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Hydrofluoroolefins (HFO) are considered as substitute to HFC refrigerant in the Organic Rankine cycle, however, the lack of studies on HFO refrigerant-lubricant mixtures hampers their further applications. This reported computational work aims to provide guidance for the development of suitable lubricants for new refrigerants. We use ab initio and large scale molecular dynamics (MD) modeling to gain insight into the molecular mechanism of interaction between polypropylene glycol dimethyl ether (PPGDE) and R1233zd(E). The ab initio calculated stable structures, interaction energies and charge distribution analysis indicate that the oxygen atoms in PPGDE and chlorine atom in R1233zd(E) play a major role in the solubility, the calculated interaction energy is -13.97 kJ mol−1 for the most stable configuration; The MD simulated density of R1233zd(E) agrees well with experimental data over a wide range of temperatures and pressures, the obtained cohesive energy density, solubility parameter, Flory−Huggins parameter and radial distribution functions (RDF) allow a characterization of the solubility properties and reveal the solvation mechanism and structure as well, PPGDE molecules begin to be attached to the Cl atoms in R1233zd(E) from the first neighbor shell of radius 2.925 A.

For the prevailing CO2 micro-channel gas-cooler (MCGC) mathematical modeling research, a big challenge for an efficient numerical model is the trade-off between fast calculation and high-precision considering the specific properties of CO2. The main purpose of this work is to propose a regression method with multiple variables for CO2 MCGC model based on the combination of Distributed Parameter Model (DPM) and Genetic algorithm (GA). The GA based prediction model of MCGC is established, which takes correlation coefficients and mean squared error into consideration. A test rig of CO2 MCGC is developed, and the experimental data used to validate the MCGC model are collected. The fixed-point iteration algorithm is utilized to reduce the complexity of simulation to accelerate the iteration speed. Moreover, an improved Taylor series expansion-based method for fast calculation of working fluid properties was proposed. The model precision was analyzed by comparing the prediction results with conducted experimental data. The improved CO2-to-air MCGC model enables to predict refrigerant outlet temperature and pressure drop within a maximum deviation of 1.2 °C and 2 kPa against the experimental data.

This study primarily aims to evaluate the cooling and heating characteristics and provide a performance comparison of CO2, R41, and several CO2/R41 blends for applications in automobile air-conditioning and heat pump systems. Experiments were conducted to reveal the effects of the refrigerant charge, compositions, ambient temperatures, and compressor speeds among the trans-critical and subcritical cycles. The performance merits of the environmental friendliness, energy efficiency, and safety of using CO2/R41 blends are emphasized. The results demonstrate that, at an optimum charge amount, the coefficient of performance of pure CO2 in the heating and cooling modes can be improved up to a maximum of 14.5% and 25.7%, respectively, by increasing the R41 mass fraction. The heating and cooling capacities were both decreased with an increasing R41 mass fraction, as the system mass flow rates decreased owing to the reduction in suction density. The overall system operation pressure, gas cooler outlet temperature, and high- and low-side pressure drops in both the heating and cooling modes were reduced significantly when the mass fraction of R41 increased from 0% to 100%. Furthermore, the effect of the CO2 mass fraction on the flammability of CO2/R41 blends was studied to discuss the advantages of its safety application. Finally, based on the experimental data, a correlation of the optimum high-side pressure for the trans-critical CO2/R41 cycle was developed, showing a deviation of ±5% from the data.

The aim of this work is to evaluate the cabin heating energy consumption and driving range for a battery electric vehicle. The vehicle was tested in climatic wind tunnel laboratory under Worldwide Harmonized Light-duty vehicle Test Cycle. A heating load model was established and used to predict the heating requirements of the vehicle in different climate conditions. Three different heat pump systems and five refrigerants are also investigated. Results show an error within 5% for the established heating load model. Compared with PTC heating, the heat pump systems lead to much lower energy consumption between 41% and 72% for different scenarios. Besides, 20% and 30% recirculation air can reduce the overall consumed energy by an average of 28.5% and 36.6%, respectively. Finally, the driving range of all the heat pump systems are much higher than that of PTC, which is between 14.2% and 31%.

