Optimization of the Gas Generator in Composite Power System with Tip-Jet Rotor

The key and bottleneck of research on the tip-jet rotor compound helicopter lies in the power system. Computational Fluid Dynamics (CFD) was used to numerically simulate the gas generator and rotor inner passage of the tip-jet rotor composite power system, studying the effects of intake mode, inner cavity structure, propellant components, and injection amount on the characteristics of the composite power system. The results show that when a single high-temperature exhaust gas enters, the gas generator outlet fluid is uneven and asymmetric;when two-way high-temperature exhaust gas enters, the outlet temperature of the gas generator with a tilted inlet is more uniform than that with a vertical inlet;adding an inner cavity improves the temperature and velocity distribution of the gas generator's internal flow field;increasing the energy of the propellant is beneficial for improving the available moment.

Convertibility in Multi-Goal and Multi-Objective Optimization

Multi-goal and multi-objective optimizations are similar techniques to achieve multiple conflicting goals/objectives simultaneously. There are several techniques for solving multi-goal and multi-objective optimization problems. The present study proposed the possibility of convertibility in solving multi-goal and multi-objective optimization problems.

Optimum path planning of mobile robot in unknown static and dynamic environments using Fuzzy-Wind Driven Optimization algorithm

This article introduces a singleton type-1 fuzzy logic system(T1-SFLS) controller and Fuzzy-WDO hybrid for the autonomous mobile robot navigation and collision avoidance in an unknown static and dynamic environment. The WDO(Wind Driven Optimization) algorithm is used to optimize and tune the input/output membership function parameters of the fuzzy controller. The WDO algorithm is working based on the atmospheric motion of infinitesimal small air parcels navigates over an N-dimensional search domain. The performance of this proposed technique has compared through many computer simulations and real-time experiments by using Khepera-Ⅲ mobile robot. As compared to the T1-SFLS controller the Fuzzy-WDO algorithm is found good agreement for mobile robot navigation.

Data Driven Vibration Control:A Review

With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing,aviation,and healthcare,the data driven vibration control(DDVC)has attracted broad interests from both the industrial and academic communities.Input shaping(IS),as a simple and effective feedforward method,is greatly demanded in DDVC methods.It convolves the desired input command with impulse sequence without requiring parametric dynamics and the closed-loop system structure,thereby suppressing the residual vibration separately.Based on a thorough investigation into the state-of-the-art DDVC methods,this survey has made the following efforts:1)Introducing the IS theory and typical input shapers;2)Categorizing recent progress of DDVC methods;3)Summarizing commonly adopted metrics for DDVC;and 4)Discussing the engineering applications and future trends of DDVC.By doing so,this study provides a systematic and comprehensive overview of existing DDVC methods from designing to optimizing perspectives,aiming at promoting future research regarding this emerging and vital issue.

Modelling to Generate Alternatives Using Simulation-Driven Optimization: An Application to Waste Management Facility Expansion Planning

Public sector decision-making typically involves complex problems that are riddled with competing performance objecttives and possess design requirements which are difficult to capture at the time that supporting decision models are constructed. Environmental policy formulation can prove additionally complicated because the various system components often contain considerable stochastic uncertainty and frequently numerous stakeholders exist that hold completely incompatible perspectives. Consequently, there are invariably unmodelled performance design issues, not apparent at the time of the problem formulation, which can greatly impact the acceptability of any proposed solutions. While a mathematically optimal solution might provide the best solution to a modelled problem, normally this will not be the best solution to the underlying real problem. Therefore, in public environmental policy formulation, it is generally preferable to be able to create several quantifiably good alternatives that provide very different approaches and perspectives to the problem. This study shows how a computationally efficient simulation-driven optimization approach that com- bines evolutionary optimization with simulation can be used to generate multiple policy alternatives that satisfy required system criteria and are maximally different in decision space. The efficacy of this modelling-to-generate-alternatives method is specifically demonstrated on a municipal solid waste management facility expansion case.

Manufacturing task data chain-driven production logistics trajectory analysis and optimization decision making method

Production logistics(PL)is considered as a critical factor that affects the efficiency and cost of production operations in discrete manufacturing systems.To effectively utilize manufacturing big data to improve PL efficiency and promote job shop floor economic benefits,this study proposes a PL trajectory analysis and optimization decision making method driven by a manufacturing task data chain(MTDC).First,the manufacturing task chain(MTC)is defined to characterize the discrete production process of a product.To handle manufacturing big data,the MTC data paradigm is designed,and the MTDC is established.Then,the logistics trajectory model is presented,where the various types of logistics trajectories are extracted using the MTC as the search engine for the MTDC.Based on this,a logistics efficiency evaluation indicator system is proposed to support the optimization decision making for the PL.Finally,a case study is applied to verify the proposed method,and the method determines the PL optimization decisions for PL efficiency without changing the layout and workshop equipment,which can assist managers in implementing the optimization decisions.

