PSA About ABS - Follow Up

Just a very short follow up about my previous ABS post. I was able to paint the part with a brush and some IKEA BEHANDLA Glazing paint. This is the paint I used on my pine night-stands in 2012, so it is a little old, but it did the trick. The smooth surface on the bottom of the stand didn't hold paint very well, but that's okay. The textured sides where the printer slowly built up the part gave lots of surface area to color and it looks like it belongs. Happy accident, but worth remembering if you want a fake wood texture. You could even expose different parts of the roll of plastic intentionally for a very interesting color pattern, but go easy or you may hurt the plastic's integrity.

2015-08-30 21.35.10 2015-08-30 21.35.30

While researching about ABS damage from UV exposure, I found out that there are now UV sensitive plastic filaments available. The world of 3D printing moves very fast and I was apparently not up to speed on some of the new materials that are out there. Below is a video of the filament being exposed (not me or my filament obviously).

PSA About ABS - A Surprising Tale in 3D Printing

A little over a year ago I got a 3D printer. I've had a great experience with it! I had printed a lot of lab hardware and some fun things, but then let it sit around for a few months while things were very busy in other parts of work. This past week was maker week in State College, so there were lots of 3D printing demonstrations around town and other maker projects. I decided I should get things fired up again. I've got a couple of new projects that will need custom enclosures built. Without being able to have a spot-welder in my apartment, I'm pretty much limited to plastics or paying for the manufacturing.

I re-leveled and calibrated the bed and z-axis mechanism. I carefully re-checked the extruder calibration to be sure that I got the appropriate amount of plastic extruded. Everything looked great, so I downloaded a simple, but utilitarian part for my Apple Watch off thingiverse. Using Slic3r, I created the GCode and sent it off to print. Everything started off like normal, so I left the printer running to deal with other tasks. I came back later and too many surprise the part had stripes!

2015-08-29 19.52.56

I've always printed with ABS plastic. It's not that I don't want to use PLA, it's just what I've always had on hand and what I know how to work with. I happened to have left the white roll of plastic on the printer after my last print. Since my printer sits next to the window, it gets some sunlight, especially in the morning hours. Not the best location, but it's the only location I can place it for the moment. It looks like the UV radiation has damaged my filament! Every time the part of the roll that was exposed heavily comes around, I get a band. For a simple charging stand, I'm not too upset. I could even try some of the ABS bleaching brews used by antique computer collectors. I may just paint the whole thing with paint that matches my wooden night stand. Maybe this will give me a false wood grain look?

I wanted to confirm that the stripes corresponded to a revolution of the spool. The whole part is darker than parts printed from the same roll months ago, but I'd guess the darker bits were on the top of the roll. By analyzing the GCode that produced the part, I calculated that the printed used about 5.1 meters of filament (<$2). The roll has been used down to a coiling diameter of about 150 mm. That means I expect about 10-11 turns of the reel for the print. I see 7-10 layers depending on what I count, so I'll call that close enough.

2015-08-29 20.34.26

Well that's the story for now. Don't leave your ABS in the sunlight, even on your machine. I've been meaning to get a dust cover for the machine and this is even more reason to do so and make sure it's opaque.

Drone Sounding Prototype

Again we have a short project post in-between the posts of the open science series (part 3 coming soon)! This time I want to share a fun little project involving cheap drones and an instrument pack that I designed on top of the Light Blue Bean module. The pack uses an HTU21D temperature/humidity sensor and a BME180 pressure sensor. I designed the board in the open-source PCB/EDA tool KiCAD. Should you want to reproduce the boards, the files to send off to a board house are available on a GitHub repository here.

I designed the pack to be a measurement device for a home, truck, or airplane of the weather enthusiast or storm chaser. Ideally it will send the data to a smartphone/tablet that then sends it out to the web or lets you do whatever you want with it. It was also a good excuse to play with the bean after hearing about it. While delivering another product to a friend, we decided to strap this sensor to a small and cheap ($33) drone and see what happened. We got some vague data, but the drone didn't get over a few meters high due to the high load. Zip ties provided some protection on takeoff/landing.

Our initial test flight with some quick plots in the background.

Our initial test flight with some quick plots in the background.

After playing we though it would be fun to do this on a drone with some more power. I grabbed a $55 drone (Syma X5C) on eBay and gave it a shot. After a couple of test flights I just couldn't get the bluetooth link to stay connected at the distances I wanted (50m).

My breakout and the bean attached to the top of the drone body.

My breakout and the bean attached to the top of the drone body.

I added a kludge that wrote data to an SD card using the OpenLog. It was extra weight since I needed two more coin cell batteries, but the drone turned out to be able to carry it to 45 m once or twice. Then the drone looses signal and shakily falls out of the sky until I can get control again. While inspiring me to drool over more advanced drones, I did get some interesting data! Some of the plots are rather small in web-view, but click on them to expand. I just didn't want a bunch of individual figures making the post scroll forever.

2015-07-27 19.17.44

First I'll show my first SD logged flight(s). Below is the altitude plot (derived from the barometric pressure sensor on-board).

A few up/down flights of the drone. The ascent in the grey box will be examined in detail.

A few up/down flights of the drone. The ascent in the grey box will be examined in detail.

