Mathematical Model for the Homogenization of Unit Load Formation

One of the most important issues in storage and transport processes is the formation of unit loads. Our main goal is to investigate the homogenization of unit load formation cases. We provide a model involving the major factors and parameters for the optimal selection of the unit load formations. Objective functions and constraints related to the basic tasks are formulated. We give a method for the selection of the optimal unit load formation equipment for a given number of products under given constraints.

Force Control Compensation Method with Variable Load Stiffness and Damping of the Hydraulic Drive Unit Force Control System

Each joint of hydraulic drive quadruped robot is driven by the hydraulic drive unit（HDU）, and the contacting between the robot foot end and the ground is complex and variable, which increases the difficulty of force control inevitably. In the recent years, although many scholars researched some control methods such as disturbance rejection control, parameter self-adaptive control, impedance control and so on, to improve the force control performance of HDU, the robustness of the force control still needs improving. Therefore, how to simulate the complex and variable load characteristics of the environment structure and how to ensure HDU having excellent force control performance with the complex and variable load characteristics are key issues to be solved in this paper. The force control system mathematic model of HDU is established by the mechanism modeling method, and the theoretical models of a novel force control compensation method and a load characteristics simulation method under different environment structures are derived, considering the dynamic characteristics of the load stiffness and the load damping under different environment structures. Then, simulation effects of the variable load stiffness and load damping under the step and sinusoidal load force are analyzed experimentally on the HDU force control performance test platform, which provides the foundation for the force control compensation experiment research. In addition, the optimized PID control parameters are designed to make the HDU have better force control performance with suitable load stiffness and load damping, under which the force control compensation method is introduced, and the robustness of the force control system with several constant load characteristics and the variable load characteristics respectively are comparatively analyzed by experiment. The research results indicate that if the load characteristics are known, the force control compensation method presented in this paper has positive compensation effects on the load characteristics variation, i.e., this method decreases the effects of the load characteristics variation on the force control performance and enhances the force control system robustness with the constant PID parameters, thereby, the online PID parameters tuning control method which is complex needs not be adopted. All the above research provides theoretical and experimental foundation for the force control method of the quadruped robot joints with high robustness.

Optimization of Minimum Power Output for Combined Heat and Power Units Considering Peak Load Regulation Ability

Northern China has rich wind power and photovoltaic renewable resources. Combined Heat and Power (CHP) Units to meet the load demand and limit its peaking capacity in winter, to a certain extent, it results in structural problems of wind-solar power and thermoelectric. To solve these problems, this paper proposes a plurality of units together to ensure supply of heat load on the premise, by building a thermoelectric power peaking considering thermal load unit group dynamic scheduling model, to achieve the potential of different thermoelectric properties peaking units of the excavation. Simulation examples show, if the unit group exists obvious relationship thermoelectric individual differences, the thermal load dynamic scheduling can be more significantly improved overall performance peaking unit group, effectively increase clean energy consumptive.

Discussion on Key Points of Wind Load of Jack-up Unit

The anti-overturning ability and structure safety of jack-up unit in in-place condition are often affected by environment loads, especially wind load. According to the MODU rule, the projected area method is used for calculating the wind load. However, the calculated results are conservative and not good for structure optimization design. In this paper, a 400 ft jack-up is studied as an example. Based on the wind tunnel test and numerical simulation method, some key points of wind load calculation, such as shielding effect, lift effect and shape coefficient of component, are discussed. The study shows that the points mentioned above, which are ignored in the MODU rule calculation, result in the conservative result.

Comparison between dynamic programming and genetic algorithm for hydro unit economic load dispatch

The hydro unit economic load dispatch （ELD） is of great importance in energy conservation and emission reduction. Dynamic programming （DP） and genetic algorithm （GA） are two representative algorithms for solving ELD problems. The goal of this study was to examine the performance of DP and GA while they were applied to ELD. We established numerical experiments to conduct performance comparisons between DP and GA with two given schemes. The schemes included comparing the CPU time of the algorithms when they had the same solution quality, and comparing the solution quality when they had the same CPU time. The numerical experiments were applied to the Three Gorges Reservoir in China, which is equipped with 26 hydro generation units. We found the relation between the performance of algorithms and the number of units through experiments. Results show that GA is adept at searching for optimal solutions in low-dimensional cases. In some cases, such as with a number of units of less than 10, GA＇s performance is superior to that of a coarse-grid DP. However, GA loses its superiority in high-dimensional cases. DP is powerful in obtaining stable and high-quality solutions. Its performance can be maintained even while searching over a large solution space. Nevertheless, due to its exhaustive enumerating nature, it costs excess time in low-dimensional cases.

