Near real-time prediction of thermophysical parameters and time-dependent boundary heat flux of participating medium

The scattering coefficient (κ_s), absorption coefficient (κ_a), thermal conductivity (λ) and time-varying boundary heat flux (q(t)) of 1D participating medium are simultaneously determined in near real time. The forward problem of coupled conductive and radiative heat transfer is solved by finite volume method (FVM). The unscented Kalman filter (UKF) and corresponding smoothing technique (UKS) are employed to estimate the q(t), λ, κ_a and κ_s. To achieve non-intrusive detection, the measured signals on boundaries of the medium (such as radiative intensity on right boundary and temperatures on boundaries) are employed as inputs in the inverse problem. The effects of measurement error covariance, process error covariance, future measured signals, sampling interval and thermophysical properties on estimation results are thoroughly investigated. The UKS algorithm, when compared to the UKF algorithm, can improve the stability and accuracy of estimated q(t), λ, κ_a and κ_s. It is found that the UKS algorithm is efficient and accurate to solve the near real-time prediction of thermophysical parameters and boundary heat flux of participating medium even with measurement errors.