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What happens when you add art to science and engineering? Our guest in this episode is Dr. Amy McCleney, a Southwest Research Institute fluids engineer. She’s collecting data in a creative, colorful way. In addition to running numbers and poring over spreadsheets, she’s gazing at beautiful images and compiling information from pictures and videos. She says it’s one way to bring more art into the sciences. So, how does this technique work? And how can art, like photos and abstract images, transform the future of engineering and technology? Listen now to learn about an inspiring approach painting new possibilities in science.
Below is a transcript of the episode, modified for clarity.
Lisa Peña (LP): They say a picture is worth a thousand words, but our guest today says pictures can be worth so much more. She's an engineer using images to solve scientific problems, extracting data from pictures and videos. You could say it's her form of art. So how does it work? And how can this approach inspire creativity and curiosity in our future innovators? That's next on this episode of Technology Today.
We live with technology, science, engineering, and the results of innovative research every day. Now, let's understand it better. You're listening to the new Technology Today Podcast, presented by Southwest Research Institute.
Hello, and welcome to Technology Today. I'm your host, Lisa Peña. Our guest today is Dr. Amy McCleney, a Southwest Research Institute Mechanical Engineer who works with fluids. Amy recently gave a TEDx Talk. For those not familiar, TED stands for technology, entertainment and design, and these short powerful speeches or talks have become really a global phenomenon.
So TEDx is an independently run event with the same short format for each talk. So in her TEDx talk, Amy described a technique where art and science live in harmony complementing one another. It's called image analysis, extracting useful problem-solving data from images and videos. And she has a vision, a future where art and creativity blend together seamlessly with science, technology, engineering, and math for colorful solutions to our biggest challenges. Amy, thank you for joining us today.
Amy McCleney (AM): Yes. Thanks for having me.
LP: OK. So first, let's talk about what you do. So you're a mechanical engineer, but you specifically work with fluids, a fluids engineer. What is fluids engineering?
AM: Sure. Fluids engineering, it's kind of a broad, I guess, type of engineering. Basically anything to do with liquid types of flows, such as water, oil, even gas. So a gas such as nitrogen. And air, air is considered a fluid. Some people don't consider it.
LP: Oh, yeah. No. I wouldn't have thought that.
AM: No. Yeah. So we deal a lot with testing different types of technologies in those environments as well as doing computer simulations to simulate fluid flow processes. And this could be anything such as oil and gas applications, space systems, nuclear environment, literally anything you can think of where there's water or--
LP: So like medical?
AM: Correct. Yeah. Any type of application where there's liquids, or airs, or gases involved.
LP: So your expertise really stretches across a number of industries from oil and gas to you mentioned nuclear, medical, all kind of woven in there. So what types of problems are you solving for these industries when they seek you out as a fluids expert?
AM: Sure. Usually it's more applied type of study. So they'll have a piece of equipment. And maybe they're prototyping a piece of equipment. And they're not quite sure how it functions. So we'll take some fluid. We'll run it through, maybe it's a pump.
We'll test the performance of the pump under different fluid conditions, whether that's increasing flow rate, changing certain conditions such as viscosity just to see how it performs so that way they can better design their pieces of equipment. So that's just one example.
LP: So as we mentioned, you are a fan of image analysis. And I hadn't heard of this concept before until I heard your talk. But I'm really just taken with it, because it's really intriguing. So tell us what image analysis is.
AM: Sure. So image analysis is a way of doing data analysis but with pictures. So you take a pretty picture or a video, and you try to extract meaningful information from that picture or video using math.
And the reason for that is because you know a picture is, like you said, worth a thousand words. And so being able to quantify what we physically can see using mathematics to explain the world around us, it's a very powerful idea and concept.
LP: OK. So how do you get from an image to math?
AM: Sure. So an image is actually constructed of pixels. So we've heard of pixels before. So when you hear the word resolution, it's common with cameras.
LP: Yeah. I think we pay attention to that with all of our phone cameras.
AM: Right. Yeah. I mean every iPhone has really good resolution now or big Samsung phones. But with resolution, what that means is that's the number of pixels of your width times the height of an image.
