Microfin Tubes Research Articles

Numerical simulation on flow boiling heat transfer characteristics of R513A in the horizontal microfin tubes

Numerical simulation on flow boiling heat transfer characteristics of R513A in the horizontal microfin tubes

Experimental investigation of heat transfer coefficient and frictional pressure drop in flow boiling of R513A: Comparative analysis of micro-fin and smooth tubes

The heat transfer coefficient (HTC) and frictional pressure drop (FPD) of R513A in flow boiling conditions experimentally investigated in the present article. The study employed micro-fin tubes (MFT1 and MFT2) and smooth tubes (ST1 and ST2) as testing tubes under various operational parameters. The test sections featured standardized tube lengths (1000 mm) and outer diameter (9.52 mm) across all tubes, with variations in mass flux (50–300 kg·m−2·s−1), vapor quality (0.02–0.96), saturation temperature (12°C, 17°C, and 22 °C) and heat flux (6, 18, and 30 kW·m−2). Research focused on evaluating influence of micro-fin enhancement on flow boiling characteristics, specifically comparing HTC and FPD between micro-fin and smooth tubes. Results indicate significant improvement on HTC within micro-fin tubes compared to smooth tubes, accompanied by reasonable increase in FPD. Additionally, the study validated experimental data against established correlations and conducted sensitivity analysis to understand influence of key parameters on HTC and FPD. Overall, findings contribute valuable insights for optimizing heat exchanger design and thermal system efficiency in flow boiling applications.

Evaluation and development of flow condensation correlations using the data from low GWP refrigerants in an axial micro-fin aluminum tube

To mitigate global warming, the world is transitioning to refrigerants with low global warming potential (GWP). Supporting this shift requires a model that can accurately predict the heat transfer and pressure drop of new refrigerants, crucial for designing efficient heat exchangers. Existing models, however, are largely based on currently deployed refrigerants and primarily developed for unexpanded micro-fin tubes with spiral angles of 6° to 30°. Their applicability to new refrigerants, especially in expanded micro-fin tubes, is uncertain. This study assesses the performance of four well-known condensation models for six emerging refrigerants—R-32, R-454B, R-454C, R-455A, R-1234yf, and R-1234ze(E)—against experimental data. Initially, the Han and Lee (2005) model shows the best prediction accuracy with a mean absolute deviation (MAD) of 22.1 %. To enhance the accuracy of heat transfer models for new refrigerants and geometries with large temperature glides, two approaches are proposed. The first approach applies a simple correction factor, reducing the MAD of the Cavallini et al. (2009) model from 68.2 % to 15.4 %. The second approach uses the variable metric method for minimization, fitting new constants to the data. This optimization results in the Kedzierski and Goncalves (1997) model achieving the highest accuracy, with a MAD of 13.1 %. For pressure drop models, the Cavallini et al. (1997) model is the most accurate with a MAD of 6.4 %, followed by the Haraguchi et al. (1993) model with a MAD of 9.4 %. Due to its simplicity, the Haraguchi et al. (1993) model is a practical option for predicting frictional pressure drop.

Experimental study on the pool boiling heat transfer of R134a outside various enhanced tubes

The paper aims to experimentally study the pool boiling heat transfer properties of R134a outside various enhanced tubes under saturation temperatures ranged from 3 to 40 °C and heat fluxes of 5.5∼76.5 kW·m-2. One smooth tube and three micro-fin tubes were selected as the tested tube. Based on the experimental results, it can be found that the pool boiling heat transfer coefficient increases firstly and decreases later with increasing heat flux, but saturation temperature and water velocity have little effect on the heat transfer coefficient. Under the same experimental conditions, refrigerant thermal resistance ratio is greater than 0.5 during the heat transfer process. The micro-fin facilitates the bubble nucleation with the heat transfer enhancement ratio varying in the range of 2.08∼4.59. Moreover, the experimental values are compared with the calculated values of three existing correlations for the pool boiling heat transfer coefficient. The experimental variables, which mainly includes saturation temperature, heat flux and tube structure, have great influences on the correlation accuracy. Finally, to eliminate the influence of the experimental variable, a new correlation was proposed by introducing ratio of fin height to outer diameter of the tested tube. After the further verification, the proposed correlation can predict the heat transfer coefficient with the mean deviation of less than 10%.

