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Abstract Global hydropower growth continues to accelerate with 25% of total capacity installed in just the last 10 years. This accelerating expansion and the important storage facility hydropower means it is increasingly important to understand the reasons for operational failures. This is a challenge because the major reason for failures involves the complex interaction of hydraulic, mechanical and electric subsystems. Historically, reliability modelling has been split in two directions, focusing on different sub-systems, and has not yet been unified. Here these approaches are unified with a novel expression of unbalanced forces. This model with operational data are validated and the important modes of oscillation in the shaft are identified. Finally, the mechanism of the first-order oscillation mode exciting a second-order mode is presented. This integrated and accurate mathematical model is a major advance in the diagnosis and prediction of failures in hydropower operation.

Thermally integrated pumped-thermal electricity storage (TI-PTES) offers the opportunity to store electricity as thermal exergy at a large scale, and existing studies are primarily focused on TI-PTES systems based on high-temperature thermal energy storage. This paper presents a thermo-economic analysis of a “cold TI-PTES” system which converts electricity into cold energy using a vapor compression refrigeration (VCR) unit and stores it at sub-ambient temperatures during the charging process, and generates electricity by using an organic Rankine cycle (ORC) working between the sub-ambient temperature and an external low-grade heat source during the discharging process. The effects of key parameters, i.e., mass flowrate and temperature of the storage medium, ORC evaporation temperature, component efficiencies, and pinch-point temperature differences, on the system performance are evaluated based on a whole-system thermo-economic model. The results reveal that the roundtrip efficiency and levelized cost of storage (LCOS) of the system increases while the electrical energy storage capacity decreases as the temperatures of the two cold storage tanks approach each other. When the temperature of the cold storage tank 1 rises from 1 °C to 8 °C while the cold storage tank 2 remains as 13 °C, there is an increase of 25% and 20% in the roundtrip efficiency and LCOS respectively while the energy storage capacity decreases by 69%. A roundtrip efficiency of 0.74 and LCOS of 0.32 $/kWh are achieved with a heat source temperature of 85 °C, using a mass flowrate and temperature of the cold storage medium of 50 kg/s and 1 °C. Furthermore, any change in cold storage medium mass flowrate changes both electrical energy storage capacity and power output by the same proportions. With a continuous high-flowrate external heat source, the LCOS can be as low as 0.17 $/kWh. By providing sufficient heat from an external heat source, the proposed system possesses a high potential for medium-to-large scale energy storage with a unique hybrid ...

Abstract Catalyst layer (CL) is the core electrochemical reaction region of proton exchange membrane fuel cells (PEMFCs). Its composition directly determines PEMFC output performance. Existing experimental or modeling methods are still insufficient on the deep optimization of CL composition. This work develops a novel artificial intelligence (AI) framework combining a data-driven surrogate model and a stochastic optimization algorithm to achieve multi-variables global optimization for improving the maximum power density of PEMFCs. Simulation results of a three-dimensional computational fluid dynamics (CFD) PEMFC model coupled with the CL agglomerate model constitutes the database, which is then used to train the data-driven surrogate model based on Support Vector Machine (SVM), a typical AI algorithm. Prediction performance shows that the squared correlation coefficient (R-square) and mean percentage error in the test set are 0.9908 and 3.3375%, respectively. The surrogate model has demonstrated comparable accuracy to the physical model, but with much greater computation-resource efficiency: the calculation of one polarization curve will be within one second by the surrogate model, while it may cost hundreds of processor-hours by the physical CFD model. The surrogate model is then fed into a Genetic Algorithm (GA) to obtain the optimal solution of CL composition. For verification, the optimal CL composition is returned to the physical model, and the percentage error between the surrogate model predicted and physical model simulated maximum power densities under the optimal CL composition is only 1.3950%. The results indicate that the proposed framework can guide the multi-variables optimization of complex systems.

