Condensation Heat Transfer Coefficient Research Articles

Evaluation of dropwise condensation from a vertical condenser tube impregnated with semisolid lubricant

AbstractThe quest for augmenting dropwise condensation heat transfer performance has been the driving force behind the exploration of innovative techniques and approaches to fulfill the desired objective. Mostly, earlier research on dropwise condensation was devoted to study condensation from horizontal tubes and plates but, rarely, addressed dropwise condensation from vertical tubes. An interesting topic of current research in dropwise condensation is to explore the application of Slippery Liquid Infused Porous Surfaces (SLIPSs) to improve the condensation performance. The aforesaid facts are the motivation behind the topics of interest in the present study. In this study, we explore the applicability of semisolid lubricant‐impregnated porous surfaces in enhancing dropwise condensation performance of a vertical condenser tube. The experiments conducted over a wide range of subcooling (5°C ≤ ΔT ≤ 65°C) in saturated steam environment showed significant improvement in condensation heat transfer coefficient of a vertical condenser tube impregnated with semisolid lubricants when compared to bare vertical condenser tube. The highest enhancement is found to be 280% at a subcooling of 40°C. Furthermore, these surfaces proficiently sustain dropwise condensation over a period of 72 hours without compromising heat transfer performance. The devised fabrication method is simpler, more cost‐effective, and less time‐consuming than earlier techniques used for creating SLIPSs. Additionally, an approach based on numerical optimization by a stochastic global optimization technique, namely, Genetic Algorithm, is proposed to retrieve the coefficients and the exponent in the mathematical expression of overall resistance, that is being used to compute the tube‐side dropwise convection heat transfer coefficient.

A novel strategy to enhance heavy oil Recovery: Condensation heat transfer calculation and 2-D visualized physical simulation

High carbon emissions and low efficiency in thermal utilization are pivotal challenges in heavy oil development. Addressing the urgent need to enhance heavy oil production, this paper introduces an innovative approach in which aerogel and flue gas serve as supplementary additives in steam thermal recovery. To investigate the mechanisms behind the improved heat transfer and oil recovery facilitated by the combination of aerogel and flue gas, condensation heat transfer experiments and 2-D visual oil displacement experiments were carried out. The condensation heat transfer coefficient, oil displacement performance, and heat utilization efficiency were analyzed and compared. The results showed the porous structure of the aerogel enhances its thermal insulation properties by adsorbing flue gas, thereby reducing the steam-to-cold object condensation heat transfer coefficient by 60.6%. The cementation between aerogel nanoparticles (NPs) retained in the formation and rock particles formed an insulating layer that impedes the upward propagation of steam heat. Combined with efficacy of flue gas foam in improving fluid flow, this promoted the horizontal expansion of the steam chamber, resulting in an 8.2% and 3.4% increase in the steam chamber’s extent compared to using steam and a combination of steam with aerogels, respectively. Moreover, the profile control and thermal insulation impacted following the NPs, lowering the water cut in the produced fluid and enhancing the oil recovery by 7.9%. Upon the application of flue gas, this enhancement in recovery extended to 9.3%.

Condensation heat transfer performance of R290 in a plate heat exchanger for electric vehicle heat pumps

In the continuous improvement of electric vehicles driving range, a heat pump system with low-global warming potential (GWP) refrigerant is a promising solution. In the present work, the condensation heat transfer coefficient (HTC) and frictional pressure drop (FPD) were experimentally evaluated in an offset strip fins embedded plate heat exchanger (PHE), using an eco-friendly, low-GWP refrigerant R290 as the working fluid. A Modified Wilson Plot Method was used to predict the heat transfer of the coolant side (water/ethylene glycol; 50/50 %). The main focus of the present study revolves around the R290-to-coolant condensation experiment, wherein we analyzed the influence of mass flux (G = 20–50 kg m−2 s−1), heat flux (q = 3–12 kW m−2), and saturation temperature (Tsat = 40–55 °C) on HTC and FPD within the PHE while variable mean vapor quality (xm). According to the results, all experimental conditions were fall in the forced-convection condensation regime. The results state that HTC increases with a rise in q, G, and xm, but decreases with a surge in Tsat. Conversely, FPD increases with a rise in G and xm, and decreases with a escalation in Tsat, while q has no significant impact. Correlations were developed to predict the Nusselt number and friction factor values of R290 with mean absolute errors of 10.6 % and 11.2 %, respectively. These correlations are compared to previous research findings, signifying their efficacy in predicting the heat transfer characteristics of R290 within PHEs.

