A model of thermal conductivity as a function of temperature and steel composition has been produced using a neural network technique based upon a Bayesian statistics framework. The model allows the estimation of conduc- tivity for heat transfer problems, along with the appropriate uncertainty. The performance of the model is demonstrated by making predictions of previous experimental results which were not included in the process which leads to the creation of the model.
International Journal of Heat and Mass Transfer 54 (2011) 2602-2608
| MAP_STEEL_THERMAL | PROGRAM:Model for thermal conductivity of steel. |
| MAP_DATA_THERMAL | DATA:Thermal conductivity data for steel. |
| Envelope | Coefficients | Davenport | Hot | Delta |
| Satoh | Fields | Piping | European welds | Poles |
| Mixed | Creep | Extraordinary ductility | Problems | Low temperatures |
| PT Group Home | Materials Algorithms |