If we take the highest and most constant climb rate ascent (gray box) and look at the temperature/dewpoint data we see rather clean results!

flight1

It was a dead still evening, just before sundown. Without any mechanical mixing,  we see radiation from the ground producing a temperature inversion (temperature increases with height here). We also see a nice dew point trend to drier air as we ascend. For fun, I calculated the lapse rate. This just means how fast the temperature changes with height. Plotting the data and fitting a line we get about +11 degrees/kilometer of height. A reasonable number. (Perhaps coincidentally about the negative of the typical dry adiabatic lapse rate? It's been too long and I didn't ever do much near ground meteorology. Thoughts appreciated.)

lapse_rate

The next evening, a very similar setup without wind, I did another sounding that got up to 45 meters. On this flight I noticed that the bumps in the temperature and dew point trends match rather well with the bump in my ascent rate. Since this drone isn't programmable, I do this by hand which is tricky to judge. It probably has to do with the sensors needing a lot of settling time to equilibrate to their surroundings (a couple of seconds). Maybe flying small circles on the way up is a solution. I also have the video from this flight if you're curious what it looks like. Nothing too interesting, but the uncontrolled descents are rather exciting. I've read about hacking better antennas on this drone for more range, so that's a thought. Before I get it much further away I want to do it in a large field to decrease the risk from a runaway drone. If this proves to be interesting enough, maybe a drone update will be in order. They are pricey though!


Flight 2 data

Flight 2 data

Open Science Pt. 2 - Open Data

For the second installment in our summer open science series, I’d like to talk about open data. This could very well be one of the more debated topics; I certainly know it always gets my colleagues opinions exposed very quickly in one direction or the other. I’d like to think about why we would do this, methods and challenges of open data, and close with my personal viewpoint on the topic.

What is Open Data?

Open data simply means putting data that supports your scientific arguments in a publicly available location for anyone to download, replicate your analysis, or try new kinds of analysis. This is now easier than ever for us to do with a vast array of services that offer hosting of data, code, etc. The fact that every researcher will likely have a fast internet connection makes most arguments about file size invalid, with the exception of very large (100’s of gigabytes) files. The quote below is a good starting place for our discussion:

Numerous scientists have pointed out the irony that right at the historical moment when we have the technologies to permit worldwide availability and distributed process of scientific data, broadening collaboration and accelerating the pace and depth of discovery…..we are busy locking up that data and preventing the use of correspondingly advanced technologies on knowledge.

- John Wilbanks, VP Science, Creative Commons

Why/Why-Not Open Data?

When I say that I put my data “out-there” for mass consumption, I often get strange looks from others in the field. Sometimes it is due to not being familiar with the concept, but other times it comes with the line “are you crazy?”  Let’s take a look at why and why-not to set data free.

First, let’s state the facts about why open data is good. I don’t think there is much argument on these points, then we’ll go on to address more two-sided facets of the idea. It is clear that open data has the potential to increase the friendliness of a group of knowledge workers and the ability to increase our collaboration potential. Sharing our data enables us to pull from data that has been collected by others, and gain new insights from other’s analysis and comments on our data. This can reduce the reproduction of work and hopefully increase the numbers of checks done on a particular analysis. It also gives our supporters (tax payers for most of us) the best “bang for their buck.” The more places that the same data is used, the cost per bit of knowledge extracted from it is reduced. Finally, open data prevents researchers from taking their body of knowledge “to the grave” either literally or metaphorically. Too often a grad student leaves a lab group to go on in their career and all of their data, notes, results, etc that are not published go with them. Later students have to reproduce some of the work for comparison using scant clues in papers, or email the original student and ask for the data. After some rummaging, they are likely emailed a few scattered, poorly formatted spreadsheets with some random sampling of the data that is worse than no data at all. Open data means that quality data is posted and available for anyone, including future students and future versions of yourself!

Like every coin, there is another side to open data. This side is full of “challenges.” Some of these challenges even pass the polite term and are really just full-blown problems. The biggest criticism is wondering why someone would make the data that they worked very hard to collect out in the open, for free, to be utilized by anyone and for any purpose. Maybe you plan on mining the data more yourself and are afraid that someone else will do that first. Maybe the data is very costly to collect and there is great competition to have the “best set” of data. Whatever the motivation, this complaint is not going to go away. Generally my reply to these criticisms goes along the lines of data citation. Data is becoming a commodity in any field (marketing, biology, music, geology, etc). The best way to be sure that your data is properly obtained is to make it open with citation. This means that people will use your data, because they can find it, but provide proper credit. There are a number of ways to get your data assigned a digital object identifier (DOI), including services like datacite. If anything, this protects the data collector by providing a time-stamp of doing data collection of phenomena X at a certain time with a time-stamped data entry. I’m also very hopeful that future tenure committees will begin to recognize data as a useful output, not just papers. I’ve seen too many papers that were published as a “data dump.” I believe that we are technologically past that now, if we can get past "publish or perish," we can stop these publications and just let the data speak for itself.

Another common statement is “my data is too complicated/specialized to be used by anyone else, and I don’t want it getting mis-used.” I understand the sentiment behind this statement, but often hear it as “I don’t want to dedicate time to cleaning up my data, I’ll never look at it after I publish this paper anyway.” Taking the time to clean up data for it to be made publicly available is when you have a second chance to find problems, make notes about procedures and observations, and make it clear exactly what happened during your experiment (physical or computational). I cannot even count the number of times I’ve looked back at old data and found notes to myself in the comments that helped guide me through re-analysis. These notes saved hours of time and possibly a few mistakes along the way.

Data Licensing

Like everything from software to intellectual property, open-data requires a license to work. No license on data is almost worse that no data at all because the hands of whoever finds it are legally bound to do nothing with it. There is even a PLOS article about licensing scientific software that is a good read and largely applies to data.

What data licensing options are available to you are largely a function of the country you work in and you should talk with your funding agency. The country or funding agency may limit the options you have. For example, any US publicly funded research must be available after a presidential mandate that data be open where possible “as a public good to advance government efficiency, improve accountability, and fuel private sector innovation, scientific discovery, and economic growth.” You can read all about it in the White House U.S. Open Data Action Plan. So, depending on your funding source you may be violating policy by hoarding your data.

