Impact of transparent exopolymer particles on the dynamics of dissolved organic carbon in the Amundsen Sea,Antarctica

The Southern Ocean is an important carbon sink pool and plays a critical role in the global carbon cycling.The Amundsen Sea was reported to be highly productive in inshore area in the Southern Ocean.In order to investigate the influence of transparent exopolymer particles(TEP)on the behavior of dissolved organic carbon(DOC)in this region,a comprehensive study was conducted,encompassing both open water areas and highly productive polynyas.It was found that microbial heterotrophic metabolism is the primary process responsible for the production of humic-like fluorescent components in the open ocean.The relationship between apparent oxygen utilization and the two humic-like components can be accurately described by a power-law function,with a conversion rate consistent with that observed globally.The presence of TEP was found to have little impact on this process.Additionally,the study revealed the accumulation of DOC at the sea surface in the Amundsen Sea Polynya,suggesting that TEP may play a critical role in this phenomenon.These findings contribute to a deeper understanding of the dynamics and surface accumulation of DOC in the Amundsen Sea Polynya,and provide valuable insights into the carbon cycle in this region.

Parallelized Implementation of the Finite Particle Method for Explicit Dynamics in GPU

As a novel kind of particle method for explicit dynamics,the finite particle method(FPM)does not require the formation or solution of global matrices,and the evaluations of the element equivalent forces and particle displacements are decoupled in nature,thus making this method suitable for parallelization.The FPM also requires an acceleration strategy to overcome the heavy computational burden of its explicit framework for time-dependent dynamic analysis.To this end,a GPU-accelerated parallel strategy for the FPM is proposed in this paper.By taking advantage of the independence of each step of the FPM workflow,a generic parallelized computational framework for multiple types of analysis is established.Using the Compute Unified Device Architecture(CUDA),the GPU implementations of the main tasks of the FPM,such as evaluating and assembling the element equivalent forces and solving the kinematic equations for particles,are elaborated through careful thread management and memory optimization.Performance tests show that speedup ratios of 8,25 and 48 are achieved for beams,hexahedral solids and triangular shells,respectively.For examples consisting of explicit dynamic analyses of shells and solids,comparisons with Abaqus using 1 to 8 CPU cores validate the accuracy of the results and demonstrate a maximum speed improvement of a factor of 11.2.

A Parallel Boundary Element Formulation for Tracking Multiple Particle Trajectories in Stoke’s Flow for Microfluidic Applications

A new formulation for tracking multiple particles in slow viscous flow for microfluidic applications is presented.The method employs the manipulation of the boundary element matrices so that finally a system of equations is obtained relating the rigid body velocities of the particle to the forces applied on the particle.The formulation is specially designed for particle trajectory tracking and involves successive matrix multiplications for which SMP(Symmetric multiprocessing)parallelisation is applied.It is observed that present formulation offers an efficient numerical model to be used for particle tracking and can easily be extended for multiphysics simulations in which several physics involved.

Particle filter based on iterated importance density function and parallel resampling

The design, analysis and parallel implementation of particle filter(PF) were investigated. Firstly, to tackle the particle degeneracy problem in the PF, an iterated importance density function(IIDF) was proposed, where a new term associating with the current measurement information(CMI) was introduced into the expression of the sampled particles. Through the repeated use of the least squares estimate, the CMI can be integrated into the sampling stage in an iterative manner, conducing to the greatly improved sampling quality. By running the IIDF, an iterated PF(IPF) can be obtained. Subsequently, a parallel resampling(PR) was proposed for the purpose of parallel implementation of IPF, whose main idea was the same as systematic resampling(SR) but performed differently. The PR directly used the integral part of the product of the particle weight and particle number as the number of times that a particle was replicated, and it simultaneously eliminated the particles with the smallest weights, which are the two key differences from the SR. The detailed implementation procedures on the graphics processing unit of IPF based on the PR were presented at last. The performance of the IPF, PR and their parallel implementations are illustrated via one-dimensional numerical simulation and practical application of passive radar target tracking.

A GPU-Based Parallel Algorithm for 2D Large Deformation Contact Problems Using the Finite Particle Method

Large deformation contact problems generally involve highly nonlinear behaviors,which are very time-consuming and may lead to convergence issues.The finite particle method(FPM)effectively separates pure deformation from total motion in large deformation problems.In addition,the decoupled procedures of the FPM make it suitable for parallel computing,which may provide an approach to solve time-consuming issues.In this study,a graphics processing unit(GPU)-based parallel algorithm is proposed for two-dimensional large deformation contact problems.The fundamentals of the FPM for planar solids are first briefly introduced,including the equations of motion of particles and the internal forces of quadrilateral elements.Subsequently,a linked-list data structure suitable for parallel processing is built,and parallel global and local search algorithms are presented for contact detection.The contact forces are then derived and directly exerted on particles.The proposed method is implemented with main solution procedures executed in parallel on a GPU.Two verification problems comprising large deformation frictional contacts are presented,and the accuracy of the proposed algorithm is validated.Furthermore,the algorithm’s performance is investigated via a large-scale contact problem,and the maximum speedups of total computational time and contact calculation reach 28.5 and 77.4,respectively,relative to commercial finite element software Abaqus/Explicit running on a single-core central processing unit(CPU).The contact calculation time percentage of the total calculation time is only 18%with the FPM,much smaller than that(50%)with Abaqus/Explicit,demonstrating the efficiency of the proposed method.

