Metal microspheres doping porous carbon (MMPC), which was prepared using in-situ pyrolysis reduction strategy, could enhance the thermal conductivity of shape-stabilized phase change material (ss-PCM) prepared by MMPC as the matrix. However, in previous studies that were reported, the preparation of MMPC needed to synthesize porous carbon by pyrolysis firstly, and then porous carbon adsorbed metal ions was pyrolyzed again to obtain MMPC, which was tedious and energy-prodigal. In this study, a one-step pyrolysis strategy was developed for the synthesis of MMPC through the pyrolyzation of wheat bran adsorbed copper ions, and the copper microspheres doping wheat bran biochar (CMS-WBB) was prepared. The CMS-WBB was taken as the supporter of stearic acid (SA) to synthesize the ss-PCM of SA/CMS-WBB. The study results about the thermal properties of SA/CMS-WBB demonstrated that the introduction of copper microspheres could not only improve the thermal conductivity of SA/CMS-WBB, but also could increase the SA loading amount of wheat bran biochar. More importantly, the CMS-WBB could be obtained by only one-step pyrolysis, which greatly simplified the preparation process and saved energy consumption. Furthermore, the raw material of wheat bran is a kind of agricultural waste, which is abundant, cheap and easy to obtain. Hence, the SA/CMS-WBB synthesized in this study had huge potentialities in thermal management applications, and a simplified method for improving the thermal properties of ss-PCMs was provided.

Polydopamine microspheres (PDAMs), synthesized using a biomimetic method, were used as a matrix for polyethylene glycol (PEG) to develop a novel high-efficient form-stable phase change material (PEG/PDAM) using a simple vacuum impregnation strategy. The PDAMs were first used as a support for the organic phase change materials, and the biomimetic synthesis of the PDAMs had the advantages of easy operation, mild conditions, and environmental friendliness. The characteristics and thermal properties of the PEG/PDAMs were investigated using SEM, FTIR, XRD, TGA, DSC, and XPS, and the results demonstrated that the PEG/PDAMs possessed favourable heat storage capacity, excellent thermal stability, and reliability, and the melting and freezing latent heats of PEG/PDAM-3 reached 133.20 ± 2.50 J/g and 107.55 ± 4.45 J/g, respectively. Therefore, the PEG/PDAMs possess great potential in real-world applications for thermal energy storage. Additionally, the study on the interaction mechanism between the PEG and PDAMs indicated that PEG was immobilized on the surface of PDAMs through hydrogen bonds between the PEG molecules and the PDAMs. Moreover, the PDAMs can also be used as a matrix for other organic materials for the preparation of form-stable phase change materials.

Two hybrid back-propagation neural network (BPNN) models optimized by two heuristic search algorithms, namely genetic algorithm (GA-BP) and particle swarm optimization (PSO-BP), are proposed in this paper to predict radial maximum wall shear stress instead of traditional computational fluid dynamics (CFD) methods. The two proposed models are trained and validated using a database of 150 radial maximum wall-shear-stress values obtained via CFD simulations. The best fit model is identified from various BPNN models based on the Akaike information criterion (AIC) and the Bayesian information criterion (BIC), balancing the trade-off between goodness-of-fit and model complexity. The model performance is evaluated by MAE, RMSE, regression coefficient (R), and Nash-Sutcliffe efficiency (NSE). The best fit model is a three-layered BPNN model consisting of a 4:4:1 topology. In almost all evaluation indicators, the two hybrid BPNN methods outperform three existing algorithms, namely classical BPNN, random forest (RF), and gradient boosting decision tree (GBDT). Both PSO-BP and GA-BP can provide a more precise assessment of radial maximum wall shear stress, with maximum errors being 5.81% and 8.24% respectively. The proposed PSO-BP prediction model is promising and has great feasibility in predicting the radial maximum wall shear stress of UHP water-jet nozzle in engineering applications.

This paper presents a statistical analysis of storm surge in Hangzhou Bay, where storm surge is known taking place frequently. This study utilises measurement data taken the period of 1974–2006. The Pearson-III-Pareto distribution model was used first to fit with extreme values based on these measurement data and was then compared with Gumbel, Weibull, Pearson-III, Pareto distribution functions for estimations of extreme storm surge values correspondingly to 100-, 200-, 400-, 500-, 700- and 1000-year return periods. The predicted values of storm surge elevation in the 1000-year return period of the new model are 2.179% higher than the Weibull distribution and 3.546% lower than the Pearson-III distribution. Expectedly, the proposed Pearson-III-Pareto distribution is a more reasonable for presenting the statistical characteristic of extreme values of storm surge.