CN104166784B - Composition distribution calculation method based on the steady-state polyol copolymer of rapid chemical cuda - Google Patents

Composition distribution calculation method based on the steady-state polyol copolymer of rapid chemical cuda Download PDF

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CN104166784B
CN104166784B CN 201410325208 CN201410325208A CN104166784B CN 104166784 B CN104166784 B CN 104166784B CN 201410325208 CN201410325208 CN 201410325208 CN 201410325208 A CN201410325208 A CN 201410325208A CN 104166784 B CN104166784 B CN 104166784B
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CN104166784A (en )
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翁金祖
陈曦
邵之江
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浙江大学
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Abstract

本发明公开了一种基于CUDA平台下的应用在稳态多元共聚中的快速化学组成分布计算方法。 The present invention discloses an internet-based applications in rapid chemical CUDA polyhydric steady state copolymerization composition distribution calculation method. 本发明针对多元共聚反应中的稳态过程,依靠聚合反应的动力学机理,以蒙特卡罗方法为基础,在统一计算设备架构(CUDA)平台下给出了一种求取多元共聚物化学组成分布的快速计算方法。 The present invention is directed to the copolymerization reaction of polyol steady state process, relying on the mechanism of polymerization kinetics to Monte Carlo method is based on obtaining a given chemical composition in a multicomponent copolymer Compute Unified Device Architecture (the CUDA) internet fast calculation method of distribution. 该方法首先通过聚合反应的动力学机理等给出了蒙特卡罗方法中所需要的不同概率,通过在CUDA平台上并行的执行蒙特卡罗方法,最终得到所需要的化学组成分布。 Firstly, by a kinetic mechanism of polymerization reaction gives the Monte Carlo method in different probabilities needed, by performing Monte Carlo method on the CUDA platform parallel to finally obtain the desired chemical composition distribution. 由于整个计算过程并行度较高,从而使得计算时间大幅度缩短,因此称为快速化学组成分布计算方法。 Due to the high degree of parallelism entire calculation process, so that the calculation time is greatly shortened, so called rapid chemical composition distribution calculation.

Description

一种基于CUDA的稳态多元共聚快速化学组成分布计算方法 Composition distribution calculation method based on the steady-state polyol copolymer of rapid chemical CUDA

技术领域 FIELD

[0001] 本发明涉及基于CUDA平台下的高分子多元共聚稳态过程中的快速化学组成分布计算技术方法。 [0001] The present invention relates to a polymer-based polyol in the copolymerization computing platform CUDA art approaches steady state process of rapid chemical composition distribution.

背景技术 Background technique

[0002] 随机数生成器,是指的能够生成随机数的函数或者程序模块。 [0002] The random number generator means capable of generating a random number function or program module. 在连续型随机变量的分布中,最简单而且最基本的分布是单位均匀分布,由该分布抽取的简单子样称为随机数序列,其中每一个个体称为随机数。 In the distribution of continuous random variable, the most simple and most basic unit of distribution is uniform, simple sub extracted by the random number sequence is called sample distribution, wherein each individual is called a random number. 独立性、均匀性是随机数必备的两个特点。 Independence, uniformity is a random number of two essential features. 包括蒙特卡罗计算方法在内的大多数算法都要求所采用的随机数序列服从均匀分布,即同一范围内的任一个数出现的概率相同。 Most algorithms including Monte Carlo calculation method are required, including the sequence of random numbers uniformly distributed used, i.e. the same probability of a any number appearing in the same range.

[0003] 蒙特卡罗方法,也称统计模拟方法,是二十世纪四十年代中期由于科学技术的发展和电子计算机的发明而被提出的一种以概率统计理论为指导的一类非常重要的数值计算方法。 [0003] Monte Carlo method, also known as statistical simulation method, is a mid-1940s due to the invention and development of computer science and technology have been presented to the theory of probability and statistics is a very important class of guidance numerical method. 该方法使用随机数(或更常见的伪随机数)来解决很多计算问题,与它对应的是确定性算法。 The method uses a random number (or more common pseudo-random number) is calculated to solve many problems, and it corresponds to a deterministic algorithm. 蒙特卡罗方法在化工领域已经得到认可和应用。 Monte Carlo method in the chemical industry has been recognized and applied. 在给定动力学机理的情况下,根据共聚系统稳态的各个状态值来计算出不同反应类型的概率;其次,设定的共聚体系中存在的分子链的数目,并进一步根据随机数生成器所生成的一系列随机数来重复的判定反应中各条链的反应情况,直到整个系统中的所有链都终止了为止。 In the case of a given kinetic mechanism, according to the respective steady state values ​​of the copolymerization system to calculate the probabilities of different types of reaction; secondly, setting the number of molecular chain present in the copolymerization system, and further according to the random number generator the generated series of random numbers repeated in the case of the reaction of the reaction is determined that the pieces of chain, so the chain until the entire system are terminated so far.

