Effects of combined heat transfer on the thermo-economic performance of irreversible solar-driven heat engines

A thermo-economic performance analysis and optimization has been carried out for an irrversible solar-driven heat engine with losses due to heat transfer across finite temperature differences, heat leak and internal irreversibilities. In the considered heat engine model, heat transfer from the hot reservoir is assumed to be simultaneous radiation and convection mode and the heat transfer to the cold reservoir is assumed to be convection mode. The effects of the technical and economical parameters on the thermo-economic performance have been investigated in order to see the collective effects of the radiation and convection modes of heat transfer. Also the optimal performance parameters of the heat engine, such as the thermal efficiency, temperatures of the working fluid and the ratio of heat transfer areas have been discussed in detail.     

Comparison of a Basic Organic Rankine Cycle and a Parallel Double-Evaporator Organic Rankine Cycle

ABSTRACT

The applications of zeotropic mixtures and multi-evaporator systems are two viable options to improve the performance of the organic Rankine cycle (ORC). This paper conducts the thermo-economic comparison of a basic ORC with R245fa/R600a and a parallel double-evaporator organic Rankine cycle (PDORC) with R245fa. Four indicators are used to evaluate the system performance: net power, cycle efficiency, area of heat exchangers, and area of heat exchangers per net power output. Submodels of condensers and evaporators are established specially for pure organic fluids and zeotropic mixtures. The performance optimization using genetic algorithm is conducted to compare the two systems quantitatively. The optimization indicates a zeotropic mixture is more profitable than a pure work fluid in a basic ORC with a worthy additional investment of heat exchanger. Though PDORC can increase net power obviously, it would decrease the thermo-economic performance of ORC.     

Multiobjective optimal dispatch of microgrid based on analytic hierarchy process and quantum particle swarm optimization

Owing to the rapid development of microgrids (MGs) and growing applications of renewable energy resources, multiobjective optimal dispatch of MGs need to be studied in detail. In this study, a multiobjective optimal dispatch model is developed for a standalone MG composed of wind turbines, photovoltaics, diesel engine unit, load, and battery energy storage system. The economic cost, environmental concerns, and power supply consistency are expressed via subobjectives with varying priorities. Then, the analytic hierarchy process algorithm is employed to reasonably specify the weight coefficients of the subobjectives. The quantum particle swarm optimization algorithm is thereafter employed as a solution to achieve optimal dispatch of the MG. Finally, the validity of the proposed model and solution methodology are confirmed by case studies. This study provides reference for mathematical model of multiojective optimization of MG and can be widely used in current research field.     

Exergy analysis and multiobjective optimization of a biomass gasification based multigeneration system

Biomass gasification is a process of converting biomass to a combustible gas suitable for use in boilers, engines and turbines to produce combined cooling, heat and power. This paper presents a detailed model of a biomass gasification system and designs a multigeneration energy system which uses the biomass gasification process for generating combined cooling, heat and electricity. Energy and exergy analyses are first applied to evaluate the performance of the designed system. Next, minimizing total cost rate and maximizing exergy efficiency of the system are considered as two objective functions and a multiobjective optimization approach based on differential evolution algorithm and local unimodal sampling technique is developed to calculate the optimal values of the multigeneration system parameters. A parametric study is then carried out and Pareto front curve is used to determine the trend of objective functions and assess the performance of the system. Furthermore, a sensitivity analysis is employed to evaluate effects of design parameters on the objective functions. Simulation results are compared with two other multiobjective optimization algorithms and effectiveness of the proposed method is verified using various performance indicators.     

Thermodynamic optimization of Stirling heat engine with methane gas using finite speed thermodynamic model

With the daily rise in environmental issues due to the use of conventional fuels, researchers are motivated to use renewable energy sources. One of such waste heat and low-temperature differential driven energy sources is the Stirling engine. The performance of the Stirling engine can be improved by finding out the optimum operating and geometrical parameters with suitable working gas and thermal model. Based on this motivation, the current work focuses on the multiobjective optimization of the Stirling engine using the finite speed thermodynamic model and methane gas as the working fluid. Considering output power and pressure drop as two objective functions, the system is optimized using 11 geometrical and thermal design parameters. The optimization results are obtained in the form of the Pareto frontier. A sensitivity assessment is carried out to observe the decision variables, which are having a more sensitive effect on the optimization objectives. Optimization results reveal that 99.83% change in power output and 78% change in total pressure drop can take place in the two-dimensional optimization space. The optimal solution closest to the ideal solution has output power and pressure drop values as 12.31 kW and 22.76 kPa, respectively.     

