How to Read a Psychrometric Chart

Raise your hand if you know how to read and interpret a Psychrometric Chart.

An esteemed colleague in the DCIM software space recently published a blog posting on the above aforementioned subject.

For everyone who just raised his or her hand, Hurray for you!  And I apologize if the following blog seems disrespectful to you; it is not intended to be.

A Visual Representation

Now for those of you who didn’t raise your hand a Psychrometric Chart is something that looks like an American Indian Dream Catcher, a beautifully woven string pattern used to capture and preserve your dreams.  It is a “visual” representation of thermal dynamic properties using data points consisting of things like:

  • Dry bulb temperature
  • Vapor pressure
  • Dew point
  • Humidity ratio
  • Enthalpy
  • Saturation temperature
  • Wet bulb temperature
  • Specific volume of dry air
  • Relative humidity

Digging Out My Old College Physics Books

Now I was a physics major in college, and in my early career did thermal consulting for companies seeking NEBS compliance on their telecommunications equipment.  Yet, I have to admit when I saw this blog subject I had to dig out my old college physics books, which were buried under my slide rule and HP12c calculator.  I began to wonder, If this is so critical to managing the thermal dynamics of a data center, why don’t we see more of these charts in our DCIM software solutions?

Seriously, this is why we invented computers.  I mean, it is a nerdy fun thing to use a sling version of the psychrometer to get two different temp readings from a wet bulb and a dry bulb to calculate the humidity (and therefore dewpoint, etc.).  But then, what do you do with that information when you get it?  I guess one thing you can do is to predict PUE using a three level perceptron neural network much like Google has done…feeling the nerd-burn?

So the Question is, “OK Great, What Do You Do With the Ability to Read This?”

In terms of an A/C system it becomes important so you can optimize cost as these factors rapidly change, or if you can predict them changing in the near future.  This gets into host cooling and dehumidification work, but to put it incorrectly but simply: it is far cheaper to over cool a space and keep the doors closed if it is going to get hot an humid outside, than it is to wait to cool both the room and humidified air from the inside space.  The real use of being able to interpret this data (“read the chart”) is to plan what to do when the environmental and predicted environmental factors change so that you’re A/C units can work optimally.

You know, thinking about this has much more relevance to legal cannabis growing than it does to data centers, as dew point and factors make a big difference in indoor crop yield.  Or, any other organic indoor growing I guess.  If we could just legalize cannabis, then we have a huge play in the indoor grow space in terms of power efficiency, and potentially space management with workflow.  Running a hydroponic farm isn’t all that different from running a datacenter; it is just that moves/add/deletes are on a more scheduled basis.  The problem is, for now, that they are a cash only business, which is too hard to work with.  Those guys would love to be able to instrument their environmentals using Nlyte.

We are currently working on “Enhancing” this research using the data we have available to us, and channeling it through our Machine Learning AI engine.  But for now, I will let the computers do the math, and safely tuck my slide rule and HP12c back into the box of musty old books.

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