There is one exception to this: Some data are export controlled, meaning that the government restricts what can be put out in the open for national security purposes. Generally this pertains to projects that have applications in areas such as nuclear weapons, missile guidance, sonar, and other defense department topics. Even in these cases, it is often that certain parts of the data may still be released (and should be), but it is possible that some bits of data or code may be confidential. Releasing these is a good way to end up in trouble with your government, so be sure to check. This generally applies to nuclear and mechanical engineering projects and some astrophysical projects.

File Formats

A large challenge to open data is the file formats we use to store our data. Often times the scientist will use an instrument to collect their data that stores information in a manufacturer specific, proprietary format. It is analyzed with proprietary software and a screen-shot of the results included in the publication. Posting that raw data from the instrument does no good since others must have the licensed and closed-source software to even open it. In many cases, the users pay many thousands of dollars a year for a software “seat” that allows them to use the software. If they stop paying, the software stops working… they never really own it. This is a technique that the instrument companies use to ensure continued revenue. I understand the strategy from a business perspective and understand that development is expensive, but this is the wrong business model for a research company. Especially considering that the software is generally difficult to use and poorly written.

Why do we still deal in proprietary formats? Often it is because that is what the software we use has, as mentioned above. Other times it is because legacy formats die hard. Research groups that have a large base of data in an outdated format are hesitant to update the format because it involves a lot of data maintenance. That kind of work is slow, boring, and unfunded. It’s no wonder nobody wants to do it! This is partially the fault of the funding structure, and unmaintained data is useless data and does not fulfill the “open” idea. I’m not sure what the best way to change this idea in the community is, but it must change. Recent competitions to “rescue” data from older publications are a promising start. Another, darker, reason is that some researches want to make their data obscure. Sure, it is posted online, so they claim it is “open”, but the format is poorly explained or there is no meta-data. This is a rare case, but in competitive fields can be found. This is data hoarding in the ugliest form under the guise of being open.

There are several open formats that are available for almost any kind of data including plain text, markdown, netCDF, HDF5, and TDMS. I was at a meeting a few years ago where someone argued that all data should be archived as Excel files because “you’ll always be able to open those.” My jaw dropped. Excel is a closed, XML based, format that you must have a closed-source program to open. Yes, Open Office can open those files, but compatibility can be sketchy. Stick to a format that can handle large files (unlike Excel), supports complex multi-dimensional data (unlike Excel), and has many tools in many languages to read/write it (unlike Excel).

The final format/data maintenance task is a physical format concern. Storage media changes with time. We have transitioned from tapes, floppy disks, CDs, and ZIP disks to solid state storage and large external hard-drives. I’m sure some folks have their data on large floppy disks, but haven’t had a computer to read them in years. That data is lost as well. Keeping formats updated is another thankless and unfunded task. Even modern hard-drives must be backed up and replaced after a finite shelf life to ensure data continuity. Until the funding agencies realize this, the best we can do is write in a small budget line-item to update our storage and maintain a safe and useful archive of our data.

Meta-Data

The last item I want to talk about in this already long article is meta-data. Meta-data, as the name implies, are data about the data. Without the meta-data, most data are useless. Data must be accompanied by the experimental description, relevant parameters (who, when, where, why, how, etc), and information about what each data item means. Often this data lives in the pages of the laboratory notebooks of experimenters or on scraps of paper or whiteboards for modelers. Scanners with optical character recognition (OCR) can help solve that problem in many cases.

The other problems with meta-data are human problems. We think we’ll remember something, or we don’t have time to collect it. Anytime that I’ve thought I didn’t have time to write good notes, I payed by spending much more time after the fact figuring out what happened. Collecting meta-data is something we can’t ever do enough of and need to train ourselves to do. Again, it is a thankless and unfunded job… but just do it. I’ve even just turned on a video or audio recorder before and dictated what I’m doing. If you are running a complex analysis procedure, flip on a screen capture program and make a video of doing it to explain it to your future self and anyone else who is interested.

Meta-data is also a tricky beast because we never know what to record. Generally, record everything you think is relevant, then record everything else. In rock mechanics we generally record stress conditions, but never think to write down things like temperature and humidity in the lab. Well, we never think to until someone proves that humidity makes a difference in the results. Now all of our old data could be mined to verify/refute that hypothesis, except we don’t have the meta-data of humidity. While recording everything is impossible, it is wise to record everything that you can within a reasonable budget and time commitment. Consistency is key. Recording all of the parameters every time is necessary to be useful!

Final Thoughts

Whew! That is a lot of content. I think each item has a lot unsaid still, but this is where my thinking currently sits on the topic. I think my view is rather clear, but I want to know how we can make it better. How can we share in fair and useful ways? Everyone is imperfect at this, but that shouldn’t stop us from striving for the best results we can achieve! Next time we’ll briefly mention an unplanned segment on open-notebooks, then get on to open-source software. Until then, keep collecting, documenting, and sharing. Please comment with your thoughts/opinions!

Happy Leap Second Day!

I'm currently in the process of getting part 2 in our open science series prepared, but wanted to drop a quick note saying happy leap second day! In just a few hours we will have our 26th leap second! The last minute of today (UTC) will have 61 seconds to make up for difference in our atomically derived time system and the imperfect system that is the Earth's rotation. The clock will read 11:59:59, 11:59:60, 12:00:00. Unusual eh? You can watch the leap second online at time.gov, spend your leap second watching a random one second video at spendyourleapsecondhere.com, or watch it nerd-style on your GPS NMEA feed.

