应用 Python-GPU 求解的实时混合试验方法研究

提出了基于 Python 和图形处理器（GPU）数值求解的实时混合试验系统。将土‐结相互作用系统作为试验模型，使用 Python‐GPU 代替 CPU 数值求解，对提出的实时混合试验系统进行了仿真与试验验证。研究结果表明，使用 Python‐GPU 对无条件稳定算法求解，积分步长 20 ms 时 GPU 实时求解模型自由度超过 24000，是同一台计算机CPU 求解规模的 7 倍左右，提升了实时混合试验的试验能力。     

大尺寸非线性实时动力子结构试验实现

实时子结构试验结合物理试验和数值计算的优点,间接增强了既有设备试验能力。受数值积分算法和加载系统控制方法的限制,该方法目前仅限于结构线性或小尺寸试件非线性动力特性研究。为了改善数值子结构求解及物理子结构控制性能,基于SIMULINK发展了闭环数值积分方法、建立了基于仿真的逆动力补偿控制策略。利用这两项技术成功实现了大尺寸试件非线性实时子结构试验,并通过数值仿真和试验验证了其性能。研究表明:发展的非线性实时子结构试验充分释放了该试验技术的潜能。     

采用等效力控制方法的非线性结构实时子结构试验

等效力控制方法用反馈控制代替数学迭代过程求解实时子结构试验隐式逐步积分方法的非线性方程。对于非线性结构试验,采用比例-微分(PD)控制器的等效力控制方法会使等效力响应产生稳态误差,从而使试验结果失真。文章用比例-积分(PI)控制器代替PD控制器以解决此问题。介绍了实时子结构试验等效力控制方法,进行了等效力控制器设计,并通过数值模拟和试验验证了采用PI控制器的等效力控制方法对非线性结构实时子结构试验的有效性。数值模拟与试验的结果都表明,对于非线性体系的试验,采用PI控制器的等效力控制方法可以取得非常好的效果。     

基于振动台的RTDHT试验及其在双层刚架结构上的应用

实时耦联动力试验(RTDHT)是一种将物理模型试验与数值求解计算实时耦联在一起的新型结构动力试验方法。该文基于振动台构建了振动台型RTDHT试验系统,并对其动力特性进行了研究。利用所构建的RTDHT试验系统,研究了一个双层刚架结构的自振特性及动力反应,其中上层框架为物理子结构,而下层框架为数值子结构,通过两部分之间的实时数据交互得到整体结构的动力响应。将双层刚架的RTDHT试验结果与CDM数值计算结果进行了对比,结果表明二者吻合较好,基于该试验系统的RTDHT试验具有较高的精度。     

设备-结构-土体系振动台实时子结构试验方法探讨

该文探讨了设备-结构-土体系振动台实时子结构试验方法的可行性,将设备-结构体系作为由振动台加载控制的试验子结构,同时将自由度缩减后的土体作为由仿真软件计算的数值子结构,试验时两者之间进行数据实时交互。首先基于分支模态子结构方法推导了设备-结构-线性土体系运动方程,并对各体系运动方程进行了变换,将其应用于设备-结构-线性土体系振动台实时子结构试验。然后结合土体在强震作用下并非全部进入非线性阶段的特点,提出采用局部非线性土模型作为数值子结构参与振动台实时子结构试验的思路,并应用分支模态子结构法与线性-非线性混合约束模态子结构法推导了设备-结构-局部非线性土体系的运动方程。设计了设备-结构-土相互作用缩尺模型,进行了各地震动作用下的设备-结构-线性土体系振动台实时子结构试验。通过比较振动台实时子结构试验结果与数值计算结果,发现两者之间吻合良好,证明该试验方法是可靠有效的。     

实时子结构实验的滑动模态控制

首先介绍了实时子结构实验技术的基本原理、过程及关键技术问题。然后建立了液压伺服实验系统的传递函数形式和状态空间形式的线性数值模型,并将滑动模态控制应用于实时子结构实验。最后对采用PID控制和滑动模态控制的实时子结构实验进行了数值仿真分析。在ElCentro地震波激励下的数值仿真结果表明:(1)滑动模态控制的控制精度和鲁棒性明显高于PID控制,(2)系统的动力特性对实验结果有较大影响,这种影响与计算时间间隔、试件的刚度、阻尼有关。     

