基于深度学习的SOFC球壳结构检测方法

为优化固体氧化物燃料电池(SOFC)制备工艺提供数据支持和理论依据,提出一种基于光学显微镜微观图像的球壳检测方法。在SOFC阳极微观图像上如果出现球壳结构,表明氧化镍(NiO)未完全还原,该现象严重影响电池的电化学性能、稳定性和使用寿命。为此,利用深度学习方法对SOFC光学显微镜图像进行球壳结构检测,分析阳极NiO的还原程度,通过预选框尺度、网络结构及参数的优化来提高检测性能。为充分利用有限的数据训练网络模型,对训练数据进行扩增。实验结果表明,该检测方法可准确有效地检测与识别形状复杂的SOFC阳极球壳结构,具有检测速度快,球壳结构定位精度较高等优点。     

基于改进RBF神经网络模型的SOFC性能预测方法

固体氧化物燃料电池(SOFC)测试存在费用高、实施困难以及耗时长等问题,因此,提出了一种基于径向基(radial basis function, RBF)神经网络的SOFC建模方法。首先采用数据驱动的方式利用RBF神经网络模型对电池中阳极、阴极、电解质厚度等微观结构对SOFC性能的影响进行分析,然后针对RBF神经网络模型参数选取困难、易陷入局部极值的问题,提出一种改进果蝇算法(improved fruit fly optimization algorithm, IFOA)对其进行优化,自动确定模型参数的同时确保其收敛于全局最优解。仿真结果表明,所提方法能够准确描述微观结构变化对SOFC性能的影响,相对于支撑向量机(support vector machine, SVM)模型能够获得更高的预测精度。     

基于广义回归神经网络的SOFC电压模型研究靠

首先介绍并分析了固体氧化物燃料电池(SOFC)的工作原理和理论电压模型.然后,针对SOFC系统过于复杂,理论电压模型存在明显不足的特点,试图绕开SOFC的内部复杂性,利用广义回归神经网络(GRNN)对SOFC系统进行辨识建模.模型以电池工作温度为神经网络辨识模型的输入量,电池电压/电流密度为输出量,利用750组实验数据作为训练样本,建立了SOFC在不同工作温度下的电池电压/电流密度动态响应模型.仿真结果表明了该方法的有效性,所建模型精度也较高.     

基于T-S模型的预测控制在SOFC中的应用

固体氧化物燃料电池(SOFC)被认为是21世纪最有潜力的发电技术之一.文章针对固体氧化物燃料电池(SOFC)具有较强的输出非线性且难以建模的特点,提出一种基于Takagi-Sugeno模型的模糊预测控制方法.文章先根据SOFC的结构建立了机理模型,然后采用模糊聚类的方法,建立了Takagi-Sugeno的预测模型来逼近模型中的非线性部分,并结合广义预测控制算法(GPC)来实现对SOFC的控制,此方法能很好的解决SOFC输出电压不稳定和难以跟踪设定功率的问题.对SOFC系统的仿真结果表明了该方法的有效性.     

基于FLUENT的燃料电池密封性的仿真分析

为了评估玻璃/Al2O3基复合密封材料在固体氧化物燃料电池（SOFC）中的密封性能,本研究利用FLUENT软件,基于多孔介质模型,对用于SOFC内部连接体和电极之间的该密封材料的泄漏量进行了模拟仿真。结果表明：采用的计算模型和模拟方法是可行的;泄漏量的大小与压力的变化正相关,与温度的变化为负相关;物性参数的差异是影响气体介质泄漏量发生变化的主要因素。通过实验的对比分析,说明本研究采用的计算模型及模拟方法正确可行,这为预测SOFC的密封泄漏提供一种可靠的评价方法。     

基于启发式动态规划的固体氧化物燃料电池优化控制研究

固体氧化物燃料电池(SOFC)系统是一个非线性系统,现存的建模方法和优化控制算法很难对其进行精确的建模及优化控制;针对此问题,采用基于数据的建模方法,对固体氧化物燃料电池系统进行BP神经网络建模,然后在此基础上,首次采用启发式动态规划(HDP)算法对固体氧化物燃料电池系统中的各种气体分压、输出电压以及温度进行优化控制;Matlab仿真结果表明,基于BP神经网络的HDP优化算法具有收敛速度快、鲁棒性强、控制精度高等优点,并使固体氧化物燃料电池系统在负载变化时很快稳定输出电压,实现了优化控制,减少能耗。     

基于FLUENT的燃料电池密封性仿真分析

为评估玻璃/Al2O3基复合密封材料的密封性能,利用FLUENT软件,基于多孔介质模型,对用于固体氧化物燃料电池(solid oxide fuel cell,SOFC)内部连接体与电极之间的玻璃/Al2O3基复合密封材料进行模拟.结果 表明:采用的计算模型和模拟方法是可行的;泄漏量的大小与压力的变化正相关,与温度的变化负相关;物性参数的差异是影响气体介质泄漏量发生变化的主要因素.通过与实验测量对比分析,证明采用的计算模型和模拟方法正确可行,为预测SOFC的密封泄漏提供一种可靠的评价方法.     

