In this article, we will dig into fatigue life theories commonly used in practice. After learning the fundamentals of fatigue in the Introduction to Fatigue article, let's start with the theories being used to express fatigue properties of the materials.

Continue reading Understanding of Fatigue Life Theories at Mechead.com.

]]>In this article, we will dig into fatigue life theories commonly used in practice. After learning the fundamentals of fatigue in the Introduction to Fatigue article, let’s start with the theories being used to express fatigue properties of the materials.

As we mentioned in the previous post that fatigue results represented in tables by Wöhler, later on, plotted in stress and life axis, and named Wöhler or SN curves. In this method, primarily elastic deformation is predominant. Failure time or cycles to failure takes a longer period. Therefore, this technique is also called **high cycle fatigue** and measured with stress and cycle to failure. Two different types of SN curves are discovered and schematically shown in the figure below. The graphs show that the stress life curve differs depending on the material properties.

Ferrous and titanium alloys represent a fatigue limit (endurance limit). For these materials, the S-N curve becomes horizontal at higher cycles; below this line, no fatigue failure is seen for the infinite number of cycles. However, most of the non-ferrous metals show a declining trend over the cycle to failure and does not have a fatigue limit.

Another approach to fatigue life estimation is strain life. Since the failures occur in a relatively small number of cycles, it is also named **low cycle fatigue**. This technique is used for circumstances where fatigue loading is associated with plastic deformation (stress above the yield point). In this method, fatigue life is characterised by the strain range. In the strain life approach, the total strain has two components namely, elastic and plastic.

\frac{\Delta\epsilon}{2}=\Delta\epsilon_a=\underbrace{\frac{\sigma^{'}_f}{E}(2N_f)^b}_{elastic}+\underbrace{\epsilon_{f}^{'}(2N_f)^c}_{plastic}

According to the plot of total strain-life, the coefficients can be listed below:

\sigma^{'}_f, fatigue strengthcoefficient \\ \epsilon^{'}_f, fatigue ductility coefficient \\ b, fatigue strength exponent\\ c, fatigue ductility exponent\\

The stress or strain life graphs above are often developed for fully reversed loading conditions where the material loaded with the ratio of -1. The mean stress is normally zero for standard developed stress-strain life graphs. However, what if you have a loading ratio different than -1 (mean stress). For these circumstances, a number of techniques were developed to find out allowable stress amplitude that your material can withstand. The most accepted methods can be listed as; Goodman, Gerber and Soderberg.

\sigma_e \text{: fatigue limit under zero mean stress} \\ \sigma_a \text{: fatigue stress amplitude} \\ \sigma_m \text{: mean stress} \\ \sigma_{TS} \text{: tensile strength} \\ \sigma_{y} \text{: yield strength} \\

Using these curves, general forms of the relationships are expressed as follows;

\sigma_a=\sigma_e(1-(\frac{\sigma_m}{\sigma_{TS}})^x)

**where x=1 for the Goodman line, x=2 for the Gerber line.**

Soderberg expressed allowable stress amplitude under mean stress using fatigue limit and yield strength as shown below;

\sigma_a=\sigma_e(1-(\frac{\sigma_m}{\sigma_{y}}))

New engineering series on Mechead. In the first article, we talk about basic concepts of fatigue methods and mechanisms as well as evaluation of fatigue approaches over history.

]]>As we engineers often start learning theories based on static problems at universities. However, once we start having a career in industry, you notice that in contrast to undergrad education we rarely used the static formulas in real life. I wouldn’t say that it is pointless to learn static problems, particularly they are the main structures of our engineering knowledge. On the other side, as Vasily Grossman said “Everything flows” or Albert Einstein, “**All things** in our universe are constantly in motion”, besides their philosophical meanings, we would all agree that most of the mechanical problems are dynamic and somehow they are subjected to altered forces/stress/pressure. As an academic who has been working with fatigue problems for more than 10 years, I will be talking about an in-depth understanding of fatigue. The fatigue series will be divided into 4 articles;

- The first one is
**Introduction to Fatigue** - we will follow with
**Understanding of Fatigue Life Theories** - then,
**Experimental Applications of Fatigue** - at last,
**Fatigue Fractures**

One of the first studies in history about how metals behave under dynamic loads was completed by August Wöhler, a railway engineer, 1852-1869 and during his axle investigations interestingly he found that;

- Materials can be subjected to failure by repeating stress that is lower than static strength
- Stress amplitude is critical to determine whether the material withstands or not.
- The higher the maximum stress the system has, the lower the stress amplitudes the material can accommodate.

