[成形过程仿真优化与集成计算材料工程]限定加热管布局的快速热循环注塑成形模具电加热系统优化

针对管道布局、最大允许能耗给定条件下快速热循环注塑成形（RHCM）注塑模具型腔表面快速均匀加热的问题，提出以单根加热棒热流密度为设计变量，以模具型腔表面升温效率和温度分布均匀性为目标，结合有限元模拟、响应面设计以及多目标粒子群优化技术来优化RHCM模具电加热系统。与优化前相比，加热系统优化后，模具型腔表面最大温差降低63.4%，加热系统总能耗降低9%。对比了不同注塑成形工艺条件下成形的平板塑件表面质量，结果表明，相对传统注塑成形（CIM）工艺，RHCM工艺将制品表面粗糙度Ra从320 nm降低到118 nm，并有效抑制了制品表面熔接痕、缩痕等缺陷；发现制品表面粗糙度与型腔表面对应点温度成负相关，说明优化后的型腔表面温度分布更有利于提升制品表面质量。     

泵站压力管道振动特性分析及减振优化设计

为探究泵站压力管道的不同布置形式对管道振动的影响,选取宁夏盐环定泵站工程二泵站#1压力管道为研究对象,建立基于流固耦合的泵站压力管道水流ALGOR数值模型,并采用DASP振动测试系统获取振动信号对模拟结果进行验证,分析压力管道水流流态对管道振动的激励特征并提出优化设计方案。结果表明,压力管道进水管处、支管总管相接处及总出水管处均有强烈的振动,但进水管的振动较出水总管振动更为强烈;减振优化设计方案中,调整支管距离对降低水流对管道振动的激励效果微弱,增大管径及改变总管走向对其效果较好,其中总管走向在2°～5°之间较佳,不仅能降低管道振动,而且能减少泵站能耗损失。研究结果可为高扬程泵站压力管道的优化设计提供理论依据。     

Anderson Acceleration for Nonconvex ADMM Based on Douglas-Rachford Splitting

The alternating direction multiplier method (ADMM) is widely used in computer graphics for solving optimization problems that can be nonsmooth and nonconvex. It converges quickly to an approximate solution, but can take a long time to converge to a solution of high-accuracy. Previously, Anderson acceleration has been applied to ADMM, by treating it as a fixed-point iteration for the concatenation of the dual variables and a subset of the primal variables. In this paper, we note that the equivalence between ADMM and Douglas-Rachford splitting reveals that ADMM is in fact a fixed-point iteration in a lower-dimensional space. By applying Anderson acceleration to such lower-dimensional fixed-point iteration, we obtain a more effective approach for accelerating ADMM. We analyze the convergence of the proposed acceleration method on nonconvex problems, and verify its effectiveness on a variety of computer graphics including geometry processing and physical simulation.     

A Stochastic Petri Net-Based Model of the Involvement of Interleukin 18 in Atherosclerosis

Interleukin 18 (IL-18) is a proinflammatory and proatherogenic cytokine with pleiotropic properties, which is involved in T and NK cell maturation and the synthesis of other inflammatory cytokines and cell adhesion molecules. It plays a significant role in orchestrating the cytokine cascade, accelerates atherosclerosis and influences plaque vulnerability. To investigate the influence of IL-18 cytokine on atherosclerosis development, a stochastic Petri net model was built and then analyzed. First, MCT-sets and t-clusters were generated, then knockout and simulation-based analysis was conducted. The application of systems approach that was used in this research enabled an in-depth analysis of the studied phenomenon. Our results gave us better insight into the studied phenomenon and allow revealing that activation of macrophages by the classical pathway and IL-18-MyD88 signaling axis is crucial for the modeled process.     

S6-11综放工作面回风巷支护设计优化

针对常村矿回采巷道变形严重,影响正常生产的问题,通过对矿井赋存条件、巷道现有支护形式和围岩变形过程分析,采用新型联合支护技术对S6-11回风巷道支护进行优化。现场实测巷道顶底板相对移近量最大120mm,两帮相对移近量最大140mm,均在允许正常范围之内。     

配煤掺烧最小煤质偏差模型及煤场存放优化

定义了混煤和锅炉目标煤的煤质偏差,给出以煤质偏差作为目标函数的掺配优化模型,通过调整某个成分的期望误差,可单独调节该成分与目标煤的接近程度;通过对模型约束条件设定,可限制低灰熔点煤的掺配份额,控制炉膛结焦,或提高采购量大的单煤的掺配比例。基于最小煤质偏差模型,针对某670 MW超临界压力直流锅炉进行掺配优化模拟,提出煤场存放的优化概念和方法。结果表明:该模型能够从复杂的单煤煤源中找到最接近目标煤质的掺配方案,优化后入炉煤质稳定,且接近目标煤质。     

