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In which I babble about my research
Okay, so this "Three Weeks for Dreamwidth" thing is now just an excuse for me to find even more New and Exciting ways to babble about my thesis. Because it's approximately all I can think about, and since you lot have had to put up with me swearing at it, maybe I can explain what it's, you know, about. That, and I need to write the abstract and this will help me formulate the words.
There's been something of a recent trend in architecture and design to have buildings with open spaces and lots of glass. (Granted, in this case, my sample size are the three-soon-to-be-four new buildings on campus) There has also been a recent push in research, both private and government-funded, for energy efficiency in buildings. Now, one thing about buildings with a lot of glass is that you have to take into consideration the thermal effects of the material when designing the heating, ventilation, and air conditioning (HVAC) system. Specifically, that much glass can create an effective greenhouse when the temperature and humidity inside, which the HVAC system must overcome to make it comfortable. The harder the HVAC has to work, the less energy efficient the system.
There are ways of modeling and characterizing the HVAC systems prior to construction, mostly based on computational fluid dynamic (CFD) models. Thing is, these are a) theoretical, and b) not in real-time. Furthermore, as a building ages, there are significant changes in the behavior of the thermal systems inside, such as those caused by the deterioration of window caulking (like how older homes can spring leaks in the windows during the winter that drive up your heating bills). The CFD models just can't always account for that.
What I've been doing is working with a research group to create a sensor network that monitors temperature, humidity, and air flow in the atrium of the newest building on campus. The sensor network is dense and covers 4 floors of the space. It sends the data back in real-time, allowing us to get an accurate picture of the conditions inside the space, and then compare the temperature and humidity to the conditions that we can pull from the Weather channel. In theory, once you know what the inside temperature is and the outside temperature, you could do things like calculate efficiencies, thermal diffusivity, etc. And then, you could do things like make a smart HVAC system that'll just know when to turn on and off, just based on the overall conditions. Or other such smart energy things.
My part has been to help with the initial deployment and calibration of the system, and to analyze the initial results. The airflow sensors have been finicky and there are significant problems with the design, because you have to have the balance between low-power (so it doesn't drain the batteries) and accurate (which is related to stability, noise, etc), and we haven't hit it yet.
But one of the beauties of engineering/scientific research is that "this doesn't work, don't do this" is just as valuable as "this works!".
There's been something of a recent trend in architecture and design to have buildings with open spaces and lots of glass. (Granted, in this case, my sample size are the three-soon-to-be-four new buildings on campus) There has also been a recent push in research, both private and government-funded, for energy efficiency in buildings. Now, one thing about buildings with a lot of glass is that you have to take into consideration the thermal effects of the material when designing the heating, ventilation, and air conditioning (HVAC) system. Specifically, that much glass can create an effective greenhouse when the temperature and humidity inside, which the HVAC system must overcome to make it comfortable. The harder the HVAC has to work, the less energy efficient the system.
There are ways of modeling and characterizing the HVAC systems prior to construction, mostly based on computational fluid dynamic (CFD) models. Thing is, these are a) theoretical, and b) not in real-time. Furthermore, as a building ages, there are significant changes in the behavior of the thermal systems inside, such as those caused by the deterioration of window caulking (like how older homes can spring leaks in the windows during the winter that drive up your heating bills). The CFD models just can't always account for that.
What I've been doing is working with a research group to create a sensor network that monitors temperature, humidity, and air flow in the atrium of the newest building on campus. The sensor network is dense and covers 4 floors of the space. It sends the data back in real-time, allowing us to get an accurate picture of the conditions inside the space, and then compare the temperature and humidity to the conditions that we can pull from the Weather channel. In theory, once you know what the inside temperature is and the outside temperature, you could do things like calculate efficiencies, thermal diffusivity, etc. And then, you could do things like make a smart HVAC system that'll just know when to turn on and off, just based on the overall conditions. Or other such smart energy things.
My part has been to help with the initial deployment and calibration of the system, and to analyze the initial results. The airflow sensors have been finicky and there are significant problems with the design, because you have to have the balance between low-power (so it doesn't drain the batteries) and accurate (which is related to stability, noise, etc), and we haven't hit it yet.
But one of the beauties of engineering/scientific research is that "this doesn't work, don't do this" is just as valuable as "this works!".
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It's kind of astonishing how much power gets eaten by data transmission. It is by far the number one drain on the battery, and its worse depending on the distance you have to have to transmit over. I had no idea before I started this project. I mean, I knew it was a power drain; I just didn't know how much.
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