Absolutely, these findings were groundbreaking at that time and he published a number of articles and reports explaining the details of his experimental outcomes. Later on, in the 1900s, metal fatigue earned particular attention from scientists and it was explained that fatigue damage starts on the surface of the material by slip lines formed on the surface of grains. The slip lines eventually develop cracks. Once a crack reaches a considerable depth, this crack propagates across the material until the section is reduced and static failure takes place.

Wöhler was representing his results in tables, however, Spangenber, who was kind of Wöhler’s assistant, plotted the tabulated results in stress and life axis. After that moment, this method became very famous and named SN/Wöhler curves since 1936.

Although a number of explanations existed about fatigue crack mechanism, in general, there is a common phenomenon, “initiation, propagation and failure”. Materials under cycling/alternating loads are subjected to stress variations (compression/tension). These variations generate microcracks that can propagate in intergranular (between the grains) or transgranular (crossing from grains) ways. By the time these micro-cracks become larger, they result in failures in the material.

Fatigue in materials due to repeated application of loads can occur in different forms; mechanical fatigue,

creep-fatigue (repeating forces in conjunction with high-temperature) and thermomechanical fatigue (the combination of temperature fluctuation and mechanical fatigue). For example, **Bhopal Disaster** was one of the biggest disasters in history due to thermomechanical fatigue.

In fatigue life formulations, stress level and loading conditions are often expressed with maximum stress, minimum stress, mean stress and stress amplitude. The figure below shows schematically the main parameters used in fatigue theories.

\sigma_{max}= Maximum \ stress \\ \sigma_{min}= Minimum \ stress \\ \sigma_{a}= Stress\ amplitude \\ \sigma_{r}= Stress\ range =\sigma_{max}-\sigma_{min} \\ \sigma_{m}= Mean \ stress=\frac{\sigma_{max}+\sigma_{min}}{2}

Lastly a stress ratio (R) is defined to characterize the fatigue loading condition.

R=\frac{\sigma_{min}}{\sigma_{max}}

R=-1 is fully reversed loading in which the material is subjected to the same amount of compression and tension stress alternatingly. R=1 is a static loading because this means that the maximum and minimum stresses are equal. R=0 is zero-based loading where the stress range varies between zero and positive/negative values.

References

1-O. Sunar, D. Fletcher and A. Beagles, “Laboratory assessment of arc damage in railway overhead contact lines with a case study on copper-silver and low oxygen content copper,” in *IEEE Transactions on Power Delivery*, doi: 10.1109/TPWRD.2020.3032798.

2-Ö. Sunar and M. Çevik , “FATIGUE ANALYSIS OF SINGLE LEAF SPRINGS WITH FINITE ELEMENT METHOD”, *Celal Bayar University Journal of Science*, vol. 11, no. 1, pp. 1-6, Jul. 2015, doi:10.18466/cbufbe.34361

3-SUNAR, OZGUN (2021) *Arc Damage Identification and Its Effects on Fatigue Life of Contact Wires in Railway Overhead Lines.* PhD thesis, University of Sheffield.

Finite element analysis often makes our analysis easier and give chance to demonstrate even so complicated situations in a couple of hours. However, sometimes If you do not know how to deal with basic fundamentals and properties of the mesh features you can turn yourself into a master of a disaster who gets horrible results even for simple issues. In this article, global mesh properties are explained to use Ansys workbench professionally and make global adjustments in the meshing strategy. Global mesh controls include sizing functions, inflation, smoothing, defeaturing, parameter inputs, assembly meshing inputs, etc. Minimal inputs Automatically calculates global element sizes based on the smallest geometric entitySmart defaults are chosen based on physics preference Sizing : Advanced Sizing Fuctions Controls the growth and distribution of mesh in important regions ofhigh curvature or close proximity of surfaces. There five options : -Off. Unavailable for Assembly Meshing -Proximity and Curvature -Curvature -Proximity -Fixed ASF: Off• The edges are meshed with globalElement Size• Then the edges are refined forcurvature and 2D proximity• At the end, corresponding face andvolume mesh is generated• Transition of cell size is defined byTransition ASF: Curvature• Determines the Edge and Face sizesbased on Curvature Normal Angle• Finer Curvature Normal …

Continue reading Global Mesh Control in Ansys Workbench at Mechead.com.

