Parameters of new three-water model based on nuclear magnetic experiment and optimization algorithm

Clastic rock reservoir is the main reservoir type in the oil and gas field.Archie formula or various conductive models developed on the basis of Archie’s formula are usually used to interpret this kind of reservoir,and the three-water model is widely used as well.However,there are many parameters in the threewater model,and some of them are difficult to determine.Most of the determination methods are based on the statistics of large amount of experimental data.In this study,the authors determine the value of the parameters of the new three-water model based on the nuclear magnetic data and the genetic optimization algorithm.The relative error between the resistivity calculated based on these parameters and the resistivity measured experimentally at 100%water content is 0.9024.The method studied in this paper can be easily applied without much experimental data.It can provide reference for other regions to determine the parameters of the new three-water model.

Numerical estimation of choice of the regularization parameter for NMR T2 inversion

Nuclear Magnetic inversion is the basis of NMR Resonance （NMR） T2 logging interpretation. The regularization parameter selection of the penalty term directly influences the NMR T2 inversion result. We implemented both norm smoothing and curvature smoothing methods for NMR T2 inversion, and compared the inversion results with respect to the optimal regular- ization parameters （（Xopt） which were selected by the dis- crepancy principle （DP）, generalized cross-validation （GCV）, S-curve, L-curve, and the slope of L-curve methods, respectively. The numerical results indicate that the DP method can lead to an oscillating or oversmoothed solution which is caused by an inaccurately estimated noise level. The （Xopt selected by the L-curve method is occa- sionally small or large which causes an undersmoothed or oversmoothed T2 distribution. The inversion results from GCV, S-curve and the slope of L-curve methods show satisfying inversion results. The slope of the L-curve method with less computation is more suitable for NMR T2 inversion. The inverted T2 distribution from norm smoothing is better than that from curvature smoothing when the noise level is high.

CONVERGENCE BALL OF ITERATIONS WITH ONE PARAMETER

Under the weak Lipschitz condition about the solution of the equation, convergence theorems for a family of iterations with one parameter are obtained. An estimation of the radius of the attraction ball is shown. At last two examples are given.

Hysteresis in the ultrasonic parameters of saturated sandstone during freezing and thawing and correlations with unfrozen water content

Determining the mechanical properties of frozen rock is highly important in cold-area engineering.These properties are essentially correlated with the content of liquid water remaining in frozen rock.Therefore,accurate determination of unfrozen water content could allow rapid evaluation of mechanical properties of frozen rock.This paper investigates the hysteresis characteristics of ultrasonic waves applied to sandstone(in terms of the parameters of P-wave velocity,amplitude,dominant frequency and quality factor Q)and their relationships with unfrozen water content during the freeze-thaw process.Their correlations are analysed in terms of their potential for use as indicators of freezing state and unfrozen water content.The results show that:(1)During a freeze-thaw cycle,the ultrasonic parameters and unfrozen water content of sandstone have significant hysteresis with changes in temperature.(2)There are three clear stages of change during freezing:supercooled stage(0℃to-2℃),rapid freezing stage(-2℃to-3℃),and stable freezing stage(-3℃to-20℃).The changes in unfrozen water content and ultrasonic parameters with freezing temperature are inverse.(3)During a single freeze-thaw cycle,the ultrasonic parameters of sandstone are significantly correlated with its unfrozen water content,and this correlation is affected by the pore structure.For sandstones with mesopores greater than 50%,there are inflection points in the curves of ultrasonic parameters vs.unfrozen water content at-3℃during freezing and at-1℃during thawing.It was found that thermal deformation of the mineral-grain skeleton and variations in the phase composition of pore water change the propagation path of ultrasonic waves.The inflection point in the curve of dominant frequency vs.temperature clearly marks the end of the rapid freezing stage of pore water,in which more than 70%of the pore water freezes.Consequently,the dominant frequency can be used as an index to conveniently estimate the unfrozen water content of frozen rock and,hence,its mechanical properties.

Valuation and Determination of Seven and Five Parameters of Photovoltaic Generator by Iterative Method

The mathematical modeling of solar cells is essential for any optimization operation of the efficiency or the diagnosis of photovoltaic generator. The photovoltaic module is generally represented by an equivalent circuit whose parameters are experimentally calculated by using the characteristic current-tension, I-V. The precise determination of these parameters stays a challenge for the researchers, making to a big difference in the models and the digital methods dedicated to their characterizations. In the present paper, We are interested to characterize the parameters of single diode and two diodes models, in order to plan the behavior of the photovoltaic generator under real functioning conditions. We developed an identification method of the parameters using Newton Raphson method by using the software Matlab/Simulink. This method is the faster technique which allows the identification of several parameters and can be used in real time applications. The results of the proposed method show an accordance with the experimental and simulated characteristics of photovoltaic generator.

