Stratified Flow Regime Research Articles

Development of analytical equations for void fraction in biphasic systems using gamma radiation and MCNP6 code

This study presents the development of mathematical equations for calculating void fractions in pipes using gamma densitometry. A traditional measurement setup, consisting of a137Cs point source and a NaI(Tl) scintillator detector, was simulated using the Monte Carlo method via the MCNP6 code. To validate the proposed equations, water-gas biphasic models were simulated in tubes with square and cylindrical cross-sections, varying diameters, and radiation sources (241Am, 137Cs, 60Co) through gamma-ray transmission. A comparative analysis with existing equations from the literature was conducted. The void fractions, determined from the transmission photopeak, were in close agreement with the actual values. The proposed equations demonstrated a maximum mean relative error of 0.21% for cylindrical tubes in stratified and annular flow regimes.

Analyzing the effectiveness of Nields constraint and stratification on dynamics of non-Newtonian fluid by executing numerical and machine learning approaches

Stratified flows are commonly observed in numerous industrial processes. For example, a gas-condensate pipeline typically uses a stratified flow regime. However, this flow arrangement is stable only under a specific set of operating conditions that allows the formation of stratification. In this study, the authors analyzed the flow attributes of Prandtl Eyring liquid past an inclined sheet immersed in a stratified medium. The flow also characterizes the features of the magnetic field along with a first-order chemical reaction. Convective boundary constraints associated with the thermosolutal exchange at the extremity of the domain are also prescribed. The fundamental equations of the study are formulated in dimensional PDEs and converted into dimensionless ODEs via similar variables. The numerical solution of the modelled setup is acquired by executing computations using shooting and RK-4 methods. The intelligent computing paradigm working on the mechanism of the back-propagated Levenberg-Marquardt strategy is also capitalized to forecast the behavior of related physical quantities. Graphs and tables are drawn to elaborate the impression of pertinent factors on flow distributions. It is perceived that the momentum profile diminishes with the magnetic field effect, whereas the opposite behavior is observed for the skin friction coefficient. The thermal and concentration distributions were found to dominate in the absence of stratification. Consideration of convective heating and concentration tends to elevate thermal and mass distributions.

Experimental design and manufacturing of a smart control system for horizontal separator based on PID controller and integrated production model

During oil production, the reservoir pressure declines, causing changes in the hydrocarbon components. To ensure better separation of produced phases, separator dimensions should also be adjusted. It is not possible to change the dimensions of the separator during production. Therefore, to improve the separation of the phases, the level of the separator needs to be adjusted. An intelligent system is required to ensure that the liquid level is maintained at the desired level for optimal phase separation during changes in reservoir pressure. In this study, a novel correlation is presented to measure the desired liquid level using new separator pressures. For this purpose, an intelligent system was built in the laboratory and tested in different operational conditions. The intelligent system effectively maintained the desired liquid level of the separator through a new correlation technique. The system accomplished this by acquiring new separator pressure readings collected by installed sensors. This approach helped mitigate the negative effects of the slug flow regime and minimized issues such as foam formation and over-flushing of the separator. It could achieve a 99.1% separation efficiency between gas and liquid phases. This was possible during liquid and gas flow rates ranging from 0 to 2.35 and 8–17 m3/h, respectively. The system could operate under bubble, stratified, plug, and slug flow regimes. Then the intelligent model obtained from lab experiments was integrated into the production model for the southern Iranian oil field. The smart model increased oil production by 13% and prevented the separator from over-flushing in 840 days.

Thermal Characterization of Subcooled Flow Boiling in a Pin-Fin Coldplate With Non-Uniform Heating

Abstract Coldplates are a crucial component in various cooling applications, such as cooling data center servers and power electronics. The unprecedented growth in electronics power density, along with the resulting ultrahigh heat fluxes, demands a transition from single-phase forced convection to two-phase flow boiling heat transfer. The majority of studies in the literature have focused on flow boiling in fin-enhanced silicon microgaps and microchannels, with only a few addressing flow boiling in millimeter-scale heat sinks. In the present study, flow boiling of HFE-7200 dielectric fluid in a millimeter-scale pin-fin coldplate is experimentally investigated under nonuniform heating conditions. Four background heaters represent the low-dissipating-power devices. On the other hand, five hotspot heaters mimic the high-heat-flux devices and generate heat fluxes ranging from 50 W/cm2 to 1000 W/cm2, corresponding to hotspot heat inputs ranging from 62.5 W to 1.25 kW, respectively. The coldplate's thermohydraulic performance is investigated for various flow rates and inlet temperature ranging from 0.5 L/min to 1.5 L/min and from 25 °C to 60 °C, respectively. A high-speed camera is utilized for a narrow field of view (FOV) flow visualization at a frame rate of 2229 fps while a digital camera is used for a wider FOV at 60 fps. Flow visualization demonstrated the transition between bubbly, slug/churn, and stratified two-phase flow regimes.