So inside your image, if you zoom in, so say you take a picture on your phone and you zoom in further, you can see how it's pixelated a little bit. So in each side of those pixels is a color, so whether it's RGB (red, green, blue), that's how computers figure out what number is associated with that color.
So you can actually open up an image in computer software such as Paint, click on an individual pixel if you zoom in close enough, and see what those numbers are. And the point of image processing is to look within those numbers to find patterns within those numbers or changes.
And if we find patterns or changes that are repeatable or something that shows, I guess, changes in terms of things moving or being tracked over video, we can extract that information and quantify what we see.
LP: So you can get usable information just by measuring the pixels?
AM: Measuring the numbers within the pixels. Absolutely.
LP: Measuring the numbers within the pixels. So how accurate is this form of data collection image analysis? How accurate is it?
AM: It's actually fairly accurate. So you can get information that is what they call subpixel accuracy. So depending on how far away you are from where you're taking the picture, or even if you're really, really close to what you're trying to take a picture of, you can get a very fine accurate measurement with just taking a single image.
LP: So how often are you using this technique in your day to day work?
AM: Sure. So I work on one or two projects a year that use image analysis as part of data analysis. And it varies, but an example of what the image analysis is but an example would be flows inside of pipelines for the oil and gas industry.
AM: They're interested in learning if I have certain flows of gas and liquid, what type of flow profile am I getting. And if I have liquid droplets that are in the gas floating around, how big are those droplets? So I can take a video, a high speed video, and get information such as what size droplets are we seeing that are traveling in the flow.
LP: Is this by like zooming in on the image or just by timing how fast the video transpires, goes through?
AM: Yeah. So it's a combination of both. You don't necessarily have to zoom in super far if you can get a magnification that gives you a range of details within the image. So say if every droplet, say we're looking at the droplets, is two pixels in size, I really can't get any information from that. But if you zoom in a little bit more, maybe I can see a variety of changes within those colors that we had talked about.
So looking at that, but also looking at image sequences. So a video is just images stacked on top of each other, right? It's kind of like when you used to draw those flip books on paper and then you drew a stick figure walking across each piece of paper. And then you flipped it.
LP: That's amazing.
AM: Yeah. Basically, that's a video. It's a way to describe what a video is like. So if you take individual frames from a video, you can track things from one image to the next using both techniques.
LP: So were you taught this technique in school? Is this something you discovered along the way? How did you get to the point where you are now using it in your projects now?
AM: Sure. It wasn't something that's typically taught, or it's not something that's typically taught at university level. I very rarely have seen this. But the first time I was exposed to this type of analysis approach in general, I did a summer research experience at University of Alabama. And the reason I chose to apply to this opportunity was because they had mentioned in the brochure that I got to work with lasers. And I was like, that's pretty cool.
LP: Sign me up.
AM: Yeah. Sign me up. I like lasers. That's fun. And I didn't really know what image analysis was. You know, it was probably my third year of school and, again, not really been exposed to that kind of thing. Up to that point it was more of school was just here's an equation, plug in the numbers type of thing. Not really scientific. More research. And when I got to REU [Research Experiences for Undergradutes], I learned about this technique called particle image velocimetry and it's basically an image analysis technique. So you use a laser as a light source to illuminate what you're trying to record on a camera. So again, get to play with lasers. That's a lot of fun.
And the intent of that project was to look at improving efficiencies for your car. So can we get, how do we look at flow structures to improve how efficient a combustion is within your car cylinder? So when that happens, looking at it, typically, you can't look at that from outside, inside of a car.
You want to look at processes inside the car. You're not able to do that. So this was making something apparent by using image analysis. So that was my first exposure. And after that, I gained some skills from that.
But really what honed everything in for me was when I went to do my master's and my PhD work, which was entirely flow visualization, looking at the dispersal of jets. And the background on that was they're looking at plumes for jets that are at the bottom of the ocean, so hot water plumes.
And what these hot water plumes do is they spread nutrients along the bottom of the ocean that allow deep sea life to prosper. So we were looking at the process of how that kind of plume disperses those nutrients.