Optimal switching policy for performance enhancement of distributed parameter systems based on event-driven control

This paper aims to improve the performance of a class of distributed parameter systems for the optimal switching of actuators and controllers based on event-driven control. It is assumed that in the available multiple actuators, only one actuator can receive the control signal and be activated over an unfixed time interval, and the other actuators keep dormant. After incorporating a state observer into the event generator, the event-driven control loop and the minimum inter-event time are ultimately bounded. Based on the event-driven state feedback control, the time intervals of unfixed length can be obtained. The optimal switching policy is based on finite horizon linear quadratic optimal control at the beginning of each time subinterval. A simulation example demonstrate the effectiveness of the proposed policy.

Optimization of Linear Filtering Model to Predict Post-LASIK Corneal Smoothing Based on Training Data Sets

Laser vision correction is a rapidly growing field for correcting nearsightedness, farsightedness as well as astigmatism with dominating laser-assisted in situ keratomileusis (LASIK) procedures. While the technique works well for correcting spherocylindrical aberrations, it does not fully correct high order aberrations (HOAs), in particular spherical aberration (SA), due to unexpected induction of HOAs post-surgery. Corneal epithelial remodeling was proposed as one source to account for such HOA induction process. This work proposes a dual-scale linear filtering kernel to model such a process. Several retrospective clinical data sets were used as training data sets to construct the model, with a downhill simplex algorithm to optimize the two free parameters of the kernel. The performance of the optimized kernel was testedon new clinical data sets that were not previously used for the optimization.

考虑源荷不确定性及用户响应行为的电力系统低碳经济调度

电力系统是未来实现碳中和目标的重要领域,随着源-荷不确定性的加剧,基于确定性模型的低碳经济调度方法无法准确描述不确定因素对碳排放的影响。针对上述问题,该文首先从系统层面构建考虑不确定性的低碳鲁棒优化模型,利用概率模型对源荷不确定因素进行建模,并采用机会约束对目标满足期望的显著性水平进行描述,通过最大化不确定因素的置信水平得出风险规避策略下的鲁棒调度方案。接着从用户层面构建基于事件驱动的用户低碳响应模型,根据系统层计算结果,通过设定事件触发的碳排放阈值定义碳排放超额事件,并以价格形式引导用户的低碳用能行为。最后通过算例分析,验证所提模型能有效量化评估系统的低碳经济调度不确定性水平,充分发挥用户侧降碳能力,实现源荷双侧低碳目标的协同。

考虑大规模风光分层接入的配电网多层协调无功优化方法

大量风光新能源分层、多点接入给配电网运行带来了电压越限等负面影响,增加了不同电压等级无功协同优化的难度。兼顾不同电压等级调节需求,从模型降维等值的角度,提出了基于等值模型的配电网多层协调无功优化方法。该方法分别针对含有无功调节设备及没有无功调节设备的两种台区系统,利用神经网络对大量台区相关运行数据进行训练得到台区拟合模型,形成不可控和可控两种类型的台区等值模型,并用这两类拟合模型分别代替两类台区系统的物理模型,使台区以数据驱动的方式参与配电网无功优化,形成馈线物理模型和台区拟合模型组成的单一电压等级物数混合优化调度模型。将原多层无功优化问题转换为单层系统优化问题,再对该单层系统无功优化调度问题进行求解。该方法可降低配电网系统优化计算规模,从而减小计算量,以无功优化数学模型与数据驱动方法相结合的方式,实现配电网馈线-台区多层协调无功优化。通过算例验证了所提方法对于解决分布式风光分层接入所导致电压越限问题的可行性、有效性和优越性,能够保障配电网的经济安全稳定运行。

考虑灵活资源及模数驱动方法的电力系统调度方法综述

可再生能源及负荷种类的增多给电力系统运行带来更大不确定性,也给电力系统经济调度带来挑战。深入分析总结灵活资源特性并对不确定性的准确建模是评估电力系统灵活性和实现经济调度的基础。基于模型或数据驱动的调度建模方法面临诸多挑战,将模型与数据驱动方式相结合,并充分发挥二者优势是电力系统优化调度的发展方向。该文从灵活资源分类及特性、系统灵活性评估方法及优化调度的模型与数据驱动建模3个方面进行了归纳整理。首先,从电网侧、供应侧及需求侧3个方面介绍了系统中的灵活资源,并总结了其调节特性。其次,介绍了权重分配、数理统计及包络区间3种常用电力系统灵活性评价指标,并总结了不同方法的适用性。然后,总结了模型驱动或数据驱动的应用现状及其各自优缺点,并对模型数据交互驱动的研究现状进行了概述。最后,对考虑灵活资源的电力系统调度方案研究进行了展望。