A Predictive Machine Learning-Based Analysis for In-Tube Co-current Steam Condensation Heat Transfer in the Presence of Non-condensable Gases

AbstractThe primary objective of this study is to develop a predictive model that accurately estimates the condensation heat transfer coefficient (HTC) in the context of in-tube co-current flow condensation, with specific consideration given to the influence of non-condensable gases (NCGs). The need to find useful models and/or correlations that can accurately predict how well a passive condenser in the Economic Simplified Boiling Water Reactor will transfer heat during condensation is greatly warranted. This innovative cooling system utilizes gravity to condense water vapor, effectively dissipating heat from the containment vessel in the event of an accident and transferring the heat into the secondary cooling system. For the development of the forecasting model, we employed Multi-Gens Genetic Programming implemented in MATLAB. This approach possesses the capability of enabling visualization and articulating a transparent mathematical expression that directly establishes the connection between inputs and outputs. This model was specifically developed for forecasting the HTC during the condensation process, considering two distinct types of NCG: air, helium, and a mixture of air and helium, in addition to data for pure steam. The model was trained using a dataset comprising 2913 data points gathered from five experimental references. The model's input comprises a varied range of parameters, including local location ($$z$$ z ), temperature of the steam/NCG mixture ($${T}_{mix}$$ T mix ), condenser wall temperature ($${T}_{wall}$$ T wall ), coolant flow rate ($$\beta_{c}$$ β c ), total pressure ($${P}_{tot}$$ P tot ), condenser inner diameter ($${D}_{1}$$ D 1 ), condenser outer diameter ($${D}_{2}$$ D 2 ), molecular weight (MM), NCG mass fraction ($${w}_{NCG}$$ w NCG ), and mixture Reynolds number ($${Re}_{mix}$$ Re mix ). The output of our model corresponds to the condensation HTC ($$h$$ h ). Four specific correlations were examined with the third correlation attaining the highest anticipated values within a deviation range of ± 30, reaching 90.9%, exhibiting good performance in terms of both performance index and coefficient of determination. This correlation was validated, and its accuracy assessed by employing an unused training dataset that included nitrogen as a NCG. The outcomes showed that 92.94% of the predicted data fell within a deviation range of ± 30%. The findings of this study signify a notable advancement in the analysis of datasets obtained from experiments. Particularly, it delves into the impact of NCGs by considering their molecular weight and mass fraction.

Experimental investigation on steam condensation characteristics in horizontal heat transfer pipe of PRS for HPR1000

The heat exchanger is an important component of the passive residual heat removal system (PRS) in the Hualong Pressurized Reactor (HPR1000). The heat released via steam condensation inside the heat transfer pipes is delivered to water stored outside of the containment. The heat transfer along the heat transfer pipes is systematically studied experimentally. Three horizontal test sections with different dimensions are designed and employed in the experiment. The test data are obtained under different pressure, steam quality, and mass flow rate conditions and compared with existing steam condensation correlation. The test results reveal that the heat transfer coefficient decreases from 41.5 kW/(m2·K) to 24.0 kW/(m2·K) with the pressure increased from 1.0 MPa to 8.3 MPa. In the higher pressure conditions, the larger condensed flow rate provides larger thermal resistance, decreasing the condensation heat transfer coefficient. The steam condensation heat transfer coefficient is increased from 14.3 kW/(m2·K) to 41.4 kW/(m2·K) as the steam quality is increased from 0.07 to 0.70. The condensation heat transfer coefficient is increased from 28.6 kW/(m2·K) to 45.8 kW/(m2·K) as the mass flow rate is increased from 0.029 kg/s to 0.072 kg/s. The maximum prediction deviation of Shah’s correlation for all test data is ±34.9 %.

