超细晶工业纯钛的变形、应变速率敏感性和激活体积

在温度为250~450 ℃、应变速率为1×10-4-1 s-1的条件下，对超细晶工业纯钛进行变速率压缩实验，计算超细晶工业纯钛的应变速率敏感性因子和激活体积，并研究超细晶工业纯钛的变形行为。研究结果表明：超细晶工业纯钛在稳态变形阶段存在流变软化效应，这是受变形过程中大角度晶界和位错活动所控制的。超细晶工业纯钛的应变速率敏感性因子和激活体积在数值上都相对较低，应变速率敏感性随着变形温度的升高而增加，但激活体积独立于变形温度。应变速率敏感性和激活体积的数值表明晶粒内部位错之间的交互作用几乎不发生，而位错与晶界之间的交互作用显著影响超细晶工业纯钛的塑性变形。     

Kinetic parameter estimation for cooling crystallization process based on cell average technique and automatic differentiation

In this paper, a cell average technique(CAT) based parameter estimation method is proposed for cooling crystallization involved with particle growth, aggregation and breakage, by establishing a more efficient and accurate solution in terms of the automatic differentiation(AD) algorithm. To overcome the deficiency of CAT that demands high computation cost for implementation, a set of ordinary differential equations(ODEs) entailed from CAT based discretized population balance equation(PBE) are solved by using the AD based high-order Taylor expansion. Moreover, an AD based trust-region reflective(TRR) algorithm and another interior-point(IP) algorithm are established for estimating the kinetic parameters associated with particle growth, aggregation and breakage. As a result, the estimation accuracy can be further improved while the computation cost can be significantly reduced, compared to the existing algorithms. Benchmark examples from the literature are used to illustrate the accuracy and efficiency of the AD-based CAT, TRR and IP algorithms in comparison with the existing algorithms. Moreover, seeded batch cooling crystallization experiments of β form L-glutamic acid are performed to validate the proposed method.     

新景矿3号煤层渗透率对有效应力敏感性实验分析

利用KDZS-Ⅱ型煤体瓦斯瞬时解吸及渗流特性测试仪在0.31、0.61 MPa气体压力条件下,开展了新景矿3号煤层渗透率对有效应力敏感性实验分析。结果表明:新景矿3号煤层渗透率对有效应力具有极强的敏感性,煤层渗透率随有效应力增加而降低,二者之间具有良好的负指数幂函数关系;相同气体压力和有效应力下各煤样试件的渗透率变化不同且分异现象显著;煤样试件的渗透率大小与孔隙度、裂隙方向密切相关,煤样试件裂隙方向平行于轴线方向、孔隙度大时,煤样试件的渗透率相对较大;煤样试件的裂隙方向垂直于轴线方向、孔隙度较小时,煤样试件的渗透率相对较小。     

Thermodynamic analysis of a novel fossil-fuel–free energy storage system with a trans-critical carbon dioxide cycle and heat pump

This paper presents and analyzes a novel fossil-fuel–free trans-critical energy storage system that uses CO₂ as the working fluid in a closed loop shuttled between two saline aquifers or caverns at different depths: one a low-pressure reservoir and the other a high-pressure reservoir. Thermal energy storage and a heat pump are adopted to eliminate the need for external natural gas for heating the CO₂ entering the energy recovery turbines. We carefully analyze the energy storage and recovery processes to reveal the actual efficiency of the system. We also highlight thermodynamic and sensitivity analyses of the performance of this fossil-fuel–free trans-critical energy storage system based on a steady-state mathematical method. It is found that the fossil-fuel–free trans-critical CO₂ energy storage system has good comprehensive thermodynamic performance. The exergy efficiency, round-trip efficiency, and energy storage efficiency are 67.89%, 66%, and 58.41%, and the energy generated of per unit storage volume is 2.12 kW·h/m³, and the main contribution to exergy destruction is the turbine reheater, from which we can quantify how performance can be improved. Moreover, with a higher energy storage and recovery pressure and lower pressure in the low-pressure reservoir, this novel system shows promising performance.     

Distributed adaptive state estimation and tracking by using active-passive sensor networks

Heterogeneous sensor networks (HSN) find a wide range of applications in the field of military and civilian environments, where sensor nodes are utilized to estimate the position of a target with both dynamics and control input being unknown for the purposes of tracking. In the HSN, nodes are considered active depending upon their ability to sense the target output while the others are taken passive. Accurate estimation requires local information exchange among the spatially located sensor nodes, so that the active nodes as well as the passive nodes converge simultaneously to the same value. The local information exchange among the nodes is dictated by a connected graph. By using the criterion of collective observability, a novel distributed adaptive estimation scheme is introduced via adaptive observer where the nodes are allowed to have different sensor modalities. Using the estimated information, a subset of active and passive nodes, referred to as mobile nodes, can track the moving target. By using a constant state feedback controller at each mobile node, the state and parameter estimation as well as the tracking errors are shown to be uniformly ultimately bounded. Simulation results verify theoretical claims.     

