Elastoplastic materials combine two principal types of behavior: *elastic deformation*, which is reversible deformation, and *plastic deformation* (or plasticity), which is irreversible and leaves a permanent deformation upon unloading. In order to model this type of material behavior, we need to use a constitutive relation that connects the stress state not only to the current strain state, but also to the previously accumulated plastic strains and to their development.

*Plastic deformation in a pressure vessel subjected to internal pressure, showing the elastic region (dark blue) and some plasticity (red).*

Generally, when there is an increase in stress and the initial yield stress (the elastic limit) is surpassed, the elastoplastic material is strained much more than for a corresponding stress increase in the elastic region. The material is hardened by plastic deformation, but the response in the plastic regime varies greatly among different materials.

For metallic materials, hardening is commonly described by three different types of behavior:

- Isotropic hardening, in which the yield surface expands with increasing stress. Loading in tension also hardens the material in compression
- Kinematic hardening, in which the yield surface translates, but stays the same size. Loading in tension will make the material softer in compression
- Mixed hardening, in which the yield surface both expands and translates

In the figures below, we can visualize the stress-strain relation for a uniaxial loading for the three types of hardening. In the first step, the material is stretched until a significant plastic strain is reached. At this point, the current yield stress, , is above the initial yield stress, . So far, the stress-strain curve follows the same path for all three types of hardening. In the second step, the loading direction is reversed and the material is compressed until the onset of yielding in compression.

*The stress-strain relation in a uniaxial load case for three hardening models: isotropic, kinematic, and mixed.*

With isotropic hardening, a material can be compressed at most before the onset of reversed yielding. With kinematic hardening, a material can be compressed at most . With mixed hardening, the compression is in between the two, having . Both kinematic and mixed hardening result in a so-called back stress or shift stress, which is a new stress level that is equally far from yielding in tension and compression. Before the onset of plasticity and in the case of isotropic hardening, the back stress is zero.

Besides this type of deformation hardening, some metallic materials also demonstrate more complex types of behavior. One example is viscoplasticity, where the plastic behavior is strain rate dependent.

You can access a collection of material models that can be used for modeling elastoplastic materials in the Nonlinear Structural Materials Module. Selecting a fatigue model, however, does not only depend on the material model, but also on the loading characteristics. We talk about the influence of loading conditions on the choice of fatigue model in a previous blog post.

When working with nonlinear materials such as elastoplastic materials, the material response of the first load cycle generally differs from the material response of the second cycle. This is caused by the first load cycle, which can both shift the yield surface and change the yield stress. The consecutive load cycles can then either oscillate around a new stress-strain state or cause further accumulation of inelastic strains. When studying fatigue, we must first find a stable load cycle, which is representative for the subsequent cycles. Therefore, when modeling elastoplastic materials, we often need to simulate several load cycles before reaching a stable load cycle.

We discuss the different types of load cycles in another blog post: Modeling Thermal Fatigue in Nonlinear Materials.

Let’s go over how to model fatigue in elastoplastic materials with two of the types of hardening, kinematic and isotropic hardening, using COMSOL Multiphysics.

Let’s take a look at the Elastoplastic Low-Cycle Fatigue of Cylinder with a Hole tutorial model. Here, the component is loaded beyond the point of yielding. The material experiences immediate stability, since a stable load cycle is obtained already during the second cycle. However, the stable load cycle consists of both elastic and plastic deformations. This is possible since the material is modeled with kinematic hardening. This means that the yield surface moves between two positions: tension and compression.

For most applications that involve kinematic hardening, a full elastoplastic analysis must be performed. The model size can be somewhat reduced by dividing the model into domains where plasticity develops and domains where only elastic deformation takes place. This method is useful because plasticity is computationally expensive to model, requiring us to evaluate an additional seven degrees of freedom as opposed to the three displacements in elastic materials.

It is common that fatigue failure originates from the presence of a notch. In this case, an approximate solution can be used; for example, the Neuber correction for plasticity based on the Ramberg-Osgood material model. Based on the elastic solution, this approximate method computes an elastoplastic stress-strain state at a notch. This method is fast, but the further away we move from the notch, the lower the accuracy of the results. This method is demonstrated in a related example model: Notch Approximation to Low-Cycle Fatigue Analysis of Cylinder with a Hole.

We can compare the two methods in the figures below. Due to high strain and multiaxial load conditions at the hole, we predict fatigue using the low-cycle-fatigue Smith-Watson-Topper (SWT) model. The results at the critical spot are similar for both methods. The computation time, on the other hand, differs significantly. For the elastoplastic model, computation time is a few minutes, compared to a few seconds for the notch approximation.

*A low-cycle fatigue prediction, based on a full elastoplastic analysis (left) and a notch approximation (right). Results display the logarithm of the number of cycles to failure. The same color scale is used in both figures.*

In another tutorial model, Standing Contact Fatigue, a surface-hardened material is subjected to a compressive load cycle. Affected by the hardening process, the tested material has three distinct layers with different material properties. The material is strong closest to the surface (the case), while it is weak deep inside (the core). In between, there is a thin transition layer where both the material properties and residual stress sharply change.

The plastic properties of the material differ through the depth. In the case layer, the hardening follows a linear isotropic model, while in the core, it follows an exponential hardening model. In the transition layer, the hardening function is exponential and parameterized. The function for the material parameters is chosen such that the material model of the transition layer at the interface with the case corresponds to the case model, and the interface with the core corresponds to the core model.

During the first load cycle, the material is compressed past the point of yielding and plasticity grows on the subsurface level. Since the yield surface expands in isotropic hardening, each consecutive load cycle that is not as high in magnitude as the first cycle will not introduce any further plasticity, thus the stable load cycle is elastic. Although high strains develop during the first load cycle, any consecutive cycle will result in small strain changes. It is therefore reasonable to assume that a stress-driven, high-cycle fatigue model is suitable for fatigue evaluation.

In the case of a predominantly compressive load, the Dang Van model is useful for fatigue modeling, since it takes the compressive mean stress into account. You can access the Dang Van model for these types of simulations in the Fatigue Module.

*Fatigue prediction in a surface-hardened material. Fatigue usage factor is displayed. The highest risk of fatigue is in the near-surface case layer, with a lower risk of fatigue in the deep core layer.*

By simulating fatigue in common types of elastoplastic materials with COMSOL Multiphysics, we can better understand and predict the occurrence of fatigue failure.