Comparative analysis of condensation heat transfer and pressure drop of low GWP near-Azeotropic mixture and R-290 in a plate heat exchanger

Downward condensation of a near-azeotropic low GWP refrigerant mixture of R290/R1270 (65%/35% wt.), named HC01, was investigated inside an offset strip-fin (OSF) flow-structured plate heat exchanger (PHE). This study tested the prototype heat exchanger with HC01 and compared the condensation heat transfer coefficient (CHTC) and pressure drop (CPD) with the pure R-290 for working conditions of refrigerant mass flux (G) = 20–50 kg/m2s, inlet vapor quality (xin) = 0.2–0.9, saturation temperature (Tsat) = 40–55 °C, and heat flux (q) = 4–13 kW/m2. It was observed that the condensation of HC01 occurred entirely within the forced convection condensation flow regime, despite a slight change in the CHTC at a lower xin as the G increased. The CHTC demonstrated an increase in response to higher values of G and q, but decreased with increasing Tsat. Similarly, the CPD exhibited an increase with higher G and a decrease with lower Tsat, whereas the q had minimal influence. Comparison results showed that the HC01 exhibited 14.2% higher average CHTC but 6.5% lower CPD than that of R-290 because of the differences in properties, such as liquid thermal conductivity, viscosity, surface tension, latent heat, and discrepancies in liquid and vapor phase densities. Correlations were generated to predict the values of the Nusselt number (Nu) and friction factor (FF) of HC01, with mean absolute errors of 12.1% and 12.7%, respectively.

Numerical study on condensation heat transfer of R1234ze(E)/R152a in a horizontal smooth tube

Stable-state numerical simulations of the condensation heat transfer properties of the novel refrigerant NCUR01 (R1234ze(E) and R152a in a mass ratio of 4:6) have been performed using the volume of fluid (VOF) multiphase flow model. The findings demonstrate that as mass flux rises, the working substance’s fluid velocity increases at the same location within the tube, boosting the condensation heat transfer coefficient in the process. The condensation heat transfer force is strengthened by an increase in heat flux, which raises the condensation heat transfer coefficient. On the other hand, when the saturation temperature rises, the condensation heat transfer coefficient falls.

Machine-learning algorithms for predicting condensation heat transfer coefficients in the presence of non-condensable gases

Condensation on surfaces in the presence of non-condensable gas (NCG) is a ubiquitous and critical phenomenon in many industrial fields. However, current empirical correlations for predicting condensation heat transfer coefficients in the presence of NCG may exhibit significant deviations from reality. Machine learning algorithms are now being dedicated to this field, but the models developed can only predict the condensation heat transfer coefficients of steam or vapor of a non-aqueous working fluid with similar physical properties to water in the presence of NCG. In the present study, a comprehensive theoretical analysis was conducted to investigate the total condensation heat transfer coefficients with NCG, and 16 dimensionless numbers were identified as input variables for machine learning models. Based on a filtered database consisting of 4377 data points extracted from 37 papers, the Spearman correlation coefficients were calculated to evaluate the relationship between the total heat transfer coefficients and the input variables, indicating the magnitude of the impact of the 16 dimensionless variables. Four machine learning models, namely Gradient Boosting Regression (GBR), Extreme Gradient Boosting (XGBoost), Random Forest Regression (RFR), and Multilayer Perceptron (MLP), were developed to predict the total heat transfer coefficients for water and non-aqueous working fluids. The mean absolute percentage errors for the four models were 1.38%, 1.63%, 3.00%, and 4.42%, respectively, with the GBR model exhibiting the highest degree of accuracy. The determination of the application scope of these models was conducted by analyzing the value ranges for each dimensionless parameter and its corresponding frequency distribution.

