Adaptive-Weighting Input-Estimation Approach to Nonlinear Inverse Heat-Conduction Problems
| Autor: | Pan-Chio Tuan, Huai-Min Wang, Tsung-Chien Chen, Shi-Gan Den |
|---|---|
| Rok vydání: | 2005 |
| Předmět: | |
| Zdroj: | Journal of Thermophysics and Heat Transfer. 19:209-216 |
| ISSN: | 1533-6808 0887-8722 |
| DOI: | 10.2514/1.8720 |
| Popis: | The inverse heat-conduction problem involves surface-heat-flux or heat-source estimation that requires only the temperatures measured at an insulated wall. This problem becomes nonlinear if the thermal properties are temperature-dependent. Innovative adaptive-weighting input-estimation inverse methodology for estimating a time-varying unknown heat source from a nonlinear thermal system is presented. This algorithm includes the extended Kalman filter (EKF) and the recursive least-square estimator. EKF recursively estimates the interior temperature of a body under a system involving noisy measurement and modeling errors. During the EKF estimation procedure, an important regression equation between the observable bias residual innovation and the thermal unknown is provided. Based on this regression model, a recursive least-square estimator weighted by the adaptive weighting factor is proposed to estimate these unknowns, defined as the input. The Kalman tuning parameter is used first to analyze the interactive relationship between measurement noise and modeling-error variance. The superior capabilities of the proposed algorithm are demonstrated through two simulated examples with different types of time-varying heat sources as the unknown inputs. Nomenclature |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|