Interested in working with me, then click here to book a slot with me.

Dear Reader,

I hope you had a good week and having a good weekend. It was quite a busy one for me as I officially it is my second week of teaching this new year. I had taken over a new course from a colleague and so it is both exciting teaching but also challenging developing the new lecture materials.

Here are the focus for this newsletter:

1. Technical reflections: Computational challenges of modelling smart stents
2. Behind the Scenes at CM Videos: Waitlist to my Live Cohort masterclass
3. Quote of the week: Niche and opportunities

Technical Reflections

Computational challenges of modelling smart stents

Last year, I started this technical reflections about my work with smart stents. In this newsletter, I am following up on that previous discussion. If you want to follow up on my initial article on this, then read it here: The development of Smart Stents for managing heart diseases. Let us continue the conversations.

My work is in the area of virtual testing assessment of material systems of which a stent is one. I have published papers exploring how computational modelling can help in the development of next-generation biodegrable stents that are manufactured by 3D printing. However, in this exciting field of smart stents, here are some thoughts I have about its computational modelling challenge and if you join me in this research, we would be dealing with some of these.

1. Constitutive modelling of biodegradable materials: The primary challenge in modeling biodegradable stents lies in developing accurate constitutive equations that reflect both the mechanical behavior and degradation mechanics of materials like PLA and PCL. These polymers degrade over time, requiring computational models that simulate time-dependent changes in material properties, such as loss of mass and mechanical stability. Factors like pH and temperature further affect degradation rates, necessitating models that account for nonlinear viscoelasticity and evolving time-dependent properties to reflect real-world conditions accurately.
2. Modelling of structural response: Stents, often with mesh-like, complex designs, provide structural support to arteries, posing a computational challenge in modeling their structural response accurately. This involves using finite element modeling tools to simulate the stent’s behavior in real-time during crimping, inflation, hold, and deflation stages in coronary angioplasty. Developing reliable finite element models of the stent within the in vivo arterial environment is essential for realistic performance prediction.
3. Understanding and modelling the dynamics of degradation: Biodegradable stents gradually lose mechanical stiffness over time, aligning with the healing process and the restoration of normal blood flow. This degradation occurs over an extended period, often beyond 24 months. A key modeling challenge is creating a numerical framework that captures these time-dependent changes in mechanical properties. Constitutive equations need to relate the polymer’s macromolecular behavior to the gradual loss of strength and material associated with degradation. Starting with an effective diffusion model can provide a strong foundation for developing this degradation-based constitutive model.
4. Understanding sensors and real-time data transmission/transmission: : Smart stents are embedded with micro- and nanoscale sensors, which add complexity to the computational model. These sensors may need to monitor blood flow, pressure, or detect changes in the stent's structural integrity. Numerically modeling these sensors involves capturing real-time, dynamic feedback mechanisms, where the stent’s data-transmitting components interact with the surrounding environment. Furthermore, simulating the piezoelectric or electromechanical behavior of sensors within a degrading polymer matrix is computationally intensive, as these properties change over time.
5. Dealing with the multi-physics paradigm : To achieve an effective in vivo model for virtual testing of stents, researchers must address the multi-physics paradigm. This involves capturing simultaneous mechanical, chemical, and biological processes, including structural responses to blood flow, chemical degradation affected by environmental factors, and biological interactions like cell growth and immune reactions. Integrating these domains requires complex algorithms and substantial computational resources, as each operates on different timescales, challenging the balance between precision and efficiency.
6. Novel 3D printing manufacturing techniques: Another challenge is developing effective 3D printing techniques for biodegradable stents. Aside from fused deposition modeling (FDM) with exception of a few others as selective laser sintering techniques, most 3D printing methods operate at high temperatures that exceed the melting points of many polymers. A suitable technique is needed to not only print uniform stent materials but also incorporate sensors at the strut level, enabling precise functionality within the stent’s complex structure. For the later, we could explore polyjet printing which operates at low temperatures, preserving sensor integrity, and enables embedding of electronics or responsive elements.

The above are research directions that need to be explored if one is to advance work in this area of smart stents. It will obviously be a few PhDs but that is the direction we are going. Please let me know if you are keen to be involved.

Behind the Scenes at CM Videos

Waitlist to my Live Cohort masterclass

I mentioned in my last newsletter that I am building a Live Cohort Masterclass on Computational Modelling. The aim is to provide sustained and detailed teaching on several of the topics I cover on the CM Videos YouTube channel ranging from RVE modelling design, Validation of RVE models, Homogenization strategies; 2D and 3D periodic boundary conditions implementation in ABAQUS, Monte carlo methods for creating randomness within a microstructure, and general solid foundations of FEM principles that wraps around these ideas. It is planned to be about 12-week masterclass with lots of interactions. I also would make myself available to the students to have 1-on-1 sessions where I can discuss specific challenges you may have with regards to your computational modelling problems. This is the very first time I am doing this and something that I have had wanted to do for a very long time.

As you can guest, this is something I am very excited about and I would like as many of my subscribers on this newsletter to be involved hence I will be periodically writing about it in this general newsletter. However, I do realize not all of you would want to hear me go on about it hence I have created a mailing list (waitlist) where I will communicate more frequently about the course design, timelines and also answer any specific queries you may have about it. If you want to be interested in joining or hearing more about the Live cohort masterclass, then click to join the wait list below.

Quote of the Week

Niches, Opportunities and RVE modelling

​

I was listening to a podcast today. The podcast is called The Think Media Podcast and hosted by Sean Cannell. I was listening to episode 383 titled: How Small Creators Land Big Brand Deals in 2025 (Complete Sponsorship Guide). I regularly listen to this podcast as Sean has been a real great mentor for my YouTube journey, especially the fact that he helps small YouTubers with actionable insights to grow their YouTube business. He also is a christian and his faith and values resonate very closely with mine. In this particular episode, he was interviewing Justin Moore who specializes in helping creators secure sponsorship revenues to fuel their creative endeavours. I am taking my quote of the week from a statement made by Justin Moore, during a podcast hosted and it goes as follows:

When I heard that, I began thinking about how true that is for life and for most of what we do in computational modelling. RVE modelling design is really about delving deeper into a specific area of a computational modelling problem so that you can find opportunities to predict more accurately micromechanical features of a structural problem. Without delving deeper into the RVE lengthscale, it is often quite challenging to predict such features as damage, evolution of crack path, boundary condition effects and interface contribution to failure for heterogeneous system. To harness the opportunities available for high predictive fidelity solutions, you would have to move from a smeared macroscale/structural scale problem to a microstructural, multi-physics and enhanced predictive fidelity model.

Therefore, in business, the more niched you are in your area of interest, the more opportunities there are to find the right market and generate significant income to fuel the business. Similarly, RVE modelling gives you an opportunity to niche down into a solution and opportunites for modelling predictive fidelity that operators at a non-niched level would never find.