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A new time step selection procedure is proposed for solving nonlinear diffusion equations.
For the calculation of these rates, it is desired to calculate of productivity index, mobility, compressibility factor, and pore volume in the new time step.
Once the positions at the new time step are known, the velocities and accelerations at the new time step are computed according to v → n + 1 = γ β Δ t ( x → n + 1 − x → n ) + ( 1 − γ β ) v → n + Δ t ( 1 − γ 2 β ) a → n, (12).
Then, before invoking the local constitutive model at every new time step, the local equivalent strain is transformed to the nonlocal one, and stresses of each integration point are updated, the corresponding history variables are stored for next step.
To deal with the nonlinear part in a simple way, we might just as well use the quantities at the previous time step to approximate those at the new time step.
Then, making use of the work presented in [89], [90], [22], this positivity study is extended to high-orders of accuracy, where new time step constraints are obtained, and proper limitation is required.
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These new time stepping schemes are necessary to get accurate and stable simulations of (forced) water waves and other non-autonomous variational systems, which we illustrate in our numerical results.
In total we now require three GPU kernels to compute gravity, one copy kernel to move particles from CPU buffers to GPU buffers, one kernel to predict the particles to the new time-step and finally, the gravity kernel to compute the forces.
Our new time-stepping method consistently demonstrates as good as or better performance than existing schemes.
The new time-stepping scheme is composed of two backward Euler steps, but has twice the convergence rate of the backward Euler scheme and alleviates the convergence problems that are often experienced when employing compressive limiter functions.
With regard to the two dimensional Dirichlet or Neumann boundary problems, our new time-stepping schemes coupled with discrete Fast Sine / Cosine Transformation can be applied to simulate the two-dimensional nonlinear Klein Gordon equations effectively.
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