胶莱盆地南部大盛群、王氏群的划分与对比

胶莱盆地在胶东半岛地质构造及造山带研究中占有重要地位,其巨厚的白垩系陆相沉积是石油系统多年的研究目标。胶莱盆地南部白垩纪的岩石地层序列较其北部更为复杂,其认识几经反复,其中尤以大盛群与王氏群和莱阳群的部分单位的关系为甚。本次工作通过详细对比其沉积层序、相互关系、古生物组合、岩石学特征及同位素年龄等因素,认为大盛群与王氏群属同一沉积层序,沉积基底相同、沉积环境相似、古生物群落时代一致,二者具有同时代、同沉积相、同岩石组合的一致性,应是一套地层,建议今后使用统一地层名称。     

消防法制建设与消防监督的相关性探究

文章从我国消防法制建设的现状出发,简要介绍了现行的消防法律法规,并详细解读了其现存问题,最后分别从消防法制建设与消防监督两个角度提出了发展对策,以期能为改善我国现阶段的消防法制建设和消防监督体系带来一定的参考意见.     

A two-phase flow meter for determining water and solids volumetric flow rates in stratified,inclined solids-in-water flows

This paper describes the design and implementation of a two-phase flow meter which can be used in solids-in-water two phase pipe flows to measure the in-situ volume fraction distributions of both phases, the velocity profiles of both phases and the volumetric flow rates for both phases. The system contains an Impedance Cross Correlation (ICC) device which is used in conjunction with an Electromagnetic Velocity Profiler (EVP). Experimental results were obtained for the water and solids velocity and volume fraction profiles in upward inclined flow at 30° to the vertical, in which highly non-uniform velocity and volume fraction profiles occur.     

Optimal-correlation-based reconstruction for distributed compressed video sensing

Distributed compressed video sensing (DCVS) is a framework that integrates both compressed sensing and distributed video coding characteristics to achieve a low-complexity video coding. However, how to design an efficient joint reconstruction by leveraging more realistic signal models is still an open challenge. In this paper, we present a novel optimal-correlation-based reconstruction method for compressively sampled videos from multiple measurement vectors. In our method, the sparsity is mainly exploited through inter-signal correlations rather than the traditional frequency transform, wherein the optimization is not only over the signal space to satisfy data consistency but also over all possible linear correlation models to achieve minimum-l₁-norm correlation noise. Additionally, a two-phase Bregman iterative based algorithm is outlined for solving the optimization problem. Simulation results show that our proposal can achieve an improved reconstruction performance in comparison to the conventional approaches, and especially, offer a 0.7–9.9 dB gain in the average PSNR for DCVS.     

内插法在相关干涉仪测向体制中的应用

测向误差的减小一直以来都是无线电测向精度提高的一个难点,对于不同的测向体制所采用的减小测向误差的方法都不尽相同。本文主要介绍的是相关干涉仪测向体制的基本原理以及通过内插法的应用来减小测向误差的方法。     

Statistical Data Analysis in the Era of Big Data

Big data is on everyone's lips and often raises emotions. On the one hand, the notion is a basis for much technological optimism, mostly directed towards new business models, or simplifications and optimizations in professional and private life. On the other hand, it is a basis for dystopic perspectives, which are targeted, e.g., at profiling of the individual and their privacy space, overarching optimization in daily life and intransparency of decision making. In this article, after a short historical prolog, it is discussed what distinguishes big data from traditional data analysis. The underlying mathematical methods are introduced and scientific successes are reported. Additionally, the risks and limits – especially regarding the derivation of causal relationships – of data analysis are discussed.     

Development of correlation equations on hydrogen properties for hydrogen refueling process by machine learning approach