[0004] 统一计算设备架构(CUDA),是显卡产商NVIDIA推出的运算平台,是一种通用并行计算架构。 [0004] Compute Unified Device Architecture (the CUDA), the graphics card manufacturers NVIDIA Release computing platform, a general purpose parallel computing architecture. 由于它包含了指令集架构以及并行计算引擎,因此能够解决很多复杂的计算问题,并且大幅度的缩短计算时间,计算效率得到明显的提升。 Because it contains the instruction set architecture and parallel computing engine, it is possible to solve many complex computational problems, and greatly reduce the computing time and computational efficiency significantly improved.

[0005] 化学组成分布,是指不同种类单体在分子链中所占比重的一类分布。 [0005] The chemical composition distribution, refers to the different types of monomers proportion occupied by a class of distributed in the molecular chain. 在高分子化学领域,聚合物的性能指标包括常见的熔融指数、平均分子量、分子量分布,然而在共聚体系中这些指标并不能完全的描述聚合物的性能,因此就需要研究更为细致的化学组成分布。 In the field of polymer chemistry, performance polymers include common melt index, average molecular weight, molecular weight distribution, but in the copolymerization system of these indicators do not fully describe the performance of the polymer, thus requiring more detailed study of the chemical composition distributed.

发明内容 SUMMARY

[0006] 本发明的目的是针对稳态多元共聚体系中自由基聚合反应的应用场景,提供一种基于CUDA的稳态多元共聚快速化学组成分布计算方法。 [0006] The object of the present invention is the use scenario for the copolymerization system steady polyol radical polymerization reaction, to provide a steady CUDA-based copolymer polyol rapid chemical composition distribution calculation.

[0007] 本发明的技术方案如下: [0007] aspect of the present invention is as follows:

[0008] -种基于CUDA的稳态多元共聚快速化学组成分布计算方法包括如下步骤: [0008] - Steady species based copolymer polyol CUDA rapid chemical composition calculated distribution comprising the steps of:

[0009] a.读取稳态多元共聚体系的状态值,包括链增长、链转移、链终止反应的动力学常数以及各类单体、链转移剂、链终止剂的浓度; . [0009] a steady state value read polyhydric copolymerization system, including chain growth, chain transfer, the kinetic constants of chain termination reactions and a variety of monomers, the chain transfer agent concentration of chain terminating agent;

[0010] b.计算蒙特卡罗方法所需要的各个概率值,包括以各类单体结尾的活性链发生链增长反应的概率?:以及以各类单体结尾的活性链向各类单体链增长的概率Pu,以各类单体结尾的活性链发生链增长反应的概率即将各类单体结尾的链增长化学反应速率乘以相应单体的浓度之后的加和,除以各类单体结尾的链增长、链转移、链终止化学反应速率乘以相应单体的浓度之后的加和,以各类单体结尾的活性链向各类单体链增长的概率即将向各类单体链增长化学反应速率乘以对应单体的浓度,除以各类单体结尾的链增长化学反应速率乘以相应单体的浓度之后的加和;以公式表示: . [0010] b calculating respective probability values ​​required Monte Carlo method, including the occurrence probability active chain end of the chain extension reaction of a monomer of various types:?, And the end of an active chain of various types of various types of monomers to monomers the chain growth probability Pu, with a probability of occurrence of the living chain end of the chain extension reaction of various types of various types of monomers soon after the chemical reaction rate of the monomer chain-end concentration multiplied by the respective monomers added and divided by the various types of single chain-end of the body, a chain transfer, chain termination after the chemical reaction rate multiplied by the concentration of the respective monomers added and the probability of the active chain end of the various types of monomer to chain growth of various types of monomers is about to various types of monomers increase chemical reaction rates multiplied by the chain corresponding to the monomer concentration, divided by the rate of chemical reactions of various types of chain-end monomer multiplied by the corresponding monomers after concentration and addition; in formula:

Figure CN104166784BD00041

[0011] [0011]

[0012] [0012]

[0013] 其中,RP1表示聚合反应中以单体i结尾的活性链链增长速率;Rtl表示以单体i结尾的活性链链转移速率;Rdl表示以单体i结尾的活性链链终止速率;[j]、[m]分别表示单体j、 单体m的浓度;kpij、kpim分别表示以单体i结尾的活性链向单体j、单体m发生链增长的化学反应速率; [0013] wherein, RP1 represents the polymerization activity to the growth rate of the monomer chain ending i; Rtl represents an active chain end of the transfer rate of the monomer i; Rdl represents an active chain end of the termination rate of the monomer i; [J], [m] denote the concentration of monomer j, the m monomer; kpij, kpim represent the chemical reaction rate, the occurrence of a chain of monomer m the active chain end of the monomer to the monomer J i;

[0014] c.将步骤b计算得到的概率值从CPU平台传递到CUDA平台上; . [0014] c calculated probability value obtained in step b is transferred from the CPU to the internet platform CUDA;

[0015] d.在⑶DA平台上开辟用于记录序列信息的存储空间以及数目等于模拟总链数的线程数; . [0015] d the total number of threads of the open-chain and storage space for recording information is equal to the number of sequences in the simulation platform ⑶DA;

[0016] e.并行的执行所有线程,在每个线程里依靠步骤b中得到的概率,顺序地判断相应活性链是否发生链增长;若否,停止该线程的模拟计算并将得到的化学组成信息按线程编号在存储空间中进行存储;若是,则继续进行判断向哪一类单体进行链增长并记录对应的化学组成信息; . [0016] e parallel execution of all threads, relying probability obtained in step b in each thread sequentially determines whether the corresponding activity chain chain growth occurs; if not, the thread stops simulation and the resulting chemical composition information thread number is stored in a storage space; if yes, proceed to a judgment which a chain monomer corresponding to the chemical composition and the recording information;

[0017] f.重复步骤e,直到获得停止信息并退出; . [0017] f repeating steps e, until stop information and exit;

[0018] g.将记录的化学组成信息从CUDA平台传递到CPU平台上; . [0018] g the chemical composition of the recorded information is transmitted to the CPU from the internet platform CUDA;

[0019] h.统计所有的化学组成信息,得到所需要的化学组成分布。 [0019] h. The chemical composition of all the statistical information needed to get the chemical composition distribution.

[0020] 所述的步骤b中所述的蒙特卡罗方法的模拟平台为CUDA。 [0020] The step b of the method of Monte Carlo simulation platform for CUDA. 步骤b中所述的蒙特卡罗方法的模拟方式为每个模拟线程都只进行一条链的模拟过程。 In step b the Monte Carlo method in an analog manner for each analog simulation process with only a thread chain. 步骤b中所述的蒙特卡罗方法的模拟顺序为并行的执行多个蒙特卡罗模拟线程。 Monte Carlo simulation of the sequence of step b according to the plurality of parallel execution threads Monte Carlo simulation. 步骤e中所述的记录信息为链中所有的化学组成信息。 In step e the information recorded in the chain all chemical composition information.

[0021] 基于⑶DA的稳态多元共聚中的快速化学组成分布计算方法的核心思想是:用每个线程进行共聚反应中每条链的模拟,并将这些线程在CUDA平台下运行实现,从而实现快速计算。 The core idea of ​​[0021] Based on the steady-state polyvalent copolymerizable ⑶DA in rapid chemical composition distribution is calculated: simulation of each chain copolymerization reaction with each thread, and these threads run CUDA platform implemented in order to achieve Quick calculations. 方法是:首先,设定需要模拟计算的链的数目以及一些系统的参数值;其次,根据给定的共聚动力学反应机理以及稳态下对应的动力学参数值和系统状态值,计算出以不同共聚物类型结尾的活性链进行各种动力学反应的概率以及在此基础上进一步向不同反应物类型反应的概率等;然后,将前面的参数值以及计算出来的概率传到CUDA平台上,以链数为线程数进行重复运算,直到满足设定的链的数目或者最大链长为止;最后,将CUDA上模拟计算出来的每条链的信息传回主程序,统计出所需要的不同化学组成比重,再把这些值整理后得到所需要的共聚物化学组成分布。 The method is: first, set the number of chains need to simulate some of the system and calculated parameter values; Secondly, according to the copolymerization kinetics of the reaction mechanism and kinetics parameter values ​​and the corresponding values ​​of the system state at the given steady-state, in order to calculate different types of copolymers probability of ending various active strand and kinetics of the reaction in a further reaction to a different type of reaction on the basis of probability; then, the foregoing parameter values ​​and the probability calculated on the spread CUDA platform, calculating the number of repetitive chain of threads, until the number of chains is set to meet the maximum or until the chain length; and finally, the analog information on the CUDA calculated each strand returns the main routine, the statistics of different chemical composition required gravity, then collates the values ​​obtained after the copolymer of the desired composition distribution.

