Condensation Heat Transfer Model Research Articles

A novel condensation heat transfer correction based on non-equilibrium film theory and degradation mechanism for zeotropic mixture

Energy conversion based on thermodynamic cycle using zeotropic mixtures is an effective strategy for advancing global low-carbon development in the context of carbon neutrality. However, the deficiencies of existing heat transfer mixtures models and the complex condensation mechanisms of zeotropic refrigerants pose significant challenges to the size design and optimization of heat exchangers, which can directly impact the economic efficiency and operational safety of thermodynamic cycle systems. Thus, there is an urgent need for a simplified and universal correction method that incorporates mixing effects to improve prediction accuracy of conventional models. In the present study, a non-equilibrium condensation heat transfer model based on film theory was developed and validated to analyze the influence of various parameters on heat transfer degradation and heat transfer coefficients of zeotropic mixtures inside horizontal circular tubes. Through multi-factor sensitivity analysis and dimensionless method, a degradation factor characterizing the gradient contributions to heat transfer degradation was proposed. Based on the proposed factor, a simplified non-equilibrium model considering mixing effect without complex computations was developed. Using the new developed non-equilibrium model, prediction of a database containing 1813 experimental points was conducted. A deviation of 15.7 % between the predicted values and the experimental values was achieved, demonstrating remarkable predictive accuracy and significant generalizability compared to existing mixture models. Extending this corrective method to various pure substance models resulted in a significant improvement in accuracy.

Heat transfer model for dropwise condensation on hydrophobic and superhydrophobic interfaces

Heat transfer models for condensation on hydrophobic and superhydrophobic interfaces are broadly available based on thermal resistance correlations. In the previous studies, very few models are presented based on the scaling factor or Nusselt number, and no model is available that directly correlates Biot number. This study develops a heat transfer model for dropwise condensation underneath a horizontal surface. The present model correlates with the Biot number to predict the heat transfer, temperature variation at the interfaces, solid-liquid, and liquid-vapor, and the growth rate of droplet condensate on the hydrophobic and superhydro-phobic interfaces by using Archimedes’ hat-box theorem. The present model is validated with analytical and experimental results against hydrophobic and superhydrophobic contact angles of similar working parameters made excellent agreements. The analytical model for dropwise condensation produces inaccurate results due to discrepancies and discontinuities due to mul-tiple correlations in the modeling. The present model is modified to obtain a continuous result using experimental data. The modified model is used for analyzing heat transfer by varying Biot numbers from 0.0001 to 1000 using Python 3.6.1 with an accuracy of 10-4. Simulation of the present model results in constant heat transfer at Bi = 4, irrespective of the contact angle. A negligible amount of coating resistance and interface resistance when Bi > 0.1, curvature effect when Bi > 0.04, droplet resistance when Bi < 0.02, the maximum liquid-vapor interface tem-perature at Bi ≈ 10, and maximum solid-liquid interface temperature at Bi ≈ 5, are presented.

Research and Application of Steam Condensation Heat Transfer Model Containing Noncondensable Gas on a Wall Surface

Steam condensation plays an important role in various engineering processes due to its excellent heat transfer performance. However, condensation in the presence of noncondensable gas has attracted great attention in recent years since noncondensable gas will have a negative effect on condensation heat transfer. The present study proposes a comprehensive model coupled with convective heat transfer, liquid film heat transfer and steam condensation for the heat transfer of condensation with noncondensable gas and uses it in the Program Integrated for Severe Accident Analysis (PISAA) for a nuclear power plant. The condensation heat transfer model has good universality, the calculation process is stable with less iteration and a fast convergence and it is verified and validated by comparing the simulation results of the PISAA and those from traditional containment analysis codes, as well the experiments from the Wisconsin condensation tests; then, a sensitivity analysis for the parameters of the heat transfer coefficient is performed. The validation results show that the average error of the condensation heat transfer coefficient is approximately 10%, and the maximum error does not exceed 30%. The deviation from the experimental data is limited in the acceptable range, which could fulfill the requirement for the analysis of containment accidents in nuclear power plants.

