Dataset of air velocity and temperature fields inside an insulated box equipped with phase change material under several operating conditions.

This article contains a description of protocol to measure air velocity field (by Particle Image Velocimetry - PIV) and temperature field (by T-type thermocouples) in an insulated box equipped with Phase Change Material (PCM) of melting point 0 °C. The influence of various conditions was studied: i) PCM position (at sidewall and at top), ii) aspect ratio of the box (height/width ∼ 1 and 1.7), iii) ambient temperature (10 °C, 20 °C and 30 °C), iv) test product initial temperature (4 °C and 10 °C) and vi) spacing beneath the load (0 mm and 20 mm). This article is related to a published research paper, it provides the dataset of all experiments which can be useful for experimenter to understand the phenomena and for expert in numerical model to validate the developed model e.g., by Computational Fluid Dynamic.

Dataset of experimental study investigation of airflow and heat transfer in an insulated box equipped with a phase change material.

This article contains a detailed description of the experimental protocol of air velocity (by particle image velocimetry - PIV) and temperature measurement (by T-type thermocouples) in an insulated box equipped with a Phase Change Material (PCM). The study was conducted in an empty box and a loaded box with extruded polystyrene slabs (XPS) and methylcellulose slabs (test product). The measurement was conducted at the middle plane and lateral plane. This article contains a complete dataset along with the illustrated figures of conducted experiment. They lead to more understanding of phenomena inside a closed cavity with a cold source and can be useful for validating numerical models, e.g., the results computed by computational fluid dynamic.

Water transport during bread baking: Impact of the baking temperature and the baking time.

The impact of the baking temperature on the moisture profile (in terms of water content), during bread baking was analyzed using a convection oven (three oven temperatures and different baking times). During baking, local water content and temperature were measured at different regions of the crust and crumb. There was found an increase in water content at the core. Water content reached a maximum level (at about 2.5%), with no effect of the baking temperature, and decreased slowly at advanced baking times. Regarding the crust, a theoretical model relating water flux to the driven force (temperature difference between the oven environment and the vaporization front) and the crust thermal resistance was validated with experimental values. Water losses were also reported. The water lost by bread contributes significantly to the energy consumption by this process and its reduction is of concern for conducting the process in a more sustainable manner. A better optimization of heat transfer between the surface (for coloration purposes) and the core (for inflation purposes) could help in this way, together with shorter baking duration and hence higher yield.

Effect of Freeze Dryer Design on Heat Transfer Variability Investigated Using a 3D Mathematical Model.

During the freeze-drying process, vials located at the border of the shelf usually present higher heat flow rates that result in higher product temperatures than vials in the center. This phenomenon, referred to as edge vial effect, can lead to product quality variability within the same batch of vials and between batches at different scales. Our objective was to investigate the effect of various freeze dryer design features on heat transfer variability. A 3D mathematical model previously developed in COMSOL Multiphysics and experimentally validated was used to simulate the heat transfer of a set of vials located at the edge and in the center of the shelf. The design features considered included the vials loading configurations, the thermal characteristics, and some relevant dimensions of the drying chamber geometry. The presence of the rail in the loading configuration and the value of the shelf emissivity strongly impacted the heat flow rates received by the vials. Conversely, the heat transfer was not significantly influenced by modifications of the thermal conductivity of the rail, the emissivity of the walls, or the geometry of the drying chamber. The model developed turned out to be a powerful tool for cycle development and scale-up.

Identification of the significant factors in food safety using global sensitivity analysis and the accept-and-reject algorithm: application to the cold chain of ham.

Deterministic models describing heat transfer and microbial growth in the cold chain are widely studied. However, it is difficult to apply them in practice because of several variable parameters in the logistic supply chain (e.g., ambient temperature varying due to season and product residence time in refrigeration equipment), the product's characteristics (e.g., pH and water activity) and the microbial characteristics (e.g., initial microbial load and lag time). This variability can lead to different bacterial growth rates in food products and has to be considered to properly predict the consumer's exposure and identify the key parameters of the cold chain. This study proposes a new approach that combines deterministic (heat transfer) and stochastic (Monte Carlo) modeling to account for the variability in the logistic supply chain and the product's characteristics. The model generates a realistic time-temperature product history , contrary to existing modeling whose describe time-temperature profile Contrary to existing approaches that use directly a time-temperature profile, the proposed model predicts product temperature evolution from the thermostat setting and the ambient temperature. The developed methodology was applied to the cold chain of cooked ham including, the display cabinet, transport by the consumer and the domestic refrigerator, to predict the evolution of state variables, such as the temperature and the growth of Listeria monocytogenes. The impacts of the input factors were calculated and ranked. It was found that the product's time-temperature history and the initial contamination level are the main causes of consumers' exposure. Then, a refined analysis was applied, revealing the importance of consumer behaviors on Listeria monocytogenes exposure.