The Hague, 10 October 2024. Did you know that you have an innate skill that grants you the ability to discern between the sound of hot and cold water flowing? For today's Thursday Soundbyte, we review some sound dynamics which lay the basic foundation for how RESONIK's technology evaluates acoustics.
Humans rely on sound on a daily basis, and according to the US National Institute of Health, sound is the second most important sense, following sight. In today's world, sound is all around us - from listening to music on our daily commute, alerting us when our washing machine is done, and communicating with others through speech.
Our brain is finely tuned to hear even the most subtle changes in frequency and pitch, which is why you can tell when your friend has received bad news just from their tone of voice. But we have capabilities that stretch beyond aiding us in social situations. We have the ability to hear differences in the temperature of liquids - and spoiler, it is because of the frequencies due to imperfections, just like how RESONIKS' AI analyzes frequencies for defects!
Before we delve into the physics behind why we can hear the difference, let's see if you can decipher between cold and hot:
The answer should be apparent, but we’ll reveal the answer later in this article.
The burbling one hears when pouring water is the result of air bubbles vibrating and therefore emitting soundwaves.
Hot and cold water have different levels of viscosity, or "thickness", due to their temperatures. Hot water, which has a low viscosity, flows more freely, creating many small air bubbles as it’s poured resulting in the chamber, or glass having a higher resonant frequency. Cold water, on the other hand, has a higher viscosity, meaning it flows more slowly creating larger air bubbles and a lower resonant frequency.
So, which one was higher and which was lower?
Glass 1 is indeed the cold, and Glass 2 the hot. Did you get it right?
The concept of air bubbles and viscosity changing the frequency is similar to how in RESONIKS, we use the changes in frequencies due to porosity or cracks and analyse them using a machine learning model, allowing us to identify defects in parts.