船舶与海洋平台结构冰载荷的高性能扩展多面体离散元方法

船舶与海洋平台结构的冰载荷是寒区海洋工程结构物设计中的关键参数,而离散元方法是有效计算结构冰载荷的重要手段. 本文采用基于闵可夫斯基和原理的扩展多面体离散元方法模拟船舶与海洋平台结构的相互作用过程. 其中,构造扩展多面体的近似包络函数并建立了基于优化模型的快速接触搜索算法;考虑单元间粘结作用的刚度软化过程建标识码元间的粘结-破碎模型. 同时,发展了 CPU-GPU 协同异构环境下的高性能并行算法. 为分析海冰与海洋结构作用中的冰载荷,采用ISO标准验证了扩展多面体离散元分析结构冰载荷的准确性. 采用离散元方法计算了船舶结构的冰载荷,研究了船舶结构表明的线载荷分布特点,并采用船舶结构冰阻力经验公式验证了计算结果的合理性. 采用离散元方法计算了平整冰区与多桩腿平台结构的相互作用,分析各桩腿上的冰载荷特点. 针对碎冰区的海冰管理过程,采用离散元方法分析了船舶结构绕行过程中的船舶和海洋平台结构冰载荷. 本文方法可有效应用于海洋结构冰载荷分析,能为极地船舶与海洋平台结构的设计和安全运行提供科学的分析手段.      

呋喃唑酮在凡纳滨对虾组织中代谢动力学研究

运用液相色谱-串联质谱法研究了呋喃唑酮药饵多次给药后在凡纳滨对虾体内的药物代谢动力学.研究结果表明：呋喃唑酮代谢物在血淋巴、肝胰脏、肌肉组织中的代谢过程均符合二室模型,其动力学方程分别为C血液=414.107×e^-0.092t＋124.451×e^-0.005t,C肝胰腺=1625.563×e^-0.019t＋125.700,C肌肉=120.434×e^-0.019t＋71.579×e^-0.001t.凡纳滨对虾血淋巴和肝胰腺中呋喃唑酮代谢物浓度在4h达到高峰,肌肉中药物浓度在2h达到最大值;最高峰时血淋巴、肝胰腺和肌肉中药物浓度平均为570.6μg·L^-1、1948.6μg·kg-1和210.0μg·kg-1.在血淋巴、肝胰腺和肌肉组织中呋喃唑酮代谢物浓度的吸收半衰期（t1/2α）分别为7.497h、36.206h和36.484h,消除半衰期（t1/2β）分别为132.525 h、1 000 479.217 h和505.637 h,总表观分布容积（V1/F）分别为55.775 L·kg^-1、17.16 L·kg^-1和161.574L·kg^-1.说明给药后呋喃唑酮代谢物在凡纳滨对虾体内消除缓慢,残留严重,尤其以肝胰脏残留最为明显.     

一种求解三维热辐射输运方程的整体预处理迭代方法及并行计算

为三维灰体热辐射输运方程的隐式离散纵标方法发展一个整体预处理迭代方法并研制并行程序.该方法采用组装线性代数方程组策略,同时求出所有离散方向上的辐射强度.借助预处理的Krylov子空间迭代法,避免复杂网格上扫描算法可能遇到的死锁问题,能够提高健壮性和计算效率.空间离散上采用一阶迎风有限体积格式.数值实验测试变形六面体网格...     

Real-Time Reconfigurable Interconnections for Parallel Optical Processing

In this letter we describe the advantages of a dynamic optical interconnection system for parallel information processing applications. The system is based on a liquid crystal television which acts as a binary phase-only spatial light modulator. We describe example algorithms where reconfigurable interconnects would be useful and present results of several interconnection topologies which have been implemented.Presented at the International Commission for Optics Topical Meeting, Kyoto, 1994.     

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将理想全二维磁流体(MHD)稳定性数值代码GATO成功移植到HL-2A高性能计算系统,并用其模拟研究了HL-2A装置一次典型弹丸注入实验的等离子体的理想MHD稳定性。利用HL-2A装置第4050次放电的实验数据,应用EFIT代码重建了几个时间片的平衡位形,然后应用GATO代码对每个平衡位形的MHD稳定性进行了计算。通过对结果进行分析比较得出初步结论,由于弹丸注入而造成的反剪切位形的致稳作用,可以提高等离子体的约束性能。     

二维多群辐射输运程序LARED-R-1的并行化

利用有向图描述数据依赖关系,应用已有的并行流水线通量扫描算法,实现基于非协调网格的二维辐射输运程序LARED-R-1的并行化.同时,采用消息缓冲技术提高并行程序的性能.经测试,对于典型的问题规模(100群、3800个网格单元、40个方向),在某并行机的64个和128个处理器上,并行程序分别获得80%和53%的并行效率.     

A minimization version of a directed subgraph homeomorphism problem

We consider a special case of the directed subgraph homeomorphism or topological minor problem, where the host graph has a specific regular structure. Given an acyclic directed pattern graph, we are looking for a host graph of minimal height which still allows for an embedding. This problem has applications in compiler design for certain coarse-grain reconfigurable architectures. In this application domain, the task is to simultaneously schedule, bind and route a so-called data-flow graph, where vertices represent operations and arcs stand for data dependencies between the operations, given an orthogonal grid structure of reconfigurable processing elements (PEs) that have restricted communication abilities. We show that the problem of simultaneously scheduling, binding and routing is NP-complete by describing a logic engine reduction from NAE-3-SAT. This result holds even when the input graph is a directed tree with maximum indegree two. We also give a |V|^3/2-approximation algorithm. J. A. Brenner’s research supported by the DFG Research Center Matheon “Mathematics for key technologies”. J. C. van der Veen’s research supported by DFG Focus Program 1148, “Reconfigurable Architectures”, Grants FE 407/8-1 and FE 407/8-2.     

Topological structure preserving numerical simulations of dynamical models

This paper brings together two methods producing numerical solutions with a statement of their quality — the nonstandard finite difference method and the method of validated computing. It deals with the construction and the analysis of reliable numerical discretizations of dynamical systems by employing these two techniques. An epidemiological model is used as a model example for their combined application.     

Parallel preconditioned conjugate gradient algorithm on GPU

We propose a parallel implementation of the Preconditioned Conjugate Gradient algorithm on a GPU platform. The preconditioning matrix is an approximate inverse derived from the SSOR preconditioner. Used through sparse matrix–vector multiplication, the proposed preconditioner is well suited for the massively parallel GPU architecture. As compared to CPU implementation of the conjugate gradient algorithm, our GPU preconditioned conjugate gradient implementation is up to 10 times faster (8 times faster at worst).