Rand Simulation Solutions Blog

Performing a Creep Deformation Analysis in Ansys Mechanical

If you’ve ever hung a heavy item on a plastic clothes hanger and come back a while later to see that the hanger has permanently deformed, then you’ve seen a phenomenon known as “Creep” occurring firsthand. When materials are subjected to sustained mechanical loads over long periods of time, they tend to slowly deform, and eventually rupture.  While this failure mode is usually not a concern at room temperature for most engineering materials, the process is accelerated at high temperatures.

Creep Deformation Analysis vs. Stress Rupture Analysis

A Creep rupture test is performed by applying a sustained mechanical load to a test specimen which is maintained at a specific temperature and the time to rupture is measured often over the course of months or even years. A Creep test is similar to a Creep rupture test, but in a Creep test, the elongation of the test specimen is monitored throughout the test. This gives information on the rate of Creep deformation that a Creep rupture test does not provide.

Before beginning an analysis, it is important to determine if a Creep deformation analysis is necessary. If the only concern is ensuring that a component does not rupture after some time, a stress rupture analysis is all that is required. The key question to ask is if the excessive deformation will interfere with the function of a component. For instance, a turbine blade that experiences excessive Creep deformation would begin to rub against the casing that contains it, causing wear and vibration issues. In this case, a Creep deformation analysis would provide useful information. On the other hand, a reformer tube used in the production of hydrogen does not have tight clearances, and the only concern is making sure that a Creep rupture does not occur before the intended design life. In this case, a Creep rupture analysis using a Larson miller curve may be all that is required.

The Three Phases of Creep

Creep can be broken down into primary Creep, secondary Creep, and tertiary Creep. Primary Creep begins at a fast rate and then slows down as it approaches the secondary phase. Secondary Creep is the longest portion of the Creep curve and occurs at a constant rate. Finally, during tertiary Creep, there is an increase in the rate of deformation and may culminate in rupture. Ansys has the capability to model primary and secondary Creep, but Tertiary Creep is not typically analyzed because rupture is impending.

 

 

The Norton model is commonly used for modeling secondary Creep. It is a power-law relation that expresses the Creep strain rate as a function of the stress and temperature.

 

 

Setting up a Creep Analysis in Mechanical

The actual process of setting up a Creep deformation analysis is relatively straightforward. First, add a Creep material model to the material in the Engineering Data section of Ansys Workbench. In the below, example, the Norton model was chosen to model secondary Creep.

 

 

To turn on Creep deformation effects for a particular load step, click on Analysis Settings, specify the current load step number, and then turn Creep effects on under Creep controls. You may want to use the first load steps to set up the initial conditions for the Creep analysis with Creep effects turned off, and then have a second load step where Creep effects are turned on. Finally, note the Creep limit ratio under Creep controls. To ensure an accurate solution, this setting limits the ratio of the equivalent Creep strain increment to the equivalent elastic strain. Typically recommended values are between one and ten.

These are the basics of implicit Creep analysis in Ansys Mechanical. If you would like a more thorough example of a Creep analysis, see Chapter 35 of the Workbench Technology Showcase.

Optimization with Ansys Workbench

Introduction

There are two main types of optimization methods available in Ansys Workbench:

1. Direct Optimization
2. Response Surface Optimization

This article will focus solely on direct optimization which is essentially a brute force attempt at finding the most optimal set of input values needed to produce the desired output value subject to a set of constraints.  The inputs can be anything from geometry to physics.  This article will use an example of trying to find the best performing airfoil using simple geometric inputs.  The objective is to minimize drag while maintaining a minimum lift and lift/drag ratio.  The input variables and their ranges are shown below:

Table 1: Optimization Variables

Figure 1: Geometry with Chord Length, LE, TE, and Angle of Attack dimensions

Analysis Setup

This analysis considers a 2D extruded airfoil with 0.2m depth created in DesignModeler and uses an airspeed of 30 MPH.  After the CFD model is set up, the user creates a Direct Optimization which defines a range of values for each input parameter.

Figure 2: Workbench Workflow

Figure 3: Input Variable Ranges

Next, output constraints are added which disqualify any set of inputs that did not meet the constraints.  In this case, the output constraints are a lift of at least 5N, a drag of at most 2N, and a lift/drag ratio of at least 5. For the drag constraint, the objective is set to minimize this value.

Figure 4: Output Constraints

After the constraints are set up, Workbench will determine an initial set of discrete input values based on the ranges provided.  For each set of inputs, there is a row of data that shows the design point (DP), its input values, and its output values.  The cases that are not run yet are shown with lightning bolts, and the table is filled out as the CFD runs are completed.

Figure 5: Inputs and Outputs

The design points shown in the table are just the initial values used to start generating an input vs output map.  After these initial points are run, the optimizer will create additional points in the design space that are likely to be good candidates that meet all specified output constraints while also attempting to minimize drag.  The user can choose settings that create more data points for better accuracy or decide on fewer data points in order to speed up the optimization process (the results from this example use fewer data points).  Sometimes certain combinations of input variables will fail due to unphysical geometry creation or other reasons.  These design points are shown with a red X.  In the case of failed points, the optimizer will just disregard the data and move on to the next point.

Figure 6: Inputs and Outputs

The figure below shows the final optimized shape to minimize drag.  If the optimization was allowed to use more data points, then a better-performing airfoil would be found.

Figure 7: Optimized Shape to Minimize Drag (AoA = 5 deg)

Figure 8: Static Pressure Contour

If the objective is modified to maximize the L/D ratio instead of minimizing drag, then this produces a longer, thinner airfoil at a smaller angle of attack.

Figure 9: Optimized Shape to Maximize L/D Ratio (AoA = 2.68 deg)

Figure 10: Static Pressure Contour

Conclusion

The Direct Optimization method is a straightforward process that allows the user to provide a simple set of inputs and constraints which Workbench then uses to determine the most optimal design.  As computing resources become faster and cheaper, more of the design process will be spent using automated optimization methods so that the engineer can spend more time on the higher-level decisions and less time generating geometries, meshes, and pre/post-processing.