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.     

Optimization of Transducer Location for Novel Non-Intrusive Methodologies of Diagnosis in Diesel Engines

The health monitoring has been studied to ensure integrity of design of engine structure by detection, quantification, and prediction of damages. Early detection of faults may allow the downtime of maintenance to be rescheduled, thus preventing sudden shutdown of machines. In cylinder pressure developed, vibrations and noise emissions data provide a rich source of information about condition of engines. Monitoring of vibrations and noise emissions are novel non-intrusive methodologies for which positioning of various transducers are important issue. The presented work shows applicability of these diagnosis methodologies adopted in case of diesel engines. The effects of changing various fuel injection parameters was analyzed. Scope of using non-intrusive technique has been analyzed by changing locations of microphone. Novelty of this worklies in exploring signal processing methods for various locations around the engine test set up. Various frequency ranges of contributing noise and vibration sources were identified. Time-Frequency analysis showed the onset of various cyclic. Based on the identification of various frequency bands, it is possible to device suitable filters in order to extract more information.     

“联用技术及元素形态”国际课程探索与实践*

充分利用线上教学的优势,对化学院本科生开设了“联用技术及元素形态”国际课程,避免了传统教学中组织、协调外籍/外地专家资源过程中必要的各种消耗,极大程度地整合教学资源、改善教学效果。在该课程的探索与实践过程中,学生们对痕量元素形态及基于等离子体质谱的各种联用技术产生了极大的兴趣,激发了他们的主动学习热情;教师之间及师生之间的沟通趋向更简单、更灵活、更实时,为后续线上线下混合式国际课程建设提供了良好的基础和借鉴。     

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.     

Generalized Jacobi spectral Galerkin method for fractional pantograph differential equation

This work is concerned with the extension of the Jacobi spectral Galerkin method to a class of nonlinear fractional pantograph differential equations. First, the fractional differential equation is converted to a nonlinear Volterra integral equation with weakly singular kernel. Second, we analyze the existence and uniqueness of solutions for the obtained integral equation. Then, the Galerkin method is used for solving the equivalent integral equation. The error estimates for the proposed method are also investigated. Finally, illustrative examples are presented to confirm our theoretical analysis.     

线上线下混合式实验教学模式的改革与探索

大学化学实验课程是培养材料、化学、化工等专业人才应用基本操作和理论解决实际问题的综合性实践训练环节。文章探讨了当前大学化学实验发展由于受到仪器设备大型化、专业化、成本大、更新快等因素限制出现瓶颈,设计了线上线下混合实验教学模式,进行相应的软硬件建设,并开展线上线下混合式实验教学模式的改革与探索。该教学模式具有良好的教学效果,并且可复制可推广,为创新人才的培养和实验教学的创新提供了有益的借鉴。     

A general framework for describing photofission observables of actinides at an average excitation energy below 30 MeV

A reasonable prediction of photofission observables plays a paramount role in understanding the photofission process and guiding various photofission-induced applications, such as short-lived isotope production, nuclear waste disposal, and nuclear safeguards. However, the available experimental data for photofission observables are limited, and the existing models and programs have mainly been developed for neutron-induced fission processes. In this study, a general framework is proposed for characterizing the photofission observables of actinides, including the mass yield distributions (MYD) and isobaric charge distributions (ICD) of fission fragments and the multiplicity and energy distributions of prompt neutrons (n_p) and prompt γ rays (γ_p). The framework encompasses various systematic neutron models and empirical models considering the Bohr hypothesis and does not rely on the experimental data as input. These models are then validated individually against experimental data at an average excitation energy below 30 MeV, which shows the reliability and robustness of the general framework. Finally, we employ this framework to predict the characteristics of photofission fragments and the emissions of prompt particles for typical actinides including ²³²Th, ^{235, 238}U and ²⁴⁰Pu. It is found that the ²³⁸U(γ, f) reaction is more suitable for producing neutron-rich nuclei compared to the ²³²Th(γ, f) reaction. In addition, the average multiplicity number of both n_p and γ_p increases with the average excitation energy.