A dynamic problem on the edges of a mode Ι crack subjected to moving unit-step loads

By the theory of complex functions, a dynamic problem on the edges of a mode Ι crack subjected to moving unit-step loads are investigated. The Riemann-Hilbert problem is easily formulated by the ways of self-similar functions. The analytical solution obtained is rather simple and concise. After the solution was utilized by superposition theorem, the relevant solution on the case of arbitrary loading can be obtained.

Optimal Load Dispatch of Gas Turbine Power Generation Units based on Multiple Population Genetic Algorithm

In this paper, a multiple population genetic algorithm (MPGA) is proposed to solve the problem of optimal load dispatch of gas turbine generation units. By introducing multiple populations on the basis of Standard Genetic Algorithm (SGA), connecting each population through immigrant operator and preserving the best individuals of every generation through elite strategy, MPGA can enhance the efficiency in obtaining the global optimal solution. In this paper, MPGA is applied to optimize the load dispatch of 3×390MW gas turbine units. The results of MPGA calculation are compared with that of equal micro incremental method and AGC instruction. MPGA shows the best performance of optimization under different load conditions. The amount of saved gas consumption in the calculation is up to 2337.45m3N/h, which indicates that the load dispatch optimization of gas turbine units via MPGA approach can be effective.

Estimating Unconfined Compressive Strength of Sedimentary Rocks in United Arab Emirates from Point Load Strength Index

In this paper, three rock types including Sandstone, Mudstone, and Crystalline Gypsum were part of a laboratory study conducted to develop a dataset for predicting the unconfined compressive strength of UAE intact sedimentary rock specimens. Four hundred nineteen rock samples from various areas along the coastal region of the UAE were collected and tested for the development of this dataset and evaluation of models. From the statistical analysis of the data, regression equations were established among rock parameters and correlations were expressed and compared by the ones proposed in literature.

Research on the Prediction of Load Regulation Capacity for Supercritical Thermal Power Unit

Both the modeling and the load regulation capacity prediction of a supercritical power plant are investigated in this paper. Firstly, an indirect identification method based on subspace identification method is proposed. The obtained identification model is verified by the actual operation data and the dynamic characteristics of the system are well reproduced. Secondly, the model is used to predict the load regulation capacity of thermal power unit. The power, main steam pressure, main steam temperature and other parameters are simulated respectively when the unit load is going up and down. Under the actual constraints, the load regulation capacity of thermal power unit can be predicted quickly.

基于KPCA-CNN-DBiGRU模型的短期负荷预测方法

本文针对已有神经网络模型在短期负荷预测中输入维度过高、预测误差较大等问题,提出了一种结合核主成分分析、卷积神经网络和深度双向门控循环单元的短期负荷预测方法。先运用核主成分分析法对原始高维输入变量进行降维,再通过卷积深度双向门控循环单元网络模型进行负荷预测。以第九届全国电工数学建模竞赛试题A题中的负荷数据作为实际算例,结果表明所提方法较降维之前预测误差大大降低,与已有预测方法相比也有大幅的误差降低。

燃煤机组锅炉负荷偏差分析模型与评价系统

“双碳”大背景下我国上网新能源装机发展十分迅速。为了加大电网对新能源发电的消纳,燃煤发电机组频繁参与深度调峰导致了机组实际负荷与自动发电控制(AGC)指令负荷出现较大偏差。针对这一技术难题,以锅炉及其辅助设备为对象,研究构建了锅炉机组本体及其辅助设备的实际出力模型、应有出力模型和最大出力模型,建立了用以分析评价锅炉机组负荷偏差原因及诊断与预警的分析评价系统,并将该系统在电厂中进行了验证。结果表明:该分析评价系统可有效地对锅炉及其辅助设备的出力故障进行早期预警和诊断,可有效提高机组运行的可靠性、安全性与经济性。

