Author: Anis Jayaram

UPC – A decisive turning point for IMFSE ?

~ Anis Jayaram ~ 1 Comment

Hello There !!!

Pursuing a master’s degree, I was in a bit of a dilemma: should I join TAMU for process safety? NTNU for RAMS? or IMFSE for fire engineering?. Even though I was working in operational safety and fire protection in the oil industry, my knowledge about fire science was very rudimentary, due to the fact that the fire engineering subjects in my undergrad degree were basically a cookbook curriculum focused on the job market. I already had mixed feelings when I decided to go ahead with IMFSE, as I felt that I was straying away from an industry that i enjoyed working in.

There was a significant shift at some point. In March 2022, I was notified by IMFSE via email about a modification in the mobility arrangement. It was conveyed that UoE will no longer provide any courses during the third semester and that UPC has become a full-time partner university in the IMFSE consortium. Upon reviewing the curriculum, I was delighted to discover the inclusion of a course specifically focused on risk and safety within the chemical industry, known as RSCI.  While one subject alone cannot replace a comprehensive master’s programme in process safety, I am pleased that I will not be abandoning my previous industry. Instead, it will be incorporated as a subject within the IMFSE programme.

“Barcelona is a vibrant and culturally rich city in Spain, renowned for its unique architecture, Mediterranean beaches, and a lively arts and culinary scene.” 

This is a typical textbook description of this happening city. You also realise that things are a bit different from the previous cities that you lived in when orientation week activities get kicked off by an anti-pickpocket lecture or demo, so guys, be wary about your surroundings.

Sometimes you may be unlucky, but for me, it has been more than 5 months since I started living in Barcelona, and except for my heart, nothing of value has been stolen from me yet. (although the clumsy me lost three water bottles; ignore that for now.)

I am not planning to write another IMFSE Practical Guide, but to share some experiences that I thought might be useful for you all. Since we were the first bunch to start semester 3 at UPC and there were no seniors to use as crutches, I believe this would be a good place to share the experience and address your questions. 

Sooooo, why did I choose UPC? 

It was not just RSCI but the other core subjects that UPC offered. These were pretty hot topics, which instilled interest in a budding fire engineer, but you are struck with the realisation that there are a handful of institutions that will teach these courses, and we all grabbed the opportunity to jump on the bandwagon  ‘to hear something straight from the horse’s mouth’. 

In the first week, we were introduced to the subjects and why UPC being part of IMFSE is a game changer. It’s been more than 10 years since the inception of IMFSE, and for the first time, we have dedicated courses in wildfires!!

It’s not surprising that this is the need of the hour, as you tend to notice how frequent the outbreak of wildfires is. A combination of climate change, changes in rainfall and wind patterns, extended periods of drought, the ever-growing WUI, disruptions in the natural fire cycle, and improper forest management have paved the way to the current crisis. As a fire engineer, it’s an amazing opportunity to learn about and understand this problem and apply engineering practices to mitigate it.

In addition to the core subjects related to wildland fire i.e Wildland fire behavior and modeling, Risk and vulnerability at WUI. You have the option to pick an elective out of these three (1-3) and the other courses are (4-5)

  1. Data analysis and pattern recognition
  2. Computer vision
  3. Technology innovation
  4. Risk and safety in the chemical industry.
  5. Advanced fire safety engineering

UPC has some moderate contact hours; you will spend an average of 20 hours a week on campus (still less than Ghent). The semester has a midterm exam around the end of October. Initially, we felt that this was going to be trouble, but I guess this favoured us all because we are able to know where we stand in following the course, and the final grades are dependent on the midterms, final exam, and coursework (assignments and projects). So if we play our cards right, we will have a certain degree of relief while appearing for the final exam. Most of the midterm exams here are MCQs, except for the electives (some electives don’t have an exam), whereas the final exams were a mix of MCQs as well as a written part.