Sensitivity of charged particle activation analysis for long-lived radioactive nuclide determination

ABSTRACT

Charged Particle Activation Analysis (CPAA) utilizing an 8-MeV proton beam has been studied for determination of 35 long-lived radioactive nuclides. We accumulated the reaction cross section and nuclear decay data by referring to nuclear database supplied by National Nuclear Data Center in Brookhaven National Laboratory. We also calculated the reaction cross sections by using statistical model code ALICE. By using the nuclear data, we have derived determination sensitivity of the radioactive nuclides relative to unit weight and specific radioactivity. The result indicates that several hardly measurable nuclides with long half-lives such as ¹³⁵Cs, ²⁴⁴Pu, ¹²⁹I, ¹²⁶Sn, ⁹³Mo, ¹⁰⁷Pd, ²³⁶U, ²⁴⁸Cm, and ²³⁷Np have high sensitivity. It may be concluded that CPAA can be applied to determination of several long-lived nuclei and will provide a quick and non-destructive analysis method.     

Permanent magnet DC motor parameters estimation via universal adaptive stabilization

This paper presents a quick and effective adaptive estimation methodology for parameters estimation of a permanent magnet (PM) DC motor. The proposed technique uses a universal adaptive stabilizer (UAS). This technique estimates PMDC motor parameters in a single experimental run using input voltage, current and speed. Over time, due to aging and wear, a motor’s parameters values do not match those in the datasheet. Mathematical proofs, experimental results supporting the proposed approach are presented. Despite the persistence of excitation condition not being imposed, the proposed technique produces good results, and is verified in earlier work on Li-ion battery parameters estimation.     

Crowd counting in complex scenes based on an attention aware CNN network

Crowd counting with density estimation has been an active research community due to its significant applications in the fields of public security, video surveillance, traffic monitoring. However, Crowd counting for congested scenes often suffers from some obstacles including severe occlusions, large scale variations, noise interference, etc. In this paper, using the first ten layers of a modified VGG16 and dilated convolution layers as the framework, we have proposed a CNN based crowd counting and density estimation model improved by the attention aware modules with residual connections. To tackle the problem of noise interference, convolutional block attention modules have been introduced into the deep network to segment the foreground and background to focus on interest information, refining deeper features of the input image. To improve information transmission and reuse, residual connections are utilized to link 3 attention blocks. Meanwhile, dilated convolution layers keep larger reception fields and obtain high-resolution density maps. The proposed method has been evaluated on three public benchmarks, i.e. Shanghai Tech A & B, UCF-QNRF and MALL, achieving the mean absolute errors of 64.6 & 8.3, 113.8 and 1.68, respectively. The results outperform some existing excellent approaches. This indicates that the proposed model has high accuracy and better robustness, which is suitable for crowd counting and density estimation in various congested scenes.     

Estimation of maximum available capacity of lithium-ion battery based on multi-view features extracted from reconstructed charging curve

A new method is developed in the paper to estimate the maximum available capacity which is an important basis for indicating the State of Health (SOH) of lithium-ion batteries. Firstly, a data reconstruction approach is proposed to pre-process the acquired data to suppress the influence of measurement noise and reduce the negative impact on estimation precision when measuring equipment adopts different sampling frequencies. Then, the variation trend of the incremental capacity curve obtained based on the reconstructed data with the battery aging is analyzed, and a health indicator (HI) including multi-view features is put forward to characterize the battery degradation more comprehensively. The multi-view features are coming from the capacity increment curve versus voltage and time, including the maximum value of the capacity increment curve, the voltage corresponding to the maximum value, other values surrounding the maximum value and so on. Finally, Support Vector Regression is used to establish a model between the extracted HI and the maximum available capacity, and two types of open source data are used to verify the performance. The experimental results show that the data reconstruction method and multi-view health indicator proposed in the paper can obtain high precision estimation results.     

A review on MRAS-type speed estimators for reliable and efficient induction motor drives

Induction machines have recently been very popular in variable-speed drives, because of their robust construction and relatively low manufacturing costs (brushless), maintenance-free and well-matured control methods. However, for high-precision control and efficiency optimization one needs the information on the rotor speed which can be measured using different speed sensors. All sensors require a mounting space and cabling, they also generate extra costs and reduce system reliability. Therefore, many of the recent research efforts have been dedicated to sensorless or encoderless electrical drives offering such considerable advantages as: lower cost, reduced size and hardware complexity of the drive system, elimination of sensor cables, lower maintenance requirements, possible operation in aggressive environment, higher noise immunity, reliable and user friendly operation. In this article all well-known sensorless techniques are shortly addressed, but the main focus is on the solutions based on the Model Reference Adaptive System (MRAS) concept. The mathematical models and schemes of all types of MRAS-type speed estimators known from the literature are gathered in this article. The comparative analysis of these speed estimators is done from the following points of view: the speed adaptation mechanism derivation based on the Lyapunov theory, stability problems near zero speed and in the regenerating operation mode, and the sensitivity of MRAS estimators to induction machine parameter changes.