Design optimization method of pipeline parameter based on improved artificial neural network

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jiangtao Mei , Junguo Cui , Lei Wu , Shilin Xu , Qiang Guo , Wensheng Xiao , Songmao Ye , Hui Zhang
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引用次数: 0

Abstract

The rationality of pipeline design is directly related to its energy efficiency, reliability, and safety. Pipeline vibration may lead to negative effects such as mechanical loss and fatigue damage. Therefore, this study utilizes pipeline optimization design to mitigate these effects. Recently, neural networks have been widely used in structure design optimization. In the study, a backpropagation neural network (BP) combined with a variant slime mould algorithm (SMA) is utilized to solve the pipeline structure design optimization problem. Pipeline transport plays a crucial role in the efficient movement of various commodities, including but not limited to gas, oil, water, and other liquid substances. The interaction between liquid and pipeline can cause pipeline vibration and even damage. Therefore, based on the simulation model considering FSI (fluid-structure interaction), machine learning methods such as BP can predict vibration characteristics of fluid-conveying pipelines. However, existing research has shown that BP has insufficient parsing ability in structure mechanics problems, especially in solving the overall characteristics of complex structures (such as maximum structural strain). This study proposes an Arithmetic-based slime mould algorithm (ACSMA) with an adaptive decision strategy and a chaotic mapping strategy. A hybrid algorithm named ACSMA-BP is presented to promote the model's prediction ability. At last, to verify the effectiveness of the proposed pipeline structure design optimization approach, the ACSMA-BP model is utilized to complete a structure design optimization case for a simulated pipeline. The numerical results indicate that compared with AOA, CWOA, ESSAWOA, NGS_WOA, and RSA, the ACSMA has the best optimization ability.

基于改进型人工神经网络的管道参数设计优化方法
管道设计的合理性直接关系到其能源效率、可靠性和安全性。管道振动可能会导致机械损失和疲劳损坏等负面影响。因此,本研究利用管道优化设计来减轻这些影响。最近,神经网络被广泛应用于结构设计优化。本研究利用反向传播神经网络(BP)结合变体粘模算法(SMA)来解决管道结构设计优化问题。管道运输对各种商品(包括但不限于天然气、石油、水和其他液体物质)的高效运输起着至关重要的作用。液体与管道之间的相互作用会导致管道振动甚至损坏。因此,基于考虑 FSI(流体与结构相互作用)的仿真模型,BP 等机器学习方法可以预测流体输送管道的振动特性。然而,现有研究表明,BP 对结构力学问题的解析能力不足,尤其是在求解复杂结构的整体特性(如最大结构应变)方面。本研究提出了一种具有自适应决策策略和混沌映射策略的基于算术的粘模算法(ACSMA)。为了提高模型的预测能力,还提出了一种名为 ACSMA-BP 的混合算法。最后,为了验证所提出的管道结构设计优化方法的有效性,利用 ACSMA-BP 模型完成了一个模拟管道的结构设计优化案例。数值结果表明,与 AOA、CWOA、ESSAWOA、NGS_WOA 和 RSA 相比,ACSMA 的优化能力最佳。
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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
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