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Mean inter-particle distance (or mean inter-particle separation) is the mean distance between microscopic particles (usually atoms or molecules) in a macroscopic body.
In physics, mean free path is the average distance over which a moving particle (such as an atom, a molecule, or a photon) travels before substantially changing its direction or energy (or, in a specific context, other properties), typically as a result of one or more successive collisions with other particles.
The mean free time for a molecule in a fluid is the average time between collisions. The mean free path of the molecule is the product of the average speed and the mean free time. [1] These concepts are used in the kinetic theory of gases to compute transport coefficients such as the viscosity. [2] In a gas the mean free path may be much larger ...
mean free path, the average distance between two subsequent collisions of the electron (ion) with plasma components: , =, ¯,, where , ¯ is an average velocity of the electron (ion) and , is the electron or ion collision rate.
Mean free time; Mean inter-particle distance; Mean radiant temperature; Measure-preserving dynamical system; Measure (physics) ... Motion graphs and derivatives;
We can take the average interparticle spacing in the gas to be approximately (V/N) 1/3 where V is the volume and N is the number of particles. When the thermal de Broglie wavelength is much smaller than the interparticle distance, the gas can be considered to be a classical or Maxwell–Boltzmann gas.
The graphs determine the local equations of motion, while the allowed large-scale configurations describe non-perturbative physics. But because Feynman propagators are nonlocal in time, translating a field process to a coherent particle language is not completely intuitive, and has only been explicitly worked out in certain special cases.
However, if the range of the interatomic potential is finite, i.e. the potentials () above some cutoff distance , the summing can be restricted to atoms within the cutoff distance of each other. By also using a cellular method for finding the neighbours, [ 1 ] the MD algorithm can be an O(N) algorithm.