Evaporator Inlet Research Articles

Developing an efficient explainable artificial intelligence approach for accurate reverse osmosis desalination plant performance prediction: application of SHAP analysis

In recent decades, securing drinkable water sources has become a pressing concern for populations in various regions worldwide. Therefore, to address the growing need for potable water, contemporary water purification technologies can be employed to convert saline sources into drinkable supplies. Therefore, the prediction of important parameters of desalination plants is a key task for designing and implementing these facilities. In this regard, artificial intelligence techniques have proven to be powerful assets in this field. These methods offer an expedited and effective means of estimating effective parameters, thus catalyzing their implementation in real-world scenarios. In this study, the predictive accuracy of six different machine learning models, including Natural Gradient-based Boosting (NGBoost), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Support vector regression (SVR), Gaussian Process Regression (GPR), and Extremely Randomized Tree (ERT) was evaluated for modelling the parameter of permeate flow as a key element in system efficiency, energy consumption, and water quality using six various input combinations of feed water salt concentration, condenser inlet temperature, feed flow rate, and evaporator inlet temperature. The next phase of this research employed the SHAP interpretability method to illustrate the impact of individual variables on the model's output. Moreover, the predictive performance of the developed frameworks was evaluated using a set of five dependable statistical measures: RMSE, NS, MAE, MAPE and R 2. These indicators were utilized to provide a robust means of gauging the precision of the model's forecasts. A comparative analysis of the outcomes, as measured by the RMSE criteria, revealed that the SVR technique (RMSE = 0.125 L/(h·m2)) exhibited superior performance compared to NGBoost (RMSE = 0.163 L/(h·m2)), AdaBoost (RMSE = 0.219 L/(h·m2)), CatBoost (RMSE = 0.149 L/(h·m2)), GPR (RMSE = 0.156 L/(h·m2)), and ERT (RMSE = 0.167 L/(h·m2)) methodologies in predicting permeate flow rates. The modelling outcomes obtained during the evaluation stage demonstrated the efficacy of the SVR algorithm in enhancing the precision of permeate flow forecasts, utilizing relevant input variables.

Performance improvement and multi-objective optimization of a two-stage and dual-temperature ejector auto-cascade refrigeration cycle driven by the waste heat

This paper presents a two-stage and dual-temperature ejector auto-cascade refrigeration cycle (TEARC) driven by the waste hot water and low-pressure exhaust steam in chemical plants. In this cycle, a throttle valve is provided between the condenser outlet and separator inlet to regulate the composition and mass flow ratio of mixture refrigerant in the two evaporators. The specific enthalpies of refrigerant at the low-temperature (LT) evaporator and medium-temperature (MT) evaporator inlet are respectively reduced via the evaporative condenser and separator, leading to an enhancement in cooling capacity. Energy, exergy, and economic (3E) analyses are conducted to compare the performance between the TEARC and the basic two-stage and dual-temperature ejector refrigeration cycle (BTERC). At the basic operating condition, the TEARC has enhancements of 9.13 % and 9.95 % in COP and exergy efficiency than those of the BTERC. According to the multi-objective optimization results, it indicates that the TEARC's COP, exergy efficiency, and levelized cost of cooling are respectively improved by 8.93 % and 5.67 % and reduced by 1.22 % compared to the BTERC at optimum operating conditions. The simulation results of the proposed cycle reveal a significant performance improvement and the potential for application in waste heat-driven refrigeration.

Carbon dioxide evaporation heat transfer coefficient prediction in porous media using Machine learning algorithms based on experimental data