© 2019 Elsevier Ltd In this article, water and TiO2-H2O nanofluids are used as heat exchange mediums. The heat transfer and resistance coefficient of corrugated tubes with different corrugation pitches are studied by experimental method. The economy of experimental system is evaluated from the aspects of quantity and quality of energy by thermal and exergy efficiencies respectively. The heat transfer performance of nanofluids in the smooth tube can be enhanced by 2.64–16.9% compared with water under the same working conditions, while the corrugated tubes can improve the heat transfer performance by 4.8–66.3%. When the mass fraction of the nanofluid is 0.5%, the corrugated tubes with different corrugation pitches can increase the heat transfer by 36.3% (P = 6 mm), 40.3% (P = 4 mm) and 44.5% (P = 2 mm) respectively. For thermal efficiency, the results prove that when the Reynolds number is larger than 6000, the comprehensive evaluation indexes of corrugated tubes are much larger than that of smooth tube, and the maximum can reach 1.5637. For exergy efficiency, the research results show that the exergy efficiency of the smooth tube is better than that of the corrugated tubes.

Remanufacturing has received extensive attention due to its advantages in material and energy saving, emission reduction and is often considered a viable approach for the realization of a circular economy. Remanufacturing ecological performance reflects the ability of an enterprise to balance economic and environmental benefits. Therefore, evaluating the remanufacturing ecological performance is of great significance for leveraging the benefits of remanufacturing and promoting the concept of sustainability and the implementation of a circular economy in the industry. To this end, a set of data-driven techniques, i.e., data envelopment analysis, R clustering and grey relational analysis, are deployed to analyze and evaluate the ecological performance of a remanufacturing process. The effectiveness and feasibility of the proposed method are illustrated via a case study of remanufacturing for hydraulic cylinder and boom cylinder. Furthermore, a number of critical factors, e.g., energy-saving rate, remanufacturing process cost and rate of remanufacturing, for end-of-life products have been identified as the key drivers impacting the remanufacturing ecological performance. So as to improve remanufacturing ecological performance, optimizing production technology, implementing lean remanufacturing and raising public acceptability over remanufacturing products are effective measures. The research results of the present work can provide support for remanufacturing enterprises to guide and improve their ecological performance and formulate better development strategies.

Abstract In this research, a parabolic trough concentrator with linear V-Shape cavity receiver was studied as the heat source of an organic Rankine cycle system. The solar organic Rankine cycle system was evaluated under exergy and economic analyses. Thermal oil was used as the solar working fluid, and ethanol was used as the organic working fluid under different turbine inlet temperatures and turbine inlet pressure. The influence of different operational parameters, including solar radiation, mass flow rate, and inlet temperature of solar working fluid was investigated on the performance of the solar organic Rankine cycle system. It was found that exergy gain and exergy efficiency of the solar system improved with increasing solar radiation, increasing inlet temperature, and decreasing the flow rate of the solar working fluid. The highest organic Rankine cycle efficiency and total efficiency were found to be 35%, and 25% at turbine inlet temperature of 592 K and turbine inlet pressure of 6 MPa, respectively. Finally, the lowest levelized cost of electricity, and the lowest payback period were calculated equal to 0.0716 (€/kWh), and 8.79 (years) for the optimum condition of the developed solar organic Rankine cycle system, respectively. The present study is beneficial for improving the performance of the solar organic Rankine cycle systems with parabolic trough concentrators in a simple and convenient way and the development of solar thermal technologies.

Abstract Energy management is essential for improving the fuel economy of plug-in hybrid electric vehicles (PHEVs). Some existing efforts have focused on optimizing fuel consumption and battery degradation, but without adequately considering the onboard electric motor's (EM) thermal dynamics. To address this research gap, this paper proposes a predictive energy management strategy considering EM thermal control. Specifically, we make three main contributions that distinguish our study from the existing studies. First, we design four velocity predictors based on the artificial neural network (ANN) and examine their prediction accuracy and computational efficiency. Second, we present a Pontryagin's Minimum Principle-based model predictive control (PMP-MPC) framework that includes EM thermal dynamics. The framework minimizes the operating costs while ensuring that the EM temperature is less than the limit value. Finally, we analyze and compare the effects of different reference temperature thresholds and preview horizon sizes on the fuel economy and EM temperature. The results demonstrate that the proposed PMP-MPC approach can effectively control the EM temperature rise and realize online applications with high computational efficiency.