2H NMR Spectroscopic Investigation of para-Nitroazobenzene Liquid Crystals

The orientational order of two liquid crystals , namely, 6 [4 (4 nitrophenylazo)phenyloxy] hexyl diethanolamines(C6) and 10 1 bromo[4 (nitrophenylazo)phenyloxy] alkane(B10) was studied by means of 2H NMR spectroscopy with hexamethylbenzene d 18 as the probe molecule. The results show that the directors in the smectic A phase of C6 and the nematic phase of B10 could be aligned, which was parallel to the magnetic field. The orientational order parameter of the solute molecules in C6 was about 0.2, while it is only 0.1 in B10, which is expected because the more ordered smectic phase tends to align solute molecules to a high level. Compared to the orientational order parameter of the solute in the SmC phase of 4[3,4,5 tris(4 dodecyloxybenzyloxy) benzoyloxy] 4 (4′ dodecyloxybenzoyloxy)biphenyl (Ⅰ) ( P 2=0.14 ), it is larger in SmA phase of C6. The relatively higher orientational order parameter of the solute in C6 is attributed to the formation of intermolecular H bonds in the SmA phase of C6.

Iterative Identification of Robot Dynamic Parameters Based on Logistic Function

The dynamic parameter identification of the robot is the basis for the design of the controller based on the dynamic model.Currently,the primary method for solving angular velocity and angular acceleration is to filter and smooth the position sequence and then form a differential signal.However,if the noise and the original signal overlap in the frequency domain,filtering the noise will also filter out the valuable information in the frequency band.This paper proposes an excitation trajectory based on Logistic function,which fully uses the information in the original signal and can accurately solve the angular velocity and angular acceleration without filtering and smoothing the position sequence.The joint angle of the excitation trajectory is mapped to the joint angular velocity and angular acceleration one by one so that the joint angular velocity and joint angular acceleration can be obtained directly according to the position.The genetic algorithm is used to optimize the excitation trajectory parameters to minimize the observation matrix’s condition number and further improve the identification accuracy.By using the strategy of iterative identification,the dynamic parameters identified in each iteration are substituted into the robot controller according to the previous position sequence until the tracking trajectory approaches the desired trajectory,and the actual joint angular velocity and angular acceleration converge to the expected value.The simulation results show that using the step-by-step strategy,the joint angular velocity and joint angular acceleration of the tracking trajectory quickly converge to the expected value,and the identification error of inertia parameters is less than 0.01 in three iterations.With the increase of the number of iterations,the identification error of inertial parameters can be further reduced.

Mathematical Modelling of Bloch NMR to Explain the Rashba Energy Features

The Bloch NMR as an analytical tool was able to address the fundamental features in the learning of spintronics. Beside confirming past assertions on the Rashba spin-orbit interaction, thermal motion of hole and electron spin and features of the quantum well, it was also able to explain the condition necessary for Rashba splitting within the quantum well. When the Rashba energy is 43 meV, it modified the Ehrenfest’s theorem to hold for an external magnetic field. The confinement potential which is the strength of the Rashba spin-orbit interaction was shown to be controlled magnetically.

基于空时级联单脉冲的多目标高效参数估计算法

比幅单脉冲最大似然算法(ACM-ML)在进行目标参数估计时需要进行距离与速度的2维松弛迭代搜索,导致了计算效率低、运算量大的问题。针对上述问题,该文提出一种基于空时级联单脉冲的高效多目标参数估计算法(M-STCMP算法)。该算法将单脉冲概念引入脉冲域,利用时域单脉冲计算目标速度,避免了ACM-ML算法中对速度的迭代搜索,将2维松弛迭代搜索降为1维搜索,有效降低了计算复杂度。考虑时域单脉冲无法同时匹配分布在不同时域主波束的速度各异的多个检测目标,进一步利用目标信号的多普勒信息,在各多普勒单元分别进行时域单脉冲测速,并搜索目标距离值。最后为抑制目标间的信号泄露,将所有目标的估计参数进行级联迭代获得高精度参数估计值。理论分析和仿真结果验证了M-STCMP算法的有效性。