The prediction of the internal heat transfer coefficient during evaporation is vital for vapor-compression refrigeration and closed-loop power cycles. Accurate measurements and understanding of CO2 heat transfer in porous evaporators are essential for optimal system design across various operating conditions. This study utilizes a reference dataset derived from previous experiments that investigated the impact of porous evaporators on CO2′s internal heat transfer coefficient under sub-critical conditions, employing gravel sand as the porous medium. The dataset encompasses key factors: gravel sand porosities ranging from 39.8 % to 44.5 %, evaporator inlet pressures between 3700 and 4300 kPa, CO2 mass flow rates from 10.7x10-5–18x10-5kg.s−1, and porous tube effective diameters spanning 1.53 x10-3–3.4x10-3m. Employing three machine learning techniques (SVM, GPR, OBEM), the study predicts the internal heat transfer coefficient using regression models. The models’ predictions are analyzed and compared to expected values for validation, evaluating their performance using four statistical criteria. Results indicate SVM, GPR, and OBEM models achieved RMSEs of 1.5471, 1.8212, and 3.6978, respectively, while MAE errors were 1.1479, 1.2418, and 2.9787, respectively. Comparison with the dimensional analysis method reveals the effectiveness of the proposed models in accurately predicting internal heat transfer coefficients. The models exhibit low uncertainty and maintain prediction quality on an extended dataset without overfitting concerns. Overall, this research contributes valuable insights for designing heat exchangers and systems in vapor-compression refrigeration and closed-loop power cycles.

Simulation study on the system performance of solar-ground source heat pump using return water of the district heating network as supplementary heating

In order to mitigate the extreme climate change and improve the utilization of renewable resources, a solar-ground source heat pump system using return water of the district heating network as supplementary heating(SGSHP-DHNSH)has been proposed. This system compared with parallel and series connections of solar assist ground source heat pump systems for simulation and analysis. The results indicate that the SGSHP-DHNSH system raises the evaporation temperature, demonstrates a high rate of renewable energy utilization, and efficiently stores heat within the buried pipes. Notably, the soil temperature in the SGSHP-DHNSH system experiences only a 0.16 °C decrease, which represents reductions of 8 % and 9.8 % compared to the series and parallel systems, respectively. Furthermore, the system COP demonstrates a commendable improvement of 20 % and 13.7 %, the evaporator inlet temperature is higher than the series and parallel systems by 6.7 °C and 5.3 °C, respectively. Moreover, the total energy consumption throughout the heating season stands at 63 MW h, reflecting a substantial 24 % and 19 % reduction in comparison to the series and parallel systems, respectively. The SGSHP-DHNSH system plays a crucial role in improving the utilization rate of renewable resources and alleviating the soil heat imbalance caused by ground source heat pump operation.

Improvised grey wolf optimizer assisted artificial neural network (IGWO-ANN) predictive models to accurately predict the permeate flux of desalination plants

Effective planning, management, and control of industrial plants and processes have exploded in popularity to enhance global sustainability in recent decades. In this arena, computational predictive models have significantly contributed to plant performance optimization. In this regard, this research proposes an Improvised Grey Wolf Optimizer (IGWO) aided Artificial Neural Network (ANN) predictive model (IGWO-ANN Model-1 to 4) to predict the performance (permeate flux) of desalination plants accurately. For this, the proposed models investigated experimental inputs four: salt concentration & feed flow rate, condenser & evaporator inlet temperatures of the plant. Besides, mean squared error (MSE) and the regression coefficients (R2) have been used to assess the models' accuracy. The proposed IGWO-ANN Model-4 shows strong optimization abilities and provides better R2 = 99.3 % with minimum errors (0.004) compared to existing Response Surface Methodology (RSM) (R2 = 98.5 %, error = 0.100), ANN (R2 = 98.8 %, error = 0.060), GWO-ANN (R2 = 98.8 % error = 0.008), models. The proposed models are multitasking, multilayers, and multivariable, capable of accurately analyzing the desalination plant's performance, and suitable for other industrial applications. This study yielded a promising outcome and revealed the significant pathways for the researchers to analyze the desalination plant's performance to save time, money, and energy.

EXPERIMENTAL STUDY OF DOMESTIC REFRIGERATOR BY USING EVAPORATIVE CONDENSER

Refrigerator has become an essential commodity rather than luxury item. It is one of the home appliance ukkigg, vapour compression cycle in it process. Performance of this system becomes main issue and many researches are still ongoing to evaluate and improve efficiency of the system. This study presents effect of evaporative condenser on COP of domestic refrigerator. The purpose of this article is to compare the COP of refrigerator by using air cooled condenser and evaporative condenser of same length and same diameter. This experiment is carried out on domestic refrigerator (185 ikr4 test rig. In this study, an innovative, evaporative condenser for residential refrigerator was introduced. A yaw compression cycle incorporating the proposed evaporative condenser was tested to evaluate the cycle performance. To allow for evaporative cooling, sheets of cloth were wrapped around condenser to suck the water sprayed on it. The thermal properties at the different points of the refrigeration cycle were measured for typical operating conditions. Further to compare both the condensers some parameters like condenser outlet temperature, evaporator inlet temperature, power consumption have taken. After investigation it was found that evaporative condenser may replace the air cooled condenser from domestic refrigerator KEYWORDS: Refrigerator, Condenser, Evaporator, COP, Refrigerating Effect, Compressor.