Multi-objective optimization for GPU3 Stirling engine by combining multi-objective algorithms

Stirling engine has become preferable for high attention towards the use of alternate renewable energy resources like biomass and solar energy. Stirling engine is the main component of dish Stirling system in thermal power generation sector. Stirling engine is an externally heating engine, which theoretical efficiency is as high as Carnot cycle's, but actual ones are always far below compared with the Carnot efficiency. A number of studies have been done on multi-objective optimization to improve the design of Stirling engine. In the current study, a multi-objective optimization method, which is a combination of multiple optimization algorithms including differential evolution, genetic algorithm and adaptive simulated annealing, was proposed. This method is an attempt to generalize and improve the robustness and diversity with above three kinds of population based meta-heuristic optimization techniques. The analogous interpreter was linked and interchanged to find the best global optimal solution for Stirling engine performance optimization. It decreases the chance of convergence at a local minimum by powering from the fact that these three algorithms run parallel and members from each population and technique are swapped. The optimization considers five decision variables, including engine frequency, mean effective pressure, temperature of heating source, number of wires in regenerator matrix, and the wire diameter of regenerator, as multiple objectives. The Pareto optimal frontier was obtained and a final optimal solution was also selected by using various multi-criteria decision making methods including techniques for Order of Preference by Similarity to Ideal Solution and Simple Additive Weighting. The multi-objective optimization indicated a way for GPU-3 Stirling engine to obtain an output power of more than 3 kW and an increase by 5% in thermal efficiency with significant decrease in power loss due to flow resistance.     

Thermodynamic multi-objective optimization of a solar-dish Brayton system based on maximum power output,thermal efficiency and ecological performance

Solar-dish Brayton system driven by the hybrid of fossil fuel and solar energy is characterized by continuously stable operation, simplified hybridization, low system costs and high thermal efficiency. In order to enable the system to operate with its highest capabilities, a thermodynamic multi-objective optimization was performed in this study based on maximum power output, thermal efficiency and ecological performance. A thermodynamic model was developed to obtain the dimensionless power output, thermal efficiency and ecological performance, in which the imperfect performance of parabolic dish solar collector, the external irreversibility of Brayton heat engine and the conductive thermal bridging loss were considered. The combination of NSGA-II algorithm and decision makings was used to realize multi-objective optimization, where the temperatures of absorber, cooling water and working fluid, the effectiveness of hot-side heat exchanger, cold-side heat exchanger and regenerator were considered as optimization variables. Using the decision makings of Shannon Entropy, LINMAP and TOPSIS, the final optimal solutions were chosen from the Pareto frontier obtained by NSGA-II. By comparing the deviation index of each final optimal solution from the ideal solution, it is shown that the multi-objective optimization can lead to a more desirable design compared to the single-objective optimizations, and the final optimal solution selected by TOPSIS decision making presents superior performance. Moreover, the fitted curve between the optimal power output, thermal efficiency and ecological performance derived from Pareto frontier is obtained for better insight into the optimal design of the system. The sensitivity analysis shows that the optimal system performance is strongly dependent on the temperatures of absorber, cooling water and working fluid, and the effectiveness of regenerator. The results of this work offer benefits for related theoretic research and basis for solar energy industry.     

Maximum power-conversion efficiency for the utilization of solar energy

The overall efficiency of solar thermal power plants is investigated for estimating the upper limit of their practical performances. This study consists of the theoretical optimization of the heat engine and the optimization of the overall system efficiency, which is the product of the efficiency of the solar collector and the efficiency of the heat engine. In order to obtain a more realistic performance of the solar thermal power plant, the solar collector concentration ratio, the diffused solar radiation and the convective and radiative heat losses of the solar collector are taken into account. Instead of the classical Carnot efficiency, the efficiency at maximum power is used as the optimal conversion efficiency of a heat engine. By means of simple calculations, the optimal overall system efficiency and the corresponding operating conditions of the solar collector are obtained. The results of the present work provide an accurate guide to the performance estimation and the design of solar thermal power plants.     

Energy production cost minimization in a combined heat and power generation systems using cuckoo optimization algorithm

In this paper, cuckoo optimization algorithm is implemented to solve energy production cost minimization in a combined heat and power (CHP) generation system. This problem is also known as combined heat and power economic dispatch problem, which looks for optimal values of power and heat generation of each CHP unit to minimize the total production cost. Cuckoo optimization algorithm is a new metaheuristic algorithm. It is inspired by the life of a bird family, called cuckoo, that special lifestyle of these birds and their characteristics in egg laying and breeding has been the basic motivation for development of this algorithm. Unlike of the some previous approaches, the effect of valve point is considered in the cost function and clearly formulated in the conventional polynomial cost function as absolute sinusoidal term. The proposed method is applied to three small (with three different test cases), medium, and large test systems in order to evaluate its efficiency and feasibility. The obtained results demonstrated a higher quality solution and superior performance of the proposed cuckoo optimization algorithm method in comparison with many existing methodologies.     