If you want to know more about leap seconds and the wacky world of time keeping systems, I encourage you to listen to our episode on time-keeping on Don't Panic Geocast. We put a lot of time into researching that show and had a lot of fun learning about how complex time really is. Happy leap second day everyone!

 

Open Science Pt. 1 - What is Science?

The topic of open science has been on my mind a lot lately, and it has also been on the list of potential posts for this blog. As I sat down to map the ideas I wanted to clarify and share, I realized that I wasn't looking at one post, but several. I think this will take us most of the summer to get through. Why? Well, there's a lot here to talk about and there are also a few exciting projects that I'm working on that will hopefully line-up with their respective blog posts.

This post series was finally pushed to the top of my stack by a few events. First, I had some great discussions on open data with some colleagues and promised to show their work as an example of open science. Next, I was frustrated by reading a high impact paper whose data and methods were decidedly not open. I also wanted to raise awareness that US federally funded research is now supposed to be open through a mandate from the Office of Science and Technology Policy (OSTP). You can read the mandate here. Finally, several people had mentioned to me that they wanted to know more about open science, some even mentioning a social science site called ResearchGate.

For this first post, we are going to ask ourselves: 1) What is science anyway? 2) Why should it be open? 3) Why is this hard? Without further delay, let's jump in!

What is science anyway?

When I was in seventh grade our science teacher, Mr.Paskiewicz, asked us to write down the answer to "what is science?" I don't remember what I wrote then, but I know that the question has never stopped being something I wonder about. While my answer changes depending on the level of detail requested, I believe that science is basically the name for structured curiosity. While this is more broad than Merriam-Webster's definition of "knowledge about or study of the natural world based on facts learned through experiments and observation," I think it encompasses a similar idea. Science is the process that we have devised to learn about universal truths of our existence in a way that safeguards us against our own human tendencies. These tendencies include bias, preconceived ideas, literature inertia, and career pressure.

In an effort to protect us from ourselves, the scientific method came into existence. The scientific method is a recursive process of making predictions, testing them, revising our ideas and models, then making more predictions. This process seems bullet-proof until we step into the equation. If a scientist is working alone (or in a vacuum as we often say), they may end up chasing a line of work that supports their "pet" hypothesis. This is easy to do! What happens if that idea turns out to be false? Sometimes we still pursue different ways to show that it may be true, not wanting to let go of our self-conceived brilliance. The missing chunk in this process was filled much after the early days of science by the sharing of discoveries in publications, and finally the peer review process was conceived.

Peer review is how we ideally are one another's check and balance system. I think that my work has made a contribution to the knowledge base that is science, so I submit it for publication. The paper (and ideally the evidence) is sent to other scientists to read, review, and comment on. These comments should concern how good the work is, but be mostly made up of recommendations or comments on the contribution. This sounds like it should be fertile ground from which we continually reap a harvest, but instead has turned into one of the most mocked processes in science. Reviews on publications are sometimes very helpful, but just as often seem to contain requests to add references (often to the reviewer's work), mundane requests to change things in the figures or writing style, or are completely unhelpful (such as "good paper"). The publication and review processes are part of the called-for revisions in open science, so we'll discuss them deeply in a later post.

Image: phdcomics.com

Image: phdcomics.com

why should science be open?

As we said above, one of the goals of science is to create a curated body of knowledge. The definition didn't say what all was included in that body of knowledge. Is it our conclusions and "laws" of nature? Sure, but it should also consist of the methods, hardware, software, and data to back up those conclusions. If we do not continually re-evaluate our "laws," we run the risk of continuing the obey a false, self-imposed, misconception.

Open science is one of the best ways to help (though not totally prevent, we are human after all) us keep a trusted and verified scientific knowledge base. Open science is the idea that our data, methods, software, hardware, ideas, notes, and all materials are available to be checked, tested, compared, and scrutinized by anyone. How available? That is part of the core argument going on currently. Should things that I worked years on and devoted thousands of hours to be posted online for anyone to download? I'm going to argue that they should. This is absolutely not a trivial thing to do, and it certainly doesn't end in a situation with no problems where scientists worldwide sit in a virtual circle around a hologram campfire singing Kumbaya. It's certainly closer than where we are now though.

One last note on this before we move on. I am not at all against companies that make software, hardware, etc. I am not against making money, we all need to make a living. I am not against patents, the patent process, or copyright. There are so many licenses out there currently (copyleft, share and share alike, CERN Open Hardware License, and many others) that it is confusing. In fact, your work needs some kind of license or it is assumed that others cannot use it! The issue is further confused when we often sign over rights to our figures and text to a publishing house when we publish a paper. I'm not sure, nor is anyone, what the best path is, so let's explore that together as well.

Also have a look at Michael Nielsen's TEDxWaterloo talk on open science (below). He shows some excellent examples of both the successes and failures of open science. (Like anything, acknowledging failures is crucial to the eventual success.)

why is this hard?

So why are all scientists and R&D folks not sitting around that virtual campfire already? There are so many road-blocks and difficult decisions we will have to make as a community that it can seem very imposing. Why would we share our hard-earned results and ideas for free? How can we get funding to do our work from a company and remain open? Where will all of this go and who maintains it? Who sets standards? This list seems pretty imposing to me and may even sound discouraging. It's not! Challenges of equal magnitude have been faced during every major revolution by information workers. No solution will be perfect, but most of the solutions we have devised have been good enough. Just think about standardization in the early days of the internet or when plans for early scientific apparatus were released from corporate secrecy.

Final thoughts

As you can see, this is a subject that I am very passionate about. My goal is going to be explaining things in a brief manner so that these are not so long that you have trouble making it through! I hope that these posts will promote discussion amongst my colleagues, both known and unknown. Please contribute by commenting: sharing your ideas is the first step!

