Category "Simulation"

From solver perspective, number of enhancements have been made but as additive manufacturing is gaining popularity these days, let’s start with what’s new is available at AM front.

Additive manufacturing functionality enhancement

  • Until past release, number of basic AM simulation features were not a part of main solver and required specific configuration to access. Starting 2018x all AM is in FD05 and in 2019x all AM is in GA.
  • Eigen strain has been added as an input/output in AM that can be accesses using existing subroutine UEPACTIVATIONVOL that now has eigenstrain as an argument. The material orientations can be defined as well as modified. As eigen strain is treated as instantaneous load, it can cause convergence problem. In such cases, eigen strain can be applied as a ramp input.

 

  • The conventional displacement of nodal output includes displacement prior to activation as well. New output variables UACT and URACT contain translational and rotational displacement only after activation.

  • Improved convergence of heat transfer analysis when linear elements are used with temperature dependent material properties.
  • The event series data is no longer limited to 53 million events. It has now been extended to 420 million events. Available from 2019xGA.
  • Property and parameter tables now available for Abaqus explicit as well starting 2019xFD01. Earlier this functionality was limited to standard only.
  • Number of heat energy outputs have been added for non-uniform moving flux. These are element internal heat energy called as EHUMDFLUX and element internal heat energy density called as EHUMDFLUXDEN. Both field and history outputs are available.
  • 2D and axisymmetric elements are not available that support *FILM parameter as well for convective heat transfer.

This blog is a part of series “what’s new in SIMULIA 2019”. Please follow our blog site regularly for next blog article on this topic.

Every year around this time, SIMULIA comes up with official declaration of new releases. That news is followed by discussion and buzzes around new functionalities and features. Last year we released a series of blog articles on new features in 2018 suite of products and we are following a similar pattern this year starting with Abaqus CAE.

  • Translation of parts and instances: Additional parameters have been introduced to ease this operation. Earlier CAE prompted to pick a start and end points to define direction vector. Now it possible to define the direction by picking global or local coordinate axis, datum axis as well as any straight edge. Moreover, the start and end point method is supplemented by a local coordinate system, if needed. Here is how user interface looks like:
  • CAE support for CAXA/SAXA element types: CAXA/SAXA element types are very useful in modeling structures that have axisymmetric geometry but not axisymmetric load. These element types are present in solver since long time but only option to use them was through manual keyword input. Now these element types are part of Abaqus CAE.
  • Optimization enhancement for additive manufacturing: Overhands can be difficult to print and they require support structures as well. It is advisable not to have overhand structures in the part subjected to AM process. Now an additional geometric restriction is available in optimization module of CAE to prevent overhangs formation.
  • Other optimization enhancements:
  • Shape optimization is often used after topology optimization to reduce hotspots. Earlier only controller based algorithm was supported for shape optimization that imposed many restrictions on choice of design responses. Now sensitivity based algorithm is also available in CAE for shape optimization. Moreover, for all types of optimization schemes, it is not possible to export the output in IGES format as well. Earlier this output feature was available only in form based native TOSCA GUI.
  • The envelop contours can not be created for complex stress values as well. Three types of complex stress contours are supported as shown below:
  • Another significant enhancement in viewer is the visualization of variable beam radius. This is applicable to the output of TOSCA sizing when beam elements are present in the structure. The name of field variable is BRADIUS.

This blog is a part of series “what’s new in SIMULIA 2019”. Please follow our blog site regularly for next blog article on this topic.

This year 2019 Abaqus release has seen number of potential enhancements in Abaqus explicit. Some are general purpose while others are tied to specific procedure and application. Let’s have a look at what’s new in the explicit basket.

  • Lumped Kinetic Molecular model: This model has been developed to simulate behavior of gases that can be of much use in air bag deployment simulation. The method is based on kinetic theory of gases which states that pressure exerted by a gas in closed chamber is a result of collisions between gas molecules as well as between gas and chamber surface. These collisions are perfectly elastic in nature. As number of molecules in a mole of gas is equal to Avogadro number (6.023e23) which is very large from computational perspective, lumped mass approach is used in Abaqus in which a gas particle is defined as a collection of many molecules. The method has been validated with analytical approaches. This method now replaces the Unified Pressure Method that cannot capture the change in pressure as the airbag expands. However, LKM is computationally more expensive than UPM. Best approach might be to use LKM during airbag expansion when pressure variation is large and then switch to UPM method. Switching time should be defined in such a case. Most expensive method is still CEL.

  • C3D10 element has been introduced in explicit that is a true second order element that offers larger stable time increment compared to C3D10M or linear element. It supports all the loads and BC’s supported by conventional continuum elements in explicit.
  • Limiting stop feature: It is not possible to stop the explicit analysis when a certain output parameter reaches a limiting value. These physical parameters may be node based such as reaction forces or element based such as equivalent plastic strains. The keyword is *FILTER.
  • Improved performance: Substantial decrease in solver time when performing large system level crash simulation over high performance cluster. Below is the example of a 5M DOF crash model on multiple cores.

