饱和尾粉砂边界面模型在FLAC3D中的开发

由于岩土材料的复杂性，FLAC3D软件自带的本构模型不能满足实际数值分析的全部要求。利用FLAC3D预留的UMD接口，在C++的编译环境下实现了一种基于临界状态模型CASM边界面模型的开发。介绍了边界面模型的基本组成，给出了开发的关键步骤。并基于三轴试验结果，利用该模型对饱和尾粉砂静/动力学特性进行了分析，论证了该模型对饱和尾粉砂的适用性。     

Molecular dynamic investigation on sulfur migration during hydrogen production by benzothiophene gasification in supercritical water

Benzothiophene (BT) is a key sulfur-containing intermediate product in the thermal conversion process of coal and heavy oil. The migration process of the sulfur element may affect the thermal utilization design of BT. In this paper, BT was used as a model compound to simulate the supercritical water gasification (SCWG) process by molecular dynamics with a reactive force field (ReaxFF) method, and the laws of hydrogen production and sulfur migration mechanisms were obtained. Increasing the molecule number of supercritical water (SCW) and increasing the reaction temperature can enhance the generation of hydrogen and promote the conversion of organic sulfur to inorganic sulfur. Water was the main source of H₂, and H₂S was the main gaseous sulfur-containing product. SCW had a certain degree of oxidation due to a large number of hydroxyl radicals, which could increase the valence of sulfur. The conversion process of BT in SCW was mainly divided into four stages, including thiophene ring-opening; sulfur separation or carbon chain broke with sulfur retention; carbon chain cleaved, and gas generation. The lumped kinetic parameters of the conversion of sulfur in BT to inorganic sulfur were calculated, and the activation energy was 369.98 kJ/mol, which was much lower than those under pyrolysis conditions. This article aims to clarify the synergistic characteristics of hydrogen production and sulfur migration in the SCWG process of BT from the molecular perspective, which is expected to provide a theoretical basis for pollutant directional removal during hydrogen production by sulfur-containing organic matters in SCW.     

基于改进LFM算法的主动解列断面搜索方法

针对传统解列断面算法复杂度高的问题,提出一种基于改进LFM算法的解列断面搜索方式。首先,基于节点间电气联系和能量转移分布熵完成电网加权复杂网络建模;其次,基于主动解列断面约束条件,对LFM算法做适应性改良;最后,通过改进LFM算法得到解列断面,并在IEEE39节点系统中验证了算法有效性。仿真结果表明,改进LFM算法可充分考虑传统解列断面的约束条件,在算法具有较低复杂度的同时对系统运行状态更强的适应性。     

The impact of hydrogen refueling station subsidy strategy on China's hydrogen fuel cell vehicle market diffusion

Lack of hydrogen refueling stations (HRSs) has hindered the diffusion of hydrogen fuel cell vehicles (HFCVs) in the Chinese transport market. By combining the agent-based model (ABM) and the experience weighted attraction (EWA) learning algorithm, this paper explores the impact of government subsidy strategy for HRSs on the market diffusion of HFCVs. The actions of the parties (government, HRS planning department and consumers) and their interactions are taken into account. The new model suggests dynamic subsidy mode based on EWA algorithm yields better results than static subsidy mode: HFCV purchases, HRS construction effort, total number of HRSs and expected HRS planning department profits all outperform static data by around 27%. In addition, choosing an appropriate initial subsidy strategy can increase the sales of HFCVs by nearly 40%. Early investment from government to establish initial HRSs can also increase market diffusion efficiency by more than 76.7%.     

Decentralized optimal tracking control for large-scale nonlinear systems with tracking error constraints

In this article, a decentralized optimal tracking control strategy is proposed for a class of nonlinear systems with tracking error constraints by utilizing adaptive dynamic programming (ADP). It should be noted that ADP technology cannot be directly used to solve decentralized optimal tracking problem of large-scale interconnected nonlinear system with nonzero equilibrium points, since that an infinite domain performance index function may result in an unsolvable solution. In addition, by introducing a smooth function, the constrained tracking error is transformed into an unconstrained one. Then, the error dynamics and a new infinite domain performance index function are designed, such that ADP technology can be used. Following the designed performance index function, the tracking error can be ensured within a small neighborhood of zero. Finally, the feasibility and the effectiveness of the proposed decentralized optimal control scheme are verified through two simulation examples.     

A review on modeling of solar photovoltaic systems using artificial neural networks,fuzzy logic,genetic algorithm and hybrid models

The uncertainty associated with modeling and performance prediction of solar photovoltaic systems could be easily and efficiently solved by artificial intelligence techniques. During the past decade of 2009 to 2019, artificial neural network (ANN), fuzzy logic (FL), genetic algorithm (GA) and their hybrid models are found potential artificial intelligence tools for performance prediction and modeling of solar photovoltaic systems. In addition, during this decade there is no extensive review on applicability of ANN, FL, GA and their hybrid models for performance prediction and modeling of solar photovoltaic systems. Therefore, this article focuses on extensive review on design, modeling, maximum power point tracking, fault detection and output power/efficiency prediction of solar photovoltaic systems using artificial intelligence techniques of the ANN, FL, GA and their hybrid models. In addition, the selected articles on the solar radiation prediction using ANN, FL, GA and their hybrid models are also summarized. Total of 122 articles are reviewed and summarized in the present review for the period of 2009 to 2019 with 90 articles in the field of {ANN, FL, GA and their hybrid models} + solar photovoltaic systems and 32 articles in the field of {ANN, FL, GA and their hybrid models} + solar radiation. The review shows the suitability and reliability of ANN, FL, GA and hybrid models for accurate prediction of the solar radiation and the performance characteristics of solar photovoltaic systems. In addition, this review presents the guidance for the researchers and engineers in the field of solar photovoltaic systems to select the suitable prediction tool for enhancement of the performance characteristics of the solar photovoltaic systems and the utilization of the available solar radiation.     

