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[en] Highlights: • An irreversible solar-driven heat engine is optimized. • Developed multi objective evolutionary approaches is used. • Power output, ecological function and thermal efficiency are optimized. - Abstract: The present paper illustrates a new thermo-economic performance analysis of an irreversible solar-driven heat engine. Moreover, aforementioned irreversible solar-driven heat engine is optimized by employing thermo-economic functions. With the help of the first and second laws of thermodynamics, an equivalent system is initially specified. To assess this goal, three objective functions that the normalized objective function associated to the power output (F_P) and Normalized ecological function (F_E) and thermal efficiency (η_t_h) are involved in optimization process simultaneously. Three objective functions are maximized at the same time. A multi objective evolutionary approaches (MOEAs) on the basis of NSGA-II method is employed in this work
[en] Highlights: • Thermodynamic model of a solar-dish Stirling engine was presented. • Thermal efficiency and output power of the engine were simultaneously maximized. • A final optimal solution was selected using several decision-making methods. • An optimal solution with least deviation from the ideal design was obtained. • Optimal solutions showed high sensitivity against variation of system parameters. - Abstract: A solar-powered high temperature differential Stirling engine was considered for optimization using multiple criteria. A thermal model was developed so that the output power and thermal efficiency of the solar Stirling system with finite rate of heat transfer, regenerative heat loss, conductive thermal bridging loss, finite regeneration process time and imperfect performance of the dish collector could be obtained. The output power and overall thermal efficiency were considered for simultaneous maximization. Multi-objective evolutionary algorithms (MOEAs) based on the NSGA-II algorithm were employed while the solar absorber temperature and the highest and lowest temperatures of the working fluid were considered the decision variables. The Pareto optimal frontier was obtained and a final optimal solution was also selected using various decision-making methods including the fuzzy Bellman–Zadeh, LINMAP and TOPSIS. It was found that multi-objective optimization could yield results with a relatively low deviation from the ideal solution in comparison to the conventional single objective approach. Furthermore, it was shown that, if the weight of thermal efficiency as one of the objective functions is considered to be greater than weight of the power objective, lower absorber temperature and low temperature ratio should be considered in the design of the Stirling engine
[en] Highlights: • Thermodynamic analysis of a hybrid CCHP system. • Sensitivity analysis is performed on the most important parameters of the system. • Pressure ratio and gas turbine inlet temperature are the most effective parameters. - Abstract: Hybrid power systems are gained more attention due to their better performance and higher efficiency. Widespread use of these systems improves environmental situation as they reduce the amount of fossil fuel consumption. In this paper a hybrid system composed of a gas turbine, an ORC cycle and an absorption refrigeration cycle is proposed as a combined cooling, heating and power system for residential usage. Thermodynamic analysis is applied on the system. Also a parametric analysis is carried out to investigate the effect of different parameters on the system performance and output cooling, heating and power. The results show that under design conditions, the proposed plant can produce 30 kW power, 8 kW cooling and almost 7.2 ton hot water with an efficiency of 67.6%. Moreover, parametric analysis shows that pressure ratio and gas turbine inlet temperature are the most important and influential parameters. After these two, ORC turbine inlet temperature is the most effective parameter as it can change both net output power and energy efficiency of the system.
[en] Highlights: • Thermodynamic modeling of Ericsson refrigeration is performed. • The latter is achieved using NSGA algorithm and thermodynamic analysis. • Different decision makers are utilized to determine optimum values of outcomes. - Abstract: Optimum ecological and thermal performance assessments of an Ericsson cryogenic refrigerator system are investigated in different optimization settings. To evaluate this goal, ecological and thermal approaches are proposed for the Ericsson cryogenic refrigerator, and three objective functions (input power, coefficient of performance and ecological objective function) are gained for the suggested system. Throughout the current research, an evolutionary algorithm (EA) and thermodynamic analysis are employed to specify optimum values of the input power, coefficient of performance and ecological objective function of an Ericsson cryogenic refrigerator system. Four setups are assessed for optimization of the Ericsson cryogenic refrigerator. Throughout the three scenarios, a conventional single-objective optimization has been utilized distinctly with each objective function, nonetheless of other objectives. Throughout the last setting, input power, coefficient of performance and ecological function objectives are optimized concurrently employing a non-dominated sorting genetic algorithm (GA) named the non-dominated sorting genetic algorithm (NSGA-II). As in multi-objective optimization, an assortment of optimum results named the Pareto optimum frontiers are gained rather than a single ultimate optimum result gained via conventional single-objective optimization. Thus, a process of decision making has been utilized for choosing an ultimate optimum result. Well-known decision-makers have been performed to specify optimized outcomes from the Pareto optimum results in the space of objectives. The outcomes gained from aforementioned optimization setups are discussed and compared employing an index of deviation presented in this work
[en] Nowadays, due to the increased greenhouse gas emissions and high energy prices, it is essential to use renewable energy sources in different industrial applications. In this study, the technical and economic assessment of using solar energy in order to preheat the process fluid before entering the furnaces in refinery is carried out. The furnace unit 400 (an indirect furnace) used in Parsian Gas Refinery, which uses natural gas as a fuel, is studied. Mid-temperature solar energy is a practical energy source in this case with respect to the temperature range of the heated process fluid by furnaces which is about 300 °C. Among different types of solar thermal systems for collecting the solar energy, the parabolic trough solar collector is selected in this research because of the advantages it offers including its temperature range and lower cost. The proposed solar thermal preheating system coupled with the furnace is modeled using TRNSYS software. In the optimum condition, the maximum amount of fuel that can be saved using solar energy for the studied furnace is 1,996,000 m3 per year which is about 23.8% of the fuel consumption of the furnace and is responsible for 3557.7 ton CO2 emissions per year. Finally, using solar heat exchanger before the furnace considering different sizes of solar farm is economically evaluated, taking into account different fuel prices and different mortgage rates. The results show that at natural gas prices more than 45 cents per m3 as the fuel, in different sizes of the system, it is perfectly economic to use solar thermal collector as a preheating system.
