Presence Of Non-condensable Gases Research Articles

Comparative analysis of four classification algorithms for fault detection of heat pumps

Heat Pumps (HPs) technology had a remarkable diffusion and growth in the Heating, Ventilation, and Air-Conditioning (HVAC) field, with an increase predicted to be greater than 200% until 2030. Therefore, efficient asset management must be achieved by ensuring their maintainability and reliability. For this purpose, several methodologies have been used in the last years for HPs fault diagnosis. These methodologies include, with the advances of computational systems and reduced costs of several components, machine learning and other artificial intelligence techniques. In the present paper, a connection weight approach is used to identify the clout of each component for the fault detection (FD) of HPs. The results show that the method provides a more comprehensive assessment of the importance of each component, yielding a reduction of 50% of the features. A comparative analysis of supervised learning classification algorithms is performed, for fault detection of an air-to-air HP in cooling mode. Such algorithms include Naïve Bayes, Support Vector Machine, Logistic Regression, and K-nearest Neighbors. The faults considered include compressor and reversing valve leakage, improper condenser and evaporator fouling, liquid line restriction, refrigerant undercharge and overcharge, and also the presence of non-condensable gases. Algorithms are compared in terms of accuracy, precision, recall, and F1 Score. The best results were achieved with K-nearest Neighbors, since it presented the highest values for these four metrics, over 99% after a cross validation considering a reduced number of components. Naïve Bayes, Support Vector Machine, and Logistic Regression also achieved over 90% performance metrics. Other models were superficially analysed and Ridge Regression with metrics performance of 100%, on average, revealed to be a promising tool for fault diagnosis in HPs.

Experimental Study of a Novel Helix-Shaped Oscillating Heat Pipe and Design Parameters for Flow Stabilization

Abstract Abstract A novel helix-shaped oscillating heat pipe (OHP) designed for enhanced heat transfer in thermal management and heat recovery was studied experimentally. Two orientations were explored: side-heated, the intended orientation in which improved fluid circulation is expected, and bottom-heated, a control resembling traditional bottom-heated OHPs. Results showed stronger circulation, reduced temperature differences, and lower start-up thresholds in most side-heated cases. This orientation achieved higher maximum heat loads at a fill ratio of 0.5, although the maximum heat load decreased at a fill ratio of 0.7. Notably, the experimental OHP attained an effective thermal conductivity over 9,000 W m-K-1 in multiple tests and a maximum heat transport of 676 W. Additional parameters were explored including heat load, fill ratio, condenser temperature, and the presence of noncondensable gases (NCGs). NCGs increased the temperature drop as expected, but also raised the maximum heat transport, indicating potential benefits in certain applications. Elevated condenser temperatures decreased temperature drops, but also reduced maximum heat transport. A previously developed OHP performance model was expanded to evaluate the novel helix-shaped OHP. The model reasonably predicted temperature drops during degassed experiments under moderate heat loads. However, most data points fell outside the model's scope, emphasizing the need to extend it to handle condenser bubble collapse. The expanded analytical models for side-heated helix OHPs highlighted restrictions on circulation that differed from traditional, bottom-heated OHPs, which explains the superior performance of the novel design.

Numerical simulation of vapor explosion bubbles in the presence of a non-condensable gas and a phase change

The complex dynamics of two flow problems of vapor explosion bubbles near a free surface and between two parallel plates are simulated by a compressible three-phase flow code. Firstly, a coupled high-order monotonicity-preserving (MP) interface capturing scheme is developed for the numerical computation of compressible three-phase flows, which is followed by validation with previous numerical and experimental data. Phase changes are taken into consideration and the empirical condensation coefficient is calibrated with the experimental data. For both two flow problems, good agreement between the numerical results and reference data is obtained. Most physical phenomena, including the free surface spike, the liquid jet before the impact and the complex interface deformations during multiple expanding and collapsing oscillations, are reasonably well reproduced. Afterwards, the effects of the initial vapor pressure and the non-condensable gas volume fraction in the surrounding water on the behavior of vapor bubbles are analyzed and discussed. It is found that both the initial vapor pressure and the non-condensable gas volume fraction have a substantial influence on the behavior of the bubble. Higher initial vapor pressure does indeed tend to result in larger bubbles. The presence of non-condensable gases tends to hinder the transfer of heat, slowing down condensation and potentially resulting in larger bubbles. When the initial vapor pressure is in the range of 10 to 20 atmospheres and the gas volume fraction is in the range of 0 to 0.2, the maximum vapor bubble volume is found to increase linearly with an increase of the amount of non-condensable gas.