]]>Finite element analysis often makes our analysis easier and give chance to demonstrate even so complicated situations in a couple of hours. However, sometimes If you do not know how to deal with basic fundamentals and properties of the mesh features you can turn yourself into a master of a disaster who gets horrible results even for simple issues. In this article, global mesh properties are explained to use Ansys workbench professionally and make global adjustments in the meshing strategy.

Global mesh controls include sizing functions, inflation, smoothing, defeaturing, parameter inputs, assembly meshing inputs, etc.

Minimal inputs

- Automatically calculates global element sizes based on the smallest geometric entity
- Smart defaults are chosen based on physics preference

Controls the growth and distribution of mesh in important regions of

high curvature or close proximity of surfaces.

There five options :

-Off. Unavailable for Assembly Meshing

-Proximity and Curvature

-Curvature

-Proximity

-Fixed

**ASF: Off**

• The edges are meshed with global

Element Size

• Then the edges are refined for

curvature and 2D proximity

• At the end, corresponding face and

volume mesh is generated

• Transition of cell size is defined by

Transition

**ASF: Curvature**

• Determines the Edge and Face sizes

based on Curvature Normal Angle

• Finer Curvature Normal Angle creates

finer surface mesh

• Transition of cell size is defined by

Growth Rate

**ASF: Proximity**

• Controls the mesh resolution on

proximity regions in the model

• Fits in specified number of elements in

the narrow gaps

• Higher Number of Cells Across Gap

creates more refined surface mesh

• Transition of cell size is defined by

Growth Rate

**Element Size**

• Element size used for the entire model

– This size will be used for meshing all edges, faces and bodies

• Default value based on Relevance and Initial Size Seed

– User can input required value as per geometry dimensions

**Min Size**

• Minimum element size that the size function will generate

• Some element sizes may be smaller than this size depending on the edge length

**Max Size**

• Maximum element size that can be grown in the interior of volume mesh

**Max Face Size**

• Maximum face size that the size function will generate

• Not supported by CutCell meshing

Basically, growth rate affects how the quality of your mesh will differ from fine to coarse behaviours.

Designing a jet engine in a couple of months with no experience before. Yes, I know it sounds silly. However, it is not impossible. Here is my story and how I completed my jet engine design starting from literally zero.

]]>Designing a jet engine in a couple of months with no experience before. Yes, I know it sounds silly. However, it is not impossible. Here is my story and how I completed my jet engine design starting from literally zero.

Before explaining how I achieved my goal of designing a jet engine, let’s have a look at the final drawing. The engine consisted of 32 components and it is not a replica of standard jet engines currently used in the industry.

I am currently a student in Materials Science and Engineering, but engines always interest me even before I attended the university. In the first year of my university education, I was introduced to a competition that was about designing for engineers. That was the first time that I heard the name “CAD”. In a short time, CAD software, Solidworks became one of my favourite interests. Briefly, I have been using CAD software for 3 years but only the first year was full of learning how to design something.

The jet engine is not the first model that I developed, my initial experience was a V6 internal combustion engine. To be honest, this was pretty much a benchmark for me, especially understanding how to turn my thought into a model. But we can talk about this engine maybe in another post.

Initially, the overall shape of the jet engine was so important. The most challenging part was the turbine blades. To design this, I created 4 planes/surfaces and connected them with splines. Later on, I connected the end-points of the splines with another 2 splines. Lastly, I applied the boundary surface command. I used cip-pattern to rotate the design around a central axis. I know that you could find a number of designs on the internet, however, I wanted to build my own jet engine. For this reason, I went through many many components which I was able to find by googling and completed my pre-drawings by hand. Here is an example below;

I must admit that this was not a straightforward operation. You must go through lots of desperation, and passionate. Learning 3D modelling is a long term process that you cannot build in one or two days. I am still in search of a number of ways to turn my ideas into reality by using much less effort than I used to spend before. However, I would still recommend a few tips for those who are keen to look for some fun by learning 3D design.

- Grabcad is a nice webpage which you can find various design ideas
- Check Udemy, even the free courses exist about computer-aided design
- My most important step is practising, keep drawing everything that you see around. Have a calliper in your hand and start measuring the objects. Then It will be easier to complete their 3D models in the software using your technical drawings.

You can reach the jet engine source file using the link as follows;

]]>Building an effective and efficient finite element model can be a difficult practice for engineering. Particularly, if you are a new learner finite element user, this sometimes gets very frustrating. We expect the model to be able to answer questions as fast as possible with most accurate results. In this article we will be talking about 3 major life-saving tips to develop more effective finite element analysis.