Classes are quite fun in UPC; as far as IMFSE is concerned, I thoroughly enjoyed the FSFD lectures in Edinburgh, and this place also had the same vibe. Classes from Professor Elsa are particularly engaging and interactive; I would say classes are bit gamified where we have mini puzzle/card games that she brings to the class, and some of these games are integral to our assignments related to risk and vulnerability at the WUI. I am going to pause here and not spoil too much about it.  The risk and safety in the chemical industry have some tasks where we work together with the local students, which promotes integration and improves your team-building skills. UPC has a wonderful team with Dr. Eulalia, Dr. Elsa, Dr. Alba, Dr. Ronan, Dr. Pascale, and Dr. Simona. Pascale is an IMFSE alumni and a former IMFSE blogger. They have expertise in Wildland fire behaviour and modelling , Risk and vulnerability in the WUI, Risk Analysis, consequence modelling , Fire brand transportation and modelling , GIS , Smoke control and many more (just briefly mentioning the parts that are relevant to us)

Field visits: L – WUI in Barcelona , R – River park community (Pont de Vilomara)

All the classes are complimented by field visits, where we had a total of 3 field parts of the curriculum during the semester.  The first visit was to the WUIs in Barcelona to understand the intricacies of this subject. Then we had a visit to one of the communities ravaged by a recent wildland fire (Pont de Vilomara) and a visit to Applus laboratories (picture it as DBI here in Barcelona—it’s a fire testing and research laboratory). We also had a tour of the campus firefighting facilities and the hydrogen lab at UPC. 

We also had a fun leisure trip to Costa Brava near the end of the semester, which was a fun day trip complete with a scrumptious lunch.

Trip to Costa Brava

My elective here was Data Analysis and Pattern Recognition (DAPR), which was a thoroughly enjoyable course because we got exposed to the trending subject of AI/ML, and by the end of the course, we were able to build and train our own neural network and become familiar with multiple ML algorithms. I am a bit biassed in saying this, but this is a very good elective, and I recommend everyone pick it because you learn some skills that are invaluable to an engineer who is going to save your skin somewhere in the future. 

More reasons to choose it may be found in my previous post: 

Speak Parseltongue?

Computer vision is also recommended if you are already familiar with DAPR, but definitely read about the course contents before picking it. I had taken the opinion of many professors before choosing the elective and essentially weighted the pros and cons.

Should I go to UPC?

While I was writing this blog, I received a bunch of questions about UPC. A popular question was, “Should I go to UPC if I am not interested in Wildfires ? “ In the beginning of the post, I stated my motivation to choose UPC. In the end, it all boils down to individual preference. For me, I saw this as an opportunity to learn about something that I was equally interested in yet clueless about. So laying everything out objectively, I would say if you are interested in wildland fires and their interaction at the urban interface, just pack your stuff and head to Barcelona and don’t look back.

But you wouldn’t miss out on a lot when you compare it with Ghent. This may or may not be a pro because here at UPC we have the subject of Advanced Safety and Fire Engineering. But Ghent has three dedicated courses on the same subject, whereas here at UPC its toned down to one course. We would be touching on key aspects of fire safety engineering when compared to Ghent, but we may lack the depth of coverage. The course still has a PBD project that carries a significant weight to the total marks.  Traditionally, this has been the case in the third semester; before UPC, it was UoE, where the course work was predominantly focused on structural fire engineering (FEA, etc.) and fire science. So I believe a lot of thought must be put into making a choice about choosing a college for the third semester. My goal was to diversify my programme and knowledge so that I could fit into a system if the need arose, plus wildland fires is definitely a pressing issue in our field and hearing it from the pioneers in the field is a bonus (I mean, this is generally case in any partner university of IMFSE)

The RSCI course offers a thorough and extensive exploration of consequence modelling and analysis. It would greatly enhance your understanding of risk, particularly in conjunction with the risk course you will be taking in Lund during semester 3.

Furthermore, you will never be deprived of any opportunities or experiences in the IMFSE programme. As an example, I have close acquaintances in both the IMFSE programme and the MFSE programme.  During my Christmas break, we used to discuss what we all learned by sharing references and key concepts and the understanding that we gained in our respective semesters. If this is a concern, I guess your friends have your back!! 

The only worthwhile minute of the abomination called: The Rise of Skywalker

See you all at Lund during the IMFSE day!!!

Regards,
Obi-Wan-Kenobi

Speak Parseltongue?

~ Anis Jayaram ~ 1 Comment

Hello there!!

Merry Christmas, folks!!

Have you ever wondered how you could create beautiful graphs, analyse them, find correlations, and smooth the data? The answer to your problems is a certain species of snake, and you probably need to learn a bit of parseltongue. I am talking about Python and how learning it will save you time analysing data and make your life a bit easier.