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.

Steam Condensation Heat Transfer in the Presence of Noncondensable Gases (NCGs) in Nuclear Power Plants (NPPs): A Comprehensive Review of Fundamentals, Current Status, and Prospects for Future Research

Efficient steam condensation is crucial for safe nuclear power plant (NPP) operations, preventing pressure buildup, overheating, and the release of radioactive materials. However, the presence of noncondensable gases (NCGs), such as air, nitrogen, hydrogen, or helium, can hinder the condensation process by creating a thermal resistance layer that impedes steam diffusion and condensation on the system’s surface. Maximizing the efficiency of steam condensation requires a thorough grasp of the fundamental processes, theories, advancements, and technical hurdles. Therefore, this work thoroughly addresses these needs, with a particular emphasis on addressing the challenges posed by NCGs by dividing the work into four thematic areas. The first theme relates to a comprehensive examination of pure steam condensation phenomena, which includes an exploration of familiar condensation scenarios and various film condensation types. The second theme examines condensation in the presence of NCGs, their mixture properties, and related theories and modelling of heat and mass transfer. The third theme investigates condensation in NPP by exploring passive cooling systems and condensation phenomena under both natural and forced convection conditions during nuclear accidents, the origin of NCGs in NPP and their transportation aspects. This is followed by experimental work related to condensation scenarios and scale. Finally, the last theme looks upon the recent advancements in computational fluid dynamics (CFD) modelling of wall condensation, system analysis codes coupling with CFD, and the implementation of machine learning (ML) for predicting the condensation HTC. By bridging the gap between fundamental knowledge and practical applications, the four thematic areas presented in this work are aimed at providing a comprehensive foundation for researchers and experts in the field of steam condensation when NCGs are involved. The ultimate objective is to bolster the safety and efficacy of NPP operations by understanding the heat and mass transfer mechanisms while mitigating the risk of catastrophic events.

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.

Liquid evaporation from nanochannel in the presence of non-condensable gas by direct simulation Monte Carlo

Liquid-vapor phase change in the form of evaporation and with the confinement of nanostructures is of vast importance in many engineering applications. The direct simulation Monte Carlo (DSMC) approach was employed to simulate the liquid evaporation from two-dimensional nanochannels in the presence of non-condensable (NC) gases. The thermodynamic properties of vapor and NC gas flow fields as well as the evaporation rate of liquid were calculated. Factors such as channel opening ratio, meniscus receding depth and the NC gas density were examined to reveal their impacts on the liquid evaporation rates. The results showed that the gas mixture flow field transforms to one-dimensional away from the channel exit. The overall vapor pressure drop can be divided into that across the liquid–vapor interface, that within the channel, outside the channel, as well as that caused by the binary diffusion. The effect of NC gas on the evaporation rate was found to be the most outstanding with pinned meniscus and with a wide channel opening, in which case the normalized evaporation rate drops to 0.43. Moreover, the Maxwell-Stefan equation could capture the evaporation data with modified diffusion lengths. For receded meniscus (250 nm deep) and small opening ratio (0.417), the effective diffusion length was as large as 1.6 times that in the one-dimensional case.

Numerical simulation on the effect of non-condensable gas on direct contact condensation of steam jet in a narrow pipe

A numerical model was developed to investigate the phenomenon of direct contact condensation involving steam-air jet in subcooled waterflow. This study encompassed a comprehensive exploration of the influence exerted by non-condensable gas from a multi-faceted perspective. These aspects included an examination of gas plume shape, distribution of air mass fraction, variation in axial thermodynamic parameters, the local heat transfer coefficient, and the local condensation rate. It was observed that the presence of non-condensable gas had the effect of extending the penetration length of the steam plume. Notably, the non-condensable gas was predominantly concentrated in proximity to the gas-liquid interface, a phenomenon that introduced hindrances to both heat and mass transfer processes. A detailed analysis of axial thermodynamic parameters further unveiled an intriguing trend. As the air content increased, the expansion of gas plume weakened, subsequently leading to a reduction in the condensation rate. This resulted in diminished pressure fluctuations in the two-phase mixing region. Moreover, the presence of non-condensable gas was shown to curtail the local heat transfer coefficient and the local condensation rate at the interface. This observation offered valuable insights into the underlying mechanisms responsible for the protraction of the gas plume.

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.