And so the technique we used, again we get to play with lasers, was image analysis, illuminating a fluid flow with a laser so we could see it, filming it on a high speed camera, and then writing computer software to extract meaningful information about the processes of how that plume transferred all those nutrients.
LP: So what did you find out?
AM: Yeah. It's actually a very complicated process. And it actually promoted more questions than anything. Yeah. It was a first step that there are a lot of conditions that go into that nutrient transfer and how turbulence and fluid flow interact with each other. It brought up more questions than really answers.
LP: But that's really neat that you were able to get to that point using image analysis--
AM: Yeah. Absolutely.
LP: --which is hugely a creative approach to, as you say, to solving a scientific problem. So that's really neat. OK. So how did you decide that you prefer to use image analysis over other techniques? I know it doesn't work for every project. But how do you decide that it's your preferred method for certain projects?
AM: Sure. Absolutely. The image analysis is unique in a way, because when you have images, it's able to store data in a simplistic format. And the reason I prefer it is because I would rather stare at a picture than stare at a spreadsheet full of numbers.
LP: Oh, but why?
AM: I know. I mean, there is this issue that has come up. It's called big data. And we hear this word big data. And what does that actually mean? Big data is we just have so much numbers and so much data we don't know what to do with it.
So using image analysis, maybe taking that information that is in our Excel spreadsheets that we have and putting it into a graphical format such as an image, maybe we can easily pull out information that previously we were able to, because all we see is numbers at that point.
LP: OK. So let's describe an image you would break down and what it can tell you. So you sent me a few pictures. And I'm really interested in this. OK. So here's what we're looking at. It's a fish. And to me, I'm just going to describe what it looks like to me, because I'm looking at a picture. It's a fish. And it has several bubbles coming up out of its mouth. And the fish is somewhat vertical or at a slant. And it looks like the bubbles are going towards a plant of some sort. But you see this picture and you see much more. So tell us what this picture really is and what it tells you.
AM: Yeah. So this picture is a picture of an animal, a fish, what is known as an archer fish. So what archer fish are known for is that they catch their prey that are insects that are typically hanging out by the leaves of trees that are overhanging the water.
LP: OK. So this little dot that I'm seeing where the bubbles are leading up, this is an insect?
AM: Yeah. Absolutely. Yeah. So the picture that I showed you is a fish that is spitting water out of its mouth trying to hit a bug that is hanging off the plant. So the bug will fall into the water and the fish will eat it.
LP: Smart fish.
AM: Yeah. It's pretty brilliant. So what's unique about this and why people study these fish is because they believe that they have some internal mechanism like chameleons that have this kind of catapult system inside of them. But when biologists have opened up these fish, they don't find that. So they're interested in trying to figure out how these fish actually do it, because the jet that they create could improve things like inkjet printing and how we print on paper as well as how we synthetically create rain for movies and stuff, so making it more like realistic.
LP: So can you extract that kind of data just by looking at this picture? Is this a picture or video?
AM: This is a video This is still image of a video. But from this image, you can extract a lot of information. So what you can do is you can take the spitting jet that the fish spits out and get information such as at the head of the jet, you see this bulge. And that shape of that bulge is the ideal shape of a rain droplet.
So using image analysis, looking within this picture and finding the pixels where those colors change, extracting the size of that, and seeing how it changes over time will tell us the underlying mechanisms of how raindrops are typically formed and how their shape is maintained.
LP: And in turn, that could, potentially, one day improve inkjet printing.
LP: OK. I think it's appropriate to say "mind blown" right now. That's really cool, because, again, I see a pretty picture of a fish, but there's so much more to be taken from that using your very creative approach. So it sounds like image analysis is just this really great way to find solutions, but is there any pushback to using this method in certain areas?
AM: I think the pushback is mainly because it's newer. And it's not well-defined and not as well known. Although it is accurate, it is, again, not traditional so people have a hard time believing or wanting to accept things that are outside the standard or norm. So I think that's what the pushback mainly is.