Application of the Fourier Transform to Improve the Accuracy of Gamma-Based Volume Percentage Detection System Independent of Scale Thickness

With the passage of time, scale gradually forms inside the oil pipeline. The produced scale, which has a high density, strongly attenuates photons, which lowers the measurement accuracy of three-phase flow meters based on gamma radiation. It is worth mentioning that the need for multiphase flow metering arises when it is necessary or desirable to meter well stream(s) upstream of inlet separation and/or commingling. In this investigation, a novel technique based on artificial intelligence is presented to overcome the issue mentioned earlier. Initially, a detection system was comprised of two NaI detectors and a dual-energy gamma source (241 Am and 133 Ba radioisotopes) using Monte Carlo N particle (MCNP) code. A stratified flow regime with varying volume percentages of oil, water, and gas was modeled inside a pipe that included a scale layer with varying thicknesses. Two detectors record the attenuated photons that could travel through the pipe. Four characteristics with the names of the amplitude of the first and second dominant signal frequencies were extracted from the received signals by both detectors. The aforementioned obtained characteristics were used to train two Radial Basis Function (RBF) neural networks to forecast the volumetric percentages of each component. The RMSE value of the gas and oil prediction neural networks are equal to 0.27 and 0.29, respectively. By measuring two phases of fluids in the pipe, the volume of the third phase can be calculated by subtracting the volume of two phases from the total volume of the pipe. Extraction and introduction of suitable characteristics to determine the volume percentages, reducing the computational burden of the detection system, considering the scale value thickness the pipe, and increasing the accuracy in determining the volume percentages of oil pipes are some of the advantages of the current research, which has increased the usability of the proposed system as a reliable measuring system in the oil and petrochemical industry.

Investigation on multiphase flow of multi-size cuttings particles and non-Newtonian drilling fluids in oil and gas horizontal well drilling using kinetic theory of granular flow

Effective cuttings transport has long been a challenge in oil and gas horizontal drilling, and inadequate wellbore cleaning can result in severe drilling accidents including stuck pipe. The transportation of multi-size cuttings by non-Newtonian drilling fluids in the horizontal wellbore with a rotating drill pipe was systematically investigated using the kinetic theory of granular flow. Collisions between particles and their two-way coupling with drilling fluid turbulence were modeled. Flow regime of multi-size cuttings has been predicted, and effects of particle size composition characteristics were studied. The spatial inhomogeneity of the non-Newtonian fluid viscosity and the sedimentation of particles result in a complex stratified flow regime for multi-size cuttings transport. Cuttings concentration and drilling fluid axial velocity distributions both show swaying phenomena with drill pipe rotation. Moreover, the secondary flow regions appear with negative tangential velocities. The cuttings transport ratio (CTR = 0.846) is the best in the case of cuttings with different particle sizes injected in a uniform concentration. While in the case where the concentration of injected multi-size cuttings is negatively correlated with particle size, the cuttings velocity fluctuations caused by collisions and frictions are the most severe, and the borehole cleaning is the worst (CTR = 0.803).

Void fraction prediction using prompt gamma neutron activation analysis and artificial intelligence

This paper presents the simulation of a water-gas biphasic system in annular and stratified flow regimes using a neutron beam from a241Am–Be radiation source. The objective is to evaluate the potential of prompt gamma neutron activation analysis (PGNAA) and artificial intelligence-based algorithms in flow measurements. A measurement geometry consisted of a neutron flat source with parallel emission and a spherical detector was developed using the MCNP6 code. Prompt gamma rays spectra were used as input data of a feed-forward multilayer perceptron (MLP) artificial neural network aiming to predict void fraction and to identify the investigated flow regimes. The results showed that the MLP was able to predict void fraction, for both flow regimes, with mean absolute percentage error within 1.58%. In addition, the same MLP achieved 100% of success in flow regime identification. Therefore, it is possible to combine PGNAA with MLP in order to investigate flow characteristics.