NEURAL NETWORK TRAINING WITH PARALLEL PARTICLE SWARM OPTIMIZER

Objective To reduce the execution time of neural network training. Methods Parallel particle swarm optimization algorithm based on master-slave model is proposed to train radial basis function neural networks, which is implemented on a cluster using MPI libraries for inter-process communication. Results High speed-up factor is achieved and execution time is reduced greatly. On the other hand, the resulting neural network has good classification accuracy not only on training sets but also on test sets. Conclusion Since the fitness evaluation is intensive, parallel particle swarm optimization shows great advantages to speed up neural network training.

Particle size segregation in hopper at a bell-less top blast furnace with parallel hoppers

Owing to a certain angle existing between a belt conveyor and the parallel hoppers,and the hoppers localizing away from the centerlines of a blast furnace,particles size segregation is likely to happen in a bell-less top blast furnace with parallel hoppers.Mastering the law of particles size segregation in hoppers could help to choose better charging parameters and optimize production and technical indices.As for the previous works on burden segregation at a bell-less top blast furnace with parallel hoppers,more attention was paid to the falling point segregation and the circumferential mass flow segregation while charging from the tilting chute,but ignoring the particle size segregation in burden hoppers as burden falls from a belt conveyor,which is the right basis of analyzing the former,and plays a significant role in controlling the gas distribution in the blast furnace.The present work takes ternary mixtures of coke in three different particle sizes to simulate the size segregation of the coke charged into the hoppers by experiments.The effect of the main striking point on size segregation is also investigated.The research shows that there exists a good linear relation between segregation coefficient k and the dimensionless main striking point when using the equation C = C_0~k to express the degree of size segregation in hoppers.The linear relation is proposed for the first time and provides a new way to predict the size segregation in hoppers,which forms a theoretical basis and technical support for reducing the size segregation degree in hoppers.

Particle-Particle算法并行化及改进

介绍了particle-particle算法的基本原理,并对串行particle-particle算法进行有效的 并行化;对并行算法的受力计算和通信过程进行改进;最后给出了实验结果,并进行相关性 能分析．

Variational Data Assimilation Method Using Parallel Dual Populations Particle Swarm Optimization Algorithm

In recent years,numerical weather forecasting has been increasingly emphasized.Variational data assimilation furnishes precise initial values for numerical forecasting models,constituting an inherently nonlinear optimization challenge.The enormity of the dataset under consideration gives rise to substantial computational burdens,complex modeling,and high hardware requirements.This paper employs the Dual-Population Particle Swarm Optimization(DPSO)algorithm in variational data assimilation to enhance assimilation accuracy.By harnessing parallel computing principles,the paper introduces the Parallel Dual-Population Particle Swarm Optimization(PDPSO)Algorithm to reduce the algorithm processing time.Simulations were carried out using partial differential equations,and comparisons in terms of time and accuracy were made against DPSO,the Dynamic Weight Particle Swarm Algorithm(PSOCIWAC),and the TimeVarying Double Compression Factor Particle Swarm Algorithm(PSOTVCF).Experimental results indicate that the proposed PDPSO outperforms PSOCIWAC and PSOTVCF in convergence accuracy and is comparable to DPSO.Regarding processing time,PDPSO is 40%faster than PSOCIWAC and PSOTVCF and 70%faster than DPSO.

Particle flow code simulation of intact and fissured granitic rock samples

This study presents a calibration process of three-dimensional particle flow code(PFC3D)simulation of intact and fissured granite samples.First,laboratory stressestrain response from triaxial testing of intact and fissured granite samples is recalled.Then,PFC3D is introduced,with focus on the bonded particle models(BPM).After that,we present previous studies where intact rock is simulated by means of flatjoint approaches,and how improved accuracy was gained with the help of parametric studies.Then,models of the pre-fissured rock specimens were generated,including modeled fissures in the form of“smooth joint”type contacts.Finally,triaxial testing simulations of 1 t 2 and 2 t 3 jointed rock specimens were performed.Results show that both elastic behavior and the peak strength levels are closely matched,without any additional fine tuning of micro-mechanical parameters.Concerning the postfailure behavior,models reproduce the trends of decreasing dilation with increasing confinement and plasticity.However,the dilation values simulated are larger than those observed in practice.This is attributed to the difficulty in modeling some phenomena of fissured rock behaviors,such as rock piece corner crushing with dust production and interactions between newly formed shear bands or axial splitting cracks with pre-existing joints.

