Abaqus Example Problems Guide Pdf

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AbaqusExplicit has capabilities that allow automatic solution mapping using adaptive meshing. Therefore, the mapping process is easier since it is contained within the analysis and the user only has to decide how frequently remeshing should be done and what method to use to map the solution from the old mesh to the new mesh as the solution progresses. Abaqus/Explicit offers default choices for adaptive meshing that have been shown to work for a wide variety of problems. Finally, solution-dependent meshing is used to concentrate mesh refinement areas of evolving boundary curvature. This counteracts the tendency of the basic smoothing methods to reduce the mesh refinement near concave boundaries where solution accuracy is important.

For Case 1 several different analyses are performed to compare the different section control options available in Abaqus/Explicit and to evaluate the effects of mesh refinement for the billet modeled with CAX4R elements. A coarse mesh (analysis COARSE_SS) and a fine mesh (analysis FINE_SS) are analyzed with the pure stiffness form of hourglass control. A coarse mesh (analysis COARSE_CS) is analyzed with the combined hourglass control. A coarse mesh (analysis COARSE_ENHS) and a fine mesh (analysis FINE_ENHS) are analyzed with the hourglass control based on the enhanced strain method. The default section controls, using the integral viscoelastic form of hourglass control, are tested on a coarse mesh (analysis COARSE) and a fine mesh (analysis FINE). Since this is a quasi-static analysis, the viscous hourglass control option should not be used. All other cases use the default section controls.

The Abaqus/Standard analyses for Case 1 compare the two hourglass control options and evaluate the effect of mesh refinement for the billet modeled with CAX4R elements. A coarse mesh (analysis COARSE_S) and a fine mesh (analysis FINE_S) are analyzed with the pure stiffness form of hourglass control. A coarse mesh (analysis COARSE_EH) and a fine mesh (analysis FINE_EH) are analyzed with hourglass control based on the enhanced strain method. A coarse mesh (analysis COARSE_EHG) with CGAX4R elements is also analyzed with hourglass control based on the enhanced strain method for comparison purposes.

No mesh convergence studies have been done, but the agreement with the results given in Lippmann (1979) suggests that the meshes used here are good enough to provide reasonable predictions of the overall force on the dies.

The material model assumed for the billet is that given in Lippmann (1979). Young's modulus is 200 GPa, Poisson's ratio is 0.3, and the density is 7833 kg/m3. A rate-independent von Mises elastic-plastic material model is used, with a yield stress of 700 MPa and a hardening slope of 0.3 GPa.

In Abaqus/Standard the rigid die is displaced by 9 mm in the axial direction using a displacement boundary condition. In Abaqus/Explicit the -displacement of the rigid die is prescribed using a velocity boundary condition whose value is ramped up to a velocity of 20 m/s and then held constant until the die has moved a total of 9 mm. The total simulation time of the Abaqus/Explicit analysis is 0.55 millisec, and the loading rate is slow enough to be considered quasi-static. In both Abaqus/Standard and Abaqus/Explicit the radial and rotational degrees of freedom of the rigid die are constrained.

For all cases the analyses are done in two steps so that the first step can be stopped at a die displacement corresponding to 44% upsetting; the second step carries the analysis to 60% upsetting. In the Abaqus/Standard simulations the solution mapping analysis restarts from the end of the first step with a new mesh and proceeds until 60% upsetting is achieved.

The interpolation technique used in solution mapping is a two-step process. First, values of all solution variables are obtained at the nodes of the old mesh by extrapolating the values from the integration points to the nodes of each element and averaging those values over all elements abutting each node. The second step is to locate each integration point in the new mesh with respect to the old mesh (this assumes all integration points in the new mesh lie within the bounds of the old mesh: warning messages are issued if this is not so, and new model solution variables at the integration point are set to zero). The variables are then interpolated from the nodes of the element in the old mesh to the location in the new mesh. All solution variables are interpolated automatically in this way so that the solution can proceed on the new mesh. Whenever a model is mapped, it can be expected that there will be some discontinuity in the solution because of the change in the mesh. If the discontinuity is significant, it is an indication that the meshes are too coarse or that the mapping should have been done at an earlier stage before too much distortion occurred.