Optimal design of Raman fibre amplifier based on terminal value optimization strategy and shuffled frog leaping algorithm

This paper introduces an evolutionary algorithm, Shuffled Frog Leaping Algorithm (SFLA), to solve the optimization problem in designing the multi-pumped Raman Fibre Amplifier (RFA). SFLA is a powerful optimizer tool because of its efficient mathematical expressions and global search capability. We utilize SFLA to determine the optimal pump wavelengths and pump powers by minimizing the gain ripple of RFA. To accelerate calculations, a terminal value optimization strategy (TVOS) is incorporated into the evolution of SFLA. This proposed strategy takes the terminal power values of pumps as the decision variables in optimization. Then, the optimal original power values of the pumps are obtained by solving the Power Coupled Equations once, without using the traditional method of repetitive guesses.The combination of SFLA and TVOS enhances the efficiency of optimization and accelerates calculation, while satisfying the design requirements of RFA.The simulation results show that nearly 65% of computational time has been saved compared with the traditional average power analysis. The 4-pumped C+L band of backward multi-pumped RFA with the average net gain of 0 dB, 1 dB and 2 dB are designed individually, where the gain ripple is less than 0.64 dB. The combination of SFLA and TVOS enhance the optimization efficiency and improve the performance of RFA with good gain profile.     

利用调度优化模型研究原油调合方案的可行性

针对某沿海炼油企业拟投用原油调合系统以稳定混炼原油的性质,设计方案要求将部分码头罐的操作模式由混储改为单储的情况,采用约束规划与数学规划复合建模,实现了码头至厂区大容积长输线模拟,建立了该企业的原油调度优化模型并研究了设计方案的可行性。针对240h调度周期、10种可加工原油、3艘到港油轮的复杂工况进行了优化计算。结果表明:该设计方案具备可行性,且油轮仍可实现到港即卸油,蒸馏装置混炼油种也可长时间保持稳定;模型的优化计算时间较短(小于15min),且对长输线收付及存油情况模拟准确,复合建模方法满足工业应用要求。     

R2D2: A scalable deep learning toolkit for medical imaging segmentation

Deep learning has gained a significant popularity in recent years thanks to its tremendous success across a wide range of relevant fields of applications, including medical image analysis domain in particular. Although convolutional neural networks (CNNs) based medical applications have been providing powerful solutions and revolutionizing medicine, efficiently training of CNNs models is a tedious and challenging task. It is a computationally intensive process taking long time and rare system resources, which represents a significant hindrance to scientific research progress. In order to address this challenge, we propose in this article, R2D2, a scalable intuitive deep learning toolkit for medical imaging semantic segmentation. To the best of our knowledge, the present work is the first that aims to tackle this issue by offering a novel distributed versions of two well-known and widely used CNN segmentation architectures [ie, fully convolutional network (FCN) and U-Net]. We introduce the design and the core building blocks of R2D2. We further present and analyze its experimental evaluation results on two different concrete medical imaging segmentation use cases. R2D2 achieves up to 17.5× and 10.4× speedup than single-node based training of U-Net and FCN, respectively, with a negligible, though still unexpected segmentation accuracy loss. R2D2 offers not only an empirical evidence and investigates in-depth the latest published works but also it facilitates and significantly reduces the effort required by researchers to quickly prototype and easily discover cutting-edge CNN configurations and architectures.     

Void region restriction for additive manufacturing via a diffusion physics approach

A longstanding challenge in additive manufacturing (AM), the presence of void regions in additively manufactured components, causes two main issues: the enclosing of build material powder in powder bed fusion techniques and limiting tool access in critical post-processing operations to remove sacrificial support structures. As topology optimization has embraced and overcome many of the obstacles of incorporating AM constraints into the underlying numerical optimization statement, there exist few solutions that directly address this fundamental void region issue. By developing computationally efficient and effective solutions to this problem, the integration of these two advanced technologies can be fully realized. Drawing on inspiration from the principles of diffusion physics, a particle diffusion void restriction (PDVR) method is presented in this work that is capable of encouraging the optimization scheme to generate final designs that are fully accessible. Additionally, this method empowers the user to choose the type of post-processing method to clear support material (eg, three-axis or five-axis milling operations, number and orientation of part set-ups) and, therefore, quantify the level of costs associated with the post-processing operation. The PDVR optimization framework is demonstrated on multiple two- and three-dimensional test problems, with physically manufactured examples depicting the real-world benefits this method admits.