实时子结构试验中显式算法对比分析

实时子结构试验技术中的一个关键问题是求解数值子结构的动力响应,而这一过程可选取合适的数值积分算法来实现。针对目前已建立的三种基于模型的显式积分算法(Chang法、CR法和实时子结构RST法),对比分析了各算法在线性系统和非线性系统中的数值特性。结果表明:三种算法在线性系统和具有刚度软化特性的非线性系统中均是无条件稳定的,在具有刚度硬化特性的非线性系统中变为有条件稳定。当结构阻尼比为零时,三种算法均无数值阻尼,且周期延长率结果完全一致,并随Ω的增加而增大;当结构阻尼比不为零时,三种算法均存在数值阻尼,且CR法的数值阻尼绝对值最小,而RST法的周期延长率最小。两个算例表明,RST法和Chang法的精度要优于CR法,但因Chang法是半显式的,因此RST法更适于实时子结构试验中数值子结构的仿真计算。     

实时子结构试验中显式算法对比分析

实时子结构试验技术中的一个关键问题是求解数值子结构的动力响应,而这一过程可选取合适的数值积分算法来实现。针对目前已建立的三种基于模型的显式积分算法(Chang法、CR法和实时子结构RST法),对比分析了各算法在线性系统和非线性系统中的数值特性。结果表明:三种算法在线性系统和具有刚度软化特性的非线性系统中均是无条件稳定的,在具有刚度硬化特性的非线性系统中变为有条件稳定。当结构阻尼比为零时,三种算法均无数值阻尼,且周期延长率结果完全一致,并随Ω的增加而增大;当结构阻尼比不为零时,三种算法均存在数值阻尼,且CR法的数值阻尼绝对值最小,而RST法的周期延长率最小。两个算例表明,RST法和Chang法的精度要优于CR法,但因Chang法是半显式的,因此RST法更适于实时子结构试验中数值子结构的仿真计算。     

基于UKF模型更新的混合试验方法

在混合试验中,将结构划分为物理子结构和数值子结构两部分。对遭遇强震下大型结构的混合试验,很难保证数值子结构仍处于弹性阶段。为确保数值子结构模型的准确性,提出基于Unscented Kalman filter (UKF) 模型更新混合试验方法。该方法假定数值子结构与物理子结构恢复力模型相同,在混合试验进行中利用物理子结构试验观测数据,采用UKF方法在线识别物理子结构模型参数,实时更新数值子结构模型参数。通过数值模拟,应用UKF方法对单自由度结构非线性模型进行在线参数识别,验证UKF方法性能;通过对弹簧试件实际试验,验证该混合试验方法的有效性。结果表明,基于UKF模型更新混合试验方法较传统混合试验方法精度更高。     

结构-地基动力相互作用的实时耦联动力试验

振动台试验中如何考虑无限地基辐射阻尼是结构动力试验中的一个难题.该文采用实时耦联动力试验(RTDHT)方法,在试验系统的数值子结构中引入地基集总参数模型,将地基的数值模型计算与结构的物理模型试验实时耦联,从而实现了考虑结构-地基动力相互作用(SSI)的结构振动台动力试验.利用该方法,对一个双层结构考虑结构-地基动力相互...     

Parallel tsunami simulation and visualization on tiled display wall using OpenGL Shading Language

Tsunami simulation consists of fluid dynamics, numerical computations, and visualization techniques. Nonlinear shallow water equations are often used to model the tsunami propagation. Tsunami inundation is modeled by adding the friction slope to the conservation of momentum. The two-step second-order finite difference MacCormack numerical method can solve these equations. It is well suited for nonlinear equations and simpler for related application development. In addition, the finite difference method can be computed in parallel. The programmable graphics hardware allows general-purpose computing on graphics processing units (GPUs) to solve the MacCormack method in parallel to speed up the simulation. Tsunami simulation data can be loaded as textures data in graphics memory, the computation processes can be written as shader programs using OpenGL Shading Language so that the operations can be computed by graphics processors in parallel. We developed different versions of the tsunami simulation and visualization programs: (i) CPU-based, and (ii) CPU–GPU collaboration to implement the MacCormack numerical method. The performance results showed that graphics hardware accelerated simulation had a significant improvement in the execution time of each computation step. Real-time simulation and visualization are made possible by the programmable shaders. Furthermore, we achieved high-performance parallel visualization on a tiled display wall with a cluster of computers.     

