Wind turbines are a crucial source of renewable energy, but their blades are constantly exposed to harsh weather conditions, which can cause damage over time. To ensure the safety and efficiency of wind turbines, it is essential to detect damage in the composite materials used to make the blades. However, detecting damage in composites can […]
Subsea cables and pipelines are the backbone of the offshore infrastructure, playing a critical role in transporting energy and fuel. These pipelines and cables are essential for keeping the lights on and powering our homes and businesses, but with this importance comes a great responsibility to ensure their safe and reliable operation. One of the
Uncharted Waters: A Deep Dive into Assessing the Fatigue Life of Subsea Pipelines Read More »
Researchers have developed a novel machine learning and pattern recognition approach to solving the wind farm layout optimization problem (WFLOP). The traditional row-by-row grids of turbines seen in many wind farms today can suffer from a 10-15% annual energy production loss due to inter-turbine turbulence called the wake effect. To minimize this loss, an optimal
This short blog post is based on the one of the question I have received from a researcher/student who follows my channel and webpage. When simulating the behaviour of materials under high strain rates or extreme loading conditions, it’s important to accurately predict when and how they will fail. Two commonly used models for this
Difference between Hashin Damage Model and Johnshon Cook Damage Model Read More »
In this blog I take you through ductile damage modelling in ABAQUS CAE using both explicit and standard analysis steps. After going through this video you will be able to answer and perform the following with full confidence. How to simulate ductile failure of a metal in ABAQUS? Tips on improving convergence in simulating ductile
Ductile Damage Simulation in ABAQUS CAE – Through Explicit and Standard Read More »
Learning to predict material behaviour through machine learning.
Material Science via Machine Learning: The How and What Data? Read More »