A Modified Correlative Model for Condensation Heat Transfer in Horizontal Enhanced Tubes with R32 and R410A Refrigerants

An experimental study was performed that compared tube side condensation heat transfer characteristics of enhanced tubes (hydrophobic surface tubes (HYD), herringbone micro fin tube (HB), and a composite hydrophobic/herringbone (micro fin) tube (HYD/HB)) to the performance of a smooth tube (ST). The condensation heat transfer coefficient (HTC) was calculated from data that were recorded for smooth and enhanced tubes that had an outer diameter (OD) of 12.7 mm. Data were collected (as a function of mass flow rate) using a couple of refrigerants (R410A and R32), for saturated temperatures of 35 °C and 45 °C, with vapor qualities that ranged from 0.8 to 0.2. Several previously reported smooth tube HTC models were used to calculate values that could be compared to experimentally obtained HTC values. The correlation model that demonstrated the best accuracy (for the conditions considered) was then modified for use with the enhanced tubes from this study. Results from the modified correlation show differences with experimental values that ranged from −10% to +17%; the new modified correlation demonstrates high prediction accuracy. An accurate correlation allows the evaluation of enhanced heat transfer tubes for use in high-efficiency heat exchanger systems. The development of this new model is significant in the study of enhanced heat transfer.

Experimental investigation on interface characteristics and heat transfer during bubble condensation in narrow rectangular channel

Direct steam contact condensation in subcooled water is widely used in various industries because of high efficiency in heat and mass transfer. A visual experimental is carried out to investigate the condensation process of single vapor bubble in narrow rectangular channel by a high-speed camera. Steam mass flux and water temperature are 0–1000 kg/(m2·s) and 57–93 °C, respectively. Four different condensation patterns are found in this study, such as chugging, ellipsoidal jetting, smooth grown bubble and rough grown bubble based on bubble behavior and surface feature. The bubble condensation period is positively correlated with initial bubble diameter. The bubble interface is smooth at low mass flow rates, while it becomes rough when the mass flow rate is high and the process of condensation is shorter. There is a bubble critical diameter during growth stage and each type of bubble will have distinctive phenomena when it reaches the critical diameter. The restriction of the narrow rectangular channel on vapor bubble is that an accelerated condensation process occurs when the bubble diameter changes from restricted to unrestricted. The factor of heat conduction is taken into account in the energy conservation equation, and the condensation heat transfer model is proposed. By deriving the energy conservation equation, a dimensionless parameter A related to the thermal conductivity of the wall and the width of flow channel is obtained. A predictive correlation for bubble diameter and a condensation Nusselt correlation are proposed, and the new correlations incorporate the dimensionless parameter A.

Film thickness, interfacial waviness and heat transfer during downflow condensation of R134a

• Condensation tests are performed with R134a in a 3.38 mm tube. • Simultaneous film thickness and heat transfer measurements are carried out. • The experimental results of R134a are compared to those of R245fa. • The interfacial waves inception is delayed for R134a due to the high vapor density. • Models for condensation heat transfer, film thickness and void fraction are assessed. Coupled measurements of liquid film thickness and heat transfer during condensation inside minichannels are rare in the literature due to the technical difficulties in performing accurate and non-intrusive measures. The availability of such experimental data is fundamental for better understanding the occurring physical mechanisms and for the proper design of heat exchangers. To cover this gap, in the present work liquid film thickness and heat transfer coefficients have been simultaneously measured during vertical downflow condensation inside a 3.38 mm inner diameter channel. Condensation tests have been run with refrigerant R134a at mass flux between 30 and 150 kg m −2 s −1 and 30 °C saturation temperature. The instantaneous liquid film thickness and interfacial waves are detected by combining two complementary optical methods: shadowgraph technique (applied to a sequence of flow pattern images recorded by a high speed camera) and chromatic confocal imaging. In the data analysis, the experimental results of R134a have been discussed in comparison to R245fa condensation data taken during a previous experimental campaign at much lower pressure. On average, R134a displays higher liquid film thickness than R245fa and the appearance of high-amplitude disturbance waves, which promote the thinning of the liquid film and enhance the heat transfer, is detected at higher mass flux for R134a as compared to R245fa. The liquid film thickness is found to decrease with the increasing mass flux for both fluids, except from 30 to 50 kg m −2 s −1 due to the transition between shear stress-driven and gravity-driven downflow condensation. The accuracy of several well-known models for the prediction of heat transfer coefficient, film thickness and void fraction has been assessed with comparison to the experimental data of both R134a and R245fa.

