Prediction Of Heat Transfer Coefficient Research Articles

Radial basis Neural Network Model to Prediction of Thermal Resistance and Heat Transfer Coefficient of Oscillating Heat Pipe Using Graphene and Acetone-Based Nanofluids

Radial basis Neural Network Model to Prediction of Thermal Resistance and Heat Transfer Coefficient of Oscillating Heat Pipe Using Graphene and Acetone-Based Nanofluids

Experimental study on heat transfer characteristics of dispersed flow during strong transient reflood process

The dispersed heat transfer correlations obtained in the quasi-steady-state heating experiment may have some limitations in the strong transient reflood process. Therefore, this article will conduct a visualization experiment on single-rod channel reflood cooling under different working conditions. By combining the transient hydrodynamic characteristics of liquid droplets and the wall temperature transient curve, the boiling heat transfer characteristics of the dispersed flow stage during the reflood process are studied. Through comparative analysis, this article found that the traditional post-CHF (Critical Heat Flux) heat transfer correlations are difficult to characterize the impact of droplet number and size changes. This article is based on the form of a divided path heat transfer mechanism model. In response to the strong transient characteristics of dispersed flow during the reflood process in a restricted channel, the single-phase steam heat transfer model and the steam-droplet interfacial heat transfer model have been modified, greatly improving the prediction accuracy. The RMS of the predicted heat transfer coefficient decreased from 0.68 in the original model to 0.24 in the new model.

Prediction of heat transfer coefficient and pressure drop of flow boiling and condensation using machine learning

Heat transfer coefficient and pressure drop are two critical parameters in diverse thermal engineering applications, and the prediction of these parameters plays a crucial role in the development of heat exchangers. Traditional methods are often reliant on empirical correlations based on limited refrigerants and experimental conditions. The present research explores the application of machine learning methods to predict the heat transfer coefficient and pressure drop of a wide range of pure and mixed refrigerants. The methodology involves the compilation of over 20, 000 experimental observations encompassing an extensive range of operating conditions, heat exchanger geometries, and refrigerants. A total of 27 machine learning algorithm models, such as regression tree, support vector machines, and deep neural networks, are trained on this diverse dataset to discern intricate patterns and dependencies. After dividing the dataset into 80% for training and the rest for validation, the machine learning prediction shows an ability to predict the heat transfer coefficient and the pressure drop with high accuracy in most of the refrigerants. Notably, the accuracy achieved by the machine learning models surpassed that of conventional correlations, highlighting their superior predictive capabilities. Furthermore, the accurate prediction of heat transfer enhancement methods added another layer of validation to the model’s effectiveness. It is noteworthy that the dataset employed in this study has been made publicly accessible online.

Heat transfer optimisation using novel biomorphic pin-fin heat sinks: An integrated approach via design for manufacturing, numerical simulation, and machine learning

With the availability of advanced manufacturing techniques, non-conventional shapes and bio-inspired/biomorphic designs have shown to provide more efficient heat transfer. Consequently, this research investigates the heat transfer performance and fluid flow characteristics of novel biomorphic scutoid pin fins with varying volumes and top geometries. Numerical simulations were conducted using four hybrid designs for Reynolds Number 5500–13500. The impact of pin fin 'top' geometrical features on the heat transfer coefficient (HTC) was evaluated by combining computational fluid dynamics (CFD), experimental data, and machine learning. The results highlighted that the new pin fins saved 6.3 % to 14.3 % volume/material usage but produced around 1.5 to 1.7 times more heat transfer than conventional square/rectangular fins. Also, manipulating pin fins via the top geometrical properties can lead to more uniform velocity and temperature distributions while demonstrating the potential for increased thermal efficiency with reduced thermal resistance. Furthermore, six machine learning models accurately predict HTC using volume and surface area as key variables, achieving less than 5 % mean absolute percentage error (MAPE). Overall, this research introduces innovative biomorphic designs with unconventional geometries, emphasising resource optimisation and efficient HTC prediction using machine learning. It simplifies design processes, supports agile product development, calls for re-evaluation of conventional heat sink geometries, and provides promising directions for future research.