Some outputs using a python script ( libraries used: Matplotlib and Pandas)

To be honest, I am a really inefficient programmer. I write code without optimising it; my goal is often to get the output rather than think about the optimisation( Which i am improving on) . I know that some of us are apprehensive about using Python and ditching Excel all together because we are not confident with our programming skills, but everything changed back in late 2022.

Meet ChatGPT: One area where this LLM ( Large Language Model) shines is being an AI-copilot in helping you code as well as refactoring your inefficient code. We are living in a time where the typical gatekeeping of the programming community is no longer present. With GPTs, we are able to code with a limited understanding of a programming language as long as you understand the problem and how you want to solve or approach it. Learning the syntax is no longer necessary, but desirable, and I am sure that in due course, after starting to use these tools, you will get a hold of the syntax and will be fairly confident in writing your own code without any crutches.

I understood the power of Python during my internship at DBI. I was spending time analysing data in Excel and creating templates, which could have been completed in mere minutes using the help of Python and the Pandas library

Image generated using DALL-E , Python and her libraries for data analysis

Libraries: what are they?

“Libraries are collections of pre-written code that users can include in their programmes to add functionality without having to write it from scratch. These libraries consist of modules and packages that provide methods, classes, and functions for various tasks, making Python programming more efficient and streamlined“

Imagine you want to build a LEGO castle. You could create every single piece of LEGO yourself (tedious and expensive), but what if you had boxes of LEGO pieces that were already made for different parts of the castle, like walls, towers, and gates? You just pick the pieces you need and put them together to make your castle. Libraries are essentially like pre-made LEGO pieces; you use them for specific tasks like plotting graphs, finding correlations, and analysing data.

Some must-have libraries are:

  • NumPy – https://numpy.org/ (A fundamental package for numerical computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a vast collection of high-level mathematical functions to operate on these arrays. Widely used in scientific computing, it is essential for performing complex mathematical operations and statistical analysis )
  • SciPy –https://scipy.org/ (Built on NumPy, SciPy is a library used for scientific and technical computing. It extends NumPy’s capabilities by adding useful features like modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers, and other tasks relevant in science and engineering.
  • Pandas – https://pandas.pydata.org/ (A powerful data manipulation and analysis tool, offering data structures like DataFrame and Series for handling and analyzing structured data. It excels in tasks such as data cleaning, transformation, and aggregation, making it indispensable in data science workflows. )
  • Matplotlib – https://matplotlib.org/ ( A plotting library for creating static, interactive, and animated visualizations in Python. It’s the go-to tool for plotting graphs and charts, essential for data visualization and the graphical representation of data)
  • Seaborn – https://seaborn.pydata.org/ (A library that works on top of Matplotlib. Seaborn is particularly known for making beautiful and informative statistical graphics in Python. It provides a high-level interface for drawing attractive and informative statistical plots. Seaborn simplifies the process of creating complex visualizations like heat maps, time series, and violin plots.)
  • scikit-learn – https://scikit-learn.org/stable/ ( libraries for machine learning in Python. It provides a range of supervised and unsupervised learning algorithms via a consistent interface. These include regression, classification, clustering, model selection, preprocessing, and dimensionality reduction. The library is built upon NumPy, SciPy, and Matplotlib)

This may seem overwhelming, but trust me,once past the initial apprehension, it is very simple and intuitive to use. I would suggest following sources to get started with:

Regarding IDE (integrated development environment ) , My favourites are Spyder and Jupyter- There are hundereds of IDEs out there , this is more like a personal choice.

Some examples

Baby steps

This is a small script to plot graphs (using Spyder IDE) , I just need to specify the column names and read the CSV output from the FDS simulation, There is an even better and more efficient way to plot using iLOC and a for loop but for the sake of interpretability, I am uploading this code

The output looks like:

Some fun with graphs

The following image below is a piece of code written on Spyder IDE to animate a graph, and since its written in Python, the readability and interpretability are pretty high. Someone with a basic understanding of programming will be able to understand the code, and if you are clueless, just paste the code in ChatGPT and ask her to explain it to you. Here, iLOC and a for loop are used to read the data, plot it, and animate it—all with 38 lines of code.

