Purpose:

We've spent a few weeks building our basic skills with using the Arduino as a data logger. Now it's time to go do some data analysis which will eventually lead to some insights into the underlying physics. This lab will largely take place in your Jupyter notebook once you have generated some data.

For those who are newer to Jupyter notebooks you may find that creating the pdf of your notebook for submission to the LMS takes some extra time. Your classmates/colleagues can be of help and our current best practices for converting the notebook to pdf are documented on this hints page.

Procedure:

For this lab you will need all your data logger equipment to gather the data. After that the work is in the Jupyterlab notebook.

i) Gather your data: You need hot water. I boiled some water in my kettle. I poured the water into a mug which had the TMP36 sensor taped to the outside of the mug. I then covered the mug with a chunk of insulation to limit the heat loss through the surface. Some reasonable insulation cover is important since what we will be looking at in the future is the difference with the cover removed.

To start the data gathering I hit the reset button on my Arduino which had already been loaded with the data logger sketch. Because I didn't have to do this remotely I had my Arduino attached to my computer and I watched the data gathering process on the serial monitor. Roughly 15 min worth of data will be sufficient for this lab.

When the data gathering was done I copied and pasted the data from the file on the SD card into a txt document, imported that file into a spreadsheet (using commas as the delimiter, and then saved the spreadsheet as a .csv file. This is the process which has been working best for me on my computer. You need to use whatever process is working for you based on previous labs.

ii) Analysis: Complete all the same analyses that I did for my data (you will only have one data set) using your own data from the experiment above. The Jupyter notebook to guide you through the analysis is on the github and is called NewtonCooling.ipynb. There are other NewtonCooling notebooks that are NOT what you want. The two data files I used (datafile1 , datafile2).

iii) Bonus Round: If you must have a bonus round then go back and take a longer set of data (25-30 min) and see if you can distinguish between the linear, quadratic, and exponential models!

LAB DELIVERABLES:

I) Turn in the pdf of your Jupyter notebook (in the LMS) showing your data as well as all the analyses (curve fits) and conclusions requested in the deliverables.