- Try it yourself: Download the tutorial models discussed in this blog post
- Learn more about applications for modeling nonlinear structural materials and fatigue on the COMSOL Blog

Contact fatigue occurs when a changing contact pressure between two parts introduces a time-dependent stress state on both the surface and subsurface level. When stresses are too high, a microcrack forms in the component, either on or under the surface. A subsurface microcrack frequently originates at some kind of defect, such as a material impurity. This microcrack grows parallel to the surface with subsequent loading. At some point, it kinks toward the surface, removing a piece of the material and leaving a shallow hole.

*The stress history when a rolling element travels along a curved raceway. The top surface shows high levels of contact pressure in red and stress-free areas in blue. The subsurface level displays high and low effective stress in red and blue, respectively.*

The three main types of contact fatigue are:

- Standing contact fatigue
- Rolling contact fatigue
- Fretting contact fatigue

In standing contact fatigue, the two objects in contact experience a relative movement in the surface’s normal direction. The movement can be very small, not visible to the human eye, or large with surface separation. The two objects are repeatedly pressed together and then released. In rolling contact fatigue, the contact fatigue is caused by an object rolling across a surface.

We won’t go into the specifics of modeling fretting fatigue in this blog post, but this type of fatigue occurs when the two objects in contact have a small relative motion along the surface, such as vibration. It might seem that the two objects move in phase on the macroscopic level, but the two surfaces can experience relative motion on the microscopic level, which leads to a fatigue failure.

We can model contact fatigue in COMSOL Multiphysics using two methods. One way is to create a *Contact Pair* at the interface between the two objects. Both objects must be modeled and a fine mesh must be applied along the two contact interfaces. This type of contact simulation is often computationally expensive.

The other way to simulate contact fatigue is to use the classical solutions associated with Hertz for contact between two elastic bodies with curved surfaces, which is described in the study of contact mechanics. One of the objects in contact is replaced by an analytical solution for the contact pressure, which is prescribed on the surface of the other body. We can do this by:

- Specifying the contact characteristics, such as the maximum pressure and contact axes, as parameters in the
*Parameters*node - Expressing the contact pressure at a given location on the surface as a variable in the
*Variables*node - Prescribing the contact pressure as a boundary load on the surface of the other body

By doing so, we don’t need to model one of the objects, which reduces the model size. Since an accurate resolution of the resulting stress state requires a fine mesh, any technique that reduces the model size is important in contact fatigue modeling.

*Settings for prescribing an analytical solution for the contact pressure on an object in contact.*

The second technique is employed in two tutorial models available in the Application Library of the Fatigue Module: Standing Contact Fatigue and Rolling Contact Fatigue in a Linear Guide. In the first example, a spherical indenter is repeatedly pressed and released over the tested material. In the second, a spherical rolling element moves along a raceway groove.

The characteristic geometric length in both models is a few millimeters, which corresponds to the radius of spherical objects in contact. The characteristic length of the contact area is about a tenth of that measurement. In the standing contact fatigue example, the radius of the indenter is 7 mm and the contact radius is 260 μm. For the rolling contact fatigue example, the radius of the rolling element is 2 mm and the two contact ellipse axes are 161 μm and 36 μm, respectively.

*There is a large difference in mesh size between that of the contact surface and the rest of the model.*

The contact surface is not the only place where a fine mesh is required. Although the highest contact stress is transferred through this small contact area, the highest effective and shear stresses, both used in fatigue analysis, are found on the subsurface level close to the surface. In the standing contact fatigue model, the highest effective stress and shear stress are located about 110 μm below the surface. In the rolling contact fatigue example, the maximum of both stress components is found about 20 μm below the surface. This is about 1% of the characteristic length of the geometrical objects, requiring a fine mesh through the depth.

The load transfer is concentrated at one location in standing contact fatigue, while the contact area travels in rolling contact fatigue. We must therefore use a material volume with a fine mesh along the entire path of the traveling object when modeling rolling contact fatigue. In some models, the size of the modeled volume can be reduced, since the contact stress only has a significant influence on the material volume within a few contact lengths from the contact point. In fatigue simulations, the stress state that is further away is insignificant.

By prescribing the contact pressure a few contact lengths before the evaluation point and then moving it to a few contact lengths after this point, we can obtain good results for the center. When modeling rolling contact fatigue, we apply the contact load to about three contact lengths before the evaluation point and then translate it to about three contact lengths after the point. Once we get the results for the center, we can use them in subsequent fatigue studies.

*The affected volume around a traveling contact pressure. The top surface displays contact pressure. The bottom volume shows the shear stress in blue for high negative values, red for high positive values, and green for stress-free regions.*

Once a steady-state load cycle is computed, we can base the fatigue evaluation on one of the models included with the Fatigue Module. The Dang Van model, for example, is frequently used for compressive load cases because it can take the influence of the compressive state into account. Moreover, the model parameters can be easily extracted from the standard pure tension and pure torsion fatigue tests. The Dang Van model is the latest extension of the Fatigue Module and is new with COMSOL Multiphysics® version 5.2a.

- Try it yourself: Download the tutorials featured in this blog post
- Read more about the new Dang Van model on the 5.2a Release Highlights page

Simulating fatigue offers valuable insight into how stress can affect the longevity of a structure and its components. This can help identify potential design problems and pave the way for the development of a safer structure. Arriving at this solution, however, often requires running several simulations to test different scenarios. Our Frame Fatigue Life demo app demonstrates how simulation apps can save you time and energy in evaluating the impact of fatigue.

In the real world, structures are regularly subjected to external stresses. The repetition of these stresses can cause structures to weaken over time, which can result in their failure. Simulation helps to identify weaknesses within designs *before* failure occurs by analyzing the impact of applied loads.

Oftentimes, many simulations must be run before reaching an optimized design. This is especially true when modeling fatigue, as small design changes have a large impact on the lifetime of the structure. Those without a background in simulation will rely on you to run new tests as modifications are made to the structure’s existing design. But what if there was a way to shift some of that workload into the hands of other individuals involved in the design and manufacturing process?

Using the Application Builder, you can turn your models into easy-to-use simulation apps. This tool will enable your colleagues and customers to run their own simulations on the app, which you can customize to include only the specific input parameters that are relevant to your design. Take our Frame Fatigue Life demo app, for instance, which can be used to evaluate the fatigue life in a frame with a cutout. Let’s explore this demo app and its features in greater detail.

The Frame Fatigue Life demo app is based on a model of a thin-walled frame with a cutout. In this example, a random moment load is applied along the three principal directions on the right end of the frame; the left end of the frame remains fixed.