A multi-energy production system utilizing an absorption refrigeration cycle, and a PEM electrolyzer powered by geothermal energy: Thermoeconomic assessment and optimization

This study aims to optimize a multi-energy production system that utilizes sustainable geothermal energy as its primary power source. The system comprises several subsystems, including a geothermal well, an organic rankine cycle (ORC) unit, an absorption chiller, and a proton exchange membrane (PEM) electrolyzer. The study examined the impact of variations in seven key parameters, including turbine inlet temperature, evaporator pinch point temperature, pump inlet temperature, evaporator inlet temperature, evaporator inlet mass flow rate, turbine efficiency, and pump efficiency, on the overall system performance. The Response Surface Methodology (RSM) was employed to find the optimum point of the system. To facilitate the practical implementation of the system, the research team assessed its viability in 10 different Iranian cities, all known for their substantial geothermal energy potential. Among the cities, Zahedan emerged as the most suitable location for deploying the system, given its robust energy production potential. The results showed that January, with a production of 626.4 MW h of electricity, and November, with a production of 622.8 MWh of electricity, are the best months for the system to operate in Zahedan city. The environmental aspect of the system also yielded noteworthy results. By setting up the proposed system in Zahedan city, it is possible to prevent the emission of 1472.411 tons of carbon dioxide by producing 7217.7 MWh of electricity throughout the year at a cost of 35337.86 $. Additionally, the proposed system can help expand 7 ha of green space, providing the electricity needed by 2349 people throughout the year.

Performance prediction model for desalination plants using modified grey wolf optimizer based artificial neural network approach

Desalination represents an effective method for alleviating water scarcity, applying algorithmic techniques to predict the performance of reverse osmosis (RO) desalination plants, Modified Grey Wolf Optimizer (MGWO) based Artificial Neural Networks (ANN) can predict the performance of membrane distillation (MD) equipment. Four experimental inputs are selected: feed salt concentration(35–140 g/h), feed flow rate(400–600 L/h), evaporator inlet temperature (60–80 ℃), and condenser inlet temperature (20–30 ℃). The permeate flux (L/h m2) is selected as the experimental output. Ten prediction models were proposed and compared with the existing models (ANN, WOA-ANN, and GWO-ANN). The results showed that the MGWO-ANN model-5 showed the best regression results: R2 = 99.3 %, mean square error (MSE)= 0.004. This model outperformed the existing ANN model (R2 =98.8 %, MSE=0.060), WOA-ANN model (R2 =99.1 %, MSE=0.005) and GWO-ANN model (R2 =98.9 %, MSE=0.007). Model-5 has a single hidden layer (H=1), 13 hidden nodes (n = 13), 10 search agents (SA=10), and 75 %− 20 %− 05 % dataset division. Its residual error is within acceptable limits (spanning −0.1 to 0.2). Optimizing the number of hidden layer nodes (n) and the number of search agents (SA) can improve the training efficiency and prediction accuracy of the model, and the MGWO-ANN model is capable of more accurately predicting the performance of desalination plants.