A stratified flow of a non-Newtonian Casson fluid comprising microorganisms on a stretching sheet with activation energy

A stratified flow may be seen regularly in a number of significant industrial operations. For instance, the stratified flow regime is typically used by gas-condensate pipelines. Clearly, only a limited set of working situations for which this flow arrangement is stable allow for the achievement of the stratified two-phase flow zone. In this paper, the authors are considered the laminar, steady and incompressible magnetohydrodynamic flow of a non-Newtonian Casson fluid flow past a stratified extending sheet. The features of bio-convection, Brownian motion, thermal radiation thermophoresis, heat source, and chemically reactive activation energy have been employed. The set of equations administered flow of fluid is converted into ordinary differential equation by suitable variables. A semi-analytical investigation of the present analysis is performed with homotopy analysis method. Endorsement of the current results with previous results is also investigated. The outcomes showed that the velocity distribution of the fluid flow lessens with higher Casson and magnetic factors. The temperature profiles of fluid flow shrinkage as the Prandtl number and Casson factor increase and enlarges with higher values of thermal radiation, magnetic, and Brownian motion factors. It is found that the growing thermophoretic and Brownian motion factors reduce the rate of thermal flow of the Casson fluid flow. In contrast, the increasing thermal stratification parameter increases the thermal flow rate of fluid.

An Experimental and Simulation Study for Comparison of the Sensitivity of Different Non-Destructive Capacitive Sensors in a Stratified Two-Phase Flow Regime

Measuring the volume fraction of each phase in multi-phase flows is an essential problem in petrochemical industries. One of the standard flow regimes is stratified two-phase flow, which occurs when two immiscible fluids are present in a pipeline. In this paper, we performed several experiments on vertical concave, horizontal concave, and double-ring sensors to benchmark obtained simulation results from modeling these sensors in stratified two-phase flow using COMSOL Multiphysics software. The simulation data was confirmed by experimental data. Due to the low number of data in the experimental method in order to extract more data, the mentioned software was used to extract more data and then compare the sensitivity of different directions of concave and double ring sensors. The simulation results show that the overall sensitivity of the concave is higher than the double-ring and the momentary sensitivity of the horizontal concave is higher in higher void fractions, and the vertical one has higher sensitivity in lower void fractions.

The use of artificial intelligence and time characteristics in the optimization of the structure of the volumetric percentage detection system independent of the scale value inside the pipe

ABSTRACT When scale builds up in a transmission pipeline, it narrows the pipe’s interior and causes losses in both power and efficiency. A noninvasive instrument based on gamma-ray attenuation is one of the most reliable diagnostic procedures for determining volumetric percentages in a variety of circumstances. A system with a NaI detector and dual-energy gamma generator simulations (241Am and 133 Ba radioisotopes) is recommended for simulating a volume percentage detection system utilizing Monte Carlo N particle (MCNP). Three-phase flow consisting of oil, water, and gas moves through a scaled pipe of variable wall thicknesses in a stratified flow regime with changing volume percentages. After gamma rays are emitted from one end of the pipe, a detector take in the photons coming from the other end. Four temporal features, including kurtosis and mean value of the square root (MSR), skewness, and waveform length (WL) picked up by the detector, were thus obtained. By training two GMDH neural networks with the aforementioned inputs, it is possible to forecast volumetric percentages with an RMSE of less than 0.90 and independently of scale thickness. The low error value, simplicity of the system, and reduction of design costs ensures the effectiveness of the suggested method and the advantages of employing this approach in the petroleum and petrochemical industries.

Identification of Horizontal Gas-Liquid Two-Phase Flow Regime using Deep Learning

Two-phase flow is of great importance in various industrial processes. A characteristic feature of two-phase flow is that it can acquire various spatial distribution of phases to form different flow patterns/regimes. The knowledge of flow regime is very important for quantifying the pressure drop, the stability and safety of two-phase flow systems and it holds great significance in petrochemical and thermonuclear industries today. The objective of this study is to develop a methodology for identification of flow regime using dynamic pressure signals and deep learning techniques. Stratified, slug and annular flow regimes were simulated using a Level-Set (LS) method coupled with Volume of Fluid (VOF) method in a 6 m horizontal pipe with 0.050 m inner diameter. Dynamic pressure signals were collected at a strategic location. These signals were converted to scalograms and used as inputs in deep learning architectures like ResNet-50 and ShuffleNet. Both architectures were effective in classifying different flow regime and recorded testing accuracies of 85.7% and 82.9% respectively. According to our knowledge no similar research has been reported in literature, where various Convolutional Neural Networks are used along with dynamic pressure signals to identify flow regime in horizontal pipe. This research provides a benchmark for future research to use dynamic pressure for identification of two-phase flow regimes. This research provides a benchmark for future research to use dynamic pressure for identification of two-phase flow regimes. This study can be extended by collecting data over broader range of flow parameters and different geometries.