*The geometry of the frame.*

Within the frame, the cutout represents the *critical part*, as this is where fatigue can occur. The fatigue lifetime is significantly reduced if the cutout is a square, since sharp corners lead to increased stress in the surrounding area. The focus here is to predict fatigue life and stress distribution at the cutout, along with the relative usage factor at the critical part.

The Frame Fatigue Life demo app takes all of the physics and functionality behind the model and makes them available in a user-friendly interface. You can use this example as a foundation for creating your own fatigue life app, configuring its layout to meet your specific needs.

When building your own app, you have the ability to create a user interface (UI) that is tailored to your particular design. Depending on the focus of your analysis, you can decide which parameters to make available for modification within your app. As the simulation expert, you can program any sequence of model actions based on the app user’s click of a button, helping to reduce human error that can occur when several actions are required to make a change to the design.

Our Frame Fatigue Life demo app features input parameters that allow changes to the frame’s geometry. For further customization, user-defined load histories and materials can also be imported. Say an app user decides to test a different material or load history. In this case, the new input file will be automatically assigned to both fatigue steps after it has been imported. This reduces the level of human error that may occur if the changes are assigned to only one fatigue step rather than both of the steps.

Upon clicking the *Solve* button, necessary checks are applied to the inputs. If they are not correctly specified, a message displays to explain the mismatch, providing app users with the opportunity to correct their mistakes. It is possible that so many changes were made that it would be difficult to identify the exact point at which the error occurred. For these types of situations, the *Reset to Default* button can be used to revert the model back to its original state, offering a clearer picture of suitable parameter values, thus helping to identify the inaccuracy.

Once the proper inputs have been entered, the app solves for fatigue life in the designated frame. To achieve this, three study steps must be computed. The first study step computes the structural response to the applied moments; the following two study steps evaluate fatigue on each side of the shell element. When working with the Model Builder, this would mean having to solve all three studies manually. In the simulation app, however, all three steps are performed automatically once the *Solve* button is selected due to a scheme created with the Method Editor.

The simulation results from the app can be compiled into a report by clicking the *Create Report* button. The report for the demo app includes the predicted stress distribution, fatigue life, and relative fatigue usage at the critical part, as well as information on the original geometry. The app’s documentation file can be found under *Open Documentation*.

The results of the simulation show both sides of the frame, with fatigue life computed on the critical side. The fatigue usage factor on either side of the frame is displayed. At the critical point, the stress cycle distribution and relative usage factor are also shown.

*Simulation results for the fatigue life app.*

Apps mark a revolutionary step forward in simulation, hiding the complexity of models in a simplified and intuitive interface that can be used by individuals throughout an organization. Extending the power of simulation not only helps to lighten your workload, giving time for additional simulation projects, but it also promotes a more integrated approach to product design and development. We encourage you to use our Frame Fatigue Life demo app as a guide to creating your own app, establishing a more efficient way of designing safe structures.

- Download the demo app: Frame Fatigue Life
- See how easy it is to create your own apps with this video: Introducing the Application Builder in COMSOL Multiphysics

Many of today’s motor vehicles rely on reciprocating piston engines as their source of power. In an internal combustion reciprocating engine, fuel combines with an oxidizer in a combustion chamber. The combustion causes the gases to expand, applying pressure to the engine’s piston, pushing it out of the chamber. The linear movement of the piston is converted to a rotating motion by way of a connecting rod, which joins the piston to the crankshaft. This continual motion exerts a great deal of stress on the connecting rod — a force that becomes greater with increasing engine speeds.

Within reciprocating engines, it is crucial that each component is analyzed critically, since one part’s failure often means replacing the entire engine. To optimize the design of this engine and ensure a long operational lifetime, we can analyze the connecting rods from the fatigue viewpoint.

The High-Cycle Fatigue of a Reciprocating Piston Engine model is based on an example of a three-cylinder reciprocating engine from the Multibody Dynamics Module. In this engine, a flywheel is mounted on the crankshaft, with the assembly supported on both ends by journal bearings. The model also features three sets of cylinders, pistons, and connecting rods that are identical. Hinge joints are used to connect the bottom ends of the connecting rods to the common crankshaft as well as to connect the pistons to the top ends of the rods. A prismatic joint is used to connect each of the cylinders to a piston.

*Reciprocating engine geometry.*

Aside from the flexible central connecting rod, it is assumed that the engine components are rigid. The cylinders are fixed and the other parts of the engine are able to freely move in space. The engine as a whole operates at 1,000 RPM and the material data is derived from structural steel, which shows a fatigue limit at 210 MPa.

Our analysis begins with the stress history in the connecting rod fillet, as a stress concentration resulting from geometrical change is assumed in this area. After a few revolutions, the engine reaches a steady-state behavior. Following the third cycle, the stress history seemingly repeats itself for each cycle, as shown in the plot below. The third principal stress dominates the connecting rod’s stress history as the part is in compression during this time. Because the first and second principal stresses are small compared to the third one, we can consider the stress state at the fillet to be uniaxial. Since the von Mises stress would be better suited in a *multiaxial* loading, we use the principal stress as the amplitude stress in the Basquin relation.

*The stress history in a connecting rod fillet.*

The following plot addresses fatigue life prediction in the connecting rod. Here, the point of focus is the fillet near the top end of the rod. According to the Basquin model, the fatigue life is predicted to be more than twenty-five billion cycles, which is a notably long operational lifetime. Although the endurance limit is not defined in the Basquin model, the relation can be used to back-calculate the fatigue life at the endurance stress to 245 million cycles. Since the model prediction gives a greater life than the back-calculated fatigue life at the endurance limit, we can assume then that the stress within the engine’s assembly is beneath the endurance limit, which as previously noted is 210 MPa for the used material, and that the connecting rod has an infinite operational life.

*The connecting rod’s fatigue life prediction.*

The initial stress history plot also indicates that the rod is designed for infinite life. With a principal stress range around 110 MPa, the stress amplitude is near 55 MPa, which is lower than the material’s endurance limit.

- Download the model: High-cycle fatigue of a reciprocating engine

Fatigue models are based on physical assumptions and are therefore said to be phenomenological. Since different micromechanical mechanisms govern fatigue under various conditions, many analytical and numerical relations are needed to cover the full spectrum of fatigue. These models, in turn, require dedicated material parameters.

It is well known that fatigue testing is expensive. Many test specimens are necessary since the impurities responsible for fatigue initiation are randomly distributed in the material. The difference in the fatigue life is clearly visible when you visualize all the test results in an S-N curve.