具有热阻和热漏的再热布雷顿循环性能分析

用有限时间热力学方法建立了一个工作在恒温热源TH、TL之间,存在热阻、热漏和再热的定常流空气标准闭式布雷顿循环模型。导出了其功率、效率的一般关系并对其进行优化,得到循环的基本优化关系;分析了在傅立叶导热定律下再热对循环最优性能的影响。     

电控汽油机点火提前角多目标优化方法研究

利用正交多项式统计建模方法建立了点火提前角对发动机动力性和排放影响的模型.在该模型的基础上,对点火提前角进行了多目标优化,讨论了不同的加权因子对优化结果的影响,采用遗传算法对线性加权评价函数进行寻优求解.最后针对WF4C27F-E型495电喷汽油机进行了点火提前角标定试验.结果表明,该方法能够对发动机各项性能进行良好的折衷,以获得综合性能最优的点火提前角.     

内可逆四热源吸收式制冷循环的热经济性能优化

应用内可逆四热源吸收式制冷循环模型，分析吸收式制冷机受传热不可逆性影响时的热经济性能。在牛顿传热定律下，导出了循环的最佳热经济性目标和制冷系数的基本优化关系和最大热经济性目标及相应的制冷系数与比制冷率；通过数值算例，得出循环参数对循环的热经济性目标、制冷系数和比制冷率的影响关系。     

A novel multiobjective charging optimization method of power lithium‐ion batteries based on charging time and temperature rise

Power lithium‐ion batteries have been widely utilized in energy storage system and electric vehicles, because these batteries are characterized by high energy density and power density, long cycle life, and low self‐discharge rate. However, battery charging always takes a long time, and the high current rate inevitably causes great temperature rises, which is the bottleneck for practical applications. This paper presents a multiobjective charging optimization strategy for power lithium‐ion battery multistage charging. The Pareto front is obtained using multiobjective particle swarm optimization (MOPSO) method, and the optimal solution is selected using technique for order of preference by similarity to ideal solution (TOPSIS) method. This strategy aims to achieve fast charging with a relatively low temperature rise. The MOPSO algorithm searches the potential feasible solutions that satisfy two objectives, and the TOPSIS method determines the optimal solution. The one‐order resistor‐capacitor (RC) equivalent circuit model is utilized to describe the model parameter variation with different current rates and state of charges (SOCs) as well as temperature rises during charging. And battery temperature variations are estimated using thermal model. Then a PSO‐based multiobjective optimization method for power lithium‐ion battery multistage charging is proposed to balance charging speed and temperature rise, and the best charging stage currents are obtained using the TOPSIS method. Finally, the optimal results are experimentally verified with a power lithium‐ion battery, and fast charging is achieved within 1534 s with a 4.1°C temperature rise.     

Multiobjective optimization study of jet impingement heat transfer through a porous passage configuration

The present study reports an optimized configuration of multijets impinging through porous passages, providing a viable solution for applications requiring localized heat transfer. The cascaded collision lattice Boltzmann numerical method is initially validated with the in-house experimental results of single jet impinging through a porous passage configuration. A multiobjective optimization study using Kriging-GA algorithm is conducted on a single jet impinging through a porous passage at a Reynolds number of 400, considering Darcy number, porosity, and porous passage height as variables and Nusselt number, nondimensional pressure drop as the conflicting objectives. The optimal parameters from the generated pareto plot are chosen attributing equal weightage to Nusselt number and nondimensional pressure drop. Finally, an optimal pitch for multijets impinging through optimized porous passages is determined to maximize heat transfer performance.     

Research on optimal calibration technology for hydrogen-fueled engine based on nonlinear programming theory

To analyze and resolve the contradiction of abnormal combustion and improving hydrogen-fueled engine power is the key for promoting the progress of hydrogen-fueled engine research. Optimal control is the most valuable technology for resolving this contradiction. In this paper, the optimal model of hydrogen-fueled engine for multi-variable, multi-objective, multi-constraint under the whole operating conditions was established. The technology was a combination of nonlinear programming theory and optimal calibration algorithm of genetic algorithm. Calibration process can be adjusted dynamically to match with the working conditions of engine by weighted function. It implements the unity of comprehensive performance optimization and individual optimization, and not only simplifies calibration process but also improves calibration speed. Furthermore, a new method that accurately and quickly calibrates MAP under the conditions of multi-variable, multi-goal and multi-constraint is provided to effectively resolve the contradiction of the abnormal combustion and improving hydrogen-fueled engine power.     