Micro-scale organic Rankine cycle (ORC) enables to convert low-temperature waste heat into electricity, which is presented as one of combined heat and power (CHP) technologies. The commonly utilized working fluid R245fa will be phased out in the near future because of significant impact to the climate change. In that case, new HFOs (hydrofluoroolefins) refrigerants are suggested as potential alternatives to R245fa because of extremely low GWPs (Global Warming Potential) and zero ODP (Ozone Depletion Potential). This paper experimentally analyzed the applicability of three HFO refrigerants as drop-in replacements to R245fa for micro-scale ORC application including R1234ze(Z), R1233zd(E) and R1336mzz(E). Control options were assigned to the refrigerant mass flow rate and the expander rotational speed. System performance indicators including cycle thermal efficiency and net power output were compared. Besides, the expansion behavior along with the heat transfer performance and pump work were also analyzed. For the whole range of operating conditions, comparing the maximum cycle thermal efficiency, R245fa is 4.6% while R1233zd(E), R1234ze(Z) and R1336mzz(E) are 4.7%, 4.5% and 3.1%, respectively; Comparing the maximum net power output, R245fa generates 11.4% more than R1233zd(E) and 3.1% than R1234ze(Z). Limited range of pressure ratio contributes to the extremely low electricity of R1336mzz(E).

R410A has been widely applied in air-to-water heat pumps for space heating, but its phase-out process is accelerated due to the high GWP. This paper investigates the heating performances of low GWP HFO mixtures (R452B and R447B) as substitutions in a sub-cooler vapor injection cycle. The results illustrate that the capacities of R452B and R447B systems are lower by 0.7–5% and 6–15% in the measured conditions, respectively. The attenuation with the ambient temperature of R452B is slower than that of R410A, while R447B is faster. The COP improvements of R452B are 4–9% and 1.4–2.4% with and without vapor injection, respectively. The COP increases of R447B are 3–12% and 0.4–3.8% with and without vapor injection, respectively. In view of injection characteristics, the optimal injection pressure ratios of R452B and R447B are slightly higher. Synthetically R452B and R447B perform more excellent in the high outlet water temperature conditions and R452B is also suitable for low ambient temperatures.

To help reduce the warming impacts, this study evaluates the drop-in performances of R-427A, R-161 and R-290 in R-22 air source heat pump with EVI (Economized Vapor Injection). The results show that the IPLV(H) of R-161 and R-290 arises 5∼8% relative to R-22 with EVI on. A reversal occurs between R-290 and the baseline with EVI on, making the IPLV(H) of R-290 superior to that of R-22 by 3.6∼5.2% from worse performance with EVI off. At -20/41 °C condition, the capacity and COP of R-290 increase by 38% and 19%, respectively. Considering COP and discharge temperatures, R-290 is better-performed to replace R-22 in high pressure ratio conditions. Based on LCCP (Life Cycle Climate Performance) model, 11% of the equivalent CO2 emission can be reduced by using R-161 and R-290. However, the discharge temperature of R-161 is high in the high pressure ratio condition. R-290 is the most synthetically beneficial alternative.

Heat pump and its efficiency are of great significance to the energy saving of electric vehicle in cold climate. This paper proposes a modified autocascade heat pump (ACHP) system using natural CO2-propane mixture. The main purpose is to enhance the heating performance compared with that of conventional heat pump. Using a thermodynamic model of the proposed ACHP cycle, the transcritical and subcritical cycle performance under various conditions are carried out from the perspective of first and second law of thermodynamics, and the transcritical operating characteristics of ACHP system is introduced for the first time. Additionally, an exergy performance comparison has been carried out. Results indicate that the ACHP system can produce more heating capacity, lower compression pressure ratio, lower heat rejection temperature for low ambient temperature conditions and it consumes less compressor work for all the temperature range researched. For transcritical ACHP cycle, the best COP is 2.7 obtained when CO2 mass fraction is 0.8 at −20 °C, which is 12.3% higher than that of CO2 single stage heat pump. For subcritical ACHP cycle, the best COP is 3.97 while heating capacity is much lower than that of transcritical cycle in the same condition. Finally, the exergy analysis confirms the feasibility of the ACHP system and points out exactly the further optimization locations in the ACHP system.

HFCs are suggested to be banned in 2020 because of high GWP (Global Warming Potential). New type of HFO refrigerant R1233zd(E) is proposed as a drop-in replacement to R245fa for organic Rankine cycle application considering the similar thermo-physical properties. In this paper, a description of previous experimental comparison between two refrigerants is presented in the first section. In the second section, further investigation in expansion procedure is implemented with a semi-empirical expander model, which is validated with experimental data based on ‘Genetic Algorithm’. Internal leakage, mechanical friction and heat transfer are presented as main irreversible losses. Input parameters are assigned to mass flow rate, expander rotational speed, supply temperature and exhaust pressure. Supply pressure, exhaust temperature and net power are computed as output results. The maximum deviation between the measured and predicted results are 3.35%, 2.24 K and 6.09% respectively. In the last section, polynomial curve-fittings of dimensionless expander efficiency are conducted for wider prediction of operating range of the expander. Values of filling factor and expander isentropic efficiency are predicted with R 2 = 99.517 % and R 2 = 97.997 % . Curve-fittings of expander efficiency can be integrated into systematic simulation, which is aimed for further optimization of cycle performance to better take advantage of new refrigerants.

