Global experience curves for wind farms

In order to forecast the technological development and cost of wind turbines and the production costs of wind electricity, frequent use is made of the so-called experience curve concept. Experience curves of wind turbines are generally based on data describing the development of national markets, which cause a number of problems when applied for global assessments. To analyze global wind energy price development more adequately, we compose a global experience curve. First, underlying factors for past and potential future price reductions of wind turbines are analyzed. Also possible implications and pitfalls when applying the experience curve methodology are assessed. Second, we present and discuss a new approach of establishing a global experience curve and thus a global progress ratio for the investment cost of wind farms. Results show that global progress ratios for wind farms may lie between 77% and 85% (with an average of 81%), which is significantly more optimistic than progress ratios applied in most current scenario studies and integrated assessment models. While the findings are based on a limited amount of data, they may indicate faster price reduction opportunities than so far assumed. With this global experience curve we aim to improve the reliability of describing the speed with which global costs of wind power may decline.     

基于伏安法润滑油总碱值测定技术的研究

介绍了润滑油总碱值的一种测定新技术——循环伏安法。研究表明，用该方法测定润滑油总碱值是可行的，具有测定结果准确、自动化程度高、操作简单、测定时间短等优点，适合进行现场润滑油测定。     

碳纳米管/ABS树脂基复合材料的力学性能和雷达波吸收性能的研究

采用竖式炉流动法制备的碳纳米管经液相阳极氧化表面处理后增强ABS热塑性工程塑料。复合材料的力学性能研究显示:碳纳米管的加入有效提高了复合材料的拉伸性能,但材料的耐冲击性能下降。当碳纳米管含量为12 wt%时,复合材料的拉伸强度由45.00 MPa提高到69.96 MPa,杨氏模量由0.75 GPa提高到1.93 GPa。对碳纳米管/ABS复合材料的反射系数测定表明:复合材料在一定频率范围内有雷达波吸收性能。     

The combustion of kerosene: Experimental results and kinetic modelling using 1- to 3-component surrogate model fuels

The oxidation of kerosene Jet-A1 and that of n-decane have been studied experimentally in a jet-stirred reactor at atmospheric pressure and constant residence time, over the high temperature range 900–1300 K, and for variable equivalence ratio (0.5≤ϕ≤2). Concentration profiles of the reactants, stable intermediates, and final products have been obtained by probe sampling followed by on-line and off-line GC analyses. The oxidation of neat n-decane and of kerosene in these conditions was modeled using a detailed kinetic reaction mechanism (209 species and 1673 reactions, most of them reversible). The present model was successfully used to simulate the structure of a fuel-rich premixed n-decane–oxygen–nitrogen flame. In the modelling, kerosene was represented by four surrogate model fuels: 100% n-decane, n-decane-n-propylbenzene (74%/26% mol), n-decane-n-propylcyclohexane (74%/26% mol), and n-decane-n-propylbenzene-n-propylcyclohexane (74%/15%/11% mol). The 3-component model fuel was the most appropriate for simulating the JSR experiments. It was also successfully used to simulate the structure of a fuel-rich premixed kerosene–oxygen–nitrogen flame.     

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.     

Pitting corrosion resistance of La added 316L stainless steel in simulated body fluids

Lanthanum (La), a rare earth element with anticoagulative and antiphlogistic function, was added into the medical grade 316L stainless steel in order to improve its biocompatibility. The corrosion resistance of the La added 316L steel in two different simulated body fluids, simulated blood plasma and Hank's solution, was evaluated. The result showed that the addition of La in the steel could largely affect the corrosion behavior of the steel. The steel with 0.01% La showed the widest passive region and the best resistance to pitting attack, within the addition range of La from 0.01% to 0.08%. The corrosion resistance improvement of La added 316L stainless steel is probably due to the effect of La on the purification of the steel, the modification of inclusions, and the passive film formation in the simulated body fluids.     

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.     

Distributed patching for mobile wireless sensor networks

Mobile wireless sensor networks (MWSNs), the continuous movement of sensor nodes, may cause complete disconnection of the network or at best a part of it. The design of such networks should guarantee that all sensor nodes at all times have a path to the sink node(s). Prolonging the lifetimes of the MWSNs is a crucial design issue but should not be at the expense of other essential functions such as connectivity. In this paper, we propose an energy efficient distributed framework for connectivity maintenance of MWSNs. In the proposed framework, sensor nodes of the MWSNs schedule and control their radio frequency (RF) modules based on dynamic coordinated reconstruction mechanism, some senor nodes may lose their connectivity with the network due to mobility, depletion of energy and/or incurred faults. Two protocols are developed to patch up the disconnection of the MWSNs. The first protocol reestablishes the network connection consuming far less energy than state-of-the-art alternatives. However, it is capable of reestablishing the connection when no more than 20% of the MWSN backbone nodes lose connection concurrently. The second protocol extends this limit to operate when up to 35% of the MWSN backbone nodes lose connection simultaneously.     

Short-term load forecasting using bayesian neural networks learned by Hybrid Monte Carlo algorithm

This paper presents a short term load forecasting model based on Bayesian neural network (shorted as BNN) learned by the Hybrid Monte Carlo (shorted as HMC) algorithm. The weight vector parameter of the Bayesian neural network is a multi-dimensional random variable. In learning process, the Bayesian neural network is considered as a special Hamiltonian dynamical system, and the weights vector as the system position variable. The HMC algorithm is used to learn the weight vector parameter with respect to Normal prior distribution and Cauchy prior distribution, respectively. The Bayesian neural networks learned by Laplace algorithm and HMC algorithm and the artificial neural network (ANN) learned by the BP algorithm were used to forecast the hourly load of 25 days of April (Spring), August (Summer), October (Autumn) and January (Winter), respectively. The roots mean squared error (RMSE) and the mean absolute percent errors (MAPE) were used to measured the forecasting performance. The experimental result shows that the BNNs learned by HMC algorithm have far better performance than the BNN learned by Laplace algorithm and the neural network learned BP algorithm and the BNN learned by HMC has powerful generalizing capability, it can welly solve the overfitting problem.