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The non-planar vortex lattice method has advantages in both the computational accuracy and efficiency.
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Both methods can improve the computational accuracy with the refinement of mesh.
Simulation results indicate that the reduction technique can significantly accelerate the simulation without worsening the convergence or losing the computational accuracy for both types of iterations, and the acceleration effect becomes more remarkable as the problem scale increases.
Numerical results in Tables 4, 5, 6, and 7 indicate that, even for a nonlinear equation, the computational accuracy of the IM method is improved for both a non-stiff problem and a stiff problem.
This simplification of integral computation not only improves the computational speed, but also improves the computational accuracy.
nature of bootstrap sampling, familiar formulas describe the computational accuracy of the Bayes estimates.
The presented method has high computational efficiency, and the computational accuracy is also verified.
Two numerical examples are illustrated to assess the performances of the computational accuracy and efficiency.
Comparisons are made to validate the computational accuracy and efficiency of the present approach.
And the influence of different spacing of control points on the computational accuracy is investigated.
The computational accuracy of the proposed model was tested for different time steps and grid size.
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