ANALYSIS OF FRICTION INDUCED VIBRATION UNDER THE ACTIVE DISTURBANCE REJECTION CONTROL FRAMEWORK 1)

自抗扰控制(active disturbance rejection control, ADRC)是一种具有两自由度控制结构的工程化方法, 由于其能够直观有效地处理多种扰动, 近些年来在许多机电系统上得到了成功应用. 当采用ADRC对带有摩擦力的机电系统进行调节时, 可能会产生极限环振动. 目前, 还没有ADRC框架下摩擦力振动精确分析的相关工作. 因此, 本文采用非线性动力学系统的分析工具对这一问题进行研究. 首先, 考虑两种典型摩擦力模型, 静态切换模型和动态LuGre 模型, 对一类二阶运动系统设计不同阶次的ADRC, 得到控制器的等效形式, 并揭示出与比例积分微分(proportional-integral-derivative, PID)控制之间的联系. 然后, 采用打靶法结合拟弧长延拓方法求解系统中的极限环, 并根据Floquet理论判断极限环的稳定性、可能出现的分岔以及分岔类型. 此外, 通过雅克比矩阵和近似数值方法对系统平衡点集的局部稳定性进行了分析. 最后, 通过数值计算研究了摩擦力模型和参数、ADRC阶次和参数对极限环和平衡点集的影响. 计算结果表明, 决定摩擦力Stribeck效应负斜率的参数$\beta$作用较大. 当$\beta>1$时, 两种摩擦力模型下的闭环系统呈现出相同的特性, 极限环会出现环面折叠分岔(cyclic fold bifurcation, CFB)且平衡点集是局部稳定的. 然而当$\beta<1$时, 两种闭环系统呈现出完全不同的特性. 此外, 不同阶次的ADRC在极限环的存在性和稳定性、平衡点集的稳定性上面的结论是相同的, 而低阶次的ADRC能够更好地解决摩擦力补偿和稳定鲁棒性之间的矛盾问题. 这些结论对实际现象的理解、ADRC阶次的选择以及参数整定提供了一定指导.     

Turbulent Energy Scale-Budget Equations for nearly Homogeneous Sheared Turbulence

For moderate Reynolds numbers, the isotropic relation between second-order and third-order moments for velocity increments (Kolmogorov's equation) is not respected, reflecting a non-negligible correlation between the scales responsible for the injection, transfer and dissipation of the turbulent energy. For (shearless) grid turbulence, there is only one dominant large-scale phenomenon, which is the non-stationarity of statistical moments resulting from the decay of energy downstream of the grid. In this case, the extension of Kolmogorov's analysis, as carried out by Danaila, Anselmet, Zhou and Antonia, J. Fluid Mech. 391, 1999 359-369) is quite straightforward. For shear flows, several large-scale phenomena generally coexist with similar amplitudes. This is particularly the case for wall-bounded flows, where turbulent diffusion and shear effects can present comparable amplitudes. The objective of this work is to quantify, in a fully developed turbulent channel flow and far from the wall, the influence of these two effects on the scale-by-scale energy budget equation. A generalized Kolmogorov equation is derived. Relatively good agreement between the new equation and hot-wire measurements is obtained in the outer region (40 < x ⁺ ₃ < 150) of the channel flow, for which the turbulent Reynolds number is R _λ≈ 36.     

Assessment of a three-component vorticity probe in decaying turbulence

Measurements in grid turbulence with a three-component hot-wire vorticity probe are compared with results obtained from a direct numerical simulation of decaying homogeneous turbulence at a similar Reynolds number. There is adequate agreement for both spectra and probability density functions of all three vorticity fluctuations. This supports the treatment of the measured data, in particular the corrections that need to be applied for the finite spatial resolution of the probe. The comparison does, however, highlight some inaccuracies in the way the instantaneous dissipation rate fluctuations were obtained experimentally.     

Surface-Modified Sb2S3 Nanoparticles and Their Tribological Behaviors in Liquid Paraffin

Sb₂S₃ nanoparticles surface-modified with S-tetradecyl N, N-dihydroxyethyl dithiocarbamate (C₁₄DTC-Sb₂S₃) have been synthesized via extraction of Sb₂S₃ colloidal particles from ethylene glycol into toluene in the presence of C₁₄DTC. The obtained products were characterized by high-resolution transmission electron microscope (HRTEM) and Fourier transformation infrared (FTIR), and their tribological behaviors as an additive in liquid paraffin were investigated using a four-ball tribometer. The results show that C₁₄DTC-Sb₂S₃ nanoparticles can significantly improve the friction reduction, anti-wear, and load-carrying properties of base oils. The preliminary lubrication mechanism was discussed based on the SEM and XPS investigation of the rubbed surfaces.     

Study on the “Tin-Ene” reaction of α-bromoacetophenone and metallic tin with aldehydes

β-hydroxy ketones were obtained in good yields by the “tin-ene” reactions of α-bromoacetophenone and metallic tin with aldehydes.     