Dynamic characteristics of refrigerant evaporation temperature in air-water heat source heat pump

In order to improve the problem of reduced heat capacity and unstable heating ability of air source heat pump (ASHP) in the heating mode during the cold season, a heat pump evaporator using air and surface water as a heat source is designed in this paper. A heat transfer unit's calculation model of an air–water heat source evaporator is established. Experimental tests and simulation studies are conducted on the refrigerant evaporation temperature and heat pump heating performance. On this basis, a refrigerant evaporation temperature prediction model is constructed using a multiple linear regression (MLR) model. The trend of refrigerant evaporation temperature under air–water heat source is analyzed by taking the winter meteorological parameters in Xiangtan City as an example. The results show that the air–water heat source heat pump (A-WSHP) has a higher refrigerant evaporation temperature and a smaller temperature fluctuation range than the ASHP. The average values of refrigerant evaporation temperatures under the experimental conditions were 5.3 °C and 4.6 °C, respectively. The temperature fluctuation ranges were 3.8 °C-6.7 °C and 2.8 °C-7.0 °C, respectively. The A-WSHP has higher heating capacity and heating stability. Air temperature, air velocity, water spray temperature and water spray flow rate are all important parameters that affect the refrigerant evaporation temperature of a heat pump. Evaporation temperature changes are positively correlated with these factors. In the refrigerant evaporation temperature prediction model, the air temperature change has the most significant effect on the evaporation temperature, followed by spray water temperature, air velocity, and spray flow rate, whose influence weights are 82.5 %, 38.8 %, 29.4 %, and 25.3 %, respectively. Selecting different temperatures of spray water and adjusting the evaporator's inlet spray flow rate can improve the refrigerant evaporation temperature and heat pump heating stability.

Enhancing CO2 Water-to-Water Heat Pump Performance Through the Application of a Multi-Objective Evolutionary Algorithm

Abstract Due to growing concerns about the environmental impact of refrigerants, carbon dioxide (CO2) heat pumps have been increasingly evaluated as efficient alternatives for conventional heat pumps. Performance analyses of CO2 heat pump water heaters (HPWHs) have been the subject of many studies, but these are typically limited to parametric analyses of air-source HPWHs. The interrelated behavior of the supercritical and subcritical thermodynamic properties, component operation, and efficiency means that a parametric study cannot adequately capture the inherent nonlinearity. Therefore, this paper, for the first time, aims to perform a multi-objective optimization on CO2 water-sourced HPWH performance in order to minimize the total component costs, maximize gas cooler (GC) heating capacity, and maximize the coefficient of performance (COP) using two different optimization scenarios. The decision variables are defined as GC pressure (75–140 bar), evaporator temperature (−19.5–0.2 °C), and GC outlet temperature for CO2 (16–36 °C). The model performance is constrained by the practical ranges of the GC and evaporator inlet and outlet temperatures for water. A coupled simulation-optimization model through python is developed using Engineering Equation Solver (EES) software and the non-dominated sorting genetic algorithm II (NSGA-II). The result of the optimal Pareto front showed that the optimal GC heating capacity changes from 19.2 to 56.7 kW, with a lowest cost of $7771 to a highest cost of $9742, respectively. When the lower bound of the GC outlet temperature was set to 32 °C, the Pareto front showed a maximum COP of 3.23, with a corresponding GC heating capacity of 44.36 kW.

Experimental investigation and regression analysis on the characteristics of the JT refrigerator operating with natural refrigerant mixtures

This paper presents the results of the analysis of the experimental characteristics of the closed-cycle Joule-Thomson (JT) refrigerator operating on a nitrogen-hydrocarbon refrigerant mixture. Designing such energy-efficient refrigeration machines involves the calculation and experimental determination of the optimal values of the “charge” composition of the mixture components. The design process must take into account the change in the “circulating” composition of the mixture components. In the paper, we present the results of regression analysis of the “circulating” composition of the mixture components of a JT low-performance refrigerator (compressor volumetric flow rate less than 25 m3/h). Such refrigerators can be used for thermostatting objects at the temperatures down to −160 °C. The results are given in the form of relationships for specific refrigeration capacity and a number of efficiency indicators. In the considered component concentration range, the relative root-mean-square regression error was less than 12.6 %. Finally, we determined the optimal concentrations of the mixture components of the JT refrigerator having a compressor with a volumetric flow rate of 4.36 m3/h which corresponds to the maximum value of specific refrigerating capacity Qt/Ve [19,87 W/(m3/h)] at the temperature of the evaporator inlet of −150 °C. The results of approbation the proposed regression method on a prototype low-temperature freezer based on a JT refrigerator operating with MMR and the limits of applicability of the developed regression models are determined. These relationships provide valuable insights for designing stock-produced devices of varying standard sizes, reducing development time and cost while leveraging comparable operating cycles and working body compositions.

