An Updated Lagrangian Particle Hydrodynamics (ULPH)-NOSBPD Coupling Approach forModeling Fluid-Structure Interaction Problem

A fluid-structure interaction approach is proposed in this paper based onNon-Ordinary State-Based Peridynamics(NOSB-PD)and Updated Lagrangian Particle Hydrodynamics(ULPH)to simulate the fluid-structure interaction problem with large geometric deformation and material failure and solve the fluid-structure interaction problem of Newtonian fluid.In the coupled framework,the NOSB-PD theory describes the deformation and fracture of the solid material structure.ULPH is applied to describe the flow of Newtonian fluids due to its advantages in computational accuracy.The framework utilizes the advantages of NOSB-PD theory for solving discontinuous problems and ULPH theory for solving fluid problems,with good computational stability and robustness.A fluidstructure coupling algorithm using pressure as the transmission medium is established to deal with the fluidstructure interface.The dynamic model of solid structure and the PD-ULPH fluid-structure interaction model involving large deformation are verified by numerical simulations.The results agree with the analytical solution,the available experimental data,and other numerical results.Thus,the accuracy and effectiveness of the proposed method in solving the fluid-structure interaction problem are demonstrated.The fluid-structure interactionmodel based on ULPH and NOSB-PD established in this paper provides a new idea for the numerical solution of fluidstructure interaction and a promising approach for engineering design and experimental prediction.

Updated Lagrangian Particle Hydrodynamics (ULPH)Modeling of Natural Convection Problems

Natural convection is a heat transfer mechanism driven by temperature or density differences,leading to fluid motion without external influence.It occurs in various natural and engineering phenomena,influencing heat transfer,climate,and fluid mixing in industrial processes.This work aims to use the Updated Lagrangian Particle Hydrodynamics(ULPH)theory to address natural convection problems.The Navier-Stokes equation is discretized using second-order nonlocal differential operators,allowing a direct solution of the Laplace operator for temperature in the energy equation.Various numerical simulations,including cases such as natural convection in square cavities and two concentric cylinders,were conducted to validate the reliability of the model.The results demonstrate that the proposed model exhibits excellent accuracy and performance,providing a promising and effective numerical approach for natural convection problems.

AN UPDATING METHOD FOR LOCAL MODEL ERRORS

The location of model errors in a stiffness matrix by using test data has been investigated by the others.The present paper deals with the problem of updating stiffness elements in the erroneous areas. Firstly,a model that bears relation to erroneous elements only is derived.This model is termed local errors model,which reduces orders and computational loads compared with global stiffness matrix. Secondly,an inverse eigenvalue method is used to update model errors. The results of a numerical experiment demonstrate that the method is quite effective.

An improved optimal elemental method for updating finite element models

The optimal matrix method and optimal elemental method used to update finite element models may not provide accurate results.This situation occurs when the test modal model is incomplete,as is often the case in practice.An improved optimal elemental method is presented that defines a new objective function,and as a byproduct,circumvents the need for mass normalized modal shapes,which are also not readily available in practice.To solve the group of nonlinear equations created by the improved optimal method,the Lagrange multiplier method and Matlab function fmincon are employed.To deal with actual complex structures, the float-encoding genetic algorithm(FGA)is introduced to enhance the capability of the improved method.Two examples,a 7- degree of freedom(DOF)mass-spring system and a 53-DOF planar frame,respectively,are updated using the improved method. The example results demonstrate the advantages of the improved method over existing optimal methods,and show that the genetic algorithm is an effective way to update the models used for actual complex structures.

Hydraulic model with roughness coefficient updating method based on Kalman filter for channel flood forecast

A real-time channel flood forecast model was developed to simulate channel flow in plain rivers based on the dynamic wave theory. Taking into consideration channel shape differences along the channel, a roughness updating technique was developed using the Kalman filter method to update Manning＇s roughness coefficient at each time step of the calculation processes. Channel shapes were simplified as rectangles, triangles, and parabolas, and the relationships between hydraulic radius and water depth were developed for plain rivers. Based on the relationship between the Froude number and the inertia terms of the momentum equation in the Saint-Venant equations, the relationship between Manning＇s roughness coefficient and water depth was obtained. Using the channel of the Huaihe River from Wangjiaba to Lutaizi stations as a case, to test the performance and rationality of the present flood routing model, the original hydraulic model was compared with the developed model. Results show that the stage hydrographs calculated by the developed flood routing model with the updated Manning＇s roughness coefficient have a good agreement with the observed stage hydrographs. This model performs better than the original hydraulic model.