基于区分应用的车轮结构分层ALM模型

基于区分应用提出具有层次车轮结构的应用层多播（ALM）模型,该模型在混乱情况下自愈性强、数据传输速度高。给出一种基于泛洪算法和区分应用的分层多播数据传输协议,根据不同应用使数据通过不同链路传输。依据网络状况变化动态优化系统拓扑结构。利用P2Psim在Linux下进行仿真实验,结果表明该模型可以提高ALM性能。     

平板式固体氧化物燃料电池的建模与仿真研究

基于气流模型以及热模型利用流体力学计算软件Fluent,建立平板式固体氧化物燃料电池（SOFC）的计算流体力学（CFD）模型。模型中采用电化学反应控制方程、质量、动量和能量守恒方程描述电池内的传热传质等物理过程,并对电池内部的运行电压、温度以及各种极化分布情况,进行了数值模拟。研究给出了顺流平板型SOFC与逆流平板型SOFC情况下,运行电压、温度和极化的分布。结果显示逆流平板型SOFC可获得更好的性能,具有更大的电功率密度和燃料利用率。     

基于敏捷开发的工作量估算模型的研究

为了能够对敏捷开发项目进行有效的工作量估算,提出了一种基于人工神经网络的工作量估算模型.根据ISBSG (international software benchmarking standard group)数据集的特点,对数据集进行预处理.利用主成分分析方法对工作量的影响因子进行压缩,得到了针对敏捷开发的综合影响因子集合.基于BP神经网络,建立了工作量估算模型.给出了该模型的具体实现步骤和实验验证,并将实验结果与其他估算方法进行比较.实验结果表明,该模型能很好地实现敏捷开发的工作量估算,且明显优于其他估算方法.     

管式SOFC数学模型及系统性能分析

固体氧化物燃料电池(SOFC)是一种高效低污染的新型能源。该文基于物质和能量守恒并耦合流体流动、热量产生和传递以及电化学知识建立了以天然气为燃料的管式固体氧化物燃料电池的数学模型,对模型进行求解,并分析系统参数(电流密度、工作压力、燃料的摩尔流量等)对系统性能(发电量、发电效率等)的影响。结果表明:SOFC系统的发电效率可以达到50.73%。通过增加单电池个数来减小电流密度可以增加系统发电量和发电效率,但是会增加系统投资;增大燃料电池的工作压力可以改善系统的性能;而增加燃料的摩尔流量(燃料利用率不变)会使系统的发电效率降低。     

Dynamic modeling,simulation, and MIMO predictive control of a tubular solid oxide fuel cell

Solid oxide fuel cells are a promising option for distributed energy stationary power generation that offers efficiencies up to 50% in stand-alone applications, 70% in hybrid gas turbine applications and 80% in cogeneration. To advance SOFC technology sufficiently for widespread market penetration, the SOFC must demonstrate improved cell lifetime from the status quo. Much research has been performed to improve SOFC lifetime using advanced geometries and materials, and in this research, we suggest further improving lifetime by designing an advanced control algorithm based upon preexisting mechanical stress analysis [1]. Control algorithms commonly address SOFC lifetime related operability objectives using unconstrained, SISO control algorithms that seek to minimize thermal transients. While thermal fatigue may be one thermal stress driver, these studies often do not consider maximum radial thermal gradients or critical absolute temperatures in the SOFC. In addition, researchers often discuss hot-spots as a critical lifetime reliability issue, but as previous stress work demonstrates, the minimum cell temperature is the primary thermal stress driver in tubular SOFCs modeled after the Siemens Power Generation, Inc. design. In this work, we present a dynamic, quasi-two-dimensional model for a high-temperature tubular SOFC combined with ejector and prereformer models. The model captures dynamics of critical thermal stress drivers and is used as the physical plant for closed-loop simulations with a constrained, MIMO model predictive control algorithm. Closed-loop simulation results demonstrate effective load-following, operability constraint satisfaction, and disturbance rejection.     

两种固体氧化物燃料电池系统的故障建模与仿真对比研究

固体氧化物燃料电池(SOFC)系统的建模方式较多,而基于机理模型的故障诊断是能够通过系统的动态趋势辨别故障的有效手段之一,但该方法对机理模型的准确性有要求.此外,不同的燃料供给系统采用的系统结构也是有差异的,进而导致在相同故障下SOFC系统的动态响应也是不同的.因此,本文基于两种燃料供应方式,提出了分别以纯氢气和天然气作为燃料的SOFC系统结构,并基于原有机理知识进行MATLAB/Simulink系统建模.经与真实SOFC系统实验对比,搭建的系统模型能够有效模拟系统在无故障状态下的动态变化;另外,在无故障模型的基础上,分别加入两类常见故障,其一为风机故障,其二为燃料供应管路泄露.最后通过仿真分析,明确了所搭建模型的合理性,且发现了两种燃料供应对SOFC系统热响应特性是不同的,对SOFC系统故障的检测和设备选型具有重要意义.     