Exploring novel approaches on impact of micro-fin tubes on flow boiling heat transfer using R134a refrigerant: A parametric case study

This paper experimentally investigates the factors influencing the flow boiling heat transfer of R134a refrigerant within smooth and micro-fin tubes having an outer diameter of 9.52 mm. The tube has a peculiar micro-fin shape with a 46° apex angle and 22° helix angle, producing a surface area 1.62 times bigger than a smooth tube of identical diameter. The main goal of the study is to understand the influences of mass flux, heat flux, saturation temperature, and average vapor quality on heat transfer characteristics. The findings are initially compared with known flow boiling correlations; moreover, performance index, penalty factor, and enhancement factor considerations are also made. The heat transfer coefficient (HTC) relies on mass flux, with significant improvements at G = 200 kg m−2 s−1, despite dry-out difficulties at high vapor quality. Intensified flow velocity from higher mass flux causes a rise in frictional pressure drop (FPD), with micro-fin tubes demonstrating superior heat transfer, owing to their larger surface area and improved swirl formation. Compared to smooth tubes, FPD is significantly influenced by vapor quality for micro-fin tubes. The study analyses the importance of micro-fin tubes to improve heat transfer in industrial applications, as well as the effects of saturation temperature and vapor quality on HTC.

Thermal behavior of ferrofluids in a microfin tube with rotating magnetic fields: Experimental analysis and artificial intelligence modeling strategies

The utilization of various tools by researchers to enhance heat transfer (HT) in fluid flow is steadily on the rise. These tools encompass both active and passive methods. This study involved an experimental examination of HT and fluid flow within a micro-fin tube. The strategies were implemented to improve Nu: ferrofluid, micro-fins, and a rotating magnetic field. Before implementation, the magnetic ferrofluid was evaluated for particle size and stability. The results were validated, demonstrating a good correlation with the anticipated outcomes. The use of a microfin tube and ferrofluid can enhance HT about 7–14 % and 7–22 %, respectively. Additionally, a rotating magnetic field can improve HT by approximately 10%–37 %. An empirical correlation derived from experimental data is introduced for Nu prediction. Additionally, employing artificial intelligence techniques such as ANFIS and GMDH, the Nu has been successfully estimated, showcasing the effectiveness of these models in predicting Nu.

Experimental comparison of flow boiling heat transfer in smooth and microfin tubes using R134a, R1234yf, and R513A

Addressing the urgent need for environmentally sustainable and efficient refrigeration systems, this study investigates the performance of low-GWP refrigerants R1234yf and R513A in comparison to the high-GWP refrigerant R134a, focusing on the heat transfer coefficient (HTC) and pressure drop during flow boiling in smooth and microfin tubes. The experimental setup involves tubes with a 6.14 mm internal diameter and 500 mm length. Data were collected across a broad range of operating conditions, including mass fluxes from 170 to 495 kg/m² s, heat fluxes from 10 to 40 kW/m², saturation temperatures of 15 °C and 25 °C, and vapor qualities from 0.15 to 1.0. In a specific case with a mass flux of 170 kg/m² s, heat flux of 23 kW/m², and a saturation temperature of 15 °C, the results indicate that the microfin tube achieves an HTC enhancement of up to 64 % compared to smooth tubes. However, R134a exhibits a higher HTC than R1234yf and R513A, approximately 5 % and 3 % higher, respectively. In contrast, R134a presents a higher pressure drop than R1234yf by about 8 %. While the pressure drop for R134a is slightly higher than R513A in the smooth tube, the microfin tube shows similar trends but more pronounced differences. This study comprehensively explores microfin tube performance with these refrigerants, offering critical insights for designing advanced refrigeration systems that balance environmental responsibility with energy efficiency. These findings were validated against predicted correlations, showing good agreement.

