Abstract
Abstract
During the service process of the thermal protection system, unreasonable results are often obtained by the calculation model due to the uncertainty of the materials and structures. The reason is that the calculation model is not competent to reflect the actual physical behavior of structures. Some simulation conditions such as the initial and boundary conditions and loads can only be obtained accurately until the system is actually in service. At present, the simulation and the experiment are carried in quiescent condition and serialization and have not been effectively combined for the research of thermal protection. Dynamic data driven method which can update the structure state and participate in task decision making by dynamic data connection between simulation and actual test system and On-line simulation and evaluation of dynamic test data is a promising trend to achieve breakthroughs in thermal protection level. In this paper, the concept of Dynamic Data Driven Application System (DDDAS) is introduced to the analysis and evaluation of thermal protection system. The typical thermal protection system is used as the research object. The framework design and implementation mechanism of dynamic data driven between simulation and test is explored, and the method of on-line analysis and evaluation under high temperature environment is carried out. The significance of the DDDAS theory and broad application prospects reveal the influence of dynamic data driven introduction on thermal response of structures.
First of all, a framework of dynamic data driven application system were designed in this paper. An overall data-driven logical framework of "sensor data-online model-test system" was constructed; An on-line fast calculation model was established to address the problem of high-temperature nonlinearity of material properties. The Kirchhoff transform was used to transform it into a linear problem. Numerical calculations were performed using the implicit finite difference method and the validity of the model was verified；Build an overall test platform and design control algorithms for system operation.
Secondly, experimental verification and accuracy analysis of dynamic data-driven analysis methods for structural thermal response are carried out. The thermal response tests under different load conditions were carried out, and the effects of grid model accuracy, number of sensors, data collection intervals, and forecast time length on the accuracy of the forecast were analyzed. The applicability of the model to different heat flow loads is verified, embodying the advantages of thermal response analysis and forecast accuracy in the dynamic data-driven model.
II
Abstract
Finally, the thermal response test of the insulation tile under the two types of accidental overload and cyclic load conditions were carried out, to achieve the online decision and feedback control of the system in the test process. For the former, the system is able to control the measurement behavior through the forecast results including change the number of sensors, interval of data collection and model grid accuracy. For the last, the test load is controlled when the prediction result exceeds the safe range, the load is reduced in advance to ensure the structural reliability. The efficient and stable dynamic data-driven implementation mechanism is adjusted and explored by the algorithm improvement and adjustment of data transmission strategy.
Keywords: Dynamic data drive, Thermal protection system, Nonlinear heat transfer, Online simulation and decision
III
目录
目录
摘要 .......................................................................................................................... I ABSTRACT ................................................................................................................ II 第1章绪论 .. (1)
1.1课题研究的背景和意义 (1)
1.2国内外研究现状 (2)
1.2.1 热防护发展现状及局限性 (2)
1.2.2 结构健康监测技术的发展 (7)
1.2.3 动态数据驱动应用系统发展概述 (8)
1.2.4 高温测试技术发展概述 (11)
1.2.5 国内外发展现状简析 (11)
1.3主要研究内容 (12)
第2章动态数据驱动应用系统框架设计 (14)
2.1引言 (14)
2.2动态数据驱动逻辑框架搭建 (14)
2.3非线性传热模型建立 (16)
2.3.1 非线性热传导的基尔霍夫变换 (17)
2.3.1 传热模型数值求解及验证 (18)
2.4试验平台设计 (22)
2.4.1 试验测试单元 (23)
2.4.2 传感及数据采集单元 (24)
2.4.3 在线仿真与控制单元 (28)
2.5本章小结 (30)
第3章热结构试验验证与精度分析 (31)
3.1引言 (31)
3.2试验方案设计 (31)
3.2.1 试样结构 (31)
3.2.2 试验过程 (32)
3.3试验验证结果精度对比分析 (33)
3.3.1 在线传热模型网格对预报精度影响 (34)
3.3.2 传感器数量对预报精度影响 (40)
3.3.3 数据采集间隔对预报精度影响 (43)
3.3.4 实时预报时长对预报精度影响 (34)
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目录
3.3.5 不同热流预报结果精度对比 (46)
3.4本章小结 (50)
第4章复杂工况试验验证 (51)
4.1引言 (51)
4.2在线决策策略制定 (51)
4.3意外超载工况结果分析 (53)
4.3.1 初期意外超载结果分析 (53)
4.3.2 中期意外超载结果分析 (55)
4.4周期载荷工况结果分析 (58)
4.5本章小结 (60)
结论 (61)
参考文献 (62)
攻读硕士学位期间发表的论文及其它成果 (66)
哈尔滨工业大学学位论文原创性声明和使用权限 (67)
致谢 (67)
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