Predicting Scale Thickness in Oil Pipelines Using Frequency Characteristics and an Artificial Neural Network in a Stratified Flow Regime

One of the main problems in oil fields is the deposition of scale inside oil pipelines, which causes problems such as the reduction of the internal diameter of oil pipes, the need for more energy to transport oil products, and the waste of energy. For this purpose, the use of an accurate and reliable system for determining the amount of scale inside the pipes has always been one of the needs of the oil industry. In this research, a non-invasive, accurate, and reliable system is presented, which works based on the attenuation of gamma rays. A dual-energy gamma source (241Am and 133Ba radioisotopes), a sodium iodide detector, and a steel pipe are used in the structure of the detection system. The configuration of the detection structure is such that the dual-energy source and the detector are directly opposite each other and on both sides of the steel pipe. In the steel pipe, a stratified flow regime consisting of gas, water, and oil in different volume percentages was simulated using Monte Carlo N Particle (MCNP) code. Seven scale thicknesses between 0 and 3 cm were simulated inside the tube. After the end of the simulation process, the received signals were labeled and transferred to the frequency domain usage of fast Fourier transform (FFT). Frequency domain signals were processed, and four frequency characteristics were extracted from them. The multilayer perceptron (MLP) neural network was used to obtain the relationship between the extracted frequency characteristics and the scale thickness. Frequency characteristics were defined as inputs and scale thickness in cm as the output of the neural network. The prediction of scale thickness with an RMSE of 0.13 and the use of only one detector in the structure of the detection system are among the advantages of this research.

Increasing the Accuracy and Optimizing the Structure of the Scale Thickness Detection System by Extracting the Optimal Characteristics Using Wavelet Transform

Loss of energy, decrement of efficiency, and decrement of the effective diameter of the oil pipe are among the consequences of scale inside oil condensate transfer pipes. To prevent these incidents and their consequences and take timely action, it is important to detect the amount of scale. One of the accurate diagnosis methods is the use of non-invasive systems based on gamma-ray attenuation. The detection method proposed in this research consists of a detector that receives the radiation sent by the gamma source with dual energy (radioisotopes 241Am and 133Ba) after passing through the test pipe with inner scale (in different thicknesses). This structure was simulated by Monte Carlo N Particle code. The simulation performed in the test pipe included a three-phase flow consisting of water, gas, and oil in a stratified flow regime in different volume percentages. The signals received by the detector were processed by wavelet transform, which provided sufficient inputs to design the radial basis function (RBF) neural network. The scale thickness value deposited in the pipe can be predicted with an MSE of 0.02. The use of a detector optimizes the structure, and its high accuracy guarantees the usefulness of its use in practical situations.

Application of Artificial Intelligence for Determining the Volume Percentages of a Stratified Regime’s Three-Phase Flow, Independent of the Oil Pipeline’s Scale Thickness

As time passes, scale builds up inside the pipelines that deliver the oil or gas product from the source to processing plants or storage tanks, reducing the inside diameter and ultimately wasting energy and reducing efficiency. A non-invasive system based on gamma-ray attenuation is one of the most accurate diagnostic methods to detect volumetric percentages in different conditions. A system including two NaI detectors and dual-energy gamma sources (241Am and 133Ba radioisotopes) is the recommended requirement for modeling a volume-percentage detection system using Monte Carlo N particle (MCNP) simulations. Oil, water, and gas form a three-phase flow in a stratified-flow regime in different volume percentages, which flows inside a scaled pipe with different thicknesses. Gamma rays are emitted from one side, and photons are absorbed from the other side of the pipe by two scintillator detectors, and finally, three features with the names of the count under Photopeaks 241Am and 133Ba of the first detector and the total count of the second detector were obtained. By designing two MLP neural networks with said inputs, the volumetric percentages can be predicted with an RMSE of less than 1.48 independent of scale thickness. This low error value guarantees the effectiveness of the intended method and the usefulness of using this approach in the petroleum and petrochemical industries.