A dual channel perturbation particle filter algorithm based on GPU acceleration

The particle filter（PF） algorithm is one of the most commonly used algorithms for maneuvering target tracking. The traditional PF maps from multi-dimensional information to onedimensional information during particle weight calculation, and the incorrect transmission of information leads to the fact that the particle prediction information does not match the weight information, and its essence is the reduction of the information entropy of the useful information. To solve this problem, a dual channel independent filtering method is proposed based on the idea of equalization mapping. Firstly, the particle prediction performance is described by particle manipulations of different dimensions, and the accuracy of particle prediction is improved. The improvement of particle degradation of this algorithm is analyzed in the aspects of particle weight and effective particle number. Secondly, according to the problem of lack of particle samples, the new particles are generated based on the filtering results, and the particle diversity is increased. Finally, the introduction of the graphics processing unit（GPU） parallel computing the platform, the “channel-level” and “particlelevel” parallel computing the program are designed to accelerate the algorithm. The simulation results show that the algorithm has the advantages of better filtering precision, higher particle efficiency and faster calculation speed compared with the traditional algorithm of the CPU platform.

Implementation of a Particle Accelerator Beam Dynamics Code on Multi-Node GPUs

Particle accelerators play an important role in a wide range of scientific discoveries and industrial applications. The self-consistent multi-particle simulation based on the particle-in-cell (PIC) method has been used to study charged particle beam dynamics inside those accelerators. However, the PIC simulation is time-consuming and needs to use modern parallel computers for high-resolution applications. In this paper, we implemented a parallel beam dynamics PIC code on multi-node hybrid architecture computers with multiple Graphics Processing Units (GPUs). We used two methods to parallelize the PIC code on multiple GPUs and observed that the replication method is a better choice for moderate problem size and current computer hardware while the domain decomposition method might be a better choice for large problem size and more advanced computer hardware that allows direct communications among multiple GPUs. Using the multi-node hybrid architectures at Oak Ridge Leadership Computing Facility (OLCF), the optimized GPU PIC code achieves a reasonable parallel performance and scales up to 64 GPUs with 16 million particles.

AN ITERATIVE PARTICLE FILTER SIGNAL DETECTOR FOR MIMO FAST FADING CHANNELS

For flat fast fading Multiple-Input Multiple-Output(MIMO) channels,this paper presents a sampling based channel estimation and an iterative Particle Filter(PF) signal detection scheme. The channel estimation is comprised of two parts:the adaptive iterative update on the channel distribution mean and a regular update on the "adaptability" via pilot. In the detection procedure,the PF is employed to produce the optimal decision given the known received signal and the sequence of the channel samples,where an asymptotic optimal importance density is constructed,and in terms of the asymptotic update order,the Parallel Importance Update(PIU) and the Serial Importance Update(SIU) scheme are performed respectively. The simulation results show that for the given fading channel,if an appropriate pilot mode is selected,the proposed scheme is more robust than the conventional Kalman filter based superimposed detection scheme.

A Rayleigh Wave Globally Optimal Full Waveform Inversion Framework Based on GPU Parallel Computing

Conventional gradient-based full waveform inversion (FWI) is a local optimization, which is highly dependent on the initial model and prone to trapping in local minima. Globally optimal FWI that can overcome this limitation is particularly attractive, but is currently limited by the huge amount of calculation. In this paper, we propose a globally optimal FWI framework based on GPU parallel computing, which greatly improves the efficiency, and is expected to make globally optimal FWI more widely used. In this framework, we simplify and recombine the model parameters, and optimize the model iteratively. Each iteration contains hundreds of individuals, each individual is independent of the other, and each individual contains forward modeling and cost function calculation. The framework is suitable for a variety of globally optimal algorithms, and we test the framework with particle swarm optimization algorithm for example. Both the synthetic and field examples achieve good results, indicating the effectiveness of the framework. .

通过包络面重构的大规模粒子并行绘制算法

针对大规模粒子高表现可视化需求,提出基于包络面重构的大规模粒子并行绘制算法。该算法以连续曲面的形式表示,绘制大规模粒子的团簇表面及其物理量分布。对算法进行了分布式并行化,从而可以通过大规模并行来处理亿以上规模的粒子数据。在算法实现上,还解决了并行计算时的块间裂缝问题,并提出了快速查找邻域粒子的方法,同时,基于可见性对粒子数据进行剔除,提高了绘制效率。由此,可以通过带光照效果的光滑曲面来高表现展示大规模粒子数据中的团簇结构及其物理量分布。实验结果表明,该算法在512核上可在5 s内完成上亿粒子的绘制,并行效率可达60%。该算法已成功应用到大规模并行非平衡分子动力学模拟等实际模拟应用中。

