Microscopic details of asphaltenes aggregation onset during waterflooding

We report detailed microscopic studies of asphaltenes aggregation onset during waterflooding of petroleum reservoirs. To achieve this objective, a series of simulations are performed on asphaltenic-oil miscibilized with water at high pressure and temperature through molecular dynamics. Results of this simulation onset are applicable to asphaltenes behavior in real crude oils. Our simulation results illustrate that the aggregation onset in waterflooding generally follows three sequential steps: (i) Asphaltene-water interaction; (ii) Water bridging; (iii) Face-to-face stacking. Then, asphaltene-water and water-water hydrogen-bonding network surround every aggregate boosting the intensity of aggregation onset. We intend to utilize such understanding of these details in our predictive and preventive measures of arterial blockage in oil reservoirs during waterflooding.     

Microtopography‐Guided Radial Gradient Circle Array Film with Nanoscale Resolution

Distribution of multimaterials at arbitrary positions with nanoscale resolution and over a large area substrate is essential to future advances in functional graded materials. Such stringent requirements are highly beyond the reach of current techniques, although newly developed 3D printing technologies are addressed. Here, a radial gradient circle array film with the distribution accuracy up to ≈18 nm is fabricated by using microtopographic substrate. A mathematical model is developed to guide the distribution of position, size, shape, and type of materials on an arbitrary section for the given morphology of substrate. The periodic electrical and mechanical properties of the radial gradient circle film are identified, which can be beneficial for further functionalization and applications, such as gradient refractive index lenses, microcoils, and microantennas.     

First principles computational studies of spontaneous reduction reaction of Eu(III) in eutectic LiCl‐KCl molten salt

Using first principles calculations, we study fundamental mechanism of spontaneous reduction reaction of Eu³⁺ to Eu²⁺ in eutectic LiCl‐KCl molten salt. We decouple the reaction Gibbs free energy into enthalpy and entropy contributions by using rigorous thermodynamic formalism. Key structural features of the solvation shell are characterized by the radial distribution function and the coordination number. Compared with Eu²⁺, the Eu³⁺ ion has a more rigid framework of the solvation shells, corroborating its stronger electrostatic interaction with neighboring ligands of Cl^? ions and a more favorable state on the aspect of enthalpy. Computations on vibrational frequency, however, pose significant contribution of vibrational entropy to the reaction Gibbs free energy for the reduction. Vibration frequency of Eu²⁺ is smaller than that of Eu³⁺, driving a more positive change of the entropy in the reduction reaction. Furthermore, an Eu²⁺ diffuses more quickly than an Eu³⁺ in the LiCl‐KCl molten salt with switching mechanism of ligand Cl^? ions in the solvation shell. Our results propose that the spontaneity of the reduction reaction is driven by the entropic contribution by overcoming the penalty of the reaction enthalpy.     

基于ANSYS的带簧油封径向力分析

提出一种考虑弹簧初应力的油封径向力有限元计算方法,即通过二维轴对称模型实现带簧油封的径向力计算。该模型中通过圆管来等效油封弹簧,并可以实现弹簧初应力现象的模拟;圆管模型的材料属性由2种状态下(弹簧装配在油封上和油封装配在轴上)弹簧的拉力转换得到,转换关系通过试验与仿真相结合的办法获得,最终实现了带簧油封2种状态下的径向力有限元计算。通过径向力测量仪验证计算结果的正确性,同时发现弹簧所产生的径向力并非百分之百转化为油封径向力。     

径向式汽轮机叶轮参数分析及优化

径向式汽轮机叶轮的叶片角度,叶轮进口直径和叶片宽度等因素直接影响着它的蒸汽利用率和机械效率。针对目前径向式汽轮机效率不高,能量损失偏大等问题,以某径向式汽轮机叶轮为研究对象,利用Proe、Ansys等软件对其进行流体域建模,然后进行网格划分、数值模拟、流场分析等一系列工作,分析了该径向式汽轮机的工作效率和损失,并提出了优化叶轮参数的具体方法。     

Comments on “Study and application of discharge calibration for submerged radial gates” by Guo X., Guo Y., Wang T., Fu H., Li J. Flow Meas. Instrum. 78 (2021) 101912, https://doi.org/10.1016/j.flowmeasinst.2021.101912

This paper discusses the capability of Guo et al.'s (2021) equations to determine the discharge of radial gates under submerged flow conditions. It was concluded that Guo et al.'s (2021) equations are associated with error reduction compared to the Incomplete Self-Similarity (ISS) theory and the calibration method. However, it does not have a significant advantage over Energy-Momentum (E-M) approach. Employing E-M principles, new equations were proposed to determine the discharge of radial gates, which has some advantages compared to Guo et al. (2021), such as (1) error reduction under partially and fully submerged flow conditions, (2) least dependence on the empirical constants, (3) uniformity of form over the entire submerged condition, and (4) no need to classify the submerged flow. Field calibration showed that the proposed equations in the present study for a single gate predict the discharge of parallel radial gates with a mean absolute error of less than 4.5% subject to the submerged operation of all open gates.     

