Good day, Lakshmanan Kasi. I'm Arjun. I've had a look through your background and your professional record. I'm interested in hearing more about your work and how you see yourself fitting in here with our faculty. Ready to start?

Yeah, good day. Thank you for your welcoming. This is Lakshmana. I'm a mechanical engineer researcher, especially in computational modeling.

Uh, high speed error mix on the.

Thermal analysis I have recently submitted my uh PhD thesis at an St. where my research focused on uh, high speed, uh, uh speed air aerodynamics with nanometer coated airfoil at super supersonic conditions. So I this this will align with my competition modeling and the experimental work very strongly validated my research data.

Thank you.

Thank you for sharing that, Professor.

Professor Kasi, as part of the technical evaluation, I'll assess your skills and expertise relevant to the position. Let's begin. Can you explain how you approach designing computational models for simulating fluid-structure interactions, and mention specific tools or methods you've used?

Computation modeling, computational fluid dynamics, both are different terms Computational fluid dynamics and especially used for that maintaining the flow dynamics and the tool used to define the flow dynamics, which is very much useful for the understanding of basic physics flow phenomena. This will describe the fluid structure interactions fluid how the fluid will interact with the.

Structure and it will proceed with the next condition, subsequent conditions, the fluid structure, it is basically focused on the material and also the physical concepts of training through the fundamentals.

So the basic conditions they are we are required for the governing equations and the boundary conditions and flow visualizations, the understanding the learning things different focus to the consideration of the.

Computational method is very much effective, so the tool used is ancestor fluid and open form is a open source software. So the both are very much.

Efficient to understand the basic flow phenomena for the beginners and also for the advanced experts.

Understood. Could you elaborate on a specific scenario or project where you implemented either ANSYS Fluent or OpenFOAM to solve a real-world problem? What were the challenges you faced, and how did you address them?

E-mail research on the X model part is very much strong, so that the validation of the X multiple work with the computational model is very much useful for my understanding and overflow. The strong valuation and additional addition to my research, the supersonic speed where the nanomaterial is used, what the focusing of interdisciplinary approach. Dynamically supplied over the.

Aerodynamic surfaces to achieve the thermal reduction on the shockwave strength reduction can be monitored and studied through experimental work. So this can be indirectly applied to using a computational software by ANSYS Fluent and also open form is initially difficult for me, but after that I understood that the basic understanding using the external concept.

Under.

Uh, software validation is, uh, interesting to know more about that conditions of the basic, uh, fluid. So the air is a fluid I have used for my research. So the air compressed to the system and it is growing through the tunnel. So that that condition is, uh, simulated, uh, with the Mac number, Mac number is. The variation of speed and the flowing blowing through the.

Controlled environment, so that the environment makes me understanding the very different phenomenon that much understanding is introduced to this software only competency software. So the assumption compared to the work and the numerical see the computational software and the interdisciplinary approach makes me that a nanometer behavior. How the nanomaterial coated nanomaterial influence influences the?

Thermal reduction and shockwave reduction. For the advanced.

Advanced aviation. So the search is very much efficient when we combine the experimental and computational fluid dynamics.

Concepts to evaluate the strong strength, high quality research output.

So this is very much I needed for my research and I replaced the X model and computational fluid dynamics models using ANSYS Fluent and the Open Phone.

Thank you for the thorough explanation, Professor Kasi. It seems you've applied computational tools effectively in research. Moving on, could you discuss a specific instance where you integrated AI or machine learning techniques within your computational analysis, particularly in materials science or manufacturing?

Yeah, for my the research, one of my one part of my research output is thermal distribution. I can I placed thermal distribution using infrared thermography for my experiment research and I applied through the CFD software to validate the experimental data. So after that I planning to introduce. Machine learning concepts to effectively develop the interdisciplinary research.

So I for that I can apply the infrared thermography, infrared thermography data feed into the machine learning models to validate the prediction, prediction of the enhanced heat transfer, enhance the heat transfer optimizes using this machine learning models. How that machine learning models can interpret and recognize the prediction of the.

So the graph theory which they used for the interpreting intermediate intermediate data transfer from the infrared thermography images to the graph theory. So this the graph theory can formulate and convert it into binary information. So the binary information is very much useful to.

Heat increase enhancement.