[0022] 本发明与现有技术相比的有益效果是:由于蒙特卡罗的模拟计算过程以多线程的形式分配给CUDA平台运行,因此并行度较高、计算时间大幅度缩短,从而实现了快速化学组成分布计算方法。 [0022] Advantageous effects of the present invention compared to prior art: Because the Monte Carlo simulation of the process assigned to run as CUDA internet multithreading, a higher degree of parallelism, greatly reduce the computing time, thereby achieving rapid chemical composition distribution calculation method.

附图说明 BRIEF DESCRIPTION

[0023]图1是基于CUDA的稳态多元共聚快速化学组成分布计算方法的主程序模块流程图; [0023] FIG. 1 is a CUDA-based copolymer polyol steady rapid chemical composition distribution calculation module main program flowchart;

[0024]图2是本发明中的蒙特卡罗模拟计算模块流程图; [0024] FIG 2 is a flowchart of the calculation module Monte Carlo simulation of the present invention;

[0025] 图3是本发明得到的化学组成分布图。 [0025] FIG. 3 is a profile of the chemical composition of the present invention is obtained.

具体实施方式 detailed description

[0026] 以烯烃二元共聚反应体系为例,对本发明的技术方案进行进一步说明。 [0026] In the olefin binary copolymer reaction system as an example, the technical solution of the present invention will be further described.

[0027] 1实例背景介绍 [0027] Background Example 1

[0028] 本发明中,以烯烃共聚反应体系为实施案例。 [0028] In the present invention, olefin copolymerization reaction system implementation case. 烯烃由于其原材料丰富且价格低廉、 容易加工成型、综合性能优良,是一类产量巨大、应用十分广泛的高分子材料。 Olefin because of its rich raw materials and low cost, easy molding processing, excellent overall performance, is a kind of huge production, it is widely used polymer materials. 在烯烃聚合体系中,由于通过共聚不仅能够扩大聚合物的品种,而且可以让一些难以进行均聚的单体参与聚合反应,因此共聚体系的应用比较广泛。 In the olefin polymerization system, since the species can be increased by copolymerizing not only polymers, but also can be difficult for some homopolymerization of the monomers participating in the polymerization reaction, the more extensive application copolymerization system.

[0029] 2聚合反应机理 [0029] The polymerization mechanism 2

[0030] 本发明中,以两元共聚为例,考虑烯烃共聚反应体系中带有末端效应的链增长反应与链转移反应,如表1所示。 [0030] In the present invention, a two copolymerized as an example, consider the olefin comonomer in the reaction system with a chain-end effects chain transfer reaction, as shown in Table 1.

[0031] 表1烯烃共聚反应机理 [0031] Table 1-olefin copolymerization reaction mechanism

[0032] [0032]

Figure CN104166784BD00051

[0033] 其中,A与B分别代表了二元共聚反应的两种单体类型;P;1是链长为r并以A结尾的活性链;if是链长为r并以B结尾的活性链;Dr是链长为r的死聚物链;PQ是空活性位;H 2为氢气;A1为助催化剂;kP,kt分别是链增长与链转移的动力学反应速率常数。 [0033] wherein, A and B represent the two monomer types dipolymerization reaction; P; 1, and r is a length of chain A of the active strand ending; if a chain length of B r and ending with active chain; Dr dead oligomer chain length r of the chain; the PQ is empty active site; H 2 is hydrogen; A1 as cocatalyst; kP, kt are kinetic reaction rate constant of chain propagation and chain transfer.

