An iterative method for suboptimal control of linear time-delayed systems

This article presents a new approach for solving the Optimal Control Problem (OCP) of linear time-delay systems with a quadratic cost functional. The proposed method can also be used for designing optimal control time-delay systems with disturbance. In this study, the Variational Iteration Method (VIM) is employed to convert the original Time-Delay Optimal Control Problem (TDOCP) into a sequence of nonhomogeneous linear two-point boundary value problems (TPBVPs). The optimal control law obtained consists of an accurate linear feedback term and a nonlinear compensation term which is the limit of an adjoint vector sequence. The feedback term is determined by solving Riccati matrix differential equation. By using the finite-step iteration of a nonlinear compensation sequence, we can obtain a suboptimal control law. Finally, Illustrative examples are included to demonstrate the validity and applicability of the technique.     

Melting heat transfer analysis of electrically conducting nanofluid flow over an exponentially shrinking/stretching porous sheet with radiative heat flux under a magnetic field

Modern magnetic nanomaterial processing operations are progressing rapidly and require increasingly sophisticated mathematical models for their optimization. Stimulated by such developments, in this paper, a theoretical and computational study of a steady magnetohydrodynamic nanofluid over an exponentially stretching/shrinking permeable sheet with melting (phase change) and radiative heat transfer is presented. Besides, wall transpiration, that is, suction and blowing (injection), is included. This study deploys Buongiorno's nanofluid model, which simulates the effects of the Brownian motion and thermophoresis. The transport equations and boundary conditions are normalized via similarity transformations and appropriate variables, and the similarity solutions are shown to depend on the transpiration parameter. The emerging dimensionless nonlinear coupled ordinary differential boundary value problem is solved numerically with the Newton-Fehlberg iteration technique. Validation with special cases from the literature is included. The increase in the magnetic field, that is, the Hartmann number, is observed to elevate nanoparticle concentration and temperature, whereas it dampens the velocity. Higher values of the melting parameter consistently decelerate the boundary layer flow and suppress temperature and nanoparticle concentration. A higher radiative parameter strongly increases temperature (and thermal boundary layer thickness) and weakly accelerates the flow. The increase in the Brownian motion reduces nanoparticle concentrations, whereas a greater thermophoretic body force strongly enhances them. The Nusselt number and Sherwood number are observed to be decreased with an increasing Hartmann number, whereas they are elevated with a stronger wall suction and melting parameter.     

Optimal-correlation-based reconstruction for distributed compressed video sensing

Distributed compressed video sensing (DCVS) is a framework that integrates both compressed sensing and distributed video coding characteristics to achieve a low-complexity video coding. However, how to design an efficient joint reconstruction by leveraging more realistic signal models is still an open challenge. In this paper, we present a novel optimal-correlation-based reconstruction method for compressively sampled videos from multiple measurement vectors. In our method, the sparsity is mainly exploited through inter-signal correlations rather than the traditional frequency transform, wherein the optimization is not only over the signal space to satisfy data consistency but also over all possible linear correlation models to achieve minimum-l₁-norm correlation noise. Additionally, a two-phase Bregman iterative based algorithm is outlined for solving the optimization problem. Simulation results show that our proposal can achieve an improved reconstruction performance in comparison to the conventional approaches, and especially, offer a 0.7–9.9 dB gain in the average PSNR for DCVS.     

Persistence of lower dimensional invariant tori in a class of reversible systems

In this paper we prove the persistence of lower dimensional invariant tori for a class of reversible systems without any non-degeneracy condition. In the proof, we use the special nature that the dimension of the frequency ω is 2 and the modified KAM iteration to prove the persistence of lower dimensional hyperbolic invariant tori, but the frequency has some small shifts from small perturbations.     