There is an entire spectrum of open-ness that runs from top-secret documents that will never see the sun to those who freely give all of their knowledge and content. The community will never reach either end of that spectrum, but it is my hope that the slow migration towards open source science can be promoted through discussion. I'm really looking forward to working through my own thoughts on this subject and hearing what others have to say about it.

The flight of the frisbee

It is finally summer and I see lots of people playing all sorts of outdoor sports. While I have never been a big sports player, it is fun to watch. There are a lot of physics involved in most sports, so maybe that is a line of posts worth investigating. I do have a post series on open source science planned that will last most of the summer, but maybe we can keep sports in mind next year?

This post is a resurrection of a post I started a couple of years ago and never got around to finishing. In an effort to tie up loose ends, here we go! How does a frisbee fly? I wondered this when we discovered that my fiancée can throw a frisbee upside down sometimes with results that flew surprisingly far. To understand what is happening we need to look at the fundamental physics of frisbee flight, make some measurements, then try to draw some conclusions. Turns out there is a lot of research on the aerodynamics of flying discs, so I'll hit the important points and leave links to let you dig down the wiki-hole with me if you wish. To start out, check out a copy of "Spinning Flight" by Ralph Lorenz at your library.

The physics of frisbee flight

While in flight, our frisbee will experience several forces governing its movement including gravity, lift, drag, and a torque from its angular momentum. We will quickly look at each of these, but we are not going to fully model the system (though it could be fun!)

Forces on a frisbee. (Image: illumin.usc.edu)

Forces on a frisbee. (Image: illumin.usc.edu)

First off, Gravity is the most obvious and intuitive of these forces. Everything is pulled towards the earth's center of mass by gravity at about 9.81m/s/s. If we just place the frisbee on the table, it will experience a force resulting from the acceleration of gravity. If we drop it, it will be accelerated downward. The velocity of falling objects has been fully investigated and we won't go into that too deeply. For now, simply assume that we could calculate the velocity of a dropped object or calculate the acceleration of an object if we know its fall rate.

Lift is what may be the most important force in this study. Believe it or not, a frisbee experiences lift following the same principles as a traditional wing or rotor blade. As the frisbee cuts through the air, some of the oncoming air goes over the frisbee, some goes under. The air streams have to meet up in the lee of the frisbee and since we cannot create or destroy air, we must have continuity and conservation. The air on top has to move faster for this to hold, it has longer to travel after all, and the air below can move more slowly. Thanks to Bernoulli's principle, we know that air moving faster will have a decreased pressure. This isn't anything new; in fact, Daniel Bernoulli published this idea in 1738! With fast air on top of the frisbee and slower air below, there is high pressure below and low pressure above the disk. From a difference in pressure we get the force of lift (more generally called a pressure gradient force).

Lift force from a pressure differential. (Image web.wellington.org)

Lift force from a pressure differential. (Image web.wellington.org)

Drag is the reason that our frisbee does not fly off into the distance. Drag is the force that slows the frisbee down as it pushes through the dense atmosphere. We could try to measure the drag through our later analysis, but that itself it a very deep task that we could spend a lot of time on.

Finally, comes the torque from the angular momentum of our frisbee. Have you tried to throw a frisbee without spinning it? In theory it would fly right? It's just a circular wing after all. Well, that turns out to be a pretty unstable way to fly your disc. Generally it will turn up, stall, and then fall like a rock. Why? Thanks to some spatial change in the lift force, the front of the disc will be lifted with slightly more force than the back, causing the disc to torque over to the back. In fact, I'll save you the trouble, you can watch my feeble attempts below. We could try to outfit our frisbee with control surfaces to help it maintain the proper angle of attack, but that is heavy, complicated, and would mean dead batteries would plague playing kids and dogs. Surely there is a more simple way?

Of course! Let's spin the frisbee when we throw it! When we throw the disc and it spins, we get gyroscopic stability and the possibility to see the cool spinning designs we print on the toy. We can determine that this torque will point vertically through the top of the frisbee. If you remember the right hand rule, you'll see that for my right-handed clockwise throw, the torque vector will point towards the ground. For the south-paws out there, your torque vector will point towards the sky. The important thing is that it will try to counteract the tendency of the lift moment to make the frisbee backflip. Granted, it will not win forever. Eventually the flight will become unstable, but we can maintain steady flight for several seconds. Depending on the force balance, we'll also expect to see the frisbee's vertical axis precess around true "up".

Taking measurements

To take some measurements of frisbee flight, I created an instrumented frisbee. Turns out, I'm not the first one to do this. Lorenz made an instrumented frisbee in the early 2000's and then improved it by adding a plethorera of sensors. I decided that a simple accelerometer would be enough for my investigation. While adding angle sensors and such would be interesting, let's keep it simple for a first pass. Maybe adding a 9 degree of freedom inertial measurement unit would be fun, but that's an idea for another time. Actually, the gyroscope data from that would be incredibly useful.

I used an Arduino Pro Mini for my micro controller, an accelerometer, and SD card logger for the sensor and logging system. I ended up just trying to read the sensor in single shot mode as fast as possible. This gave a data rate of around 180-200 Hz with time-stamps in microseconds on each packet. Sure, we could make this part a little more slick, but again, the KISS principle rules for these first hacks at a problem . Power comes from a pair of CR2032 coin cell batteries. All of this was hot glued down and hopefully made as aerodynamic as possible without coweling the whole assembly. Should we wish to improve this, I would directly solder the wires to the boards instead of using header connectors and cover everything in kapton tape.