[0034] 3主程序模块 [0034] The main program module 3

[0035] 主程序模块主要负责模拟工作的准备工作以及最后的数据整理工作,如图1所示。 [0035] The main program module is responsible for simulated operation of the preparations and the last data collation, as shown in FIG. 准备工作有:根据给定的系统状态值计算出进行蒙特卡罗模拟所需要的每个参数值,并开辟与设定的总链数相等的线程数。 Preparations are: the parameter value for each Monte Carlo simulation based on a given desired value calculation system state, and to open the total number of threads is set equal to the chain. 数据整理工作有:统计每个线程模拟计算出来的每条链中的单体A与B的比重信息得到所需要的化学组成分布。 Collation data are: the proportion of statistical information calculated by simulation for each thread in each chain monomers A and B to obtain the desired chemical composition distribution. 4蒙特卡罗模拟计算模块[0036] 每个线程下的蒙特卡罗模拟计算流程图如图2所示,具体步骤如下: Monte Carlo simulation module 4 [0036] Each thread in the Monte Carlo simulation flowchart shown in Figure 2, the following steps:

[0037] 步骤一:获取从主程序传过来的系统参数值(rn,rA,r B,fA,fB,qtA,qtB,PP,iast, PPi ast,A),跳到步骤二; [0037] Step a: acquiring system parameter values ​​(rn, rA, r B, fA, fB, qtA, qtB, PP, iast, PPi ast, A) passed over from the main program, jumps to step II;

[0038] 步骤二:将链长(r)置零,每条链的最后一个单体的标记(last)置零,A与B单体个数的记录值(ru,n B)置零,跳到步骤三; [0038] Step 2: chain length (r) to zero, marking the last one monomer each strand (Last) zero, A and B monomers recording the number of values ​​(ru, n B) is set to zero, jumps to step III;

[0039] 步骤三:利用随机数生成器生成一个(0,1)随机数,根据活性链最后一个单体的标记last来判断生成的随机数与具体哪个概率值进行比较,从而判定是执行链增长还是链转移;如果是链增长,则跳到步骤四;如果是链转移,则跳到步骤七; [0039] Step Three: using a random number generator (0,1) random number is determined according to the activity of a single strand of the last marker to the last random number generated which specific probability value, thereby determining the implementation chain growth or chain transfer; if the chain is, skip to step four; if it is a chain transfer, skip to step seven;

[0040] 步骤四:执行r自加操作,并再次生成一个(0,1)随机数,同样根据活性链最后一个单体的标记last来判断生成的随机数与具体哪个概率值进行比较,从而判定是执行单体A 链增长还是单体B链增长;如果是单体A链增长,则跳到步骤五;如果是单体B链增长,则跳到步骤六; [0040] Step Four: r from performing add operation, and generates a (0,1) random numbers again, also in accordance with the last active strand single marker to determine the last generates a random number with a particular probability value which is compared to A determination is performed monomeric chain extender or chain extender monomer B; if A is a monomeric chain growth, skip to step five; if monomer B is a chain, then jumps to step six;

[0041] 步骤五:执行nA自加操作,同时把last赋为A,跳到步骤三; [0041] Step Five: self-add operation performed nA, while the last assigned to A, jumps to step III;

[0042] 步骤六:执行nB自加操作,同时把last赋为B,跳到步骤三; [0042] Step Six: the operation performed nB from Canada, while the last assigned as B, jumps to step III;

[0043] 步骤七:保存所需要的信息(r,nA,nB),程序运行结束。 [0043] Step 7: save the information (r, nA, nB) required, the program ends.

[0044] 5对比效果 [0044] 5 contrast

[0045] 本发明中,设定值如下: [0045] In the present invention, the set values ​​are as follows:

[0046] rn=1000,rA=5.0,rB = 0.2,fA=0.6,fB = 0.4,qtA=0.5,qtA=0.5 [0046] rn = 1000, rA = 5.0, rB = 0.2, fA = 0.6, fB = 0.4, qtA = 0.5, qtA = 0.5

Figure CN104166784BD00061

[0051] [0051]

Figure CN104166784BD00071

[0052] [0052]

