太阳能热风发电技术研究进展

介绍了太阳能热风发电的产生背景、技术原理和特点。太阳能热风发电能够实现由太阳能到空气动能,最终到电能的转变,具有环保无污染、运行维护简单、缓解常规能源消耗等特点。简要介绍世界上第1座试验性质的太阳能热风发电站。对太阳能热风发电技术相关研究历程从试验研究和理论研究2个方面做了综述,重点介绍了澳大利亚电站的建设进展及最新相关学术研究进展,包括过渡段理论及试验研究、系统数值模拟和太阳能热风发电技术在高山地区的应用等。介绍我国太阳能资源情况,指出我国太阳能资源丰富,适合建造大型太阳能热风发电站。     

Development of a small DMFC bipolar plate stack for portable applications

The direct methanol fuel cell (DMFC) is regarded as a promising candidate in portable electronic power applications. Bipolar plate stacks were systematically studied by controlling the operating conditions, and by adjusting the stack structure design parameters, to develop more commercial DMFCs. The findings indicate that the peak power of the stack is influenced more strongly by the flow rate of air than by that of the methanol solution. Notably, the stack performance remains constant even as the channel depth is decreased from 1.0 to 0.6 mm, without loss of the performance in each cell. Furthermore, the specific power density of the stack was increased greatly from ∼60 to ∼100 W l⁻¹ for stacks of 10 and 18 cells, respectively. The current status of the work indicates that the power output of an 18-cell short stack reaches 33 W in air at 70 °C. The outer dimensions of this 18-cell short stack are only 80 mm × 80 mm × 51 mm, which are suitable for practical applications in 10–20 W DMFC portable systems.     

基于决策树技术的日前市场清算电价预测

以澳大利亚昆士兰州电力市场为背景,采用决策树技术,建立了提前日的电力市场清算电价预测模型。详细介绍了决策树技术的原理及其在电价预测中的实现方法,考虑了非电价因素对电价预测的影响。实际结果表明该方法具有良好的预测效果和直观性。     

Important variables in explaining real-time peak price in the independent power market of Ontario

This paper uses support vector machines (SVM) based learning algorithm to select important variables that help explain the real-time peak electricity price in the Ontario market. The Ontario market was opened to competition only in May 2002. Due to the limited number of observations available, finding a set of variables that can explain the independent power market of Ontario (IMO) real-time peak price is a significant challenge for the traders and analysts. The kernel regressions of the explanatory variables on the IMO real-time average peak price show that non-linear dependencies exist between the explanatory variables and the IMO price. This non-linear relationship combined with the low variable-observation ratio rule out conventional statistical analysis. Hence, we use an alternative machine learning technique to find the important explanatory variables for the IMO real-time average peak price. SVM sensitivity analysis based results find that the IMO's predispatch average peak price, the actual import peak volume, the peak load of the Ontario market and the net available supply after accounting for load (energy excess) are some of the most important variables in explaining the real-time average peak price in the Ontario electricity market.     

Modeling,numerical optimization,and irreversibility reduction of a dual-pressure reheat combined-cycle

Optimizing the gas-turbine combined-cycle is an important method for improving its efficiency. In this paper, a dual-pressure reheat combined-cycle was modeled and optimized for 80 cases. Constraints were set on the minimum temperature-difference for pinch points (PP_m), superheat approach temperature-difference, steam-turbine inlet temperature and pressure, stack temperature, and dryness fraction at the steam-turbine’s outlet. The dual-pressure reheat combined-cycle was optimized using two different methods; the direct search and the variable metric. A technique to reduce the irreversibility of the steam generator of the combined cycle was introduced. The optimized and the reduced-irreversibility dual-pressure reheat combined-cycles were compared with the regularly-designed dual-pressure reheat combined-cycle, which is the typical design for a commercial combined-cycle. The effects of varying the inlet temperature of the gas turbine (TIT) and PP_m on the performance of all cycles were presented and discussed. The results indicated that the optimized combined-cycle is up to 1% higher in efficiency than the reduced-irreversibility combined-cycle, which is 2–2.5% higher in efficiency than the regularly-designed combined-cycle when compared for the same values of TIT and PP_m. The advantages of the optimized and reduced-irreversibility combined-cycles were manifested when compared with the most efficient commercially-available combined cycle at the same value of TIT.     

