Search

Building integrated photovoltaic thermal (BIPV/T) systems offer a highly effective means of generating clean energy for both electricity and heating purposes in residential buildings. Hence, this article introduces a new BIPV/T system to optimally minimize the energy consumption of a household residential building. The meticulous design of the proposed BIPV/T system is accomplished through MATLAB/Simulink® dynamic modeling. Performance analysis for the BIPV/T system is performed under different seasonal conditions with in-depth techno-economic analyses to estimate the expected enhancement in the thermal, electrical, and economic performance of the system. Moreover, a sensitivity analysis is conducted to explore the impact of various factors on the energetic and economic performances of the proposed BIPV/T system. More so, the two-layer feed-forward back-propagation artificial neural network modeling is developed to accurately predict the hourly solar radiation and ambient temperature for the BIPV/T. Additionally, a multi-objective optimization using the NSGA-II method is also conducted for the minimization of the total BIPV/T plant area and maximization of the total efficiency and net thermal power of the system as well as to estimate the optimized operating conditions for input variables across different seasons within the provided ranges. The sensitivity analysis revealed that higher solar flux levels lead to increased electric output power of the BIPV/T plant, but total efficiency decreases due to higher thermal losses. Moreover, the proposed NSGA-II shows a feasible method to attain a maximum net thermal power and optimal total efficiency of 5320 W and 63% with a minimal total plant area of 32.89 m2 that attained a very low deviation index from the ideal solution. The levelised cost of electricity is obtained as 0.10 $/kWh under the optimal conditions. Thus, these findings offer valuable insights into the potential of BIPV/T systems as a sustainable and efficient energy solution for residential applications.

Building integrated photovoltaic thermal (BIPV/T) systems offer a highly effective means of generating clean energy for both electricity and heating purposes in residential buildings. Hence, this article introduces a new BIPV/T system to optimally minimize the energy consumption of a household residential building. The meticulous design of the proposed BIPV/T system is accomplished through MATLAB/Simulink® dynamic modeling. Performance analysis for the BIPV/T system is performed under different seasonal conditions with in-depth techno-economic analyses to estimate the expected enhancement in the thermal, electrical, and economic performance of the system. Moreover, a sensitivity analysis is conducted to explore the impact of various factors on the energetic and economic performances of the proposed BIPV/T system. More so, the two-layer feed-forward back-propagation artificial neural network modeling is developed to accurately predict the hourly solar radiation and ambient temperature for the BIPV/T. Additionally, a multi-objective optimization using the NSGA-II method is also conducted for the minimization of the total BIPV/T plant area and maximization of the total efficiency and net thermal power of the system as well as to estimate the optimized operating conditions for input variables across different seasons within the provided ranges. The sensitivity analysis revealed that higher solar flux levels lead to increased electric output power of the BIPV/T plant, but total efficiency decreases due to higher thermal losses. Moreover, the proposed NSGA-II shows a feasible method to attain a maximum net thermal power and optimal total efficiency of 5320 W and 63% with a minimal total plant area of 32.89 m2 that attained a very low deviation index from the ideal solution. The levelised cost of electricity is obtained as 0.10 $/kWh under the optimal conditions. Thus, these findings offer valuable insights into the potential of BIPV/T systems as a sustainable and efficient energy solution for residential applications. [ABSTRACT FROM AUTHOR]

To meet the high-performance requirements of new energy vehicle drive, the optimization design of 8/6 Switched Reluctance Motor is realized based on finite element parametric modeling of the motor. Firstly, the initial design of motor structure parameters is carried out based on the mathematical model of Switched Reluctance Motor, and the simulation model of the motor is built using RMxprt platform, and the debugging of the characteristics of the wide speed range of the motor is finished. Then, the parametric finite element model of the motor is generated, and the stator and rotor pole arc coefficients of the motor are selected as the optimization variables, and the multi-objective compromise optimization of the torque characteristics and efficiency of the motor is carried out by using the Quasi-Newton method weighting method. Finally, the magnetic field distribution, torque characteristics, efficiency and speed range characteristics before and after optimization are compared, proving that the optimized Switch Reluctance Motor can achieve multi-objective performance optimization. The motor designed by this modeling optimization method can improve the requirements of vehicle driving better. [ABSTRACT FROM AUTHOR]

