Shedding vortex simulation method based on viscous compensation technology research

Owing to the influence of the viscosity of the flow field, the strength of the shedding vortex decreases gradually in the process of backward propagation. Large-scale vortexes constantly break up, forming smaller vortexes. In engineering, when numerical simulation of vortex evolution process is carried out, a large grid is needed to be arranged in the area of outflow field far from the boundary layer in order to ensure the calculation efficiency. As a result, small scale vortexes at the far end of the flow field cannot be captured by the sparse grid in this region, resulting in the dissipation or even disappearance of vortexes. In this paper, the effect of grid scale is quantified and compared with the viscous effect through theoretical derivation. The theoretical relationship between the mesh viscosity and the original viscosity of the flow field is established, and the viscosity term in the turbulence model is modified. This method proves to be able to effectively improve the intensity of small-scale shedding vortexes at the far end of the flow field under the condition of sparse grid. The error between the simulation results and the results obtained by using fine mesh is greatly reduced, the calculation time is shortened, and the high-precision and efficient simulation of the flow field is realized.     

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.     

Experimental and numerical simulation of effects of CO2/N2 concentration and initial temperature on combustion characteristics of biomass syngas

Biomass syngas is a form of renewable energy with very broad application prospects, and it has different combustion characteristics according to the fuel composition and processing technology of biomass syngas. The influence of combustion composition, diluent and temperature variation on combustion characteristics were studied in this paper. The FFCM-1 mechanism was used to investigate the combustion characteristics of CO/CH₄/H₂ under varied diluents CO₂/N₂ and temperature by using spherical expansion flame method and ANSYS CHEMKIN-PRO. The experimental laminar burning velocity was compared with the simulation results of FFCM-1 mechanism. The results reveal that the experimental data are in good agreement with the simulation results, which are somewhat different under the condition of rich fuel. The laminar burning velocity decreases significantly with the increase of diluent CO₂/N₂, with the effect of diluent CO₂ being more significant. The laminar burning velocity increase dramatically with the increase of initial temperature, and the adiabatic flame temperature also decreases with the increase of diluent. The reduction caused by diluent CO₂ is much larger than that caused by diluent N₂. The change of initial temperature also affects the adiabatic flame temperature, but the range of variation is not as pronounced as that of diluent. Not only was the interaction between the combustion characteristics of CO/CH₄/H₂ under different diluents and temperature changes explored in this paper, but the influence mechanism was also revealed in depth.     

Cyclohexane‐1,3‐dione as a derivatizing agent for the analysis of aldehydes by micelar electrokinetic chromatography with diode array detection

Aldehydes are important compounds in a large number of samples, especially food and beverages. In this work, for the first time, cyclohexane‐1,3‐dione (CHD) was used as a derivatizing reagent aiming aldehyde (formaldehyde, acetaldehyde, propionaldehyde, and valeraldehyde) analysis by MEKC‐DAD. The optimized separation of the derivates was performed using a voltage program (+20 kV, 0–15 min.; +23 kV, 15–17 min.) at a temperature of 26°C, and using as the running buffer a mixture containing 100 mmol/L of sodium dodecyl sulfate and 29 mmol/L of sodium tetraborate at pH 9.2, with maximum absorbance at 260 nm. CHD was compared with two other derivatizing agents: 3‐methyl‐2‐benzothiazolinone hydrazone and phenylhydrazine‐4‐sulfonic acid. The CHD‐aldehyde derivatives were also characterized by LC‐MS. The calibration curves for all aldehydes had r² above 0.999 and LODs ranged from 0.01 to 0.7 mg/L. The optimized methodology was applied in sugar cane brandy (cachaça) samples successfully. CHD showed to be an alternative derivatization reagent due to its stability, aqueous solubility, high selectivity and sensitivity, reduced impurities, and simple preparation steps.     

基于内聚力模型的快速荷载下CFRP-混凝土界面剪切剥离模拟

鉴于纤维增强复合材料(Fiber Reinforced Polymer, FRP)已在混凝土结构加固中普遍应用, 能有效提高结构的承载和变形能力. 为揭示动载作用下FRP-混凝土界面的剥离机理, 本文基于ABAQUS平台, 采用内聚力模型模拟CFRP-混凝土界面层, 实现了快速荷载下CFRP-混凝土界面剥离的高效模拟. 结果表明: CFRP表面应变在加载过程中由加载端向自由端方向传递, 随着加载速率的提高, 界面承载能力也随之提高; 界面峰值剪应力也存在显著的应变率效应; 模拟结果与试验结果基本吻合, 说明了新方法的有效性.     

梁柱简化模型在自立塔塔线体系动态响应中的适用性研究

对输电塔进行合理简化可以提高塔线体系动力学仿真的效率。本文给出自立塔梁柱简化模型的计算方法，并提出利用梁柱简化模型计算方法建立自立塔塔线体系整体模型，同时采用桁梁混合模型建立精细化塔线体系整体模型，对两种模型塔线体系静力特性及振型和固有频率等动力特性进行对比分析。以脱冰工况为例，采用生死单元技术将施加在输电线节点上的集中质量单元杀死来模拟脱冰，实现对塔线体系动力学响应的有限元模拟，研究塔线体系简化模型在动态响应中的适用性。结果表明，两种模型弯曲变形误差小，低阶的振型相同，固有频率值误差小，动力特性基本相同；脱冰工况下，自立塔节点位移和塔材内力时程曲线一致，在提高计算效率的情况下，能有效保证计算精度。     

Stochastic sensitivity analysis of coupled acoustic-structural systems under non-stationary random excitations

This paper presents a new sensitivity analysis method for coupled acoustic–structural systems subjected to non-stationary random excitations. The integral of the response power spectrum density (PSD) of the coupled system is taken as the objective function. The thickness of each structural element is used as a design variable. A time-domain algorithm integrating the pseudo excitation method (PEM), direct differentiation method (DDM) and high precision direct (HPD) integration method is proposed for the sensitivity analysis of the objective function with respect to design variables. Firstly, the PEM is adopted to transform the sensitivity analysis under non-stationary random excitations into the sensitivity analysis under pseudo transient excitations. Then, the sensitivity analysis equation of the coupled system under pseudo transient excitations is derived based on the DDM. Moreover, the HPD integration method is used to efficiently solve the sensitivity analysis equation under pseudo transient excitations in a reduced-order modal space. Numerical examples are presented to demonstrate the validity of the proposed method.