汽车动力传动系仿真与优化

为了在汽车设计过程同时兼顾汽车的动力性与燃油经济性,建立了汽车动力性与燃油经济性的一般数学模型,编制程序计算了常用的动力性参数并绘制多种动力特性曲线.建立了同时考虑汽车动力性和燃油经济性为目标函数的优化模型,特别引入了加权因子的概念,使得优化结果可以根据不同设计需要有所侧重,最后得出变速器各档速比及主减速比等传动系主要参数的优化值.结合实例,对某两厢小型轿车进行计算与优化,对优化前后主要动力性参数进行分析比较,验证了仿真程序的可行性并得出了传动系优化结果.     

Nano-structured solid oxide fuel cell design with superior power output at high and intermediate operation temperatures

A solid oxide fuel cell (SOFC) with a thin-film yttria-stabilized zirconia (YSZ) electrolyte was developed and tested. This novel SOFC shows a similar multilayer set-up as other current anode-supported SOFCs and is composed of a Ni/8YSZ anode, a gas-tight 8YSZ electrolyte layer, a dense Sr-diffusion barrier layer and a LSCF cathode. To increase the power density and lower the SOFC operating temperature, the thickness of the electrolyte layer was reduced from around 10 μm in current cells to 1 μm, using a nanoparticle deposition method. By using the novel 1 μm electrolyte layer, the current density of our SOFC progressed to 2.7, 2.1 and 1.6 A/cm² at operation temperatures of 800, 700 and 650°C, respectively, and out-performs all similar cells reported to date in the literature. An important consideration is also that cost-effective dip-coating and spin-coating methods are applied for the fabrication of the thin-film electrolyte. Processing of 1 μm layers on the very porous anode substrate material was initially experienced as very difficult and therefore 8YSZ nanoparticle coatings were developed and optimized on porous 8YSZ model substrates and transferred afterwards to regular anode substrates. In this paper, the preparation of the novel SOFC is shown and its morphology is illustrated with high resolution SEM pictures. Further, the performance in a standard SOFC test is demonstrated.     

Design of a grey-prediction self-organizing fuzzy controller for active suspension systems

Self-organizing fuzzy controllers (SOFCs) have excellent learning capabilities. They have been proposed for the manipulation of active suspension systems. However, it is difficult to select the parameters of an SOFC appropriately, and an SOFC may extensively modify its fuzzy rules during the control process when the parameters selected for it are inappropriate. To eliminate this problem, this study developed a grey-prediction self-organizing fuzzy controller (GPSOFC) for active suspension systems. The GPSOFC introduces a grey-prediction algorithm into an SOFC, in order to pre-correct its fuzzy rules for the control of active suspension systems. This design solves the problem of SOFCs with inappropriately chosen parameters. To evaluate the feasibility of the proposed method, this study applied the GPSOFC to the manipulation of an active hydraulic-servo suspension system, in order to determine its control performance. Experimental results demonstrated that the GPSOFC achieved better control performance than either the SOFC or the passive method of active suspension control.     

Retrieve Snow Depth of North of Xinjiang Region from ARMS 2 Data based on Artificial Neural Network Technology

Based on the characteristics of the microwave signal responding to the snow depth，we use AMSR2 brightness temperature，geo\|location and terrain factor as the inputs of ANN，and snow depth as the desired output to develop an efficiency snow depth retrieve model.We compared the influence of combinations of TB，geo-ocation and terrain factors on the retrieve of snow depth.It is reviewed in this article that，TB of horizontal polarization，latitude perform better than vertical polarization and longitude respectively.Combination of slope and aspect is superior to other combinations of terrain factors.Besides，there are equivalent influence on snow depth of geo\|location and terrain factors.Finally，we compare the performance of four optimal ANN models under different input combinations.At last，we found that the ANN consists TB，latitude，longitude，slope and aspect as inputs is the best model which might fairly simulating the snow depth of Beijiang.     

Nonlinear modeling of a SOFC stack based on ANFIS identification

An adaptive neural-fuzzy inference system (ANFIS) model is developed to study different flows effect on the performance of solid oxide fuel cell (SOFC). During the process of modeling, a hybrid learning algorithm combining backpropagation (BP) and least squares estimate (LSE) is adopted to identify linear and nonlinear parameters in the ANFIS. The validity and accuracy of modeling are tested by simulations and the simulation results reveal that the obtained ANFIS model can efficiently approximate the dynamic behavior of the SOFC stack. Thus it is feasible to establish the model of SOFC stack by ANFIS.