LP: But with more engineers and scientists like you bringing this method in, perhaps it will become the norm sooner than later.
LP: Is that your hope?
AM: Yes. Absolutely. I mean, I love it. I mean it's a beautiful way of solving problems. And so, again, I would much rather stare at a picture trying to retain information or describe information than looking at a spreadsheet.
LP: Yeah. I think you're not the only one there. Let's talk about the bigger picture here. You are a huge proponent of bringing creativity into solutions for scientific problems. And in your TEDx talk, you describe an enlightening survey of your family and friends. I loved it. So what was the survey? And what did you find out?
AM: Sure. So part of my TED Talk was to see with my family and friends what their perceptions were about people who do science types of activities or what their perception of what artists do on daily activities. And because I actually have, we both have scientists and artists in my family, I was very curious what side they pulled from.
But what I found out from that survey is people do still think that we are either right side or left sided brained, that we're one thing or the other, that scientists are intuitive. They sometimes lack social skills, that they are super analytical, or, if you're an artist, you're intuitive and that you draw from emotion and are creative, but really that there is not this great combination of two, or at least that's the perception of what my family and friends thought. And I think that's the perception of most people out there as well.
LP: So you asked them for personality traits of an artist and personality traits of a scientist. And you've got these really boxed answers of scientists being analytical and, as you said, lacking social skills, et cetera, an artist being creative and like all the happy adjectives and laidback adjectives, but what do you feel about these boxed beliefs about these two disciplines that could be problematic?
AM: I think the problem with that lies when you have someone who is both creative as well as more technical and they don't typically fit into either of these boxes. They feel like they're a combination of both of those. So unfortunately, when you try to box people into one side or the other, they think that there's something wrong with them. And they think that they shouldn't be pursuing what they're pursuing.
And that's, you know, the misconception that we're trying to get rid of is that you can be both of these things. And that's what I'm a big advocate for the STEAM movement which is STEM (science, technology, engineering and math), plus the arts, which make STEAM. And what the STEAM movement is is trying to break away from that division between arts and sciences and trying to drive those two components together.
LP: And you said that the A in STEAM is actually bigger than art. It really is creativity and a creative approach to problem solving. What are your thoughts on that?
AM: Yeah. Absolutely. I think the A should be a C for creativity but the acronym STECM, I mean that doesn't ring off the tongue.
LP: Yeah. Doesn't sound as good.
AM: Not as good. But I think there is this people interchange art and creative. So people, if you ask someone if they're creative, they think artistry immediately, but really it's not that. I think creativity is more like problem solving. And it's problem solving with relevance and novelty, relevance meaning 'Can I actually solve the problem?' and novelty meaning, 'How unique did I solve the problem?' So really the A, the art, is really drawing from that creativity in terms of did I use novelty to solve my problem.
LP: And imagine how far technology, and science, and engineering, I mean, we've already seen huge great things happening in these fields. But with maybe a more creative approach, maybe we'd see even more happening.
AM: Yes. Absolutely. And there was, in 2011. President Obama during one of his State of the Union addresses said this is our generation's "Sputnik" moment. We need to start finding ways to get our younger generation wanting to be our future innovators of tomorrow.
And that was the big push of promoting or getting students and kids more interested in STEM fields. But if you're still marketing STEM in a way that's not appealing to students or kids, it doesn't matter what you do. They're not going to be interested. So finding creative ways and ways that make them interested is really what should be focused on in the STEAM movement.
LP: So I really loved one part of your TEDx Talk in particular and you told the audience what you wanted to be at six years old when you grew up. And I thought it was just the best. So would you mind sharing with our listeners?
AM: Yeah. I told the audience that when I was six years old, I wanted to grow up to be an astronaut-ballerina-dolphin trainer. And it's true, which is amazing.
LP: Yeah, why not.
AM: Yeah. And you know, I didn't understand as a kid why no one wanted to be that, because I thought that was awesome. That's an amazing career.
LP: Totally legit career path.
AM: Yeah. So you know growing up, I think all kids go through that where they want to be these multiple things and they can't make up their mind. And I mean, I think that's what's beautiful about kids' minds.
