Engineering topics
Wang, Yakun
Publications and source records attributed to Wang, Yakun.
Carbon-encapsulated niobium carbonitride with high volumetric capacitance and wide potential windows in aqueous pseudocapacitors
Electrochemical capacitors deliver high power and have long operational lives; yet, their energy densities are limited. Compared to carbon-based electrical double-layer capacitors, pseudocapacitors with an aqueous electrolyte are predicated to provide both high gravimetric and volumetric capacitance while being cost-effective and eco-friendly. However, their narrow operational potential windows limit the practical delivery of energy. In this study, we synthesized novel NbCxN1-x nanoparticles encapsulated in a uniform carbon layer. This pseudocapacitive material exhibits a high volumetric capacitance, a wide potential window stability (~345 F cm –3 , 2.1 V, in 1 M of Li 2 SO 4 ), and a long cycling life (>10 000 cycles). Moreover, the symmetric cells (2.2 V, in 1 M of Li 2 SO 4 ) exhibit high energy density as well as excellent cyclability and rate performances. These results offer new chances for the design of aqueous energy storage devices having a wide applied potential difference and excellent performance.
A dynamic data-driven method for dealing with model structural error in soil moisture data assimilation
Attributing to the flexibility in considering various types of observation error and model error, data assimilation has been increasingly applied to dynamically improve soil moisture modeling in many hydrological practices. However, accurate characterization of model error, especially the part caused by defective model structure, presents a significant challenge to the successful implementation of data assimilation. Model structural error has received limited attention relative to parameter and input errors, mainly due to our poor understanding of structural inadequacy and the difficulties in parameterizing structural error. In this paper, we present a dynamic data-driven approach to estimate the model structural error in soil moisture data assimilation without the need for identifying error generation mechanism or specifying particular form for the error model. The error model is based on the Gaussian process regression and then integrated into the ensemble Kalman filter (EnKF) to form a hybrid method for dealing with multi-source model errors. Two variants of the hybrid method in terms of two different error correction manners are proposed. The effectiveness of the proposed method is tested through a suit of synthetic cases and a real-world case. Results demonstrate the potential of the proposed hybrid method for estimating model structural error and providing improved model predictions. Compared to the traditional EnKF without explicitly considering the model structural error, parameter compensation issue is obviously reduced and soil moisture retrieval is substantially improved.