Controllable Quantum Network Coding Scheme Based on Quantum Walk

Quantum entanglement is one of the key methods in quantum information processing, but it is difficult to prepare quantum entanglement. Quantum walk is widely used in quantum computation and quantum simulation, it can be applied to the preparation of quantum entangled states. In this paper, a controllable quantum network coding scheme based on quantum walk is proposed. With the help of quantum walk, the scheme preliminary realized the entanglement distribution of butterfly network, reduced entanglement resources and enhanced scalability. According to the existing technology, it is feasible to implement the quantum network coding scheme proposed in this paper.

     

Gaussian mixture model-based target feature extraction and visualization

Journal of Visualization - Effective extraction and visualization of complex target features from volume data are an important task, which allows the user to analyze and get insights of the complex...     

Counter-current flame propagation in a cylindrical bed of activated carbon saturated with artificial volatile matter in an upward flow of O2/N2 mixture

A counter-current flame propagation process, or traverse combustion, in a cylindrical bed packed with activated carbon (AC) saturated with artificial volatiles was studied experimentally to appreciate the effect of homogenous volatile oxidation in the combustion process of packed solid. The experiments were conducted with an upward flow of a 50/50 v/v O₂/N₂ mixture. Flame propagation velocity and total carbon consumption during traverse combustion period, from ignition to the time when the flame propagated to the bed bottom, were determined. Ethanol, isooctane and benzene, were used respectively as the artificial volatile. Results show that compared to the case of AC alone, the flame propagation velocity was reduced while the combustion duration extended in the presence of volatile. The visible gas phase flame due to homogeneous volatile combustion traversed in the opposite direction of the oxidizing gas stream, competing with heterogeneous carbon oxidation for available oxygen. The proportion of oxygen consumed by homogeneous oxidation was the highest for ethanol, up to 87–94%, while isooctane and benzene accounted for 30–65% and 27–63%, respectively. Despite the competing homogeneous volatile combustion, the total amount of carbon consumed in the heterogeneous carbon oxidation was even increased in the cases of isooctane and benzene due to the extended combustion durations and relatively higher proportions of O₂ consumed by heterogeneous carbon oxidation than in the ethanol case.     

Recognition of Vehicles Entering Expressway Service Areas and Estimation of Dwell Time Using ETC Data

To scientifically and effectively evaluate the service capacity of expressway service areas (ESAs) and improve the management level of ESAs, we propose a method for the recognition of vehicles entering ESAs (VeESAs) and estimation of vehicle dwell times using electronic toll collection (ETC) data. First, the ETC data and their advantages are described in detail, and then the cleaning rules are designed according to the characteristics of the ETC data. Second, we established feature engineering according to the characteristics of VeESA and proposed the XGBoost-based VeESA recognition (VR-XGBoost) model. Studied the driving rules in depth, we constructed a kinematics-based vehicle dwell time estimation (K-VDTE) model. The field validation in Part A/B of Yangli ESA using real ETC transaction data demonstrates that the effectiveness of our proposal outperforms the current state-of-the-art. Specifically, in Part A and Part B, the recognition accuracies of VR-XGBoost are 95.9% and 97.4%, respectively, the mean absolute errors (MAEs) of dwell time are 52 and 14 s, respectively, and the root mean square errors (RMSEs) are 69 and 22 s, respectively. In addition, the confidence level of controlling the MAE of dwell time within 2 min is more than 97%. This work can effectively recognize the VeESA and accurately estimate the dwell time, which can provide a reference idea and theoretical basis for the service capacity evaluation and layout optimization of the ESA.     

Predicting Bitcoin (BTC) Price in the Context of Economic Theories: A Machine Learning Approach

Bitcoin (BTC)—the first cryptocurrency—is a decentralized network used to make private, anonymous, peer-to-peer transactions worldwide, yet there are numerous issues in its pricing due to its arbitrary nature, thus limiting its use due to skepticism among businesses and households. However, there is a vast scope of machine learning approaches to predict future prices precisely. One of the major problems with previous research on BTC price predictions is that they are primarily empirical research lacking sufficient analytical support to back up the claims. Therefore, this study aims to solve the BTC price prediction problem in the context of both macroeconomic and microeconomic theories by applying new machine learning methods. Previous work, however, shows mixed evidence of the superiority of machine learning over statistical analysis and vice versa, so more research is needed. This paper applies comparative approaches, including ordinary least squares (OLS), Ensemble learning, support vector regression (SVR), and multilayer perceptron (MLP), to investigate whether the macroeconomic, microeconomic, technical, and blockchain indicators based on economic theories predict the BTC price or not. The findings point out that some technical indicators are significant short-run BTC price predictors, thus confirming the validity of technical analysis. Moreover, macroeconomic and blockchain indicators are found to be significant long-term predictors, implying that supply, demand, and cost-based pricing theories are the underlying theories of BTC price prediction. Likewise, SVR is found to be superior to other machine learning and traditional models. This research’s innovation is looking at BTC price prediction through theoretical aspects. The overall findings show that SVR is superior to other machine learning models and traditional models. This paper has several contributions. It can contribute to international finance to be used as a reference for setting asset pricing and improved investment decision-making. It also contributes to the economics of BTC price prediction by introducing its theoretical background. Moreover, as the authors still doubt whether machine learning can beat the traditional methods in BTC price prediction, this research contributes to machine learning configuration and helping developers use it as a benchmark.