We would like to share two challenges that, if solved, could improve our domestic lives.
Firstly, having appliances that are as unobtrusive as possible is a strong desire, unwanted noise can cause a negative impact on relaxation. A key target for refrigerators is low sound level, a key noise source is the capillary tube. The capillary tube effects the phase change that is required for the refrigerant to be in the gaseous state in the evaporator for cooling. Noise is generated during this process due to two phases being present within the flow through the tube. The challenge is to create a numerical model and analysis of refrigerant flow properties in order to estimate the acoustic behaviour.
Secondly, we would like to maximise the information that can be gathered from our new range of connected devices. By analysing the data generated during usage we would like to be able to predict faults and understand user behaviour in more detail. The challenge regarding fault prediction is the scarcity of the failure data and the impact of false positives. Due to the number of units in the field, a relatively small fraction of false positives can remove the ROI from such an initiative. We would like to understand if advanced machine learning methods can be used to reduce this risk.
Further Information
Whilst domestic appliances or white goods are a standard product in our everyday lives, the technology areas that have been developed to achieve high performance and efficiency at low cost are numerous. Beko’s parent company, Arcelik, have a research campus that includes teams working on fluid dynamics, thermodynamics, materials science, data analytics, IOT, electronics amongst many others.