 Yes, I'm happy to make the next introduction. So it's my great pleasure to introduce to you all, Dr. Sadegh Dabiri, like the other talks we heard this morning, Dr. Sadegh Dabiri has recently been promoted to associate professor and I want to compliment the organizers, Arvin Ramon and the others putting this together because I see a lot of interesting themes that are really part of the cohort we are honoring and hearing from today. We just heard from the previous speaker, some of the outside, the purely academic support that Sadegh and this has provided and that network of others from a similar cultural background, I think is important to support one another. Like all of the candidates, Dr. Dabiri has a background in mechanical engineering. So maybe this is a case where mechanical engineers can branch out and go into research in a wide variety of areas. Dr. Dabiri has an appointment in agricultural engineering. He has a partial appointment in mechanical engineering as well. And really the connection to Carson in the first presentation is Dr. Dabiri's research focuses on developing computational tools for analyzing complex flows in fluid systems. So much more on the theoretical and on the fundamental and small scale with large scale applications that sort of compliment the experimental work that we heard in the first talk. I want to echo the previous introducers, congratulations to this entire group of fairly new associate professors, congratulations all of you on all that you've done and all you've accomplished to get to this point. And I'm happy to turn the floor over now to Sadegh Dabiri to talk about how modeling tools can have applications not only in combustion as we heard in the first talk but in a wide variety of other areas as well and applications where fluids are important parts of engineered systems. Dr. Dabiri. Thank you Dr. Bojair for introduction and for kind words. I have prepared an overview of the research that's going on in my group. They all fall into the same area of multi-scale modeling of multi-phase flows but the wide range of applications. Before I start, this is basically my current research, my current students and Adonai, who basically have done all of the work, all of the hard work for their, and the results that I'm going to show you some of them. So our group is mostly focused on basically modeling of multi-phase flows, mostly gas liquid flows with different type of applications. We look at the transport and mixing, look at the cavitation, flow boiling for basically cooling off the microchips and for surface phenomena, like we're looking at the effect of surfactants in basically in a two-phase flow environment and we are mostly focused on the very fundamentals but sometimes we look at the applications and the first thing I wanna talk about is cavitation. So this is mostly not this video that we will see. Usually it's not associated with cavitation but the phenomena that happens is actually cavitation. So this is actually a detonation under water surface that actually itself creates a very large wave but the actual destructive force actually comes after the initial wave. So this is how we have enough of the detonation so you get the bubble and then when that bubble collapses is basically when you get all this energy being concentrated at one point. And the same, so this is basically a cavitation bubble collapsing under water. The same phenomenon actually is miniaturized and used for basically breaking up the kidney stones. Where the inception process is different here use acoustic sound, acoustic waves to concentrate and create the cavitation near the, basically near the kidney stones and the collapse of those bubbles will lead to this breakup of this. And so this has been well established and basically being used now. Next phase is that to even basically focus on more critical areas like looking at the blood-brain barrier which is actually a issue for transferring large molecule drugs into the, from the blood into the brain and cavitation is actually a potential mechanism to creating cavitation for microbubbles inside this, inside the vessels to create basically a locally, essentially a locally hole that you can transport some of the drug into the, through this blood-brain barrier. And so this work is the, so we already had a very simple simplified case look at the basically cavitation that is induced by laser. And even for this bubble that's gross and collapses and then rebounds even for this simple formula, it's difficult to actually get the accurate model that predicts what is happening during this process. So this is the experiment is done in Dr. Blasso's lab and we have the idea is that we actually we need to include more physics to create a model that can predict the cavitation process. So to see the cavitation actually when you see these bubbles growing and collapsing after the rebound, the first, the initial behavior is easily predicted but as you get through further rebounds it's becomes more and more difficult and the error actually increases. So what we have done is to include more physics including the heat and mass transfers during this growth and collapse of the bubbles. And with that, we've been able to improve the models that are, that have been present to actually be able to get a better prediction of what is happening during this phenomenon. So this is on one end and on the other end sometimes we don't want to look at all of the details of the something like cavitation. So we want to have a simplified model. So in this case, we are actually looking at the cavitation model for basically for a generator pump. So this is actually a generator pump. It's very common and that's used for basically pumping fuel or oil or liquids. And if you want to optimize this then you don't really have enough time to do detailed simulation. So you want to have a basically long parameter model for optimizing that so you can run it quickly. And if you look at the cavitation bubble the red line here is actually shows the behavior that you will get but we don't really want to simulate this. We want to have something what we get is that this we were able to by looking at the energy balance for this basically for cavitation bubble we were able to get this behavior, the average behavior of the bubble during this gross and collapse process. And so this is the experiment itself was done in