I smell a (lab) rat June 25, 2014Posted by mareserinitatis in engineering, research, work.
Tags: engineering, hardware, lab coats, lab work, simulations, troubleshooting
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There are times in one’s life when we have to reinvent ourselves. This has been one of those times for me.
I’m turning into a lab rat.
I’m much more comfortable in front of a computer, designing simulations. I vastly prefer debugging programs to troubleshooting hardware.
ESD jackets look fugly on me. (Okay…I know they aren’t flattering on anyone, but it’s yet one more annoyance with the whole ‘working in the lab’ thing.)
I hate taking data.
However, whether I like it or not, I’ve been stuck in the lab for the better part of a month. My student left a month ago, and that leaves me to do a lot of the testing and troubleshooting on the latest project. I had hoped she’d be here through the end of the month, but she decided a post-graduation job was more important. (I can’t say I blame her.)
I really miss running simulations.
Beautiful, elegant models March 27, 2014Posted by mareserinitatis in engineering, geology, physics, research.
Tags: engineering, interdisciplinary research, modelling, models, simulations
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I’m interested in the different uses of the word model. Of course, the most common reference (outside of science and engineering) is to someone who wears expensive clothes. Upon encountering such a model, most of us in the sciences and engineering wonder how they could charge so much for so little fabric.
In science and engineering, however, I’m discovering that I don’t like the use of the word because it’s ubiquitous and therefore nearly useless. The problem I’ve run into is that everyone uses it but not necessarily for the same things. In one field (or to one person), it means the equations describing a phenomenon. In another field, it’s a computational model incorporating those equations in a specific configuration. In yet a third field, it can describe a computational framework. Then there are models that are simple calculations to describe inputs and outputs of a system. And finally, I’ve also heard someone refer to it as a non-quantitative description of a process.
I’m slowly realizing that a model depends on what you and your field emphasize. It’s used to describe an abstraction or an idea of the process, but what you’re describing as a model is extremely dependent on your training.
I think I may go back to using it to describe the walking mannequin.
Review me, critique me, pan me, print me March 14, 2013Posted by mareserinitatis in engineering, papers, research.
Tags: computers, engineering research, papers, research, simulations
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One of the first things I remember asking my MS advisor was how much detail should I include in a paper for publication. He said to make sure there was enough for someone else to replicate the work. When reviewing papers myself, I also look at this as one of the major criteria for publication.
I have tried very hard to stick with this rule of thumb, though there are things I overlook. Given most of my work is simulation, I sometimes forget that there are certain things which I tend to always do in my work, and not everyone does. Or maybe there’s a setting I never use and so the default stays in place. However, someone else may have a different default for that particular setting. And on and on. Regardless, I do my best.
The past couple weeks, I’ve been working on a new set of simulations. I’m basically taking widgets that other people have designed and seeing if I can use them for a particular, and somewhat unusual, application. I think it’s a rather interesting approach to the problem, but I keep getting mucked up. The reason is that several of the widgets I wanted to use are not described adequately in the papers. I’m not talking about some esoteric setting: some of these papers show widgets that don’t give physical dimensions of any of the parts! I have come across three different papers, all suffering the same problem.
I have decided that these papers are going in the round file. I was, at first, inclined to write to some of the authors of these papers and see if I could get some clarification. However, after encountering the third one, I decided it wasn’t worth the effort and decided to use papers from people who are more careful. I’m lucky in that there are several approaches to making these widgets, so I can be picky. That isn’t always the case, however.
I’m sitting here wondering first why the authors didn’t think to include this information and, second, what were the reviewers doing?! It’s not like these are complicated widgets with a million parts. Is it just my field of research? Am I the only one who replicates other people’s work? As much as I think peer review is awesome, I kind of feel like some people have fallen down on the job. It makes me appreciate those third reviewers that much more.
Model Building: the integration of art and math January 3, 2011Posted by mareserinitatis in computers, electromagnetics, engineering, math.
Tags: emag, models, simulations
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As time goes on, I am really enjoying the trend toward more sophisticated computational tools. (Although I should also specify that I’m not crazy about the increasing prices…)
In elementary school, I learned to program in basic. I was using a TI computer hooked up to a TV. I had a tape recorder to save my programs. My first program was to make a ‘christmas card’ complete with blinking lights. This was an example program in the TI Basic text that came with the computer. I also very quickly learned the meaning of the word ‘debug’ because some of the flashing lights were floating above the Christmas tree rather than on it.