A prediction model based on data-driven method for velocity and heat transfer coefficient of falling-film liquid on horizontal tube

The prediction of liquid film velocity and heat transfer coefficient of horizontal-tube falling-film flow is important for the investigation and enhancement of the flow and heat transfer behavior. To date, the prediction mainly relies on semi-empirical-semi-theoretical formulas based on data fitting. This prediction method has many limitations and assumptions on working conditions, and the error is large when it is extended to practical applications. In this paper, a support vector regression machine prediction method based on sine cosine algorithm optimization is proposed to improve the prediction accuracy of the liquid film velocity and heat transfer coefficient. And the database was established by conducting horizontal-tube falling-film flow and heat transfer experiments. Results show that the support vector regression model has the best prediction performance with small sample size in comparison with other four machine learning methods and traditional correlation based on Least Square method. The RMSEs of liquid film prediction model and heat transfer prediction model are only 0.0281 and 0.0117 respectively. And the MAPEs are 8.58 and 2.63 respectively. The liquid film velocity and heat transfer coefficient were accurately predicted based on relevant parameters. The importance of these parameters was also analyzed.

Prediction of Heat Transfer Coefficient of Two Phase Flow Sodium Reactor Based on Artificial Neural Network

Liquid sodium metal is one of the main coolants for fast neutron reaction in nuclear reactors with high heat load and high thermal conductivity. Sodium reactor is one of the most promising and commercialized reactor types in modern nuclear power system. Because the chemical properties of liquid sodium metal are very active, so in a sealed environment, it is easy to react with oxygen, water and other substances, resulting in an explosion. Therefore, it is necessary to design a reasonable experimental circuit and conditions under the appropriate experimental environment to carry out the experiment. With liquid metal sodium as the experimental medium, this paper studies the flow heat transfer characteristics of a two-phase flow sodium reactor by establishing a mathematical model. The key heat transfer parameters in a two-phase flow sodium reactor are predicted by building an artificial neural network model, and the parameters affecting the network performance are adjusted by the artificial neural network. The error of the final result predicted by the adjusted artificial neural network model is within the range of ±10%. The prediction results show that the prediction by the artificial neural network model has higher prediction accuracy.

Research on vacuum glass insulation performance prediction based on unsteady state multivariate data screening and multi-model fusion self-optimization

A self-optimization regression method based on multivariate data screening and multi-model fusion is proposed for the regression prediction of vacuum glass insulation performance. Firstly, we analyzed the positive correlation between the temperature change rate and heat transfer coefficient. Secondly, we combined the technique of multi-distribution overall diffusion to generate virtual sample data with multiple variables corresponding to the small neighborhood of the target variable, addressing the issue of insufficient sample size. We conducted quantity and threshold screening on the generated data to enhance the effectiveness of the virtual sample data. The screened data and original training set data are integrated to form new training samples. Then, the limited-memory Broyden Fletcher Goldfarb Shanno with boundary constraints (L-BFGS-B) algorithm is introduced to optimize the parameters of multiple models to improve the accuracy of model regression prediction. Finally, through the self-searching optimization framework structure, the virtual sample data screening and optimization of various model parameters are updated. Eventually, the optimal model for predicting the heat transfer coefficient of vacuum glass insulation performance is determined from multiple models. In order to validate the effectiveness of self-optimization regression method, we conducted insulation performance heat transfer coefficient predictions under 30 different ways for vacuum glass. Experimental results demonstrate that the self-optimization regression method based on multivariate data screening and multi-model fusion can obtain the most effective regression prediction model, achieving accurate, rapid and intelligent prediction of vacuum glass insulation performance.

