Posts Tagged "simulation"

Many of our Abaqus customers don’t know that the Computational Fluid Dynamics approach (CFD) is not the only method of modeling fluids in Abaqus. There are many other possibilities and the right approach depends on the physics of the problem. This blog post discusses the multi physics methods of modeling fluids in Abaqus.

  • CFD method: This is the well-known and traditional method for fluids modeling. It’s based on Eulerian formulation, in which material flows through the mesh and can be accessed through the Abaqus/CFD solver. Application example: Flow through exhaust systems.
  • CEL method: This is a coupled Eulerian Lagrangian method primarily used in problems involving unbounded fluids where fluids free surface visualization is required. It’s also possible to simulate interaction between multiple materials, either fluids or solids. This method is accessible through Abaqus/explicit solver. Application example: Fluid motion in washing machine.
  • SPH method: This is a smooth particle hydrodynamics approach primarily used to model unbounded fluids that undergo severe deformation or disintegrate into individual particles. This method uses a Lagrangain approach in which material moves with the nodes or particles and can be accessed through the Abaqus/explicit solver. This method can be used for fluids as well as for solids. Application example: bird strike on an aero structure.

We can compare these three methods against multiple parameters such as materials, contact, computation speed, etc. to understand their applications and limitations:

  • Material considerations:

SPH method is most versatile in terms of material support. SPH supports fluids, isotropic solids as well as anisotropic solids.

CFD is the only technique that can model fluid turbulence

CFD is the only technique to model porous media

CFD and CEL allows material flow through the mesh: Eulerian

  • Contact considerations:


Additive manufacturing is not a new technology – it was introduced in the manufacturing industry in late 80s for very niche applications. Stereolithography, a variant of additive manufacturing, was introduced in 1986 for rapid prototyping applications; however, its true potential remained hidden for a long time. Additive manufacturing primarily refers to methods of creating a part or a tool using a layered approach. As a still-evolving technology, it now covers a family of processes such as material extrusion, material jetting, direct energy deposition, power bed fusion, and more.

Additive manufacturing expands design possibilities by eliminating many manufacturing constraints. Contrary to rapid prototyping and 3D printing, there has been a shift of focus to functional requirements in additive manufacturing; however, these functional requirements may deviate from what is expected due to many factors typical of an additive manufacturing process.

  • Change in material properties: Mechanical and thermal properties of a manufactured part differ from raw material properties. This happens due to material phase change which is typical to most additive manufacturing applications.
  • Cracking and failure: The process itself generates lots of heat that produces residual stresses due to thermal expansion. These stresses can cause cracks in material during manufacturing.
  • Distortion: Thermal stresses can lead to distortion that can make the part unusable.

The additive manufacturing process is not certifiable yet, which is a major barrier in widespread adoption of these processes commercially. The ASTM F42 committee is working on defining AM standards with respect to materials, machines, and process variables.

The role of Simulation in additive manufacturing

  • Functional design: The first objective is to generate a suitable design that meets functional requirements, then subsequently improve the design through optimization methodologies that work in parallel with simulation.
  • Generate a lattice structure: Many of the parts manufactured through AM have a lattice structure instead of a full continuum. One objective of simulation in AM is to generate a lattice structure and optimize it using sizing optimization.
  • Calibrate material: As mentioned before, the material properties of a final part can differ substantially from that of the raw material. The next objective is to capture the phase transformation process through multi-scale material modeling.
  • Optimize the AM process: Unwanted residual stresses and distortions can develop in the process. It is necessary to accurately capture these physical changes to minimize the gap between the as-designed and as-manufactured part specs.
  • In service performance: Evaluate how the manufactured part will perform under real life service loads with respect to stiffness, fatigue, etc.


Now let’s discuss each of these objectives in more detail, with respect to SIMULIA. […]

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