If you are interested in trying this yourself, the Arduino sketch is at the bottom of this post.

frisbee_label

To approximate the speed of the frisbee I will have some video of the flights that we can look at to get some rough numbers to work with. These were just filmed with a DSLR camera, so this is something you can try at home! The newer iPhones are actually even faster than this camera, but I didn't have a tripod mount handy for my 6+ when I filmed this.

Data analysis

First, let's look at the film of a flight to figure out how fast the frisbee throw is on average. We could look at how long the flight was and how far is was and get an average velocity with v=d/t. That's great, but we can do one better! Through the magic of image tracking, we can get the position of the frisbee in each frame of the video and calculate the velocity profile during the flight. While probably not totally necessary, why not?

I'll use the Tracker video analysis software for this. We read in the video, setup some coordinate systems, markers, etc, and let it churn. If the frisbee veers in the third dimension (into or out of the screen) we won't get that information because we are just tracking its center in a 2D picture. Hence, I'll try to keep the throws gentle! We'll look at the stall, upside-down, and normal throws:

To keep this short, I'll tell you that the forward speed of these throws looks to top out at about 5 m/s. Again, we could go down the hole of getting drag from the slight deceleration in forward speed with time, but it really doesn't slow us much... the ground beats drag to stopping our frisbee.

We can see that the stall throw drops very quickly without really flying. In fact, it mostly is flipping end-over-end. The regular and upside-down throws look like they have a similar flight profile from the video. This means I must have not been as consistent as I had hoped with my throwing strength. We know that without the lift component that the upside-down throw should follow a more parabolic path than the regular throw. Also, my regular throw was pretty weak to keep the frisbee in the frame and minimally veering, so it didn't have enough lift to show us long periods of stable flight.

 

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Next, let's look at the accelerometer data. The data is stored in a text file with the millisecond time stamp, and then each of the three axes acceleration measurements. I've plotted the radial (outward from the center) and vertical accelerations. Since the accelerometer was mounted near the rim of the frisbee, we will see relatively large signals from the wobble in flight.

We'll start with the normal throw this time. The accelerometer is roughly calibrated at the factory, but don't worry about the absolute values too much here. We see me pull back to throw as a upturn in the radial, then a large negative (outward) acceleration from the spinning of the disc during flight. Roughly a couple of g's here. The vertical is interesting through. We see the roughly -10m/s/s from the Earth's gravity as a prepare to throw and after the landing, but during flight we see a near zero vertical acceleration that trends downward. What is it? Lift! This is the flight of the frisbee that is gradually reduced as drag slows us and the angle of attack becomes non-ideal. We are expecting that we don't totally counteract gravity because flight is not sustained and our frisbee does not go on forever. This was a pretty gentle and short flight, but followed our expectations in terms of the forces at work. We can even see some precession in the vertical in the neighborhood of about 4 times/second. Unknown-3

 

Next, we'll look at the stall throw. This isn't spinning so we don't expect to see a lot of radial acceleration once the throw leaves our hands, but we do expect to see some lift for a short period of time, then a stall and fall.  That is what we get, too! The spike in the blue curve at 0.5 seconds is my push to accelerate the frisbee, then there are few other radial accelerations recorded (except the impact). There should be some small accelerations from the flip of the disc, but they are tiny here. The vertical trend up and down just before 1 second is the frisbee flipping over once. The only real lift is just a tiny fraction of a second before the front is lifted up. After that, we are really just in free-fall.

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Finally, the elusive upside-down throw. The frisbee starts out upside-down, so the acceleration of gravity now shows as positive (look after the landing for example). We still see radial acceleration from the spinning and we also see a reduction in the vertical acceleration. This can't be lift, but is probably some axis mis-alignment on the sensor. We still see precession as the torque tries to keep the disc horizontal.

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What did we learn?

We learned about all of the forces at play in the flight of a frisbee, lift, drag, etc. We measured some flight paths and acceleration profiles. These were not quite as clear cut as I had hoped though. We still saw that flying right side up works pretty well, but upside down "flight" is basically spin-stabilized falling with a lot of forward momentum. Throwing with no spin quickly results in a pitch-up and stall.

We'll see what happens with this. If people are interested we could think about adding an IMU to the setup with better positioning and balance. We could also just put a light on a frisbee and track it with a time-lapse photo. This turned out to be a fascinating look at flight, acceleration measurement, and video tracking! If you are wondering about numerical modeling, there is a really nice report from MIT that develops a good model.

#include <SPI.h>
#include <SD.h>
#include <Wire.h>
#include <Adafruit_Sensor.h>
#include <Adafruit_ADXL345_U.h>

/* Assign a unique ID to this sensor at the same time */
Adafruit_ADXL345_Unified accel = Adafruit_ADXL345_Unified(12345);

const int chipSelect = 4;

File dataFile;

void displaySensorDetails(void)
{
sensor_t sensor;
accel.getSensor(&sensor);
Serial.println("------------------------------------");
Serial.print ("Sensor: "); Serial.println(sensor.name);
Serial.print ("Driver Ver: "); Serial.println(sensor.version);
Serial.print ("Unique ID: "); Serial.println(sensor.sensor_id);
Serial.print ("Max Value: "); Serial.print(sensor.max_value); Serial.println(" m/s^2");
Serial.print ("Min Value: "); Serial.print(sensor.min_value); Serial.println(" m/s^2");
Serial.print ("Resolution: "); Serial.print(sensor.resolution); Serial.println(" m/s^2");
Serial.println("------------------------------------");
Serial.println("");
delay(500);
}

void displayDataRate(void)
{
Serial.print ("Data Rate: ");