[0053] 其中,^表示数均链长;rA表示以A结尾的活性链向A链增长的反应速率除以以A结尾的活性链向B链增长的反应速率;rB表示以B结尾的活性链向B链增长的反应速率除以以B 结尾的活性链向A链增长的反应速率;fa表示单体A的浓度除以A的浓度与B的浓度的加和;fB 表示单体B的浓度除以A的浓度与B的浓度的加和;qtA表示以A结尾的链转移、链终止化学反应速率乘以相应单体的浓度之后的加和除以以A和B结尾的链转移、链终止化学反应速率乘以相应单体的浓度之后的加和;qt B表示以B结尾的链转移、链终止化学反应速率乘以相应单体的浓度之后的加和除以以A和B结尾的链转移、链终止化学反应速率乘以相应单体的浓度之后的加和;巧表示单体A在共聚物中的平均摩尔分率;巧表示单体B在共聚物中的平均摩尔分率;PpA表示以A结尾的活性链发生链增长反应的概率;PAA表示以A结 [0053] where ^ represents the number average chain length; of rA represents the reaction rate at the end of the reaction rate of A-chain to A-chain active growth divided by the active end of the A chain to the B chain growth; rB represents ending B activity the reaction rate of the chain to the B-chain-reaction rate divided by the active end of the B chain to the a chain growth; FA represents the concentration of the monomer concentration divided by the concentration of a and B and a plus; fB represents a monomer B divided by the concentration of a and B the concentration of the additive; Qta a represents the chain transfer to the end of the chain terminator, after the chemical reaction rate multiplied by the concentration of the respective monomers added and divided by the end of the a and B chain transfer, chain termination after the chemical reaction rate multiplied by the sum of the concentration of the corresponding monomers; B represents Qt to the end of the B chain transfer, chain termination and chemical reaction rates multiplies after concentration divided by the corresponding monomers a and B to end chain transfer, chemical reaction rate multiplied by the chain termination after the addition and the concentration of the corresponding monomers; Qiao a represents the average molar fraction of the monomer in the copolymer; Qiao represents an average molar fraction of B monomer in the copolymer ; PPA represents the probability of an active chain to chain extension reaction occurs in the end a; a junction of PAA expressed in 的活性链发生向A链增长反应的概率;PpB表示以B结尾的活性链发生链增长反应的概率;PBA表示以B结尾的活性链发生向A链增长反应的概率;P pQ与PpQA分别是用来进行判断链增长与具体向哪一类单体链增长的初始概率。 The probability of response to A chain-active strand occurs; ppb represents probability active chain to chain growth reaction end B; the PBA represents the active chain end of the B probability of response to A chain growth occurs; P pQ with PpQA are used for determining probability of chain growth and particularly to the initial monomer which chain growth.

[0054] 根据这些参数值,套入前面的模块中,可以得到化学组成分布如下图3所示: [0054] The values ​​of these parameters, the module sets the foregoing, the chemical composition distribution can be obtained as shown in Figure 3:

[0055] 整个程序分别在CPU平台下串行运行、在CUDA平台下并行运行,各自所需时间如下表2所示: [0055] Serial entire program are running in CPU platform, running in parallel CUDA platform, each required time as shown in Table 2:

[0056] 表2运行时间表 [0056] Table 2 Run schedule

[0057] [0057]

Figure CN104166784BD00072

[0058] 进一步的,可以计算得到加速效果: [0058] Further, the acceleration effect can be calculated:

[0059] [0059]

Figure CN104166784BD00073

[0060]可以看到,在CUDA平台下进行蒙特卡罗的模拟计算能够大幅度的提升计算效率, 从而实现快速化学组成分布计算技术。 [0060] It can be seen Monte Carlo simulation can greatly enhance computational efficiency in CUDA platform, enabling fast chemical composition distribution calculation technique.

Claims (3)