*An S-N curve. The black squares represent individual fatigue tests.*

Since the *S-N curve* — also called the Wöhler curve — is one of the oldest tools for fatigue prediction, there is a good chance that the material data is already available in this form. Many times, the data is given for a 50% failure risk. If you do not have access to the material data, you are faced with a testing campaign.

When you are done, pay attention to the statistical aspect and, at each load level, select the same reliability when constructing an S-N curve. This is important since the S-N curve is expressed in a logarithmic scale where a small difference in input has a large influence on the output. Then, S-N curves for different reliability levels fall under each other and you should select an appropriate level for your application. For noncritical structures, a failure rate of 50% might be acceptable. However, for critical structures, a significantly lower failure rate should be chosen.

Always pay attention when you combine fatigue data from different sources. Make sure that the testing conditions and the operating conditions are the same.

Another aspect of fatigue testing considers the mean stress that has a substantial influence on the fatigue life. In general, fatigue tests performed at tensile mean stress will give a shorter life than tests performed at a compressive mean stress. This effect is also frequently expressed using the *R-value* (the ratio between the minimum and maximum stress in the load cycle). Thus, with decreasing mean stress (or R-value), the fatigue life increases.

In the Fatigue Module, the *Stress-Life* models do not take into account this effect. When using these models, you need to choose material data obtained under the same testing conditions as the operating one.

In the cumulative damage model, the Palmgren-Miner linear damage summation uses an S-N curve. However, in this model, the S-N curve is specified with the R-value dependence and the mean stress effect is accounted for.

*The mean stress effect.*

In case you use a material library and the fatigue data is specified using the maximum stress, you can easily convert it to the stress amplitude using

\sigma_a=\frac{\sigma_{\textrm{max}}(1-R)}{2}

where is the stress amplitude, is the maximum stress, and is the R-value.

The stress-based models seem to be fairly simple. For example, the Findley and the Matake models use the expressions

\left(\frac{\Delta\tau}{2}+k\sigma_{\textrm{n}}\right)_{\textrm{max}} =f

and

\left(\frac{\Delta\tau}{2}\right)_{\textrm{max}}+k\sigma_{\textrm{n}} =f

respectively. They depend on only two material constants: and . These material parameters are, however, nonstandard material data that can be related to the endurance limit of the material.

Note that the actual values of and differ between the two models. The analytical relation is somewhat cumbersome to obtain since the stress-based models are based on the critical plane approach and you need to find a plane where the left-hand sides of the above relations are maximized. This is basically done by expressing the shear and the normal stress as a function of the orientation using the Mohr’s stress circle, maximizing by setting the derivative to zero, and simplifying the resulting relation.

The different steps of the data manipulation will not be shown here. For the Findley model, the material parameters are related to the standard fatigue data using

\frac{f}{\sigma_U(R)} = \frac{(1-R)^2+2k\beta+4k^2}{2\beta(1-R)},\ \ \ \beta=\sqrt{(2k)^2+(1-R)^2}

Here, is the R-value and is the endurance limit. The argument of the endurance limit indicates that the stress is R-value dependent. For the Matake model, the relation is somewhat simpler and given by

\frac{f}{\sigma_U(R)}=0.5+\frac{k}{1-R}

Since both relations have two unknown material parameters, you need endurance limits from two different types of fatigue tests. To illustrate this, consider a case where one endurance limit is obtained by alternating the load between a tensile and a compressive value, . In the second case, the load is cycled between a zero load and a maximum load, . For the Findley model, this leads to

\left\{

\begin{array}{lr}

\frac{f}{\sigma_U(-1)}=\frac{1}{2}\left(k+\sqrt{1+k^2}\right)\\

\frac{f}{\sigma_U(0)}=\frac{1}{2}\left(2k+\sqrt{1+4k^2}\right)

\end{array}

\right.

\begin{array}{lr}

\frac{f}{\sigma_U(-1)}=\frac{1}{2}\left(k+\sqrt{1+k^2}\right)\\

\frac{f}{\sigma_U(0)}=\frac{1}{2}\left(2k+\sqrt{1+4k^2}\right)

\end{array}

\right.

The pair of equations must be solved numerically. Here is the strategy:

- Eliminate between the two equations. This is trivial since it always appears as a linear term.
- Now, you have a nonlinear equation for only. Since has a rather small variation (usually between 0.2 and 0.3), it is easy to solve even by pure trial and error.
- Given the computed , evaluate using either of the original equations.

For the Matake model, the two fatigue tests lead to

\left\{

\begin{array}{lr}

\frac{f}{\sigma_U(-1)}=\frac{1}{2}+\frac{k}{2}\\

\frac{f}{\sigma_U(0)}=\frac{1}{2}+k

\end{array}

\right.

\begin{array}{lr}

\frac{f}{\sigma_U(-1)}=\frac{1}{2}+\frac{k}{2}\\

\frac{f}{\sigma_U(0)}=\frac{1}{2}+k

\end{array}

\right.

which you can solve analytically.

I would like to share a few examples where the discussed fatigue models are used:

- Findley and Matake models are used to predict fatigue in the example of High-Cycle Fatigue Analysis of a Cylindrical Test Specimen.
- The S-N curve is used in the tutorial model from the Structural Mechanics Module of a bracket.
- The S-N curve with R-value dependence is used in the fatigue prediction of a model of a frame with a cutout.

A fatigue model can be selected in different ways. Expert knowledge is a good starting point. It may so be that, within your organization, there is prior knowledge on the topic if a similar application has been analyzed already. Alternatively, you may also find expert knowledge through a literature search. Since about 90% of all structural failures are caused by fatigue, there is a great chance that another engineering team has already analyzed a similar application to yours.

When there is no prior knowledge on the fatigue case, a suitable fatigue model can be proposed based on a few questions regarding loading conditions and expected fatigue failure. In the diagram below, I have summarized the key questions you should ask when evaluating fatigue using the Fatigue Module.

*Selection of the fatigue model type.*

First, you need to determine whether the external load is random or if your application is subjected to a constant cycle. A load that is not truly random, but has sequences of non-constant load cycles, could also fall into this category.

The stress history for random loads introduces a complex load scenario in the structure that requires an advanced evaluation technique to quantify the stress response. If your application is subjected to random loading, you can evaluate fatigue using the Cumulative Damage feature, where the random load is converted into a stress range distribution, rather than the single constant stress cycle — which is assumed for the other evaluation techniques.