GPU accelerated lattice Boltzmann model for shallow water flow and mass transport

A lattice Boltzmann method (LBM) for solving the shallow water equations (SWEs) and the advection–dispersion equation is developed and implemented on graphics processing unit (GPU)‐based architectures. A generalized lattice Boltzmann equation (GLBE) with a multiple‐relaxation‐time (MRT) collision method is used to simulate shallow water flow. A two‐relaxation‐time (TRT) method with two speed‐of‐sound techniques is used to solve the advection–dispersion equation. The proposed LBM is implemented to an NVIDIA ^® Computing Processor in a single GPU workstation. GPU computing is performed using the Jacket GPU engine for MATLAB ^® and CUDA. In the numerical examples, the MRT‐LBM model and the TRT‐LBM model are verified and show excellent agreement to exact solutions. The MRT outperforms the single‐relaxation‐time (SRT) collision operator in terms of stability and accuracy when the SRT parameter is close to the stability limit of 0.5. Mass transport with velocity‐dependent dispersion in shallow water flow is simulated by combining the MRT‐LBM model and the TRT‐LBM model. GPU performance with CUDA code shows an order of magnitude higher than MATLAB‐Jacket code. Moreover, the GPU parallel performance increases as the grid size increases. The results indicate the promise of the GPU‐accelerated LBM for modeling mass transport phenomena in shallow water flows. Copyright © 2010 John Wiley & Sons, Ltd.     

A Graphical Processing Unit–Based Parallel Implementation of Multiplicative Algebraic Reconstruction Technique Algorithm for Limited View Tomography

This article proposes an efficient two-dimensional (2D) pixel-driven multiplicative algebraic reconstruction technique (PdMART) on a general purpose graphical processing unit (GPU), Nvidia graphics card GTX-275. It has been tested on numerical data and also on real data that have been obtained from the micro–computed tomography scanner installed at University of Manchester. We have used real data having 90 projections and 256 rays in each projection to test the algorithm. The real data has been obtained by scanning the graphite core object of size 30 mm × 30 mm. It has been found that GPU can help PdMART to generate the weight matrix for the 256 × 256 pixel grid within a second which is very fast compared to any sequential machine. Experimental results reveal that PdMART on GPU is computationally inexpensive. Preliminary results also indicate much better performance (as compared to popular Fourier methods) for cases of limited-view projection data as is the case for the upcoming laminographic tomography machines.     

Satellite Cloud-Derived Wind Inversion Algorithm Using GPU

Cloud-derived wind refers to the wind field data product reversely derived through satellite remote sensing cloud images. Satellite cloud-derived wind inversion has the characteristics of large scale, computationally intensive and long time. The most widely used cloud-derived serial--tracer cloud tracking method is the maximum cross-correlation coefficient (MCC) method. In order to overcome the efficiency bottleneck of the cloud-derived serial MCC algorithm, we proposed a parallel cloud-derived wind inversion algorithm based on GPU framework in this paper, according to the characteristics of independence between each wind vector calculation. In this algorithm, each iteration is considered as a thread of GPU cores, and each thread block array of GPU allocates n*32 threads, and the many thread blocks are allocated to the thread grid. The parameters of the algorithm are passed from CPU to GPU global memory and the storage spaces are previously created on the GPU device before the functions of algorithm are executed. The test results of multiple sets of different inversion models on the NVIDIA Geforce GT and the 4-core 8-thread Core i7-3770 CPU show that the algorithm significantly improves the inversion efficiency. The acceleration ratio is up to 112, and the parallel experiment acceleration ratio is also impressive.     

Graphics processing unit parallel accelerated solution of the discrete ordinates for photon transport in biological tissues

As a widely used numerical solution for the radiation transport equation (RTE), the discrete ordinates can predict the propagation of photons through biological tissues more accurately relative to the diffusion equation. The discrete ordinates reduce the RTE to a serial of differential equations that can be solved by source iteration (SI). However, the tremendous time consumption of SI, which is partly caused by the expensive computation of each SI step, limits its applications. In this paper, we present a graphics processing unit (GPU) parallel accelerated SI method for discrete ordinates. Utilizing the calculation independence on the levels of the discrete ordinate equation and spatial element, the proposed method reduces the time cost of each SI step by parallel calculation. The photon reflection at the boundary was calculated based on the results of the last SI step to ensure the calculation independence on the level of the discrete ordinate equation. An element sweeping strategy was proposed to detect the calculation independence on the level of the spatial element. A GPU parallel frame called the compute unified device architecture was employed to carry out the parallel computation. The simulation experiments, which were carried out with a cylindrical phantom and numerical mouse, indicated that the time cost of each SI step can be reduced up to a factor of 228 by the proposed method with a GTX 260 graphics card.     