LP: Yeah. I think it's your wanting to be an astronaut-ballerina--
AM: -dolphin trainer.
LP: --dolphin trainer, yes. I think it's just really telling of how a child's mind naturally blends the two disciplines together of art and science, creativity and science. I mean, why not? And kids, I think, are so interested in doing experiments and trying out experiments, because they can be messy and fun.
And I think it's all in the same area there of bringing in that creativity to really open their minds and help them want to engage more in the STEM fields, so I think that's a great approach. So what would you like to see children learning around creativity and science?
AM: I guess in terms of getting kids involved in science, it really is based on what interests them. So say a student or kid is really interested in cooking. Maybe it's talking to them and giving them a real world experience of how the science of fat, salts, and acids go together to create this beautiful dish or even ask eating them how would they describe the perfect grilled cheese, and then going into the science of how you make a perfect grilled cheese. You know, it's finding out what they're interested in integrating that into something that they can take away from.
LP: Do you know off the top of your head what the science is in a perfect grilled cheese?
AM: I mean, I know what I like.
LP: It's ooey gooey.
AM: Yeah. The ooey gooeyness and the butter. I mean, more butter the better.
LP: Right. So basically, you think the best-case scenario is to really see this blending of science and creativity. Doesn't have to be two separate things, two different conversations. You can have one conversation about both.
AM: Yes. Absolutely. I think it's good to bring both into the picture. And as we grow older, we lose that novelty that we had as kids, by novelty again being unique thinkers. You know, wanting to grow up to be an astronaut-ballerina-dolphin trainer, no one told me that was wrong.
So solving problems when you're a kid, you get more creative. So honing in on that creativity when you're young and letting them know that that creative thinking process and that critical thinking process to solving problems is good, and that it should be expanded upon is the way to go.
LP: So what was the road from astronaut-ballerina-dolphin trainer to mechanical engineer? What was the in-between there?
AM: Yeah. No. There is a big jump. From wanting to grow up to be multiple things, I, in high school, I thought I was going to be a band director or even create marching band field shows. And I mean, I think about it now, and that's kind of a mathematical process on its own. But I've always been interested in astronomy.
And I think it was because I saw these beautiful photographs of our universe. You know, you always see beautiful pictures of the Milky Way, and Saturn, and in the planets. And I was like, yeah, that's really attractive. And I guess pulling from that wanting to be an astronaut it pushed that a little bit forward.
But I decided to go into aerospace engineering because I thought, I was like, yeah, that's a great way to get there. But what I found through that process is it's a lot more than that. There's a lot that goes into it.
But I found my own way and my own unique way to make the experience of engineering fun for myself. And that was through finding this path of image analysis, making it more creative for me and making it appealing for me.
LP: Yeah. I love that you did that, because you don't think of mechanical engineering and fun or creativity. But that's really telling that how you're able to pull in your own techniques to make it a creative process.
So do you have any tips for parents, or grandparents, or even big brothers and sisters listening to help us encourage creativity and curiosity in young minds? I know you just gave us some really great ideas, but anything else off the top of your head?
AM: Yeah. I think doing hands-on activities with kids is always a good idea and then explaining the science behind it.
LP: I think for those of us not science minded, that might be the challenge, but there's so much information available nowadays.
AM: Oh yeah. I mean, online, you can find anything on Pinterest, or any social media outreach on STEM or STEAM activities for kids, whether it's creating a rain cloud with food coloring and shaving cream--
LP: That sounds fun.
AM: --yeah, there's a whole bunch of stuff out there that is really easy to do with your kids and explains a lot about everyday real world problems.
LP: So it's kind of a challenge for parents too to get a little bit more creative in how we approach science with our kids.
AM: Yes. Absolutely.
LP: I've really enjoyed hearing about your ideas and your approach to solving these big scientific problems with a touch of creativity. And as a parent to a kindergartner, I think you really got me thinking about how to bring in some more creative moments into his learning which is great. So thank you for joining us.
AM: Thank you.
LP: And that wraps up this episode of Technology Today.
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