Optimize 100 validate the effective prediction of E transfer enhancement using the machine learning models from the machine learning models. So most of the machine learning models can be very much effective but few can only achieve the R square accuracy. So for that the boost required for the machine learning model and it can be very much gradient gradient for the effective research collaboration and also the machine learning.

Patients used for the nanometer conditions, nanometer is very much effectively used in this research. So this nanometer can influence the behavior of the shock strength reduction on the thermal reduction in the SuperSonics speed aviation.

Noted, Professor Kasi. You've detailed how AI and computational tools interplay to augment your research, particularly in thermal and shockwave management in aviation contexts. Let’s pivot slightly. How do you approach the process of teaching complex theoretical and laboratory concepts in computational modeling to ensure students with diverse academic backgrounds grasp the material effectively?

First, I think I have very much focused to provide better communication and also the fundamental things to the biggest student and the worst thing happened in the student. So by applying the basic concept and providing the visual thing visualization. Played learning outcome based learning The outcome was the education.

And the the feedback given to by the students and also the thing which is very much easily understand using the graphics, any computational simulation graphics, first thing they need to understand the.

Basic flow, basic fluid whenever they're required before that they need to understand the basic thing which we I normally used in our real world examples. This real world scenario only attract the weaker students to involve in the subjects, then they can easily grab the concepts and use. By grabbing the concepts, we can easily. Provide the better.

Solutions to the students for the CFB competition through dynamics methods or any artificial intelligence or machine learning methods to feed the students to better to be better in the students perspective. So the laboratory session is very much effective for the students to understand the basic concepts into the handling experience. This handling experience makes them to understand the better view of the conditions and the real world.

Scenarios. What they actually require to know about that?

It's commendable that you focus on foundational understanding, real-world applications, and hands-on laboratory experience to cater to students' diverse needs. As a follow-up, how do you evaluate students in such a way that ensures both theoretical understanding and practical proficiency in computational modeling?

They they need they they need to understand the theoretical love theoretical theoretic classes and then the outcome of the theoretic classes only replicate to the computational modeling. Before that the fundamental thing equation they need to know, but before that they need to know that how the things simulate. The simulate only can understand and can create the. Newer things and also can create the interest to the.

Students mental mindset so the student is very much involved when they when we provide the exactly what they require to simulate and how they can proceed and how it can proceed, provide the information this will.

Improve the understanding and also it can very much effective for the computational model tools. Computational model is what we know fundamentally can be applied, so the applied terms can be replicated. So before the CFD models they need to understand first theory classes. Theory classes they can use through simulation. Only graphics simulation. Not exactly the computational model simulation. Graphic simulation is a basic simulation.

So they can easily what actually it requires and what it actually it behaves. So this one basic interest interest can be created within the students and it can apply to the computational model computational simulation. After that the computational simulation they can understand the what the scenario can happen if, if and if so or if, if and if, if. Suppose if. If it is not means what happens if it is happen means if.

What happens the conditions on the scenarios are very varying by using the students. So this kind of understanding can create the research integrity to the students by using this the theoretical classes on the computational modeling softwares and simulations classes and also the laboratory sessions.

Your approach integrates theoretical rigor, graphical simulations as foundational steps, and hands-on computational practice, ensuring a comprehensive learning journey for your students. It’s clear that you aim to nurture not just understanding but also research curiosity among them. Moving to the next area, could you describe your experience in guiding student research projects, particularly in computational modeling? How do you ensure the projects are rigorous and contribute meaningfully to the field?

When my research progressing in my early stage research, my professor providing some some of the students postgraduate students are done with the students to mentoring the CFD simulations to the basic knowledge so.

This will improve my understanding and providing the teaching experience to the students for the undergraduate and postgraduate students for my professors students. So this will increase my interest to the teaching CFD simulations to the particularly. Focused on this CFD simulations to the students will improve in my research for area. So the area where I choose an is interdisciplinary. So this CFD is applied to any discipline where this can.

Somewhat physics, later subjects, departments, anything. So I inform my students are post post Ledger students. I have a mentor, some microalgae slurry. This microalgae slurry is very new to me, but I am very much interested to apply the CFD concept into this area. From that I can. The post graduate student is very much involved in the CFD. Concept so the combination of my CFD knowledge to the.

Apply the domain where it can need the area, whether it is a airspace, whether it is a energy, whether it is a mechanical, whether it is a.