As I went through high school and then college, I picked up other variants of basic as well as ForTran and C. I found programming to be relatively boring, however, unless I got to do something related to graphics. Being able to create images, and better yet, make them move, made programming a lot of fun for me. I think my favorite formal programming experience was learning Logo, where I used it exclusively on old Apple IIs to draw pictures.
To be perfectly honest, however, drawing pictures doesn’t have the same fascination for me as it used to. I really enjoy it, but I like kicking it up a notch.
When I build my models for electromagnetic simulation, I start by creating a physical model. It’s drawing, but it’s mathematical. Items have to be a precise size, so more often than not I have to spend time doing some arithmetic to get things to fit together correctly. I draw shapes, but I may have to modify them using a mathematical relationship to the other objects.
More fun than drawing, however, are building models where you can change the dimensions of various objects. Take an interdigital capacitor, as an example:
There are a lot of things I might like to change if I were to model one. How long are the fingers? How wide? How much spacing between them? What thickness should the substrate be? Or superstrate? How about the metal?
An IDC is a simple example. When I model something that has several parts, I have to make sure that I create every part with a set of variables that will change the part dimensions if the model changes. This is where the math can be very fun. If I take my capacitor example above, I have two parts. If I change the finger lengths, I will have to change the position of one or both of the pieces that bridges all the fingers. The bridge piece will change length if I change the finger width. And if I change the gap, both the position and length will change.
Debugging these models can be fun, as well. I am often amused if I forget to apply a variable to a dimension that is supposed to change because the results will look very strange. If I don’t change the position of a bridge piece when I change the length of the fingers, it’s going to be a big mess. But it’s fun to see what the mess looks like.
Anyway, building models is a very fun aspect of my job, one that requires both math and a bit of artistry. It requires an eye for detail, because you need to make sure that everything lines up as it should be. The fact that I get to apply electromagnetics to it is a bonus.
Repost: Simulations December 29, 2010Posted by mareserinitatis in computers, electromagnetics, engineering, geophysics, research, science.
Tags: electromagnetics, simulations
After reading this post and participating in the discussion, I felt that perhaps reposting this from the old blog was in order.
After posting this morning about how I hate computers, I figured I should temper that.
One thing I hear an awful lot of is how people don’t trust simulations. (They also don’t trust math, but let’s take one thing at a time.)
An awful lot of science can be done through simulations. However, as soon as you tell someone that you got your science out of a computer program that feeds you data or makes pretty pictures, you may have just said you made science with your kid chemistry set and drew your data in crayons.
Skepticism about computer methods is a good thing as long as you know where to draw the line. A couple years ago, I went to a tutorial session on different computation methods used in electromagnetic compatibility (EMC). At the end of the tutorial, a spontaneous discussion about the reliability, drawbacks, and validation of simulations came up. I’ll summarize some of the main points and talk about how I have addressed them.
I guess the first thing to address is that there are many different methods to simulate things, and these methods have drawbacks. As an example from electromagnetics (EM), folks often use something called Finite Element Method (FEM). FEM is not unique as an EM tool…it was actually first developed to examine mechanical engineering problems (think stress and strain). It works very well for electromagnetics as well, with one caveat: whatever your modeling needs to be enclosed. If you don’t have an enclosed area (say a shielded box over a circuit), FEM can’t mesh space infinitely. There are methods that have been developed to deal with items when are radiating in open-space. One is called a Perfectly Matched Layer (PML) which matches the impedance your radiator sees at the edge of the space and then attenuates the field beyond that area.
I give this example because, as someone who has worked with antennas using FEM-based software, it’s important to understand these things. I didn’t, at first, and it took a lot of work to figure out if the software was even simulating correctly.
How did I do it? I used a method that everyone who is a good simulation researcher does: I validated my simulations. In antennas, I started out by modeling simple known devices to see if the results matched the theoretical value. Since the equations to compute these values are based on the same equations as the theoretical value, they should be pretty close. Next, as my devices increased in complexity, I used another computational EM code called Method of Moments (MoM). MoM is awesome because it works differently than FEM. FEM jumps straight into calculating fields while MoM calculates the currents on an antenna (for example) and then is able to compute field at any given point. Once I was able to get simulations that matched either an analytical result or the other code, I could be fairly certain that I’d gotten the kinks out.
