Turbulence Chapter 4: Laminar-turbulent Transition Modeling
This chapter focuses on the modeling of laminar–turbulent transition in RANS simulations. The transition region affects boundary layer behavior, drag, and heat transfer, and cannot be captured by fully turbulent models. Different transition mechanisms are introduced, followed by an overview of modern transition models based on intermittency, laminar kinetic energy, and empirical correlations. The summary includes Fluent-specific implementations and practical considerations for setup and mesh requirements.
Turbulence Chapter 3: Near-Wall Modeling
In this chapter, we journey into the turbulent zone right next to the wall — where sharp gradients, subtle balances, and small-scale chaos control the drag, heat transfer, and flow separation that engineers care about. We explore how near-wall turbulence is structured, how CFD models like wall functions or enhanced wall treatments handle it, and why roughness and mesh strategy matter more than you might expect. This is where the wall stops being just a boundary and becomes the real battleground of turbulence.
IBM Data Engineering: Python
Structured course notes from IBM’s Python for Data Science and AI. Includes syntax basics, functions, pandas, NumPy, file I/O, REST APIs, and web scraping. Focus on practical reference and tools for real-world data projects.
IBM Data Engineering: Introduction
A practical, high-level walkthrough of modern data engineering: tools, architecture, pipelines, wrangling, security, governance, and real-world practices. Based on the IBM Introduction to Data Engineering course, this post distills key lessons for both beginners and transitioning professionals.
Turbulence Chapter 2: Turbulence Anisotropy in RANS
Reynolds-Stress Models (RSM) aim to capture the directional complexity of turbulence where simpler models fail. This post breaks down the theory behind RSM, explains when and why it’s needed, and offers intuitive analogies and stability tips — all framed through the Socratic questions we use throughout the course.
Turbulence Chapter 1: Review of RANS-Boussinesq Models & Statistical Turbulence Description
Turbulence modeling is at the core of modern Computational Fluid Dynamics (CFD), bridging the gap between theoretical fluid mechanics and practical engineering applications. This guide explores the fundamentals of turbulence, from the Reynolds-Averaged Navier-Stokes (RANS) approach and the Boussinesq hypothesis to improved RANS models like Realizable k-ε, RNG k-ε, and curvature-corrected models. With a focus on practical CFD applications, we delve into turbulence production limiters, near-wall treatments, and Fluent best practices. This structured study consolidates critical turbulence modeling concepts, equipping CFD engineers with the knowledge to select and implement the most suitable models for their simulations.
Exploring SQL: Building a Foundation in Data
Structured Query Language (SQL) is an essential tool for any data-driven project. In this post, I share my experience with the SQL course by Luke Barousse, covering everything from basic queries to advanced concepts like CTEs and subqueries. I also explain how I plan to apply these new skills in real-world SQL projects at work.
Kaggle Playground series s5e1: Tough Beginnings
This blog post covers my first attempt at a Kaggle competition, where I explored data, engineered features, and trained models to predict sticker sales. Despite ranking around 1500th, I learned valuable lessons and discovered top competitor strategies that I'll dive into next.
Interpolation and Mapping in Fluid-Structure Interaction: Connecting the Dots
Mapping and interpolation bridge the gap between incompatible fluid and structure meshes in FSI simulations. This post explores key techniques like bucket search algorithms, spline methods, and their real-world applications.
From Metals to Insulation: The science of Conductive Heat Transfer
Conductive heat transfer is all around us, from the warmth of a cozy jacket to the chill of a metal surface. Dive into the science of how energy flows through solids, liquids, and gases, and discover why understanding conduction is key to improving everything from home insulation to electronics cooling.
2-Way Fluid-Structure Interaction: From Explicit to Implicit Coupling and Beyond
Dive into fluid-structure interaction (FSI) methods, from coupling algorithms to remeshing and stabilization techniques. Discover how these tools power real-world simulations in engineering and science.
Citizen Data Scientist, Module VII: The Power of Hypothesis Testing in Decision Making
In this blog, we demystify hypothesis testing, explaining the key concepts, steps, and practical examples that make it an indispensable tool for decision-making. We also showcase a practical assignment testing Python loops versus NumPy for performance
Citizen Data Scientist, Module VI: Mastering Models for Learning: A Deep Dive into Bagging, Neural Networks, and More
Learn about machine learning models like Random Forest, Neural Networks, and K-means clustering. This detailed guide explains concepts intuitively, with examples like predicting ice cream sales and classifying handwritten digits
Citizen Data Scientist, Module V: Unsupervised Learning: Discovering hidden patterns
Explore the world of unsupervised learning with intuitive examples. Learn about clustering, dimensionality reduction, and anomaly detection, and discover how these techniques reveal hidden structures in data.
1-Way Coupling in Fluid-Structure Interaction: Wind, Cooling, and Structural Response
Dive into the world of physics coupling and discover how fluids and structures interact. This guide covers the basics of 1-way coupling, the monolithic vs. partitioned approach, and the key differences between explicit and implicit time discretization methods.
Citizen Data Scientist, Module IV: Applying Data Science in Practice: Feature Engineering, Scaling, and Selection
Learn how to apply data science in practice by mastering essential preprocessing techniques like feature engineering, scaling, and selection. This post explains the importance of each step, with practical examples on how to improve your machine learning models.
Citizen Data Scientist, Module III: Measuring Model Performance: Metrics That Matter
Evaluating a machine learning model's performance is crucial to ensure it works well with unseen data. In this post, we explore key metrics for regression and classification, such as R², MSE, precision, recall, and the confusion matrix. With examples that clarify concepts like recall's importance in high-risk scenarios, we also explain k-fold cross-validation to enhance model reliability
Citizen Data Scientist, Module II: Supervised learning: Predicting the Future with Labeled Data
Supervised learning is at the heart of many machine learning applications, helping models make predictions based on labeled data. From predicting house prices to classifying emails, this blog post explores the basics of supervised learning, covering regression, classification, decision trees, and key concepts like gradient descent—all in an accessible and intuitive way.
Fluid-Structure Interaction for Beginners: From Bridges to Blood Flow
In this post, we explore the world of Fluid-Structure Interaction (FSI), a key area of study that reveals how fluids and solids influence each other in systems like bridges, airplanes, and even the human body. From basic principles to real-world examples, this guide will help you understand how these forces shape the structures we interact with every day.
Citizen Data Scientist, Module I: Introduction to Data Science: Laying the Foundation
In this first module of the Citizen Data Scientist course series, we explore the foundational principles of Data Science. From understanding key concepts like machine learning and the CRISP-DM process to getting hands-on with Python and essential libraries, this post lays the groundwork for your journey into the world of data-driven decision-making.