This blog is a part of series “what’s new in SIMULIA 2019”. Please follow our blog site regularly for next blog article on this topic.

One of the noticeable change that has been made in 2019 solver is its capability to handle large models. SIMULIA has noticed that in recent past customers have shown increased interest in dealing with very large models with 2M degrees of freedom or more. There are multiple reasons for this requirement. First is scalability. More and more customers are interested in large system level simulations compared to part or assembly level. Second is fidelity and accuracy. Mesh size is getting finer to capture behavior at micro level instead of macro level.

When it comes to solving very large models, iterative solver has many advantages compared to direct solver such as less memory consumption, scalability and speed. A new hybrid iterative solver scheme has been introduced that offers more flexibility for choosing number of MPI ranks and number of threads per rank for a given node. These parameters can be defined in the abaqus_v6 environment file. This is equivalent to DMP+SMP allowing efficient memory management.

The iterative solver is available in 3DExperience 2018x FD05 and 2019x FD01 release. It supports many more Abaqus features compared to conventional iterative solver such as gaskets, friction, plasticity, creep, periodic boundary conditions etc.

This blog is a part of series “what’s new in SIMULIA 2019”. Please follow our blog site regularly for next blog article on this topic.

Simulations in Aerospace and Defense companies have a well-defined workflow. They have two separate teams for composites products: one for design and other for simulation. Composites ply design is primarily done by design engineers. These are the folks that determine the composites material as well as ply thicknesses and stackings in different regions of the composites part. CATIA composites design and manufacturing workbench has all sorts of tools to help designers achieve their objectives. We have discussed these workbenches in past.

However, because of FAA and other regulations in place, design has to be validated with FEA simulation and for most of the non linear workflows, Abaqus is the right solver choice. Though CATIA does provide an environment for Abaqus pre-processing, the preferred method in Aerospace industry is to use Abaqus CAE user interface. This is because of two reasons. First reason is better meshing capabilities offered by Abaqus CAE and second is tight coupling of Abaqus CAE with underlying solver. The obvious questions that arises is “how to move the ply information from CATIA to Abaqus CAE.”

The answer is composites link in collaboration with composites modeler for Abaqus CAE. The composites link exports the ply data from CATIA in form of layup file. Based on workflow, three options are possible.

  • Export only the ply data: When mesh is already in Abaqus CAE environment.
  • Export ply data with CATIA mesh: When meshing has been done in CATIA Analysis environment.
  • Export ply with external mesh file: When Abaqus input file needs to be merged with ply data.There are further options to export data either with or without taking change in orientations due to wrinkling into account as done by composites fiber modeler. Once the mesh and layup comes in Abaqus CAE environment, it is possible to explode the shell data based on ply thickness and create solid elements from shells. Abaqus CAE automatically creates section properties and assignments based on modified ply orientations. It is further possible to visualize ply orientations on each ply as well as ply stack plots on element by element bases. Once the data transfer and visualization is complete, the entire advanced analysis set up such as bird strike, fracture or delamination can be defined in Abaqus for analysis.

The structural model creation app of 3D Experience 2017x is primarily the pre-processing application for structural models that is available in Structural Analysis Engineer (DRD) and Mechanical Analyst (SMU) roles. This article briefly explains the meshing algorithms available in the given app. The mesh creation module of structural model creation app looks like this:

This meshing algorithm is simplest and easiest to use to create continuum linear/quadratic tetrahedral meshes. Global mesh specifications can be applied to control overall mesh size and local mesh specifications can be used to make finer or coarser mesh regions of the model. Few advanced parameters such as minimum mesh size and quality control factors are available.

 

 

In this technique the surface of geometry is meshed with triangular meshes which then fill the inside volume with solid elements using the specified volume growth parameter. Basic parameters such as mesh size, element order and absolute sag are available in this method. This technique captures the surface geometry of the model more precisely than the Octree Tetrahedron mesh method. Accordingly, the mesh appears a lot smoother.

 

 

This algorithm fills solid geometry with tetrahedral elements working inwards from an existing surface mesh. The approach is like tetrahedron mesh. However, in this approach support is an existing surface mesh instead of solid geometry. The order of filled element type is independent of the order of surface element type.

 

 

This meshing scheme is a big bonanza for Abaqus CAE users, specifically those users who have been using Abaqus CAE for computationally fluid dynamics applications. Unlike other FEA pre-processors, 3D Experience platform offers one click linear hexahedral meshing of complex geometries through this meshing technique. For CFD applications, this includes boundary layer as well. Moreover, no fluid domain geometry needed to create CFD mesh. Application computes fluid domain automatically based on certain input/output parameters. The ratio of Hexahedral elements to other elements depends on the complexity of geometry. Higher the geometry complexity, lower is the ratio.