You can find more details about this computation method in my previous blog post “Random Load Fatigue“.

At constant load cycles, the structure is affected by a repeatable load sequence. In this case, you need to determine whether the loading is proportional or non-proportional.

In proportional loading, the orientation of the principal stresses and strains does not change during the load cycle. Another way to discriminate between these two cases is to consider the characteristics of the external load. With one source of the external load, the structural response is defined by a stress tensor where all components change *in phase*. When the external load is applied in multiple points or if you have a traveling load, the components of the stress tensor can change *out of phase*. These two types of load cycles require different techniques for fatigue evaluation.

In proportional loading, the direction of the largest stress or strain that controls fatigue is clear. This was probably the type of application you worked with when you took your first class in fatigue. Back then, the load was always sinusoidal and classical methods such as the *S-N curve*, also called the Wöhler curve, were used.

In the Fatigue Module, the *Stress-Life* and *Strain-Life* models can evaluate fatigue at proportional loading. These models are based on a fatigue-life curve, which provides a direct relation between the fatigue life and the applied stress or strain amplitude.

One model in the Stress-Life family requires extra attention: The *Approximate S-N curve* (see figure below). In the model, you specify two points on the S-N curve. The first one is the transition between the high- and low-cycle fatigue, while the second defines the endurance limit. The advantage of this model is that it does not require any substantial knowledge of the fatigue material data, since the two required points can be related to the ultimate tensile strength. Although it is a rough approximation, it is a good starting point when you lack material data.

*The approximate S-N curve model. The index t denotes the transition point, while the index e denotes the endurance limit point.*

The Stress-Life models are suitable for simulating high-cycle fatigue, while the Strain-Life models are frequently used in the low-fatigue regime. The transition between the low- and high-cycle fatigue varies, but is usually somewhere in the span of 1,000 to 10,000 cycles.

The challenge for non-proportional loading is to determine the range of the fatigue-controlling parameter. Since the direction of principal stresses and strains changes, so does the direction of the parameter that gives the highest impact on fatigue life.

In the Fatigue Module, this type of application can be assessed with the *strain-based* and *stress-based* models that I discussed in the blog entry “Fatigue Prediction Using Critical Plane Models“. These are called *critical plane models* because they evaluate many orientations in space in search for the critical plane where fatigue is expected to occur.

The strain-based models are suitable for fatigue prediction at low-cycle fatigue, while the stress-based models are frequently used to predict high-cycle fatigue. Most of the fatigue models predict the number of cycles until failure. The stress-based models predict a fatigue usage factor, which is the fraction between the applied stress and the stress limit. This indicates to the user whether the stress limit has been exceeded and failure is expected or if the component will hold for the expected fatigue life. You can view the fatigue usage factor as the inverse of a safety factor.

In some cases, the stress or strain alone is not sufficient to characterize the fatigue properties. You can then use the *energy-based* models. These combine the effect of stress and strain into energy, which is released or dissipated during a load cycle.

The energy-based models are frequently used in nonlinear materials in the low-cycle fatigue regime. Since the energy can be calculated in different ways, the energy-based models can be used in proportionally and non-proportionally loaded applications.

The blog post titled “Modeling Thermal Fatigue in Nonlinear Materials” demonstrates the use of the energy-based models.

I would like to share a few examples to demonstrate how the different model types are used:

- In the example Random Load Fatigue in a Frame with a Cutout, the fatigue model takes into account a random load consisting of 1,000 load events.
- The classical SN-curve is used in fatigue evaluation of a proportionally loaded bracket.
- In the example model High-Cycle Fatigue Analysis of a Cylindrical Test Specimen, fatigue is predicted in a non-proportionally loaded test specimen. The model also shows how to obtain material parameters from two types of fatigue tests.
- An energy-based fatigue criterion is used in the Thermal Fatigue of a Surface Mount Resistor model, where the dissipated energy in a viscoplastic solder joint eventually leads to failure.

If you have any questions about your fatigue modeling application, please contact us.

]]>Using COMSOL Multiphysics, we implemented a wear model and validated it by simulating a pin-on-disc wear test. We then used the model to predict wear in an automotive disc brake problem. The results we found showed good agreement with published wear data.

*Wear* is the process of the gradual removal of material from solid surfaces that are subjected to sliding contact. It is a complex phenomenon that is relevant to many problems involving frictional contact, such as mechanical brakes, seals, metal forming, and orthopedic implants. The rate of wear depends on the properties of the contacting materials and operating conditions.

Archard’s law is a simple but widely used wear law that relates the volume of material removed due to wear to the normal contact force , sliding distance , material hardness , and a material-related constant

W=\frac{KF_N L_T}{H}

In our work, we considered a modified version of Archard’s law:

\.{w}=k(H,T)p_N V_T

This modified law relates the wear depth at any point to the normal contact pressure , magnitude of sliding velocity , and a constant that is a function of the material and temperature. The wear constant may be computed from experimental wear data, which is typically in the form of weight loss for a specific contact pressure and velocity.

Wear equations are not directly available in finite element analysis (FEA) codes, although their implementation in COMSOL Multiphysics is straightforward. We incorporated the wear equations within our simulations by defining boundary ordinary differential equations (ODEs) on the destination contact surfaces with the wear depth as the independent variable. The wear depth is then used as an offset between contacting surfaces (e.g., brake pad and disc) within the contact formulation in COMSOL Multiphysics. In particular, contact is enforced when the penetration between the contact surfaces is equal to the wear depth , as shown in image below.

*Modification of contact gap calculation: is the wear depth, is the gap, and is the contact pressure.*

This wear algorithm is very efficient since it does not involve altering the nodal locations to account for material loss due to wear. It is only suitable, however, for cases where the wear depth is significantly less than the width of the contact surface.

You can enhance this wear algorithm by including more sophisticated effects, such as anisotropic wear behavior, dependence on the mean and deviatoric stresses in the solid (not just the contact pressure), threshold pressure/stress below which no wear occurs, and more. The assumption of small wear depth must still hold for this modeling approach to be accurate.

We validated the new, contact-offset-based wear model implementation by simulating a pin-on-disc wear test. Only a small section of the disk is modeled, as shown below.

*Pin-on-disc wear test model.*

The disc in this model is much stiffer than the pin and all the wear is assumed to occur in the pin. A force is applied to the pin, resulting in a circular, Hertzian-type contact pressure distribution. A constant tangential velocity is then applied to the disc. The graph below shows how the wear depth varies radially along the pin at four time instances. The total volume loss, calculated as the integral of wear depth over the contact surface, was similar to the value calculated using Archard’s law.

