Geopolymer concrete-filled pultruded composite beams – Concrete mix design and application

The limited research on the geopolymer concrete mix design for targeting a specific strength is identified an obstacle for their effective design and wide use. In this paper, a mix design procedure has been proposed for fly-ash based geopolymer concrete and its use as infill hybrid composite beam is investigated. Then, the structural performance of geopolymer concrete filled hybrid composite beam is investigated to determine their possible application in civil infrastructure. Firstly, a detailed procedure of mix design for fly-ash based geopolymer concrete is presented. Secondly, three hybrid beams filled with geopolymer concrete were prepared and tested in a four-point bending setup to evaluate their flexural modulus and modulus of rupture. Numerical and analytical evaluation of the behaviour of hybrid beam were performed and results showed a good agreement with the experimental investigation. Thirdly, the suitability of the beam for a composite railway sleeper is evaluated and compared with existing timber and composite sleepers. Finally, the beams’ performance in a ballast railway track is analysed using Strand7 finite element simulation software and the results showed that the new concept of using geopolymer concrete as infill to pultruded composite section satisfied the stiffness and strength requirements for a railway sleeper.     

Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.     

随车起重机伸缩臂截面多目标优化

为了提高某火箭炮弹药装填车随车起重机伸缩臂的结构刚度,减轻结构质量,建立了随车起重机伸缩臂参数化模型,并对伸缩臂在水平受载状态下进行仿真分析。以伸缩臂截面尺寸参数为设计变量,借助最优拉丁超立方试验设计建立样本空间,构造响应面多项式函数近似模型。在此基础上,利用NSGA-Ⅱ型遗传算法对伸缩臂自身重量、挠度进行多目标优化。优化结果表明:伸缩臂质量减轻了24.5%,减重效果明显。文中所采用的将参数化建模、有限元分析和数值寻优相结合的优化方法,可为起重机伸缩臂截面优化提供一定的参考。     

基于射流冲击作用的舰机适配性数值分析与优化

准确分析和控制射流冲击对航母甲板环境的影响是新型喷气偏流板设计和布局的关键所在。为了确定射流冲击影响最小的偏流板布局，运用有限体积法，采用分区混合网格方案，结合雷诺时均纳维斯托克斯（RANS）方程和SST k-ω湍流模型对喷气偏流板在不同布局下的射流冲击效应进行三维数值模拟。选取舰载机双发动机全加力状态时喷气偏流板与发动机距离不同、喷气偏流板倾角不同共12种布局组合进行射流冲击效应的对比计算,计算结果显示了喷气偏流板各布局下的流场参数、传热特性、尾喷口温升、冲击力和力矩等分布规律。定性和定量分析了燃气射流冲击下温度场和速度场的危险区域，结果表明，偏流板与发动机距离5 m、偏流板倾角45°时的布局更有利于将燃气射流向上引导。在此基础上，基于倾角最小化原则及二次导流原理优化设计了一种导流隔热性能好、工作稳定性高的被动隔热式喷气偏流板装置。     

Alloy optimisation of bainitic steel for large plastic mould

Based on the phase transformation theories, especially the T₀ concept of bainite transformation, alloy optimisation of bainitic steel with carbides has been carried out aiming at the produce of plastic mould with large cross-section. The effect of manganese and silicon on proeutectoid ferrite and bainite transformation is explored by dilatometric analysis, XRD and different microscopy techniques. The results show that after the alloy optimisation, the transformation of proeutectoid ferrite is suppressed and when the cooling rate is lower than 0·1°C?s^??1, the new lower bainite transformation appears by decreasing carbon capacity of austenite and promoting carbide precipitation. Industrial production proves that the optimised alloy SDP1 can meet the demand for the plastic mould with the thickness of 1050?mm.     