Machine-learning-based heat transfer and pressure drop model for internal flow condensation of binary mixtures

Machine learning regression models were developed to predict the heat transfer coefficient and frictional pressure gradient during condensation of three zeotropic mixtures (ethane/propane, R245fa/pentane, and R410A) in micro- and macro-channels (0.76 mm < D < 14.45 mm). 1032 data points were used to train, test, and validate four machine learning models: support vector regression (SVR), random forest regression (RFR), gradient boost (GB), and artificial neural networks (ANN). For data not used for training, SVR predicted the apparent Nusselt number the best, with a mean absolute percent error (MAPE) of 5.0%, while GB predicted the two-phase friction factor the best with a MAPE of 5.5%. To determine the impact of the additional heat and mass transfer resistances in zeotropic condensation, Shapley analysis was performed on the models to weigh the importance of all dimensionless parameters used to predict the heat transfer and pressure drop. Overall, the three most important parameters to predict heat transfer were the Reynolds number of both phases and a dimensionless temperature glide; while the most important parameters to predict pressure drop were the Bond number, Weber number, and vapor-phase Reynolds number. The importance of mass transfer is discussed along with the applicability of other simple mixture models.

Condensation heat transfer of R1234ze(E) and its A1 mixtures in small diameter channels

R1234ze(E) has emerged in the recent years as low Global Warming Potential substitute for R134a in refrigeration and air-conditioning systems. As a drawback, R1234ze(E) is classified as a mildly flammable fluid (A2L class) and, in the search for non-flammable alternatives to R134a, hydrofluorocarbon/hydrofluoroolefin binary mixtures can be considered. In the present work, condensation tests are performed with R1234ze(E) and non-flammable binary mixtures R450A (R1234ze(E)/R134a at 58.0/42.0% by mass) and R515B (R1234ze(E)/R227ea at 91.1/8.9% by mass) inside two channels with inner diameter equal to 3.38 mm and 0.96 mm. R515B is an azeotropic mixture whereas R450A is a near-azeotropic blend (temperature glide 0.6 K at 40 °C). Heat transfer coefficients are measured at 40 °C saturation temperature and mass flux from 40 kg m−2 s−1 to 600 kg m−2 s−1. Flow pattern visualizations are recorded by a high-speed camera in the 3.38 mm inner diameter tube. Two-phase pressure gradients are measured in the 0.96 mm test section at mass flux equal to 200 and 400 kg m−2 s−1. The prediction accuracy of condensation heat transfer and two-phase pressure drop models is assessed against the experimental results. A comparative study between the tested fluids and R134a, accounting for both the heat transfer coefficients and the two-phase pressure drops, is performed.

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.

Application of the machine learning technique for the development of a condensation heat transfer model for a passive containment cooling system

A condensation heat transfer model is essential to accurately predict the performance of the passive containment cooling system (PCCS) during an accident in an advanced light water reactor. However, most of existing models tend to predict condensation heat transfer very well for a specific range of thermal-hydraulic conditions. In this study, a new correlation for condensation heat transfer coefficient (HTC) is presented using machine learning technique. To secure sufficient training data, a large number of pseudo data were produced by using ten existing condensation models. Then, a neural network model was developed, consisting of a fully connected layer and a convolutional neural network (CNN) algorithm, DenseNet. Based on the hold-out cross-validation, the neural network was trained and validated against the pseudo data. Thereafter, it was evaluated using the experimental data, which were not used for training. The machine learning model predicted better results than the existing models. It was also confirmed through a parametric study that the machine learning model presents continuous and physical HTCs for various thermal-hydraulic conditions. By reflecting the effects of individual variables obtained from the parametric analysis, a new correlation was proposed. It yielded better results for almost all experimental conditions than the ten existing models.