Performance and scalability of Fourier domain optical coherence tomography acceleration using graphics processing units

Fourier domain optical coherence tomography (FD-OCT) provides faster line rates, better resolution, and higher sensitivity for noninvasive, in vivo biomedical imaging compared to traditional time domain OCT (TD-OCT). However, because the signal processing for FD-OCT is computationally intensive, real-time FD-OCT applications demand powerful computing platforms to deliver acceptable performance. Graphics processing units (GPUs) have been used as coprocessors to accelerate FD-OCT by leveraging their relatively simple programming model to exploit thread-level parallelism. Unfortunately, GPUs do not "share" memory with their host processors, requiring additional data transfers between the GPU and CPU. In this paper, we implement a complete FD-OCT accelerator on a consumer grade GPU/CPU platform. Our data acquisition system uses spectrometer-based detection and a dual-arm interferometer topology with numerical dispersion compensation for retinal imaging. We demonstrate that the maximum line rate is dictated by the memory transfer time and not the processing time due to the GPU platform's memory model. Finally, we discuss how the performance trends of GPU-based accelerators compare to the expected future requirements of FD-OCT data rates.     

一种笔记本电脑水冷散热装置研究

针对笔记本电脑散热问题设计了一种新型外置无强迫对流散热器,通过设置折流板和导流槽增大换热面积,采用实验和数值方法对散热器的结构参数及散热性能进行了研究。结果表明,研制的散热器具有较好的散热性能,可以满足CPU及GPU的散热要求。     

A new sparse matrix vector multiplication graphics processing unit algorithm designed for finite element problems

Recently, graphics processing units (GPUs) have been increasingly leveraged in a variety of scientific computing applications. However, architectural differences between CPUs and GPUs necessitate the development of algorithms that take advantage of GPU hardware. As sparse matrix vector (SPMV) multiplication operations are commonly used in finite element analysis, a new SPMV algorithm and several variations are developed for unstructured finite element meshes on GPUs. The effective bandwidth of current GPU algorithms and the newly proposed algorithms are measured and analyzed for 15 sparse matrices of varying sizes and varying sparsity structures. The effects of optimization and differences between the new GPU algorithm and its variants are then subsequently studied. Lastly, both new and current SPMV GPU algorithms are utilized in the GPU CG solver in GPU finite element simulations of the heart. These results are then compared against parallel PETSc finite element implementation results. The effective bandwidth tests indicate that the new algorithms compare very favorably with current algorithms for a wide variety of sparse matrices and can yield very notable benefits. GPU finite element simulation results demonstrate the benefit of using GPUs for finite element analysis and also show that the proposed algorithms can yield speedup factors up to 12‐fold for real finite element applications. Copyright © 2015 John Wiley & Sons, Ltd.     

Microstructure-based knowledge systems for capturing process-structure evolution linkages

This paper reviews and advances a data science framework for capturing and communicating critical information regarding the evolution of material structure in spatiotemporal multiscale simulations. This approach is called the MKS (Materials Knowledge Systems) framework, and was previously applied successfully for capturing mainly the microstructure-property linkages in spatial multiscale simulations. This paper generalizes this framework by allowing the introduction of different basis functions, and explores their potential benefits in establishing the desired process-structure-property (PSP) linkages. These new developments are demonstrated using a Cahn-Hilliard simulation as an example case study, where structure evolution was predicted three orders of magnitude faster than an optimized numerical integration algorithm. This study suggests that the MKS localization framework provides an alternate method to learn the underlying embedded physics in a numerical model expressed through Green’s function based influence kernels rather than differential equations, and potentially offers significant computational advantages in problems where numerical integration schemes are challenging to optimize. With this extension, we have now established a comprehensive framework for capturing PSP linkages for multiscale materials modeling and simulations in both space and time.