Free convection of hybrid nanofluids in a C-shaped chamber under variable heat flux and magnetic field: simulation, sensitivity analysis, and artificial neural networks

In the present investigation, the free convection energFree convection of hybrid nanofluids in a C-shaped chamber under variable heat flux and magnetic field: simulation, sensitivity analysis, and artificial neural networksy transport was studied in a C-shaped tilted chamber with the inclination angle α that was filled with the MWCNT (MultiWall Carbon Nanotubes)-Fe3O4-H2O hybrid nanofluid and it is affected by the magnetic field and thermal flux. The control equations were numerically resolved by the finite element method (FEM). Then, using the artificial neural network (ANN) combined with the particles swarm optimization algorithm (PSO), the Nusselt number was predicted, followed by investigating the effect of parameters including the Rayleigh number (Ra), the Hartmann number (Ha), the nanoparticles concentration (ϕ), the inclination angle of the chamber (α), and the aspect ratio (AR) on the heat transfer rate. The results showed the high accuracy of the ANN optimized by the PSO algorithm in the prediction of the Nusselt number such that the mean squared error in the ANN model is 0.35, while in the ANN model, it was optimized using the PSO algorithm (ANN-PSO) is 0.22, suggesting the higher accuracy of the latter. It was also found that, among the studied parameters with an effect on the heat transfer rate, the Rayleigh number and aspect ratio have the greatest impact on the thermal transmission intensification. The obtained data also showed that a growth of the Hartmann number illustrates a reduction of the Nusselt number for high Rayleigh numbers and the heat transfer rate is almost constant for low Rayleigh number values

Optimization of a large horizontal-axis wind turbine using comparison between two hybrid evolutionary algorithms

Twist and chord distribution are important parameters in optimal blade shape of MW class wind turbines. Although this is a serious challenge for the designer, their optimum design is an imperative for success in reaching the optimal blade shape. In this regard, the use of a suitable tool can be very effective in achieving this important goal. Furthermore, in order to improve the performance of large wind turbines, an objective function involving the combination of parameters of solidity and power coefficients are provided. The output power of the wind turbine is presented as the power coefficient of this objective function. Based on the presented objective function, it is sought to maximize power generation and minimize solidity. A decrease in solidity can cause weight loss of blades and ultimately reduce costs and consumed materials. One of the useful skills in solving optimization problems with high accuracy and convergence rate is hybridization of intelligent optimization algorithms. In this paper, we provided a comparison between two hybrid Evolutionary algorithm methods as genetic-based bees algorithm (GBBA) and harmony search-based bat algorithm (HSBBATA) optimization algorithms for designing the optimum shape of MW wind turbine blades. The present paper introduces two hybrid algorithms called HSBBAT and GBBA. GBBA is a novel population-based hybrid algorithm (proposed method). Therefore; we have tried using the hybrid algorithms to achieve better results for finding global optimum points with higher power generation and convergence rate for optimal blade shape of large wind turbines. On the other hand, defining three different scenarios for twist and chord, it is intended to achieve an optimal distribution for these aerodynamic parameters. The results show that the suggested algorithm GBBA, proves to have better results compared to HSBBATA

Exergy analysis and optimization of the Rankine cycle in steam power plants using the firefly algorithm