A condensation heat transfer model with light gas effects in non-condensable gas mixtures

In this study, the condensation heat transfer on a vertical tube in a steam-air‑helium mixture was investigated. The effect of helium on condensation heat transfer was analyzed using a heat and mass transfer analogy model and a new effective diffusivity which was proposed based on the Maxwell-Stefan diffusion model. The condensation heat transfer was increased at a low helium content and decreased at a high helium content, which could be explained by the competition induced by the increasing diffusivity due to helium and reduction in density difference between the bulk conditions and wall. In addition, the vapor content and wall subcooling influenced the effect of helium. An experimental investigation was conducted to analyze the helium effect on condensation heat transfer. The helium content was measured using a gas analyzer, while the vapor content was obtained under the assumption that the gas was saturated. The results of the experiment were consistent with the theoretical analyses, although the helium effect at a low helium content was stronger than that predicted by the theoretical model. An improved helium effect factor was proposed after fitting the theoretical model to the experimental data, and the model predicted the effect of helium reasonably well.

Global sensitivity analysis of a pure steam dropwise condensation heat transfer model

Established heat transfer models for dropwise condensation (DWC) consider wetting behavior, surface structure and nucleation dynamics to calculate the heat flux. However, model results often deviate from experiments, in part due to uncertainties of the model input parameters. In this study, we apply quantitative sensitivity analysis to a pure steam DWC heat transfer model in order to attribute the variation of the model result to its input parameters. Four scenarios with different variations of the model parameters are discussed and sensitivity coefficients for each parameter are calculated. Our results show a high sensitivity of the model result towards the coating thickness, the contact angle and the nucleation site density, underlining the need to accurately determine these parameters in DWC experiments.

The coupling process of steam condensation and falling film evaporation with non-condensable gas

For the steam condensation process inside a horizontal tube under small temperature difference, the operating parameters such as steam flow rate and temperature difference are narrowed in limited variation ranges to guarantee the high heat transfer performance. When the steam is mixed with non-condensable gas, the effective temperature difference shows more complexity compared with pure steam condensation. In this paper, the condensation process of the mixed steam in the tube cooled by the falling film evaporative seawater outside the tube is calculated with the steam mass flow rate of 2–9 kg/(m2·s), the evaporation temperature of the seawater of 50–70 °C, the total temperature difference of 2–4 °C and the inlet non-condensable gas (NCG) mass fraction of 1–4%. The mixed steam condensation heat transfer model combined with distributed parameter model is established and validated to couple the heat transfer on both sides of the tube. The local saturation temperature depression of the mixed steam condensation is divided into two factors, one caused by the flow resistance and the other caused by the non-condensable gas mass fraction. Both types of saturation temperature depression increase with the increase of the inlet non-condensable gas mass fraction. The mass flow rate and the temperature difference are found to have more significant impact on the total temperature depression on the saturation temperature. Besides, the saturation temperature is found to have no significant impact on the condensation heat transfer of the mixed steam. The mass flow rate corresponding to the maximum heat transfer of the heat transfer tube is presented.

Internal condensation heat transfer in a hydrophobic near-horizontal tube with high pressure and low mass flux

Based on the potential of hydrophobic surface treatment leading to a significant increase in condensation heat transfer, in this study, condensation phenomena of water with hydrophobic coating in a nearly-horizontal tube were experimentally investigated whether increase by hydrophobic surface modifications is retained in extreme flow conditions. Various pressurized conditions (0.6 ≤ P ≤ 1.5 MPa) for different mass fluxes (7.0 ≤ G ≤ 27.5 kg/(m2·s)) were tested. The hydrophobically-coated tubes showed 8.95 times higher heat transfer coefficient h than non-coated tube at P = 0.6 MPa, but the increase became negligible as P approached 1.5 MPa. The result is consistent with the hypothesis that dropwise condensation is promoted by the hydrophobic coating at low P, where surface tension σ is high, but the effect is not sustained as P increases and σ decreases. Under a given P, h increased as G increased. The high G is expected to encourage droplet departure in the partial dropwise mode, and this increase in departure rate contributes to increase h. The experimental results are described with a newly-proposed dropwise condensation heat transfer model for hydrophobically-coated tubes that have dropwise condensation in the stratified flow regime.