Application of Neural Network and Dual-Energy Radiation-Based Detection Techniques to Measure Scale Layer Thickness in Oil Pipelines Containing a Stratified Regime of Three-Phase Flow

Over time, oil pipes are scaled, which causes problems such as a reduction in the effective diameter of the oil pipe, an efficiency reduction, waste of energy, etc. Determining the exact value of the scale inside the pipe is very important in order to take timely action and to prevent the mentioned problems. One accurate detection methodology is the use of non-invasive systems based on gamma-ray attenuation. For this purpose, in this research, a scale thickness detection system consisting of a test pipe, a dual-energy gamma source (241Am and 133Ba radioisotopes), and two sodium iodide detectors were simulated using the Monte Carlo N Particle (MCNP) code. In the test pipe, three-phase flow consisting of water, gas, and oil was simulated in a stratified flow regime in volume percentages in the range from 10% to 80%. In addition, a scale with different thicknesses from 0 to 3 cm was placed inside the pipe, and gamma rays were irradiated onto the pipe; on the other side of the pipe, the photon intensity was recorded by the detectors. A total of 252 simulations were performed. From the signal received by the detectors, four characteristics were extracted, named the Photopeaks of 241Am and 133Ba for the first and second detectors. After training many different Multi-Layer Perceptron(MLP) neural networks with various architectures, it was found that a structure with two hidden layers could predict the connection between the input, extracted features, and the output, scale thickness, with a Root Mean Square Error (RMSE) of less than 0.06. This low error value guarantees the effectiveness of the proposed method and the usefulness of this method for the oil and petrochemical industry.

Towards numerical simulation of high part load pulsations in Francis turbines

One of the most interesting operating points of Francis turbines is the high part load point, which is characterized by synchronous pressure pulsations of large amplitude. Physical mechanisms of these pulsations are not well understood. Experimental observations show that the frequency of the pulsations is 1.5 to 4 times higher than the runner rotation frequency and there exists a 180 degree phase shift of pressure signal measured in the spiral casing and in the draft tube. Numerical modeling this operating point is challenging as it has to account several complex phenomena: cavitation, rotating vortex rope and interaction of the turbine flow with the whole water system of the test rig. In the present paper we carried out a series of 1D-3D calculations of a model turbine in an attempt to simulate the high part load pulsations. 1D hydro-acoustic equations were used for the upstream waterways, while 3D Reynolds averaged Navier-Stokes model of two-phase mixture was used for the turbine. Although the main factors were taken into account, these computations were not able to capture the high part load pulsations. Namely, the frequency and amplitude of the pressure pulsations were significantly lower than those in the experiment, and there was no phase shift in turbine domain. In order to investigate the shortcomings of the present model, we considered a problem of propagation of disturbances in two-phase “liquid-gas” flows. Several 2D test cases were numerically investigated using both incompressible and compressible gaseous phase model. It was shown that in the absence of phase transfer the compressible model correctly represents the speed of sound in both homogeneous and stratified flow regimes. From the other hand, in case of phase transfer the model damps out propagation of pressure disturbances in upstream direction. This behavior explains the failure of the present 1D-3D model to correctly describe the acoustic properties of the two-phase flow in the draft tube, playing a crucial role in the development of high part load pulsations.

Steam condensation in the presence of non-condensable gas inside a nearly horizontal tube under separated flow

An experiment for steam condensation in the presence of a non-condensable gas was conducted in a nearly horizontal tube with an inner diameter of 40 mm and a downward slope of 3°. The multi-dimensional distributions of the local air–steam mixture temperature and heat transfer coefficient were measured in the stratified and annular flow regimes. The influence of the non-condensable gas on the local heat transfer coefficient was investigated by varying the inlet steam mass flux, air mass fraction, and pressure. It was found that the film-wise condensation appearing in the upper region of the tube was significantly deteriorated by the air layer near the wall, but little effect was observed in the convective heat transfer by the condensate accumulated in the lower region. The degradation effect on the condensation increased further downstream due to the air accumulation near the cold wall. In addition, an empirical correlation for the degradation factor of the condensation heat transfer coefficient was developed, and it showed a mean absolute error of 10.4% against the circumferential average heat transfer coefficient of the present experimental data. This correlation is applicable to separated flow covering inlet air mass fractions of 0.01–0.56, pressures of 1–5 bar, and inlet air–steam mixture Reynolds numbers of 7,500–166,000.

