发明公开
- 专利标题: STRUCTURAL DYNAMIC PARAMETER IDENTIFICATION METHOD AIDED BY rPCK SURROGATE MODEL
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申请号: US18135218申请日: 2023-04-17
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公开(公告)号: US20230334198A1公开(公告)日: 2023-10-19
- 发明人: Maosen CAO , Yazhou JIANG , Tongfa DENG , Yifei LI , Yufeng ZHANG , Lei SHEN , Li CUI , Zeyu WANG , Jiayi PENG
- 申请人: Jiangxi University of Science and Technology , Hohai University , China Three Gorges Construction (Group) Co., Ltd. , JSTI Group
- 申请人地址: CN Ganzhou
- 专利权人: Jiangxi University of Science and Technology,Hohai University,China Three Gorges Construction (Group) Co., Ltd.,JSTI Group
- 当前专利权人: Jiangxi University of Science and Technology,Hohai University,China Three Gorges Construction (Group) Co., Ltd.,JSTI Group
- 当前专利权人地址: CN Ganzhou; CN Nanjing; CN Beijing; CN Nanjing
- 优先权: CN 2210402995.9 2022.04.18
- 主分类号: G06F30/23
- IPC分类号: G06F30/23 ; G06F30/13 ; G06F17/13
摘要:
A structural dynamic parameter identification method aided by a rPCK surrogate model comprises the following steps. Establish a finite element model that roughly reflects the structural system to be analyzed. Establish the dynamic parameter space sample set. The structural system response space sample set driven by the dynamic parameter space sample set is established by using the probabilistic finite element analysis. The robust polynomial Chaos Kriging surrogate model is obtained by mapping the dynamic parameter space sample set to the structural system response space sample set. The measured structural system response is used to drive the rPCK surrogate model, and then Bayesian inference is used to identify the structural dynamic parameters. The mean value of Bayesian posterior estimation is used as the estimated value of structural dynamic parameters. The proposed method creates conditions for establishing a high-fidelity finite element model of the actual engineering structural system.
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