Modeling The Physical World

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59 Responses

  1. Avatar Aaron W says:

    What kinds of systems do you normally construct models of?

    I do chemical transport and kinetics myself.Report

    • Avatar Oscar Gordon in reply to Aaron W says:

      I’m general fluids & heat transfer, although I dabble in structural mechanics & I’ve done kinetics & kinematics, but not anytime recently.

      I understand the basics of chemical transport, but I’ve never built or used a model of it.Report

      • Avatar Aaron W in reply to Oscar Gordon says:

        Cool.

        Just a couple small quibbles. I’d hesitate to say that statistical mechanics relies on a “statistical model” even though they both have the word statistical in them. It’s more about the statistics of having a large number of individual particles that can all carry different (but random) states such as the spin of an electron, etc. Many thermodynamic properties that we’re used to talking about such as temperature, enthalpy, etc. are simply the average of an ensemble of many, many states. This differs quite significantly from a “statistical model” of a system where you observe x over and over and use the average to predict x in the future. These kinds of models lack explanatory power in a way that statistical mechanics (quite elegantly imo) has in spades.

        Also, this is really, really minor, but some of your equations are uuuuugly. I know they’re just shown for shock and awe purposes, but something about italicized sine and cosine makes me shudder.Report

        • Avatar Oscar Gordon in reply to Aaron W says:

          Perhaps I’m wrong but I was thinking statistical mechanics is along the same vein as turbulence modeling, in that observed behaviors are used to form a model with significant predictive capabilities, but the model itself is an imperfect representation of the phenomena. Which is why there are numerous turbulence models, each being quite useful within certain domains, but not perfect & even less so outside of the preferred domain. Of course, direct numerical simulation of the turbulent structures is possible, just massively expensive.Report

  2. Avatar DensityDuck says:

    Note that CAD models can be wrong, as well.

    Obviously there are machining rounds (the rounded insides of corners cut by an end mill), but a sufficiently-detailed CAD model can imply a level of design maturity that doesn’t exist. It’s a bit of a Catch-22; people always say they want to see a fully-detailed model, but they’re used to only seeing that level of detail in the final fully-approved model, so when you show it to them they respond to Obvious Mistakes as though you’d already sent the part down to the shop and were cutting metal.Report

    • Avatar Oscar Gordon in reply to DensityDuck says:

      When it comes to modeling the interaction of CAD to a physical situation, sometimes the detail is important, sometimes not.

      If I’m modeling an airplane in flight, all the joints and seams are not important to the overall analysis, as I can apply a drag factor to the analysis to account for their assumed presence (the factor which is usually determined by experiment). If I’m doing a structural analysis of the geometry, then the fact that a given corner is rounded instead of a defined angle is potentially critical to the analysis & has to be represented in the CAD.Report

  3. Avatar Stillwater says:

    Oscar,

    Interesting stuff. I think, anyway. 🙂 I wish I could make a substantive comment about it.

    keep in mind this is primarily for the uninitiated.

    {{Gulp!}}

    One thing I can say is that some talking heads on the TV the other day were discussing whether a robot could play a round of golf better than a human. I found that interesting, actually, since a robot would be less likely to make egregious errors off the tee, which means on balance the Bot would be hitting successive shots from +/- uniformly flat lies and eliminating most of the “problem” shots where human creativity and feel are required.Report

    • Is the robot constrained to a fixed configuration? If I were designing a robot to hit the ball consistently well, I would design the lower part to allow for reconfiguration on the spot. In effect, the robot would never have to adjust its swing for having the ball sit higher or lower than its feet, or with one foot higher or lower than the other. Just reconfigure so that the part that strikes the ball is always level and always in the same position relative to the ball.

      How much of the total game is the robot responsible for? Good golfers play the hole backwards in their head — I want to putt from here, and the best chance of getting the ball in that position is to hit a fairway shot from here, and that means I have to hit something other than driver from the tee. Is the software going to be responsible for that overall planning?

      And of course there’s the whole issue of playing against an opponent, not the course. The summer after my junior year in college I was in a position to hustle some doctors and dentists on Wednesday afternoons. On more than one occasion, with confidence in my ability to hit certain shots and the situation one of the other players was in, I hit the ball into a sand trap on purpose. Or missed a putt intentionally. There’s a lot more to the game than the mechanics of hitting the ball.Report

      • Avatar Stillwater in reply to Michael Cain says:

        They didn’t really get into it all that deeply. The conversation started when one of em said that robots have perfect swings everytime and the conversation just sorta took off, briefly, from there. But if I were to design a bot golfer, I’d certainly allow it to adjust it’s “feet” so’s it’s “torso” was level and plumb every swing. I mean, why not?