双并列转子永磁直驱电机转子不平衡力分析与优化

双并列转子永磁直驱电机受轴间距限制,设计时会切掉部分定子铁心和绕组,造成转子电磁力不平衡。为最大限度地抑制转子所受的不平衡电磁力,该文从双并列转子结构出发,分析了电机绕组电感参数不对称的原因,提出通过合理设计线圈跨距和分布平衡三相电感。在此基础上,构建转子所受总电磁力的数学模型,理论分析各个分力的来源及影响因素,研究各个分力对转子所受总电磁力的影响,提出了优化占空角、优化耦合间距、采用定子端部不对称结构、采用转子偏心结构和切除部分单元电机五种优化方法,实现对转子所受电磁力的优化,探讨了不同方法的优化效果,以及其对转矩脉动的影响,总结了不同优化方法的优缺点。

基于数据驱动的配电网无功优化

传统无功电压控制由于分布式电源、储能以及柔性负荷的接入面临计算速度和精度上的挑战。该文提出了一种基于数据驱动的配电网无功电压优化方法,通过跟踪实际系统的运行参数,实现无功电压的主动控制。在极限学习机中引入自动编码器构建深度学习机制,利用自动编码器建立极限学习机输入-输出的直接耦合关系,实现无监督学习和有监督学习有机结合,缩短训练模型的迭代过程;利用蒙特卡洛法基于分布式电源、负荷预测信息构建配电网运行场景,利用深度极限学习机挖掘运行场景优化运行与无功调压设备状态间的内在联系,建立电网运行场景与系统无功调压策略的映射关系。该文提出的基于数据驱动的无功优化方法不依赖实际系统潮流计算,能够实现配电网运行状态的跟踪和无功调节设备的优化调度,为配电网无功电压的主动控制打下基础。

统一大市场建设对城际经济协同发展的影响研究

城际经济协同发展是我国现代化城际发展格局构建的重要方向,而统一大市场建设在城际经济协同发展进程中发挥着重要作用。文章基于2011—2020年全国243个地级及以上城市构成的29403组两两城市对样本,实证检验统一大市场建设对城际经济协同发展的影响效应及其作用机制和调节效应。研究发现:统一大市场建设显著促进我国城际经济协同发展,在处理内生性问题和进行稳健性检验后结论依然成立;处于同一区域内部的城市间统一大市场建设推动经济协同发展的作用强于不同区域城市间的作用;统一大市场建设可以通过产业结构优化和市场需求驱动,促进城际经济协同发展;经济关联度可以强化统一大市场建设对城际经济协同发展的促进作用,而地理临近性的调节作用并不显著。

基于频率响应特性的储能辅助电网调频方法

新能源渗透率的提高增加了电网频率控制的复杂度,储能辅助电网调频能在一定程度上缓解该问题,但受储能运行的安全性与经济性约束,要求调频措施更具针对性。文中对此展开研究,提出一种基于频率响应特性的储能辅助电网一次调频方法。首先,在储能辅助电网调频模型基础上,选择惯性加下垂的储能辅助电网调频综合控制方法,通过电网频率变化率(rate of change of frequency,RoCoF)、频率偏差与调频需求的关联性分析,设计基于频率响应特性的调频需求分区规则;然后,根据不同调频需求对应的分区判断,对储能有功输出方式进行动态调整,以响应调频需求的不确定性,并在此基础上,针对调频需求与储能出力需求、储能出力强弱与其循环使用寿命间的矛盾关系,通过多目标优化问题的设计与求解来予以平衡;最后,仿真结果验证了所提方法能够在保证电网调频效果的基础上,有效降低储能充放电深度。

