Ansys Mechanical 2023R1 – the Top 5 features

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Published on
2023-06-15
Written by
Mikko Hinkkanen

Ansys Mechanical 2023R1 – the Top 5 features

Every year, Ansys Mechanical releases new features that push the limits of structural analysis. The latest version, Ansys 2023 R1, introduces advancements in artificial intelligence/machine learning (AI/ML) resource predictions, topography optimization, and more. These updates enable users to perform more accurate, efficient, and customizable structural simulation analyses.

Geometry Based (Re-)Associativity (GBA)

One notable improvement is the Geometry-Based Reassociativity (GBA) feature.

Mechanical is known for its ability to work with underlying geometry throughout the analysis process, meshing, set up, and solving. Users who have been using Ansys Mechanical for a long time know that making edits to the geometry can sometimes result in lost associativity, making previously defined settings in Mechanical underdefined. While Ansys Workbench had some capabilities to reassociate the model setup after geometry changes, the process was only sometimes bulletproof.

With the introduction of Ansys 2023 R1, the frustration of encountering question marks in the tree due to lost associativity is a thing of the past. Now, users can efficiently edit the model and utilize the new Scoping Wizard tool to detect and restore scoping automatically.

Different geometry parts will be color-coded when importing an updated model into Mechanical based on their associativity status. Users can review the list to identify which elements have been reassociated and which have not. Elements that are successfully reassociated are colored green, elements with multiple matches are colored yellow, and elements that cannot be automatically reassociated are colored red.

Geometry-Preserving Mesh Adaptivity (GPAD)

Have you ever encountered a complex model in Mechanical that you were unfamiliar with and had no prior knowledge of where critical stress and strain areas might be? In the past, there were two methods to address this situation:

Generate a coarser mesh, solve the model, and add more detail to important areas.

Generate an overrefined mesh right from the start to capture important areas accurately.

Ansys understands that these methods can be time-consuming. That’s why they have introduced a new feature to enhance the efficiency of durability studies. This new feature is called Geometry-Preserving Adaptive Meshing (GPAD). It eliminates the need for an initially overly detailed mesh and removes the need for guesswork in mesh sizing.

With GPAD, you can begin a simulation with a less detailed mesh. As the model is being solved, the solver continuously monitors quantities like stress variation in different regions and automatically refines the mesh accordingly. This refinement is done by aligning the mesh with the underlying computer-aided design (CAD) geometry, getting closer to the actual shape of the model rather than relying on the coarser mesh used in the previous solution. Since the re-meshing occurs during the solution phase, it improves accuracy without imposing a significant computational burden.

Resource Prediction

FEA simulations are becoming more complex with various elements, materials, contacts, joints, boundary conditions, loads, etc. At the same time, the use of high-performance computing resources is rapidly increasing, both on-premises and in the cloud. At this stage, determining the optimal hardware requirements, such as memory and CPU count, is essential.

In the simulation world, where solving time and cost are essential factors, knowing the ideal amount of CPU resources that offer the best scalability for efficient time and cost management is crucial. Advancements in resource prediction now enable you to address this issue by predicting the necessary memory, solving time, and solver scaling performance even before running the simulation.

Resource prediction utilizes machine learning-based ML algorithms to forecast the required memory and solve time for complex simulation models. These algorithms analyze millions of anonymous data points from previously solved simulations and compare that data with the model you are currently working on to provide accurate predictions. This feature works with linear static and modal analyses using iterative and direct solvers. It offers the scale-up performance of up to 32 cores.

Topography Optimization

Over the years, Ansys Mechanical has integrated various optimization techniques such as parametric study, lattice optimization, and shape optimization. In the latest version, Ansys 2023 R1, Ansys introduces a new capability called topography optimization.

When designing manufactured structures, the performance heavily depends on the mass, particularly when the structure is subjected to dynamic loads. Therefore, reducing weight is crucial. However, traditional optimization methods could be more effective when dealing with thin structures, mainly when assembly and design constraints exist.

In such cases, topography optimization is the most suitable approach. It allows us to identify optimal locations of mesh nodes using free morphing techniques without altering the thickness or shape of the design. Various controls can be applied to ensure the design remains manufacturable. This approach helps enhance aspects such as noise, vibration, harshness (NVH); fatigue; crash performance; and/or reduce the structure’s weight.

Contact Configuration

Setting up contacts for the body in white (BIW) simulations can be a complex and time-consuming process. “Body in White” refers to the vehicle’s body structure before adding other components like the engine, interior, and exterior panels. BIW simulations involve various interconnected and linked features and different types of contacts, including adhesives, welding, riveting, and more. Previously, users had to manually create multiple contacts and carefully configure them on the correct sides of their shells.

In Ansys Mechanical 2023R1, improvements have been made to simplify the setup process. Now, users can specify that the target surface is double-sided, taking into account both positive and negative normal target surfaces. This eliminates the need to create multiple contact definitions, saving time and effort.

Mikko Hinkkanen, Customer Success Manager

 

 

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