        You bring up an interesting point about shot selection tho. My guess is that advanced enough programming could allow for a robot to compute the odds-to-par/birdie correlated with the right miss given the contours of a green and bunkers, etc. But really, you’d know more about that than I do. It just seems like something that could certainly be done.Report

        • Certainly this falls within the scope of AI planning problems. Those can get “hard” in a hurry, especially if you need an optimal solution and it turns into a search problem. For this, easy approximations are probably sufficient.

          Sometimes even the best players fail to do this. Some years back, a guy came to the tee of the last hole of the British Open leading by three strokes. It was a nasty par four, but all he needed was a five to win (according to rumor at the time, the jeweler had already started engraving his name on the trophy). The right strategy was hit a five-iron into the fairway, hit another five-iron to the front of the green, chip on, two-putt. Hell, I could have played it for five when I was younger. Instead the guy hits driver into trouble, then tries to make a very difficult shot to recover and gets into even more trouble, ends up with a seven and loses the tournament when the last player in the group behind him makes a lucky three.Report

    • Avatar Oscar Gordon in reply to Stillwater says:

      There is a pretty good mathematical model out there for golf ball flight. It’s much more complicated than simple ballistics, but it is easily programmed into software.

      Of course, for a robot to hit the mark consistently every time, it needs to know more than the target. It also needs current, real time weather conditions. We do that by watching the flags on the course, and trees blowing, etc, so the robot would need a similar sensor suite and the software to process that data, along with game strategy.

      Oh, and thanks, I hope it was at least informative. If I get good feedback, I’ll dive into this all a bit further.Report

      • Avatar Stillwater in reply to Oscar Gordon says:

        Oh, I love the basic idea here, Oscar. (Emphasis on “basic”…)

        Wind was one thing I thought of too. Usually the caddy provides that type of info to the golfer to limit the amount of calculations the person swinging the club has to do, and I don’t know exactly where the caddy gets that information from. Presumably, by reading the weather reports before the round and looking at flags and tree-tops and such. Seems to me it’d be harder for a bot to make correct adjustments based on wind than on “correct miss” given a pin placement and contours of the fairway, etc. It may be one of those things a human can intuit based on years of experience in varied conditions.Report

  4. Avatar Jaybird says:

    Reading this made me think primarily “i r dum” but, after that, various questions about how, given the limitations of models, how that sort of thing would best be gamable in practice.

    I mean, I grew up being taught that the ancient Hebrews had a tradition with regards to prophets where a prophet who got something wrong would then be stoned to death as a false prophet. Fair enough. This, of course, resulted in prophets who specialized in saying that stuff would happen years and generations in the future. Okay, that makes sense. So the religious leaders wrote this stuff down. Well, the scrolls that wrote down false prophecies were then burned.

    As such, the prophecies that were really oblique and relied heavily on metaphor were the ones that stuck around.

    I suppose arguments could be made that these prophecies came true, of course.

    Anyway, in that sort of vein, we know that models that are proven incorrect in short order are then scrapped as not being accurate.

    Wouldn’t this reward the models that can’t be falsified for a good long while or the stuff modeling stuff that isn’t easily verified?Report

    • Avatar Kim in reply to Jaybird says:

      Yeah probably. Except nobody FUNDS models for years upon years in the future.
      Climatology (the study of “what will happen this year” rather than what will happen tommorrow) is all about predicting next year (and the next century, but if you can’t predict next year to a decent degree, you throw out the whole model).Report

    • Avatar Oscar Gordon in reply to Jaybird says:

      @jaybird

      Note my three rules at the bottom. One of the reasons I wrote this post is because of climate change. I’ve talked with lots of climate modelers, and while I can often be critical of the assumptions made in their models, I can be critical of them because they are clear about what assumptions were made and how problematic those assumptions potentially are. They are also, outside of the climate darlings, annoyed with how often their own discussions of the limitations are ignored or glossed over in the media. From what I hear, a lot of medical researchers have similar issues with media reporting on studies.

      So for everyone here, there are always assumptions, always room for error. We address that error through additional studies and by playing with the variables & constraints. Modeling gives us a lot more flexibility for those variables & constraints at extremely low cost.Report

      • Avatar Kim in reply to Oscar Gordon says:

        Oscar,
        I’m certainly quite furious when people report that “it was the hottest year on record” and forget to mention “not statistically significant”. That’s Just Wrong.

        “The worst thing about real-time modeling is when you do it too well…”
        “How’s that?”
        “My colleagues say, ‘it’s running wonderfully, but there’s all these heat alarms going off…so many warnings…'”Report

    • Avatar Aaron W in reply to Jaybird says:

      @jaybird The limitations you’re speaking of often give modeling a somewhat bad reputation in the scientific community. As such, new models are often treated with a large dose of skepticism (with good reason). Anyone who actually knows what they’re doing will carefully spend time trying to validate their model against several different experiments under different conditions. Even then, scientists or engineers are still human. Sometimes people tend to fall in love with their model and forget that reality is the ultimate arbitrator of right and wrong.

