未知参数空间与时域空间双向压缩的热参数反向辨识

New parameter identification method based on bidirectional compression in the parameter space and time domain

极端环境中热参数的实时在线监测是保证结构安全与稳定工作的前提,但高温高压环境会增加测量装置的失效风险,给原位测量带来困难.针对这一问题,提出一种参数空间与时域空间双向压缩(PSTDC)的代理模型方法,在不损伤结构的同时达到对热参数快速辨识的目的.通过对数据集合进行两次压缩获取相关信息,一次压缩对数据的参数特征进行提取,二次压缩获得包含参数和时间共同信息的特征基,并结合径向基函数训练热参数重构的代理模型网络,快速搜索任意参数的时域系统响应;最后,通过多点测量得到结构低温区域的附加瞬态温度信息,最小化瞬时测量数据与模型响应之间的最小二乘函数,得到最优的热参数组合.通过二维平板算例和三维发动机燃烧室热参数辨识算例,验证了本方法的有效性,在误差不超过0.1%的前提下,与传统共轭梯度法和Levenberg-Marquardt法相比,本方法计算效率可以提高一到两个数量级;通过测点位置和噪声分析,证明了算法的稳定性.

The real-time monitoring of thermal parameters under extreme temperature is important to ensure structural security and stability.However,high-temperature and high-pressure surroundings increase the failure risk of measurement devices and bring difficulties to in-situ measurement.To address the above key technical problems,this paper investigates database acquisition and time-domain surrogate models for parameter identification.An efficient method of measuring thermal parameters by compressing data in unknown parameter spaces and time domains is presented;the method consists of two aspects:construction of(1)the surrogate model and(2)the inversion parameters.On the one hand,Latin hypercube sampling is used to obtain an appropriate density of sample points in the parameter space.The transient temperature information of sample points is acquired offline via the free element method,which is used to establish a space-parameter-time 3 D database.Next,the database is decomposed by using a proper orthogonal decomposition method in the parameter space and the time domain.In the time series,the parameter related to POD bases are generated by compressing the space-parameter data slice;in the parameter space,the POD bases,which contain common information about parameters,are secondarily compressed,and time is generated.Next,in combination with the radical basis function surrogate model,a reconstructed parameter model called PSTDC surrogate model is established.On the other hand,the additional information obtained from transient temperature measurements taken at locations in low-temperature areas is used for the estimation of the unknown parameters on the basis of the minimization of the least squares norm.The selected optimization algorithm is the genetic algorithm,which is a stochastic algorithm with globality and randomness.In this manner,the scale of input and output is reduced,and the mapping between unknown parameters and the time-domain physical field can be achieved.In addition,forward simulations with high computational cost is arranged offline;once the space-unknown parameter-time database is established,the efficiency of online parameter identification is greatly improved.Two numerical examples,i.e.,2 D plate to temperature-dependent thermal conductivities and 3 D combustor to coefficients of heat convection,are analyzed to verify the correctness,stability,and accuracy of the proposed method.Compared with the CGM and LM algorithms,the PSTDC surrogate model is more efficient by an order of magnitude or two,and it ensures an error of less than 0.1%.The stability of the proposed method is proven by the analysis of the location of measurement points and noise.