Mixtures of phase transforming fluids and gases: Phase field model and stabilized isogeometric discretization

Liquid–vapor phase change in the presence of non-condensable gases is a classical problem, which continues to challenge continuum modeling. Here, we propose a new model based on the phase field method, which describes the dynamics of the non-condensable gas, phase change and flow simultaneously. The model is built by extending van der Waals and Korteweg’s theory for phase-transforming mixtures. The model equations have fourth order partial differential operators which presents significant challenges to standard spatial discretization methods. In addition, the isentropic form of the model equations is not hyperbolic at the liquid–gas interface, which inhibits the direct application of most algorithms for systems of hyperbolic equations. We propose a novel numerical scheme based on Isogeometric Analysis and the Taylor–Galerkin method, and also implement a residual based discontinuity capturing scheme. We show numerical results, at micron length scales, to study the accuracy and stability of our algorithm. We study the flow of a shock wave past a liquid droplet to test our numerical algorithm when steep gradients are present in the solution. Finally, we use the model to study the problem of gas–vapor bubble collapse near a solid wall.

Experimental study on enhancement condensation heat transfer in tube by foam metal in presence of non-condensable gas

Condensation heat transfer in tube is widely applied in industrial production, and the heat transfer process is often weakened by non-condensable gas (NCG) in actual production. Enhancing condensation heat transfer is beneficial to improve production efficiency, which has always been a hot topic in current research. Foam metal material with large specific surface area and good thermal conductivity is an ideal material to enhance heat transfer. In order to study enhancement heat transfer effect and optimize structure of foam metal, this paper investigated condensation heat transfer in tube strengthened by foam metal in presence of NCG experimentally. Section shape of foam metal is annular, and the pores per inch (PPI) of foam metals is 10, 15, 20 respectively. The effects of PPI value, steam/air mixture mass flow, and NCG mass fraction on heat transfer coefficient (HTC) and flow resistance are studied. The results reveal the following: (1) Compared with smooth tube, the foam metal enhances heat transfer significantly, and HTC increases by 1.5-2.3 times. (2) At same steam/air mixture mass flow, 10PPI foam metal tube has the highest HTC compared to others. (3) With increase of NCG mass fraction and PPI value, pressure drop increases and the HTC decreases. Based on experimental data, pressure drop and HTC correlations are developed. This paper provides an important technical basis for foam metal material application in enhancement heat transfer area.

CO2 capture using steam ejector condenser under electro hydrodynamic actuator with non-condensable gas and cyclone separator: A numerical study

The concept for condensation of steam and CO2 separation in a negative CO2 emission gas power plant involves the utilization of a steam ejector condenser (SEC) for direct-contact condensation of vapor with inert gas (CO2) on a spray of subcooled liquid, integrated with a separator to produce pure CO2. Due to the increasing diffusion resistance and reduced convective heat transfer between the steam and subcooled water phases in the presence of non-condensable gas (CO2), the study utilized an electrohydrodynamic (EHD) actuator to enhance heat transfer rate in the SEC. To optimize CO2 purification, the effect of single, dual and quadruple inlets on separation efficiency was analysed. In the SEC, the Eulerian-Eulerian multiphase model is employed, treating water as the continuous phase and the compressible gas mixture (steam and CO2) as the dispersed phase. The standard k-ε model is chosen to depict the turbulence in the ejector. The separator is transient, turbulent, and three-dimensional, using the control volume method. The RSM turbulent model and mixture model are utilized to simulate the turbulent two-phase flow in the gas–liquid separator. The findings indicated that when the mass flux of steam and voltage are increased, the condensation heat transfer coefficient also increases. For a mass flux of steam of 51 (kgm2.s), the condensation heat transfer coefficients were measured to be 0.98, 1.029, 1.08, and 1.134 (MWm2.K) at electrode voltages of 0, 20, 25, and 30 kV, respectively. In addition, a single-inlet cyclone attains a separation efficiency of 95.1 %, while incorporating two inlets improves the performance to 97.9 %. However, the most remarkable outcome is witnessed in cyclones with four inlets, where an impressive separation efficiency of 99.9 % is achieved.

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.

Estimating parameters of plate heat exchanger for condensation of steam from mixture with air as a component of heat exchanger network

The condensation of vapours in the presence of non-condensable gas is happening in a number of industries. Its efficient realisation must be considered in optimisation of Heat Exchanger Networks (HENs) to increase efficiency of energy usage. In these processes, Plate Heat Exchanger (PHE) with enhanced heat and mass transfer can be effectively used. The method of preliminary selection of the PHE heat transfer area, plate size and corrugation geometry is proposed for the case of steam condensing from its mixture with air. The method uses the developed earlier one-dimensional mathematical model, modernized for PHE consisting from commercially produced plates. The heat transfer area of PHE is minimised by proper selection of plate sizes and geometrical characteristics of its corrugation. The optimisation variables are the corrugation angle to the main flow direction inside PHE channel, the corrugations depth and the length of plate. The case study illustrates the application of the method in case of utilisation low grade heat from exhaust gases after process of drying by superheated steam. The method is implemented as a computer program which can be included as a Dynamic Link Library module in software for HEN optimisation.