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.

Visual observations of steam–air submerged horizontal jets at low mass flux

Steam jet condensation at low mass flux is important especially in start-up, shutdown or accident conditions of various applications. Visual experiments of steam–air submerged jets through horizontal pipe were conducted in the ranges of mass flux 0–––20 kg/m2s, air mass fraction 0–––0.02, and water temperature 20 − 80℃. In pure steam jets, three condensation regimes of no bubbling, intermittent bubbling, and continuous bubbling were identified in steam jets. In no bubbling regime, rolling up and billows of plane shear flow were observed. In intermittent bubbling regime, large bubble was generated, detached and collapsed intermittently, accompanied by fluid oscillations. The bubble forming frequency increased with the rise of water temperature. Condensation heat transfer coefficients tends to decrease as the steam mass flux increases. In continuous bubbling, fluid oscillation no longer occurred. The bubble forming frequency decreased with the rise of water temperature. Condensation heat transfer coefficients increase as the steam mass flux increases. In the cases with shallow submerged depth, air-entrained vortex, surface water jets, and splash were observed after bubble condensation. In steam–air jets, condensation became stable and small bubbles increased. The condensation regime transitioned from intermittent bubbling to continuous bubbling earlier.

Condensation Flow of Refrigerants Inside Mini and Microchannels: A Review

Nowadays, the demand for obtaining high heat flux values in small volumes has increased with the development of technology. Condensing flow inside mini- and microchannels has been becoming a promising solution for refrigeration, HVAC, air-conditioning, heat pumps, heat pipes, and electronic cooling applications. In these applications, employing mini/microchannels in the condenser design results in the working fluid, generally refrigerant, undergoing a phase change inside the mini/microchannels. On the other hand, the reduction in the hydraulic diameter during condensation gives rise to different flow regimes and heat transfer mechanisms in the mini- and microchannels compared to the conventional channels. Therefore, the understanding of fluid flow and heat transfer characteristics during condensation of refrigerant inside mini- and microchannels has been gaining importance in terms of condenser design. This study presents a state-of-the-art review of condensation studies on refrigerants inside mini- and microchannels. The review includes experimental studies as well as correlation models, which are developed to predict condensation heat transfer coefficients and pressure drop. The refrigerant type, thermodynamical performance, and compatibility, as well as the environmental effects of refrigerant, play a decisive role in the design of refrigeration systems. Therefore, the environmental impacts of refrigerants and current regulations against them are also discussed in the present review.

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 research on condensation flow and heat transfer characteristics of immiscible binary mixed vapors on different wettability wall surfaces

In the process of biomass gasification syngas waste heat recovery, water and organic compounds in the syngas will condense at the same time, resulting in immiscible condensates adhering to the wall, which can reduce the heat exchanger heat transfer efficiency and working life. Changing the wettability of the wall surface can affect the condensate flow state on the wall surface, thus affecting the condensation heat transfer performance. Water and organic cyclohexane were used as immiscible working fluids in this paper, the condensation flow and heat transfer characteristics of the mixed vapors on hydrophilic, super-hydrophilic, and super-hydrophobic wall surfaces were experimentally investigated. The results showed that cyclohexane in immiscible condensates existed as a liquid film on hydrophilic, super-hydrophilic, and super-hydrophobic wall surfaces, while the water existed in droplets on the hydrophilic and super-hydrophobic wall surfaces, and there were two forms of channels and droplets on the super-hydrophilic wall surface. The condensation heat transfer coefficient on the super-hydrophobic wall surface was higher than that on the hydrophilic wall surface and the super-hydrophilic wall surface, with a maximum of 34% higher than hydrophilic and 45% higher than super-hydrophilic, which was related to the smaller droplet departure diameter on the super-hydrophobic surface. In addition, the super-hydrophilic wall surface had better heat transfer performance than the hydrophilic wall surface only when the immiscible condensate was a "film-channel" flow pattern. The results can provide guidance for enhancing the condensation heat transfer of the water and organic compounds binary mixed vapors.