A new heat transfer correlation based on flow patterns for hydrocarbon refrigerants condensation flow

To accurately predict heat transfer coefficients for hydrocarbon refrigerants condensation flow in heat exchangers is imperative for designing efficient systems. While various studies have proposed numerous correlations for predicting heat transfer coefficients, some of these correlations are based on fitted experimental data and are limited to predicting specific conditions. The prediction accuracy and capability for different working fluids remain to be evaluated. Currently, most researches mainly focus on pure working fluids, with relatively little attention given to mixed working fluids. In this study, six commonly used heat transfer correlations are evaluated and a new heat transfer correlation based on flow patterns is proposed. The research results indicate that the existing correlations have poor universality for predicting hydrocarbon refrigerants, and the prediction accuracy needs to be improved. The correlation developed in this study demonstrates good prediction performance for condensation heat transfer coefficients of 413 sets of hydrocarbon refrigerants, including methane, propane, ethane/propane, and methane/propane. The overall mean relative deviation (MRD) is −1.77%, and mean absolute relative deviation (MARD) is 8.12%. The correlation developed in this study can provide theoretical guidance for predicting condensation heat transfer coefficient for pure and mixed hydrocarbon refrigerants, aiding in the design of efficient heat exchangers.

Analysis of the factors affecting heat transfer performance and prediction of heat transfer coefficient for MVR evaporator: A case study

The heat transfer coefficient of evaporation is one of the key factors affecting the design and management of evaporators. This paper presented an industrial case to investigate the influencing factors on the evaporator's heat transfer coefficient, as well as the interaction between the influencing factors. Besides, a comparison of accuracies was made between the heat transfer coefficients predicted by physical models and the support vector regression (SVR) model. The study results showed that due to the interaction between the influencing factors, changes in the heat transfer coefficient varied greatly from the laboratory results. The impact of other variables must be controlled for in industrial research. There were big errors between the results predicted by the early experimental or numerical models and the observed results. The root mean square error (RMSE) values and mean absolute percentage error (MAPE) values of the physical models were all greater than 150 and 5%, respectively, and their R2 values were all lower than 0.50. The data mining-based SVR predictions were more accurate than the predictions by physical models, with a maximum relative error of 5.38% and a minimum relative error of −5.60%.

Deep Learning Based Prediction of Heat Transfer Coefficient Using Spectrogram Images from Boiling Sound

Deep Learning Based Prediction of Heat Transfer Coefficient Using Spectrogram Images from Boiling Sound

A hybrid approach towards evaluation of average heat transfer coefficient in twin-roll strip casting mold

Continuous casting stands out as one of the most efficient and productive methods for producing billets, slabs, or thin sheets while conserving energy. Understanding the solidification dynamics and achieving defect-free sheets in continuous casting necessitates the accurate prediction of heat transfer coefficient (HTC) at the mold metal interface. Accurate prediction of HTC helps in selecting process parameters such as superheat temperature and casting velocity, which directly affect the production rate and quality of the cast component. Determining the HTC is a complex process, as it is influenced by various process parameters such as casting size, casting speed, superheat temperature, surface roughness, type of water used as a coolant, nozzle design and wettability. Therefore, the present study proposed a hybrid approach for finding HTC in a twin roll strip casting mold through a solidified shell shape, which is influenced by the factors affecting the heat transfer at the mold-metal interface. This approach integrates the experimental and simulation investigations. The numerical investigation used a three-dimensional coupled fluid flow and heat transfer equations with an assumed range of HTC (100–2000 W/(m2K)). The solidified shell shape for different HTCs was compared with the experimental solidified shape form at the outer wall of the mold using the solidification criteria. Results indicate that the initial HTC range of 100–1000 W/(m2K) does not lead to blockage of the graphite mold outlet. However, as HTC values approach 1000 W/(m2K) to 1400 W/(m2K), the melt obstructs the mold exit based on solidification criteria. The HTC values (1350 ± 150 W/(m2K)) obtained from the proposed methodology for mold, align well with the HTC values (1183 ± 593 W/(m2K)) calculated using lamellar spacing of the Al-Mg2Si composite microstructure. This hybrid approach presents a novel and straightforward method for determining HTC, offering accurate predictions that can enhance the precision of continuous casting models and provide a more nuanced understanding of solidification behavior.

