水流损失和热补偿共同作用对增强型地热系统(EGS)产能影响的研究

基于青海共和盆地-3705m地热田实测数据,结合流固耦合传热理论并运用Comsol软件,建立了离散型裂隙岩体流体传热模型。考虑水流损失和热补偿共同作用,模拟得到了开采过程中上、下岩层(盖层和垫层)为绝热不渗透、传热不渗透、渗透传热时,储层(上、下岩层和压裂层)温度场的变化特征,分析了产出流量、水流损失、产出温度、产热速率的变化规律。研究结果表明:采热过程中产出流量始终小于注入流量;产出流量增幅速率先增大后减小,最后趋于稳定,前3a产出流量增幅超过总增幅量的3/4;忽略水流损失,将高估产热速率,采热初期甚至达到考虑水流损失时产热速率的3倍以上;考虑水流损失,产热速率呈先快速上升再趋于稳定后逐渐下降的趋势,最优开采时间为3a^11a;研究上、下岩层对产出温度的影响,仅考虑传热,采热寿命延长5.43%,同时考虑渗流传热时,采热寿命延长2.71%;采热前9a,水流损失占主导作用,即流入上、下岩层水流损失对产热速率的影响高于热补偿效应,开采10a后,热补偿效应占主导作用;同时考虑水流损失和热补偿效应得到的产热速率变化规律与实际工程更为符合,建议选择低渗透能力的上、下岩层延长增强型地热系统(EGS)运行时间。     

一种基于序贯估计的直达声区水面舰船 被动测距方法 *

该文利用基于射线的盲解卷积方法，从直达声区的水面舰船噪声中提取出船和锚系于海底的垂直接收阵之间的时域信道响应，并利用直达波在不同阵元相对于参考阵元的到达时间差，通过序贯方法，利用射线模型和声速剖面信息，对水面舰船距接收阵的距离进行了估计。通过处理海深约为580 m的2016年美国圣巴巴拉海峡的实验数据，对1.6~3.5 km直达声区范围内Anna Maersk商船与垂直阵之间的距离进行了估计，验证了测距方法的有效性，并将结果与系统测量值和几何方法的估计值进行了比较。由于该方法不需要对海底参数进行估计，所以在海底参数未知时要优于传统匹配场方法；在声速剖面存在跃层且海底为多层分布的复杂信道条件下，该方法仍能对距离进行有效估计，且与测量值的相对误差在6%以内，小于几何方法的估计误差，测距结果精度较高。     

一类零膨胀广义线性模型极大似然估计的相合性与渐近正态性

讨论了响应变量为单参数指数族且在零点处膨胀的广义线性模型的大样本性质，对其参数进行了极大似然估计，给出了一些正则条件.基于所提出的正则条件，证明了模型参数极大似然估计的相合性与渐近正态性.     

More efficient time integration for Fourier pseudospectral DNS of incompressible turbulence

Time integration of Fourier pseudospectral DNS is usually performed using the classical fourth-order accurate Runge-Kutta method or other second- or third-order methods, with a fixed step size. We investigate the use of higher-order Runge-Kutta pairs and automatic step size control based on local error estimation. We find that the fifth-order accurate Runge-Kutta pair of Bogacki and Shampine gives much greater accuracy at a significantly reduced computational cost. Specifically, we demonstrate speedups of 2× to 10× for the same accuracy. Numerical tests (including the Taylor-Green vortex, Rayleigh-Taylor instability, and homogeneous isotropic turbulence) confirm the reliability and efficiency of the method. We also show that adaptive time stepping provides a significant computational advantage for some problems (like the development of a Rayleigh-Taylor instability) without compromising accuracy.     

The Application of Fractal Transform and Entropy for Improving Fault Tolerance and Load Balancing in Grid Computing Environments

This paper applies the entropy-based fractal indexing scheme that enables the grid environment for fast indexing and querying. It addresses the issue of fault tolerance and load balancing-based fractal management to make computational grids more effective and reliable. A fractal dimension of a cloud of points gives an estimate of the intrinsic dimensionality of the data in that space. The main drawback of this technique is the long computing time. The main contribution of the suggested work is to investigate the effect of fractal transform by adding R-tree index structure-based entropy to existing grid computing models to obtain a balanced infrastructure with minimal fault. In this regard, the presented work is going to extend the commonly scheduling algorithms that are built based on the physical grid structure to a reduced logical network. The objective of this logical network is to reduce the searching in the grid paths according to arrival time rate and path’s bandwidth with respect to load balance and fault tolerance, respectively. Furthermore, an optimization searching technique is utilized to enhance the grid performance by investigating the optimum number of nodes extracted from the logical grid. The experimental results indicated that the proposed model has better execution time, throughput, makespan, latency, load balancing, and success rate.     

A volatolomic approach using cerumen as biofluid to diagnose bovine intoxication by Stryphnodendron rotundifolium

An innovative volatolomic approach employs the detection of biomarkers present in cerumen (earwax) to identify cattle intoxication by Stryphnodendron rotundifolium Mart., Fabaceae (popularly known as barbatimão). S. rotundifolium is a poisonous plant with the toxic compound undefined and widely distributed throughout the Brazilian territory. Cerumen samples from cattle of two local Brazilian breeds (‘Curraleiro Pé-Duro’ and ‘Pantaneiro’) were collected during an experimental intoxication protocol and analyzed using headspace (HS)/GC–MS followed by multivariate analysis (genetic algorithm for a partial least squares, cluster analysis, and classification and regression trees). A total of 106 volatile organic metabolites were identified in the cerumen samples of bovines. The intoxication by S. rotundifolium influenced the cerumen volatolomic profile of the bovines throughout the intoxication protocol. In this way, it was possible to detect biomarkers for cattle intoxication. Among the biomarkers, 2-octyldecanol and 9-tetradecen-1-ol were able to discriminate all samples between intoxicated and nonintoxicated bovines. The cattle intoxication diagnosis by S. rotundifolium was accomplished by applying the cerumen analysis using HS/GC–MS, in an easy, accurate, and noninvasive way. Thus, the proposed bioanalytical chromatography protocol is a useful tool in veterinary applications to determine this kind of intoxication.     

The effect of probe-Ohmic environment coupling type and probe information flow on quantum probing of the cutoff frequency

This work aims to compare results of dissipating and pure dephasing single-qubit probes in characterizing the cutoff frequency of a harmonic reservoir Ohmic spectral density. In particular, we proved that a dissipating single-qubit improves the estimation precision of the cutoff frequency estimator. The information backflow and outflow of the dissipating and the dephasing single-qubit are compared. The relation between the quantum Fisher information and the information outflow and backflow of the probe is obtained. The results show that difference between the values of information outflow and backflow of a dissipating single-qubit is larger than a dephasing single-qubit. So, a single-qubit probe extracts more information from an Ohmic reservoir by increasing the difference between the values of information outflow and backflow of the probe.     

一类改进最大熵方法的可调参数分析

在结构可靠性分析中,引入含可调参数的转换函数能对传统的最大熵方法进行改进,获得更高的失效概率预测精度。但是,此可调参数的最佳取值很难确定。针对这一问题,引入概率守恒方程,从功能函数转换前后所得概率密度函数出发,建立其最大熵值的变化关系,给出转换前后最大熵值之差的理论形式。通过对三种典型单调非线性转换函数开展算例研究,发现功能函数转换前后的最大熵值之差与转换函数的最佳可调参数值有关。改变可调参数值驱使最大熵值之差变化的同时,改进最大熵方法能遍历到更好的失效概率估计值。