电推进粒子网格法模拟中计算加速方法的研究综述

粒子网格法(PIC)模拟电推进装置等离子体时具有很强的第一性,但是模拟过程中计算负载很大,故PIC模拟的计算加速方法不可或缺。本文从电推进装置基本性质、低温等离子体物理特性和PIC算法特点作为切入点,明确了PIC方法在电推进装置模拟过程中计算负载高的原因;结合国内外的研究现状,从建模、时空尺度、算法与并行三个层面介绍了对应计算加速方法的原理和效果;对各类计算加速方法进行了总结和展望。

基于云计算的舰船通信网络入侵检测方法研究

为在面临大规模网络攻击或突发攻击时,提高入侵检测的实时性,提出基于云计算的舰船通信网络入侵检测方法。通过云计算的MapReduce编程模型,设计MapReduce并行化的遗传量子粒子群优化算法,在舰船通信网络数据内,提取网络入侵特征;利用MapReduce并行化熵聚类算法,确定径向基函数神经网络的基函数中心;确定基函数中心后,在MapReduce编程模型的Map函数内,输入网络入侵特征样本,训练神经网络,优化神经网络权值,通过Reduce函数输出训练结束指示,完成神经网络训练;在完成训练的MapReduce并行化径向基函数神经网络内,输入特征样本,输出舰船通信网络入侵检测结果。实验证明,该方法可有效提取舰船通信网络入侵特征;在不同网络攻击类型下,该方法均可精准完成舰船通信网络入侵检测。

基于PCA/PSO的3T1R并联机构性能优化

根据少自由度并联机构应用广泛的优点,提出了一种3T1R并联机构,该机构具有构型简单、结构对称、定位精度高等特点,可应用于小范围的精密操作,或者是大范围的搬运、分拣以及喷涂等领域。基于方位特征方程的拓扑分析理论,对该并联机构完成了自由度种类以及数目的分析与验证;基于闭环矢量法完成了运动学模型建立,并通过位置正逆解算例验证了运动学的合理性。基于位置逆解方程利用极限边界搜索法分析了3T1R并联机构可达工作空间;通过速度分析建立了速度雅可比矩阵,并根据该矩阵分析机构的定位精度与可操作度性能指标。利用主成分分析(PCA)与粒子群算法(PSO)对3个性能指标进行优化设计,并对优化结果进行了分析,最终优化后可达工作空间体积从0.2933m3提高到0.4231m3,定位精度误差放大因子从15.5044减小至4.4308,可操作度指数从9.7027减小至1.3996。

正交-响应面法在PBM细观参数标定中的应用

数值模拟作为研究岩石力学特性、再现细观裂纹演化的主要途径,已受到大量关注。现有的数值模拟参数标定方法主要为试错法及正交试验法,但二者都未能充分考虑细观参数交互作用的影响,模拟精度欠佳且宏观破坏形态与室内试验存在较大差异。因此,采用正交-响应面法相结合的数值分析方法,首先通过正交试验筛选出具有显著影响的平行黏结模型(PBM)细观参数,其次应用响应面法(RSM)研究其交互作用对模型试样宏观参量的影响规律,最后结合岩石宏观破坏形态提出一套PBM参数标定流程。结果表明:有效模量E*与刚度比k n/k s对弹性模量E影响显著;k n/k s、接触摩擦系数μ、最小颗粒半径R_(min)对泊松比ν影响显著;黏聚力c与法向黏结强度σc及其交互作用对单轴抗压强度UCS影响显著,应用响应面法计算分析得出的细观参数的模拟值与试验值误差绝对值小于7%,且二者应力应变曲线力学特征相似,宏观破坏形态相同,证明所提出的PBM细观参数标定流程具备科学性和可靠性。

基于权参数优化的并行深度学习光伏功率预测

针对不同应用场景下光伏数据波动模式差异较大时,现有的光伏发电功率预测模型存在精度及适应性不足的问题,提出了一种具有权参数自适应性的并行深度学习光伏发电功率预测框架。该框架包含2种可以并行预测的深度学习算法单元(Attention-Seq2Seq单元、Transformer单元)以及一个权参数自适应优化单元。基于所提出的并行深度学习框架,对光伏发电功率进行预测,并分别与Attention-Seq2Seq、Transformer模型的预测结果进行了对比验证。结果表明:基于权参数优化的并行深度学习光伏功率预测框架弥补了不同数据波动模式下单一算法预测精度和适应性不足的问题,也可以有效解决时间序列预测中的长距离依赖问题,较单一算法预测精度更高,其平均绝对误差和均方根误差在夏季典型日最大降幅分别是41.18%和45.59%,在冬季典型日最大降幅分别是81.13%和82.86%。