The whole-life ratchetting and fatigue failure, as well as the ratchetting-fatigue interaction, of polyamide-6 (PA6) were investigated by performing a set of uniaxial stress-controlled cyclic tests at room temperature and with different stress levels. The effects of mean stress, stress amplitude and stress ratio on the evolution of ratchetting strain and the fatigue life of PA6 are discussed. Simultaneously, the evolution of damage variable, which is defined as a function of equivalent modulus, is summarized to reflect the interaction of ratchetting and fatigue damage. The experimental results show that the uniaxial whole-life ratchetting and fatigue life of PA6 are sensitive to the mean stress, stress amplitude and stress ratio. The evolution of damage variable and its dependence on the stress level are similar to that of whole-life ratchetting, and present a tri-phased feature with respect to the damage rate. By comparing the fatigue lives obtained in the asymmetrical and symmetrical tests, a detrimental effect of ratchetting deformation on the fatigue life of PA6 is found.

It is well-known that conventional multiaxial fatigue life prediction models are generally limited to specific materials and loading conditions. To remove this limitation, a novel attempt is proposed in this work based on the deep learning (i.e., an improvement of artificial neural network in machine learning approaches, which is powerful to learn representations of data with multiple levels of abstraction). To comprehensively evaluate the prediction capability of proposed deep learning-based method, six series of existing fatigue data of different materials are, respectively, analyzed, in which the main loading conditions concerned in the low-cycle and high-cycle fatigue researches are included, such as loading modes (stress-controlled/strain-controlled modes), loading levels (stress/strain amplitude and mean stress/strain), and loading paths (uniaxial/multiaxial and proportional/non-proportional paths), as well as for low-cycle and high-cycle fatigue regimes. Comparison of the predicted and experimental results shows that: all the loading conditions mentioned above can be handled satisfactorily by the proposed deep learning-based method; excellent prediction accuracy is achieved, and the predicted lives in each study case fall almost within the scatter band of 1.5 times. In addition, four groups of specifically designed data are used to evaluate the extrapolation capability of the proposed method, and the results show that the extrapolation capability gets weaker if the distinctions between the loading paths involved in the training dataset and test one increase.

This paper develops an artificial intelligence-based automated valuation model (AI-AVM) using the decision tree and the boosting techniques to predict residential property prices in Singapore. We use more than 300,000 property transaction data from Singapore’s private residential property market for the period from 1995 to 2017 for the training of the AI-AVM models. The two tree-based AI-AVM models show superior performance over the traditional multiple regression analysis (MRA) model when predicting the property prices. We also extend the application of the AI-AVM to more homogenous public housing prices in Singapore, and the predictive performance remains robust. The boosting AI-AVM models that allow for inter-dependence structure in the decision trees is the best model that explains more than 88% of the variance in both private and public housing prices and keep the prediction errors to less than 6% for HDB and 9% for the private market. When subject the AI-AVM to the out-of-sample forecasting using the 2017-2019 testing property sale samples, the prediction errors remain within a narrow range of between 5% and 9%.

Organic Rankine cycle (ORC) enables to harness waste heat resource to generate electrical power. R245fa is assumed as an appropriate working fluid for low-temperature waste heat recovery. However, strict regulations of CO2 emissions stimulate scientists to search for potential alternatives to R245fa. This paper presents a comparative analysis of various latest low-GWP refrigerants for ORC application. An energetic-economic-environmental evaluation model is established and further integrated into the non-sorted genetic algorithm (NSGA-II) for multi-objective optimization (MOO). A classic decision-making method is employed to screen out the optimal refrigerant for current study. Apart from that, the whole-year ambient conditions in Sydney are taken into consideration to screen refrigerants. Comparing the cycle thermal efficiency, R1224yd(Z) leads to 3.3% higher than R245fa. Comparing the economic cost, it saves 10.4% more than R245fa. Comparing the whole year’s performance, R1224yd(Z) exhibits 11.2% higher cycle thermal efficiency than R245fa while R1234ze(Z) saves 9.85% more economic cost than R245fa. R1224yd(Z) is considered as the optimal working fluid for current ORC system. R1224yd(Z) leads to 16% higher L E C T s than R245fa. Comparing the maximum L E C T s , R1224yd(Z), R1234ze(Z) and R1233zd(E) is 16%, 9.2% and 13.5% higher than R245fa, respectively.