Investigation of Heat Transfer Performance in Deionized Water–Ethylene Glycol Binary Mixtures during Nucleate Pool Boiling

Pool boiling heat transfer is recognized as an exceptionally effective method, widely applied across various industries. The adoption of non-azeotropic binary mixtures aligns with the environmental objectives of modern industrial development and enhances the coefficient of performance (COP) in numerous systems. Therefore, investigating the boiling heat transfer characteristics of these mixtures is crucial to improving their industrial usability. In this study, mixtures of ethylene glycol and deionized water (EG/DW) in varying concentrations were chosen as the working fluids. A comprehensive experimental setup was developed, followed by a series of experiments to assess their pool boiling performance. Simultaneously, the thermophysical parameters of these mixtures underwent detailed examination and analysis. The research revealed that the concentration of EG in the mixture markedly affects its thermal properties and temperature glide, both of which are crucial in influencing the heat transfer coefficient. Additionally, six established heat transfer coefficient prediction correlations, primarily designed for pure fluids, have been employed. However, their application to non-azeotropic mixtures under experimental conditions revealed significant deviations. To address this issue, the present study modified existing correlations with the temperature slip characteristics of non-azeotropic mixtures. This process involved recalibrating the wall superheat values in the correlations to reflect the local temperature differential at the boiling point, thereby customizing them for application to non-azeotropic mixtures. The modified correlations highlighted the unique behaviors of non-azeotropic mixtures in boiling heat transfer, demonstrating improved compatibility with these mixtures in a deviation within a permissible 20% range compared with experimental results.

Investigation of convective heat transfer in a cold flow pebble bed nuclear reactor using advanced pebble probe heat transfer sensor

The aim of this study was to investigate the local heat transfer coefficients between the pebbles and the coolant gas in a PBR using an advanced measurement technique that consists of a heated pebble probe, a micro-foil heat flux sensor mounted flush on the surface of the heated pebble probe, and a thermocouple in the center of the bed void in front the sensor. In this work, the local heat transfer coefficients were derived at various axial levels, radial and angular locations and at different superficial inlet gas velocities that cover both transitional and turbulent flow conditions. The local heat transfer coefficients were found to be 22-32%, 34-39%, and 18-20% higher near the wall at superficial inlet gas velocities of 0.3,1.2, and 2m/s, respectively. This is due to higher volumetric flow at the wall where larger void in the pebble bed exists compared to the center region of the bed where the flow of the gas in the packed bed follows the least resistance path. Large deviations were obtained between the experimental overall heat transfer coefficients in the bed and the predictions of seven correlations that were selected from the literature. Furthermore, these correlations cannot predict the local heat transfer coefficients inside the PBR. This necessitates the development of new correlations for the prediction of the local heat transfer coefficients using the data obtained in this work. A pseudo-3D correlation was developed and was found to provide predictions that are in good agreement with the experimental values for the conditions used, with an averaged absolute relative error (AARE) of 3.33% at high Reynolds numbers for our operating and design conditions.

Evaluating the generality of machine learning-based universal models used for prediction of condensation heat transfer coefficient in mini/macro channels

This research concerns the development of universal models founded on machine learning to predict the condensation heat transfer coefficient. In this regard, a consolidated database has been employed encompassing 8340 data points which belong to the flow condensation inside mini/macro channels. The database consists of 25 working fluids with hydraulic diameters ranging from 0.42 to 20.8 mm, mass velocities ranging from 13.1 to 1400 Kg m−2 s− 1 and reduced pressures ranging from 0.03 to 0.9. Four machine learning regression models namely artificial neural network (ANN), gradient-boosted regression (GBR), random forest regression (RFR) and support vector regression (SVR) are trained utilizing the database, and subsequently their accuracy and generality are compared. A multitude of dimensionless parameters are regarded as features, while three parameters specifically the heat transfer coefficient, Nusselt number and Nusselt number correction factor are each considered individually as targets. Within each model, the optimal values of the important hyper-parameters are tuned through appropriate search methods. In order to evaluate the generality of the models, the idea is to sequentially exclude each individual database from the consolidated database. The comparison of accuracy and generality of the models reveals that the RFR model using Nusselt number correction factor as target with a mean absolute relative deviation (MARD) of 3.01 % exhibits the highest level of accuracy, whereas the RFR model employing Nusselt number as target with a MARD for the predicted excluded values equal to 19.49 % demonstrates the superior generality.

