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.

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.

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.

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 .

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.

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.

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.

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).

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。

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%。

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。

基于响应面法和ARO算法的桥梁有限元模型修正

针对桥梁结构模型修正过程中计算量大、全局寻优难等问题,提出一种基于响应面法和人工兔优化算法(artificial rabbits optimization, ARO)的桥梁有限元模型修正方法。该方法通过F检验法明确对结构响应影响显著的参数并建立响应面模型作为桥梁有限元模型的代理模型进行迭代计算,然后通过静动力联合修正法基于特征响应的响应面模型计算值和试验值构建目标函数,并通过人工兔优化算法对目标函数进行迭代计算进而获得精确的有限元模型参数。采用该方法对变截面连续刚构黄河特大桥有限元模型进行修正,结果显示:基于显著性分析结果建立的桥梁响应面模型相关系数大于0.98,且平均绝对误差低于0.05;模型修正后主梁关键截面竖向位移最大相对误差由32.97%降至5.72%,平均相对误差由20.67%降至3.61%,桥梁前3阶竖向自振频率平均相对误差仅为0.18%。可见,采用所提方法修正的模型能用于评价桥梁结构实际受力性能。

基于粒子群敏度更新的拓扑优化方法

为了进一步减少拓扑优化过程中出现的灰度单元,提出一种基于粒子群优化和分区加权敏度过滤的敏度更新方法。引入粒子群状态更新表达式,以寻找周围单元的敏度极值,将其在一定权重下与经过过滤的敏度值相加,组成新的敏度更新策略。结合变密度法和基于粒子群的敏度更新策略,以柔度最小化为目标,利用5个经典算例验证本文方法的可行性和有效性。优化结果表明,基于粒子群敏度更新的拓扑优化方法能有效减少灰度单元并能快速收敛。

大跨径石拱桥动力特性分析与有限元模型修正

【目的】研究云南省开远市长虹桥动力性能及得到较为准确的有限元分析模型。【方法】通过环境激励法对长虹桥进行动力特性测试,并分析该桥实测模态参数;根据实测模态参数结果,对有限元模型中的弹性模量、泊松比等参数进行修正;基于理论与实测的模态分析结果,得出长虹桥较为准确的有限元分析模型。【结果】结果表明:石拱桥的跨中、L/4、3L/4、L/8和7L/8处的振动变形较大,在运营监控和检测时,应该在这5个位置布置测点,以便进行实时监测;在环境激励下,该桥没有产生侧向弯曲振动和竖向弯曲振动强度相当的扭转振动,说明结构具有良好的抗扭刚度;各阶阻尼比均在合理范围内。利用模态参数修正后的频率相关性从55.2%降至最低0.6%,数值计算结果与试验结果基本一致,参数依然具有物理意义,修正后的有限元模型能够较为准确地反映结构低阶动力特性。相关性最大为-2.4%,最小为0.6%,满足相关性在±5%以内,可作为桥梁后续检测工作的理论模型。【结论】长虹桥动力性能良好,修正后的有限元模型贴合实际桥梁。

车联网中最小化峰值信息年龄的任务卸载策略

为解决车联网中数据密集型任务对信息新鲜程度极为敏感的问题,针对车路协同场景提出一种最小化峰值信息年龄策略。在所提策略中,将峰值信息年龄定义为任务更新间隔、传输时间以及等待时间三者之和的期望,将变量高度耦合的非线性优化问题转化为任务卸载比例和任务更新间隔两个子问题。在任务卸载比例问题中,证明最小峰值信息年龄的存在条件并获得最优卸载比例。通过内点罚函数法求解车辆任务更新间隔问题。给出两个子问题的交替迭代策略,实现全局车辆峰值信息年龄最小。数值仿真结果验证了所提策略在减少全局车辆峰值信息年龄方面的有效性。

基于ACBO-GM联合方法的高维变量结构模型修正

工程结构模型修正常面临变量维度高、非线性程度强等现实问题,其模型修正的精度和效率受到严重影响,为改善这一不足,提出基于改进物体碰撞算法(Advanced Colliding Bodies Optimization,ACBO)和高斯白噪声扰动(Gaussian-white-noise Mutation,GM)的联合修正方法。在联合方法中,ACBO实现从传统的“一对一”到“优对多”的碰撞模式的转变,GM则用于保证碰撞过程中种群的多样性,可有效提升结构模型修正效率。基于一系列测试函数,对比分析标准物体碰撞算法(Colliding Bodies Optimization,CBO)和联合优化方法的优化性能;给出基于ACBO-GM联合方法的修正流程,并将其应用于钢框架修正算例和悬臂梁修正实例,对比研究基于联合修正方法与基于遗传算法(Genetic Algorithm,GA)的修正精度和效率。研究表明,ACBO-GM联合优化方法在精度和效率方面都明显优于传统的CBO方法,该优化方法的可行性得到验证;基于ACBO-GM联合方法在钢框架和悬臂梁两个工程结构修正案例中的应用,其修正精度均与基于GA方法的精度一致,但修正效率显著优于基于GA的修正方法。

基于响应面法与遗传算法的梁式桥有限元模型更新

为了使自主开发的智能健康监测平台产生的海量监测数据利益最大化,在平台中添加结构分析模块,扩大平台的应用范围。针对有限元模型分析的准确性与可靠性,提出一种将响应面法和遗传算法相结合的有限元模型更新方法。首先,根据设计资料建立结构理论有限元模型并确定结构更新参数;其次,采用中心复合试验设计方法获取样本点并计算结构响应;接着,基于逐步回归分析进行显著性检验并利用最小二乘法拟合得到更新参数与特征响应之间的响应面函数关系;最后,联合结构静动力响应构造目标函数,利用遗传算法对其进行迭代优化求解,并将最优解代入理论有限元模型中,从而实现结构有限元模型更新。以简支梁为例,选择以弹性模量和截面惯性矩为更新参数,以频率和位移为结构响应,结合动静载试验结果,对其进行有限元模型更新,验证所提方法的有效性。结果表明:模型更新后的有限元模型计算分析结果与实测值的相对误差大幅度降低,且计算值更加接近于实测值。基于响应面法与遗传算法的有限元模型更新方法可以为智能健康监测平台提供更加准确的有限元模型,对损伤识别和预警评估等模块提供了便利。