Defining HEC-RAS 2D Computational Equations
HEC-RAS can perform two-dimensional unsteady flow routing using 2D computational equations. HEC-RAS provides four methods for computing the flow field in a 2D mesh, each of which may be selected from the Unsteady Flow Computational Options dialog box.
Follow the steps below to use the various 2D computational equation options:
- From the Analysis ribbon menu, select the Unsteady Flow Computational Options command.
- The Unsteady Flow Computational Options dialog box will be displayed as shown below.
Refer to this article in our knowledge base to learn more about the Unsteady Flow Computational Options command. - Select the 2D Flow Options panel of the Unsteady Flow Computational Options dialog box as shown below.
- The 2D Flow Area Computational Parameters section of the 2D Flow Options panel allows the user to set computational options and tolerances for the 2D computational module.
The following sections describe the various advantages and disadvantages of the HEC-RAS 2D computational equations.
2D Computational Equations in HEC-RAS
The Computational Equation dropdown combo box of the 2D Flow Area Computational Parameters section allows the user to select one of the four available 2D computational equations for computing the flow field in a 2D mesh. The following 2D computational equations are provided in the dropdown combo box:
- Diffusion Wave
- SWE-Eulerian-Lagrangian Method
- SWE-Eulerian Method
- SWE-Local Inertia Method
Refer to this article in our knowledge base to learn more about 2D computational equations.
Advantages and Disadvantages of HEC-RAS 2D Computational Equations
Each 2D computational equation has specific applications depending on the modeling requirements and the nature of the hydrodynamic processes involved. Choosing the right computational equation is always crucial for achieving accurate and efficient modeling outcomes. It is important to understand that each 2D computational equation has distinct advantages and disadvantages.
The following sections describe the benefits and drawbacks of various 2D computational equations.
Diffusion Wave Equation
The Diffusion Wave equation method is the default solver, and it allows the user to model various modeling situations accurately. In general, many flood applications will work fine with the Diffusion Wave equations. The Diffusion Wave equation is one of the computational methods used for modeling the 2D flow area. Some of the advantages and disadvantages of using the Diffusion Wave equation are as follows:
Advantages
- This computational method is suitable for scenarios where flow is primarily driven by gravity and friction, such as flood extent estimation.
- This computational method allows computations to run faster and with greater stability and the methodology can handle larger time steps.
- This computational method is good for assessing the potential effects of dam breaks and interior areas due to levee breaches.
- The methodology is good for computing approximate global estimates, such as flood extent.
- The methodology is good for quick estimations before a full momentum equation (SWE) run.
Disadvantages
- It does not account for fluid acceleration changes and is less accurate for detailed hydrodynamic studies where wave propagation is significant.
- This computational method is not suitable for sharp contractions and expansions.
- This methodology is not effective for predicting detailed velocity distributions in channels or around objects.
- This computational method does not work well for mixed flow regimes and hydraulic jumps.
Because the user can easily switch between 2D computational equations, each solver can be tried for a given model to see whether the usage of the Diffusion Wave equation is preferable to the Shallow Water Equations.
The decision to use the Diffusion Wave equation should be based on the specific requirements of the user, considering factors like the level of detail required, computational resources available, and the nature of the hydraulic phenomena being modeled.
Refer to this article in our knowledge base to learn more about scenarios in which the Diffusion Wave equation can be used.
SWE-Eulerian-Lagrangian Method
The SWE-Eulerian-Lagrangian Method (SWE-ELM) is the original solution for the Shallow Water Equations in HEC-RAS modeling and is suitable for a wide range of conditions. Some of the advantages and disadvantages of the SWE-Eulerian-Lagrangian Method are as follows:
Advantages
- This computational method provides detailed and accurate hydrodynamic modeling.
- The SWE-Eulerian-Lagrangian Method also has options for modeling turbulence and Coriolis effects.
- This computational method requires smaller computational intervals than the Diffusion Wave method in order to run stably.
- This computational method is suitable to use for flat-sloping river systems where slopes are usually less than 1 ft/mile.
These advantages make the SWE-Eulerian-Lagrangian Method a robust choice for complex hydraulic modeling, thus providing engineers with a powerful computational tool for detailed analysis and decision-making in water resources projects.
Under certain types of modeling conditions, the SWE-Eulerian-Lagrangian Method should be used for greater accuracy. The users can try the multiple equation sets and efficiently compare the answers by selecting the equation set to use and running the simulation. It is suggested that users first create a new Plan file and then use a different equation set to easily compare the results.
Disadvantages
- This computational method requires more computational power, resulting in longer run times.
- This computational method can become numerically unstable in rapidly changing flow directions.
Refer to this article in our knowledge base to learn more about scenarios in which the Shallow Water Equation can be used.
SWE-Eulerian Method
The SWE-Eulerian Method is one of the computational equations used for 2D flow area modeling. Some of the advantages and disadvantages of the SWE-Eulerian Method are as follows:
Advantages
- This computational method is very effective in conserving mass and momentum over the computational domain.
- This computational method produces less numerical diffusion than the original Shallow Water equation.
- This computational method provides a very detailed look at specific areas. For example, it is particularly useful for examining changes in water surfaces and velocities at and around hydraulic structures, piers/abutments, and areas with tight contractions and expansions.
Disadvantages
- More complex and computationally intensive than simpler methods, potentially leading to longer computational times.
- The increased complexity of the equations used in the SWE-Eulerian Method might require a deeper understanding of fluid dynamics and numerical modeling.
Though the SWE-Eulerian Method solution method is more momentum-conservative, it may require smaller time steps and produce longer run times. Refer to this article in our knowledge base to learn more about scenarios in which the SWE-Eulerian Method can be used.
SWE-Local Inertia Method
The SWE-Local Inertia Method has specific advantages and disadvantages that make it suitable for certain modeling scenarios. Some of the advantages and disadvantages of the SWE-Local Inertia Method are as follows:
Advantages
- This method is computationally more efficient due to its simplified approach.
- This computational method ignores the advection, diffusion, and Coriolis terms in the momentum equation, which results in a system of equations that is much simpler to solve.
- The simplification of the equations makes this method easier to implement and understand, especially for straightforward modeling scenarios where high precision in momentum conservation is not critical.
Disadvantages
- For detailed studies involving complex interactions of flow with structures or in highly variable flow conditions, this method might not capture all the necessary dynamics, leading to less accurate results.
- This computational method has a limited application scope.
- This method ignores certain dynamic aspects of fluid motion (like advection, diffusion, and Coriolis effects), and it may not be suitable for scenarios where these factors significantly influence the flow, such as in highly dynamic and turbulent waters.
This method is a good choice for simpler, less dynamic modeling scenarios where speed and computational efficiency are more critical than capturing complex flow dynamics. For more complex scenarios, other methods like the SWE-Eulerian-Lagrangian Method or SWE-Eulerian Method equations may be more appropriate.