基于扩展Kalman滤波的热耗散系数识别

Identification of heat dissipation coefficient based on extended Kalman filter

器件内部的热耗散系数信息反映了内部器件的工作状况。但是受客观条件的限制,内部的热耗散系数信息无法直接测量,因此需要通过可测量的间接信息,识别内部耗散系数信息,从而实时监测发热器件的工作状况。上述问题数学上可描述为,利用可测量信息,探求内部热耗散系数的不确定信息,进而给出器件工作的指导建议。基于上述原因,考虑在随机噪音模型下,利用可测边界的不确定性信息,识别内部阻尼系数(热耗散系数)。这是一个非线性的热传导反问题,故基于扩展Kalman滤波、全变差,设计反演算法识别热耗散系数。数值实验表明该算法可有效地识别边界阻尼系数。

The internal heat dissipation coefficient information of the device reflects the operating condition of the internal device.However,due to the limitation of objective conditions,the internal heat dissipation coefficient information cannot be directly measured.Therefore,it is necessary to identify the internal dissipation coefficient information with the indirect data to monitor the operations of electronic devices in real time.The problem can be considered as the identification of uncertainty information of the internal heat dissipation coefficient from certain measured data and provide suggestions for electronic device operations.Therefore,based on the above mentioned reasons,it is considered to identify the internal damping coefficient(heat dissipation coefficient)by using the uncertainty information of the measurable boundary under the stochastic noise model in this paper.It is a nonlinear inverse heat conduction problem,then an iteration algorithm,based on the idea of extended Kalman filtering and total variation(TV),is proposed to identify the heat dissipation coefficient.Numerical experiments show that this algorithm can effectively identify the boundary damping coefficient.