Good day, Dr. S. Madhankumar. 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?

Yes, good evening.

Yeah. Thank you. So first of all, I would like to thank you for giving this wonderful opportunity.

Good evening. Please start by introducing your professional journey in academia.

To present myself in front of you so myself Dr. S Madan Kumar. So right now I'm doing my post doctoral research at UH Indian Institute of Technology, Guwahati.

In the department of mechanical engineering. So coming to my educational qualification, I did my uh, be mean mechanical engineering from Sauna College of Technology Salem, which is affiliated to Anna University and I have received uh, a bronze medal during my.

B within CGP of 8.74 and also I have received first class with distinction so later on I have did my uh yummy in engineering design from Government College of Technology Coimbatore.

Uh and uh within CGP of 8.78 and with the first class with distinction. And recently in June 2024 I have completed my.

PST from Anna University.

Before before doing this postdoc, I was working in one private institution, namely Sri Krishna College of Engineering and Technology, which is located at Coimbatore as an assistant professor in the department of Mechatronics Engineering. So this institution was in NIRF top 100 for the past eight years.

In the title of computational experimental and machine learning studies of solar drying device with thermal energy storage you need and apart from that.

So I have almost around 7.5 years of teaching experience along with the research in in that Sri Krishna College of Engineering and Technology and in IIT Guwahati I have one year of experience. So in total I have 8.5 years of teaching and research experience and also I have like Center for research organization for doing.

Supervisorship under Anna University, so right now I'm guiding one scholar.

And uh, my key strength is research and development. So I, uh, till now I have published 62 or Scopus indexed publication among the 24 or Q1 publication and 34 conference proceedings and four book chapters.

So far in this 8.5 years of my academic experience, I have received around 900 citations for my repeated publications with high impact publications and within h-index of 15 and I-10 index of 27. So among that publications, one of my publication has publicity in Applied Energy Journal which has an impact factor of 11.446 and my cumulative impact factor from my publication was one.

8.96 And in addition to that research publication I was actively reviewing the manuscripts in the reputed publishers like in Elsevier. So in the last year I have reviewed around 30 publications and in addition to publication reviewing so I have published many conference, I have presented many articles, many.

Like works in conferences and also I actively involved in like editorial board member.

In many uh, like uh.

Uh, conference proceedings and journal in that, uh, I have, I, I, I have invited as a special editor.

For the special issue in AP Conference proceedings for two editions of International Conference.

And also I have like organized 2 international conferences during my teaching experience in the department of Mechatronics Engineering at Sri Krishna College of Engineering Technology.

So in in the conference title was International Conference on like Innovations in Robotics, Intelligent Control and Automation. So both editions were bubbly in AAP conference proceedings. In the 1st edition it was around 75 articles was published and in the second edition it was 84 articles. Both editions were indexed in Scopus.

And also I have at least one book, four book chapters in, uh, like repeated publishers. In addition to the publication, I have bubbly star like 12 utility patents in ITR and 23 design. 23 of design design patterns got registered.

And also I have received some proposals from CSIR, IET and SKCETC fund. It cost around like 4-4 point 5,00,000.

And coming to my academic or during my academic career I have I have held in many responsibilities in the institution as well as academic. So in the institution I was the like I was actor acted as a deputy controller of examination for nine months and R&D overall coordinator for the publications lab audit member, NAC criteria work member as well as I was work doing NRF ranking also. And also I was act as a residential warden for.

Around six years. So coming to my department level responsibilities, I was acted as a timetable coordinator, NBA or works ERP coordinator, R&D coordinator for the department and students mentor for many events and hackathons, Board of studies conduction website coordinator, industry interaction coordinator, MOOC as well as higher studies coordinator.

So apart from this, I have uh, brought 3 or best practices to the department as well as the institution. So I, I have initiated the department magazine as well as department video. So both we have worked along with the students to publish the department activities to the societies students as well as other department faculty members. And also I have introduced the faculty seminar series to the department.

