Imposing accurate wall boundary conditions in corrective‐matrix‐based moving particle semi‐implicit method for free surface flow

Corrective matrix that is derived to restore consistency of discretization schemes can significantly enhance accuracy for the inside particles in the Moving Particle Semi‐implicit method. In this situation, the error due to free surface and wall boundaries becomes dominant. Based on the recent study on Neumann boundary condition (Matsunaga et al, CMAME, 2020), the corrective matrix schemes in MPS are generalized to straightforwardly and accurately impose Neumann boundary condition. However, the new schemes can still easily trigger instability at free surface because of the biased error caused by the incomplete/biased neighbor support. Therefore, the existing stable schemes based on virtual particles and conservative gradient models are applied to free surface and nearby particles to produce a stable transitional layer at free surface. The new corrective matrix schemes are only applied to the particles under the stable transitional layer for improving the wall boundary conditions. Three numerical examples of free surface flows demonstrate that the proposed method can help to reduce the pressure/velocity fluctuations and hence enhance accuracy further.     

对称向量拟均衡问题有效解的存在性

利用标量化方法建立对称向量拟均衡问题有效解的存在性定理。作为标量化方法的应用，利用这一方法得到向量变分不等式和拟向量变分不等式有效解的存在性定理。     

Efficient Piezoelectric Energy Harvesting from a Discrete Hybrid Bismuth Bromide Ferroelectric Templated by Phosphonium Cation

Bismuth containing hybrid molecular ferroelectrics are receiving tremendous attention in recent years owing to their stable and non-toxic composition. However, these perovskite-like structures are primarily limited to ammonium cations. Herein, we report a new phosphonium based discrete perovskite-like hybrid ferroelectric with a formula [Me(Ph)₃P]₃[Bi₂Br₉] ( MTPBB ) and its mechanical energy harvesting capability. The Polarization-Electric field (P-E) measurements resulted in a well-defined ferroelectric hysteresis loop with a remnant polarization value of 2.1 μC cm⁻². Piezoresponse force microscopy experiments enabled visualization of the ferroelectric domain structure and evaluation of the piezoelectric strain coefficient (d₃₃) for an MTPBB single crystal and thin film sample. Furthermore, flexible devices incorporating MTPBB in polydimethylsiloxane (PDMS) matrix at various concentrations were fabricated and explored for their mechanical energy harvesting properties. The champion device with 20 wt % of MTPBB in PDMS rendered a maximum peak-to-peak open-circuit voltage of 22.9 V and a maximum power density of 7 μW cm⁻² at an optimal load of 4 MΩ. Moreover, the potential of MTPBB -based devices in low power electronics was demonstrated by storing the harvested energy in various electrolytic capacitors.     

白云石制备似立方体状方解石型碳酸钙晶体及其机理研究

以轻烧白云石粉、氯化铵和二氧化碳为原料,在未使用晶型控制剂的情况下,通过蒸氨-沉钙过程制备出了似立方体状碳酸钙。研究了反应温度、溶液中钙离子浓度、通气速率、搅拌速度以及陈化时间对碳酸钙中方解石相含量以及晶体形貌的影响,并探索了沉钙反应的晶型控制机理。结果表明,在反应温度40 ℃、钙离子浓度0.05 mol/L、通碳速率100 mL/min、搅拌速度400 r/min和陈化时间2 h的条件下,制备出形貌规整、粒径分布均匀的似立方体状碳酸钙,平均粒径为5~10 μm。该研究为提升白云石的使用价值、生产高附加价值的碳酸钙产品,以及提高白云石资源的利用率提供理论基础。     

自掺杂SnO2微球的水热合成及其可见光催化性能

本研究采用水热法,以柠檬酸为螯合剂,通过控制n(Sn⁴⁺)/n(Sn²⁺)的数值,合成了由具有丰富氧空位的SnO₂纳米晶体组装成的微球。通过X射线衍射(XRD)、扫描电子显微镜(SEM)、透射电子显微镜(TEM)、傅里叶红外光谱(FT-IR)、X射线光电子能谱(XPS)及UV-Vis漫反射光谱对SnO₂纳米微球进行表征分析,结果表明:在酸性水热条件和柠檬酸的螯合作用下,二氧化锡纳米晶体聚集形成微球;在Sn⁴⁺/Sn²⁺摩尔比例为3:7时,其微球尺寸最小,整体分散性较好;同时适量二价锡离子的掺杂使得该样品氧空位浓度达到最佳,氧空位的存在将使得样品光吸收范围拓展至可见光,因而该样品显示出较强的可见光催化效率,在8 min内完全降解甲基橙。     

Wave packet dynamics of nonlinear Gazeau–Klauder coherent states of a position-dependent mass system in a Coulomb-like potential

AD = 1 position-dependent mass approach to constructing nonlinear quantum states for a modified Coulomb potential is used to generate Gazeau–Klauder coherent states. It appears that their energy eigenvalues are scaled down by the quantum number and the nonlinearity coefficient. We study the basic properties of these states, which are found to be undefined on the whole complex plane, and some details of their revival structure are discussed.     

Optimization of heterogeneous Catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methods

There is a growing attention to the bio and renewable energies due to fast depletion of fossil fuels as well as the global warming problem. Here, we developed a modeling and simulation method by means of artificial intelligence (AI) for prediction of the bioenergy production from vegetable bean oil. AI methods are well known for prediction of complex and nonlinear process. Three distinct Adaptive Boosted models including Huber regression, LASSO, and Support Vector Regression (SVR) as well as artificial neural network (ANN) were applied in this study to predict actual yield of Fatty acid methyl esters (FAME) production. All boosted utilizing the Adaptive boosting algorithm. The important influencing parameters on the biodiesel production such as the catalyst loading (CAO/Ag, wt%) and methanol to oil (Soybean oil) molar ratio were selected as the input variables of models while the yield of FAME production was selected as output. Model hyper-parameters were tuned to maintain generality while improving prediction accuracy. The models were evaluated using three distinct metrics Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R². Error rates of 8.16780E-01, 4.43895E-01, 2.06692E + 00, and 3.92713 E-01 were obtained with the MAE metric for boosted Huber, SVR, LASSO and ANN models. On the other hand, the RMSE error of these models were about 1.092E-02, 1.015E-02, 2.669E-02, and 1.01174E-02, respectively. Finally, the R-square score were calculated for boosted Huber, boosted SVR, and boosted LASSO as 0.976, 0.990, 0.872, and 0.99702, respectively. Therefore, it can be concluded that although the boosted SVR and ANN models were better models for prediction of process efficiency in terms of error, but all algorithms had high accuracy. The optimum yield of 83.77% and 81.60% for biodiesel production were observed at optimum operating values from boosted SVR and ANN models, respectively.