Output :

Some advanced use cases (some boring stuff):

A.I. in Fire

Some fun playing around this project https://www.hackster.io/stefanblattmann/real-time-smoke-detection-with-ai-based-sensor-fusion-1086e6

The collection of training data is performed with the help of IOT devices since the goal is to develop an AI-based smoke detector device. Many different environments and fire sources have to be sampled to ensure a good dataset for training. A short list of different scenarios which are captured:

  • Normal indoor
  • Normal outdoor
  • Indoor wood fire, firefighter training area
  • Indoor gas fire, firefighter training area
  • Outdoor wood, coal, and gas grill
  • Outdoor high humidity
  • etc.

The dataset is nearly 60,000 rows long. The sample rate is 1 Hz for all sensors. To keep track of the data, a UTC timestamp is added to every sensor reading. we basically analyse what attributes contribute to a fire alarm There are 13 key attributes that trigger an alarm so we first try to understand how each parameter correlates with each other, how sensitive each are, and how they affect the state of the detector

Good luck using Excel to do this work!

The heatmap below shows correlations between the attributes.

Some Classification Algorithms applied on the database

Everyone loves Artificial Neural Networks (training an ANN on this dataset)

Output :

Analysing the result

  • The left column (with labels “0” and “1” in the rows) shows the actual true labels from the data. Here, “0” means that there was no fire alarm, and “1” means that there was a fire alarm.
  • The top row (with labels “0” and “1” in the columns) shows the predicted labels by the model. Again, “0” stands for no fire alarm predicted, and “1” stands for a fire alarm predicted.
  • The cell with “4325” represents the number of times the model correctly predicted that there was no fire alarm (true negative).
  • The cell with “11142” represents the number of times the model correctly predicted that there was a fire alarm (true positive).
  • The cell with “63” shows the number of times the model incorrectly predicted that there was no fire alarm when there was one (false negative).
  • The cell with “128” shows the number of times the model incorrectly predicted that there was a fire alarm when there wasn’t one (false positive).

Conclusion

I know I have not given a step-by-step guide on how to set everything up; this was not the intention, but my idea was to introduce the vast amount of resources out there just waiting to be used. I had a vague idea of these before IMFSE but never actually used them because:

“You never actually use these until and unless you need to use them”—wise words from a fellow IMFSE student, but you don’t have to approach it likewise, because once you start to play with these tools, you will understand that it’s easy to write a couple of lines of code and produce amazing graphs and enable yourself to perform cleaner analysis with these libraries. First-year IMFSE students who start their second semester at Lund would find these particularly interesting because we have weekly FDS assignments, and it would be ideal to start practicing and applying this. It may be a bit tedious to write the initial code but after that, you will realise that you need to put incrementally minimal effort and tweak your code bare minimum for every subsequent use.

Addressing the Elephant in the room – ChatGPT and other LLMs

This is something that I want to stress and is a bit worrysome , Do not use ChatGPT to complete your assignments/write exams (people have tried that and have got caught ) there is a limit to how you can effectively use these tools to aid your workflow. These LLMs have their strenghts and weakness and understanding it is paramount in effectively using them. They will never replace (atleast now) the critical thinking process that we put in drawing conclusions to a problem that we solve. The usage of ChatGPT that I mentioned here is merely for aiding you to code and refactor it and due diligance must be practised for further usage in the academia, for more details you can read the following articles

Also you can read this neat post by Professor Wojciech Węgrzyński ( this neatly sums up what I intent to say ) https://www.linkedin.com/feed/update/urn:li:activity:7044612939760189440/

I am not an expert programmer , I just know where to look for resources and get the work done, but feel free to get in touch with me ( https://www.linkedin.com/in/anisjayaram/ ) or drop a comment if you are particularly interested in anything mentioned in the blog so that I could potentially write a detailed post to address them.

Special credits to my group mates Matheus Pontes Lima (https://www.linkedin.com/in/mp97/) and Vanessa Valdeabella (https://www.linkedin.com/in/vanessavaldeabella/) – the graphs and figures are from some of our work here at UPC.

Regards,

Obi-Wan-Kenobi

Apprehensions, weird buildings, and an appreciation.

~ Anis Jayaram ~ Leave a comment

Hello there!

