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Abstract The control volume method has been widely used to describe the rate of exergy destruction due to process irreversibility in a laminar fluid flow stream. In this work, we demonstrate that when the approach is applied to turbulent flows, and the fluid properties are based on averaged values, some information may be lost. We have applied the Reynolds time average method to the set of equations of exergy transfer for turbulent flows and found that the exergy destruction is mainly due to two factors, i.e. the mean flow dissipations and the fluctuating motion dissipations. As a case study, this result is used to model the incompressible turbulent flows through a duct with constant heat flux. The distributions of exergy destruction due to mean flow and fluctuating motion viscous dissipations as well as to mean flow and fluctuating motion heat transfer irreversibility in radial and in axis directions are obtained. The numerical solution of an example with water passing through the duct is obtained and the mechanisms of irreversibility analyzed. It is shown that for a given fluid, the total exergy destruction per unit length is a function of the geometrical parameters and the boundary conditions as well as the Reynolds number. It implies that the design of a thermal system can be optimized through minimizing the exergy destruction in the system.

Abstract The application of the power generated by the proposed magnetic energy-harvesting suspension (MEHS) is to power a wireless sensor in the MEHS. In this paper, the generated powers of the MEHS at various excitations have been theoretically analyzed and experimentally validated. Firstly, the dynamic mechanism of the proposed MEHS is revealed and investigated. Secondly, the analysis expression of energy harvesting is obtained to find the related variables that affect the energy harvesting, and the influence parameters on the energy harvesting characteristics are analyzed numerically. The experimental tests are carried out to verify the numerical analysis and investigate the effect of various excitations and external load resistances on the energy harvesting characteristics. Experimental results demonstrate that the maximum output power of the MEHS can be obtained by changing the excitation frequency, excitation amplitude and external load resistance. Furthermore, the peak output power is generated when the excitation frequency is equal to the natural frequency, and the generated peak output power is 0.34 W at the excitation frequency 3.3 Hz. Meanwhile, the self-powered sensing experiments of the MEHS have been successfully verified in the laboratory, which lays the foundation for further application in a real vehicle.

This work presents the simulation and evaluation of a renewable hybrid power plant for off-grid fully autonomous operation on an intermediate-sized island in the Aegean Sea. A stand-alone energy system including storage facilities is simulated, optimized and analyzed relying on real-case weather and demand data of a relatively large remote community. Optimization of the power plant structure shows that to ensure continuous off-grid energy generation, even under extreme conditions, the combination of more than one renewable technology is required. The hybrid power plant consists of a pumped-storage hydropower plant, photovoltaic cells and wind turbines. Energy surplus of the power plant is used in the incorporated electrolyzer to generate a secondary product, hydrogen. Robust operation of the plant results in 48% of the energy generated stemming from the photovoltaic system and 52% from the wind turbines. The pumped-storage hydropower plant has a mean annual power output of 1.0 MW. The total mean annual efficiency of the hybrid plant is found to be 14.4%. Although stand-alone operation was achieved with the proposed plant, this requirement led to net energy output restrictions, capacity oversizing and large storage facilities. Fontina Petrakopoulou would like to thank the Universidad Carlos III de Madrid, the European Union's Seventh Framework Programme for research, technological development and demonstration (grant agreements nº 600371 and 332028), the Ministerio de Economía y Competitividad (COFUND2014-51509) and Banco Santander. Peer reviewed

Oil shale is regarded as one of the most promising alternative energy resources. In China, oil shale retorting technologies mainly consist of (ⅰ) conventional Fushun retorting technology, (ⅱ) gas full circulation retorting technology, and (ⅲ) Dagong retorting technology. There have been till now few quantitative analyses of the three technologies. This paper focuses on thermoeconomic analysis of three processes of these technologies. Results show that the exergy destruction of the Dagong retorting process is 38.6%, much lower than that of the Fushun retorting process, 65.7%. The total capital investment of the gas full circulation retorting process is the highest, 1.7 billion CNY, followed by the Dagong retorting process, 1.4 billion CNY and the Fushun retorting process, 1.2 billion CNY. The ROI (Return on investment) of the Dagong retorting process is the highest, 18%, while that of the Fushun retorting process is the lowest, 8.6%.

Abstract Precise analysis and forecasting of energy consumption not only affects energy security and environment of a nation but also provides a useful decision basis for policy makers. This study proposes a new enhanced optimization model based on the bagged echo state network improved by differential evolution algorithm to estimate energy consumption. Bagging is applied to reduce forecasting error and improve generalization of network. Further, three parameters of echo state network are optimized using differential evolution algorithm. Thus, the proposed model combines the merits of three techniques which are echo state network, bagging, and differential evolution algorithm. The proposed model is applied to two comparative examples and an extended application to verify its accuracy and reliability. Results of the comparative examples show the proposed model achieves better forecasting performance compared with basic echo state network and other existing popular models. Mean absolute percentage error of the proposed model is 0.215% for total energy consumption forecasting of China. Therefore, the proposed model can be a satisfactory tool for forecasting energy consumption because of its high accuracy and stability.