Proximal Point-Like Method for Updating Simultaneously Mass and Stiffness Matrices of Finite Element Model

The problem of correcting simultaneously mass and stiffness matrices of finite element model of undamped structural systems using vibration tests is considered in this paper.The desired matrix properties,including satisfaction of the characteristic equation,symmetry,positive semidefiniteness and sparsity,are imposed as side constraints to form the optimal matrix pencil approximation problem.Using partial Lagrangian multipliers,we transform the nonlinearly constrained optimization problem into an equivalent matrix linear variational inequality,develop a proximal point-like method for solving the matrix linear variational inequality,and analyze its global convergence.Numerical results are included to illustrate the performance and application of the proposed method.

MEMORY-EFFICIENT PATH METRIC UPDATE METHOD IN MAP DECODER IMPLEMENTATION

A novel memory efficient path metric update is proposed for Maximum A Posteriori(MAP) decoder of turbo codes to reduce the memory requirement of state metric information calcu-lation. For MAP decoder,the same memory can be shared by the forward and backward metrics with this metric update scheme. The forward and backward metrics update can be performed at the same time. And all of the extrinsic information can be calculated at the end of metric update. Therefore,the latency and area in the implementation will be reduced with the proposed metric update method.

On the Convergence of the Column-Updating Method

In this paper, we prove the local and Supcrlinear convergence theorem of the column-updating method for n>2. This is an oped problem for the convergene theory of the column-updating method given by Martinez in the Intcrnational Conference of the NATO-ASI (Italy, 1994).

Multi-group ant colony algorithm based on simulated annealing method

To overcome the default of single search tendency, the ants in the colony are divided into several sub-groups. The ants in different subgroups have different trail information and expectation coefficients. The simulated annealing method is introduced to the algorithm. Through setting the temperature changing with the iterations, after each turn of tours, the solution set obtained by the ants is taken as the candidate set. The update set is obtained by adding the solutions in the candidate set to the previous update set with the probability determined by the temperature. The solutions in the candidate set are used to update the trail information. In each turn of updating, the current best solution is also used to enhance the trail information on the current best route. The trail information is reset when the algorithm is in stagnation state. The computer experiments demonstrate that the proposed algorithm has higher stability and convergence speed.

Graph Regularized Sparse Coding Method for Highly Undersampled MRI Reconstruction

The imaging speed is a bottleneck for magnetic resonance imaging( MRI) since it appears. To alleviate this difficulty,a novel graph regularized sparse coding method for highly undersampled MRI reconstruction( GSCMRI) was proposed. The graph regularized sparse coding showed the potential in maintaining the geometrical information of the data. In this study, it was incorporated with two-level Bregman iterative procedure that updated the data term in outer-level and learned dictionary in innerlevel. Moreover,the graph regularized sparse coding and simple dictionary updating stages derived by the inner minimization made the proposed algorithm converge in few iterations, meanwhile achieving superior reconstruction performance. Extensive experimental results have demonstrated GSCMRI can consistently recover both real-valued MR images and complex-valued MR data efficiently,and outperform the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values.

A CLASS OF DECOMPOSITION METHODS FOR LARGE-SCALE SYSTEMS OF NONLINEAR EQUATIONS

This paper presents a new decomposition method for solving large-scale systems of nonlinear equations. The new method is of superlinear convergence speed and has rather less computa tional complexity than the Newton-type decomposition method as well as other known numerical methods, Primal numerical experiments show the superiority of the new method to the others.