Novel dimensionless predictive flow pattern map for HFOs inside microscale enhanced tubes

Models designated to predict flow patterns in microscale geometries with enhanced surfaces such as micro-finned tubes are scarce in the literature and new low-GWP HFOs could benefit from the utilization of such geometries. Therefore, HFO1234ze(E)’s condensation flow patterns were subjected to visualization inside micro-finned tubes ranging from 4 to 7 mm outer diameters with various geometrical figures. The saturation temperature was set to 30 °C, vapor qualities ranged in the scope of 0.01 to 0.9, and mass fluxes in the magnitude of 100 to 400 kg m-2 s-1. Four distinctive flow patterns are observed, namely intermittent, annular, wavy-stratified, and transitional. Stratification only transpired at low mass fluxes (mainly below 100 kg m-2 s-1) and intermittent flow was only present at vapor qualities close to full condensation. The range in which transitional flow is observable shrinks with a progressive trend of mass flux. The impact of diameter was observed to be negligible, however, a more meticulous assessment highlights smaller ranges of vapor qualities in which transitional flow is present for the tube of 5 mm OD whose helix angle is substantially larger. Datapoints were juxtaposed to previous models of Doretti et al., Chen et al., Mandhane et al., and Jige et al. and the results attested to an inadequacy of accurate predictions made for tube of 4 mm OD. Noting the absence of surface tension force in the aforementioned maps, a novel flow pattern map based on modified Froude versus modified Weber numbers provided an accurate prediction for the three cases under study. Ultimately the model was also deemed fairly suitable for visualization datasets collected from literature.

Heat Transfer Estimation in Flow Boiling of R134a within Microfin Tubes: Development of Explainable Machine Learning-Based Pipelines

Heat Transfer Estimation in Flow Boiling of R134a within Microfin Tubes: Development of Explainable Machine Learning-Based Pipelines

Micro-fin tubes for improved flow boiling heat transfer in refrigeration systems: a performance comparison with R134a and R407c refrigerants

Micro-fin tubes for improved flow boiling heat transfer in refrigeration systems: a performance comparison with R134a and R407c refrigerants

Comparative analysis of entropy generation in smooth and micro-fin tubes using R513A refrigerant: A parametric study

Currently, although significant advancements have been made in understanding heat transfer coefficients and pressure decreases in different tube shapes, there is still a noticeable lack of detailed studies on the generation of entropy under flow boiling conditions. In this work, the entropy generation in micro-fin tubes (MFT1 and MFT2) and smooth tubes (ST1 and ST2) in flow boiling conditions experimentally investigated with refrigerant R513A. Research focused on evaluating influence of different input parameters on entropy generation, specifically contribution of heat transfer coefficient (HTC) and total pressure drop (TPD) on entropy generation for all testing tubes. As at a heat flux of 6 kW·m−² and a saturation temperature of 12 °C, MFT1 shows HTC contributions to entropy generation ranging from 0.032 to 0.156 W·K−1, while MFT2 ranges from 0.03 to 0.14 W·K−1. TPD contributions for both MFT1 and MFT2 range from 0.001 to 0.04 W·K−1. Hence, MFT2 shows better results than MFT1 as low entropy generation required for a good heat exchanger. Among input parameters, heat flux displays the highest sensitivity, indicating its significant influence on total entropy generation variations, while vapor quality, mass flux, and saturation temperature also demonstrate notable sensitivity. This research helps us design systems that transfer heat more effectively while using less energy. Understanding these factors can lead to more efficient heat exchangers and other thermal systems.