The analysis of the exergy efficiency has always been considered as a fundamental criterion to study the behavior of the thermodynamic cycles. In this research, the exergy analysis of a steam power plant for generating electricity with Rankine thermodynamic cycle is carried out. Zarand steam power plant, which is located in the Kerman province, is considered as a case study. In order to optimize these thermodynamic processes and to achieve the highest exergy efficiency value, some primary parameters were considered as the decision variables. By changing the values of these parameters, an attempt was made to enhance the exergy efficiency by using a novel approach. The six decision variables, which are, output temperature and pressure values of the boiler, as well as the output pressure values of the four stages of the turbine, were chosen on the basis of probability of variations in a certain range of electricity generation parameters for the studied power plant. The exergy efficiency was considered as the objective function. Afterwards, optimization of the power plant by employing the firefly algorithm, which is one of the relatively latest invented algorithms for solving the optimization problems, was carried out. The firefly model performs the optimization process inspired by the behavior and action of fireflies to attract mates and reject enemies. For the purpose of analysis of the exergy efficiency, at the first stage, the optimization of exergy efficiency function was performed for the studied steam power plant, and then the results were compared with the solutions obtained using the genetic and particle swarm optimization algorithms. Final results are indicative of the fact that by appropriate changes in the decision variables and employing the firefly algorithm, the exergy efficiency of the thermal power plant increased from 30.1 to 30.7037 percent. This increase was equivalent to 0.6037 for the cycle, and compared to the results obtained from the genetic and swarm particle optimization algorithms, it was 0.04% and 0.0398% higher, respectively

Application of PCM in a Zero-Energy Building and Using a CCHP System Based on Geothermal Energy in Canada and the UAE

In this research, the optimization of energy consumption of zero-energy buildings using PCMs in the two study cities of Vancouver and Dubai and its energy supply with a multi-generation geothermal system is discussed. PCMs used in the walls and roofs of designed buildings are of two types, namely PCM (solid) and PCM (liquid). By optimizing the energy consumption of the residential complex in two study cities, it is finally possible to choose the best mode in optimal conditions to reduce energy consumption in the residential complex, reduce the costs of the residential complex, and reduce the environmental pollution. The results showed that the amount of electricity consumption, heating, and cooling of the residential complex during the year in the city of Vancouver is 8493.55, 7899.1, and 1083.97 kWh, respectively, and in the city of Dubai, the values are 9572.1, 8.99, and 18,845.44 kW, respectively. Also, by optimizing the energy consumption of residential complexes in Vancouver and Dubai, it is possible to reduce CO2 emissions by 2129.7 and 2773.2 kg/year, respectively. The electricity consumption of the residential complex in Dubai is 11.26% and the carbon dioxide emission is 23.20% more. In the end, a multi-generation system is proposed to meet the energy consumption of a six-unit zero-energy residential complex with 120 m2 and two bedrooms in Vancouver, Canada. By setting up the study system in the city of Vancouver, 237,364.6 kWh of electricity, 425,959.4 kWh of heating, and 304,732.8 kWh of electricity can be produced in one year. According to the investigation, the geothermal system can easily provide the energy consumption required by residential buildings

Performance Evaluation and Optimization of a Photovoltaic/Thermal (PV/T) System according to Climatic Conditions

Population and economic growth, industrial activities, development of technology, and depletion of fossil fuels have all led to increasing energy demand. As a result, there is an increasing ambition towards implementation of sustainable energy sources. In this study, first, a review of the literature is conducted to learn about various methods and objectives for optimization of photovoltaic and thermal (PV/T) systems. Then, a case study is considered, and the seasonal and hourly solar radiation are studied. Further, two methods of multiobjective evolutionary algorithm based on decomposition (MOEA/D) and multiobjective particle swarm optimization (MOPSO) are compared. On this basis, the energy and exergy efficiencies are analyzed for a proposed PV/T system. The outcomes are validated by taking into account the previous studies, and a sufficient agreement is found indicating the validity and accuracy of the results. It is also found that the efficiency rates for both energy and exergy soar with a rise in the ambient temperature. Additionally, a growth in the warm water flow rate from 0.4 to 1 kg/s increases the exergy efficiency by 0.6%. It is concluded that the MOEA/D method outperforms the MOPSO in terms of the optimization of the proposed PV/T system