This is Obi-Wan, and this is my first blog. So here goes nothing.

Before starting my IMFSE journey, I was a typical “Sarkari Babu”—this is a colloquial term used to describe officials working for the government. I was basically responsible for managing the safety of a sour gas process complex located in the middle of the Arabian Sea, and it was quite a transition for me, who was familiar with the process industry, into the built environment. I carried a lot of apprehension as I started my semester in Edinburgh and it is often true that we fear the unknown

“Dr. Angus I don’t have a structural engineering background. How hard is this course going to be for me?”

This was the first question that I asked Dr. Angus Law after he introduced his course “Structural Design for Fire”, having worked the last 5 years in operational safety and fire protection, plus my undergraduate degree in safety and fire engineering had no major structural subjects in the curriculum. My apprehension grew even more when I realised IMFSE was sharing this course with SAFE (structural and fire safety engineering) and third-semester IMFSE students. At this point, I started to freak out, realising that most of these students have structural engineering backgrounds and some even have advanced structural engineering degrees.

Dr. Law basically told me that it wouldn’t be a cakewalk for me, but at the same time, he stressed that the course is designed not to test our structural engineering skills, and he was absolutely right because right after the exam results were declared and we received the exam statistics, I realised that I was in the extreme right corner of the bell curve, so this is for the Semester 1 guys- don’t be alarmed or tensed if a particular subject is not something that is familiar to you; you are all in good hands because, just remember what Dumbledore said: “Help will always be given at Hogwarts to those who ask for it,” and its often true that

“There are more things to alarm us than to harm us, and we suffer more often in apprehension than reality.“

The subjects at Edinburgh instigated a newfound interest in me, appreciating those “weird buildings”. For a larger period of my life, I was ignorant about architecture and often wondered about the troubles that designers have gone through to produce something that looks beautiful but serves no practical purpose (no disrespect). A special friend of mine helped me change my perspective by teaching me to value even the most abstract of designs, and as my time in IMFSE went on, I grew fascinated by the unique engineering challenges that these designs pose as well as the fact that fire protection challenges in the build environment can be exciting as architects become more creative, whereas this was typically not the case in the process industry. Now my brain is slowly wired to think stuff like, “Oh, how would the fire behave travelling along this structure?” or “Making guestimates on evacuation time?” and, of course, looking for affordances and emergency exits every time I enter a building.

Weirdly beautiful

A beautifully weird or is it weirdly beautiful building?

The first time I heard about this building was during the introductory lectures on structural design for fire (pardon my ignorance because I was used to the process industry). This building was also highlighted in the SDF course module “Keeping it Cool.”, At that moment, I was not able to appreciate the issue with this building since I was not very familiar with it or, in general, the built environment. As the course progressed, the fire dynamics lectures by Dr. Ricky Carvel introduced us to the legend Dr. Margaret Law, her research, and her contributions in designing this structure.

The mobility of the IMFSE programme enabled me to roam around Europe for a bit (of course the programme comes first before the excursions 🙃). So while backpacking through Italy during the Easter break, Farith Hinojosa Coca (my brother from another mother and fellow IMFSE enjoyer :v ) and I ended up in Paris on our way back to Lund. Hence, we decided to take a look at this structure; seeing her in all its glory really shifts the perspective, especially going back to the 1970s, when the engineers worked with a fraction of the resources that we have at our disposal now.

We were amazed by the facade of this building, and this is something that every fire engineer needs to see. This structure is none other than the Pompidou Building in Paris, which is considered to be an architectural masterpiece, and its innovative design and incorporation of advanced fire protection systems have helped to set a new standard for building safety and fire protection engineering. I am not going to spoil everything for you, but by the end of the semester, you guys will appreciate this structure and understand the challenges of having a facade as a load-bearing member. All hail, Margaret Law!

Not to be a killjoy, I am dropping a link here. Keep reading and appreciate the seminal works of one of the most prominent pioneers in our field

https://www.arup.com/perspectives/publications/books/section/engineering-fire-safety-some-selected-papers-from-margaret-law

The IMFSE programme is truly remarkable because it accounts for people from different backgrounds; we all started with different perceptions, different skill sets, and different outlooks. After year 1, I can confidently say that we are all on the same page when it comes to fire.

I am grateful to be part of this journey.

Regards,

General Kenobi