Numerical simulation of the effects of toothed structure on the flow boiling characteristics of micro-fin tubes

Abstract The numerical simulation of R141b flow boiling in micro-fin tubes (MFTs) with a 1 mm equivalent diameter is conducted using the VOF and the Lee models. An analysis of the boiling heat transfer (HT) characteristics has been performed, considering impacts of the four factors. The most suitable correlation of boiling HT has also been evaluated. The summarized findings are as follows: the BHT coefficient within the MFTs consistently rises with increased mass velocity, heat flux, and saturation temperature. However, there is a presence of a partially dry-out region inside the MFTs at high heat flux. Additionally, the enhancement of boiling HT by elevating saturation temperature is not prominent in the low vapor quality range. HT coefficients of the MFTs with various fin structures ranked in magnitude are as follows: tapered MFTs > trapezoidal MFTs > circular MFTs. The model proposed by Wu is identified as the best predictor for flow boiling HT performance in horizontal MFTs, although its prediction accuracy is limited in the low vapor quality range.

Entropy Generation of R513A Condensation Flow Inside the Horizontal Microfin Tubes

Abstract It is widely acknowledged that the pressure drop increases with enhanced heat transfer in heat exchanger tubes, and entropy generation analysis serves as an effective method to comprehensively evaluate heat transfer and pressure drops. This paper conducts experimental research on in-tube condensation heat transfer using refrigerants R513A and R134a in six test tubes, comprising both smooth and microfin tubes with outer diameters of 9.52 mm and 12.7 mm, respectively. The microfin tubes are available in two types, with 60 and 65 fins, respectively, and a helix angle of 18 deg. The experimental conditions included mass fluxes of 50–250 kg/m2 s and condensation temperatures of 35 °C, 38 °C, and 40 °C. The findings indicate that replacing R134a with R513A is feasible. The 9.52 mm tube exhibits superior overall heat transfer performance compared to the 12.7 mm tube, and the 60-fin microfin tube outperforms the 65-fin tube in terms of heat transfer efficiency. This suggests that microfin tubes with smaller diameters and an optimal number of fins are more effective in enhancing condensation heat transfer performance.

Machine and deep learning driven models for the design of heat exchangers with micro-finned tubes

The design of micro-finned tube heat exchangers is a complex task due to intricate geometry, heat transfer goals, material selection, and manufacturing challenges. Nowadays, mathematical models provide valuable insights, aid in optimization, and allow us to explore various design parameters efficiently. However, existing empirical models often fall short in facilitating an optimal design because of their limited accuracy, sensitivity to assumption, and context dependency. In this scenario, the use of Machine and Deep Learning (ML and DL) methods can enhance accuracy, manage nonlinearity, adjust to varying conditions, decrease dependence on assumptions, automatically extract pertinent features, and provide scalability. Indeed, ML and DL techniques can derive valuable insights from datasets, contributing to a comprehensive understanding. By means of multiple ML and DL methods, this paper addresses the challenge of estimating key parameters in micro-finned tube heat exchangers such as the heat transfer coefficient (HTC) and frictional pressure drop (FPD). The methods have been trained and tested using an experimental dataset consisting of over a thousand data points associated with flow condensation, involving various tube geometries. In this context, the Artificial Neural Network (ANN) demonstrates superior performance in accurately estimating parameters with MAEs in the range below 4.5% for both HTC and FPD. Finally, recognizing the importance of comprehending the internal mechanisms of the black-box ANN model, the paper explores its interpretability aspects.

Research on the R513A condensation heat transfer characteristics in horizontal microfin tubes

R513A has been proposed as alternative to R134a due to its lower GWP. So, it is necessary to investigate the condensation heat transfer characteristics and pressure drop of R513A in the horizontal tubes. The 12.70-mm-OD and 9.52-mm-OD tubes were selected for condensation tests. It was found that the R134a condensation heat transfer coefficient in the 12.70-mm-OD microfin tube was similar to the R513A condensation heat transfer coefficient, and the deviation was about −3% ∼ +9%; R513A yielded 4.44% ∼ 16.01% lower pressure drop than R134a. The R513A heat transfer coefficient of the microfin tube was about 1.42–1.74 times of that of the smooth tube, and the pressure drop was 39.11% ∼ 76.63% than that in the smooth tube. Additionally, the condensation heat transfer coefficient and pressure drop of the 9.52-mm-OD test tube were 23.88% ∼ 41.62% and 40.79% ∼ 71.86% higher than those of the 12.70-mm-OD tube, respectively. The micro-fin structure inside the microfin tube resulted in efficient heat transfer, but also increased the fluid flow power consumption. Moreover, as the tube diameter decreased, the effects of sheer stress and surface tension increased. Furthermore, experimental data were also compared with previous correlations proposed for microfin tubes. More experiments are still required to develop more applicable theoretical correlations in future.