The Potential of Wind for Energy Production and Water Pumping in Saravan County

Sustainable sources of energy are vital for energy production in remote areas which have difficult access to electricity and grid. Thus, in this paper an initial evaluation of wind resource for over 18 months was done to evaluate the potential of wind energy as a power generation source in a remote village in Saravan county, southeastern Iran. The Weibull distribution is employed to model the wind data at three heights: 10, 30 and 40 meters. The Weibull distribution presented in this study indicates a good compatibility with the measured wind data. Different wind speed parameters such as monthly and diurnal wind speed profiles at different heights, wind direction, turbulence intensity, and etc. have been estimated and analyzed. The results showed the studied site has not the sufficient wind speed and power for development of commercial wind power plants. But the studied site may be suitable for development of small and residential wind turbines. Therefore in the next part of study, energy production of different small wind turbines has been estimated. It was concluded that one of the small wind turbines which has the highest net energy production of 33,685 kWh/ yr and highest capacity factor of 25.6% can be suitable for non-grid connected electrical and mechanical applications, such as local consumption, battery charging, and water pumping. In the last phase of study, the water pumping potential of the studied area has been investigated

Optimization of a trigeneration cooling, heating, and power system with low-temperature waste heat from 4E points of view

Waste heat recovery from buildings and/or industrial processes for generating different kinds of energy such as electricity, cooling, and heating is very effective for achieving higher efficiency of supply, reduced energy consumption, and lower carbon footprint. In this research, a novel waste heat-driven combined Kalina-Organic Rankine Cycle system for simultaneous supply of cooling, heating, and power is proposed, optimized, and investigated. For this, based on a parametric study, the system is analyzed from a 4E (energy, exergy, economic, environmental) perspective. Then, the cycle is optimized by using response surface methodology and taking exergy and economic parameters as the objectives. The simulations are conducted in TRNSYS, and the design of experiments technique is used for finding the best combination of the design variables. The possibility of using the proposed Cogeneration Cooling, Heating and Power System (CCHPS) for providing the energy demand of some buildings in Dubai is investigated. The results reveal exergy efficiencies of 54.16%, a total cost of 23458.07 $/years for the designed cycle

Design and optimization of a gas turbine regenerator with fixed pressure drop using GA and firefly algorithms

The present study investigates eight design parameters such as seal coverage, core porosity, core volume ratio, core thickness, dimensionless core rotation rate, inner diameter of the core, air mass flow rate and exhaust mass flow rate to design and optimize a regenerator of a 20-MW power generation gas turbine with fixed pressure drop. The application of GA and Firefly algorithms to optimize the effectiveness of the regenerator is presented to demonstrate the efficiency and accuracy of the proposed algorithms. The effect of change in the seal coverage, core porosity, core volume ratio and dimensionless core rotation rate are evaluated as important design parameters having influence on the size and mass of the core of the regenerator. This could be done through fixing each of these parameters, while the other seven design parameters are selected as variables to optimize the effectiveness. The results show that the selection of all eight-design parameters proposed as operating variables is necessary to optimize the parameters to achieve the proper design of this regenerator

Ocean thermal energy conversion (OTEC) system driven with solar-wind energy and thermoelectric based on thermo-economic analysis using multi-objective optimization technique

In the present study, we investigated a multiple energy generation system based on the use of solar, wind and ocean thermal energy for the study area with high renewable energy potential. In this study, all existing capacities in coastal areas (wind, sun, ocean temperature difference) are used to design a renewable system. The present system consists of subsystems including organic Rankin cycle (ORC), wind turbine, thermoelectric, heat exchanger and flat panel solar collector. EES thermodynamic software has been used to model the system and obtain thermodynamic results. The system designed for the city of Bushehr, which has good ocean thermal energy, wind energy and solar radiation potential, has been studied. The most important effective and practical parameters in the proposed system are collector area, solar radiation, and wind turbine speed. The results of the investigation of the system in Bushehr City showed that the production power of the system is 4,840,913.8 and 1,138,957.02 kWh compared to the climate changes. Also, the amount of freshwater produced by the system is 103,495.99 and 429,080.51 m3/h compared to the climate changes. Finally, in order to optimize the designed system, the response level multi-objective optimization method has been used to find the best set of objective functions and decision variables. The two objective functions of this optimization included the exergy efficiency of the whole system and the system cost rate. The most optimal value of exergy efficiency was 13.25% and the cost rate was 72.17 $/hour