Many more meshing techniques exist such as beam mesh, surface triangle mesh, octree triangle mesh for shapes of varied topologies. To know more, please contact us.

 

 

 

 

 

In previous blog article on simulation in collaborative environment, we introduced SIMULIA 3D Experience platform and discussed the concept behind its inception, the reason why it exists in the industry and briefly discussed its four integrated components: 3D modeling, information intelligence, social & collaboration and simulation. All four of these collectively form 3D Compass. This blog article explains the configuration of simulation component in more detail.

In general, 3D Experience platform uses few specific terms with respect to its configuration: Personas, Roles, Apps and Extensions. These terms primarily govern how various platform functionalities are bundled, licensed and made available to users.

Personas: Defines the job responsibilities of a group of users. Every user has at-least one persona. Configuration requires estimation of number of personas and number of users in each persona.

Roles: Based on job activities, each persona has to be mapped with a specific role. The platform offers a library of different roles. Each role is a bundle of sellable license features or apps.

Apps: An app can be defined as a group of functionalities to achieve a specific task. For example, a fluid model creation app offers multiple GUI features to define fluid model such a fluid domain creation, fluid mesh, boundary layer mesh, fluid material definition etc.

Extensions: These are a bundle of top-up apps that can be added to a given role to enhance its overall functionality.

Role Categories

Remember that in case of standalone point solutions, we discussed two broad categories: designer level solutions that are primarily CAD embedded vs. expert level solutions with their own graphical user interfaces and complex workflows. In 3D Experience platform as well, Simulation roles can be differentiated into engineer roles and analyst roles. There is also a third category of roles called as process roles. Each of these types of roles require some basic platform roles as pre-requisites.

Extensions vs. Roles

Each extension can be ideally treated as a mini role. Extension, if compatible with a given role can be added to it to enhance its functionality without duplicating any app available in the role. Here is an example for demonstration.

Mechanical Analyst and FEM Specialist are roles with number of apps as shown above. Now if an SMU user needs SIMULIA model assembly design app, there are two ways to do it. An expensive approach would be to procure entire SFM role that would not only increase cost substantially but would also duplicate apps common between SMU and SFM. An economical approach is to add the SMA extension with only relevant app to the SMU role.

Simulation capacity: Tokens vs credits

A token is a governor of maximum amount of simulation that can run concurrently. More tokens mean more concurrent simulation. A token is a renewable simulation resource. Once simulation is complete, tokens are returned to the token license pool. Jobs can be submitted either on premise or on cloud. Both these options have different token categories.

A credit is a consumable computation resource that is not replenished once the simulation is complete. There is no limit on how fast a credit may be consumed during concurrent simulation. The user procures compute credits that gets consumed as simulation progresses. The rate of credit consumption is directly related to speed of simulation. Quite often, analysts prefer to use tokens as one-time investment. The credits are used to meet occasional peak demand when tokens are not enough to meet simulation capacity needed.

In future blog articles related to 3D Experience platform, we will discuss various roles available for stress analysis as well as their underlying apps, extensions and simulation capacity.

 

The definition of warranty varies depending on the nature of product being used by the consumer. For a product as intangible as an insurance policy, warranty may be defined as percentage of eligible claims settled by the insurance company in given time-period. However, manufacturing companies produce tangible products. In such scenarios warranty is almost always defined either in terms of time or in terms of in service load cycles product is expected to survive. Both parameters are often related to each other depending on the frequency of use. Moreover, in service load values varies a lot from one geography to the other as well as from one user to the other. Have you ever come across a car that is always driven on roads of a constant surface quality? Have you ever seen two drivers who maneuver their cars in a perfect identical fashion? If not, there is a statistical aspect to be considered as well. Nevertheless, customer do not realize how difficult it could be to define a warranty of a physical product. They treat it as a simple number on warranty card and if the product fails before the warranty expiry period, they feel cheated and their response is often something like this:

It is true that several critical parameters should be considered to define warranty of a physical product with precision. Many of such parameters are either very difficult to measure in house or to collect from external resources due to their seasonal, spatial or statistical nature. Thus, it is impossible to narrow down warranty failure instances to absolute zero. However, it is possible to reduce cost and unpleasant experience associated with such instances by reducing their frequency.

In case of a physical engineering product, often used in transportation, aviation or healthcare industry, virtual testing for durability is an effective method of containing warranty costs. Here we are talking about using finite element analysis approach using FEA codes such as Abaqus in combination with durability codes such as fe-safe.