switch(accel.getDataRate())
{
case ADXL345_DATARATE_3200_HZ:
Serial.print ("3200 ");
break;
case ADXL345_DATARATE_1600_HZ:
Serial.print ("1600 ");
break;
case ADXL345_DATARATE_800_HZ:
Serial.print ("800 ");
break;
case ADXL345_DATARATE_400_HZ:
Serial.print ("400 ");
break;
case ADXL345_DATARATE_200_HZ:
Serial.print ("200 ");
break;
case ADXL345_DATARATE_100_HZ:
Serial.print ("100 ");
break;
case ADXL345_DATARATE_50_HZ:
Serial.print ("50 ");
break;
case ADXL345_DATARATE_25_HZ:
Serial.print ("25 ");
break;
case ADXL345_DATARATE_12_5_HZ:
Serial.print ("12.5 ");
break;
case ADXL345_DATARATE_6_25HZ:
Serial.print ("6.25 ");
break;
case ADXL345_DATARATE_3_13_HZ:
Serial.print ("3.13 ");
break;
case ADXL345_DATARATE_1_56_HZ:
Serial.print ("1.56 ");
break;
case ADXL345_DATARATE_0_78_HZ:
Serial.print ("0.78 ");
break;
case ADXL345_DATARATE_0_39_HZ:
Serial.print ("0.39 ");
break;
case ADXL345_DATARATE_0_20_HZ:
Serial.print ("0.20 ");
break;
case ADXL345_DATARATE_0_10_HZ:
Serial.print ("0.10 ");
break;
default:
Serial.print ("???? ");
break;
}
Serial.println(" Hz");
}

void setup(void)
{
Serial.begin(9600);
Serial.println("Accelerometer Test"); Serial.println("");

/* Initialise the sensor */
if(!accel.begin())
{
/* There was a problem detecting the ADXL345 ... check your connections */
Serial.println("Ooops, no ADXL345 detected ... Check your wiring!");
while(1);
}

/* Set the range to whatever is appropriate for your project */
accel.setRange(ADXL345_RANGE_16_G);
// displaySetRange(ADXL345_RANGE_8_G);
//accel.setRange(ADXL345_RANGE_4_G);
// displaySetRange(ADXL345_RANGE_2_G);

/* Display some basic information on this sensor */
displaySensorDetails();

/* Display additional settings (outside the scope of sensor_t) */
displayDataRate();
//displayRange();
Serial.println("");
Serial.print("Initializing SD card...");
// make sure that the default chip select pin is set to
// output, even if you don't use it:
pinMode(SS, OUTPUT);

// see if the card is present and can be initialized:
if (!SD.begin(chipSelect)) {
Serial.println("Card failed, or not present");
// don't do anything more:
while (1) ;
}
Serial.println("card initialized.");

char filename[15];
strcpy(filename, "ACCLOG00.TXT");
for (uint8_t i = 0; i < 100; i++) {
filename[6] = '0' + i/10;
filename[7] = '0' + i%10;
// create if does not exist, do not open existing, write, sync after write
if (! SD.exists(filename)) {
break;
}
}

dataFile = SD.open(filename, FILE_WRITE);
if( ! dataFile ) {
Serial.print("Couldnt create ");
Serial.println(filename);
while(1);
}
Serial.print("Writing to ");
Serial.println(filename);
}

void loop(void)
{
/* Get a new sensor event */
for(int i=0; i < 100; i++){
sensors_event_t event;
accel.getEvent(&event);
dataFile.print(millis());
dataFile.print(",");
//log_float(event.acceleration.x,999,8,5);
dataFile.print(event.acceleration.x,5);
dataFile.print(",");
//log_float(event.acceleration.x,999,8,5);
dataFile.print(event.acceleration.y,5);
dataFile.print(",");
//log_float(event.acceleration.x,999,8,5);
dataFile.print(event.acceleration.z,5);
dataFile.println("");
}
dataFile.flush();
}

Nepal Earthquake Ground Motion Around the World

The recent earthquake in Nepal is truly a tragic event. Currently it has claimed over 5000 lives and the more remote regions will not be reached for days to weeks. It is really very hard to comprehend the intensity of ground motion for such an event. If you want to know more technical details about the event, I encourage you to look at the official USGS event page and Chuck Ammon's blog post. We also will talk about earthquake details on the "Don't Panic Geocast" tomorrow (Friday).

For now I wanted to share some animations of the ground motion associated with the event. I tweeted some of these earlier in the week and got a great response, so I wanted to collect them all in one place with some maps. First off a quick map of the main shock and many aftershocks (circle area goes with the magnitude, color the age).

2015-04-30 10.37.19

Let's start with a ground motion visualization from a station in Tibet, China. This station "clipped". This means the instrument hit the limits of the motion it could measure. This particular station is about 650 km (400 miles) from the earthquake. There is another instrument that measures strong motion closer to the earthquake, but the data had some holes that made animation very difficult. (I guess that's another feature to add to the program!)

Screen Shot 2015-04-30 at 10.40.56 AM

Next, we look a little further away at Kabul. While the shaking wasn't very strong (much smaller accelerations), we begin to see more interesting waveforms as phases are getting separated by traveling a greater distance of 1650 km (1025 mi).

Screen Shot 2015-04-30 at 10.41.07 AM

If we move much further away to the U.S., we see a very long record of motion. I made two animations for the U.S., one near where I grew up in Arkansas and one from the instrument in the basement of the geology building here at Penn State. There are some really great Rayleigh waves (the circular motion) around 3:51 in the Arkansas video and 3:18 in the Pennsylvania video.

Screen Shot 2015-04-30 at 10.41.36 AM

Screen Shot 2015-04-30 at 10.41.44 AM

I hope you find these videos interesting! There is a lot of possible post material in each one, but I wanted to be sure to get them out in a timely and collected way. The program to make these is completely open source on GitHub: https://github.com/jrleeman/SeismoVisualize and was inspired by the visualization of Mike Cleveland and Chuck Ammon.