Neural network prediction of cooling heat transfer characteristics of supercritical R1234ze (E) in horizontal tube

<sec>The prediction of heat transfer coefficients or wall temperatures of heat exchanger tubes is an important research topic in supercritical heat transfer, which is extremely significant for the application of supercritical fluids in industrial production and the design of the entire thermal system. At present, the empirical correlation method is the most widely adopted prediction method, but its predicted heat transfer coefficient still has significant difference from the actual data near the pseudo-critical temperature. Therefore, some scholars proposed using artificial neural networks to predict the heat transfer performance of supercritical fluids in tubes. On the basis of previous researches, this work further explores the effectiveness of artificial neural network in predicting supercritical heat transfer, focusing on the influence of input parameters on neural network prediction results and the influence of genetic algorithm optimization on the prediction results.</sec><sec>In this research, a neural network prediction model for supercritical R1234ze(E) cooled in horizontal straight tubes is established and compared with the modified D-B heat transfer correlation. The result shows that the input parameter has great influence on the prediction accuracy of BPNN, and not all BPNN input parameter combinations can bring better prediction results than heat transfer correlation. The combination of <inline-formula><tex-math id="M7">\begin{document}$ {{Re} _{\text{b}}} $\end{document}</tex-math><alternatives><graphic specific-use="online" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20240283_M7.jpg"/><graphic specific-use="print" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20240283_M7.png"/></alternatives></inline-formula>, <inline-formula><tex-math id="M8">\begin{document}$ {Pr _{\text{b}}} $\end{document}</tex-math><alternatives><graphic specific-use="online" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20240283_M8.jpg"/><graphic specific-use="print" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20240283_M8.png"/></alternatives></inline-formula>, <inline-formula><tex-math id="M9">\begin{document}$ {{{\rho _{\text{b}}}} \mathord{\left/ {\vphantom {{{\rho _{\text{b}}}} {{\rho _{\text{w}}}}}} \right. } {{\rho _{\text{w}}}}} $\end{document}</tex-math><alternatives><graphic specific-use="online" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20240283_M9.jpg"/><graphic specific-use="print" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20240283_M9.png"/></alternatives></inline-formula>, <inline-formula><tex-math id="M10">\begin{document}$ {{{{\overline C }_{\text{p}}}} \mathord{\left/ {\vphantom {{{{\overline C }_{\text{p}}}} {{C_{{\text{pw}}}}}}} \right. } {{C_{{\text{pw}}}}}} $\end{document}</tex-math><alternatives><graphic specific-use="online" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20240283_M10.jpg"/><graphic specific-use="print" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20240283_M10.png"/></alternatives></inline-formula>, <inline-formula><tex-math id="M11">\begin{document}$ {{{\lambda _{\text{b}}}} \mathord{\left/ {\vphantom {{{\lambda _{\text{b}}}} {{\lambda _{\text{w}}}}}} \right. } {{\lambda _{\text{w}}}}} $\end{document}</tex-math><alternatives><graphic specific-use="online" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20240283_M11.jpg"/><graphic specific-use="print" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20240283_M11.png"/></alternatives></inline-formula>, <inline-formula><tex-math id="M12">\begin{document}$ {{{\mu _{\text{b}}}} \mathord{\left/ {\vphantom {{{\mu _{\text{b}}}} {{\mu _{\text{w}}}}}} \right. } {{\mu _{\text{w}}}}} $\end{document}</tex-math><alternatives><graphic specific-use="online" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20240283_M12.jpg"/><graphic specific-use="print" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="12-20240283_M12.png"/></alternatives></inline-formula> features the best prediction performance. The AAD and Error<sub>max</sub> of the prediction result for the trial set are only 2.02 % and 9.34 %, which are far lower than the prediction deviation of the heat transfer correlation, and the predictions of the trend of <i>h</i> in the high temperature region, the maximum value of <i>h</i> and the position of the peak value of <i>h</i> are more precise than heat transfer correlation. Moreover, this research compares GA-BP model with BP model under two different fitness value calculation methods to reveal the effectiveness of GA-BP in enhancing the prediction accuracy of supercritical heat transfer, concluding that when the same dataset is adopted for network training and fitness value calculation, over-fitting will occur and the GA-BP cannot further improve the prediction accuracy; when different datasets are used to train the network and calculate the fitness value, the generalization ability of the network will be strengthened, and the root mean square deviation and the maximum deviation of the prediction result can be further reduced.</sec><sec>This work will provide a practical tool for predicting the cooling convection heat transfer of supercritical R1234ze (E) in horizontal tubes, laying the foundation for its application in trans-critical heat pump systems, and providing inspiration for potential research directions of ANN in supercritical heat transfer prediction.</sec>