Fundamental research on the condensation heat transfer of the hydrocarbon-mixture energy in a spiral tube described by a universal model using flow pattern based and general modes

The transportation of liquefied natural gas (LNG) is a crucial aspect of global energy application and security. However, there are still some challenges in liquefaction. This study establishes systematic experiments to investigate the condensation two-phase flow and heat transfer process of hydrocarbon-mixture energy (methane/ethane/propane/isobutane) in a spiral tube. The main influencing parameters, including mass flux (200–560 kg/(m2·s)), operating pressure (2–4 MPa), and vapor quality (0–1) on the heat transfer intensity and frictional pressure drop were analyzed. Different two-phase flow patterns were observed and categorized them as bubble flow, intermittent flow, stratified-wavy flow, and annular flow. For bubble flow and intermittent flow, vapor quality is found to remarkably affect the heat transfer; regarding stratified-wavy flow and annular flow, gravitational force and inertia force are found to prominently influence the heat transfer, respectively. Additionally, a newly universal model includes split flow patterns mode (SFPM) and the general mode (GM) has been proposed, which is with high-precision for describing the condensation heat transfer coefficients of different flow patterns. Furthermore, the analytical results show the SFPM's root mean square error and mean absolute relative deviation are 97.35 W/(m2·K) and 0.036.

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.

CFD simulation data report: heat transfer coefficient and pressure drop of natural gas flow condensation in spiral tube

CFD simulation data report: heat transfer coefficient and pressure drop of natural gas flow condensation in spiral tube

NUMERICAL AND EXPERIMENTAL INVESTIGATIONOF STEAM FILM CONDENSATION ON A VERTICAL TUBE

NUMERICAL AND EXPERIMENTAL INVESTIGATIONOF STEAM FILM CONDENSATION ON A VERTICAL TUBE

Enhancing the vertical downward condensation heat transfer of a plate heat exchanger by electrochemical etching

This research employed electrochemical etching to generate microscale roughness on a plate heat exchanger (PHE) thereby enhancing condensation heat transfer in a vertical downward flow. The effects of the mass flux, vapor quality, heat flux, saturation temperature and microscale surface roughness on the condensation heat transfer coefficient (HTC) and frictional pressure drop (FPD) of R-513a flowing through a PHE was experimentally investigated. Experimental measurements were made under various heat fluxes of 5–15 kW/m2, mass fluxes of 60–80 kg/m2s, saturation temperatures of 40–50 °C, and mean vapor qualities of 0.1–0.7. HTC increased by up to 28, 26, 74, and 32.5 % as the heat flux, mass flux, average quality, and microscale surface roughness were respectively increased, but decreased by 19 % with the saturation temperature. FPD increased as the mass flux and average vapor quality increased, whereas it was reduced as the refrigerant saturation temperature increased. In contrast, the heat flux had little to no influence on FPD. Correlations for predicting the Nusselt number and friction factor were postulated by considering influential dimensionless numbers during heat transfer. Maximizing the Nusselt number and thermal performance factor was ultimately resolved using a multi-objective genetic algorithm to find a Pareto front solution. Consequently, the optimal surface roughness was found to be 9.94 μm.

Surface functionalization enabling highly effective phase-change heat transfer of R1234yf: A molecular dynamics study