Modeling,design, and optimization of a cost‐effective and reliable hybrid renewable energy system integrated with desalination using the division algorithm

People in the Middle East are facing the problem of freshwater shortages. This problem is more intense for a remote region, which has no access to the power grid. The use of seawater desalination technology integrated with the generated energy unit by renewable energy sources could help overcome this problem. In this study, we refer a seawater reverse osmosis desalination (SWROD) plant with a capacity of 1.5 m³/h used on Larak Island, Iran. Moreover, for producing fresh water and meet the load demand of the SWROD plant, three different stand‐alone hybrid renewable energy systems (SAHRES), namely wind turbine (WT)/photovoltaic (PV)/battery bank storage (BBS), PV/BBS, and WT/BBS are modeled and investigated. The optimization problem was coded in MATLAB software. Furthermore, the optimized results were obtained by the division algorithm (DA). The DA has been developed to solve the sizing problem of three SAHRES configurations by considering the object function's constraints. These results show that this improved algorithm has been simpler, more precise, faster, and more flexible than a genetic algorithm (GA) in solving problems. Moreover, the minimum total life cycle cost (TLCC = 243 763$), with minimum loss of power supply probability (LPSP = 0%) and maximum reliability, was related to the WT/PV/BBS configuration. WT/PV/BBS is also the best configuration to use less battery as a backup unit (69 units). The batteries in this configuration have a longer life cycle (maximum average of annual battery charge level) than two other configurations (93.86%). Moreover, the optimized results have shown that utilizing the configuration of WT/PV/BBS could lead to attaining a cost‐effective and green (without environmental pollution) SAHRES, with high reliability for remote areas, with appropriate potential of wind and solar irradiance.     

Parameter identification of proton exchange membrane fuel cell via Levenberg-Marquardt backpropagation algorithm

It is essential to develop an accurate model of proton exchange membrane fuel cell (PEMFC) for a reliable operation and analysis, in which unknown parameters usually need to be determined. The inherent nonlinear, strong coupling, and diversification of PEMFC model seriously hinder traditional methods to identify the parameters. For the sake of overcoming these thorny obstacles, Levenberg-Marquardt backpropagation (LMBP) algorithm based on artificial neural networks (ANNs) is proposed for PEMFC parameter identification. Furthermore, the performance of LMBP is thoroughly evaluated and compared with four typical meta-heuristic algorithms under three cases. Simulation results indicate that LMBP performs a higher accuracy and faster speed for parameter identification. In particular, accuracy and convergence speed can achieve as much as 99.8% and 95.9% growth via LMBP, respectively.     

动态循环荷载下大坝混凝土拉压转换损伤本构模型构建及影响研究

目前混凝土坝抗震安全评价中,对于动态循环荷载下大坝混凝土损伤本构模型中从受拉状态向受压状态转变过程(拉压转换)中的混凝土弹性模量变化,一般都基于商用软件ABAQUS的"单边效应"假设,认为混凝土弹性模量立即恢复为初始弹性模量。上述假设的合理性缺乏试验验证,直接影响混凝土坝抗震安全评价的可靠性。通过对全级配大坝混凝土拉压转换全过程试验成果的观察分析,发现混凝土从受拉状态进入受压状态后弹性模量并非立即恢复为初始弹性模量,而是由损伤后弹性模量连续渐进恢复至初始弹性模量,期间原有的受拉残余应变在压应力作用下迅速减小到较小数值,因此ABAQUS的"单边效应"假设并不符合实际情况。本文以全级配大坝混凝土拉压转换全过程试验成果为基础,提出受拉损伤发生后,拉压转换时受压应力应变关系采用双折线模型的思路,据此推导了相应的应力-应变关系解析表达式,构建了更接近混凝土拉压转换时真实状况的本构关系数值模型。通过实际工程的计算分析,发现常用的受压弹性模量立即恢复为初始弹性模量的损伤本构模型过高估计了坝体刚度,可能带来偏于不安全的评价结果。     

退火炉内罩中气体流动及钢卷传热的数值模拟

摘要：轧制力预报一直是热连轧过程控制模型的核心，浅层神经网络对复杂函数的表示能力有限，而深度学习模型通过学习一种深层非线性网络结构，实现复杂函数逼近。利用深度学习框架TensorFlow，构建了一种深度前馈神经网络轧制力模型，采用BP算法计算网络损失函数的梯度，运用融入Mini batch策略的Adam优化算法进行参数寻优，采用Early stopping、参数惩罚和Dropout正则化策略提高模型的泛化能力。基于上述建模策略，针对宝钢1880热连轧精轧机组的大量轧制历史数据进行了建模实验，对比分析了4种不同结构的前馈网络预测精度。结果表明，相比于传统SIMS轧制力模型，深度神经网络可实现轧制力的高精度预测，针对所有机架的预测精度平均提升21.11%。