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In VERTISF development, a virtual reality platform and numerical models were hosted on different computers and connected by a network to improve simulation speed.
In order to improve simulation speed, this paper takes the following heuristic load shedding strategies: Firstly, assuming that all load points are not shed, when the case does not meet the following conditions: (1) the smallest load is shed firstly, repeat this process until it satisfies (1).
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Smaller granularities failed to improve simulation results.
Aside from those sampling techniques, this paper proposes a novel approach to further improve MC simulation speed with almost the same accuracy.
The results demonstrate excellent accuracy of the present method, and drastically improved numerical simulation speed.
The proliferation of four-dimensional imaging technologies, increasing computational speeds, improved simulation algorithms, and the widespread availability of powerful computing platforms is enabling simulations of cardiac hemodynamics with unprecedented speed and fidelity.
We then examine the improvement in simulation speed by re-profiling the SSE-enabled codes and report the improved runtimes.
By using an improved delta circuit model, simulation speed can be improved automatically due to the dynamic step control in transient analysis.
The results show that the parallel computing improves precision and increases the simulation speed of the dam-break flow, the simulation of 320 h flood process can be completed within 1.6 h on a 16-kernel computer; a speedup factor of 8.64× is achieved.
In this way, we gain several advantages: i) the efficiency of the algorithm is improved for a given box size; ii) a fractional number of molecules can be realized, which is important for irreversible reactions; and iii) the simulation speed can be improved because the calculations can be easily parallelized.
This fast simulation model is able to obtain almost identical simulation results as the detailed switching model while simulation speed can be improved by approximately 10,000 times.
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