Mentorship - In Memory of Prof. Ernst Deiter-Schmitter

Prof. Schmitter (far right)

Prof. Schmitter (far right)

I had been thinking about a blog post on the importance of being a mentor in an academic setting (or any other setting really). Unfortunately I lost one of my early mentors and wanted to write a short story to show the impact that being a mentor can have.

When I was in high-school I was fascinated by tornadoes and electric fields. I still am. I decided that I wanted to research the connection, so I began to read lots of literature on the subject (my first exposure to peer-reviewed papers) and looking for places I could contribute. I landed on the Yahoo Groups page of a group dedicated to ULF/VLF studies. After asking some very basic questions, I ended up chatting with Prof. Schmitter through e-mail. After weeks of communication and beginning to design an instrument, he inquired as to which institution I was at. I replied that I was a high school student, expecting to never hear back from him. The response was exactly the opposite. Ernst re-doubled his efforts to help me undertake a project.

This effort wasn't to check off a box on a funding agency outreach goal sheet, but it was a true excitement to help a student learn. I was very excited about the whole thing! We did design an instrument that I constructed at my home in Arkansas. Some friends and I took it to the field and collected data. After returning, I sent Ernst the data and he suggested we write a publication.

Having never written anything more than a detailed physics lab report before, this was an incredible learning experience.  We worked the paper over a few times and had a couple of Skype calls about it. He submitted the paper, absorbing the cost of publication. That was my first scientific article.

Did I mention that he was a professor in Germany? We never met in person. I had seen his recent publications come out and kept meaning to email and update him that I had again been working in electrostatics. I didn't get it done. It had been too many years since we talked. I do have a new instrument that we designed some years ago that still needs to be built and tested, but it will surely be many times more difficult now.

What is the lesson in this other than contacting people before you can't anymore? It's is that you never know who you will inspire. Without pushes from Prof. Schmitter I probably wouldn't have finished the project and published anything. That publication helped me get a foot in the door of the research field. That's how I found out I wanted to do research as a career (thanks to several other amazing mentors). The lesson is that taking the time to talk to interested students is one way to have a lasting impact, even after your time.

I consider myself incredibly lucky to have had as many amazing mentors and teachers at every stage of my life. I'm in much deeper service debt than I can ever hope to pay off in one lifetime. I want to thank everyone reading this for encouraging me through direct contact or just by supporting this blog with your readership. This post serves to show that everyone of you make a difference in the lives of others everyday.

* I looked back through our emails had we had nearly 200 pages of email exchange before I had finished my first year of college. That's a lot of information!

Knowing the Fundamentals

phdcomics.com

phdcomics.com

Ok, I've been sitting on this topic for awhile, but I was recently inspired to revive this post after being asked some very general questions by a tour group that came through the lab. Next time we'll be back to doing some data collection and analysis. Maybe gravity tide measurements? Anyhow, on with the topic of the day: knowing the fundamentals.

On a (now old) episode of the podcast Technical Difficulties, Gabe Weatherhead (@macdrifter) was chatting with Brett Terpstra (@ttscoff) and Rob Trew (@complexpoint). The show was mostly about how everyone got started writing computer code and some tool suggestions. One line made me stop on my walk home to type it into my reminders to write this post.

Rob quoted a line from the Windows 95 API Manual (a programming interface manual for the non-programmers out there). It said "The nature of an expert is not someone who knows all the details, it's someone who understands the fundamentals really well." Rob points out that therein lies the key to problem solving. This statement really resonated with me when I looked back on problems that I've encountered in the past, both scientific and technological.

We often think of an expert as someone that is in the top few percent of the knowledge leaders in their field. Experts should know all of the details of their subject, including the latest "bleeding edge" research right? While many experts do stay up to date, I began re-examining the people that I considered to be experts.

The professor may be the ideal example of this. While academics often get the connotation of the aloof and socially insulated genius, it's really not true. (In fact, our academic heroes are just people too, listen to the latest Nerds on Draft for that side story.) Professors have to teach the same material over and over again during their career. Sure, they should be pushing the frontiers of their field within their research group, but that's not what should be done in the education potion of the career. When you teach something, you end up deeply learning it yourself. In fact, that is part of the value in teaching! Be continually re-iterating the fundamentals to ourselves, we can stay primed to approach a new problem with a honed set of tools.

What could these fundamentals be? Well, that depends on your work. Maybe it is knowing the basics of programming or how to do basic chemical balance/thermodynamics calculations. Maybe it is knowing the fundamental operation of the product that you sell, or knowing the backstory to a concept you are helping someone with (such as the history of a topic).

I can't count the number of times that I've been trying to figure out a solution to a problem or how to build something when, after hours of no progress, something will make me start again. This time I look from a fundamentals viewpoint and can generally see a way to a solution or at least enough of the way to be able to ask an intelligent question.

Ideally, we are prepared for this way of problem solving by getting the basics of many fields during our undergraduate careers. Unfortunately that doesn't always happen. We have all sat in a math class, economics class, etc when the professor goes deep into a subject that they adore and leaves us in the dust. Another common occurrence is that the application of the fundamentals is not shown or sometimes not even implied. Not that students should be guided by the hand to the solution, but sometimes a firm nudge is necessary. I didn't necessarily appreciate this early in my undergraduate career, but later became a mass consumer of basic knowledge.

Next time you are on Amazon or in the library, browse over to a section with a topic of interest and pick up an introductory book. Read some sections, try some problems, and you'll be amazed at the other angles you can suddenly see as avenues of attack to a problem. You can even pickup some of your old text books and remind yourself of the fundamentals that all too often slip from our minds with time.