مدل‌سازی انتقال حرارت در کنسرو حاوی عصاره مالت در بسته‌بندی نیمه‌سخت بر پایه آلومینیوم

در این پژوهش مدل عددی انتقال حرارت در کنسرو عصاره مالت با بریکس 60 در بسته‌بندی آلومینیومی نیمه‌سخت با نرم‌افزار فلوئنت توسعه داده شد. شکل هندسی کنسرو حاوی نمونه، توسط نرم افزار گمبیت رسم و شبکه بندی مناسب با فاصله گره هایcm 0.1 و cm0.2 اعمال شد. سپس انتقال حرارت درون کنسرو در طول فرآیند حرارتی با استفاده از نرم افزار فلوئنت شبیه سازی شد. پروفیل های دمایی فرآیند حرارتی، شکل و محل ناحیه کند حرارتی در کنسرو نمونه بررسی شد. همچنین تاثیر وجود سرفضا (فضای خالی بالای ظرف) بر انتقال حرارت مورد بررسی قرار گرفت. شبکه بندی مناسب برای شبیه سازی، شبکه با فاصله گره های cm0.2 بود. نتایج شبیه سازی نشان داد که محل ناحیه کند حرارتی در کنسرو عصاره مالت دارای سرفضا در پایان مرحله حرارت دهی در محدوده cm3.27-z>3.46- ، cm0.48>  y<3.44-، cm3.76

heat transfer modeling of malt syrup in semi-rigid aluminum based packaging

introductionthermal processing is an important method of canned food production (farid abdul ghani, 2004). estimation of the heat transfer rates is essential to obtain optimum processing conditions and to improve product quality. in addition, a better understanding of the mechanism of the heating process will lead to an improved performance in the process and to some energy savings (abdul ghani et al., 1999). computational fluid dynamics (cfd) is an efficient way to study flow behavior and temperature distribution of thermal processing in the food technology (ghani et al., 2003). as the semi-rigid aluminum packaging market recently has been introduced, there is limited information about the temperature distribution during the heating process of such containers. in this paper the temperature distribution was predicted and location of cold zone was determined. the effect of headspace (air and water vapor) in heat transfer mechanism was investigated. materials and methodsphysical propertiesmalt extract properties such as density, specific heat, thermal conductivity and viscosity values are necessary for the equations solution. viscosity and density of the sample was measured as a function of temperature (vatankhah et al., 2015). specific heat and thermal conductivity of sample were estimated using the mass fraction of its constituents. for simulation, the experimental results were applied by piecewise-linear method in the material part of the software to describe viscosity, thermal conductivity and specific heat. experimental methodologyfor the experimental, a thermocouple probe was located at point (0, 0, -2.76) in a semi rigid aluminum based packaging to measure the temperature distribution inside the container. then the package was filled with malt extract (°brix ~ 60) and then the package was sealed at 280 °c using alcan machine. another thermocouple was placed near the containers, in the water cascading barriquand steriflow retort. the thermocouples were attached to ellab data logger by pt100 cables. the data logger was connected to a personal computer and e-val 2.1 software was used to export time temperature profile of each thermocouple in 1 min intervals.  geometry and meshinggambit 2.3.30 was used to develop geometry and set of grid (0.2 cm, and 0.1 cm mesh size) was performed. then software of fluent 6.3.26 with 3-d, double precision, pressure-based solver, implicit formulation, unsteady time, laminar flow was applied to solve the system of the governing equations (vatankhah et al., 2015). boundary conditions and initial valuesunsteady temperature function was imposed to all faces of the geometry in 1 min time intervals. no-slip boundary condition was supposed for velocity components relative to boundaries. the boundary conditions used at top surface, bottom surface and side walls included: t = tw, vx = 0, vy = 0 and vz = 0. the initial temperature was assumed as the first temperature which was measured by the thermocouple at the starting time of processing. solution methodologyfluent software was used to solve the navier-stokes and energy equations simultaneously. a preset convergence limit of 10−3 for continuity and momentum equations and 10−8 for the energy equation were used, in order to achieve an appropriate convergence. the under-relaxation factors were adjusted smaller than 1 to obtain a good convergence of the numerical solution. simplec algorithm was used for pressure-velocity coupling. results and discussionthere was no significant difference between predicted and experimental temperatures for point (0, 0, -2.76) in models with and without head space using t-test (p<0.01).  temperature contours of predicted models (with headspace) were similar to model without headspace at the different stages of the process.