Analysis of debris flow control effect and hazard assessment in Xinqiao Gully,Wenchuan M_(s)8.0 earthquake area based on numerical simulation

Xinqiao Gully is located in the area of the 2008 Wenchuan M_(s)8.0 earthquake in Sichuan province,China.Based on the investigation of the 2023"6-26"Xinqiao Gully debris flow event,this study assessed the effectiveness of the debris flow control project and evaluated the debris flow hazards.Through field investigation and numerical simulation methods,the indicators of flow intensity reduction rate and storage capacity fullness were proposed to quantify the effectiveness of the engineering measures in the debris flow event.The simulation results show that the debris flow control project reduced the flow intensity by41.05%to 64.61%.The storage capacity of the dam decreases gradually from upstream to the mouth of the gully,thus effectively intercepting and controlling the debris flow.By evaluating the debris flow of different recurrence intervals,further measures are recommended for managing debris flow events.

Groundwater flow through fractured rocks and seepage control in geotechnical engineering: Theories and practices

Groundwater flow through fractured rocks has been recognized as an important issue in many geotechnical engineering practices.Several key aspects of fundamental mechanisms,numerical modeling and engineering applications of flow in fractured rocks are discussed.First,the microscopic mechanisms of fluid flow in fractured rocks,especially under the complex conditions of non-Darcian flow,multiphase flow,rock dissolution,and particle transport,have been revealed through a com-bined effort of visualized experiments and theoretical analysis.Then,laboratory and field methods of characterizing hydraulic properties(e.g.intrinsic permeability,inertial permeability,and unsaturated flow parameters)of fractured rocks in different flow regimes have been proposed.Subsequently,high-performance numerical simulation approaches for large-scale modeling of groundwater flow in frac-tured rocks and aquifers have been developed.Numerical procedures for optimization design of seepage control systems in various settings have also been proposed.Mechanisms of coupled hydro-mechanical processes and control of flow-induced deformation have been discussed.Finally,three case studies are presented to illustrate the applications of the improved theoretical understanding,characterization methods,modeling approaches,and seepage and deformation control strategies to geotechnical engi-neering projects.

An Improved Hyperplane Assisted Multiobjective Optimization for Distributed Hybrid Flow Shop Scheduling Problem in Glass Manufacturing Systems

To solve the distributed hybrid flow shop scheduling problem(DHFS)in raw glass manufacturing systems,we investigated an improved hyperplane assisted evolutionary algorithm(IhpaEA).Two objectives are simultaneously considered,namely,the maximum completion time and the total energy consumptions.Firstly,each solution is encoded by a three-dimensional vector,i.e.,factory assignment,scheduling,and machine assignment.Subsequently,an efficient initialization strategy embeds two heuristics are developed,which can increase the diversity of the population.Then,to improve the global search abilities,a Pareto-based crossover operator is designed to take more advantage of non-dominated solutions.Furthermore,a local search heuristic based on three parts encoding is embedded to enhance the searching performance.To enhance the local search abilities,the cooperation of the search operator is designed to obtain better non-dominated solutions.Finally,the experimental results demonstrate that the proposed algorithm is more efficient than the other three state-of-the-art algorithms.The results show that the Pareto optimal solution set obtained by the improved algorithm is superior to that of the traditional multiobjective algorithm in terms of diversity and convergence of the solution.

Reinforcement learning for wind-farm flow control:Current state and future actions

Wind-farm flow control stands at the forefront of grand challenges in wind-energy science.The central issue is that current algorithms are based on simplified models and,thus,fall short of capturing the complex physics of wind farms associated with the high-dimensional nature of turbulence and multiscale wind-farm-atmosphere interactions.Reinforcement learning(RL),as a subset of machine learning,has demonstrated its effectiveness in solving high-dimensional problems in various domains,and the studies performed in the last decade prove that it can be exploited in the development of the next generation of algorithms for wind-farm flow control.This review has two main objectives.Firstly,it aims to provide an up-to-date overview of works focusing on the development of wind-farm flow control schemes utilizing RL methods.By examining the latest research in this area,the review seeks to offer a comprehensive understanding of the advancements made in wind-farm flow control through the application of RL techniques.Secondly,it aims to shed light on the obstacles that researchers face when implementing wind-farm flow control based on RL.By highlighting these challenges,the review aims to identify areas requiring further exploration and potential opportunities for future research.