Enhancing energy and exergy performance of a cascaded refrigeration cycle: Optimization and comparative analysis

This study delves into the realm of energy and exergy analysis, aiming to optimize the performance of a refrigeration cycle by utilizing cycle Coefficients of Performance (COPs). The conventional heat pump cycle is replaced by a cascaded cycle, and this novel configuration is scrutinized through the lenses of the first and second laws of thermodynamics. The analysis is conducted within the EES software environment, encompassing the components of the cycle governed by these laws. The refrigerant employed is R407C. Through comprehensive energy and exergy analyses, significant insights emerge. Assuming a consistent refrigeration capacity, the study unveils the potential for a remarkable 21% increase in the coefficient of performance and a substantial 32% enhancement in the efficiency of the second law of thermodynamics within the cycle. Subsequently, a deeper exploration ensues, involving the selection of cascaded cycle refrigerants—R290, R134a, and R500. The impact of pivotal parameters such as inlet pressure, outlet temperature difference of the intermediate heat exchanger, evaporator inlet temperature, and condenser outlet on cycle performance and efficiency is meticulously compared for each refrigerant. Of particular interest is refrigerant R500, which, under specific conditions, drives the cycle and system COPs up by 7% and 2%, respectively, relative to R407C. Furthermore, the study reaches the pinnacle of exergy efficiency. A staggering increase of up to 8% in cascaded cycle exergy efficiency and 3% in the overall R500 refrigerant system exergy efficiency is achieved under consistent temperatures. This multifaceted research demonstrates the potential for significant enhancements in the performance and efficiency of refrigeration cycles through judicious optimization and strategic selection of refrigerants.

Performance Analysis of Atmospheric Water Extraction by Refrigerator Cum Air Conditioner

India is blessed with warm and humid climatic conditions in most of its states containing huge amount of water in atmosphere which can fulfill the increasing water demands. Atmospheric water extraction is one of the technologies where fresh water is obtained from the ambient air by condensation. The atmospheric water extracting device is working on vapour compression refrigeration cycle. In this cycle the evaporator inlet temperature is maintained at a temperature lower than the dew point temperature of the atmospheric incoming air. Therefore, the moisture present in the air condenses over the coil and it will be collected. The amount of condensate depends on psychrometric conditions of incoming air. The present work concludes that atmospheric water extracting devices are most effective in hot and humid regions. when the humidity level in atmosphere is being 56%, 8.3ml/min water is extracted and when the humidity level in atmosphere is being 36%, 1.7ml/min water is extracted.

Determination of optimum parameters using different nano fluids in heat pipe heat exchangers with response surface method

In this study, optimum parameters affecting the thermal efficiency of heat pipes were determined experimentally with response surface method using different types of nanofluids in heat exchangers. Experimental design was carried out at three levels for four parameters affecting thermal efficiency. Experiments were made for Al2O3, TiO2 and SiO2 at 0.2-0.4-0.6% concentrations. Nanofluid suspensions were formed using isopropyl alcohol as the base fluid. The inlet temperatures in the evaporator region were determined as 30-60-90 °C, and the Reynolds numbers in the condenser region were determined as 7200, 14400 and 21600. The thermal efficiencies of the heat pipes were calculated as a result of the experiments carried out in accordance with the experimental design created with the response surface method. The accuracy of the model was tested with analysis of variance (ANOVA) and optimum parameters were obtained as nanoparticle SiO2, concentration 0.32%, evaporator inlet temperature 90 °C and Reynolds number 21600 in the condenser.