滤波辨识(11):多变量CARARMA系统的滤波递阶广义增广迭代参数辨识

针对多变量受控自回归自回归滑动平均(M-CARARMA)系统,利用滤波辨识理念和递阶辨识原理,研究和提出了滤波递阶广义增广梯度迭代辨识方法、滤波递阶多新息广义增广梯度迭代辨识方法、滤波递阶递推广义增广最小二乘迭代辨识方法、滤波递阶多新息广义增广最小二乘迭代辨识方法等。这些滤波递阶广义增广迭代辨识方法可以推广到其它有色噪声干扰下的线性和非线性多变量随机系统中。

滤波辨识(12):多变量OEARMA系统的滤波辅助模型递阶广义增广迭代参数辨识

针对多变量输出误差自回归滑动平均(M-OEARMA)系统,即多变量Box-Jenkins系统,利用滤波辨识理念和辅助模型辨识思想,研究和提出了滤波辅助模型递阶广义增广梯度迭代辨识方法、滤波辅助模型递阶多新息广义增广梯度迭代辨识方法、滤波辅助模型递阶递推广义增广最小二乘迭代辨识方法、滤波辅助模型递阶多新息广义增广最小二乘迭代辨识方法等。这些滤波辅助模型递阶广义增广迭代辨识方法可以推广到其它有色噪声干扰下的线性和非线性多变量随机系统中。

基于空域自适应单脉冲的抗干扰雷达多目标参数估计算法

基于空时级联单脉冲的多目标参数高效估计算法(M-STCMP)是一种高效的雷达多目标参数估计算法,但在干扰环境下其参数估计性能将严重下降。文中提出了基于空域自适应单脉冲的抗干扰多目标参数估计(AM-STCMP)算法。首先,将空域自适应单脉冲技术引入M-STCMP算法,自适应形成和差波束抑制干扰信号,提高各目标的信干噪比,并迭代修正自适应和差波束的鉴角曲线;其次,采用空时级联单脉冲技术估计各目标的距离-速度两维参数;最后,采用Relax算法高精度迭代估计各目标的角度-速度-距离三维参数。理论分析和仿真结果验证了AM-STCMP算法的有效性,其参数估计性能接近无干扰情形。

基于MATLAB的民航发动机涡轮叶片寿命建模

针对民航飞机维修工作中发动机涡轮叶片普遍存在故障率较高的问题,收集某型民用航空发动机涡轮叶片的可靠性数据,并运用三参数Weibull分布建立该型发动机涡轮叶片的可靠性寿命模型。在模型求解的数值计算过程中,为保证模型的计算精度,采用经典的牛顿迭代法及三参数相关系数优化法对涡轮叶片的寿命数据进行拟合分析及计算;同时为减少计算工作量及提高数值计算过程中人工智能的参与度,基于MATLAB软件对上述数值计算方法编写计算程序,最后对计算结果进行K-S假设检验。结果表明:对涡轮叶片寿命数据进行可靠性分析时,运用Weibull分布建立的数学模型符合客观规律;同时牛顿迭代法、三参数相关系数优化法及MATLAB计算程序的有效利用,保证了计算结果的精度。

滤波辨识(14):类多变量输出误差ARMA系统的滤波辅助模型递阶广义增广迭代参数辨识

针对类多变量输出误差自回归滑动平均(M-OEARMA-like)系统,利用滤波辨识理念和递阶辨识原理,研究和提出了滤波辅助模型递阶广义增广梯度迭代辨识方法、滤波辅助模型递阶多新息广义增广梯度迭代辨识方法、滤波辅助模型递阶广义增广最小二乘迭代辨识方法、滤波辅助模型递阶多新息广义增广最小二乘迭代辨识方法。这些滤波辅助模型递阶广义增广迭代参数辨识方法可以推广到其他有色噪声干扰下的线性和非线性多变量输出误差随机系统中。

滤波辨识(13):类多变量方程误差ARMA系统的滤波递阶广义增广迭代参数辨识

针对类多变量方程误差自回归滑动平均(M-EEARMA-like)系统,利用滤波辨识理念和递阶辨识原理,研究和提出了滤波递阶广义增广梯度迭代辨识方法、滤波递阶多新息广义增广迭代梯度辨识方法、滤波递阶广义增广最小二乘迭代辨识方法、滤波递阶多新息广义增广最小二乘迭代辨识方法。这些滤波递阶广义增广迭代参数辨识方法可以推广到其他有色噪声干扰下的线性和非线性多变量方程误差随机系统中。