Interfacial heat transfer with non-condensable gas in ASTEC V2.2: Application to severe accidents study during PWR cold shutdown states

In a diphasic flow, the presence of non-condensable gas has an important impact on interfacial heat transfer, especially at low global pressure. In severe accidents studies, such flow can be commonly encountered. For example, in pressurised water reactors, high concentration of non-condensable gas can be found in the reactor coolant system in case of late core reflooding with a high oxidation rate (high hydrogen concentration) or in case of accidents happening during the cold shutdown of the reactor, when the reactor coolant system has been partly drained (high air concentration). Such flows with non-condensable gas are challenging to compute for severe accident system codes. A new model is implemented in ASTEC v2.2 to improve the interfacial heat transfer calculation in presence of non-condensable gas. The model gives a more accurate estimation of the heat transfer by assuming that it is mainly driven by vapour diffusion in the gas phase. The new model is applied to study cold shutdown states for 1300 MWe pressurized water reactors. A complete calculation of the cooling, depressurisation and draining of the reactor can be successfully performed. In order to show ASTEC new capabilities, a first accident scenario with loss of the residual heat removal system is also presented.

Steam condensation on a circular tube in the presence of non-condensables (air) in passive containment cooling system

Steam inside the containment should be condensed to reduce the system pressure after severe accidents such as loss of coolant accident (LOCA) and main steam line break (MSLB). Due to the absence of an active coolant system, the passive containment cooling system (PCCS) can be used to improve safety by condensing the steam on a test section (coolant) tube surface. However, the steam condensation is degraded by the presence of non-condensable gases like air. The condensation heat transfer coefficient is dependent on operating parameters such as system pressure, air-mass fraction, and wall subcooling and geometric parameters such as tube diameter, tube length, and angle of orientation.In the present study, experiments are conducted on a vertical tube to estimate the condensation heat transfer coefficient in the presence of air. The system pressure ranges from 2 - 5.5 bar, air-mass fraction of 0.3 - 0.8 and wall subcooling of 27 - 80°C. The influence of test section tube diameter (48.2 mm, 32 mm, 22 mm, and 16 mm) and inclination angles (90° and 68° from the horizontal) are investigated. Based on the experimental data, a correlation for condensation heat transfer coefficient in terms of system pressure, air-mass fraction, wall subcooling, tube diameter and angle of inclination is proposed. The condensation heat transfer coefficient increases with the increase in the system pressure and decreases with the increase in the air mass fraction, wall subcooling and condenser tube diameter. With the decrease in the diameter of the vertical condenser tube from 48.2 mm to 16 mm, an increase in the condensation heat transfer coefficient of 66% is observed. A non-dimensional correlation for condensate Nusselt number as a function of Grashof number, air-mass fraction and Jakob number is also proposed. This correlation predicts 80% and 90% experimental data within 20% and 30% uncertainty range, respectively.

Computational Fluid Dynamics Modeling of Steam Condensation in the Presence of Noncondensable Gas in Inclined Tubes

In this paper, film condensation of steam in inclined tubes in the presence of noncondensable gases is modeled using numerical method. A single-phase fluid model incorporating a wall-function boundary condition is proposed to simulate the heat and mass transfer interaction between the liquid condensate and the gas-vapor mixture region. A user defined function is implemented to impose the proposed boundary to the governing equations for steady state incompressible flow. The performance of applying this boundary condition and subsequent treatment of the heat transfer at the liquid-mixture interface is validated against published experimental results. The calculated profiles of local and average Nusselt number for various inlet conditions compares favorably to the previous published experimental results and correlations. For all simulated cases, increases in inlet and bulk concentrations of noncondensable gases resulted in a decrease in the thermal performance of the condensing tube. Additionally, it is found that the inclination angle of the condensing tube can impact the local distribution of the heat transfer coefficient and Nusselt number, while there is only small effect on the average along the length of the entire tube.

