Then next day saw some routine logger replacements at our favorite coastal cave. It’s relatively shallow, making it a good place for a shakedown dive, and we have plenty of air time to develop funky new underwater deployment procedures for our little bots. The fact that it’s also an un-decorated mud pit is just gravy, as it means that no one bothers our instruments like they would in the prettier high-traffic caves. Strong tidal influence also provides a good range in the data and this cave has been continuously instrumented since our alpha trials back in 2013.
Of course that also means this location has seen more instrument failures than anywhere else, and again it delivered some bad news with the good: showing that even a great sensor can eventually crack under pressure:
This logger had been performing well since mid 2015, and initial consumption curves indicated it should go a year or more on 3AA’s. But we had some hints in the last dataset that the temp sensor was struggling, and this time it burned through a set of batteries in less than six weeks, with the 9808 reporting imaginary temperature excursions very similar to those we saw from the TMP102 failures last year. Each was accompanied by a significant hit to the power supply, which would imply that we probably had some kind of pressure induced self heating going on inside the IC. (perhaps because of a short?)
That’s a bit of a bummer, because the MCP’s offer better off the shelf accuracy than most of the other temperature sensors in that price range. So unless I want to put the breakout inside the housing, I’m forced back to the humble DS18B20’s that I used at the beginning of the project. I had a feeling this might happen, so on this trip we deployed a special 3-sensor cap on one of the loggers, with sensors inside the body, embedded in epoxy, and one projecting directly into the water. Hopefully that will let me determine how much lag we get from moving the temp sensors inside the pvc shell. Since we typically don’t take readings more than once every 15 minutes, I’m hoping we don’t see much effect from the different arrangements. During basement testing, thermal inertia rounded off short temperature peaks significantly, but that was at a five minute sampling interval, in air rather than water.
All three flow sensors delivered beautiful records, and the worst build actually provided the best insight into the need for sensor calibration. Logger #69 had a 5° tilt error due to an adhesive failure during the build. At that time I was still learning how to calibrate the sensors, so I was doing estimates of sensor mounting offsets by simply averaging the readings from an overnight test where I hung the logger on a pivot joint. The relatively large error on #69 meant that I almost rejected that sensor cap outright, but I figured we should put the unit in a high velocity site to test if it was still usable. I later did a full calibration on that unit (with Magneto 1.2 software) to generate a more complete set of axes bias & scaling factors.
With the latest flow data in hand, I could now see how much difference the extra effort of full calibration makes:
Those two corrected sets differ by a maximum of 0.5 degrees, implying that I can generate pretty good correction factors for older gen units in the field by simply hanging them in a closet over night. This also gives me a way to keep tabs on sensor drift without completely reprogramming the loggers, which I usually don’t have time for during fieldwork.
This is probably ‘good enough’ for our high flow sites, but for the deeper saline deployments (where the flows creep along in the sub-cm/second range) I’ll probably have to go full Monty since half a degree might be a significant portion of the total signal. And I have accumulated enough compass calibrations by now to show that in comparison to the well behaved acclerometers, magnetometer correction factors are all over the map with bias & offsets sometimes approaching 20%.
Working with a five degree mounting offset…
Once you get down into the weeds, the factory offsets are larger than your signal, potentially hiding the phenomenon you are looking for. And you still have the challenge of filtering out the relatively high acclerometer noise, without destroying the more subtle tidal signals…
I can only imagine what kind of incredible calibration challenges faced the people who made first direct observations of a gravitational wave, but I think I’m beginning to understand the sentiment behind this awesome tattoo…
Hackaday just posted about an elegant new tilt sensor idea, using a variable differential transformer filled with ferrofluid. This principle of variable inductive cores has been used for years by YSI to detect salinity, but seeing this fellows approach makes me want the look at the technique again, if I can figure out a way to reduce the power consumption…
We had another MCP9808 from that gen. go down, but this time the power curve was the exact opposite of what I am used to seeing for IC sensor failures:
While the sensor was throwing out a string of ‘Maximum Reading’ errors, the power consumption for the logger was normal. Then the sensor started delivering normal data again, but was obviously drawing enough juice to take the rest of the logger down quickly. Weird…