基于模拟退火算法的汽轮机扩容改造的方案优化设计

在对智能型多目标算法——模拟退火法进行研究的基础上，将此算法应用到汽轮机扩容改造的方案优化设计计算上，通过具体实例分析了此算法的效果，从而为深入研究透平级的多目标优化设计奠定了基础。     

Energy,exergy and ecological analysis and multiobjective optimization of the hydrogen-fueled Scimitar engine with fixed nozzle geometry

In this study, an energy, exergy and ecological analysis and multiobjective optimization of the Scimitar engine with fixed core nozzle outlet geometry are carried out at hypersonic cruise conditions. A single-objective optimization is performed first, which revealed that overall efficiency and coefficient of ecological performance are maximized with different optimum nozzle outlet areas, and it propounded the need for a multiobjective optimization. The single objective optimization also showed that decreasing the hydrogen fuel mass flow rate and cruise altitude together with increasing the air mass flow rate and cruise speed improve the performance of the engine. Then, the multiobjective optimization is performed with the utopia point method. It is concluded that for fuel and air mass flow rates of 3.99 kg/s and 178.6 kg/s, respectively, and cruise speed and altitude of Ma = 5.2 and 22 km, respectively, the optimum core nozzle outlet area is 4.00 m², when equal weight factors are used for overall efficiency and coefficient of ecological performance. A comparison with the base scenario results showed that the overall efficiency has increased from 55.1% to 57.3%, and the engine size is reduced from 5.38 m² to 4.00 m² with the multiobjective optimization.     

Multiobjective optimization for the optimal heat pipe working parameters based on Taguchi's design of experiments

A heat pipe (HP) is a device transferring large quantities of heat through a small area of the cross-section with very small deviations in temperature. The thermal impedance of HP is lower while thermal conductance is higher. HPs are designed for controlling temperature, amplification of heat flux, and diminution. HPs are being used in the cooling of aircraft and electronics, solar energy, systems of heat recovery, and nuclear reactors. Complex mathematical formulation demands experimentation to acquire the physical phenomena. Performing experiments is a tedious task with increasing the working parameters and their assigned levels. In such situations, a systematic statistical approach (viz., the Taguchi method) has to be adopted to minimize the number of experiments and to provide the information for the full factorial design of experiments. This paper adopts the modified Taguchi method and applies a simple and reliable multiobjective optimization concept to determine the optimal HP working parameters (viz., heat input, inclination angle, and flow rate). In the optimization process, efficiency, thermal resistance, and overall heat transfer coefficient are the performance indicators (PIs). Empirical relations are developed and validated for the PIs in terms of the HP working parameters. The recommended Taguchi's orthogonal array to perform a few tests may not have the set of optimal working parameters. Additional tests are to be performed to confirm the estimates of the PIs for the optimal working parameters. Confirmation test results in the present study indicate close-to/within the estimated range.     

A Multiagent-System-Based Intelligent Reference Governor for Multiobjective Optimal Power Plant Operation

A large-scale power plant requires optimal set points, namely references, in several control loops for multiobjective optimal operation. In a 600-MW oil-fired drum-type boiler power unit, the set points considered are for the main steam pressure and reheater/superheater steam temperatures. The set points should be mapped with the varying unit load demand and satisfy the conflicting requirements in power plant operation. In practice, the set points are obtained using fixed nonlinear functions in the unit master control in a plant, which are designed for the single objective of load tracking with heat balance. However, it does not allow for process optimization under the multitude of conflicting objectives, which may be newly introduced and different from the initial design objective. This paper presents a methodology, multiagent-system-based intelligent reference governor (MAS-IRG), to realize the optimal mapping by searching for the best solution to the multiobjective optimization problem that tackles conflicting requirements. In searching for the optimal set points, a heuristic optimization tool, particle swarm optimization, is utilized to solve the multiobjective optimization problem. The IRG is designed based on the proposed MAS to operate at a higher level of automation, to execute asynchronous computations, and to reduce the computational complexity. The approach provides the means to specify optimal set points for controllers under a diverse operating scenarios online.      

热分析法在吸收式制冷机中的应用

由于影响吸收式制冷机的因素很多，针对吸收式制冷机性能评价的复杂性，运用COP分析法、Yong分析法、热经济学分析法对吸收式制冷机性能进行评价，得到三种方法各自的特点，COP分析法是从热力学第一定律对系统进行衡算，Yong分析法是热力学第一定律与热力学第二定律相结合的产物，热经济分析法是经济优化技术与Yong分析法的结合．分析了它们在吸收式制冷机性能优化过程中所起的作用。