is not accounted for in available DWC models. In the present paper, DWC of steam has been promoted on an aluminum sol-gel coated surface. Heat transfer coefficients and droplets departing diameters have been measured at 107 °C saturation temperature, heat flux of 335 kW m−2 and average vapor velocity between 2.7 m s−1 and 11 m s−1. A method for the estimation of the droplet departing radius in presence of non-negligible vapor velocity is here proposed. The equation accounting for vapor velocity has been included in the model by Miljkovic et al. [1] for heat transfer coefficient prediction during DWC and has been assessed using the present data and two other datasets from independent laboratories.

Theoretical analysis of annular film flow evaporation inside microfin tube

This study developed a theoretical model of film evaporation heat transfer in the microfin tube under a high vapour quality region. A liquid film flow model in the micro groove was constructed. The thickness and heat transfer characteristics in the thin-film region and meniscus region were analysed. As a result, the heat transfer coefficient increases as the vapour quality and the mass flux increase. Before the dryout occurs, the heat transfer coefficient reaches a peak due to the thinned meniscus film thickness. The theoretical predicted heat transfer coefficients were compared with available experimental data under actual tube geometries and operating conditions, and were well consistent with the experimental data. This result indicated the present theoretical analysis is beneficial for understanding the heat transfer enhancement mechanism in the microfin tubes.

Heat transfer on microstructured surfaces with pool boiling of various liquids

Recommendations for predicting heat transfer coefficients and critical heat fluxes are developed on the basis of available experimental data on heat transfer and critical heat fluxes for boiling of different liquids on microstructured surfaces realized by deformed cutting method. Microstructured surfaces allow intensifying heat transfer 1.1 to 6 times. Due to the variable wettability of microstructured surface elements, critical heat fluxes increase over 4 times. The proposed criteria equations allow predicting heat transfer coefficients with an error of 30%, and critical heat fluxes with an error of 30-35%. The equations are of interest for designing cooling systems for microelectronic devices, heat and mass transfer devices, boiling zones of heat pipes and thermosyphons, etc.

An estimation of pressure rise and heat transfer rate for hybrid nanofluid with endoscopic effects and induced magnetic field: computational intelligence application

This article reflects on the use of computational intelligence technique based on feed-forward nonlinear input–output artificial neural network for the prediction of pressure rise and heat transfer coefficient in peristaltic pumping of Ree–Eyring hybrid nanofluid through an endoscope. Gold nanoparticles are dispersed in Al2O3/blood nanofluid for hybridization with 0.02% and 0.05% volume fraction of Al2O3 and Au, respectively. Estimation parameters include Ree–Eyring fluid parameter (0.05–0.2), magnetic parameter (1.0–3.0), radius ratio (0.7–0.9), and Brinkman number (0.2–0.6). Homotopy analysis method is employed to achieve experimental data from proposed mathematical model. Computational results for profiles of ΔP and Z are illustrated graphically, and it is found that M and α lead to amplify pumping while more pumping is needed in the case of rising χ. Moreover, heat transfer coefficient enhances with the increase in Br and χ. Experimental data are then validated using artificial neural network model. Levenberg–Marquardt backpropagation training algorithm is used in supervised learning. The degree of fitting the data is assessed on the bases of optimized decision threshold of mean square error. Network outputs are presented using error histogram, time-series responses, performance, and train-state plot which show that proposed technique is able to provide robust and very accurate output with least mean square error, strong correlation between target and network output with R = 1, trained with optimized weights at decreasing gradient, and a better convergence with small mu at each corresponding epochs. Further, comparison of analytical results with previously published literature shows a good agreement.

Prediction of Heat Transfer Coefficient for Condensation Process using CFD by Varying Fluid Velocities

Prediction of Heat Transfer Coefficient for Condensation Process using CFD by Varying Fluid Velocities

Spray Cooling Heat Transfer above Leidenfrost Temperature

This study considers spray cooling starting at surface temperatures of about 1200 °C and finishing at the Leidenfrost temperature. Cooling is in the film boiling regime. The paper uses experimental techniques for the study of which spray parameters are necessary for good prediction of spray cooling intensity. The research is based on experiments with water and air-mist nozzles. The following spray parameters were measured together with a heat transfer coefficient: water flowrate, water impingement density, impact pressure, droplet size and velocity. Derived parameters as droplet kinetic energy, droplet momentum and droplet Reynolds number are used in the tested correlations as well. Ten combinations of spray parameters used for correlation functions for the heat transfer coefficient (HTC) are studied and discussed. Correlation functions for prediction of HTC are presented and it is shown which spray parameters are necessary for reliable computation of HTC. The best results were obtained when the parameters impact pressure and water impingement density were used together. It was proven that the correlations based only on water impingement density, which are the most frequent in literature, can not provide reliable results.

Machine learning algorithms to predict flow condensation heat transfer coefficient in mini/micro-channel utilizing universal data

Miniature condensers utilizing mini/micro-channel has been recognized as one effective technique for designing a compact heat rejection device. However, because of the complex behaviors in phase-change systems like flow condensation, accurately predicting heat transfer coefficients can be a challenging task. In this study, a large database is utilized to develop machine-learning based models for predicting condensation heat transfer coefficients in mini/micro-channels. A consolidated database of 4,882 data points for flow condensation heat transfer in mini/micro-channels is amassed from 37 sources that includes 17 working fluid, reduced pressures of 0.039 – 0.916, hydraulic diameters of 0.424 mm – 6.52 mm, mass velocities of 50 < G < 1403 kg/m2s, liquid-only Reynolds numbers of 285 – 89,797, superficial vapor Reynolds number of 44 – 389,298, and flow qualities of 0 – 1. This consolidated database is utilized to develop four machine learning based models viz., Artificial Neural Netoworks (ANN), Random Forest, AdaBoost and Extreme Gradient Boosting (XGBoost). A parametric optimization is conducted and ANN and XGBoost showed the best predicting accuracy. The models with dimensionless input parameters: Bd, Co, Frf, Frfo, Frg, Frgo, Ga, Ka, Prf, Prg, Ref, Refo, Reg, Rego, Suf, Sug, Sufo, Sugo, Wef, Wefo, Weg, and Wego predicted the test data for ANN and XGBoost models with MAEs of 6.8% and 9.1%, respectively. The optimal machine-learning models performed better than a highly reliable generalized flow condensation correlation. Models were also able to predict excluded datasheets with reasonable accuracy when data points including the specific working fluid were part of the training dataset of the remaining datasheets. The work shows that machine learning algorithms can become a robust new predicting tool for condensation heat transfer coefficients in mini/micro channels.