So this, uh, webinar series was introduced to, uh, share the knowledge to share the cutting edge research to the other faculty members. So this was conducted twice in a month. And also I have organized many events in the like conferences, FTPS, uh, STTP. And then I have organized workshop as, as well as I have delivered, uh, my lecture in the workshops, seminars and et cetera. And also I have completed many new move courses in NPTEL I.

Completed four more courses and in Coursera I have completed 11 new courses and also I'm happy to share that I have completed 8 modules of NA Triple T.

So in in my academic career I have handled uh, many subject. So since I was working with uh, mechatronics department I have handled uh, robotic stream subject, thermal related subject as well as design related subject. So I have Elvis with uh, some software tools like AutoCAD, SolidWorks, ANSYS, open form, Matlab, Minitab and design expert. And also I'm good with uh, some research tools like Edra, Max Origin plot and tech plot.

And also I'm actively encouraged the students to participate in hackathons, SIH hackathon and other private hackathons, as well as uh, I have arranged internship offers for the students who are in final year as well as I have motivated students to participate in court events as well as drone events.

So coming to my achievements during my academic career. So I have received certificate of recognition for the best research work during my PST from the online university.

And also I have received a third prize for the presentation I have given during the National Science Day celebration at IIT Ropar.

And also I have received Young Scientist award from the BD Technology Factory for the good number of publications in high reputed journals and participations in FDPS workshop and et cetera. And in my previous working institution, from the previous working institution, I have received best faculty in achieving faculty best high-impact factor publication, campus ambassador and et cetera.

And also, I'm happy to share with you that I have received 320 citations in the last year. That is also one of my biggest achievement as an researcher.

Thank you.

Thank you for sharing such a detailed account of your academic journey and achievements, Professor.

Thank you.

You're welcome, Professor. Let's proceed—are you ready to discuss your technical skills in depth?

Yes.

Yeah. So my research type PST research title was computational experimental and machine learning studies of solar drying device with thermal energy storage you need. So my aim is to like to dry the food products with the help of the solar drying device. So here the novelty of my work is to develop the best solar driving device as well as the portable solar driving device. So for that. So I have reviewed many literatures. So in the literatures the people the researchers have used.

Let's start with Computational Modelling. Can you describe a specific computational model you developed, focusing on how it was constructed and applied to solve a real-world problem?

Uh, the flat plate or solar collector. So in that flat plate we can have only less, we can, uh, we can observe the only very less, uh, quantity of solar radiation. So to increase the efficiency of the flat plate solar collector, so I have introduced the corrugated type of solar collector. So corrugated type of observer plate in the solar collector. So the, the comparison between the flat and corrugated type was so in flat plate.

We have only the less surface area when compared with the corrugated because it has kind of V shape. So therefore when when we have the more space, more surface area to collect the solar radiation, we can produce more raw heat heat energy.

So before moving to the experimental studies of the proposed system, I have used some computational modeling like and I have first I have used the computational model that with the help of a ANSYS fluent. So with the help of that ANSYS computational modeling. So I have analyzed the thermal behavior of the proposed solar collector. So in the solar collector apart from the corrugated observer plate, I have introduced the thermal energy storage.

Below that solar below the observer plate so that this proposed system can extend off drying or extend of producing the hot air even after 6:00 PM like after evening.

So with that sense so I have used. I have created the computational model first using the ANSYS fluent.

Then I have analyzed the miss analysis grid independence. So after grid independence, I have viewed the like thermal behavior in terms of temperature, in terms of pressure drop, as well as in terms of the velocity. So in this, uh, with this computational modeling, I have studied the effect of mass flow rate, studied the effect of solar radiation as well as the ambient condition. So with that computational modeling, the help of that computational modeling.

Have identified 3 optimal setup, 3 optimal uh conditions, 3 optimal kind of design setup to move forward to the experimental thing. So after done my experimentations for drying the food products. So once I have collected the data with the help of the sensors again I have used one more computational modeling that is called machine learning. So in in my analysis I have used five different machine learning.

Analysis namely uh, like uh, in terms of regression analysis, I have used multiple criteria uh, regression, random forest, K nearest neighbors and the next G boost as well as in and I have used.

