Understanding Percolation Threshold p_c on Triangular Lattices

In summary, the percolation threshold is the probability at which a lattice must be filled in order to have a non-zero chance of a spanning cluster in an infinite lattice. This is typically represented as p_{c} and on triangular lattices, the threshold is 0.5. However, it is possible to construct a spanning cluster with less than half of the lattice filled, making the threshold more of a guideline than a rule. Books on the subject state that above the threshold, there will always be a spanning cluster, while below it there will not be.
  • #1
bubbloy
15
0
i am having some trouble understanding the meaning of what a percolation threshold is [tex] p_{c}[/tex].
apparently on triangular lattices a threshold of 0.5 is the result on any sized lattice.
however i can definitely think of a way to fill in half the points on a triangle lattice and not have it span across the lattice.
similarly, one could make a zig zag line of connected sites to span a lattice without using anywhere near 1/2 the points. so is the percolation threshold a number at which you can expect to see percolation?

all the books I am reading seem to say that above it everything has a percolating net and below it there are no possible percolating nets.

thanks a lot,

josh S
 
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  • #2
well in case anyone was wondering, the percolation threshold is the probability with which you need to fill in the nodes on a lattice so that in the limit of an infinite lattice, there is a non zero chance that you will have a spanning cluster going across it. for example, one could construct a spanning cluster across an infinite lattice by connecting a line of edges across the whole thing and in this example the probability you fill the lattice with is 0 (one row/infinite rows) but the probability of this happening is so low that it does not survive in the infinite limit.
 
  • #3


The percolation threshold, denoted as p_c, is a critical value that represents the point at which a system transitions from a disconnected state to a connected state. In the context of triangular lattices, it is the probability at which a randomly chosen lattice site is occupied, leading to the formation of a percolating cluster that spans the entire lattice.

The value of p_c is dependent on the specific lattice structure and can vary for different types of lattices. In the case of triangular lattices, p_c is known to be 0.5, meaning that if half of the lattice sites are occupied, there is a high likelihood of a percolating cluster spanning the lattice.

It is important to note that p_c is a statistical measure and does not guarantee the formation of a percolating cluster in every single instance. As you mentioned, there are ways to fill in half of the lattice points without creating a percolating cluster. However, on average, at p_c, there is a high chance of a percolating cluster forming.

In summary, the percolation threshold is a critical value that represents the point at which a system transitions from a disconnected state to a connected state. In the case of triangular lattices, it is a statistical measure and not a guarantee of percolation in every instance. It is used as a reference point to understand the behavior of percolation in different lattice structures.
 

1. What is percolation threshold p_c on triangular lattices?

The percolation threshold p_c on triangular lattices is a critical value that represents the point at which a network of interconnected nodes starts to transmit a signal or flow. It is a measure of the connectivity of the lattice and can be thought of as the minimum amount of nodes that need to be connected in order for percolation to occur.

2. How is p_c calculated on triangular lattices?

p_c is typically calculated using computer simulations or mathematical models. In simulations, random nodes are chosen and connected until the percolation threshold is reached. In mathematical models, the connectivity of the lattice is analyzed to determine the critical value.

3. What factors affect the value of p_c on triangular lattices?

The value of p_c can be influenced by several factors, including the shape and size of the lattice, the type of connectivity (e.g. nearest neighbor or next-nearest neighbor), and the presence of any defects or obstacles in the lattice.

4. How does the value of p_c on triangular lattices compare to other lattice types?

The value of p_c on triangular lattices is often lower than on other lattice types, such as square or hexagonal lattices. This is due to the unique connectivity of triangular lattices, where each node has three nearest neighbors compared to four or six in other lattice types.

5. What are the applications of understanding p_c on triangular lattices?

Understanding p_c on triangular lattices has applications in various fields, such as physics, chemistry, and biology. It can help predict the flow of fluids in porous materials, the spread of diseases in networks, and the behavior of complex systems in nature. It also has practical applications in engineering and designing efficient networks for communication and transportation.

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