*Wear depth vs. radial distance in the pin-on-disc model.*

We also used the model to predict wear in an automotive disc brake problem, which is similar to the Heat Generation in a Disc Brake model that can be downloaded from the COMSOL Model Gallery. We developed a 3D thermal-structural disc brake model involving representative brake disc/rotor and brake pads.

*Disc brake model used in the COMSOL Multiphysics wear simulation.*

The structural and thermal processes are coupled through frictional heat generation, thermal expansion, and thermal contact. Both physics fields are also coupled to the wear depth evolution boundary ODE. We used a fully-coupled direct solver that converged rapidly, keeping solution times similar for problems with and without wear.

The results for both the pin-on-disc validation example and the disc brake problem were in good agreement with published wear data. In the disc brake example, the model captured the non-uniform wear rate that is typically observed on brake pads; it was higher near the outer radius and leading edge, as shown below.

*Typical brake pad wear depth profile.*

We will present more of our results, including contact pressure and wear contours, at both the Cambridge and Boston stops of the COMSOL Conference 2014.

Nagi Elabbasi, PhD, is a Managing Engineer at Veryst Engineering LLC. Nagi’s primary area of expertise is modeling and simulation of multiphysics systems. He has extensive experience in finite element modeling of structural, CFD, heat transfer, and coupled systems, including fluid-structure interaction, conjugate heat transfer, and structural-acoustic coupling. Veryst Engineering provides services in product development, material testing and modeling, and failure analysis, and is a member of the COMSOL Certified Consultant program.

]]>Modular orthopedic devices, common in replacement joints, allow surgeons to tailor the size, material, and design of an implant directly to a patient’s needs. This flexibility and customization is counterbalanced, however, by a need for the implant components to fit together correctly. With parts that are not ideally matched, micro-motions and stresses on mismatched surfaces can cause fretting fatigue and corrosion. Researchers at Continuum Blue Ltd. have assessed changes to femoral implant designs to quantify and prevent this damage.

Take a few steps and see how your hips rotate. You’ll find that your body weight is continuously shifting between the left and right sides, while your legs bend, swing, and then straighten out with each step. Thus, a good modular hip replacement system will need to be able to freely allow for the natural motions of the human body — walking, running, or going up and down stairs. In addition to this, it has to be durable enough to take the continually changing, and sometimes excessive, loads placed on it during these movements, while being comprised of lightweight materials that fit and interact well with the body.

Modular implants often include stems, heads, cups, or entire joint systems. A range of materials from steel and titanium alloys to polymers and ceramics offer the surgeon many options depending on the needs of the patient. However, material and geometric selections affect the amount of wear and tear that will occur over time, so certain combinations of components are better than others. With so many different factors at play, it is not surprising that these assemblies require tight tolerances and the right material combinations to function properly and last a lifetime.

*Virtual implantation of hip replacement in resected patient femur.*

Studying how a modular combination of parts will behave under dynamic loads and stresses is a crucial part of the design and decision-making process. In order to understand the available combinations better and aid medical professionals in decisions, engineers at Continuum Blue have modeled three combinations of modular femur stem and head implants to investigate the *fretting fatigue*; the fatigue wear caused by the repeated relative sliding motion of one surface on another.

The femur head contains an angled channel for the neck of a femur stem, which in turn must be tapered correctly to fit the channel. The engineers studied three different geometric configurations using different materials for the head and stem to determine which of the three was best for minimizing fretting fatigue.

*Different stem and head configurations with an ideal fit, positive mismatch, and negative mismatch.*

Using kinematic load data from Bergmann et al. and based on averages from four patient sets, Continuum Blue created a COMSOL Multiphysics simulation to analyze the cyclic loading on a femur head. They used their model to determine the loading at different points during a walking gait cycle, knowing that the load would change at different locations in the rotation, and validated their results against the kinematic data.

*Simulation results showing the dynamic loads and stresses during the gait cycle.*

Material fatigue can be determined by studying the mean stress and stress amplitude that occur during the cyclic loading of the joint. Like the loading in the femur head shown earlier, the stresses in the femur stem will change over the course of a gait cycle. With regular leg movements, the stresses observed will take on an oscillation that reflects the repeated motion of the person walking.

*SN curves for the titanium stem and cobalt chromium head used in the study.*

Continuum Blue assessed the three configurations with two different materials: a cobalt chromium alloy for the head and a titanium alloy for the stem of the modular implant. For each material domain, they calculated the stresses observed over a single gait cycle and related these to both the SN curves of the material and the micro-motions of the contact surfaces. This allowed them to predict the number of cycles the device could undergo before fretting fatigue became an issue.

*Areas where fretting fatigue occurs over gait cycle for each configuration.*

Their results showed a surprising fact: the “ideal” fit, where the femur head channel is exactly aligned to the sides of the femur stem, was *not* found to be the best configuration for minimizing fretting fatigue. Rather, the configuration with a slight positive misalignment turned out to be a better choice, exhibiting lower stresses and overall fretting fatigue.

Through their simulation, Continuum Blue was able to predict the stress, contact pressure, and areas most susceptible to fretting fatigue at different points in a gait cycle. There are many other factors that will be accounted for in future research, such as the sensitivity of the implant to varying degrees of misalignment; additional designs and geometric changes; different materials; and the effects of surface finishes, coatings, or roughness that may impact the results. However, their modeling work offers a unique promise for evaluating the lifetime of a modular implant device. It was validated as an accurate way to predict the wear and tear that will occur for these three configurations of the implant. If you ever need a joint replacement analysis — you’ll know who to call.

- COMSOL Conference 2012 presentation: “Fretting Wear and Fatigue Analysis of a Modular Implant for Total Hip Replacement“

The numerical simulation of applications containing the aforementioned challenges can be tackled using the Nonlinear Structural Materials Module, which provides a collection of predefined nonlinear material models, in combination with the Fatigue Module, which contains fatigue models for many different applications.

When the temperature changes, materials want to expand or contract. In applications consisting of several different parts, this thermal deformation will be constrained, since the thermal expansion coefficients differ between various materials. The situation is more challenging in the presence of *nonlinear* materials.

Material nonlinearity implies that the deformation is not proportional to the loading. The nonlinearity of different materials can be roughly divided into *reversible* and *irreversible* nonlinearity. Reversible nonlinearity is also called elastic nonlinearity, which means that the strain state returns back to the initial state once the external load is back at its starting point.