Finite Element Model Updating of Complicated Beam-Type Structures Based on Reduced Super Beam Model

A finite element model updating technique for complicated beam-type structures is presented in this study.Firstly, a complicated beam-type structure is reduced to a reduced super beam model with a much smaller degree of freedom by using the reduced super beam method, which is based on the classic plane cross-section assumption and displacement interpolation function of beam theory.Then based on the reduced super beam, the analysis of eigensolutions and eigensensitivities from the reduced eigenequation are processed for model updating, which will greatly reduce the computational effort when compared to the traditional model updating methods performed on the global model.Optimization techniques are adopted for updating the difference of modal dynamic properties, resulting in optimal values of the structural parameters.Finally, a complicated stiffened cylindrical shell model and a practical missile structure, served as the illustrative examples, are employed for model updating application, which demonstrate that the reduced super beam-based method is both effective and highly efficient.

关于数据库更新UPDATE命令的算法

本文从教学方法的角度讨论用其它工作区中数据库更新当前工作区中数据库的更新命令 UPDATE的算法，并通过实例加以说明 .

Low-complexity soft-output signal detector based on adaptive pre-conditioned gradient descent method for uplink multiuser massive MIMO systems

In multiuser massive Multiple Input Multiple Output(MIMO)systems,a large amount of antennas are deployed at the Base Station(BS).In this case,the Minimum Mean Square Error(MMSE)detector with soft-output can achieve the near-optimal performance at the cost of a large-scale matrix inversion operation.The optimization algorithms such as Gradient Descent(GD)method have received a lot of attention to realize the MMSE detection efficiently without a large scale matrix inversion operation.However,they converge slowly when the condition number of the MMSE filtering matrix(the coefficient matrix)increases,which can compromise the efficiency of their implementation.Moreover,their soft information computation also involves a large-scale matrix-matrix multiplication operation.In this paper,a low-complexity soft-output signal detector based on Adaptive Pre-conditioned Gradient Descent(APGD-SOD)method is proposed to realize the MMSE detection with soft-output for uplink multiuser massive MIMO systems.In the proposed detector,an Adaptive Pre-conditioner(AP)matrix obtained through the Quasi-Newton Symmetric Rank One(QN-SR1)update in each iteration is used to accelerate the convergence of the GD method.The QN-SR1 update supports the intuitive notion that for the quadractic problem one should strive to make the pre-conditioner matrix close to the inverse of the coefficient matrix,since then the condition number would be close to unity and the convergence would be rapid.By expanding the signal model of the massive MIMO system and exploiting the channel hardening property of massive MIMO systems,the computational complexity of the soft information is simplified.The proposed AP matrix is applied to the GD method as a showcase.However,it also can be used by Conjugate Gradient(CG)method due to its generality.It is demonstrated that the proposed detector is robust and its convergence rate is superlinear.Simulation results show that the proposed detector converges at most four iterations.Simulation results also show that the proposed approach achieves a better trade-off between the complexity and the performance than several existing detectors and achieves a near-optimal performance of the MMSE detector with soft-output at four iterations without a complicated large scale matrix inversion operation,which entails a big challenge for the efficient implementation.

Real-Time Update of Sequence Placement Logic for High Arch Dams Based on Evidence Weight Discount

Sequence placement logic plays a significant role in construction simulation of high arch dams and directly affects the simulation process and results. To establish a sequence logic for dam block placement, the construction scheme, real-time construction process, and random factors of the site all need to be considered in detail. There are few studies available currently that take all these factors into consideration. To address this problem, a real-time update of sequence placement logic for high arch dams based on evidence weight discount is proposed in this study. First, the subjective weight of the dam block sequence priority criteria is built using a consistent matrix method based on the construction scheme. Second, using evidence theory, dynamic objective weight of the priority criteria and basic probability assignment is built. Finally, using a weight self-adaptive adjustment method and comprehensive evidence discounting, the placing probabilities of different dam blocks are obtained. A case study indicates that this method can realize real-time update of sequence placement logic. © 2017, Tianjin University and Springer-Verlag Berlin Heidelberg.