In any given fatigue analysis workflow, a structural FEA solver as well as fatigue solver are present. The output from FEA solver serves as one of the input for fatigue solver. The FEA simulation is carried out either by applying a unit load or the entire variable load depending on the nature of problem. The minimum needed output may be either principal stresses or combination of principal stresses, principal strains and temperatures depending on the physics of the problem. The second input for fatigue solver is the in-service load data files and it may be optional in certain cases. These are real time digital files that capture fluctuation of loads in different directions over a given time. They are created using data acquisition techniques and are compatible with well-known fatigue solvers such as fe-safe. Once they are entered in the workflow, they serve as a multiplier to respective unit load data set to generate a 3D stress cycle. Both uniaxial and multi axial load scenarios are supported along with multiple block loadings.

The third input is the fatigue material properties. This could be either a stress-life curve or strain-life curve depending on physics of the problem any type of fatigue algorithm used. A good news is that fe-safe has a well-defined material databank of commonly used metals and alloys. If needed, this material data can be customized.

Once these three inputs are defined, fatigue problem definition is complete and solver is executed to provide the output in desired forms: cumulative damage, damage per block, cumulative life in terms of hours/days/load cycles etc. The numeric values are all printed in text files while a fatigue contour can be seen in a binary file compatible with most FE post processors.

An advanced fatigue workflow could be one involving fatigue optimization. In this workflow, it is possible to define fatigue as one of design response that could be minimized using shape optimization code such as Tosca. It is further possible to incorporate various types of manufacturing constraints in such an optimization.

Though a fatigue simulation workflow is well defined, it is not easy to execute. It is still beneficial to adopt this methodology because physical testing is often time consuming and manual hand calculations are not valid for complex loading cycles. If customers understand the complexity involved, they may be able to accommodate margin of errors in product warranty cards.

Computational fluid dynamics role of 3DX is the one going through tremendous enhancements compared to other roles available on the platform. In this blog article, I am going to highlight a few key enhancements with respect to the scenario modeling and the underlying solver.

Multiple reference frames

Most of CFD users are familiar with concept of moving fluid boundaries. Traditionally these problems have been solved using coupled Eularian – Lagrangian (CEL) techniques. In simple words this technique can be defined as a combination of two fluid spaces: one near the boundary in which fluid moves with the mesh and other away from the boundary in which fluid moves through the mesh. A whole new concept has been introduced in 3DX CFD to solve such problems. It is moving reference frames that can either translate or rotate with respect to a global reference frame. Either entire fluid domain or a portion of it can be assigned to this frame of reference. The governing equations are solved in this reference frame. Interfaces are created between moving and stationary frames to maintain motion continuity.

 

Compressible flows

Compressible flows become a concern when speed of flow is high. While this may not be relevant for companies designing exhaust manifolds or valves, an analyst trying to study the exterior air flow drag on a fighter jet moving at one tenth the speed of sound would feel the need of compressible air flow. The new release can simulate transonic flows up-to Mach number of 1.2.

Modeling of porous media

Are you looking to model components such as catalytic converters or air filters? Well, these products have a permeable medium that allows restrictive flow of air through it. Modeling such flows require porous media functionality that is now supported in 3DX CFD.

These along with many other enhancements are now a part of 3D experience 2018x platform. If you wish to know more, please feel free to contact us.

When it comes to Abaqus structural solver, picture is clear. There is a standard (implicit) solver as well as an explicit solver. Each of these has its own merits and demerits that we have discussed in previous blog articles. However, in CFD there appears to be only one solver. So…

Is it implicit or explicit!!

Well, if you look at the underlying parameters of the solver, it appears to be hybrid. The solver talks about inner loop and outer loop convergence. That makes user feel that solver is implicit and it requires matrix based calculations. This is true for both steady state as well as transient flows. But then when we talk about transient flow parameters, solver mentions CFL number that primarily governs the step size of transient flow. Higher CFL number results in larger step sizes but beyond a certain value of CFL, the flow may become unstable. This looks more like an explicit scheme where stability plays a role. But then, the transient flow also requires convergence that is not an explicit property. So where do we stand.

The answer is somewhat mixed. The CFD solver of Abaqus is implicit by nature. However, it does not follow all the traits of structural implicit solver. One big difference is that CFD implicit solver is not unconditionally stable. While the explicit structural solver just exits if time increment exceeds its critical value, the CFD solver continues at larger than desired CFL numbers but it may give non-realistic flow results. The other big difference is the physical quantities involved. The structural implicit solver looks at force and displacement residuals for convergence. The CFD solver looks at momentum, pressure and velocity residuals.

SIMULIA has made good efforts in 2018x release of CFD solution on the 3D Experience platform in terms of making the solver fully implicit so that it can handle large time increments. There have been other solver enhancements to improve accuracy and reduce solution time. Wish to know more about SIMULIA CFD techniques! Please get in touch.

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