So by introducing, uh, and by using these algorithms for my, uh, uh, the data which I have got it from the experimental analysis. So I have, uh, like, uh, used, uh, the Google colab with the Python codes. With that codes, I have like created the algorithm and then I have analyzed. So from that, I have identified the best algorithm to predict the output responses from my solar drying device. So with the help of this computational modeling using ANSYS.

Fluent as well as the machine learning analysis, I have minimized to the like working time as well as I have analyzed with many or like input parameters to.

Find out the optimal.

Output value. In addition to that I have cloud. I have integrated the computational modeling as well as the machine learning analysis to predict the thermal behavior of the solar collector. So in that case I have created three different kind of solar collecting device in addition to the general flat plate thing. So so that I have identified the best setup to implement to the.

The agricultural like food food industries for.

Uh, trying the food applications.

Thank you.

Thank you, Professor. You've clearly outlined a rigorous methodology for your computational modeling and its integration with machine learning. Let's proceed—how have you applied AI or ML to materials science or manufacturing, particularly beyond renewable energy?

So we can apply this concept. So I have learned introduction to Python learning from NPTL. So with that help of that course as well as I have used to the I have used five different kind of algorithms in my PST work. So with the help of this knowledge we can apply this algorithms to the material science applications. So let us take one machining applications like electrochemical machining or electrical discharge machining. So in this kind of.

Machining process we have like input parameters as well as output parameters. So we can apply these regression models to any kind of the labeling data. So so we can have for example in electrochemical machining we have like a current voltage pulse on time, off time and feed rate as are all the input parameters. So in output we may get like. Electrode wear rate or material removal rate?

And surface roughness, sometimes we may have over cut also so. So to find out or to predict the output parameters with the help of the input labeled parameters, we can apply this regression algorithms to find out the prediction to find out the optimal one as well as we can apply this algorithm to predict the variable predict the responses.

And also I wanted to share you some of my experience during my postdoctoral research. So in my postdoctoral research, I have did, uh, research in, uh, computational fluid, uh, dynamics on hydrokinetic turbine. So in this, uh, uh, in my postdoctoral research, I have created, AH, Darius hydrokinetic turbine. So in this hydrokinetic turbine, we have many input parameters.

Like the water flow velocity and the tip speed ratio of the turbine plates and the profiles of the turbine blades as well as et cetera. In the output, we may have like woman coefficient of the turbine or torque coefficient as well as the output power of the turbine. So in my postdoctoral fellowship also I have applied the regression models to predict the power coefficient of the.

Turbine which I have analyzed.

Thank you.

Thank you, Professor. That provides valuable insight into your approach to applying computational techniques and machine learning beyond renewable energy. Next, let's verify your proficiency in computer programming and computational analysis. Can you discuss a situation where you had to write or adapt code for a specific computational need, detailing both the programming languages and methodologies you employed?

Yeah, sure. So thank you for this question. So coming in like let us take the computational modeling in ANSYS Fluent. So in ANSYS Fluent, so first we have to create the computational model for the problem which we want to solve it. So for that computational domain, so we have to create a 3D model. So in after developing that computational model.

We need to do the GRIS grid independence analysis to, to like, uh, to analyze, uh, the quality of the grid, what we have generated using the skewness as well as the orthogonal quality. So the skewness is nothing, but it is a measure of the miss quality that uh, uh, good for. Missed quality that quantifies how much you sell or defines definites from its ideal as from its equal equilibrium shape.

So when we have the skewness value equal to 0 then we can say it as a good quality. And coming to the orthogonal quality it measures of how much. Clearly the angles between the mesh elements are equals to 90°. So therefore for this case we have to go for the maximum value that if our grid gives the grid value is equals to 1 then we can say it as a good quality.

So after finalizing this, uh, grid part, so we have to go for the algorithm that, uh, that is kind of a codes, We have to use it for, uh, doing the solutions of our problem. So in that case, we have to think about, uh, whether our problem is the steady state problem or unsteady state problem. So if it is steady state, the temperature in a system does not change over time. And also we have to see about the momentum equations energy.

