Isight is probably best known for its capabilities to do optimization. However, this is not the only thing Isight can do. Isight also includes the option to create an approximation, for example. With this, a mathematical equation is fitted to the actual behaviour of one or more components. This approximation can then be used in other Isight models, so it is not necessary to run the actual component(s) each time. This can save a lot of time. In this blog, I’ll show how to make an approximation in Isight and what results data is available.
One of the benefits of doing simulations, is that it is easy to change various parameters to assess their influence on results. When doing this, often the same post-processing is needed for more than one analysis. Of course you can manually open each .odb, create the right images and save them, but this can be quite a lot of (boring!) work and the chance of making a mistake is definitely there. Therefore, it is often beneficial (and more fun) to create a script to do this automatically. In this blog, I will show how you can create a script to automate the creation of images using Abaqus.
Let’s start with a problem:
“A ladder hangs over the side of a ship anchored in port. The bottom rung of the ladder touches the water. The ladder is 30 cm wide and 270 cm long. The rungs are 1 cm thick and the distance between them is 34 cm. If the tide is rising at a rate of 15cm per hour, how long will it be before the water reaches the top rung?”
Topics: problem soving in FEA Analysis
Maybe you recognize this: you set up your model, start running the job, open the monitor window and ... it stays empty, longer than you would like. The simulation is taking longer than you hoped or expected and you wonder: "how can I do this faster (without significantly reducing the accuracy of the results)?" In this blog I'll discuss some ideas and experience we've had related to simulation speed.
In a previous blog, I have shown how to model welding for a simple case with Abaqus. Here sequential thermal-structural analysis is used to apply temperatures calculated in a thermal analysis in a subsequent structural analysis. Also, the model change option is used to remove and reactivate bead material, simulating its deposit.
A similar approach can be used for more complex cases. However, defining such a model can be very time consuming because many steps with different model changes, film conditions etc. need to be defined.
To simplify the process of setting up such a model, the Abaqus Welding Interface (AWI) has been developed.
Part 1: Manual weld definition
Welding is a process that joins materials by causing fusion: the base metal is melted and typically a filler material is added to form a pool of molten material. When simulating the welding process in order to assess the structural behavior of a welded joint, there are two main challenges when setting up the model:
- Thermal and structural behavior needs to be coupled to each other
- Material needs to be added during the welding process. This also changes the boundaries and thus the location of the boundary conditions.
It’s one of those special times of the year again: an extra day off, a good reason to eat (additional) chocolate, perhaps a visit to the family and … a chance to set up an analysis in a different manner than I normally would.
While Abaqus is used most for mechanical analysis, there are other options as well. Thermal analysis is one of the possibilities. In this blog, I will show how to set up such a thermal analysis using Abaqus.
Abaqus 2018 is now available. In this blog I'll list the most significant new features and enhancements, and explain how to obtain and install Abaqus 2018.
Topics: Abaqus 2018
What do you do when you want to find a good design?
Have a brain storm session, come up with different designs, try each one out and choose the best one?
Or let the computer help you, by changing parameters such as dimensions and finding the design that matches the requirements best, possibly using Isight?
While such a parametric optimization is powerful in finding the best variation around a theme, non-parametric optimization can come up with a completely new theme: a new design concept. In this blog, we’ll take a look at non-parametric topology optimization using Tosca and Abaqus.
With the Winter Olympics coming up, being Dutch and in need of an applicable topic, we’ll use a Dutch invention; the clap skate (see Wikipedia) as example, and focus on the yellow part in the figure below.
First a regular Abaqus analysis is set up, and then Tosca is used to optimize the topology in this analysis.