Experimental investigation on the heat transfer and pressure drop characteristics of R600a in a minichannel condenser with different inclined angles

• The heat transfer and pressure drop of R600a in a compact condenser was studied. • The effect of inclined angle in both horizontal and vertical directions was analyzed. • The calculation method of logarithmic mean temperature difference was modified. • New heat transfer correlation was proposed with a mean average deviation of 9.8%. • New friction factor correlation was proposed with a mean average deviation of 7.3%. The condensation heat transfer and pressure drop characteristics of R600a in a multi-louvered fins compact heat exchanger were studied in detail. Experiments were carried out at saturation pressures from 530 to 620 kPa, mass fluxes from 25 to 41.25 kg∙(m 2 ∙s) −1 , air temperature from 25 to 35 °C and inclined angles from 0° to 180° in horizontal and vertical directions, respectively. The effects of mass flux, saturation pressure, air temperature, and inclined angle were analyzed and discussed. The results indicated that both heat transfer coefficient and pressure drop increased with the increase of mass flux and decreased with the increase of saturation pressure. Both heat transfer coefficient and pressure drop were significantly affected by the inclined angle, while the heat transfer capacity of the condenser was almost independent of the inclined angle. The experimental data were compared with several well-known condensation heat transfer models. Based on analysis results, the modified method to calculate the logarithmic average temperature difference was proposed. This predicted method could decrease the deviations of those models by 15%. In addition, the improved correlations for heat transfer and friction factor were proposed and predicted the present experimental data well with 90% of the data points within the bandwidth of ±15% and ±12%, respectively.

A neural network model for free-falling condensation heat transfer in the presence of non-condensable gases

Condensation heat transfer has been widely studied for various applications such as power generation, water desalination, data centers, chemical and pharmaceutical syntheses, heating and air conditioning systems, and especially, the passive containment cooling system (PCCS) in nuclear power plants. The PCCS is designed to reduce the risk resulting from a loss of AC power or compounding human error. In a hypothetical accident situation, one of the main safety systems, the PCCS, condenses water vapor with gravity-driven force to remove the heat from the containment vessel. Although the condensation heat transfer in the presence of non-condensable gases for predicting the heat transfer performance of the PCCS has been successfully investigated, there is still no generalized model or correlation available. In this work, we focused on the development of universal models for predicting the heat transfer coefficients of free-fall condensation heat transfer on the external surfaces of vertical tubes in the presence of non-condensable gases. For this, we created a consolidated database covering a broad range of geometric values and operating conditions, including tube hydraulic diameter Dh = 10–41.2 mm, tube length L = 0.3–3.5 m, total pressure Ptot = 0.15–2.0 MPa, wall subcooling temperature ΔTsub = 5–71 K, average condensation heat transfer coefficients of 90 ≤ ͞h ≤ 19,400 W/m2K, and Reynolds numbers of the film of 8 ≤ Refilm ≤ 7160. We analyzed the influence of varying the geometric and operational parameter values by using this database. Conventional machine learning techniques such as nonlinear regression and multilayer perceptron (MLP) neural network methods were adopted to predict the condensation heat transfer rate based on the consolidated database. Moreover, the prediction accuracies of the condensation heat transfer rates of the proposed prediction models and 12 relevant correlations were compared by using the consolidated database. The proposed nonlinear regression model exhibited good prediction accuracy with a mean absolute error (MAE) of 12.7 % for average ͞h, which is much lower than those achieved by previously proposed relevant correlations. In addition, the MLP neural network model showed excellent prediction accuracy with an MAE of 4.2 % for the consolidated database.

Universal condensation heat transfer and pressure drop model and the role of machine learning techniques to improve predictive capabilities

A novel, universal model is proposed to predict the condensation frictional pressure drop and heat transfer coefficient for horizontal microchannel and macrochannel flows. Over 4000 data points across the entire vapor quality range are collected with mass fluxes ranging from 50 – 800 kg m−2 s−1, reduced pressures from 0.03 – 0.96, and hydraulic diameters from 0.1 – 14.45 mm. The working fluids include synthetic refrigerants (R1234ze(E), R134a, R245fa, R404A, R410A), hydrocarbons (pentane and propane), and natural refrigerants (ammonia and carbon dioxide). Several models, including flow-regime based correlations and machine learning regression models (support vector regression, random forest, and artificial neural networks) are developed and compared with each other. Overall, machine learning algorithms, especially the random forest model, predict the data best for the pressure drop and heat transfer, with an absolute average deviation of about 4% for both models.