一种采用记忆神经网络和曲线形状修正的负荷预测方法

针对分布式电源和新型负荷容量累积造成负荷影响因素多元化和不确定性特性增强的问题,文中提出一种采用记忆神经网络和曲线形状修正的负荷预测方法。在负荷峰值预测中,采用最大信息系数计算负荷峰值与影响因素的非线性相关性,实现对输入特征的筛选;综合考虑负荷峰值序列的长短期自相关性和输入特征与负荷峰值的不同程度相关性,结合Attention机制和双向长短时记忆(bidirectional long short-term memory,BiLSTM)神经网络建立负荷峰值预测模型。在负荷标幺曲线预测中,通过误差倒数法组合相似日和相邻日,建立负荷标幺曲线预测模型;针对预测偏差的非平稳特征,利用自适应噪声的完全集成经验模态分解和BiLSTM网络建立误差预测模型,对曲线形状进行修正。应用中国北方某城市的区域电网负荷数据为算例,验证了所提模型的有效性。

真空平板玻璃托盘集装包装系统设计与跌落测试验证

目的对真空平板玻璃进行托盘集装包装系统设计,并进行跌落仿真测试,验证包装方案的可靠性。方法提出运输包装设计五步法。第1步产品及流通环境调研,得到相关信息;第2步包装方案设计及建模,选用瓦楞纸板、胶合板、EVA泡沫和捆扎带等包装材料,利用SolidWorks软件进行三维建模,建立各跌落工况下的几何模型;第3步有限元模型建立,对各包材选用合适的材料模型,并利用试验测得相关性能参数,在所建几何模型的基础上进行接触设置、网格划分和载荷设置;第4步有限元模型可靠性验证,对模型进行实际试验和相应仿真测试,两者结果对比分析;第5步多工况下仿真测试,涉及跌落、冲击、振动等。结果展示最具代表性的角、面和棱跌落仿真测试与相应试验结果,最大误差小于10%,验证了有限元模型的可靠性。进一步展示了跌落下各块玻璃的响应加速度,其中峰值加速度远小于真空平板玻璃脆值所允许的加速度,满足防护要求。结论基于上述运输包装设计五步法,所提出的包装方案满足跌落防护要求,也证实了基于该五步法进行包装防护设计的可行性。

基于二次分解双向门控单元新型电力系统超短期负荷预测

在新型电力系统中,电力负荷随机性和波动性较强,现有预测方法难以对其实现高精度预测。为此,提出一种基于二次分解和双向门控循环单元的超短期负荷预测模型。首先,针对电力负荷的强随机性和强波动性,利用自适应噪声完备经验模态分解对电力负荷历史序列进行初步分解,使负荷序列更加平稳。随后,对初步分解得到的强非平稳分量运用连续变分模态分解进行二次分解,降低其预测难度。最后,为充分学习电力负荷的时序特征,在预测过程构建基于双向门控循环单元的超短期电力负荷预测模型。实验结果表明,该模型相较于现有优秀预测模型有更高的预测精度。

基于CNN-BiGRU-Attention的短期电力负荷预测

针对目前电力负荷数据随机性强,影响因素复杂,传统单一预测模型精度低的问题,结合卷积神经网络(Convolutional neural network,CNN)、双向门控循环单元(Bi-directional gated recurrent unit,BiGRU)以及注意力机制(Attention)在短期电力负荷预测上的不同优点,提出一种基于CNN-BiGRU-Attention的混合预测模型。该方法首先通过CNN对历史负荷和气象数据进行初步特征提取,然后利用BiGRU进一步挖掘特征数据间时序关联,再引入注意力机制,对BiGRU输出状态给与不同权重,强化关键特征,最后完成负荷预测。试验结果表明,该模型的平均绝对百分比误差(Mean absolute percentage error,MAPE)、均方根误差(Root mean square error,RMSE)、判定系数(R-square,R~2)分别为0.167%、0.057%、0.993,三项指标明显优于其他模型,具有更高的预测精度和稳定性,验证了模型在短期负荷预测中的优势。

