非全日制研究生线上线下混合式教学模式研究 ——以《现代电气控制技术》为例

随着教育信息化技术的发展，线上线下混合式教学模式已成为一种趋势。为了解决非全日制研究生课堂教学召集困难、效果不佳等问题，提出了基于“互联网＋虚拟仿真技术”的线上线下混合式教学模式。本文以控制工程专业学位研究生为例，结合《现代电气控制技术》课程，探讨了线上线下混合式教学实现途径、师生互动方法、项目驱动案例教学法以及电气控制系统虚拟仿真实验平台的构建方法，切实提升非全日制研究生的培养质量。     

波形驱动下多参数约束高分辨率反演方法——以四川盆地渝西地区龙马溪组页岩气为例

基于叠后地震波形指示反演在薄储层预测方面的优势，以及叠前弹性参数较叠后弹性参数信息更丰富、对储层的敏感性更高的特点，根据岩石物理资料，应用地震驱动+储层构型约束的高精度叠前随机反演方法，探寻定量表征优质薄页岩的技术，以期为深层页岩气地质甜点预测提供技术支撑。首先，基于道集波形相似性、AVO特征和空间距离的三变量优选方法提取结构相似的井数据作为空间估值样本，然后建立待判别道集初始模型；其次，以统计的弹性阻抗作为先验信息，应用“基于叠前道集特征指示的马尔科夫链-蒙特卡洛随机模拟算法”进行叠前地震波形指示反演，最终得到高精度的叠前弹性参数反演成果。实际应用表明，应用所提方法有效预测了龙一段一亚段1-2小层优质页岩厚度，基于特征参数的井震高频模拟精确模拟了龙一段一亚段1-2小层地质甜点参数，为页岩气勘探提供了技术支撑。     

Effect of CdO addition on photon,electron, and neutron attenuation properties of boro-tellurite glasses

This research article aims to study the effect of CdO addition on the radiation shielding characteristics of boro-tellurite glasses in the composition of 50B₂O₃ - (50-x) TeO2- xCdO, where x = 0, 10, 20, 30, 40 and 50 mol%. These glasses were exposed to gamma radiation and the transmitted gamma photons were evaluated for energies varying from 15 keV to 15 MeV using Geant4 simulation toolkit. The number of transmitted photons was then used to characterize the gamma shielding for the studied glasses in terms of linear/mass attenuation coefficients, MFP, Z_eff, and HVL. The simulation outcomes were theoretically confirmed by using Phy-X software. The beta (electron) shielding characterization of the involved glasses was also investigated by determining the projectile range and stopping power using ESTAR software. Additionally, the fast neutron shielding characterization of the glasses was achieved by evaluating removal cross-section (Σ_R). The results reveal that the CdO has a small influence on the shielding performance of the boro-tellurite glasses against gamma, beta, and neutron radiations. The shielding performance of the boro-tellurite glasses was compared with that of common shielding materials in terms of MFP. It can be concluded that the boro-tellurite glasses regardless of the concentration of CdO content have promising shielding performance to be used for radiation applications.     

Flow state monitoring of gas-water two-phase flow using multi-Gaussian mixture model based on canonical variate analysis

As a highly complex and time-varying process, gas-water two-phase flow is commonly encountered in industries. It has a variety of typical flow states and transition flow states. Accurate identification and monitoring of flow states is not only beneficial to further study of two-phase flow but also helpful for stable operation and economic efficiency of process industry. Combining canonical variate analysis (CVA) and Gaussian mixture model (GMM), a strategy called multi-CVA-GMM is proposed for flow state monitoring in gas-water two-phase flow. CVA is used to extract flow state features from the perspective of correlation between historical data and future data, which solves the cross correlation and temporal correlation of multi-sensor measurement data. GMM calculates the possibility that the current flow state belongs to each typical flow pattern and judges the current flow state by probability indicators. It is conducive to follow-up use of Bayesian inference probability and Mahalanobis distance-based (BID) indicator for flow state monitoring, which avoids repeated traversal of multiple CVA-GMM models and improves the efficiency of the monitoring process. The probability indicators can also be used to analyze transition flow states. The method combining the probabilistic idea of GMM with the deterministic idea of multimodal modeling can accurately identify the current flow state and effectively monitor the evolution of flow state. The multi-CVA-GMM method is validated by using the measured data of the horizontal flow loop of gas-water two-phase flow experimental facility, and its effectiveness is proved.     

Effects of water and carbon dioxide pressure on the adhesion of Na2SiO3 and K2SiO3 binders on silica sand surface: Comparison of experimental data and molecular dynamics simulation

In this study, the effects of different water amounts, CO₂ blowing pressures, Na₂O:SiO₂ and K₂O:SiO₂ ratios were studied on the bonding strength of Na₂SiO₃ and K₂SiO₃ binders. It was concluded that the increase in water content had an adverse effect on the bonding strength of CO₂-hardened Na₂SiO₃ sand. The blowing pressure did not have a linear relationship with the bonding strength, but it was closely related to the diffusion coefficient of CO₂. Based on scanning electron microscopic results, it was inferred that the low strength was caused by the formation of lamellar crystals after the adhesive was hardened. It was found that the low strength was caused by the formation of lamellar crystals after the adhesive was hardened. Based on molecular dynamics simulations, different pressures and water contents had a great influence on the diffusion coefficient of CO₂ in the silicate binder system. This research provides an important theoretical background to improve the technology of CO₂-hardened Na₂SiO₃- and K₂SiO₃-bonded sands during the casting process.     