Materials that exhibit irreversible nonlinearity can sustain permanent damage when loaded and will not return to the initial state upon unloading. An example of this phenomenon is shown in the figure below, where a surface mount resistor with a nonlinear solder material is subjected to a thermal cycle.

*Displacement in a surface mount resistor at the end of a thermal load cycle. Blue color denotes zero displacement.*

The material nonlinearity is a creep mechanism that deforms the material once it is subjected to a stress field — even when the stress field is held constant. Since the thermal expansion of the different parts of the surface mount resistor is non-uniform (greater in the printed circuit board on the bottom and smaller in the resistor on top), the assembly is stressed during a thermal load cycle.

Once the thermal load has reached the end of a load cycle, and returned to the initial temperature, a permanent deformation (creep strain) is left in the solder joints on both ends of the resistor. The permanent deformation in the solder joints prevents the remaining parts from returning to the initial state. You can see this in the figure where the resistor is compressed and bulges, while the printed circuit board is elongated.

Another type of material nonlinearity occurs when the permanent deformation only depends on the applied load and does not deform at a constant stress. This is called *plasticity* and can be demonstrated simply by bending a paper clip back and forth. If the applied force is too high, the paper clip will remain in a deformed state that does not change with time. A combination of plasticity and creep is called *viscoplasticity* and is yet another nonlinear material behavior.

Repeated loading and unloading can cause fatigue cracks. Before the fatigue life can be evaluated, you must obtain a stable load cycle. When working with nonlinear materials, many load cycles are often required before the material’s response stabilizes. Generally speaking, the nonlinear material response to a cyclic load can be summarized by three cases: immediate stability, shakedown, and ratcheting.

- In the case of the
*immediate stability*, the second load cycle will already give a stable stress-strain response that is representative for each consecutive load cycle. This is demonstrated with the dotted black line in Case (a) in the figure below. - At
*shakedown*, the elongation stops first after a certain number of cycles. Therefore, a large number of cycles may need to be simulated. See Case (b). - In
*ratcheting*, Case (c), the material experiences a continuous elongation until failure. This case is the most challenging from a fatigue point of view since a stable load cycle is never obtained. In this case, you must generally simulate all cycles from initial state to failure.

*Material response to a repeated load cycle: (a) immediate stability, (b) shakedown, and (c) ratcheting.*

There is no universal model that predicts fatigue for all nonlinear materials, and many models have been proposed over time. In the 1950s, Coffin and Manson examined fatigue in metals and proposed an exponential relation between the fatigue life and the plastic strain for the low-cycle fatigue regime.

Following this pioneering work, many researchers proposed slightly modified models, where the plastic strain has been replaced with a different strain measure, such as creep strain, plastic shear strain, total shear strain, and others. Below, you can see a comparison between two strain measures (effective creep strain and the shear creep strain) in a surface mount resistor model, which was taken from our Model Gallery:

*Development of the creep strain in a solder joint. Effective creep strain, to the left, and shear creep strain, to the right.*

Both strain measures are highest at the interface between the solder and the resistor, which coincides with the position of a thermal fatigue crack in real applications.

For many applications, strain alone is not sufficient for fatigue predictions. Instead, energy might be more suitable since it combines the effect of stress and strain. In the 1960s, Morrow proposed an exponential relation between the fatigue life and the cyclic plastic strain energy. This model has later been modified to depend on other energy quantities, such as creep strain energy, total strain energy, stress-strain hysteresis energy, viscoplastic strain energy, and others.

Many times, the fatigue-controlling energy quantity is a nonstandard energy variable that requires a separate computation. This can be done in COMSOL Multiphysics, as demonstrated in the example of accelerated life testing, where the nonlinear material has two creep mechanisms. The first one controls strains at low stresses and the second one controls strains at high stresses. The fatigue, on the other hand, is controlled only by the energy dissipation caused by the creep development at high stresses.

The strain development as well as the energy dissipation by different mechanisms is calculated in individual distributed ODE interfaces:

*Model set-up for evaluating user-defined creep strains and energies using ODE interfaces (to the left). A comparison of the results between the user-defined constitutive relations and the predefined material model from the Nonlinear Structural Materials Module (to the right). The green line is the dissipated energy at low stresses, the red line is the dissipated energy at high stresses, the dotted turquoise line is the combined dissipation by both mechanisms, and the blue line is the dissipated energy calculated with the material model from the Nonlinear Structural Materials Module.*

Fatigue cracks are frequently encountered at interfaces of sharp geometrical changes and in corners. Those places are also well-known for causing numerical singularities. Thus, a point evaluation there can give misleading results.

Darveaux proposed a model that uses an energy volume average. This approach reduces the sensitivity to meshing in critical places and predicts life based on the surrounding state. In the figure below, we use the Darveaux model to predict fatigue life based on the dissipated viscoplastic strain energy in a ball grid array.

*Fatigue life based on the average dissipated creep energy. All joints in two ball grid arrays are analyzed in a full model on the left-hand side, and to the right, a detailed study of the critical solder joint in a submodel is shown.*

At first, all solder joints are analyzed in order to identify the critical one. Then, the critical joint is reanalyzed in a detailed study using a submodeling technique described in a previous blog post. The fatigue life in the thin layers at the interface with other materials, where cracking is expected, is finally predicted. Since the model evaluates a volume average, the results are calculated per domain.

We can evaluate the Coffin-Manson model with different strain options in the *Strain-based* fatigue feature. The Morrow and the Darveaux models with different energy options can be evaluated using the *Energy-based* fatigue feature.

To wrap this up, I’d like to share a few examples where the thermal fatigue of nonlinear materials is simulated:

- The Thermal Fatigue of a Surface Mount Resistor model demonstrates how to perform a fatigue evaluation based on creep strain and the dissipated creep energy using the Coffin-Manson and Morrow type relations.
- In the Energy-Based Thermal Fatigue Prediction in a Ball Grid Array example, a microelectronic microchip containing several viscoplastic solder joints is analyzed. The fatigue life is based on the Darveaux energy volume average. This model also demonstrates how to analyze large models using the concept of submodeling.
- Fatigue life prediction, based on a more exotic energy and strain representation, is modeled in the Accelerated Life Testing example. Here, a material behavior with two creep mechanisms is evaluated and fatigue life, based on one mechanism, is predicted. The separation of strains in the two mechanisms requires recalculation of individual strains using separate ODE interfaces.