A framework for data regression of heat transfer data using machine learning

Machine Learning (ML) algorithms are emerging in various industries as a powerful complement/alternative to traditional data regression methods. A major reason is that, unlike deterministic models, they can be used even in the absence of detailed phenomenological knowledge. Not surprisingly, the use of ML algorithms is being explored also in heat transfer applications. It is of particular interest in systems dealing with complex geometries and underlying phenomena (e.g. fluid phase change, multi-phase flow, heavy fouling build-up). However, heat transfer systems present specific challenges that need addressing, such as the scarcity of high-quality data, the inconsistencies across published data sources, the complex (and often correlated) influence of inputs, the split of data between training and testing sets, and the limited extrapolation capabilities to unseen conditions. In an attempt to help overcome some of these challenges and, more importantly, to provide a systematic approach, this article reviews and analyses past efforts in the application of ML algorithms to heat transfer applications, and proposes a regression framework for their deployment to estimate key quantities (e.g. heat transfer coefficient), to be used for improved design and operation of heat exchangers. The framework consists of six steps: i) data pre-treatment, ii) feature selection, iii) data splitting philosophy, iv) training and testing, v) tuning of hyperparameters, and vi) performance assessment with specific indicators, to support the choice of accurate and robust models. A relevant case study involving the estimation of the condensation heat transfer coefficient in microfin tubes is used to illustrate the proposed framework. Two data-driven algorithms, Deep Neural Networks and Random Forest, are tested and compared in terms of their estimation and extrapolation capabilities. The results show that ML algorithms are generally more accurate in predicting the heat transfer coefficient than a well-known semi-empirical correlation proposed in past studies, where the mean absolute error of the most suitable ML model is 535 [Wm2K-1], compared to the error using the correlation of 1061 [Wm2K-1]. In terms of extrapolation, the selected ML model has a mean absolute error of 1819 [Wm2K-1], while for the correlation is 1111 [Wm2K-1], indicating a disadvantage of the use of semi-empirical models, although the comparison was not entirely suitable, given that the correlation was used as is and no training was done. In addition, feature selection enables simpler models that depend only on features that are potentially most related to the target variable. Special attention is needed however, as overfitting and limited extrapolation capabilities are common difficulties that are encountered when deploying these models.

Experimental study of R600a boiling in microfin tube

This paper presents experiments to investigate flow boiling and pressure gradients of R600a. The study employed microfin and smooth copper tubes simulating conditions in a domestic refrigerator, using low mass fluxes (MFs) and saturation temperatures (Tsat). The test sections featured a tube length of (400 mm), an inner diameter of (4.3 mm), and an outer diameter of (5 mm). Test conditions varied, including MFs of (25, 35, and 45 kg m−2 s−1), vapor quality from (0.18–1), Tsat of (−15, −9, and −5 °C), and HFs of (10, 14, and 18 kW m−2). Both tubes shared identical dimensions, clearly comparing microfin impact on flow boiling characteristics. The study evaluated enhancement factor (EF), penalty factor (PF), and overall efficiency (η) by comparing heat transfer coefficients (HTC) and pressure gradients in the microfin tube versus the smooth tube. Results indicate a significantly boosted HTC in the microfin tube compared to the smooth tube, with a reasonable increase in pressure drop. Under specific conditions (HF of 10 kW m−2, Tsat of −9 °C, and MFs of 25 kg m−2 s−1), the average HTC is enhanced by 325%, while the pressure drop is increased by 150%. Experimental HTC and frictional pressure gradient comparisons aligned well with values predicted by existing literature correlations.