The regenerative supercritical CO2 Brayton cycle (RSCBC) has great development potential in waste heat recovery and utilization, and it is necessary to carry out performance analysis and optimization. This article first applies the theory of finite time thermodynamics to establish a RSCBC model with finite temperature difference heat transfer, irreversible compression, irreversible expansion and other irreversible factors under variable temperature heat source conditions, and then uses the data samples to construct the corresponding neural network model. Based on the NSGA-Ⅱ algorithm, the working fluid mass flow rate, the pressure ratio, the heat conductance distribution ratios of the regenerator and the heater are chosen as optimization variables, multi-objective optimization is carried out with the goals of cycle thermal efficiency, net power output, ecological function and exergy efficiency. The results show that the use of neural network models to predict cycle performance can save a lot of calculation time compared to traditional calculation methods; after optimization, the positive ideal point is not on the Pareto front, which shows that the four optimization objectives are mutually restricted and affect each other. The results by using Shannon Entropy method for decision-making have a lower deviation index, and those by using TOPSIS and LINMAP methods for decision-making are consistent with each other; for the results by using Shannon Entropy method for decision-making, the cycle thermal efficiency and net power output can reach 38.4% and 12.047 MW respectively, which can be increased by 31.15% and 43.29% compared to those for the initial design point respectively, and the ecological function and exergy efficiency can reach 7.6274 MW and 73.2% respectively, which are 4.2588 times and 33.75% higher than those for the initial design point respectively. The obtained results can provide some guidance for the optimal design of the RSCBC in real engineering.

To solve the problem of multi-objective performance optimization based on ant colony algorithm, a multi-objective performance optimization method of ORC cycle based on an improved ant colony algorithm is proposed. Through the analysis of the ORC cycle system, the thermodynamic model of the ORC system is constructed. Based on the first law of thermodynamics and the second law of thermodynamics, the ORC system evaluation model is established in a MATLAB environment. The sensitivity analysis of the system is carried out by using the system performance evaluation index, and the optimal working parameter combination is obtained. The ant colony algorithm is used to optimize the performance of the ORC system and obtain the optimal solution. Experimental results show that the proposed multi-objective performance optimization method based on the ant colony algorithm for the ORC cycle needs a shorter optimization time and has a higher optimization efficiency.

In order to further improve the efficiency of the gas turbine, a novel combined supercritical CO 2 regenerative cycle and organic Rankine cycle using zeotropic mixtures for waste heat recovery of gas turbine is proposed. The zeotropic mixtures used in the present study are cyclopentane/R365mfc. Exergoeconomic analysis is reported for the proposed system and parametric studies have been carried out to investigate the effect of system parameters on the exergy efficiency and the unit cost of electricity. The multi-objective optimization method based on genetic algorithm is chosen to obtain the optimum system parameters. The results show that the overall values of the exergoeconomic factor, the optimal exergy efficiency and the optimal unit cost of electricity of the proposed system are 31.88%, 62.23% and 3.95 cent/kW h, respectively. The obtained result reveals the superiority of the proposed combined regenerative S-CO 2 cycle and ORC system compared to the combined basic S-CO 2 cycle and ORC system, the combined recompression S-CO 2 cycle and ORC system. Therefore, the proposed system is suitable for gas turbine waste heat recovery, and it has advantages of deep utilization of waste heat, high efficiency and low cost. [ABSTRACT FROM AUTHOR]

Jet impingement cooling technology is usually applied to combustor liner with large heat load. With the continuous improvement of high capacity and low emission of heavy-duty gas turbines,the research on low resistance and high cooling efficiency cooling technology is particularly important. To meet these urgent needs, based on the blue whale skin structure, a new array impingement target surface cooling structure was proposed from the perspective of bionics. The flow and heat transfer characteristics of the array impingement bionic target surface cooling structure were numerically simulated under high Reynolds number (Re) and high Heat flux density (q) conditions. The influence of groove depth (H , 1 to 3 mm), radius ratio (R/H, 1 to 3) and spacing ratio (P/H, 10 to 20) on the cooling performance of impingement target surface was analyzed. Multi-objective performance optimization of the bionic structure was carried out by using the second-order response surface approximation model (RSM) and the second-generation non-inferior sequencing genetic algorithm (NSGA-II), and the performance of the optimized structure was tested. The results show that the above optimization method is accurate and effective, and the optimal bionic structure is H = 1.212 mm, R/H = 1.132, P/H = 12.078. Compared with the plane target surface, the average Nusselt number (Nu ave), flow friction factor (f), and comprehensive thermal coefficient (F) of the optimal bionic groove structure increased by 14.5%, 1.1% and 14.1%，respectively. The structure and multi-objective optimization method proposed in this paper can provide a certain reference for the design of new efficient cooling structure of heavy-duty gas turbines combustor liner. • A innovative array jet impingement cooling structure with bionic target surface for heavy-duty turbine combustor liner is proposed. • The flow and heat transfer characteristics of the bionic target surface impinged by array jet was analyzed, and the multi-objective performance optimization is carried out. • The performance of the optimized bionic structure was tested experimentally. [ABSTRACT FROM AUTHOR]