A deep residual neural network identification method for uneven dust accumulation on photovoltaic (PV) panels

Parameter extraction of the SOFC mathematical model based on fractional order version of dragonfly algorithm

Artificial neural network modeling and optimization of the Solid Oxide Fuel Cell parameters using grey wolf optimizer

An accurate assessment of urban resilience can provide decision-makers with a better understanding of urban resilience and valuable references for developing relevant urban resilience development strategies. However, most works of existing literature focus on evaluating urban resilience from a static perspective and few studies focus on the spatio-temporal evolution characteristics of urban resilience. Therefore, an urban resilience measurement model combined with the BP neural network with a genetic algorithm is established. The Chengdu-Chongqing Urban Agglomeration is taken as a case study. In addition, the spatial-temporal evolution is analyzed by the convergence model to explore the difference among the cities in Chengdu-Chongqing Urban Agglomeration. The main results of this study show that: (1) the distribution of urban resilience in Chengdu-Chongqing Urban Agglomeration has a double-headed structure, which differs from other urban agglomerations in China. (2) the four subsystems of resilience reveal exciting results that the ecological, economic and social resilience in the Chengdu-Chongqing urban agglomeration all presented a polarized pattern, and the infrastructure resilience is balanced. (3) The temporal evolution of urban agglomeration resilience indicates that the resilience gap between cities in Chengdu-Chongqing Urban Agglomeration is narrowing from 2011 to 2019. 

Analysis on the spatial-temporal evolution of urban agglomeration resilience: A case study in Chengdu-Chongqing Urban Agglomeration, China

Modeling the SOFC by BP neural network algorithm

Modelling and flow rate control methods for anode tail gas circulation intake system at SOFC

The flow field distribution of solid oxide fuel cells significantly affects the performance of the stack. The flow uniformity can be improved and the power generation efficiency can be improved by optimizing the gas distribution structure of the stack. Based on the simplified 6kW stack model, the stack gas distribution structure with two-stage buffer cavity was designed, and the stack model was numerically simulated by ANSYS Fluent software. The BP neural network model, which can predict the uniformity of the outlet of the integrated stack, is established successfully. The parameters of the gas distribution structure are analyzed and optimized by using the orthogonal test and BP neural network. The results show that at the same time considering pile distribution structure under the condition of surface area and uniformity, when the first stage inlet buffer chamber depth is 40 mm, the channel width is 40 mm, the secondary inlet buffer chamber depth is 80 mm, can effectively reduce the electric pile distribution structure, surface area, to reduce heat loss, at the same time guarantee the integrated electric reactor outlet flow uniformity of more than 96%, greatly improves the efficiency of power generation.

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Zhenzhong Xue

Papers

Modeling the SOFC by BP neural network algorithm

Modelling and flow rate control methods for anode tail gas circulation intake system at SOFC

Optimization of SOFC stack gas distribution structure based on BP Neural network and CFD