PARAMETRIC STUDY AND DEVELOPMENT OF A CORRELATION FOR NUSSELT NUMBER IN NANOFLUID JET IMPINGEMENT

Numerical simulations for nanofluid ( water with Al<sub>2</sub>O<sub>3</sub> nanoparticles) jet impinging perpendicularly on a flat circular heated p late have been performed. A heated p late is subjected to constant heat flux boundary condition. A two-phase modified mixture mo del was used for the prediction of heat transfer coefficient, and comparisons are made with standard mixture model. Present results for average Nusselt number are validated with experimental data available in the literature. Though a standard mixture model predicted heat transfer with accepted accuracy, it was found that accuracy of modified mixture model is better (around 5% improvement) compared to standard mixture model. Thereafter, parametric study was performed considering nozzle exit Reynolds number (Re), spacing ratio (<i>H/D</i>), nanoparticle volume fraction (φ), and nanoparticle diameter (dp) on heat transfer prediction. The results reveal that particle diameter in the range 10-100 nm has no effect on the Nusselt number, Furthermore, heat transfer increased with increasing Reynolds number and volume fraction. However, spacing ratio shows first increasing and then, decreasing trend (similar to a log-normal distribution curve) in the prediction of heat transfer. Finally, a new correlation was developed for Nusselt number using nonlinear regression analysis. In the correlation, a two-phase multiplier was used, which is the ratio between two-phase Nusselt number (Nu<sub>nf</sub> ) and single-phase Nusselt number (Nu<sub>sp</sub>). The simplified correlation is found to predict data with maximum error of 8.9%, average error of 2.74% and <i>R</i><sup>2</sup> = 0.955.

PREDICTION OF NH3 CONDENSATION HEAT TRANSFER COEFFICIENT WITH ARTIFICIAL NEURAL NETWORK AND MULTIPLE REGRESSION METHODS

Soğutma sektöründe kullanılan halokarbon veya sentetik içerikli bileşiklerin çevreye olumsuz etkilerinden dolayı günümüzde kullanımları uluslararası protokollerle sınırlandırılmıştır. Bu sebeple, NH3 gibi düşük küresel ısınma ve ozon tüketme faktörlerine sahip organik bazlı soğutucu akışkanların kullanımı ön plana çıkmıştır. NH3, yüksek ısıl kapasitesi ve düşük viskozite gibi avantajlı termo-fiziksel özelliklerinden dolayı son yıllarda iklimlendirme endüstrisinde tercih edilmektedir. İklimlendirme sistemleri geliştirilirken, belli bağıntılarla sistemin ısı transfer katsayısı ve basınç düşüşü tahmin edilmelidir. Ancak NH3’ün diğer akışkanlardan farklı termo-fiziksel özelliklere sahip olması sebebiyle, literatürde NH3 için yoğuşma rejiminde ısı transfer katsayısını ve basınç düşüşünü yüksek doğruluk oranında veren bir model bulunmamaktadır. Bu çalışmada, yatay ve düz borularda yoğuşma rejiminde bulunan NH3 akışkanı ile yapılmış literatür çalışmalarından veriler alınarak çoklu regresyon ve yapay sinir ağları metotları ile ısı transferi katsayısı tahmini yapılmıştır. Sonuçlar literatürdeki bilgiler ışığında tartışılmış ve öneriler sunulmuştur.