基于随机抽样的衰变热不确定度量化研究

为研究核数据引起的核素存量及导出量的不确定度,在自主程序GNET上实现了基于随机抽样的不确定度量化方法。利用贝叶斯更新方法获得裂变产物独立产额的协方差数据,弥补裂变产额协方差数据缺失。对热中子引起的235U一次裂变后衰变热不确定度进行了计算。结果表明,裂变产物产额的不确定度贡献占主要部分。该算例表明GNET程序具备了核素存量的不确定度量化功能。

基于响应面法的钢管混凝土系杆拱桥有限元模型修正

为探讨将钢管混凝土系杆拱桥有限元模型修正方法应用于实际工程性能评估的有效性,以某80 m单拱面钢管混凝土系杆拱桥为工程背景,采用Midas Civil与ANSYS有限元软件分别建立全桥梁格模型与拱脚精细化数值模型,基于径向基函数网络的响应面法对此多尺度模型进行修正。首先根据结构特点和实桥试验选取修正参数与目标函数,以中心复合试验设计构造不同修正水平下的修正参数样本,通过桥梁有限元模型计算得到对应的目标函数样本,并利用F检验法对修正参数的显著性水平进行判断;然后,以修正参数样本与目标函数样本分别作为响应面的输入输出,对径向基函数网络进行拟合训练;最后在修正范围内随机生成修正参数样本,并利用训练好的径向基网络预测对应的目标函数,采用期望度函数D进行优化处理以完成有限元模型的修正。研究结果表明:对于单拱面钢管混凝土系杆拱桥的有限元模型,可以通过径向基函数网络的响应面法进行修正,对比实测数据,修正后的模型误差有明显下降,可作为结构再分析的基准模型;对于使用不同程序建立的多尺度桥梁有限元模型,模型修正方法依旧有效,修正后的参数具有明确的物理意义,其误差可降到工程实践接受范围之内。研究结果为建立与修正钢管混凝土系杆拱桥的有限元模型提供参考。

融合蚁群算法框架下区域资源的动态调度模型

针对目前电力作业人力资源调度主要依赖于人工经验的现状,文中提出了基于改进蚁群算法的区域资源动态调度模型。该模型以花费总时间与总成本最小为优化目标,考虑了任务的资源需求、先后顺序等方面的约束条件。同时,结合区域资源动态调度模型的特性,对传统蚁群算法进行了改进设计,提出了优化的编码解码方法、蚁群动态更新机制及2-opt局部搜索方法等策略。算例分析结果表明,所提改进蚁群算法相比于传统蚁群算法具有更快的收敛速度和更高的计算准确度。在实际电力作业调度中,对比传统蚁群算法,所设计模型能够减少总时间约12%,降低总成本约10%。

基于改进SCHO算法的局部遮荫光伏MPPT研究

针对局部遮荫工况下光伏阵列最大功率点跟踪(MPPT)存在的易陷入局部功率峰值点、跟踪时间长、跟踪精度低等问题,提出一种基于改进双曲正余弦优化(SCHO)的控制算法.其采用立方混沌映射初始化,提高初始候选解集的遍历性,并利用贝塔分布的概率特性修正SCHO算法的切换标准,提高算法与候选解集寻优进程的适配性.同时,对SCHO算法中的全局最优解、个体当前解引入平衡权重更新策略,且采用透镜成像反向学习策略对寻优后期的候选解进行扰动,采用比例收缩法对寻优空间进行动态钳位,提高算法的全局勘探及局部开发能力.Matlab仿真结果表明,相比其他控制算法,本文提出的改进SCHO算法能缩短MPPT时间、提高MPPT精度,故具有更优的MPPT性能,可为进一步提升光伏发电效率提供参考.

低秩张量填充的循环算法

针对张量填充问题,提出一种低秩张量填充的循环算法。以交替方向乘子法为基础,对子问题循环更新,有效地减少了算法在迭代过程中张量展开、矩阵折叠以及奇异值分解的计算花费。同时,在合理的假设条件下,给出了算法的收敛性分析。最后,通过数值实验与HaLRTC算法、DR-TR算法及LRTC-Logdet算法的对比,验证了算法相对其它方法的有效性。