叠合盆地凸起区多期复杂断裂特征及形成机制——以准噶尔盆地车排子凸起为例

以高精度三维地震资料为基础,结合构造演化恢复和区域构造背景,系统研究了准噶尔盆地车排子凸起断裂分布和形成机制。结果表明,车排子凸起发育深部和浅部2套断裂体系,深部断裂体系和浅部断裂体系分别发育3种断裂样式。车排子凸起共经历5个构造演化阶段,其中,晚二叠世挤压-逆冲控制了深部断裂体系的初始形成,晚侏罗世逆冲-走滑控制了深部断裂体系的最终定型,新近纪叠加掀斜、局部伸展控制了浅部断裂体系的形成。不同断裂体系在油气成藏过程中均起到了重要作用。其中:深部断裂体系的红车断裂是重要的油源断裂,低序级断层控制形成了石炭系火山岩优质储集体;浅部断裂体系使得油气向浅部层系运移调整,同时形成了断块圈闭、断鼻圈闭和断层-岩性圈闭等丰富的圈闭类型。     

Multilevel adaptive control of nonlinear interconnected systems

This paper presents an adaptive backstepping-based multilevel approach for the first time to control nonlinear interconnected systems with unknown parameters. The system consists of a nonlinear controller at the first level to neutralize the interaction terms, and some adaptive controllers at the second level, in which the gains are optimally tuned using genetic algorithm. The presented scheme can be used in systems with strong couplings where completely ignoring the interactions leads to problems in performance or stability. In order to test the suitability of the method, two case studies are provided: the uncertain double and triple coupled inverted pendulums connected by springs with unknown parameters. The simulation results show that the method is capable of controlling the system effectively, in both regulation and tracking tasks.     

Systematic convergence of nonlinear stochastic estimators on the Special Orthogonal Group SO(3)

This paper introduces two novel nonlinear stochastic attitude estimators developed on the Special Orthogonal Group with the tracking error of the normalized Euclidean distance meeting predefined transient and steady‐state characteristics. The tracking error is confined to initially start within a predetermined large set such that the transient performance is guaranteed to obey dynamically reducing boundaries and decrease smoothly and asymptotically to the origin in probability from almost any initial condition. The proposed estimators produce accurate attitude estimates with remarkable convergence properties using measurements obtained from low‐cost inertial measurement units. The estimators proposed in continuous form are complemented by their discrete versions for the implementation purposes. The simulation results illustrate the effectiveness and robustness of the proposed estimators against uncertain measurements and large initialization error, whether in continuous or discrete form.     

Design structure matrix (DSM) method application to issue of modeling and analyzing the fault tree of a wind energy asset

The perpetual energy production of a wind farm could be accomplished (under proper weather conditions) if no failures occurred. But even the best possible design, manufacturing, and maintenance of a system cannot eliminate the failure possibility. In order to understand and minimize the system failures, the most crucial components of the wind turbines, which are prone to failures, should be identified. Moreover, it is essential to determine and classify the criticality of the system failures according to the impact of these failure events on wind turbine safety. The present study is processing the failure data from a wind farm and uses the Fault Tree Analysis as a baseline for applying the Design Structure Matrix technique to reveal the failure and risk interactions between wind turbine subsystems. Based on the analysis performed and by introducing new importance measures, the “readiness to fail” of a subsystem in conjunction with the “failure riskiness” can determine the “failure criticality.” The value of the failure criticality can define the frame within which interventions could be done. The arising interventions could be applied either to the whole system or could be focused in specified pairs of wind turbine subsystems. In conclusion, the method analyzed in the present research can be effectively applied by the wind turbine manufacturers and the wind farm operators as an operation framework, which can lead to a limited (as possible) design‐out maintenance cost, failures' minimization, and safety maximization for the whole wind turbine system.     

Control of self-assembly in micro- and nano-scale systems

Control of self-assembling systems at the micro- and nano-scale provides new opportunities for the engineering of novel materials in a bottom-up fashion. These systems have several challenges associated with control including high-dimensional and stochastic nonlinear dynamics, limited sensors for real-time measurements, limited actuation for control, and kinetic trapping of the system in undesirable configurations. Three main strategies for addressing these challenges are described, which include particle design (active self-assembly), open-loop control, and closed-loop (feedback) control. The strategies are illustrated using a variety of examples such as the design of patchy and Janus particles, the toggling of magnetic fields to induce the crystallization of paramagnetic colloids, and high-throughput crystallization of organic compounds in nanoliter droplets. An outlook of the future research directions and the necessary technological advancements for control of micro- and nano-scale self-assembly is provided.