Experimental investigation of the convective heat transfer coefficients at different angular orientations inside the void in a cold flow pebble bed reactor using a non-invasive heat transfer pebble probe

This study focuses on investigating the effect of the surface location of the pebble inside the bed, by varying the angular orientation of the heat transfer pebble probe and the velocity of the gas on the convective heat transfer coefficients in a cold-flow pebble bed reactor. An advanced non-invasive technique consisting of a cartridge heated copper pebble, a micro-foil sensor and a thermocouple probe was used to obtain accurate measurements of the local heat transfer coefficients from three surface locations inside the void. The effect of the wall on the local convective heat transfer coefficients was highlighted due to the low aspect ratio of the bed (bed diameter to pebble diameter (5cm)) used in this work. The local measurements showed, for the first time ever, that the surface location inside the void substantially impacts the heat transfer coefficients, with higher heat transfer coefficients at the center of the void and bottom of the void. Furthermore, the measured overall heat transfer coefficients showed good agreements with the predictions of the correlations of [52,21] and [14]. A second order polynomial model with an R2=0.9807 and an average absolute relative error (AARE) equal to 9.08%, was developed for the prediction of the local heat transfer coefficient within the design and operation conditions of this work. The accurate local heat transfer data collected in this work is valuable as benchmark data for the validation of computational fluid dynamics (CFD) simulations and heat transfer calculations.

Transfer learning model to predict flow boiling heat transfer coefficient in mini channels with micro pin fins

Flow boiling in mini channels with micro pin fins is a promising heat sink technique to achieve high-efficient aircraft thermal management. The accurate prediction of its heat transfer coefficient is critical for the practical design of two-phase heat exchanger based on mini channels with micro pin fins. Previous investigation shows that heat transfer coefficient prediction accuracy of the machine learning method is generally better than that of conventional empirical correlations. However, the machine learning method cannot guarantee its prediction accuracy among different data domains. To extend the application region of the conventional machine learning method, a transfer learning framework was proposed in present study. First of all, an experimental system was built to acquire test data from different sample domains (i.e., the diamond pin fins with different geometries). Then a conventional machine learning model was developed based on the deep learning method. Furthermore, the developed deep learning model was adjusted with transfer learning process, and the performance of these two kinds of models (i.e., conventional machine learning model and transfer learning model) was comprehensively evaluated. Results showed that the conventional machine learning model had a good prediction accuracy with an overall deviation of 4.11 % but was only confined in the same data domain as training data. Differently, the transfer learning model with 70 % data set from the new domain can achieve appropriate prediction in the new domains with deviation of 4.28 %. The results of this paper demonstrated that the conventional machine learning model can extend into different domains with reasonable prediction accuracy through transfer learning frameworks. It will benefit the practical design of high-efficiency heat exchangers for aircraft thermal management.

Ammonia heat pumps for district heating: thermo-economic optimization analysis based on evaporator geometry for either direct expansion or chilled water configurations

Electric heat pumps are recognized as a key technology for decarbonization and have received increasing policy support in several countries over the last few years. These devices offer a highly efficient form of electric heating and could make an important contribution to the transition to a low carbon future, especially in case of district heating systems. Within this context, this paper presents a thermo-economic optimization analysis of a 7.35 MW electric heat pump system employed for district heating purposes and using ammonia as working fluid. The implemented code gathers multiple sub-models calibrated ad-hoc from real data or manufacturers’ datasheets. The heat exchangers are simulated by considering phenomenological equations and well-known heat transfer coefficient and pressure drop prediction methods. The optimization analysis is performed at constant condensation temperature that guarantees in/out hot water temperatures of 30/62 °C and is focused on the matching between compressor and evaporator heat exchanger, by considering either a direct expansion system or a chilled water heat pump. Both the coefficient of performance (COP) and the set-up costs are considered as optimization performance indicators for the construction of the Pareto front.