Based on the experimental results in [1,2], a semi-empirical low-cycle fatigue life prediction model is constructed for a polyamide-6 (PA6) polymer, in which the rate dependence of fatigue life and the effect of ratchetting strain on the life are considered. Since the fatigue damage in PA6 is not only loading-cycle-dependent but also time-dependent, a function to describe the complicate effect of stress rate on the fatigue life is obtained. In the proposed model, the detrimental effect of ratchetting strain on the fatigue life is characterized by introducing a function of mean stress. Comparison of the predicted and experimental results shows that the proposed model presents a good prediction. Furthermore, the neural network based method is also used to correlate the low-cycle fatigue data of PA6. The results show that the established neural network based approach achieves a better prediction to the fatigue life of PA6 with the occurrence of ratchetting than that by the proposed semi-empirical model, which demonstrates a possibility to apply the neural network based machine learning method to deal with the fatigue of polymer.

Among various techniques of utilizing waste heat, ORC is receiving more and more attention for its high efficiency and flexibility. One of the toughest tasks in ORC waste heat recovery system is working fluid selection. The aim of the study is to investigate the effect of physical properties on overall ORC system costs and propose a general method of refrigerant’s performance assessment of ORC system. The internal relationship between the enthalpy of vaporization, molecular weight, and molecular complexity, and the impact on investment cost were analyzed theoretically. The analysis and experiment using R12333zd(E), R1234ze(Z) and R1366mzz(E) as well as R245fa explain the internal mechanism of the effect of different working fluids on NPIT, which represents the ratio of the net power output to the total cost. The economic performances of several new environmental-friendly refrigerants are evaluated using the NPIT assessment. The results show that R12333zd(E) has the best performance and its maximal NPIT value is 0.0625, followed by R1234ze(Z), R1366mzz(E) and R245fa. In addition, when R1233zd(E) performs excellently, evaporation and condensation temperature are 127 °C and 30 °C, respectively. The physical properties of R1233zd(E) and R245fa are extremely close, which makes R1233zd(E) be an alternative refrigerant without redesigning components of ORC system. NPIT is the answer to the challenge of complex boundary conditions and system types with various operational parameters and it could guide the selection of operating condition and design of ORC system equipment.

Rate-dependent whole-life ratchetting and fatigue failure of polyamide 6 were explored by conducting a series of stress-controlled uniaxial fatigue tests at room temperature. Meanwhile, the temperature rises on the surface of specimens were recorded to reflect the self-heating occurred during the fatigue tests. The experimental results demonstrate that ratchetting strain rate is sensitive to the variation of prescribed stress rate, and the whole-life ratchetting of PA6 presents a tri-staged evolution feature at high stress rate but a four-staged one at low stress rate. Two failure modes (i.e., the necking controlled by unstable self-heating and resultant large ratchetting strain; and the fracture controlled by the low-cycle fatigue damage) are experimentally observed in the tests at different stress rates. By considering the relationship between ratchetting evolution and temperature rise, it is found that whether increasing the stress rate retards or promotes the ratchetting is dependent on the magnitude of self-heating produced at each fatigue stage, and whether it will be beneficial or detrimental to the total fatigue life is determined by the competition of resultant strain hardening and self-heating softening.

With carbon emission levels on the rise, rapid and far-reaching action is needed to counteract global warming. Among many available strategies, CO2 is nowadays more and more often proposed as a solution for heating, cooling and refrigeration purposes since the extremely low global warming potential and being natural. In order to overcome the inherently low efficiency in high-temperature conditions and high operating pressure especially in transcritical cycles, an updated review of the advances on modified technologies to solve the drawbacks of CO2 refrigeration is provided and recent progress on the energy efficiency improvement is summarized. First, the basic principles of the CO2 refrigeration cycle and important performance characteristics are discussed. Then, a detailed discussion on different modified technologies as well as their operating fundamental, technical features and performance are provided, followed by a summary of previous studies. At the end of this review, conclusion and perspectives on the future development of this field are presented.

Organic Rankine cycle can be used to harness waste heat to generate electricity. The cycle performance is dependent on expander behavior, where the design of lubricant oil loop is significant. The effects of oil on expansion machine rely on the prevention of leakages and friction losses. However, the lubricant oil is inevitably mixed with refrigerant, which may in turn affect system performance. This paper is targeted to investigate the impact of lubricant oil ratios on a 2 kW ORC prototype using R245fa as working fluid. The heat source temperature is fixed at 125 °C. Two degrees of freedom including the mass flow rate and expander rotational speed are adjusted to explore the system performance. Cycle thermal efficiency, expander isentropic efficiency, filling factor and heat transfer performance are analyzed. The results show that injection of lubricant oil has significant influence on the organic Rankine cycle system. The optimum cycle thermal efficiency is 4.7%, which is obtained under high oil ratio. The system efficiency is more sensitive under lower lubricant oil charges, as 10.25% increase in cycle thermal efficiency is observed when oil-concentration varies from 0.12% to 1%. The hermetic scroll expander is more sensitive to various oil-concentration than the plate heat exchanger.