[en] Mixed convection flow of aluminium-oxide nanoparticles in water through a circular tube was modelled using the discrete phase model and implemented on ANSYS-Fluent 17.0 through customised user-defined functions. The inclination angle was varied to study its effect on the migration and deposition of the nanoparticles. Experimentally determined thermo-physical properties were used in the analysis instead of theoretical or empirical models from the literature. Varying inclination angles were found to significantly affect the migration and deposition of nanoparticles. A critical angle of maximum deposition of approximately 30° was found for volume concentrations 1%, 3% and 5%. The effect of varying inclination angle on the heat transfer coefficient was minimal for low angles of inclination between 0 and 35% and decreased significantly after 40%. The effect of Saffman’s lift, thermophoretic, Magnus and Brownian effects were also investigated, and results show that thermophoretic and Brownian effects were most dominant effects.
[en] The present study investigated and predicted the relative viscosity of multiwall carbon nanotube/carbon (60/40)/SAE 10 W 40/(Society of Automotive Engineers) SAE 85 W 90(50/50) at different temperatures and the different volumetric fraction by applying artificial neural networks based on experimental data. Several samples of nanofluid were provided by adding nanoparticles in 0%, 0.1%, 0.3%, 0.5%, 0.8% and 1% volumetric concentrations. Dynamic viscosity of the nanofluid was measured in the temperature range of 25–50 °C. Initially, a self-organizing 6 × 6 hexagonal network was used. A total of 36 neurons were chosen. The winner neuron was neuron 25, having assigned the most data to itself. Then 25 neurons were used for the neural network, which had a very good performance. Temperature and concentration were considered as input variables, while the relative viscosity was the output parameter of the neural network. Mean-square error, correlation coefficient and standard deviation were utilized in order to assess the results. Based on the obtained results, the best model was double-layer perceptron neural network with 25 neurons. The mean square error, correlation coefficient and standard deviation were equal to 2.0193e−008, 1 and 0.00021082, respectively. Therefore, the model is able to predict relative viscosity with appropriate accuracy.
[en] Highlights: • Thermo-ecological modeling of irreversible three-heat-source absorption heat pump is performed. • The latter is achieved using NSGA algorithm and thermodynamic analysis. • Various decision makers are carried out to indicate optimum values of outputs obtained with optimization process. - Abstract: Throughout present research, optimization investigations of an irreversible absorption heat pump system on the basis of a new thermo-ecological criterion. The objective functions which considered are the specific heating load, coefficient of performance (COP) and the ecological coefficient of performance (ECOP). Three objective functions of the ECOP, COP and the specific heating load are optimized simultaneously using the multi-objective optimization algorithm NSGAII. COP and ECOP are maximized and specific heating load is minimized in order to get the best performance. Decision making is done by means of three methods of LINAMP and TOPSIS and FUZZY. Finally, sensitivity analysis and error analysis was performed for the system
[en] Due to the enhanced thermophysical specifications of nanofluids, such as thermal conductivity, these types of fluids are appropriate candidates for heat transfer fluids. Nanostructure dispersion in the base fluid increases the dynamic viscosity which affects fluid flow in thermal devices. In order to facilitate design of thermal devices, it is crucial to have accurate predictive models for thermophysical properties of nanofluids. Dimensions of nanoparticles, working temperature and the concentration of nano-sized particles in the fluid are among the most influential factors in predicting dynamic viscosity of nanofluids. In the present research, four LSSVM-based algorithms including GA-LSSVM, PSO-LSSVM, HGAPSO-LSSVM and ICA-LSSVM are employed to model the dynamic viscosity of Al2O3/water. Results revealed that the generated models are accurate tools to calculate the dynamic viscosity of the nanofluid on the basis of the mentioned variables. The highest obtained coefficient of correlation belongs to GA-LSSVM which is equal to 0.9871, while this value for PSO-LSSVM, HGAPSO-LSSVM, and ICA-LSSVM algorithms are 0.9855, 0.9855, and 0.9846, respectively. Another utilized criterion for evaluating model accuracy is MSE value. Results revealed that the MSE values for HGAPSO-LSSVM, GA-LSSVM, PSO-LSSVM, and ICA-LSSVM are 0.00854, 0.00855, 0.00896 and 0.00979, respectively.
[en] Intelligence methods, including Artificial Neural Networks (ANNs) and Support Vector Machine, are among the popular approaches for modeling the engineering systems with high accuracy. Nanofluid’s thermal conductivity depends on several factors such as the dimensions of nanoparticles, their concentration, synthesis method and temperature. Intelligence methods are appropriate tools to precisely estimate nanofluids’ thermal conductivity. Different methods and structures are used for the modeling of this property. In the present article, the related studies, using intelligence methods in thermal conductivity estimation, are comprehensively reviewed. According to the literature review, the accuracy of the predictive models has an association with their structure, utilized functions, selected input variables and employed algorithm. For instance, compared with mathematical correlations, obtained by curve fitting, ANNs are more accurate. Moreover, it is concluded that the structure of the NN, including numbers of hidden layer and neurons, can noticeably influence their performance. In the reviewed articles, trial and error are performed to distinguish the most favorable structure of ANNs. Due to the dependency of the models on the input variable, considering all the factors affecting the nanofluid’s thermal conductivity results in higher precision of the models.