Research on model reduction technique of temperature field of steam generator heat transfer tube based on neural network method
编号:69 访问权限:仅限参会人 更新:2024-09-08 17:37:13 浏览:113次 口头报告

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摘要
Steam generator is the key equipment in the secondary loop system of nuclear reactor, and its internal flow and heat transfer directly affect the efficiency of nuclear power conversion. The high precision numerical model is an effective method to analyze the 3D temperature field in the steam generator, but the computational efficiency of the traditional high resolution numerical model can not meet the needs of practical applications. In order to improve the computational efficiency, a reduced order model of heat transfer problem is constructed by using the Proper Orthogonal Decomposition (POD) decomposition method. The model takes the typical numerical solutions as samples, and then establishes the mapping relationship between the boundary conditions and the reduced order system to realize the rapid reconstruction of the three-dimensional temperature field. The test examples of steam generator heat transfer tube show that compared with the full-order model, this reduced order model can achieve 3~4 orders of magnitude acceleration effect.
关键词
Steam generator,Flow heat transfer,Model reduction,Accelerated calculation,Neural network
报告人
He Zhang
哈尔滨工程大学

稿件作者
He Zhang 哈尔滨工程大学
Bo Wang 哈尔滨工程大学
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重要日期
  • 会议日期

    09月23日

    2024

    09月25日

    2024

  • 09月24日 2024

    报告提交截止日期

  • 09月25日 2024

    注册截止日期

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Harbin Engineering University (HEU)
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