Solving PDE with smoothing - Time step Query

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In summary, the use of a dissipative smoothing term in an Euler CFD code helps reduce numerical oscillations, but if the time step is too small, it can introduce errors and decrease the accuracy of the solution. This is because the numerical dissipation is applied on a larger scale than the physical phenomena, dampening out smaller-scale features and reducing accuracy. Therefore, using a larger time step can minimize the effects of the smoothing term and improve the accuracy of the solution.
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Timeforheroes0
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Hi,
I'm solving an Euler CFD code using the Lax-Wendroff method. It contains a dissipative smoothing term which I'm looking to minimise to optimise the accuracy. The timestep and smoothing terms are uncoupled, however different stable time steps result in different accuracy once the calculation has converged when the smoothing factor is constant.
This doesn't make sense in my mind.. I understand why it would converge slower with a lower stable timestep, but how is the accuracy lower as well (i.e. the effects of smoothing is more pronounced)
Thanks for any help as I can't get my head around it.
 
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The dissipative smoothing term in an Euler CFD code is there to help reduce or eliminate numerical oscillations, which can arise from the use of a numerical method that is not perfectly stable. The way it works is by introducing a small amount of numerical dissipation (i.e. "smoothing") to the solution, which reduces the amplitude of the oscillations.However, if the time step is too small then this numerical dissipation can be overly effective and can actually introduce errors to the solution. This is because the numerical dissipation is applied on a larger scale than the physical phenomena being simulated, so it can effectively damp out smaller-scale features in the solution that should be present. As a result, the accuracy of the solution can suffer. So, in summary, the larger the time step, the less pronounced the effect of the numerical dissipation will be, leading to improved accuracy for the solution.
 

Related to Solving PDE with smoothing - Time step Query

1. What is a PDE and why is it important to solve it?

A PDE, or partial differential equation, is a mathematical equation that involves multiple variables and their partial derivatives. It is important to solve PDEs because they are used to model many physical phenomena and can help us understand and predict the behavior of complex systems in fields such as physics, engineering, and economics.

2. What is smoothing and how does it relate to solving PDEs?

In the context of solving PDEs, smoothing refers to a technique used to improve the accuracy and stability of numerical methods. It involves applying a low-pass filter to the solution, which smooths out any high-frequency oscillations and can prevent numerical instabilities.

3. What is a time step and why is it important in solving PDEs with smoothing?

A time step is the size of the interval between two consecutive time points in a numerical solution of a PDE. It is important in smoothing because it affects the accuracy and stability of the solution. A too small time step may lead to unnecessary computational cost, while a too large time step may result in numerical errors.

4. How do I choose an appropriate time step for solving PDEs with smoothing?

The appropriate time step depends on the specific problem and the numerical method being used. In general, a smaller time step will result in a more accurate solution, but at the cost of increased computational time. It is important to balance accuracy and efficiency when choosing a time step.

5. Are there any limitations to solving PDEs with smoothing?

While smoothing can improve the accuracy and stability of numerical methods for solving PDEs, it is not a universal solution. It may not be effective for all types of PDEs and can also introduce some smoothing errors. Additionally, the choice of smoothing technique and time step can also impact the overall accuracy of the solution.

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