The decay of stably stratified grid turbulence in a viscosity-affected stratified flow regime

The decay of stably stratified turbulence generated by a towed rake of vertical plates is investigated by direct numerical simulations (DNS) of temporally evolving grid turbulence in a linearly stratified fluid. The Reynolds number $Re_M=U_0M/\nu$ is 5000 or 10 000 while the Froude number $Fr_M=U_0/MN$ is between 0.1 and 6 ( $U_0$ : towing speed; $M$ : mesh size; $\nu$ : kinematic viscosity; $N$ : Brunt–Väisälä frequency). The DNS results are compared with the theory of stably stratified axisymmetric Saffman turbulence. Here, the theory is extended to a viscosity-affected stratified flow regime with low buoyancy Reynolds number $Re_b$ , and power laws are derived for the temporal variations of the horizontal velocity scale ( $U_H$ ) and the horizontal and vertical integral length scales ( $L_H$ and $L_V$ ). Temporal grid turbulence initialized with the mean velocity deficit of wakes exhibits a $k^{2}$ energy spectrum at a low-wavenumber range and invariance of $U_H^2L_H^2L_V$ , which are the signatures of axisymmetric Saffman turbulence. The decay of various quantities follows the power laws predicted for low- $Re_b$ Saffman turbulence when $Fr_M$ is sufficiently small. However, the decay of $U_H^2$ at $Fr_M=6$ is no longer expressed by a power law with a constant exponent. This behaviour is related to the scaling of kinetic energy dissipation rate $\varepsilon$ , for which $\alpha =\varepsilon /(U_H^3/L_H)$ is constant during the decay for $Fr_M\leq 1$ while it varies with time for $Fr_M=6$ . We also examine the experimental data of towed-grid experiments by Praud et al. (J. Fluid Mech., vol. 522, 2005, pp. 1–33), which is shown to agree with the theory of low- $Re_b$ Saffman turbulence.

Prediction of Horizontal Gas–Liquid Segregated Flow Regimes with an All Flow Regime Multifluid Model

The generalized multifluid modelling approach (GEMMA) has been developed to handle the multiplicity of flow regimes and the coexistence of interfaces of largely different scales in multiphase flows. The solver, based on the OpenFOAM reactingEulerFoam family of solvers, adds interface resolving-like capabilities to the multifluid solver in the cells occupied by large interfaces. In this paper, GEMMA is further developed to predict stratified and slug flow regimes in horizontal ducts. The suppression of the turbulence and the wall-like behaviour of large interfaces is modelled with an additional dissipation source. This enables an accurate prediction of the velocity and of the turbulence kinetic energy in a stratified channel flow and the capturing of the formation and the travel of liquid slugs in an annulus. Large interfaces are identified and tracked, not only in the smooth and wavy stratified regimes but also in the much more perturbed interfaces of liquid slugs. The present work confirms GEMMA to be a reliable approach to provide all flow regime modelling capabilities. Further development will be focused on large interface momentum-transfer modelling, responsible for the overestimation of the interfacial shear and the limited liquid excursion during slugs, and the extension to interface break-up and the entrainment of bubbles and droplets, to handle the entire range of regimes encountered in horizontal flows.

Influence of the Co-Condensation of Water and n-Heptane on Top of the Line Corrosion

Top of the line corrosion (TLC) is a serious concern in wet gas transportation pipelines operating in a stratified flow regime. Extensive research on TLC has been conducted in hydrocarbon-free environments while, in production environments, condensable hydrocarbons are always present. When water and hydrocarbons co-condense, the condensate comprises two immiscible liquids which have different wettability and corrosivity. In this study, the co-condensation process was investigated using a borescope for visual observation and a set of conductivity probes for quantification of water-wetted area. The corrosion behavior of carbon steel under water-only condensation and water/hydrocarbon co-condensation scenarios was evaluated with weight loss measurements and surface analytical techniques (scanning electron microscopy, energy dispersive spectroscopy, and 3D profilometry). The results showed that water and hydrocarbon condensation processes interacted with each other. Even though the rate of hydrocarbon condensation was much greater than that of water, the hydrophilicity of the steel surface always ensured that it would be mainly wetted by water. However, the presence of n-heptane interfered with the water droplet coalescence and segregated water into smaller droplets which affected the water chemistry. This resulted in a potentially higher pH and iron ion concentration in the water droplet as compared to a water-only scenario. The corrosion rate consequently decreased and was found to be less dependent on the water condensation rate in the co-condensation scenario, as compared to a pure water system.