The model is built and meshed using Abaqus/CAE. The solution mapping for the Abaqus/Standard analysis is done by extracting the two-dimensional profile of the deformed billet from the output; the user must enter commands into the command line interface at the bottom of the Abaqus/CAE main window. To extract the deformed geometry from the output database as an orphan mesh part, use the command PartFromOdb, which takes the following arguments:nameThe name of the orphan mesh part to be created.

The command PartFromOdb returns a Part object that is passed to the command Part2DGeomFrom2DMesh. This command creates a geometric Part object from the orphan mesh imported earlier. It takes the following arguments:

Once the profile of the deformed part has been created, the user can switch to the Mesh module, remesh the part, and write out the new node and element definitions to be used in the mapping analysis. The Python script file billet_rezone.py is included to demonstrate the process described above.

Adaptive meshing consists of two fundamental tasks: creating a new mesh, and remapping the solution variables from the old mesh to the new mesh with a process called advection. A new mesh is created at a specified frequency for each adaptive mesh domain. The mesh is found by sweeping iteratively over the adaptive mesh domain and moving nodes to smooth the mesh. The process of mapping solution variables from an old mesh to a new mesh is referred to as an advection sweep. At least one advection sweep is performed in every adaptive mesh increment. The methods used for advecting solution variables to the new mesh are consistent; monotonic; (by default) accurate to the second order; and conserve mass, momentum, and energy. This example problem uses the default settings for adaptive mesh domains.

Input files referred to in this guide are included with the Abaqus release in compressed archive files. To reproduce the graphical representation of the solution reported in some of the problems, modifications to various options (such as step time, display options, output requests, solver precision, output frequency, etc.) may be required.

Parametric study script (.psf) and user subroutine (.f) files can be fetched in the same manner. All files for a particular problem can be obtained by leaving off the file extension. The abaqus fetch utility is explained in detail in Fetching sample input files.

It is sometimes useful to search the input files. The findkeyword utility is used to locate input files that contain user-specified input. This utility is defined in Querying the keyword/problem database.

In addition to this guide, there are two other guides that contain worked problems. The Abaqus Benchmarks Guide contains benchmark problems that provide evidence that the software can produce a result from a benchmark defined by an external body or institution such as NAFEMS. The tests in this guide are sufficient to show accuracy and convergence compared to benchmark data. The Abaqus Verification Guide contains a large number of tests that are intended to provide evidence that the implementation of the numerical model produces the expected results for one or several well-defined options in the code.

Mobile devices like tablets and smart phones are increasingly becoming an important part of our lifestyle. One of the critical features that determines the overall quality of such a device is the audio quality of the speaker. The most commonly used speaker in mobile devices is based on the principle of moving coil transduction, as described by Jackman et al. (2009). An electromagnetic field applied on the voice coil generates a mechanical driving force (known as the Lorentz force) that generates the sound by imparting motion to the diaphragm. There are several key design challenges in the selection of the transducer and its placement. Some of the important issues to be addressed in the audio system design are discussed below.

The audio quality is reduced due to the inherent nonlinearities such as the total harmonic distortion. Even for microspeakers with very low diaphragm motions or excursions, the nonlinearities due to the mechanical resistance at the restraints (in addition to acoustic losses in ports) tend to be dominant. The nonlinearities are guided not so much by the displacement but by the velocity of the diaphragm excursion (Klippel and Knobloch, 2013). Hence, there is a need to model the nonlinearities that can be appropriately modeled in the time domain to reproduce the physically accurate solution. Simulating the audio response as a harmonic analysis in the frequency domain may provide inaccurate results, especially at higher levels of nonlinearity.

This example discusses a methodology that simulates the diaphragm excursion (and the subsequent acoustic response) due to electromagnetic excitation as a transient problem. The electromagnetic circuit of the speaker is assumed to have no direct contribution to the structural response of the system and, hence, is modeled independently as a reduced-order logical system in Dymola. The structural-acoustic response of the diaphragm in the speaker assembly is modeled with its full three-dimensional finite element representation in Abaqus/Explicit. The Abaqus model and the Dymola model interact with each other through the co-simulation technique in the time domain.

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