华东阳, 王寿喜, 郭乔, 刘丹. 基于粒子群算法的液体管道仿真模型参数校正方法[J]. 油气储运, 2020, 39(12): 1386-1393. DOI: 10.6047/j.issn.1000-8241.2020.12.011
引用本文: 华东阳, 王寿喜, 郭乔, 刘丹. 基于粒子群算法的液体管道仿真模型参数校正方法[J]. 油气储运, 2020, 39(12): 1386-1393. DOI: 10.6047/j.issn.1000-8241.2020.12.011
HUA Dongyang, WANG Shouxi, GUO Qiao, LIU Dan. Correction method for parameters of liquid pipeline simulation model based on particle swarm optimization algorithm[J]. Oil & Gas Storage and Transportation, 2020, 39(12): 1386-1393. DOI: 10.6047/j.issn.1000-8241.2020.12.011
Citation: HUA Dongyang, WANG Shouxi, GUO Qiao, LIU Dan. Correction method for parameters of liquid pipeline simulation model based on particle swarm optimization algorithm[J]. Oil & Gas Storage and Transportation, 2020, 39(12): 1386-1393. DOI: 10.6047/j.issn.1000-8241.2020.12.011

基于粒子群算法的液体管道仿真模型参数校正方法

Correction method for parameters of liquid pipeline simulation model based on particle swarm optimization algorithm

  • 摘要: 针对液体管道模型参数存在偏差从而导致管道仿真模型与实际管道系统存在偏差的问题,提出一种管道模型参数校正方法。首先,根据管道内流体运动特性,建立流体动力学模型,分析管道模型不准确性产生的原因;其次,分析管长、内径、高程差、粗糙度等参数的不准确性对模型精度的影响;然后将参数校正问题转换为最优化问题,建立参数校正模型,并以粒子群算法求解该优化问题;最后,基于室内实验和仿真实验数据验证校正结果准确性、校正后模型适应性以及校正方法适用性。结果表明:对于准确性,基于26组室内试验数据的参数校正结果平均相对误差在2%以内;对于适应性,管长和内径校正后,分别有超过84.6%和96.1%的模型精度提升;对于适用性,10组仿真模型的参数校正结果相对误差均在1%以内,参数校正后,模型适应性显著提高,模型适应率从0.04%~0.53%降至0.01%~0.18%。

     

    Abstract: In view of the deviation between the simulation model and the actual pipeline system due to the deviation ofliquid pipeline model parameters, a pipeline model parameter correction method was proposed. At first, a fluid dynamicsmodel was established based on the fluid motion characteristics in the pipeline to analyze the reasons for the inaccuracyof the pipeline model. Then, the influence of the inaccuracy of pipeline parameters such as length, inner diameter, elevationdifference and roughness on the accuracy of the model was analyzed. Thus, the parameter correction problem was transformedinto an optimization problem. A parameter correction model was established, and the optimization problem was solved by particleswarm optimization algorithm. Finally, the accuracy of the corrected results, the adaptability of the corrected model, and theapplicability of the correction method were verified based on the laboratory experiments and simulation data. The verificationresults show that: In terms of accuracy, the average relative error of the parameter correction results based on 26 test data sets iswithin 2%. As for adaptability, after the correction of pipe length and inner diameter, the precision is improved for more than84.6% and 96.1% of the models respectively. In view of applicability, the relative error of the parameter correction results basedon 10 simulation models is within 1%. After parameter correction, the adaptability of the models is improved obviously andthe adaptive rate is reduced from 0.04%-0.53% to 0.01%-0.18%.

     

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