And the knowledge of these microglia slurry can be very much effective to produce the publication in they put a journals chemical engineering technology. This is very much impressed and also very much motivated me to provide the CFD simulations and provide the CFD tool to the under gadget and post-registered students by mentoring this CFD softwares and CFD modeling. So I am very much motivated to.

Provide the better quality educations to the uh students uh, for the undergraduate and postgraduate by mentoring these kind of research output by this kind of uh research, uh, knowledge can be improved by providing the better quality understanding and if the impactful research.

It’s impressive to hear how you’ve mentored students by integrating Computational Fluid Dynamics (CFD) concepts into diverse domains, such as microalgae slurry research, resulting in impactful publications. This demonstrates your ability to guide meaningful and interdisciplinary student-led projects. Let’s turn to your ability to publish research. Could you highlight a key publication of yours, focusing on its contribution to the field and any challenges you faced during its development?

Yeah, enhance the heat transfer optimization by using this infrared thermography data to the high speed aerodynamics applications in the Journal of Heat Transfer Research. It is very much core area journals to replicate work, which is very much highly recognized.

By a condition by applying these concepts into the machine learning models and COD modeling, the data obtained is very much crucial to handling the experimental data to be validated so. So by getting this exactly the answers for the experimental results is failing to that draft TV concept and it can be fit into the. Again the draft theory concept can convert this data into the binary concept. That is very much efficient to handle the data by. Too much of variations in the data is critical and crucial to.

Conclude the statement for the specific application. So by converting the binary concepts can provide the better research output and better concept clear clear to the understanding of the results. So this will be very much effective to provide the data to the machine learning models.

Machine learning models can improve the prediction accuracy and it provides the Oscar accuracy greater than point double name 999, so this will very much affect you for this my other search in X model numerical and also the application of machine learning into this integrating factor to get this research effective to for my thesis.

Your work on optimizing heat transfer through the integration of infrared thermography, CFD modeling, and machine learning is notable, especially given the high accuracy achieved and its application in advanced aerodynamics. It’s clear that your interdisciplinary approach contributes significantly to the field of thermal research and machine learning in engineering contexts. Switching slightly, let's discuss your experience with industry engagement or consultancy. Can you describe a project or collaboration with industry where you applied your computational expertise? If none, how would you approach such collaborations?

By introducing my projects in that aerospace laboratory and aerospace company in this one Aeronautics Limited in that early stages the Internet gig is very much useful and efficient to handle the different kind of model hand holding capability in that industry point of view. So that kind of. Matching machine. That kind of concept and the tool holding a capability is to be designed.

Using CFD, but using the material handling, take handling of environment, the temperature is transferred to that holding capability and it can be very much useful to the Society of the air aerospace in a forum. So I think basic thing second I think I have applied in the industry is. Some battery concept used before the CFD simulations.

Like they they they one of the industry, they're looking for the battery cap to maintain to monitor the temperature of the.

Bolt in the battery. The bolt in the battery is very much heated during the charging. That battery charging means the bolt can heated, bolt temperature can raise it. So that raising temperature can be monitored and it can be functioned and it can be optimized using this CFD. I have applied my CFD knowledge.

But this is this cannot be recognized by any publication, but my research CFD is very efficient to use for the the kind of application for reducing or monitoring the keyboard temperature in the battery system, battery environment. So this is this this kind of approach and this kind of industry oriented.

Big, uh, Methodist, where I have, uh, experience so far, I even after this, any kind of, uh, uh, innovative or new, new or, uh, information they required to apply my CFD knowledge means really to collaborate with the industry for activities and they improve the CFD concepts into the any problem solving techniques.

Your engagement with industry, particularly your CFD applications to aerospace material handling and thermal management in battery systems, highlights your ability to translate advanced computational expertise into practical problem-solving scenarios. It's evident that you’re well-equipped to bridge academic research with industrial applications. Let me finalize this evaluation to ensure all your relevant strengths are captured.

Professor, let's begin with your experiences as an educator. Could you share an introductory course or fundamental concept in computational modeling that you particularly enjoy teaching, and why?

I am very much impressed by applying the real world scenario or anything we may imagine using this kind of wonderful tool, less accommodation free dynamics tool. So this is this is very much efficient for cost reducing and also we can easily apply anytime, anywhere by any. There is no time restriction, there is no.

Time limit. There is no restriction for the cost estimation.