Model Predictive Control Strategy of Multi-Port Interline DC Power Flow Controller

There are issues with flexible DC transmission system such as a lack of control freedom over power flow.In order to tackle these issues,a DC power flow controller(DCPFC)is incorporated into a multi-terminal,flexible DC power grid.In recent years,a multi-port DC power flow controller based on a modular multi-level converter has become a focal point of research due to its simple structure and robust scalability.This work proposes a model predictive control(MPC)strategy for multi-port interline DC power flow controllers in order to improve their steady-state dynamic performance.Initially,the mathematical model of a multi-terminal DC power grid with a multi-port interline DC power flow controller is developed,and the relationship between each regulated variable and control variable is determined;The power flow controller is then discretized,and the cost function and weight factor are built with numerous control objectives.Sub module sorting method and nearest level approximation modulation regulate the power flow controller;Lastly,theMATLAB/Simulink simulation platformis used to verify the correctness of the establishedmathematicalmodel and the control performance of the suggestedMPC strategy.Finally,it is demonstrated that the control strategy possesses the benefits of robust dynamic performance,multiobjective control,and a simple structure.

Competitive and Cooperative-Based Strength Pareto Evolutionary Algorithm for Green Distributed Heterogeneous Flow Shop Scheduling

This work aims to resolve the distributed heterogeneous permutation flow shop scheduling problem(DHPFSP)with minimizing makespan and total energy consumption(TEC).To solve this NP-hard problem,this work proposed a competitive and cooperative-based strength Pareto evolutionary algorithm(CCSPEA)which contains the following features:1)An initialization based on three heuristic rules is developed to generate a population with great diversity and convergence.2)A comprehensive metric combining convergence and diversity metrics is used to better represent the heuristic information of a solution.3)A competitive selection is designed which divides the population into a winner and a loser swarms based on the comprehensive metric.4)A cooperative evolutionary schema is proposed for winner and loser swarms to accelerate the convergence of global search.5)Five local search strategies based on problem knowledge are designed to improve convergence.6)Aproblem-based energy-saving strategy is presented to reduce TEC.Finally,to evaluate the performance of CCSPEA,it is compared to four state-of-art and run on 22 instances based on the Taillard benchmark.The numerical experiment results demonstrate that 1)the proposed comprehensive metric can efficiently represent the heuristic information of each solution to help the later step divide the population.2)The global search based on the competitive and cooperative schema can accelerate loser solutions convergence and further improve the winner’s exploration.3)The problembased initialization,local search,and energy-saving strategies can efficiently reduce the makespan and TEC.4)The proposed CCSPEA is superior to the state-of-art for solving DHPFSP.

Flow control performance evaluation of a tri-electrode sliding discharge plasma actuator

Tri-electrode sliding discharge(TED)plasma actuators are formed by adding a direct current(DC)exposed electrode to conventional dielectric barrier discharge(DBD)plasma actuators.There are three TED modes depending on the polarity and amplitude of the DC supply:DBD discharge,extended discharge and sliding discharge.This paper evaluates the electrical,aerodynamic and mechanical characteristics of a TED plasma actuator based on energy analysis,particle image velocimetry experiments and calculations using the Navier-Stokes equation.The flow control performances of different discharge modes are quantitatively analyzed based on characteristic parameters.The results show that flow control performance in both extended discharge and sliding discharge is more significant than that of DBD,mainly because of the significantly higher(up to 141%)body force of TED compared with DBD.However,conductivity loss is the primary power loss caused by the DC polarity for TED discharge.Therefore,power consumption can be reduced by optimizing the dielectric material and thickness,thus improving the flow control performance of plasma actuators.

Experimental investigation of dynamic stall flow control using a microsecond-pulsed plasma actuator

To alleviate the performance deterioration caused by dynamic stall of a wind turbine airfoil,the flow control by a microsecond-pulsed dielectric barrier discharge(MP-DBD) actuator on the dynamic stall of a periodically pitching NACA0012 airfoil was investigated experimentally.Unsteady pressure measurements with high temporal accuracy were employed in this study,and the unsteady characteristics of the boundary layer were investigated by wavelet packet analysis and the moving root mean square method based on the acquired pressure.The experimental Mach number was 0.2,and the chord-based Reynolds number was 870 000.The dimensionless actuation frequencies F+ were chosen to be 0.5,1,2,and 3,respectively.For the light dynamic regime,the MP-DBD plasma actuator plays the role of suppressing flow separation from the trial edge and accelerating the flow reattachment due to the high-momentum freestream flow being entrained into the boundary layer.Meanwhile,actuation effects were promoted with the increasing dimensionless actuation frequency F+.The control effects of the deep dynamic stall were to delay the onset and reduce the strength of the dynamic stall vortex due to the accumulating vorticity near the leading edge being removed by the induced coherent vortex structures.The laminar fluctuation and Kelvin-Helmholtz(K-H) instabilities of transition and relaminarization were also mitigated by the MP-DBD actuation,and the alleviated K-H rolls led to the delay of the transition onset and earlier laminar reattachment,which improved the hysteresis effect of the dynamic stall.For the controlled cases of F+=2,and F+=3,the laminar fluctuation was replaced by relatively low frequency band disturbances corresponding to the harmonic responses of the MP-DBD actuation frequency.