      For example, I reviewed a paper once where they only measured one thing in an experiment (the loss of a chemical from the surrounding gas). However, the associated model had 15 to 16 different parameters they had to fit to get the “right” answer in several different spatial areas within the experiment. They used this complicated model because they had in several other papers that had more measurements to test it against. Despite being somewhat based on an experiment, several parts of their model were also based off assumed physical quantities that you could never verify with an experiment. (In fact, they referenced their own models (!!) as justification for the values they used.) Suffice to say I reviewed it (in brief) as “This is over fit, can a simpler model capture the experiment without so many assumptions?” That one didn’t go over very well. 🙂

      Anyway, the point of that long-winded anecdote is that you’re right that some modelers do make assumptions that can never be proven wrong as part of their models. Sometimes it’s a necessary evil (see this entire post above), but good modelers know that they’re making assumptions. Thus, they are much more careful about claiming those assumptions ARE assumptions, and not letting their ego get in the way of recognizing when they’re flawed. And knowing the kinds of compromises you have to make to get a model of a physical system to work, I can’t even imagine trying to do something like an economic model, which has way too much chance of subtle bias creeping in.Report

  5. This is a bit OT, but I have a sciencey question I am hoping someone can answer.

    Due to a set of circumstances I won’t get in to, we had a room at the old house that was full of baking soda and moisture (via humidifier). Somehow, the combination of the two (along with the friction in my pocket) just ripped the paint off my ecigarette batteries. Most of my other batteries have nicks of paint missing here and there, but that one has almost all the paint off of it. (Didn’t all come off immediately, but fell off very quickly).

    Anyway, I would actually kind of like to be able to reproduce this, but I can’t seem to. So it’s not just the baking soda and moisture, but I can’t think of what else it is.

    Anyway, can anyone give me some “idiot’s guide” insight on the chemical reactions of baking soda, moisture, and paint?Report

    • Avatar Christopher Carr in reply to Will Truman says:

      Just a guess, but paints usually contain ionic compounds that serve as pigments (http://www.compoundchem.com/2014/03/21/inorganic-pigment-compounds-the-chemistry-of-paint/). What color was your paint? I can probably guess exactly what reaction occurred if you tell me what color.

      In any case, the pigment probably reacted with the baking soda. Ionic double displacement reactions usually occur in water, since this medium allows for the interactions that allow the reaction to take place, which explains why the moisture accelerated it.

      Actually, “baking soda blasting” is a technique that’s sometimes used to remove paint. Baking soda’s chemical formula is NaHCO3. “Washing soda”, more commonly used for paint removal, is Na2CO3 – so, just one hydrogen different.Report

    • Avatar Chris in reply to Will Truman says:

      Baking soda in water is actually a great paint remover, especially from metal. When I was a teenager, I helped a friend of mine’s parents renovate their historic home, which included restoring a bunch of painted metal fixtures. We would boil baking soda in water and then soak the metal in it for a few hours, and the paint could then just be brushed off.

      I don’t know why this works, chemically, but a quick tour of Google suggests it’s a common method, particularly for antiques.Report

  6. Avatar Chris says:

    Dude, I’ve been looking forward to this post for the, what, 2 or 3 years you’ve been hinting at it? 😉 Can’t read it now, but I’m excited to read it later.Report

  7. Avatar Burt Likko says:

    Well, I tried to read the post anyway. It became a bit too complex for me about halfway through, so all I can say is “D-a-a-a-amn.” Both in response to the author’s obvious intelligence, care for, and love for the subject matter, and in response to my relief that I did not pursue an academic career path of the hard sciences because I’d have washed out.

    I can say that while I don’t know if @r-tod plays golf, I do, and the mathematical model of what happens when club strikes ball is so arcanely removed from the physical discipline required to cause contact with the ball to happen just right on a reliable basis that I must admit of little ability to understand whether, and if so how, my puzzling through the technique described in the post will help me keep my head down and hold my left arm straight during my swing.Report

    • Avatar Oscar Gordon in reply to Burt Likko says:

      Burt,

      You’ll have noticed that I completely avoided the whole bit about the swing itself.

      If we put you in for Tod, all the HD video we have of your swing would allow us to look at your swing, find the error in it, and help you adjust. After that, it’s practice & presence of mind until you’ve acquired the relevant muscle memory.

      I suppose we could wire the cameras up to a computer that looks for your head held too high and your arm bent, and then gives you a zap every time you fail to meet the criteria. Pain is a wonderful motivator.Report

  8. Avatar Tod Kelly says:

    If your model has Tod hitting the ball with the club in any way at all, it’s fairly flawed.Report

  9. Avatar Oscar Gordon says:

    There was a reason I picked golf, and that is because golf balls don’t conform exactly to the ballistic equations, certainly not the way a heavy artillery shell, or even a rifle bullet would. Those dimples affect how a golf ball flies, and while the ballistic equations are a good approximation, an accurate flight path would need to be informed by the local weather.