This will improve our knowledge and you can easily apply any any kind of any, any time of trials, any number of trials to understand the concepts and how this actual physics behave, how this actually evolved. So this kind of understanding is not explicitly obtained using experimental, but it can request more time for experimental understanding. It need patience and also. The targeted number of.

Ours is very much higher compared to CFD competition mix. The targeted number of hours is comparatively low. It can easily varying the concepts, it can easily varying the parameters to get the better results and the acute accuracy by achieving this this kind of scenarios and environments. So this will impress me and also I can provide a better. Sharing my knowledge and the experience to the students to the through the curriculum, through the.

Subject courses by teaching these courses and it will improve the students research output and students academic output can be improved by this I can. Explain and I can improve the concept of industry oriented industry linked with the CFD. How this can happen, how it will happen? How this can improve the industry projects in industry outcomes by by giving the real world scenarios and also the application.

Of the CFD.

Thank you, Professor. It’s evident you are passionate about integrating computational modeling with real-world scenarios to enhance understanding and research output. Allow me a moment.

Our scheduled time for the interview is now up. Before we conclude, do you have any questions related to the role or the company?

Professor Kasi, I noticed in your resume that you pursued a PhD in Mechanical Engineering with significant research experience, specifically with tools like OpenFOAM and ANSYS Fluent. Could you elaborate on one of the key research projects from your PhD and explain how you integrated computational modeling within it?

Yeah, of course. Actually my research I focused experimentally the experimental conduction conducting the thermal analysis and software distribution of nanometer coated airfoils operating at a supersonic speed. It can be achieved through experimental by using a supersonic internal. So this kind of exact scenario can be achieved through CFD simulation by creating the supersonic tunnel and also the exact scenario what happened to the real world physical phenomenon. So this can.

Still using this uh, access free event and the open form by comparing the results efficient by both the computational softwares. So it will improve my knowledge and also the research efficiency in this experimental validation. So this can very much effective to by nanomaterial coating is applying over the aerodynamic surfaces to improve the shockwater strength detection and also the thermal distribution. Reducing by influencing the by influence of.

Nanometer coating.

So.

The boundary layer separation, the boundary layer forming in this nanometer coating by 5 micrometer thickness of graphene is used for this experimental concept. So this exact scenario is created in this complex 2 dynamics after Nancy's are fluent and also the open form to validate the and obtain the pressure distribution, pressure shock strength and the flow phenomena using scalar and imaging and infrared thermography using the infrared thermography.

And also the optical optical diagnostics techniques to understand the flow of phenomenon in this supersonic speed.

Supersonic flow, so the CFD software can improve. CFD software can provide the effective, effective validation of the experimental data using the access period and also the open form. So this will very much affect useful for my understanding and also the research output efficiency in my study. So this this can improve the exact scenario in this.

The research outcomes and impactful research is provided by this kind of combination, integrating the experimental and the computational techniques.

Thank you, Professor Kasi, for providing a thorough overview of your research project. It seems you successfully combined advanced computational tools with experimental techniques, which is commendable. I’d like to explore further. How do you ensure that the CFD simulations accurately represent the experimental conditions, especially when handling complex phenomena like shockwave interactions and thermal distribution? Could you detail any specific validation techniques you employed?

In Ancestry. Almaraz turtleneck models.

Some density based dissolvers solver can be used for the conditions which is similar to that of real world scenario. So this kind of applications based solvers can be useful in this available in this ANSYS fluent software. This can enrich the conditions which is very much suitable to the.

Supersonic winter conditions. So it is optimized excel data and also improve the conditions parameters which is required for the obtained results. So the data which is feed into that CFD is the exact data where we can obtain from the experimental. So this will reduce the this will provide the error percentage of five 5% to 10%. Rather percentage. It is very much agreeable to the.

Condition equivalence. So this will improve the understanding. This will improve the better research providing by combining this experimental and validating the experimental data to the numerical data.

That’s a well-structured response, Professor Kasi. You’ve highlighted how specific solvers, like the density-based ones in ANSYS Fluent, align simulations closely with experimental conditions, and your error margins of 5-10% are certainly reasonable for real-world equivalence. Now, on the teaching side, how would you design a lesson or module to introduce undergraduate students to the concepts of CFD applied to supersonic flows, given how advanced and nuanced these topics can be? What foundational elements would you prioritize?

Time is up! Ending the interview now.