The energy transition which refers to shift of the energy system from fossil-based resources to renewable and sustainable energy sources becomes a global issue to mitigate the progression of climate change. Hydrogen can play an important role in long-term decarbonization of energy system and achievement of carbon neutrality. Currently, the utilization of hydrogen in the energy system is focused on a road transportation sector as a fuel in a vehicle fleet.Compressing gaseous hydrogen is the most well-established technology for storage in hydrogen-fueled vehicles. The refueling hydrogen requires short filling time while ensuring the safety of storage tanks in a vehicle. However, a fast filling of hydrogen in high pressure leads to a rapid temperature rise of hydrogen stored in tank. Therefore, many numerical and experimental studies have been carried out to analyze the filling process. Various thermo-physical properties of gaseous hydrogen such as density, viscosity, and thermal conductivity are required for the numerical studies and the accurate hydrogen properties are essential to obtain reliable results.In this work, a polynomial equation is proposed with respect to temperature and pressure in ranges of 223.15 K < T < 373.15 K and 0.1 MPa < P < 100.1 MPa to present various hydrogen thermo-physical properties by adopting different coefficients. The coefficients are determined by a machine learning method to regress the equation using a great number of reference data. The equation is trained, tested, and validated using different datasets for each property. The order of the equation has been changed from 2 to 5. Then, the accuracies are estimated and compared with respect to the order. The average relative errors (REs) of the 5th order equation are assessed to lower than 0.3% except for molar volume and entropy. The accuracy of the equation is also examined with experimental data and other correlation equations for density, viscosity, and thermal conductivity which are required for numerical simulations of hydrogen refueling. The proposed equation presents better accuracy for viscosity and thermal conductivity than literature equations. In density calculation, a literature equation shows better performance than the proposed equation, but the difference between their accuracies is not so significant. In calculation time comparison, it is revealed that the proposed equation rapidly responses adequate to computational fluid dynamics (CFD) simulations.Results of the study can provide accurate and reliable hydrogen property values in a fast and robust means specifically for simulation of hydrogen refueling process, but not restricted only to the process. Correlation equations proposed in the present work can aid in optimizing a hydrogen value chain including production, storage, and utilization by providing accurate hydrogen property.     

Polynomial regression and interpolation of thermodynamic data in Al–Si–Mg–Fe system

A numerical technique for constructing thermodynamic databases has been proposed. This technique offers accurate calculations of solidification temperature, phase fractions, and solute concentrations of specific alloys in quaternary systems. The thermodynamic data is extracted by calling the TQ-interface (Thermodynamic Calculation Interface) from Thermo-Calc software, and modeled through efficient computational approaches such as polynomial regression and interpolation. This method is described in three parts. First, the applicability of regression functions is demonstrated on the Al–Si binary phase diagram. Second, the way of combining polynomial regression and interpolation is applied to model the Al–Si–Mg ternary system. Finally, the A356 alloy, which belongs to the Al–Si–Mg–Fe system, is modeled by a series of sub-ternary systems using regression and interpolation. The valid accuracy of the method is demonstrated by comparing the present results with those calculated using Thermo-Calc software. The application of the TQ-interface to solidification processes in Scheil and lever-rule models is also included. The results indicate that this method can offer accurate thermodynamic parameters for the A356 alloy in Al–Si–Mg–Fe system and reduce CPU time significantly when applied to solidification simulation. Several problems and the corresponding strategies for high order functions, unsmooth variations of thermodynamic information and partition coefficients are discussed to improve this method. This technique can also be applied to other specific alloys with small variations of thermodynamic variables in quaternary systems.     

基于改进的CEEMDAN与关联维数的石化轴承故障特征提取

针对石化机组轴承振动信号难以自动区分的问题,提出一种基于改进的自适应噪声完备集合经验模态分解(CEEMDAN)与关联维数的石化轴承故障特征提取方法。选取某故障诊断重点实验室实测的轴承故障数据中4种工况下的轴承振动信号进行测试分析,采用改进的CEEMDAN分解测得的振动信号得到多个模态分量IMF,对得到的高频分量进行叠加求和后求取数据的嵌入维数和延迟时间并进行相空间重构,结合G-P算法求不同嵌入维数下的关联维数进行特征提取。通过极限学习机进行实验,准确率达到92.5%,证明了方法的有效性。     

Probabilistic analysis of site-specific load-displacement behaviour of cement-fly ash-gravel piles

Cement-fly ash-gravel (CFG) piles comprise one of the novel ground-improvement methods. They are attracting considerable attention, as their beneficial role in accessible soft foundations is being increasingly exploited by the surging urban industrial development in China. Despite their recognised significance, however, the load-settlement behaviour of CFG piles is poorly understood, particularly in the context of probabilistic assessments. This study focuses on six full-scale CFG pile load test datasets, consisting of 245 samples collected at six sites in the Beijing region, under static axial compression loading. As a part of this investigation, a regression curve is applied to the load-settlement data for each load test in the database using a two-parametric hyperbolic or power law curve-fitting equation. Moreover, an assessment of the multiple load-displacement curves, based on the full set of pile load measurements conducted at a particular test site, reveals that the scatter observed in the regression parameter values is mainly caused by the inherent soil variability.Thus, a bivariate copula-based mixed distribution is chosen to represent the dependence between these regression parameters. A simple copula-based simulation model is used to estimate the reliability index at any specific allowable settlement for the serviceability limit state (SLS) design. The correlation coefficients in the copula-based distributions of the regression parameters are proven to have an impact on the reliability index of this pile foundation. A scatter analysis of the load-displacement behaviour provides insight into the probabilistic design of site-specific CFG pile foundations.