Dr. Vakal's lab at MAHA. And with this model we can actually reduce the time and at the same time get more accurate results that is based on the basically energy balance between the during the growth and collapse of the bubbles. Another area that we have focused on is basically looking at the modeling of the two phase heat transfer. Basically one of the limitations for the basically increasing the power of microchips is the thermal management. And it's the one of the solutions is for to have basically two phase heat transfer where you have basically a phase change that's happening right in a microchannel on the chip and by having this phase change you can actually have a higher heat transfer rate. But actually doing the simulation for this is very difficult because we end up with a very small length scale between basically a very thin film between the bubble and the surrounding and it's very difficult to resolve that. So one of my students, Pramod he basically developed this multi-scale model where he actually solved for the dynamics of the bubble but with the full governing equations and we look at the reduced order model for asking for the thin film that we have between the vapor and the bubble. Another area that we have been focused on is looking at the basically swarm of bubbles and droplets in stratified environments and the mixing that they cause. So one of the motivations for this is that basically whenever you have a swarm of like if you have basically all these at the bottom of the ocean not all of these oils get to the surface some of it is actually leaves basically strapped in the middle layer here because of just how the density is balanced with the rest of the with the between the oil droplets and the water. And it's important to basically be able to predict how much is from what you get to the surface how much oil actually has been released. So what you get from here was for example, for the last oil that we have in the Gulf of Mexico what reached the surface was much less than what was actually expected was was seen that basically comes out of this broken part of the bottom of the sea. So what we are actually looking at here is basically dispersion of the droplets and the swarm of drops that's basically rising in stratified flow. So from homogeneous, basically uniform environment to basically more and more stratified where we have basically because of this salinity or temperature, you have a change of density and you see what actually happens that the dispersion of the droplets increases. And at the same time, we also look at this the mixing that happens during this rise of these droplets and this mixing itself is actually helpful for some applications especially when we cut the water reservoirs we want to limit the evaporation from the surface of water reservoirs. And if you can mix that and mix this basically the water in the reservoir you can actually remove the hot layer on top of the reservoir which basically increases the evaporation. So we have been looking at this mixing phenomena and how we can quantify that and how we can basically create a general model for that. Right, so that was basically a review of the work that has been going on in my group. There are some other topics that I skipped but I just want to thank my students, Dave on all of the work hard work here in my group and thanks for the organizers for organizing this event. Thank you very much for the great talk there, Sadeg. The floor is open to any questions that might be people listening on Zoom, please type them in the chat or if there's some questions in the room. Sadeg, please go ahead and recognize those individuals as well. Going back to Dr. Raman's opening remarks. Yeah, he brought this up. What's some advice that you would give junior faculty of what not to do, something that you wished you hadn't done? I started collaborating with the faculties later toward the end, but I think that was something that I wish I have started earlier. Because in the many, say, initially we had from other institutions, that's not really, it's at Purdue, but collaboration with the more senior colleagues, sometimes you may not get the credit for that, enough to credit, but I have not really seen that here at Purdue and that's something that I think that I encourage my colleagues who just started to definitely see collaboration with everyone. Great, there was a question on the chat, Sadeg, from Fatima, just a very general question. How has teaching helped you be a better researcher in your field? I would say one thing is that you, basically you learn any topic, if you wanna learn it really well, you have to basically teach that. That's one of the things that if you start, if you're teaching a subject, some of them, then that's actually the time that you see all the questions appears in your mind about all different aspects of this. So I think that's one way that the teaching can actually help with the research, just basically providing the large picture, thinking from that point of view. Thank you very much for sharing the research in your life. I kind of want to fill out the first question about collaborations. So when you talk about prefer earlier for collaborations, it's referred to the collaboration within Purdue or could it be collaboration beyond Purdue and how did they get those connections? How did you meet those people? Did you reach out, send an email, say, wanna have coffee or did you meet them in workshop or yeah, something like that? Right, yes, and now actually if, I don't know when you have started, but if you have started during the whole COVID, it's, I completely see that it could be more difficult, but yeah, just talking to colleagues and just maybe stopping by their office. And for outside collaboration, I would say the conferences are definitely what's the best place to find people with the similar interests from other institutes. Excellent, are there any more questions in either in the food room or those joining on Zoom through the chat? Okay, well, thank you very much to all of our participants, to the heads and everything. We really appreciate your attendance, whether in the food room here or virtually where most of you are. So thank you very much. And this concludes this celebrating our associate professor's session. Our next one will be on November 10th. So be watching for the e-registration for it. Thanks so much.