  1. 1. 一种基于CUDA的稳态多元共聚快速化学组成分布计算方法,其特征在于包括如下步骤: a. 读取稳态多元共聚体系的状态值,包括链增长、链转移、链终止反应的动力学常数以及各类单体、链转移剂、链终止剂的浓度; b. 计算蒙特卡罗方法所需要的各个概率值,包括以各类单体结尾的活性链发生链增长反应的概率P1以及以各类单体结尾的活性链向各类单体链增长的概率P lj: A CUDA-based polyol copolymer rapid steady stoichiometric composition distribution method, comprising the steps of:. A steady state value read copolymerized polyol system, comprising a chain growth, chain transfer, termination reactions power chain and a variety of learning constant monomers, the chain transfer agent concentration of chain terminating agent;. b probability values ​​calculated for each Monte Carlo method required, including the probability of occurrence active chain end of the chain extension reaction of the various types of monomers and P1 in order to increase the probability of living chains to end all kinds of kinds of monomer chain monomer P lj:
    Figure CN104166784BC00021
    其中,Rpi表示聚合反应中以单体i结尾的活性链链增长速率;Rtl表示以单体i结尾的活性链链转移速率;Rd1表示以单体i结尾的活性链链终止速率;[j]、[m]分别表示单体j、单体m 的浓度;kpij、kpim分别表示以单体i结尾的活性链向单体j、单体m发生链增长的化学反应速率;所述的步骤b中所述的蒙特卡罗方法的模拟平台为CUDA;所述的步骤b中所述的蒙特卡罗方法的模拟方式为每个模拟线程都只进行一条链的模拟过程; c. 将步骤b计算得到的概率值从CPU平台传递到CUDA平台上; d. 在CUDA平台上开辟用于记录序列信息的存储空间以及数目等于模拟总链数的线程数; e. 并行的执行所有线程,在每个线程里依靠步骤b中得到的概率,顺序地判断相应活性链是否发生链增长;若否,停止该线程的模拟计算并将得到的化学组成信息按线程编号在存储空间中进行存储;若是,则继续进行 Wherein, Rpi denotes a polymerization reaction to increase the active chain end of the rate of the monomer i; Rtl represents an active chain end of the transfer rate of the monomer i; RdI represents an active chain end of the termination rate of the monomer i; [J] , [m] denote the concentration of monomer j, the m monomer; kpij, kpim represent the active chain end of the monomer to the monomer i j, m monomer chain growth rate of a chemical reaction; said step b in the Monte Carlo simulation platform for the CUDA; Monte Carlo simulation method of the embodiment of step b for each of the analog simulation process with only one thread chain;. c calculated in step b probability value obtained from the CPU is transmitted to the internet platform CUDA;. d on the CUDA platform open space for storing information, and recording the sequence number is equal to the total number of threads of the analog chain;. e parallel execution of all threads, each thread rely probability obtained in step b is sequentially determined whether the corresponding activity chain chain growth occurs; if not, stop the chemical simulation of the composition and the resulting thread by thread number information stored in the storage space; if yes, keep going 判断向哪一类单体进行链增长并记录对应的化学组成信息; f. 重复步骤e,直到获得停止信息并退出; g. 将记录的化学组成信息从⑶DA平台传递到CPU平台上; h. 统计所有的化学组成信息,得到所需要的化学组成分布。 Judgment based monomer to which chain growth and chemical composition corresponding to recording information;. F repeating steps e, until stop information and exit;. G The chemical composition of the recording information from the internet to the CPU platform ⑶DA; h. the chemical composition of all statistical information to obtain the required chemical composition distribution.
  2. 2. 根据权利要求1所述的一种基于CUDA的稳态多元共聚快速化学组成分布计算方法, 其特征在于所述的步骤b中所述的蒙特卡罗方法的模拟顺序为并行的执行多个蒙特卡罗模拟线程。 According to one of the claim 1 copolymerized polyol CUDA-based steady-state flash stoichiometric composition distribution method, characterized in that the execution order of the plurality of Monte Carlo simulation method according to the step b is parallel Monte Carlo simulation threads.
  3. 3. 根据权利要求1所述的一种基于CUDA的稳态多元共聚快速化学组成分布计算方法, 其特征在于所述的步骤e中所述的记录信息为链中所有的化学组成信息。 According to one of the claim 1 copolymerized polyol CUDA-based steady-state flash stoichiometric composition distribution method, characterized in that said step e of the chain information recording all chemical composition information.
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5301118A (en) * 1991-11-18 1994-04-05 International Business Machines Corporation Monte carlo simulation design methodology
CN102063544A (en) * 2011-01-04 2011-05-18 浙江大学 Multicore parallel solving method for computation of polymer molecular weight distribution
CN102289559A (en) * 2011-05-30 2011-12-21 复旦大学 Monte Carlo simulation prediction radical copolymerization of the copolymer sequence distribution system

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5301118A (en) * 1991-11-18 1994-04-05 International Business Machines Corporation Monte carlo simulation design methodology
CN102063544A (en) * 2011-01-04 2011-05-18 浙江大学 Multicore parallel solving method for computation of polymer molecular weight distribution
CN102289559A (en) * 2011-05-30 2011-12-21 复旦大学 Monte Carlo simulation prediction radical copolymerization of the copolymer sequence distribution system

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