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Here we present a spatially resolved model for simulation of flow reduction, seston depletion and individual mussel growth inside a longline farm based on information about farm configuration (spacing between longlines, farm length and stocking density) and background environmental conditions (current speed, seston concentration and temperature).
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This 3N-dimensional space is called configuration space.
Configuration space is particularly useful for describing what is known as constraints on a problem.
According to the principle of virtual work, any infinitesimal virtual displacement in configuration space, consistent with the constraints, requires no work.
Thus, in this case, the constraint has reduced the number of independent coordinates from 3N to 6. Rather than restricting the behaviour of the system to a portion of the original 3N-dimensional configuration space, it is possible to describe the system in a much simpler 6-dimensional configuration space.
The collection of all possible configurations represents the configuration space.
Table 1 Configuration space for the cache hierarchy.
Formally, the quantum state of an object which consists of N particles is defined in 3N dimensional configuration space.
While the former obviously implies the latter, it is equally obvious that decoherence need not imply separation in configuration space.
This extends the range of configuration adjustment and avoids being limited to a local configuration space.
This paper aims to define a new configuration space, called flow pattern configuration spaces (FPCS), as a new computational tool for LCM process design.
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