服役环境下动车车底装备滑槽疲劳载荷谱的编制与验证

随着动车组向更高速度发展,对列车轻量化的需求愈发迫切。在现有的动车组平台中,将没有横梁底架的车体改为底架滑槽吊挂能够显著减轻设备框架的质量和体积,实现列车轻量化目的。然而,滑槽吊挂引起的最为关切的问题是结构的疲劳强度能否满足需求。为了了解结构的疲劳强度性能,基于服役中动车组车下设备振动加速度,结合现有标准体系,采用服役全周期内工况组合和载荷外推方法,采用累积损伤原理和疲劳试验加速法,编制107次循环次数下的等效振动幅值,并开展滑槽结构疲劳强度试验。研究结果表明:与既有标准体系相比,考虑服役环境的车下设备垂向载荷小于标准值,横向和纵向载荷大于标准值;当轻量化高速动车组大质量车下设备采用滑槽吊挂方式时,结构可以满足30年设计寿命要求。

32位RISC微处理器Load/Store部件的设计

简要介绍了一个32位RISC微处理器芯片ARS03的体系结构,阐述了处理器内部各个模块的功能。着重讨论了其中的Load/Store部件的设计,以及对关键路径的优化措施。Verilog仿真、综合和静态时序分析的的结果表明设计达到了预定要求。目前ARS03微处理器已经进入后端流程,不久就将使用0.25微米的工艺进行流片。

串联式混合动力车辆动力单元载荷补偿策略研究

为解决串联式混合动力车辆动力单元突加重载时发动机转矩负荷率过高导致运行异常的问题,设计了一种基于模糊控制的载荷补偿策略,通过控制发电机转矩来调节发动机转矩负荷率,避免发动机在临界条件工作。仿真和试验结果表明,该策略有效改善了动力单元工作特性,使其可以稳定平滑地过渡到预期工作点,提高了动力单元工作的鲁棒性。

曹娥江流域水环境容量与污染负荷分配研究

以曹娥江流域为研究对象,计算水环境容量,将其分配至各控制单元并细化到污染源类型,提出各控制单元污染物的削减量,以期提升曹娥江水环境质量。结果表明:1)2016—2021年,曹娥江流域内的屠家埠、汤曹汇合口和曹娥江大闸闸前3个监测断面的COD、氨氮、TP均达到《地表水环境质量标准》(GB 3838—2002)Ⅲ类要求。2)COD、氨氮、TN、TP入河量分别为16013.20、959.10、3658.53、416.05 t/a,且主要以非点源为主。3)基于满足曹娥江流域水体监测断面和入海断面水质达标的约束条件下,COD、氨氮、TN、TP水环境容量分别为47945.18、2435.81、6863.19、474.98 t/a。流域内COD还有剩余水环境容量,不需削减,氨氮、TN、TP分别需削减6.91、501.52、128.34 t/a。

TEB:GPU上矩阵分解重构的高效SpMV存储格式

稀疏矩阵向量乘法(SpMV)是科学与工程领域中一个至关重要的计算过程,CSR(compressed sparse row)格式是最常用的稀疏矩阵存储格式之一,在图形处理器(GPU)平台上实现并行SpMV的过程中,其只存储稀疏矩阵的非零元,避免零元素填充所带来的计算冗余,节约存储空间,但存在着负载不均衡的问题,浪费了计算资源。针对上述问题,对近年来效果良好的存储格式进行了研究,提出了一种逐行分解重组存储格式——TEB(threshold-exchangeorder block)格式。该格式采用启发式阈值选择算法确定合适分割阈值,并结合基于重排序的行归并算法,对稀疏矩阵进行重构分解,使得块与块之间非零元个数尽可能得相近,其次结合CUDA(computer unified device architecture)线程技术,提出了基于TEB存储格式的子块间并行SpMV算法,能够合理分配计算资源,解决负载不均衡问题,从而提高SpMV并行计算效率。为了验证TEB存储格式的有效性,在NVIDIA Tesla V100平台上进行实验,结果表明TEB相较于PBC(partition-block-CSR)、AMF-CSR(adaptive multi-row folding of CSR)、CSR-Scalar(compressed sparse row-scalar)和CSR5(compressed sparse row 5)存储格式,在SpMV的时间性能方面平均可提升3.23、5.83、2.33和2.21倍;在浮点计算性能方面,平均可提高3.36、5.95、2.29和2.13倍。