On the volume power density of radial thermoelectric generators

We examine the volume power density of radial thermoelectric generators (TEGs). Radial, or tubular, TEGs have been considered as an alternative to the usual flat-plate TEGs due to its improved geometric match to typical curved heat sources and high surface power density. However, surface power density is not the only important performance index in realistic situations. Especially for TEGs with inorganic materials that have high raw material prices, volume power density can be important as well. In this note, an analytic model of a radial TEG is studied with a numerical trial-and-error approach for investigating its volume power density. At the same time, an alternative, approximate method of estimating the maximum power of the radial TEG is presented. Using these two approaches, we estimate the volume power density of a skutterudite-based radial TEG and compare the results to those of a flat-plate TEG. The volume power density of the radial TEG is significantly lower than that of the flat-plate TEG. For example, our calculation for a representative case with free convection on the cold side shows that the volume power density of the radial TEG will be 107 W/m³ at best. The result improves with forced convection, and our calculation for a representative case with forced convection on the cold side exhibits the maximum volume power density of 24 100 W/m³ . All these values turn out to be smaller roughly by one order of magnitude than the maximum volume power densities of comparable flat-plate TEGs. Such a low volume power density indicates lower economic feasibility of the radial TEG with expensive inorganic thermoelectric materials. This is also explicitly discussed by presenting the high cost per watt of the radial TEG. It is therefore suggested that radial TEGs with less expensive organic materials may be more acceptable than those with inorganic ones.     

Prediction and sensitivity analysis of CNTs-modified asphalt’s adhesion force using a radial basis neural network model

Abstract

The expected longer service life of modified asphalt can be jeopardized by different environmental factors, such as moisture, oxidation, etc. which affect the desired properties by altering the adhesive property. An insight into knowledge of the adhesive property of the asphalt can help in providing more durable asphalt pavement. The study attempted to develop different models of adhesive properties of polymers and carbon nanotubes (CNTs) modified asphalt binders. The polymer-CNT modified asphalt is processed to prepare different types of samples, by simulating the damage due to moisture and oxidization, following the corresponding standard method. An Atomic Force Microscopy (AFM) was employed to assess the nanoscale adhesion force of the tested samples following the existing functional group in asphalt. Finally, the study has developed Radial Basis Function Neural Network (RBFNN) as a function of different parameters including; asphalt chemistry (i.e. AFM tip type and constant), type and percentages of polymers and CNTs and different environmental exposures (oxidation, moisture, etc.) to predict the nano adhesion force of asphalt. It is observed that the adhesive property of the Styrene–Butadiene modified asphalt is more consistent compared to the Styrene–Butadiene–Styrene modified asphalt, while the presence of Single-Wall Nanotubes (SWNT) is observed to affect the adhesive properties of asphalt significantly as compared to Multi-Wall Nanotubes (MWNT). The higher accuracy level of RBFNN model also indicates that the functional group (tip-type) adding with the percentages and types of polymers and CNTs significantly affect the adhesive properties of asphalt.     

巨型工程机械子午线轮胎高强度、低生热胎面基部胶配方开发

通过对国外知名品牌37.00R57和40.00R57规格巨型工程机械子午线轮胎胎面基部胶组成分析和物理性能测试,对我公司巨型工程机械子午线轮胎胎面基部胶配方进行优化设计。新开发配方半成品和成品胶料动态损耗因子分别降低37.9%和26.6%,胶料物理性能达到竞品水平,成品轮胎TKPH提升13.6%。     

Prediction of process parameters of water-assisted injection molding based on inverse radial basis function neural network

Because of the introduction of new processing parameters in water-assisted injection molding (WAIM), processes control has become more difficult. First, design of experiment (DOE) was carried out by using optimized Latin hypercubes (Opt LHS). On the basis of this, computational fluid dynamics (CFD) method was used to simulate and calculate hollowed core ratios and wall thickness differences of cooling water pipe at different positions. Then inverse radial basis function (RBF) neural network model reflecting the fitting relationship between processing parameters and molding quality was established, and accuracy of the model was detected by cross validation. Finally, expected molding quality was applied to predict processing parameters, and the obtained molding quality under the predicted processing parameters was verified by computer aided engineering (CAE) simulation and experimental methods. The results showed that mean relative precisions of processing parameters such as melt temperature, delay time, short shot size, water pressure, and mold temperature for inverse RBF model were 98.6%, 93.6%, 98.5%, 93.9%, and 97.9%, respectively, which met the accuracy requirements. Furthermore, compared with expected values of hollowed core ratios and wall thickness differences, the average errors of CAE and experiment were 2.3% and 4.9%, respectively.