Patience and continuity equations for the for the problem to be solved and apart from that which pressure model we are using like second order upwind scheme for momentum and pressure velocity scheme. We have to choose that far that we have to go for the Presto approach for adjusting the pressure as well as we have to use the couple coupling model that we can say it is a simple or coupling model. So in simple is nothing but we can like couple.

The pressure and velocity contours and also which model we need to use like OK Omega or K epsilon or.

Simply steady state model. So the model we have to choose. So based on this model we have to do our algorithm. So with that algorithm, uh, we have to give the input or we have to give the like conditions to the software. ANSYS flow in software, then softwares will do the analysis then it will give the preprocessing, post processing data like velocity contours, pressure contours and turbokinetic energy.

And he coming to the, uh, Python codes, it is like a, uh, like a grammatical code only. So this is very, uh, simple kind of codes when compared with the CC plus plus programming languages. So we in Python codes, first we have to analyze the input data. So in this input data, so we have to check whether the given data is labeled or unlabeled data. So after. After verifying it, if the given data has labeled data, we can go further.

And etcetera.

Regression models.

Our supervisor of machine learning approaches, if it is uh, unlabeled data, we have to choose like uh, unsupervised machine learning concepts. So in supervised machine learning we have like, uh.

Regression based problem as well as the classification based problems also like categorization problem also.

So if it is regression for example for prediction of some output responses or to optimize the output responses, we can go for any regression data. So in the current stage, current scenario, the many researchers have used Xgboost or kind of regression models to predict their data. So coming to the Python codes, so we have to 1st we have to train the X variable so.

Father, we have to use code as X train. Similarly we have to uh train the YY variable that is the output variable. For that we have to use Y train as a code.

So in that if we have like a mass flow rates, if we have like temperatures, we have to we have to like predefine our variable label like for M Moss for eight we can give MFR and for temperature we can give it as a tem. So like that. So based on our convenient we can give the coding. So this is kind of simple English language. So after training the data. So we have to so first we have to import the data software importing.

We have to split up the data for training, testing as well as validation. So in most of the cases we used like 70 percentage of the data for training and 15 percentage of data for testing and remaining 15 percentage for validation.

So far once we have confirmed we have to go for a training hour data so far that for example, if you are if we are using like a a linear regression based problem. So if in that linear regression problem we have a equations like Y is equals to MX plus C. So in that case we have to fix the constant as well as the that slope M value we have to identify based on that perceptron. So after this I analyzing the codes.

Analyzing the after done this, uh, initial training with the help of the train data, we have to apply this equation that trained equations from the trained data to the, uh, testing data. So once the testing from the testing, we can do like statistical measures like R square, RMSC and et cetera. So for that, we have to do a code as XY test like that, so Y test of RMSC of the.

Data variable. So from that our results we can identify whether the develop the algorithm or develop the model will give the best results based on AR square statistical matter values. If basic usually the R square value if more than .9 or .95 that model gives the better relationship good relationship between the input and output variable. Similarly we can apply the.

Codes Python codes for different.

Uh, like a different modeling Also Thank you.

Thank you for that detailed explanation, Professor. Your experience clearly shows a strong command of both computational tools and programming. Moving forward, let's shift our focus to your teaching abilities. Could you describe your approach to effectively teaching both theory and laboratory courses in your area of expertise?

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

Yeah, I wanted to know so before that I regarding that teaching and laboratory handling. So I have 7.5 years of experience. With that experience, I can effectively teach the subject related to computational modeling and thermal sciences. So in my past experience, I have handled the thermodynamics course for the B.Tech students in that course.

There will be a there. There was a concept related to the CFD, so I had a good experience.

With the handling that, uh, CFD concepts and regarding that, uh, laboratory effective teaching. So usually in laboratory cases, we have like 10 to 10 to 15, uh, uh, laboratory experience. So apart from that for effective learning, for easy understanding, the easy understanding of the concept, I will be given like, uh.

Like virtual kind of laboratory components to the students as a prerequisite to come before to know before coming to the experiments so that I will do it and coming to the question. So I wanted to know about what kind of role I will be get if I got selected.

No, thank you. Thank you for your clarification on the road.

Yes.