太阳能/氢能长航时无人机重量能量耦合分析

提出一种太阳能/氢能长航时无人机重量能量耦合分析方法,即在太阳能/氢能长航时无人机的概念设计阶段,通过在质量与能量迭代求解过程中嵌入与能量相关的参数的求解,从而计算出相应总体参数下的质量和能量情况,得到无人机基本性能指标的估算。通过重量能量耦合分析方法,无人机的质量迭代计算得到收敛、能量结余系数和有效载荷系数2个重要设计指标在求解过程中获得收敛。由于提出的分析方法可完成无人机重量估算,及重要设计指标的估算,因而对于太阳能/氢能长航时无人机的概念设计具有重要意义。     

相位恢复算法在仿真与实验上的研究

相位恢复算法一直存在着精确度不高,收敛速度慢甚至停滞不前等问题。将基于光强传输方程(TIE)法与G-S迭代算法混合提高了相位恢复的精确度,梯度算法的提出加大了迭代步长,使得收敛速度加快。采用GS-TIE算法和振幅加成梯度算法分别从仿真和实验的角度去比较分析恢复的效果。通过对二维图像仿真得出,振幅加成梯度算法在收敛速度上是GSTIE迭代算法的3倍,精确度是GS-TIE迭代算法的10倍。从实验结果得知,GS-TIE恢复的相位清晰可见,轮廓明显,在边缘处过度均匀,而振幅加成梯度算法相对比较模糊,在轮廓边缘处过度不均匀,悬差较大。     

Failure load prediction and optimisation for adhesively bonded joints enabled by deep learning and fruit fly optimisation

Adhesively bonded joints have been extensively employed in the aeronautical and automotive industries to join thin-layer materials for developing lightweight components. To strengthen the structural integrity of joints, it is critical to estimate and improve joint failure loads effectually. To accomplish the aforementioned purpose, this paper presents a novel deep neural network (DNN) model-enabled approach, and a single lap joint (SLJ) design is used to support research development and validation. The approach is innovative in the following aspects: (i) the DNN model is reinforced with a transfer learning (TL) mechanism to realise an adaptive prediction on a new SLJ design, and the requirement to re-create new training samples and re-train the DNN model from scratch for the design can be alleviated; (ii) a fruit fly optimisation (FFO) algorithm featured with the parallel computing capability is incorporated into the approach to efficiently optimise joint parameters based on joint failure load predictions. Case studies were developed to validate the effectiveness of the approach. Experimental results demonstrate that, with this approach, the number of datasets and the computational time required to re-train the DNN model for a new SLJ design were significantly reduced by 92.00% and 99.57% respectively, and the joint failure load was substantially increased by 9.96%.     

大空间建筑室内热环境CFD模拟中壁温及室温的求解

应用PHOENICS软件模拟某体育馆的室内热环境,将壁温作为模拟的第一类边界条件。建立了室内表面对流辐射耦合换热方程,着重讨论了采用表面换热分析法求解表面传热系数以求解壁温的方法。因壁温计算和室温计算互相耦合,采用迭代计算的方法求解壁温和室温。模拟计算结果与实测结果基本吻合。     

钢结构施工变形预调值及分析方法

一般情况下钢结构的设计位形只是施工的目标位形,不能作为确定构件加工和安装位形的直接依据。为保证施工的顺利进行以及竣工时结构的位形满足设计要求,施工过程中需对结构设置变形预调值。介绍了钢结构施工变形预调值的概念,构造了钢结构施工变形预调值计算的一般迭代法、正装迭代法、倒拆迭代法和分阶段综合迭代法。其中,一般迭代法可用来计算采用满堂脚手架法施工的钢结构变形预调值。正装迭代法可用来计算各种施工方法下钢结构的变形预调值,但当结构复杂杆件比较多时,分析过程中常出现算法不收敛的问题。倒拆迭代法收敛性高,但计算工作量较大。分阶段综合迭代法可以用来计算闭环复杂结构的变形预调值,克服迭代不收敛问题,其计算工作量较小。     

一种增广拉格朗日优化方案及其非连续变形分析实现

接触力计算精度是关乎非连续变形分析结果有效性的关键因素之一。经典非连续变形分析法(DDA)采用罚法施加块体间的接触约束,实现简单,但合理的罚值选取困难。因此,提出了一种增广拉格朗日优化算法,改进DDA中接触约束的处理。通过结合开闭迭代算法和自适应罚值更新方案等,提出并实现了多面体DDA增广拉格朗日算法及其优化方案。最后,设计了2个经典数值算例,计算结果表明:改进后的三维DDA提高了计算精度且保证了计算效率,可用于复杂多面体块体系统的非连续力学行为分析,如大型节理岩体工程稳定性分析。