Implementation of a conjugate gradient algorithm for thermal diffusivity identification in a moving boundaries system

On-line identification of temperature-dependent thermal conductivity

“…Crucial steps are the sensitivity problem resolution and the computation of the gradient (adjoint problem resolution). These steps are presented hereafter (see [9][10][11][12][13][14] for examples related to CGM implementation in thermal context). …”

“…Sensitivity problem is described by a partial differential equation (PDE) system (see [9], for example). Then, considering ( ) , x t δθ solution of this sensitivity system, descent depth k γ can be computed at iteration k :…”

A model based predictive tool for fire safety intumescent coatings design

“…The model structure has been validated for thermal fluxes occurring in violent, brief explosions and in several fire configurations. Identification of several key-parameters has been investigated in [10] or in [11] since while steel substrate characteristics are well-known; those of the intumescent layers (carbonaceous, viscous and ablative) have to be carefully considered, regarding the lack of precision of the values available in literature. The main objective of the proposed communication is to develop a predictive tool in order to estimate the virgin paint thickness required in calibrated accidental scenarios (investigated hazard have to be realistic: radiative fluxes consecutive to fires, thermal waves created by sudden explosions, aggression duration, blast effect, etc.).…”

Conjugate Gradient Method Applied to Cortical Imaging in EEG/ERP

“…Recent research can be found that uses the CGM for problems such as the determination of local boiling heat fluxes (Egger et al, 2009), the spatial distribution of Young's modulus (Fehrenbach et al, 2006), 3D elastic full-waveform seismic inversion (Epanomeritakis et al, 2008). Other applications can be found in other journals e.g., thermal diffusivity in plasma (Perez et al, 2008;Yang et al, 2008) and conductivity changes in impedance tomography (Zhao et al, 2007), proving, if it were necessary, the wide use of CGM in many different fields of application. Nevertheless, despite some attempts to use inverse methods such as the CGM in EEG/ERP, there is a lack of studies on the application of CGM to inverse problems in electroencephalography and/or event related potentials.…”