Researchers in other areas (say, global climate change) validate as well. While I would assume their approach would have to accurately reflect any analytical results, they can validate more complex code by seeing if their code generates something fairly similar to actual events and known history.
The final step for validation, in my experience, is to take the code and run it using an example of something more complicated. Usually, this is the point where you start looking for interesting journal articles to reproduce.
Now, in all fairness, I know that people don’t always follow these procedures, which is where I believe people should start to be skeptical of results. In fact, the last step of validation can be the hardest even though it’s probably the most important. I know that in my short life time in computational electromagnetics, I’ve had the misfortune of coming across papers which predicted a result, but it’s totally different from my results. In a couple cases, I ended up writing authors to find out that they had misprinted some dimensions on something On the other hand, you don’t want to pursue that route until you’ve exhausted all your other options. In my case, moving part of a device by a just a few millimeters (at high frequencies, a significant chunk of a wavelength) changed the resonance frequency of the entire device. That’s why learning how best to utilize built-in placement functions rather than hand entering things is preferable.
However, those papers aren’t all that common (I hope…but I can say I haven’t hit too many). More often than not, good researchers have tried to test their code to make sure it is accurate and representative of that which they are trying to model. They have also reproduced previous known results to show that their method is sound.
The next time someone tries to tell you it’s just a model, you can reply by asking them how much they know about code validation. If you read this entire post, there’s a good chance you’ll know more about it than they do.
Things you don’t learn in physics December 3, 2010Posted by mareserinitatis in electromagnetics, engineering, grad school, physics.
Tags: engineering, physics, sea urchins, simulations
One of my fondest memories of doing my masters degree was when I decided that I was going to prove out everything I’d been doing in my simulation work. This was in the days before we had our nice anechoic chamber.
I’d been designing antennas, so we took a cartful of antiquated equipment into the middle of a field, where we would ostensibly not have a lot of electrical interference and took a bunch of patterns.
When I got back in and was able to analyze my data, I realized that aside from the fact that my pattern seemed somewhat misaligned, I had something that looked like a sea urchin.
For all the effort of dragging that equipment around, it turned out that I’d managed to ruin things by having my cell phone in my pocket: it operates in approximately the same frequency range as my antennas, and the spikes were probably the phone attempting to communicate with a nearby tower.
That was the day that I learned how difficult it can be to go from equations or simulations to making something that actually works the way you want it to. When working on my undergrad in physics, I became adept at looking at analytical solutions at things, but I don’t think I got a really good handle on what they really meant by “ideal case” until I went into engineering.
Right now, I’m starting on a new project that involves building certain package components. I was discussing the project with the engineer who will be designing and building them (where I will be modeling and possibly testing them). We were discussing the electrical properties of one of the objects, and without thinking, I reached up, grabbed a textbook, and started discussing the theoretical behavior.
He made some comment about “that’s how grad students think”.
We have this problem. He is not sure what his yield will be, so he wants to make a whole bunch of variations on the design. However, each variation for me means a lot of time in terms of building my models and then computer time running them. I’d really like to keep my modeling work limited to about a dozen designs so that I’m not sitting there, still working on things next year at this time.
But when you build these devices, you don’t know what your tolerances are and how difficult it will be to make the device to spec. There may be problems with process, and so you may have to make a lot of changes to see what works.
It’s almost certain that it’s not going to operate the way it does in the simulations.
Sometimes it makes me think that modeling is a somewhat useless exercise. On the other hand, who has time to build and test hundreds of devices to see what works? (I mean…other than people like me and the other engineer.) At least with modeling, you have an idea of how it should work, and you can also tell if something is really amiss. Once you understand how your device deviates as it goes from simulation to testing, you may have something that makes it easy to predict what device will work for a particular application. Or maybe you can incorporate more and more of the real-life dynamics that affect your device into your model to make it increasingly realistic.
The scientist in me loves to come up with predictive models. It’s just that sometimes I feel like I’m trying to develop a perfectly spherical sea urchin.