Experimental analysis on hybrid adsorption and open air cycle humidification dehumidification (AD–OHD) desalination system

In the current status, an Adsorption Desalination System (ADS) has been found to have lower water production capacity than conventional desalination technologies. This study proposes a hybrid desalination system integrating the Adsorption (AD) and Open air cycle Humidification-Dehumidification (OHD) process to overcome the challenge of lower water production capacity in ADS. This hybrid system known as AD-OHD system comprises of a single bed ADS and a Humidification-Dehumidification (HDH) unit. The main objective of this work is to analyse the performance of the hybrid AD-OHD system based on parameters such as Gain Output Ratio (GOR) and amount of desalinated water production. The performance of the hybrid AD-OHD system is studied under three different operating conditions of switching (tswt) and sorption time (tspt), namely, tswt > tspt, tswt = tspt, and tswt < tspt. The results indicate that the optimal working condition is achieved when the hybrid AD-OHD system operates at condition tswt < tspt. The amount of desalinated water and GOR under the optimal condition is found to be 1.12 l/h and 0.78, respectively. The study also conducts experimental investigations to evaluate the GOR of the hybrid AD-OHD system under different operating conditions, including the temperature of the water entering the evaporator and the temperature of the hot and cooling water input to the adsorbent bed. Experimental data indicates a positive correlation between GOR and hot water temperatures, while increased cooling water temperature reduce GOR due to reduced adsorption behaviour. Although the evaporator inlet water temperature initially boosts GOR, it declines as the adsorption capacity diminishes. The study highlights the superior performance of the hybrid AD-OHD system compared to both the standalone single-bed ADS and open cycle HDH desalination systems. The hybrid AD-OHD desalination system exhibits an 80 % improvement over the single-bed ADS system and a 50 % improvement over the open cycle HDH system in desalinated water production. Additionally, a theoretical model of the adsorption desalination cycle is developed and validated, demonstrating the viability of the AD-OHD system for desalination applications.

Performance Analysis of Using Hydrocarbon Mixed Refrigerant R32-R290 as an Alternative to R410A in Reducing the GWP Value of Household Split Air Conditioners

An experiment conducted on the mixed refrigerant R32-R290, which is used in household air conditioners, is aimed at reducing the global warming potential and improving the environmental CO2 quality. This mixed refrigerant has a lower GWP and density than the refrigerant R410A. This research was conducted to determine the effect of the increased mass charge of R290 as a mixed refrigerant on the performance of split air conditioner. Three percentage compositions of 20%/80%, 25%/75%, and 30%/70% were applied as a change to R410A in household air conditioners. For the testing equipment condition, the air inlet evaporator and condenser temperatures were 17°C (300 K) and 35°C (308 K), respectively. In the first performance test, refrigerant mass charge of 100% (340 g) was employed. In the second test, mass charge of 40% (136 g), 50% (170 g), and 60% (204 g) were utilized for mixed refrigerant R32-R290. Compared to the performance of R410A, the experimental results for the mixed refrigerant indicated a 0.5% increase in the air outlet evaporator temperature, a 1% decrease in the air condenser outlet, a 29.7% increase in the average COP, a 12.9% decrease in the electrical power consumption from 545.12 W to 474.7 W, a 9.1% increase in the average exergy destruction average from 401.5 W to 441.6 W, and a 19.7% increase in the average exergy efficiency from 24.2 W to 30.1 W.

Performance analysis of a novel solar assisted air source heat pump

Solar assisted heat pumps (SAHPs) are energy-efficient and environmentally friendly heating systems that utilize solar energy in different configurations. In this study, the potential benefits of heating the evaporator inlet air using solar energy as a new method were investigated using a developed correlation. The study focused on a 4 kW air conditioner that achieved a COP value of 4.17 for heating with an indoor temperature of 20C and an outdoor temperature of 7C. The aim was to determine how the COP value, evaporator inlet air temperature, and total power consumption changed based on the amount of solar energy transferred when the system was supported using the mentioned method. It was observed that a solar support of 5000 W increased the COP value of the SAHP from 4.17 to 4.99.