Phase-change heat transfer has been demonstrated to be an effective thermal management approach for high-power electronics and power generators. In this work, we propose a strategy using surface functionalization to enhance the phase-change heat transfer of an environmentally friendly refrigerant, i.e., hydrofluoroolefins (HFO)-based R1234yf. Using molecular dynamics simulations, we find that the evaporation (condensation) heat transfer coefficient (HTC) at self-assembled monolayer (SAM) functionalized gold (Au) surfaces can be increased by ∼7 (∼5) times compared to that at the planar Au surfaces. The improvement in phase-change heat transfer of R1234yf at functionalized Au surfaces arises from the high thermal conductance across functionalized Au/R1234yf interfaces and the strong vibrational couplings between SAM molecules and R1234yf molecules. On the one hand, the introduced SAM layer can increase the interfacial thermal conductance (ITC) between Au and R1234yf owing to the strong bonding between Au and the SAM molecules, as well as the strong vibrational couplings at low-frequency modes (0–6 THz) among Au, SAM, and R1234yf. On the other hand, the thermal energy exchange during the evaporation (condensation) process can be improved by surface functionalization through the vibrational couplings between SAM and R1234yf at high frequency (∼12 and ∼30 THz) regions. This dual vibrational coupling will then allow the SAM layer to work as an intermedia to bridge the vibrations between Au and R1234yf molecules, which in return benefits the phase-change heat transfer. By further characterizing the evaporation and condensation processes, we find that the evaporation rate of R1234yf is increased by ∼50% at most at the wall superheat <70 K when the Au surfaces are functionalized using SAMs. When the wall superheat is above 70 K (i.e., 100 K for the planar Au surface), the evaporation of R1234yf becomes like film evaporation in which clusters of R1234yf molecules move off the surface together, which limits the enhancement (i.e., ∼100% at most) of evaporation HTC at the functionalized Au surfaces. At the same time, the condensation rate at functionalized Au surfaces is found to be increased by 15%–100% compared to that of the planar Au surfaces. By elucidating the mechanisms governing phase-change heat transfer on functionalized surfaces, our results provide an efficient strategy to enhance the cooling efficiency of the HFO refrigerants, which may benefit the design of surfaces with high phase-change heat transfer capabilities.

Machine learning-driven approach for predicting the condensation heat transfer coefficient (HTC) in the presence of non-condensable gases

This research focuses on developing a predictive model that can effectively estimate the condensation heat transfer coefficient (HTC) on vertical tube surfaces during free-fall. The model takes into consideration the presence of non-condensable gases. The main objective is to assemble an extensive and varied database that encompasses different geometric parameters and operational conditions. The motivation here stems from the urgent need to develop practical models and/or correlations that can accurately predict the condensation heat transfer efficiency for industrial heat exchangers affected by non-condensable gases, with a particular focus on passive containment cooling systems (PCCS) employed in nuclear power plants (NPPs). This innovative cooling system utilizes the force of gravity to condense water steam, effectively removing heat from the containment vessel in the event of an accident. For the development of the model, we employed the neural network toolbox in MATLAB to construct an Artificial Neural Network (ANN) known as a Multi-Layer Perceptron (MLP) network. This model was designed to predict the HTC during the condensation process involving two different types of Non-Condensable Gases (NCG): Air and Nitrogen, along with Helium as a representative light gas. To build the model, we utilized a dataset comprising 1,888 data points sourced from 21 experimental references. The input layer of our model receives a range of parameters, encompassing total pressure (Ptot), subcooling temperature (ΔTsub), mass fraction of non-condensable gases (wnc), wall length (L), hydraulic diameter (Dh), the ratio of the Helium to non-condensable gases (RHe/nc), liquid density (ρl), gas density (ρg), liquid viscosity (μl), gas viscosity (μg), saturation temperature (Tsat), latent heat (hfg), liquid thermal conductivity (kl), and gas specific heat (cp,g). The output data of our model corresponds to the condensation HTC (h). To ensure consistent scaling, the input data was normalized by scaling each feature within the range of 0 to 1. On the other hand, the output data underwent a transformation using the natural logarithm. By utilizing Pearson correlation coefficient analysis and importance analysis for parameter selection, a new correlation for heat transfer was developed and evaluated. The developed machine learning model exhibited outstanding effectiveness in precisely forecasting the condensation HTC with an accuracy range of ± 10 % and ± 20 %. The outcome of this investigation represents the notable progress in analyzing extensive datasets gathered through experiments. Specifically, it addresses the previously unexplored influence of Helium gas by harnessing the capabilities of machine learning.