Related Experiment Video
Updated: May 6, 2026

Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography
Published on: February 25, 2015
Monte Carlo renormalization-group analysis of percolation
Albert Brown1, Alexander Edelman, Jason Rocks
1Physics Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15217, USA.
We introduce a Monte Carlo renormalization group method to calculate critical behavior in percolation models. This approach determines renormalized bond probabilities and critical exponents for various dimensions and models.
Area of Science:
- Statistical Physics
- Computational Physics
Background:
- Percolation models are widely used to study phase transitions and critical phenomena.
- Calculating critical behavior and exponents in these models can be computationally intensive.
Purpose of the Study:
- To present a novel Monte Carlo renormalization group (MCRG) approach for analyzing critical behavior in percolation systems.
- To demonstrate the calculation of renormalized bond probabilities and critical exponents.
Main Methods:
- The study employs a Monte Carlo renormalization group (MCRG) technique.
- The MCRG approach is applied to determine renormalized bond probabilities.
Main Results:
- The MCRG method successfully calculates critical behavior for percolation models.
- Renormalized bond probabilities and critical exponents were determined for two-dimensional bond percolation.
Conclusions:
- The described MCRG approach is effective for calculating critical exponents in percolation models.
- This methodology is adaptable to different percolation models and dimensions, offering broad applicability.
More Related Videos
10:10Three-Dimensional Particle Shape Analysis Using X-ray Computed Tomography: Experimental Procedure and Analysis Algorithms for Metal Powders
Published on: December 4, 2020
11:34Controlled Synthesis and Fluorescence Tracking of Highly Uniform PolyN-isopropylacrylamide Microgels
Published on: September 8, 2016
Related Concept Videos
The Buckingham Pi Theorem
Entropy Change in Reversible Processes
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
Dimensionless Groups in Fluid Mechanics
Debye–Huckel–Onsager Conductance Equation
Propagation of Uncertainty from Random Error
Kohlraush’s Law and its Applications