面向精细化频率恢复要求的受端电网响应驱动自适应减载控制策略

随着电网中频率敏感性装备的大量接入,近年来电网安全导则中规定了更加精细化的频率恢复标准(frequency recovery criterion,FRC),旨在使频率敏感性装备能够在受扰后频率动态过程中安全穿越,保障系统频率安全运行。为此,该文提出一种面向精细化FRC要求的受端电网响应驱动自适应减载控制策略。以FRC为控制导向设计基于模糊控制的智能自适应减载控制器,并引入防止减载控制过切和欠切的推理机制,控制器可根据频率轨迹演变状态进行“判定-决策-控制”的实时自适应减载动作;为提升减载控制性能,进一步提出以频率恢复满足FRC要求为约束、减载代价最小为目标的控制器参数整定模型及其优化求解方法;综合各负荷站点优化减载模糊控制器构成响应FRC要求的分散式减载控制策略,可完全实时自适应决策减载要素,使频率恢复满足FRC要求基础上有效减小减载控制代价。通过对单直流馈入的简单受端系统算例和多直流馈入的省级受端电网仿真分析,验证该文所提减载策略的有效性和适用性。

绳驱桁架结构伸展臂的主动振动控制

伸展臂由于受到空间温度变化和卫星机动动作的影响,不可避免地会产生振动。在阻尼较低的空间环境中,这种振动衰减较慢,影响伸展臂的正常工作。因此,合适的振动控制方法对于伸展臂的正常工作至关重要。本文采用拉绳作为作动器的空间桁架结构伸展臂,研究其作动绳上铰链串联顺序的优化和模型预测控制律的设计,这两部分内容均考虑了作动绳的单边约束和饱和约束。首先,通过ANSYS软件获取结构的模态信息;然后结合系统可控性原理和遗传算法计算出作动绳上铰链串联最优顺序;最后,在数值仿真中,通过设计的控制率对一个由四个单元组成的桁架结构伸展臂的振动控制过程进行仿真分析。结果表明,提出的作动绳串联铰链的方式能够有效抑制伸展臂的振动。

基于样本划分的数据驱动报童问题研究

大数据时代的到来为企业库存管理带来了前所未有的机遇,同时也对其相应的决策方法提出了新的挑战。高效利用实时市场数据支撑库存管理智能决策已经成为企业提升库存管理效率的关键。传统库存管理研究是基于需求预测的基础上再做库存决策。而近年来流行的数据驱动库存管理方法是跳过需求预测的过程,直接建立需求数据与库存决策的关系。本文研究数据驱动的经典报童问题,其随机需求分布是未知的,报童只有若干期历史的需求数据。本文基于数据样本划分提出了新的数据驱动鲁棒优化方法来解决报童的订货问题。该方法通过将需求数据样本划分成不同的区间来构造随机需求分布的模糊集,从而求解相应的鲁棒优化问题。理论证明了该方法随着样本量的增加,其解收敛于最优解。数值实验表明相对已有的鲁棒方法,新方法具有相对弱的鲁棒性,但平均性能更好,并且计算复杂度相对小很多。同时表明本文提出的样本划分鲁棒方法比SAA等数据驱动方法具有更好的平均性能和更强的鲁棒性。

基于扩张状态观测器的过热汽温系统建模与参数智能辨识

可再生能源的高渗透率给电网供需匹配带来严峻挑战的同时,燃煤机组需要承担着大量的调峰调频任务,这对过热汽温系统的安全稳定运行造成了一定威胁,因此有必要建立面向热工控制的汽温数学模型。考虑到迟延型扩张状态观测器(time-delayed extended state observer,TD-ESO)的总扰动信号中含有大量模型信息,提出一种基于ESO补偿模型的参数智能优化和信息提取方法,即以总扰动中未知信息量最小为目标,采用改进沙丘猫算法对模型参数优化并提取总扰动中已知模型信息补偿至ESO的输入端。在仿真算例方面,线性和非线性系统的测试结果表明,所提辨识方法对有无输入迟延的两种系统均有良好的适用性和较高的精度;在实际应用方面,基于超超临界二次再热机组的过热汽温系统数据进行模型辨识与验证,同样表明该建模方法是合理、准确的。因此,该文所建立的模型能够为汽温系统的控制策略设计和性能优化等方面提供有价值的参考。

混合式教学在理实一体化类课程中的应用——以工业机器人现场编程为例

理实一体化类课程是高职院校最为标志性的课程,利用手段丰富的混合式教学来提升该类课程的教学效果,从而达到人才培养质量的提升。通过精品在线开放课程和学习通平台来进行线上线下混合式教学,以解决该类课程目前在教学过程中存在的一些局限,特别是如何充分利用线上的教学数据,如何分析整理数据,以数据驱动的模式来实时优化课堂的教学设计。通过教学实践总结,对教学方法的创新、教学手段的应用进行了梳理与探讨,为理实一体化类课程的教学改革提供一定的借鉴。