Thermoelastic evaluation of composite laminates using digital imaging processing

The study of the variation of the curvature of non-symmetric composite laminates with temperature provides a measure of the magnitude of the thermal stresses and the mechanical behavior of the material with temperature. In the present work, an experimental method based on the pioneer use of a digital camera and image processing method is proposed to perform such measurements. Taking advantage of the method and by comparing experimental and CLT, extended-CLT and FEM simulation results, an evaluation of the residual stresses is described and an approach providing more precise determination of the residual thermal stresses in non-symmetric laminates is proposed.     

Grid sensitivity analysis for the calibration of a prognostic meteorological model in complex terrain by a screening experiment

In this study we present an application of a sensitivity analysis to identify a set of important factors that are allowed to be calibrated in the grid setup of a prognostic meteorological model for laboratory-based simulations. The use of a calibrated grid is of paramount importance for repeated procedures by leaving unnecessary information unprocessed and inevitably reducing run times. Identification and evaluation of sensitivity, importance and uncertainty elements is attempted by the design of a ‘good practice experiment’ for site-specific calibration. The factors of varied grid size and resolution, based on a one-factor-at-a-time approach, are used for the determination of local sensitivity in the area of interest. A total of five simulations was performed for a grid configuration study that aimed to calibrate The Air Pollution Model.     

Noble metal water gas shift catalysis: Kinetics study and reactor design

Based on the water gas shift (WGS) catalytic mechanism on precious metal catalyst, a Langmuir–Hinshelwood (LH) kinetics model was derived for the operating conditions of syngas from natural gas reforming at near-ambient pressure. A power law kinetics model was also presented for comparative purpose. These two kinetics models were integrated in a dynamic distributed reactor model for design of full-scale WGS reactors for a natural gas fuel processing system. Modeling results indicated that the LH kinetics model gives predictions of reactor performance closer to the experimental data. Using the LH kinetics model, optimization of operating conditions for the high-temperature shift (HTS) and low-temperature shift (LTS) reactors was also attempted.     

CAE技术在汽轮机高温强度计算中的应用

为分析某百万千瓦超超临界汽轮机高压缸旋转部件在高温以及高离心力载荷下的强度状况,建立高压缸第一级动叶片的模型.根据蠕变试验数据,采用最小二乘法拟合幂蠕变方程中的各个参数,并详细介绍各参数的拟合、优化和验算过程;用CFD分析软件计算流体域内的对流传热系数并插值到有限元计算程序中,分析叶根倒角处危险区域的蠕变应力状况.数据验算结果表明,利用拟合数据计算得到的蠕变应力值与试验值比较接近,证明该方法可靠;CAE计算结果表明,叶根倒角处危险区域蠕变应力考核合格,并且有充足的安全裕量.     

稀疏差分网络和多监督哈希用于高效图像检索

针对基于深度哈希的图像检索中卷积神经网络（CNN）特征提取效率较低和特征相关性利用不充分的问题,提出一种融合稀疏差分网络和多监督哈希的新方法SDNMSH（sparse difference networks and multi-supervised hashing）,并将其用于高效图像检索。SDNMSH以成对的图像作为训练输入,通过精心设计的稀疏差分卷积神经网络和一个监督哈希函数来指导哈希码学习。稀疏差分卷积神经网络由稀疏差分卷积层和普通卷积层组成。稀疏差分卷积层能够快速提取丰富的特征信息,从而实现整个网络的高效特征提取。同时,为了更加充分地利用语义信息和特征的成对相关性,以促进网络提取的特征信息能够更加有效地转换为具有区分性的哈希码、进而实现SDNMSH的高效图像检索,采用一种多监督哈希（MSH）函数,并为此设计了一个目标函数。在MNIST、CIFAR-10和NUS-WIDE三个广泛使用的数据集上进行了大量的对比实验,实验结果表明,与其他先进的深度哈希方法相比,SDNMSH取得了较好的检索性能。