Traffic flow of connected and automated vehicles at lane drop on two-lane highway: An optimization-based control algorithm versus a heuristic rules-based algorithm

This paper investigates traffic flow of connected and automated vehicles at lane drop on two-lane highway. We evaluate and compare performance of an optimization-based control algorithm(OCA) with that of a heuristic rules-based algorithm(HRA). In the OCA, the average speed of each vehicle is maximized. In the HRA, virtual vehicle and restriction of the command acceleration caused by the virtual vehicle are introduced. It is found that(i) capacity under the HRA(denoted as C_(H)) is smaller than capacity under the OCA;(ii) the travel delay is always smaller under the OCA, but driving is always much more comfortable under the HRA;(iii) when the inflow rate is smaller than C_(H), the HRA outperforms the OCA with respect to the fuel consumption and the monetary cost;(iv) when the inflow rate is larger than C_(H), the HRA initially performs better with respect to the fuel consumption and the monetary cost, but the OCA would become better after certain time. The spatiotemporal pattern and speed profile of traffic flow are presented, which explains the reason underlying the different performance. The study is expected to help for better understanding of the two different types of algorithm.

Autonomous Multi-Factor Energy Flows Controller (AmEFC): Enhancing Renewable Energy Management with Intelligent Control Systems Integration

The transition to sustainable energy systems is one of the defining challenges of our time, necessitating innovations in how we generate, distribute, and manage electrical power. Micro-grids, as localized energy hubs, have emerged as a promising solution to integrate renewable energy sources, ensure energy security, and improve system resilience. The Autonomous multi-factor Energy Flow Controller (AmEFC) introduced in this paper addresses this need by offering a scalable, adaptable, and resilient framework for energy management within an on-grid micro-grid context. The urgency for such a system is predicated on the increasing volatility and unpredictability in energy landscapes, including fluctuating renewable outputs and changing load demands. To tackle these challenges, the AmEFC prototype incorporates a novel hierarchical control structure that leverages Renewable Energy Sources (RES), such as photovoltaic systems, wind turbines, and hydro pumps, alongside a sophisticated Battery Management System (BMS). Its prime objective is to maintain an uninterrupted power supply to critical loads, efficiently balance energy surplus through hydraulic storage, and ensure robust interaction with the main grid. A comprehensive Simulink model is developed to validate the functionality of the AmEFC, simulating real-world conditions and dynamic interactions among the components. The model assesses the system’s reliability in consistently powering critical loads and its efficacy in managing surplus energy. The inclusion of advanced predictive algorithms enables the AmEFC to anticipate energy production and consumption trends, integrating weather forecasting and inter-controller communication to optimize energy flow within and across micro-grids. This study’s significance lies in its potential to facilitate the seamless incorporation of RES into existing power systems, thus propelling the energy sector towards a more sustainable, autonomous, and resilient future. The results underscore the potential of such a system to revolutionize energy management practices and highlight the importance of smart controller systems in the era of smart grids.

Numerical Investigation of Flow Separation Control over an NACA0018 Airfoil Using Sweeping Jet Actuator

As an active flow control technology and with the advantages of no moving components, the Sweeping jet actuator has become a hotspot in the field of flow control. However, the linear relationship between oscillation frequency and momentum coefficient in a sweeping jet actuator makes it difficult to determine the dominant factors that affect control effectiveness. Decoupling the oscillation frequency and momentum coefficient, as well as determining the control mechanism, is the focus of studying the sweeping jet actuator. In this study, a novel sweeping jet actuator is designed using synthetic jets instead of feedback channels and applied to the flow separation control of NACA0018 airfoil. This article studies the control effect under three oscillation frequencies of F+ = f × c/U∞ = 1, 10, 100 and three momentum coefficients of Cμ = 0.45%, 0.625%, 0.9%. The numerical results indicate that all three oscillation frequencies have good control effects on flow separation, and the control effect is best when F+ = 1, with the maximum lift coefficient increasing by approximately 14% compared to the other two cases. And the sweeping jet actuator has a better ability to control flow separation as the momentum coefficient increases. By decoupling the characteristics of the sweeping jet actuator and conducting numerical analysis of the flow control effect, it will promote its better engineering application in the field of flow control. .