基于改进初值带遗忘因子的递推最小二乘法的锂电池参数辨识

锂电池荷电状态(state of charge,SOC)的准确估计依赖于精确的锂电池模型参数。在采用带遗忘因子的递推最小二乘法(forgetting factor recursive least square,FFRLS)对锂电池等效电路模型进行参数辨识时,迭代初始值选取不当会造成辨识精度低、收敛速度慢的问题。为此,将电路分析法与FFRLS相结合,提出基于改进初值带遗忘因子的递推最小二乘法(improved initial value-FFRLS,IIV-FFRLS)。首先,通过离线辨识得到各荷电状态点对应的等效电路模型参数并进行多项式拟合;然后,利用初始开路电压(open circuit voltage,OCV)和OCV-SOC曲线获得初始SOC,代入参数拟合函数得到初始参数;最后,将初始参数带入递推公式得到IIV-FFRLS迭代初始值。对4种锂电池工况进行参数辨识,结果表明:与传统方法相比,IIV-FFRLS的平均相对误差、收敛时间分别减小58%、23%以上;IIV-FFRLS具有更高的辨识精度与更快的收敛速度。

基于自适应迭代学习的多智能体系统编队控制

针对带未知时变参数的非线性多智能体系统的编队问题,提出一种分布式自适应迭代学习控制策略。首先,通过傅里叶级数对系统的不确定参数进行展开,采用一个收敛级数序列处理傅里叶级数展开产生的截断误差,结合多智能体运行过程中的编队误差推导自适应迭代学习控制律和参数更新律;其次,针对领导者动态对大部分智能体都是未知的情况,设计新的辅助控制来补偿未知动态和避免未知有界干扰;然后,基于李亚普诺夫能量函数证明了在所设计控制律作用下多智能体系统编队误差随着迭代次数的增加在有限时间内趋于0;最后,将该控制策略运用到多无人机编队系统中,并通过搭建半物理实验平台,验证了控制方法的有效性。实验结果表明该控制方法可以确保多智能体快速形成所需编队,并且每个智能体在有限时间内可以精确跟踪期望轨迹。所提方法充分考虑了多智能体系统的参数不确定性以及抗干扰的能力,为实际应用中复杂多智能体系统的精确控制提供了有效的方法。

油脂低场核磁(LF-NMR)检测参数的优化试验

适当的低场核磁共振(LF-NMR)检测条件对于保障数据的可靠性具有重要意义。该文研究了大豆油LF-NMR检测过程中仪器参数、样品温度及体积等对检测结果稳定性及重现性的影响。结果表明,将2.5 mL油脂样品置于核磁试管中,并在32℃恒温10 min,采样前稳定1 min,在TR=2 000 ms,τ=200μs,EchoCount=5 000个,NS=4次,TD=500 050,SW=250 kHz参数条件下采集信号,可保证检测结果稳定可靠。研究结果可为后期应用LF-NMR技术进行油脂品质检测提供适当的检测条件参考。

基于低场核磁共振(LF-NMR)弛豫特性的油脂品质检测研究

研究了低场核磁共振(LF-NMR)检测过程中的仪器参数、检测温度及体积等对大豆油、棕榈油和猪油弛豫特性检测结果的影响,并给出了适用于含固态脂类油脂样品的LF-NMR检测条件,即:TR=2000ms,τ=250μs,EchoCount=6000个,检测体积2.5mL,检测温度50℃。并应用建立的方法对多种地沟油样品进行了检测。结果表明,基于油样的LFNMR弛豫特性,可有效区分8种固态油样和10种液态地沟油,检测正确识别率可达75%和100%,有效避免了假阳性,且S21峰比例面积可有效反映样品的掺杂梯度变化,说明了该方法对固态和液态地沟油也具有较好的识别能力,在后期油脂品质检测应用中具有较好的可行性。

石油沥青质的NMR测定及其模型分子推测

从6种不同原油中分离提取了正己烷不溶的沥青质,测定了沥青质的1HNMR(NuclearMagneticResonance)和13CNMR谱,从不同类型氢和碳原子的质量分数计算得到了一系列平均结构参数,结合相对分子质量测定和元素分析,给出了沥青质基本结构单元的平均分子式,推测了模型分子的结构。结果表明,沥青质的基本结构单元可以用稠环芳烃连接环烷烃和烷基侧链并含氧、氮和硫等杂原子的单元表示,结构单元之间形成缔合体,缔合数为4～6。