    One thing to note for this. The shape of the object in flight is important, but so is the object’s other motions.

    So a cube in flight behaves differently from a sphere in flight, which behaves differently from a bullet shape in flight. Set the items spinning and things change radically. A sphere spinning on an axis perpendicular to the path of flight will acquire some lift and push it off the ballistic flight path (Magnus Effect). If it spins on an axis aligned with the flight path, it will resist the effects of a cross wind and tumbling (spin stabilization – which is why gun barrels are rifled, so the exiting round is spin stabilized).

    All of this is to say that when a model is being built, missing a detail like this can throw your model off, usually by quite a lot. Which is where the skills of an investigator/troubleshooter come into play – figure out what was missed or assumed incorrectly & correct for it.Report

  10. Avatar zic says:

    Just wow.

    Before I bashed my head in, I would probably have been able to learn and understand this stuff; math was my thing. After (and it’s 40 years after,) I’m still constantly relearning how to speak; which is probably why I gravitated to writing.

    My kid does some modeling as he designs quad copters; particularly important for calculating propeller angles and programming the flight controllers. He’s working on a new one now, the last model dropped in a lake last week; battery problems. The radio he used has a battery level monitor, but the user interface has it several pages in, and (I don’t understand this,) it only works in a short range but not over the range that the radio actually works to control the copter.

    It was a painful (and expensive) learning experience; one that will probably lead to him learning how to program the radios himself.

    Good to have a mad rocket scientist around the house.

    Thank you, @oscar-gordon , I’ll re-read this tomorrow, and work the wiki, see if I can better understand it; tonight, it’s all greek to me.Report

  11. Avatar James K says:

    Good stuff Oscar, there is some particular trickiness with social science modelling, but I recognise a lot of the general principals you’re describing.Report

    • Avatar Oscar Gordon in reply to James K says:

      Oh I imagine the variables & assumptions at play with social science modeling are a lot more ambiguous than what I deal with, even if the equations are usually more straightforward.

      And thanks!Report

  12. Avatar Morat20 says:

    What I do for a living is, in fact, modelling software. (Mind you, I’m not an engineer. I help implement their crazy mad engineering and science into software that does said mad science for another engineer who then decides if they can do X without it breaking off and killing someone).

    One tidbit I’ve learned over the years is there are different TYPES of models for even the most basic problem. Say, for instance “Part of my engine has a crack in it.”. There are models that you can use to help determine how the crack started (complex ones that model all the bits and slowly — oh god so slowly — map out stresses in ridiculous detail and show you exactly what pressures did what). There are ones that ignore HOW it started and instead tell you whether it will grow — or how fast it will grow. Others will tell you how long you can do “something” to a specific bit of metal before the odds of it developing a threatening crack happen.

    So I’ve seen guys model stresses on parachute parts for re-entry capsules (answering critical questions like “If we do this, will this piece of metal snap like a twig?”) and guys basically saying “If there’s a crack on this bit of an airplane, but that’s too tiny for our inspections to see, how long can we fly this part without worrying about that crack growing to the point it can break it?”

    I’ve also seen guys get really, really, really excited about aluminum. I mean really excited. I wonder if that’s how I sound when I find a new tweak for a machine learner.Report

    • Avatar Oscar Gordon in reply to Morat20 says:

      This is one of those areas I hope to tackle in a future post.

      And you & I have similar jobs.Report

    • Avatar Oscar Gordon in reply to Morat20 says:

      PS for those interested, @morat20 is talking about fatigue analysis & crack or fracture propagation. Things structural Aero is always concerned about & is devilishly tricky to model (although we have good ways to predict it on structures we have lots of experience with).Report

      • It’s kind of amusing that we have extraordinarily thorough models to analyze fatigue in a piece of metal, but blithely assume that a guy working 80 hours a week gets twice as much work done as a guy working 40.Report

      • Avatar morat20 in reply to Oscar Gordon says:

        I’m only in it for the fun videos of stuff failing under load. 🙂 Double points if the thing is filled with water so there’s a massive splash.

        One of the NASA branches (don’t remember which) was testing some fire extinguisher for use on Station (water with compressed air as a driver) and as part of the test process, they tested it to burst. (I think it required something like 3 times the mandated maximum load to produce a burst vessel).

        The video of the burst is…impressive. The bottom of the vessel leaves an impression in a concrete wall ten feet away.

        Pressurized vessels are nothing to fool around with!

        But yep. Lately it’s been crack growth, metal fatigue, and other fun stuff like that. (And man, some of the software is expensive! Do you know what an Abaqus or StressCheck license runs? Good god!)Report

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