CFD modeling of airflow in a kitchen environment : Towards improving energy efficiency in buildings

The building sector accounts for a large fraction of total energy consumption. Thus, there is a need to find methods for improving the energy efficiency of homes and houses. Among all the building services, heating, ventilation and air conditioning (HVAC) systems play an important role in building energy use. In HVAC, proper ventilation is very important because it helps in controlling the indoor air quality and providing thermal comfort. Computational fluid dynamics (CFD) is a highly used tool to predict the design, function and analysis of ventilation systems. This research report presents a computational model in conjunction with laboratory tests for better understanding of the indoor climate of kitchen environments. A test room located at South-Eastern Finland University of Applied Sciences in Mikkeli was chosen for the case study. A kitchen hood was installed in the room. The purpose of the hood was to allow mixing of the air coming through the supply inlets with the contaminants in the kitchen and effectively remove those contaminants through the outlets in the hood. In the experimental measurements, CO2 was used as a tracer gas and its mixing with the air was studied. CFD-predicted distribution of the tracer gas around the room, using various airflow rates for the air and CO2, was compared with the experimental measurements of the CO2 concentrations at different locations in the room. Comparison between the experimental and CFD results showed that computer simulations have great potential in studying airflows in ventilated spaces when applied in the proper way. It was observed that ventilation airflow rates have a large effect on the overall airflow distribution in the indoor environment. When the airflow rate is unnecessarily high, energy is wasted. A higher airflow rate does not necessarily help to remove pollutants from the kitchen environment and also increases the energy cost. Instead, a lower airflow rate helps with proper mixing of the air and results in a lower energy cost. However, the determination of an optimal airflow rate that is energy-efficient and provides acceptable indoor airflow quality needs further research. The research conducted in this project will be useful for professionals working on the design and operations of commercial kitchen hoods

Towards application of uncertainty quantification procedure combined with experimental procedure for assessment of the accuracy of the DEM approach dedicated for granular flow modeling

There is a high demand for accurate and fast numerical models for dense granular flows found in many industrial applications. Nevertheless, before numerical model can be used its need to be always validated against experimental data. During the validation, it is important to consider how the measurement data sets, as well as the numerical models, are affected by errors and uncertainties. In this study, the uncertainty quantification for the Discrete Element Method (DEM) model was performed based on selected quantities of interest (QOI), which were measured at a test rig. The uncertainty quantification was performed with open-source Dakota code, and the Latin hypercube sampling technique was used to determine test points. Various correlations between the input and output data were investigated to assess the impact of the possible input data errors on the output values determined by the solver. The results were validated against the measurement data from a novel in-house experimental test rig. The novel character of this work is developed procedure for study the impact of the uncertainties related with the input data on numerical results delivered by DEM model. In-house algorithm written using OpenCV libraries for determining particle motion characteristics from image dataset was used for results determination and postprocessing.SCOPUS: ar.jinfo:eu-repo/semantics/publishe