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The simulation process can be understood easily by creating transparent programs by creating sequences of these compact R functions.
Thirty canopies reconstructed from the same MC data sample (describing 250 leaves, i.e. less than 20% of crown leaf number) by both procedures indicated that the variability associated with each simulation process can be neglected, i.e. MC leaf sampling played the principle role in driving foliage reconstruction and deciding canopy geometrical traits.
Details of simulation process can be referred to some previous literature [49, 50].
In the case of a cell-based system, the entire simulation process can be observed in the visualization environment.
The related PC based FDTD simulation process can be found in our former published papers [24, 26].
Ignoring or specifying incorrect diffusion rate during simulation process can lead to overestimate or underestimate the oil recovery caused by the injected gas.
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Consequently, the accurate of water quality simulation processes can be enhanced.
To cope with this, the candidate solutions, in a simulation optimization process, can be compared on various possible environments, using such principles as those proposed by Taguchi.
In addition, we extend GPU-MHD to support the visualization of the simulation results and thus the whole MHD simulation and visualization process can be performed entirely on GPUs.
In order to pursue cost savings and a time efficient design, the development of numerical simulations of the process can represent a valid choice for engineers.
A detailed analytical investigation that is based on numerical simulation suggests that this process can potentially produce 100 L/day distilled water with an average specific electric energy consumption as low as 3.6 kWh/m3 using a total of eight 2 m2 flat plate solar collectors.
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