Flow modeling of high-viscosity fluids in pipeline infrastructure of oil and gas enterprises

Today, the issues related to solving the problem of finding an effective distribution of oil flows through the system of oil pipelines in order to reduce the total energy consumption are relevant. The solution to this problem is connected with selection of rational pumping modes for various technological sections of oil pipelines using modern methods of mathematical programming or new techniques for improving the energy and transport characteristics of oil.Reducing energy consumption during pumping of crude through oil trunk pipelines can be achieved by various methods. Numerous investigations in this direction are mainly carried out to save energy on separate single-line pipelines. However, due to the development of the network of trunk oil pipelines in the world over the past decades, the issues of energy efficient management of oil flows throughout the entire oil pipeline system of oil and gas enterprises become urgent.This paper analyses parameters for pipeline transport of high-viscosity and heavy oils. The article proposes a method for assessing the rheological properties of oil for further planning of pumping taking into account the preservation of oil quality and an increase in energy and transport characteristics. The proposed solutions and tasks for predicting changes in the viscosity-temperature characteristics of the flow for blends of different oil types are especially relevant in the current conditions of an increase in the share of oil production with complex rheological characteristics. Results of the presented investigations may be used for planning the measures of efficient transportation of high-viscosity and heavy oils.     

An improvement of the Space-Time Image Velocimetry combined with a new denoising method for estimating river discharge

The Space-Time Image Velocimetry (STIV) is a time-averaged velocity measurement method, which takes river surface images as the analysis object, and detects the Main Orientation of Texture (MOT) in a generated Space-Time Image (STI) to obtain one-dimensional velocities on the water surface. The STIV has great potential in real-time monitoring of river flow owing to its high spatial resolution and low time complexity. However, the generated STI contains a lot of noise and interference texture, which is inevitable in practical applications. The practicality of the STIV is severely limited by the low-quality STI. To solve this problem, a denoising method based on the filtering technology is proposed and combined with different texture detection algorithms in this paper. The accuracy of this method is verified through a comparative field experiment with an impellor-style current meter. The experimental results show: (1) By using this new denoising method, the robustness and accuracy of the STIV are significantly improved no matter what kind of texture detection algorithm is adopted; (2) Among all the texture detection algorithms, the FFT-based STIV combined with the new denoising method performs best. The relative errors of the surface velocities are controlled within 6%, and the relative errors of the discharges are controlled within ±4%.     

Neural network-based hybrid models developed for free radical polymerization of styrene

In the present work, the free radical polymerization of styrene is modeled by considering the phenomenology of the process (a simplified model, which does not include the diffusional effects, gel, and glass effects) in combination with an empirical model represented by an artificial neural network. Differential evolution (DE) algorithm, belonging to the class of evolutionary algorithms, is applied for developing the neural models in optimal forms. For improving the results—predicted conversion and molecular weights as function of time, temperature, and initiator concentration—different combinations between phenomenological model and neural network are tested; also, individual and stacked neural networks have been developed for the polymerization process. This methodology based on hybrid models, including neural networks aggregated in stacks, provides accurate results.     

Flow regime recognition in a long pipeline-riser system based on signals at the top of the riser

To identify conveniently multiphase flow regimes in subsea pipeline-risers, we study in this paper experimentally two-phase flows in a 1657 m long pipeline with an S-shaped riser to simulate field experiment, within a wide range of gas and liquid velocities. Three flow regimes, namely severe slugging, transitional flows, and stable flows, are analyzed based on three differential pressure and one pressure signals at the top of the riser; comparatively speaking, the positions of these signals in the experimental system are similar to those of the sea level signals in industrial fields, which are easy and less expensive to obtain. The obtained signals are decomposed into six scales via a multi-scale wavelet analysis, and further four statistical parameters on each scale are extracted, including mean values, standard deviations, ranges, and mean values of absolute. We compared the effects of six SVM classifiers with different kernel functions on the recognition rate of flow regimes, and it is found the recognition rates of SVM classifier with quadratic and cubic kernel functions are the highest. Further, the principal component analysis is employed to reduce the dimension of statistical parameters and it indicates that the recognition rate tends to increase with the rising number of principal components from 1 to 6, and it remains constant if the principal component number is further increased. Moreover, The results suggest that the recognition rate obtained from the pressure difference between the top of the riser and the separator peaks, and then it comes that from the pressure signal at the top of the riser, and that for the pressure difference signal at the top of the riser is the least satisfying one. As for the optimal differential pressure signals between the top of the riser and the separator, the results show that the recognition rate increases rapidly from 70.2% to 90.4% when the sample duration rising from 2.3 s to 18.6 s, and when the sample duration exceeds 74.4 s, the recognition rate exceeds 92.9% and remains unchanged.     

Flow conditioning system for tri-sonic high pressure aerothermal testing

Blowdown testing offers a cost-effective experimental tool to replicate the aerothermal conditions in numerous high speed systems. The wind tunnel must replicate the inlet operating conditions, while the spatial and time dependent inlet flow conditions should be assessed carefully. This paper provides a design methodology and rules that ensure adequate flow conditioning in high inlet pressure wind tunnels suitable for subsonic and supersonic operation with mass-flow limits ranging from 1 kg/s to 25 kg/s, Reynolds numbers from 10³ (1/m) to 4x10⁷ (1/m), and Mach numbers from 0.01 up to 6. The quality of the proposed flow conditioning system was evaluated using stereo PIV measurements combined with hotwire, Pitot probe, and total flow temperature traverses.