Experimental study of liquid line temperature drop as a fault feature for detecting severe liquid line restrictions in residential air conditioning systems

Liquid line restrictions (LL) can occur in an air conditioning system in multiple ways, such as a clogged filter drier, kinked liquid line, etc., and impact the performance of the system. In this study, the objective is to test and refine fault diagnostic methods for LL faults. We tested the effects of four common installation faults in a laboratory - LL, improper evaporator airflow, improper refrigerant charge, and the presence of non-condensable gas in the refrigerant, singly and in combination. They were tested on a split residential heat pump and an air conditioner in cooling mode. The results are used to validate a method that uses the liquid line temperature drop caused by LL faults as a feature to detect LL faults. The sensitivity of this temperature drop to LL faults and other faults were also studied, so that an effective threshold for declaring an LL fault can be ascertained. The experiments are the first to show that this feature is effective in detecting LL faults even when multiple other faults are present, and that it does not cause false positives when no LL is present.

Dropwise Condensation in Ambient on a Depleted Lubricant-Infused Surface.

Durability of a lubricant-infused surface (LIS) is critical for heat transfer, especially in condensation-based applications. Although LIS promotes dropwise condensation, each departing droplet condensate acts as a lubricant-depleting agent due to the formation of wetting ridge and cloaking layer around the condensate, thus gradually leading to drop pinning on the underlying rough topography. Condensation heat transfer further deteriorates in the presence of non-condensable gases (NCGs) requiring special experimental arrangements to eliminate NCGs due to a decrease in the availability of nucleation sites. To address these issues while simultaneously improving heat-transfer performance of LIS in condensation-based systems, we report fabrication of both fresh LIS and a lubricant-depleted LIS using silicon porous nanochannel wicks as an underlying substrate. Strong capillarity in the nanochannels helps retain silicone oil (polydimethylsiloxane) on the surface even after it is severely depleted under tap water. The effect of oil viscosity was investigated for drop mobility and condensation heat transfer under ambient conditions, i.e., in the presence of NCGs. While fresh LIS prepared using 5 cSt silicone oil exhibited a low roll-off angle (∼1°) and excellent water drop (5 μL) sliding velocity ∼66 mm s-1, it underwent rapid depletion as compared to higher viscosity oils. Condensation performed on depleted nanochannel LIS with higher viscosity oil (50 cSt) resulted in a heat-transfer coefficient (HTC) of ∼2.33 kW m-2 K-1, which is a ∼162% improvement over flat Si-LIS (50 cSt). Such LIS promote fast drop shedding as is evident from the little change in the fraction of drops with diameter <500 μm from ∼98% to only ∼93% after 4 h of condensation. Improvement in HTC was also seen in condensation experiments conducted for 3 days where a steady HTC of ∼1.46 kW m-2 K-1 was achieved over the last 2 days. The ability of reported LIS to maintain long-term hydrophobicity and dropwise condensation will aid in designing condensation-based systems with improved heat-transfer performance.

Thermal modeling and simulation of a single-flash geothermal power plant involving non-condensable gas: a case study of Kamojang geothermal field in Garut, West Java, Indonesia

The presence of non-condensable gases (NCGs) in a geothermal fluid disrupts the vacuum process in the condenser, reducing turbine efficiency and decreasing the total power output of the geothermal power plant (GPP). Therefore, to optimize the thermodynamic efficiency of a GPP, NCGs should be removed using a gas removal system. Since there is a substantial lack of design and simulation software for a GPP including NCG removal alternatives, in this study, we aimed to model and develop a software-based interface to simulate mass and energy balance involving an NCG fraction in a single-flash GPP as well as examine the thermodynamic performance of the gas removal system, which is the most important step in the planning and designing phase of a GPP. This software was validated using outputs of Kamojang GPP Units 2, 3, and 4 located at Kamojang geothermal field, Garut, West Java, Indonesia. Units 2 and 3 use two ejectors which are installed in series, and Unit 4 utilizes a hybrid system (HS) that is mostly a combination of vacuum pumps and a steam jet ejector (SJE). Our results showed that Kamojang GPP Units 2 and 3 generate 55.295 MW of power with an absolute error of 0.53%, whereas Unit 4 generates 60.218 MW of power with a 0.36% absolute error concerning the field data. These results correspond with the expected minimum error; therefore, our model’s parameters are considered valid and can be used for simulation. We found that using the simulation, the total steam saved by the HS at Kamojang GPP Units 2 and 3 was 534 kW. Furthermore, the net power production was reduced by 1.6% for the HS and 2.03% for the SJE with every 1% increase in the NCG fraction. The power requirement of the vacuum pumps remained less than the power generated by the motive steam which the ejector requires to dispose of the same amount of NCG, indicating that Kamojang GPP Units 2 and 3 will be more efficient if an HS is used.