An artificial neural network-based numerical estimation of the boiling pressure drop of different refrigerants flowing in smooth and micro-fin tubes

Abstract In thermal engineering implementations, heat exchangers need to have improved thermal capabilities and be smaller to save energy. Surface adjustments on tube heat exchanger walls may improve heat transfer using new manufacturing technologies. Since quantifying enhanced tube features is quite difficult due to the intricacy of fluid flow and heat transfer processes, numerical methods are preferred to create efficient heat exchangers. Recently, machine learning algorithms have been able to analyze flow and heat transfer in improved tubes. Machine learning methods may increase heat exchanger efficiency estimates using data. In this study, the boiling pressure drop of different refrigerants in smooth and micro-fin tubes is predicted using an artificial neural network-based machine learning approach. Two different numerical models are built based on the operating conditions, geometric specifications, and dimensionless numbers employed in the two-phase flows. A dataset including 812 data points representing the flow of R12, R125, R134a, R22, R32, R32/R134a, R407c, and R410a through smooth and micro-fin pipes is used to evaluate feed-forward and backward propagation multi-layer perceptron networks. The findings demonstrate that the neural networks have an average error margin of 10 percent when predicting the pressure drop of the refrigerant flow in both smooth and micro-fin tubes. The calculated R-values for the artificial neural network’s supplementary performance factors are found above 0.99 for all models. According to the results, margins of deviations of 0.3 percent and 0.05 percent are obtained for the tested tubes in Model 1, while deviations of 0.79 percent and 0.32 percent are found for them in Model 2.

Heat transfer characteristics of R134a flow boiling in a microfin tube under typical ORC pressures based on comparison with a smooth tube

The experimental investigation on the flow boiling heat transfer of an organic working fluid under typical organic Rankine cycle (ORC) operating conditions is insufficient, which directly affects the performance of the ORC system. In this paper, the flow boiling characteristics of R134a in a horizontal microfin tube were experimentally investigated in the reduced pressure range 0.6 ∼ 0.9. The general convection heat transfer characteristics of R134a flow boiling, along with the effects of heat flux, mass flux, pressure, and rewetting conditions, were investigated in a microfin tube and compared with that in a smooth tube. Results show that the main flow regime in the microfin tube under typical ORC operating pressures is the stratified flow with dry-out occurring at the top surface. The microfin effectively enhances the heat transfer and restrains the wall temperature separation through inducing strong helical flow due to buoyancy and centrifugal forces. With the heat flux increasing, the micro-fin strengthens the nucleate boiling by enhancing the vaporization cores, leading to more pronounced heat transfer enhancement compared with that in the smooth tube. The heat transfer on the bottom surface shows almost negligible sensitivity to variations in mass flux. However, in regions of high vapor quality, an increase in mass flux results in higher heat transfer coefficients on the top surface. Due to the interaction between intensified nucleate boiling and weakened gas convection when pressure is increased, it slightly affects the heat transfer coefficient. Unlike the smooth tube, the rewetting in the microfin tube can be maintained within a larger range of vapor quality due to the spiral flow caused by the micro-fin.

Influence of Microfin Tube on Heat Transfer during Flow Boiling of R134a Refrigerant

This research looks into the domination of flow boiling on the efficiency and sustainability of refrigeration and air conditioning systems. Specifically, it focuses on the characteristics related with heat transfer of refrigerant R134a within both smooth and microfin tubes, each having 9.52 mm outer diameter. The features of microfin tube have distinct configuration with a 46° apex angle and a 22° helix angle, resulting in a surface area 1.62 times greater in comparison to smooth tube. Through a comprehensive experimental analysis, the study examines the influence of key parameters (mass flux; saturation temperature; heat flux; and average vapor quality) on heat transfer coefficients (HTCs). Notably, the HTC exhibits significant enhancements at mass flux of G = 125 kg m⁻2·s⁻1, encountering challenges related to dry‐out at elevated vapor quality levels. The results reveal that microfin tubes consistently outperform smooth tubes, exhibiting heat transfer coefficients up to 270% higher across various operational conditions. Furthermore, the experimental data are rigorously validated through computation analysis with authentic flow boiling heat transfer models, demonstrating strong agreement.