Prediction and Analysis of Elevator Traffic Flow under the LSTM Neural Network

Elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion within elevator systems. Many passengers experience dissatisfaction with prolonged wait times, leading to impatience and frustration among building occupants. The widespread adoption of neural networks and deep learning technologies across various fields and industries represents a significant paradigm shift, and unlocking new avenues for innovation and advancement. These cutting-edge technologies offer unprecedented opportunities to address complex challenges and optimize processes in diverse domains. In this study, LSTM (Long Short-Term Memory) network technology is leveraged to analyze elevator traffic flow within a typical office building. By harnessing the predictive capabilities of LSTM, the research aims to contribute to advancements in elevator group control design, ultimately enhancing the functionality and efficiency of vertical transportation systems in built environments. The findings of this research have the potential to reference the development of intelligent elevator management systems, capable of dynamically adapting to fluctuating passenger demand and optimizing elevator usage in real-time. By enhancing the efficiency and functionality of vertical transportation systems, the research contributes to creating more sustainable, accessible, and user-friendly living environments for individuals across diverse demographics.

基于广义粗糙集的不确定条件下的Flow Shop调度

在实际生产调度过程中,加工时间的不确定性是普遍存在的,因此引用广义粗糙变量来表示不确定的工件处理时间,定义粗糙加法运算,precision以及ratio,建立了处理时间不确定性的FlowShop调度问题的粗糙规划模型,提出了粗糙遗传调度算法将其应用于调度模型的求解,仿真实验证明了算法的有效性。

加工时间依赖开工时间的Flow Shop调度问题

讨论了加工时间依赖开工时间的Flow shop 调度问题。在这类问题中,工件的加工时间是开工时间的简单线性函数,机器间满足某种优势关系。对于这类问题,当目标函数是极小化最大完工时间时,尽管比相应的经典问题复杂,但仍存在多项式算法。如果目标函数是极小化加权完工时间和或极小化最大延误,则经典问题中的结论未必成立。

调整时间可分离的无等待Flow Shop调度问题

研究了 3台机器调整时间可分离的无等待FlowShop调度问题 ,目标函数为极小化加权完工时间和·基于对问题的分析 ,给出了目标函数的一般表示·对某些特殊情况 ,给出了问题存在多项式最优算法的充分条件·在此条件下 ,将单机调度问题的WSPT规则应用到无等待FlowShop调度问题 ,得到了求解无等待FlowShop问题最优调度的分派规则·通过反例指出了文献中的某些错误·

利用博弈演化算法求解置换Flow shop调度问题

在研究了流程工业中的Flowshop问题的基础上,建立了相应的数学模型,利用基于经济学博弈论的演化博弈算法对经典问题进行了求解,仿真结果证明了算法的有效性。

用混合算法求解Flow shop调度问题

研究了流程工业中的Flowshop调度问题,针对免疫算法的随机性和不确定性,结合分枝定界方法的特点,提出了一种基于免疫算法和分枝定界方法的混合调度算法,仿真结果表明该算法不仅能有效解决调度问题,而且提高了搜索效率。

一类Flow Shop调度问题最优调度区间摄动鲁棒性

调度的鲁棒性是调度应用中的一个重要问题.本文从最优调度不变的角度研究了调度的鲁棒性问题.首先定义了最优调度的区间摄动鲁棒性,即当问题中某些参数在各自的区间上变化时最优调度保持不变的性质.然后对比例FlowShop调度问题(任给一个工件它在各台机器上的加工时间都相同)进行了研究.通过一个引理我们证明了本文的结果,该引理指出了r个参数的大小次序与它们的变化区间的相交关系之间的联系.本文的结果是目标函数为完成时间总和时在加工时间扰动下最优调度具有区间摄动鲁棒性的三个充分必要条件,目标函数为最大拖期时间时及目标函数为拖后工件个数时在加工时间和/或交付期扰动下最优调度具有区间摄动鲁棒性的若干充分条件.这些结果与调度在一个由变化参数构成的超矩形的一些顶点上的最优性有关.文中给出了使用这些结果的例子.

不确定条件下不同交货期窗口的Flow Shop调度

研究了具有不同交货期窗口的Flow Shop的提前/拖期调度问题,并考虑了处理时间的不确定性。采用三角模糊数表示不确定性信息,引入两种模糊运算,建立了问题的模糊规划模型,并采用“中间值最大隶属度”的算法将模糊规划模型转化为清晰的非线性规划模型。针对清晰的数学模型提出了基于遗传算法的优化方法,最小化对所有工件提前/拖期惩罚。最后,大量仿真实验验证了算法的有效性。

模糊交货期Flow Shop调度文化进化算法研究

基于文化算法及文化进化思想设计了的文化进化算法,通过上层文化空间的经验知识指导下层个体进化搜索的方向及步长,通过模拟人类社会文化进化的机制实现文化空间的进化与更新,最后将算法应用到模糊Flow Shop问题的求解,用Matlab编程仿真测试.结果表明,此算法解决生产调度优化问题是可行的,而且其搜索性能优于简单遗传算法及模拟退火算法.