Information theory-based dynamic feature capture and global multi-objective optimization approach for organic Rankine cycle (ORC) considering road environment

Efficient capture and global optimization of organic Rankine cycle (ORC) operating features in road environment is the key to obtain actual waste heat recovery potential. The complexity of time-varying characteristics intensifies the nonlinear coupling relationship between ORC performance and parameters. This paper first designs and builds an ORC experimental system for recovering waste heat from internal combustion engine (ICE). The experimental system includes ICE and its measurement subsystem, ORC subsystem, expander lubrication subsystem, cooling subsystem, and ORC measurement subsystem. R245fa is used as working fluid; The expander adopts a specially designed single screw expander. The performance of ORC waste heat recovery is tested by adjusting the operating conditions of the ICE, expander, pump, and valve opening. The temperature range of waste heat is 627–686 K. Subsequently, a dynamic feature capture and global multi-objective optimization approach for ORC is proposed. The results show that the precision of dynamic feature capture can be improved at least 41.78% with the introduction of information theory in complex environment. Frequent fluctuation of vehicle speed is not conducive to the improvement of evaporator inlet temperature at working fluid side. As the speed of the vehicle is greatly reduced, the evaporator inlet temperature drops sharply. The frequent fluctuation of speed will intensify the nonlinear correlation between operating parameters and system performance and make the system performance more dependent on the coupling correlation between operating parameters. The approach proposed in this paper can provide a new idea for efficient capture of dynamic characteristics and global multi-objective optimization of ORC in road environment.

Experimental study on dynamic thermal characteristics of novel thermosyphon with latent thermal energy storage condenser

Emergency cooling systems are an essential part of data centers. A water tank is usually used as an emergency cooling source to provide cold thermal energy; however, tanks are bulky and additional uninterrupted power supplies (UPSs) are needed. For flexible emergency cooling, a novel thermosyphon integrated with a latent thermal energy storage condenser (TLTESC) is developed and experimentally studied. The effects of the refrigerant filling ratio and inlet conditions on the dynamic thermal performance are analyzed. With an increase in the refrigerant filling ratio from 48.7% to 82.5%, the cooling capacity decreases; the maximum cooling capacity decreases from 4.68 to 2.03 kW. The superheating temperatures for all cases are always zero, indicative of the two-phase refrigerant being at the evaporator outlet. During the entire operating period, the refrigerant temperatures at the vapor line are considerably higher than those at the liquid line. Moreover, the refrigerant pressure at the evaporator inlet is the highest. Under the optimal filling ratio, the outlet air temperature increases and the maximum cooling capacity increases from 3.6 to 4.8 kW with the inlet air temperature increasing from 30 to 40 °C. The cooling capacity increases with air flow rate during the first half, after which the situation is reversed. The accumulated energy increases slightly as the air flow rate increases. The thermal performance is investigated to promote the application of TLTESC in data center.

Thermodynamic performance of the fractionated auto-cascade refrigeration cycle coupled with two-phase ejector using R1150/R600a at −80 °C temperature level

The auto-cascade refrigeration cycle (ACRC) is suitable for low temperature refrigeration fields. From the perspective of fractionation purification matching expansion work recovery, a fractionated auto-cascade refrigeration cycle coupled with the two-phase ejector (FACRC-TPE) using environmentally-friendly R1150/R600a is proposed in this paper. In the novel cycle, the two-phase ejector is substituted for the throttle valve at the liquid stream passage from the separator bottom to recover expansion work and cut down the energy consumption, while the fractionation heat exchanger is applied for purification of low boiling point component in the vapor stream from the separator top to boost the cycle exergy efficiency and achieve a refrigeration temperature below −80 °C. The energetic and exergetic analysis methods are used to evaluate and compare the performances of the three cycles, and comparisons of superiority and irreversibility of FACRC-TPE are also made with the unfractionated auto-cascade refrigeration cycle with the two-phase ejector (UACRC-TPE) and the fractionated auto-cascade refrigeration cycle (FACRC). The results show that the cooperative application of the fractionation heat exchanger coupled with the two-phase ejector not only significantly boosts COP of FACRC-TPE and obtains lower refrigeration temperature, but also cuts down the total exergy loss of FACRC-TPE. With R1150 mass fraction varying in the range of 0.29–0.48, the average COP of FACRC-TPE is 10.57% higher than that of UACRC-TPE, and is 15.18% higher than that of FACRC, whereas FACRC-TPE has a lower evaporator inlet temperature of −100.48 °C to −87.96 °C, as compared with UACRC-TPE. In addition, FACRC-TPE obtains the highest exergy efficiency of 32.23%, while the UACRC-TPE exergy efficiency of 26.99% is slightly higher than the FACRC exergy efficiency of 25.19%.