Thermal conductivity (λ) of frozen foods was modelled using meat composition data and temperature (T) dependent λ of solid components. The λ of non-ice/water components showed slight variation with T. Subsequently, the influence of λice = f(T) on the predicted λ of frozen foods was studied using a variety of models with continuous and dispersed phases. The predictive models were tested against experimental data of 13 frozen meat products in T range from -40 to -1°C and compared with the λ data obtained for constant λice (2.21 W/m⋅K). Variable λice = f(T) noticeably improves the performance of models with constant λice, which were under-predicting experimental data, and worsens the models whose predictions originally followed closely to experimental data. The use of λice = f(T) was beneficial to models with the under-predictive performance, particularly below -5°C, while between -1 and -5°C no noticeable changes were spotted.
Tarnawski, V.r., Bovesecchi, G., Coppa, P., L. e. o. n. g., W.h. (2007). Modelling Thermal Conductivity of Frozen Foods with Temperature Dependent Thermal Conductivity of Solid Components. In Proc. of the 22nd International Congress of Refrigeration (pp.929-938). Beijing : Intenational Institute of Refrigeration.
Modelling Thermal Conductivity of Frozen Foods with Temperature Dependent Thermal Conductivity of Solid Components
Bovesecchi,G;COPPA, PAOLO;
2007-01-01
Abstract
Thermal conductivity (λ) of frozen foods was modelled using meat composition data and temperature (T) dependent λ of solid components. The λ of non-ice/water components showed slight variation with T. Subsequently, the influence of λice = f(T) on the predicted λ of frozen foods was studied using a variety of models with continuous and dispersed phases. The predictive models were tested against experimental data of 13 frozen meat products in T range from -40 to -1°C and compared with the λ data obtained for constant λice (2.21 W/m⋅K). Variable λice = f(T) noticeably improves the performance of models with constant λice, which were under-predicting experimental data, and worsens the models whose predictions originally followed closely to experimental data. The use of λice = f(T) was beneficial to models with the under-predictive performance, particularly below -5°C, while between -1 and -5°C no noticeable changes were spotted.Questo articolo è pubblicato sotto una Licenza Licenza Creative Commons