CFD validation of condensation heat transfer in scaled-down small modular reactor applications, Part 2: Steam and non-condensable gas

This paper presents the computational fluid dynamics (CFD) validation and scaling assessment of the condensation heat transfer (CHT) models in the presence of non-condensable gas for the passive containment cooling system (PCCS) of the small modular reactor (SMR). The STAR-CCM+ software with 3D scaled-down SMR containment geometries was used in CFD simulations with steam and non-condensable gas (NCG). The limitations and approximations of the previous studies were resolved to avoid scaling distortion and uncertainties. Air was used as the NCG gas with steam. The multi-component gas model was used to define the steam-NCG mixture, and the condensation-seed parameter was used as the source term for the fluid film model. Three different turbulence models were used to check the heat flux performances and temperature distributions on the coolant side. The heat flux was estimated from the axial coolant bulk temperature, which was identical to the test data reduction method. An implicit-unsteady numerical solver was applied to the conjugate heat transfer models between the gas, liquid, and solid regions. Detailed simulations were performed, and simulation results were validated with the measured parameters experimentally. The condensation heat transfer performance was quantified using non-dimensional numbers and compared for different scaled geometries to identify the scaling distortions.

Influence of different categories of condensates on film condensation of R-134a on in-line arranged 3D finned tubes

The condensates generated in the process of film condensation, such as condensing, falling, and acting condensates, are identified and defined to obtain precise inundation effect model of 3D finned tube bundle. Furthermore, the influence of three categories of condensates on the film condensation under different heat fluxes and condensing temperatures is experimentally investigated via the homologous method. A test bench with a test section contains two arrays of 8 horizontal tubes is initially constructed. Experimental results show that the inundation effect coefficient decreases from 1.00 to 0.78 as acting condensate Re increases from 75 to 1860. The influence of heat flux on three categories of condensates is more significant than that of condensing temperature. As tube row depth increases from 1 to 6, the proportion of condensing condensate Re to acting condensate Re decreases from 100% to 15.0%, whereas the proportion of falling condensate Re to acting condensate Re increases from 0 to 85.0%. Furthermore, the condensation heat transfer model of single 3D finned tube and inundation effect model are established, and the deviations of results between experiments and models are almost within ±3.0%. The deviations of inundation effect coefficients between experiment and model proposed in this study are 92.5% and 50.0% less than those between experiment and models using routine method and considering single condensate with homologous method, respectively. Moreover, the inundation effect model proposed in this study reveals that different proportions of condensing or falling condensates to acting condensate lead to different inundation effect coefficient at the same acting condensate Re. The research results will lay the foundation for further revealing the mechanism of film condensation on 3D finned tube bundle and guide the thermal design of 3D finned tubes applied in shell-and-tube condensers.

Enhancement of vapor condensation heat transfer on the micro- and nano-structured superhydrophobic surfaces

Recently, micro- or nano- structured surfaces haven been developed to enhance condensation heat transfer, water harvesting and self-cleaning. However, at large subcoolings, condensate floods the subcooled substrate, thus deteriorating the heat transfer efficiency. Here, the superhydrophobic surfaces with micropillared and nanopillared structures are proposed to enhance heat transfer at large subcoolings. The influence of micropillar spacing and surface subcooling on the droplet dynamics and heat transfer performance is experimentally investigated using microscopic visualization techniques. In addition, the microscopic modeling of condensation heat transfer on the microstructured surfaces is performed using the mesoscopic lattice Boltzmann method. The results demonstrate that the droplet size distribution on the micropillared surface is significantly smaller over that of the nanostructured surface. The heat transfer coefficient decreases with the increase of micropillar spacing. As the subcooling rises, although the condensate floods the substrate, the heat transfer coefficient of the S10R30 (S10R30 represents the micropillar arrays with s = 10 μm and 2r = 60 μm) surface is enhanced by 26.4% compared to the hydrophobic nanostructured surface. This is because the height of liquid film is the same of order of magnitude as the micropillars, reducing the thermal resistance caused by the liquid layer. Combining environmental scanning electron microscope (ESEM) observations and LB simulation results, it is concluded that the droplets first nucleate at the bottom corner of micropillars. In addition, the condensate droplets merge to form a film, fill the micropillar gaps, and cover the entire micropillars, leading to a sharp decrease in heat flux. These findings provide a theoretical and experimental guidance for the development of condensing surfaces to enhance heat transfer.