You can find all of these example models in the Fatigue Module Model Library.

*If you are interested in learning more about fatigue modeling in nonlinear materials, join the Nonlinear Structural Materials Modeling and Fatigue Evaluation webinar on May 15 ^{th}.*

The idea behind critical plane models is that failure is caused by a crack. The crack will form and run on a plane, a *critical plane*, that has the most favorable stress/strain conditions for either crack growth, crack propagation, or both events. Planes that experience the highest normal stresses and strains are usually good candidates for a critical plane.

The stress state in a point in a structure can be described with a two-dimensional tensor with three normal and three shear components. The magnitude of those stresses changes once the examined volume element is oriented in a different direction. This means that if we make a cut through a volume element and evaluate stresses on the newly created plane, its stress state will change depending on the orientation. In case of plane stress conditions, the stress state reduces to two normal stresses and one shear stress that also differ depending on the surface normal.

*Stress conditions on different planes.*

A plane in a volume element has one normal and two shear stress/strain components. A critical plane model utilizes those stress/strain components to define the critical plane in its own specific way. For example, the *Normal stress* criterion considers a plane with the largest normal stress range, the *Findley* model searches for a plane where the combination between the normal and the shear stress ranges is maximized, while the *Matake* criterion, on the other hand, evaluates planes with the highest strain range. From the picture above, it is clear that all of the planes have different orientations.

For plane stress conditions, the critical plane can be obtained with analytical expressions. The situation becomes more challenging when the load is non-proportional and the stress state is multiaxial. We must then search for the critical plane numerically and evaluate the load history in each examined plane orientation. This is done in the Fatigue Module with the *Stress-based* and the *Strain-based* models.

*Fatigue evaluated with critical plane criteria: Normal stress, Findley, and Matake. Note from the editor, 2/24/14: This image has been updated with results from COMSOL Multiphysics version 4.4.*

In the Stress-based models we can calculate *Normal stress*, *Findley*, and *Matake* criteria. These are evaluated according to the fail-safe philosophy — calculating the *fatigue usage factor* that determines whether the experienced fatigue load is above or below a fatigue limit. The material parameters for those models can be easily calculated from the results of standard fatigue tests. *Stress-based* models are usually used in the high-cycle fatigue domain where plasticity is very limited.

The *Strain-based* models evaluate strains or combination of strains and stresses when defining a critical plane. Those models can be seen as a modified, combined Basquin and Coffin-Manson strain-life relation. They predict the number of cycles to failure. In the Fatigue Module, there are three *Strain-based* models: *Smith-Watson-Topper (SWT)*, *Wang-Brown*, and *Fatemi-Socie*. Those models are suitable for low-cycle fatigue where strains are usually large.

I’d like to share three examples of fatigue evaluation based on the critical plane evaluation. Two of them evaluate high-cycle fatigue, and the last low-cycle fatigue. You can find these in the Fatigue Module.

- The Cylindrical Test Specimen model evaluates all three stress-based criteria on a non-proportionally loaded cylindrical test specimen.
- The Structural and Fatigue Analysis of a Shaft with Fillet model demonstrates how to perform high-cycle fatigue. It also show how to calculate fatigue material data based on fatigue tests in reversed axial tension and pure torsion.
- The Low Cycle Fatigue Analysis of Cylinder with a Hole model shows how to perform fatigue study in presence of plasticity. In such a case it is important to first obtain a stable load cycle before a fatigue analysis can be performed.

*If you’re interested in learning more about fatigue prediction modeling, watch the archived webinar Fatigue Modeling with COMSOL.*

Random loads introduce a variety of stresses, with different magnitude, into a structure. It is therefore important to identify overall trends in the stress history. *Rainflow cycle counting* is a popular method to transfer the variable load history into a discrete stress distribution that is characterized by certain mean stress and stress amplitude. In COMSOL Multiphysics, the stress distribution of the Rainflow counting is visualized in a new plot type, called *Matrix Histogram*.

*Stress distribution based on the Rainflow cycle counting method.*

A classic way of obtaining fatigue life is via the S-N curve. It relates the stress amplitude to the number of loading cycles a material can withstand. In variable loading however, the stress amplitude is not constant and, instead, you must use an alternative model that calculates damage contribution of each cycle. You might use the Palmgren-Miner linear damage rule, a widely used method, to capture this. In the Fatigue Module, the Palmgren-Miner rule processes the stress distribution of the Rainflow counting and relates it to the limiting S-N curve. In order to capture the mean stress effect, so that damage increases with the increasing mean stress, the S-N curve is specified with an argument for the R-value.

The fatigue analysis consists of two steps. First, you calculate the structural response of a load cycle. Next, you perform a fatigue evaluation. When the number of load events is large in a random load analysis, the simulation of the load cycle is time-consuming, but the calculation time can be greatly reduced if the nonlinear effects are not present in the simulation. In that case, the stress cycle can be prescribed with help of superposition. This is selectable with the *Generalized loads* analysis type in the Cumulative Damage feature. There, the load cycle is not prescribed load-step by load-step, but instead the history of an external load is decomposed into few generalized loads with corresponding load histories.

*The external load simulated using three generalized loads and corresponding time histories.*

The Cumulative Damage calculation, based on the generalized loads, can be summarized in following steps:

- Define generalized loads
- Prescribe generalized loads in a structural study
- Compute structural response to generalized loads
- Define load histories for all generalized loads
- Prescribe load histories to corresponding generalized loads
- Compute fatigue analysis

The first three steps are done in a structural prestudy, while the last three are done in a fatigue study.

I’d like to share two examples of simulating Cumulative Damage, with you. Both can be found in the Fatigue Module. In one of the examples, the load cycle is prescribed step-by-step, and in the other one, superposition is used via the Generalized loads option.

The “Frame with Cutout” example uses the Generalized loads option. Here, the fatigue response to 1,000 load events is simulated. The total computation time with the Generalized loads option is 8 minutes, while the load event by load event calculation takes 1.5 minutes for each load event, thus the total load cycle would require a full day of computation time. Moreover, large amount of data needs to be saved in order to be processed by the fatigue study. With the Generalized loads option you don’t need to spend this much time on your computations.

The model “Cycle Counting in Fatigue Analysis — Benchmark” compares results of the Rainflow counting against an ASTM standard. The results based on the Palmgren-Miner are compared against hand calculations.

*If you’re interested in learning more about fatigue modeling, tune into our Fatigue Modeling with COMSOL webinar on July 30 ^{th}, 2013.*