Continue reading 3 Tips to Improve Your FE Model at Mechead.com.

]]>Building an effective and efficient finite element model can be a difficult practice for engineering. Particularly, if you are a new learner finite element user, this sometimes gets very frustrating. We expect the model to be able to answer questions as fast as possible with most accurate results. In this article we will be talking about 3 major life-saving tips to develop more effective finite element analysis.

Simplifying your model comes first when starting your finite element analysis. Of course, model simplification is not possible in all situations, however, we can try our best to perform the FE analysis without using the unnecessary parts of the design. The model simplification sometimes can be either minor geometrical corrections (Figure 1); such as, removing small fillets/rounds which will not affect the global displacement calculations, or major component reductions (Figure 2), for giving a very basic example, you do not need to model the whole chair and person to analyse if the chair legs will withstand the maximum load of 1000 N. You can simply degrade the forces affecting the legs using remote connections (You read more about remote points here).

More of an advanced simplifying; Mechanical shock results show negligible global and local results when very small chip components are included (left) vs. when they are excluded (right) in Figure 3.

You can also take the simplifying to further stage although in very complicated cases using submodeling. (Read more about submodeling)

We have known that there are a number of properties we can control during mesh operation such as; defeaturing, mesh size, advance sizing, refinement, proximity etc. (Here you can learn more about meshing properties) However, here we would like to talk about more choosing the correct and most efficient element types for your solutions. More often than not, CAD geometry will be composed entirely of three-dimensional bodies. However, in an FEA model, it may be advantageous to mesh some of those bodies with shell elements rather than solid 3D elements.

Shell elements are 2D approximations of the 3D geometry that store the thickness of a body as a physical property. They can be used for thin-walled geometries with a length much greater than the thickness of the body, and when the shear deformation is insignificant (e.g., a sheet metal chassis or the walls on a soda can). There are also special shell and beam reinforcement elements that can be used to model the thin copper layers inside a printed circuit board (PCB). New features in Ansys Sherlock allow for the rapid generation of these reinforcement geometries. These reinforcements enable the user to capture the effect the traces have on the board deformations efficiently.

It may seem intuitive to assume that 3D meshing yields more detail, which provides more accurate results. But this is not always the case. Particularly in cases of large bending, solid elements often create artificially stiff structures when they are used to mesh thin-walled geometries, resulting in inaccurate simulations. In addition, it can be very difficult to refine the mesh and generate enough elements through the thickness of a thin-walled structure to achieve accurate displacement and stress results.

Furthermore, if the geometry is complex enough, thin-walled structures may result in a poor-quality mesh when solid elements are used, creating sliver-like elements with poor aspect ratios, negatively affecting results.

When determining whether to use hexahedral (hex-six face) elements or tetrahedral (tet- three face) elements in an FEA model configuration, it’s important to keep in mind the overall shape and complexity of the object itself. The general rule of thumb is to mesh with hexahedral elements if possible. Hex or “brick” elements generally result in more accurate results at lower element counts than tetrahedral elements. However, if the object contains acute angles or other complex geometries, it may be necessary to mesh with tetrahedral elements.

It is preferable to simplify the model enough to mesh it entirely with bricks, but this is not always feasible. For complex geometries that require tet meshes, take care to ensure the mesh does not result in inaccurate results. This usually means higher element counts, high order elements and longer run times.

For these reasons, any model simplifications like fillet removal or body splitting that allow for hex meshing without significantly changing the geometry are highly recommended.

Mesh size simply refers to the characteristic edge length of an element. A smaller mesh size will result in more elements in the model, resulting in longer run times and more accurate results. First-order elements have nodes only at the corners of the elements (linear elements) and calculate displacement linearly between nodes. Second-order elements (quadratic elements ) include midside nodes between the corners and calculate displacement quadratically. The additional detail in second-order elements typically results in increased accuracy, but at a significantly increased computational cost.

At this point we should ask ourselves that do we really need these centre nodes in our analysis? How precisely will they affect our solution? It is better to find a balance between order and size of the elements.

Determining proper load applications is an important analysis step. Load applications are the model inputs that the object is being tested for, such as a specific event like a thermal cycle, shock from a drop, vibration or static flexure.

One common example is determining whether loads applied should be applied as static or transient. For example, if an engineer is simulating the flexure of a structure during assembly, it may be acceptable to model the load as a static displacement because strain rates are likely to be much slower and results time-independent. However, if an engineer is modeling a similar deflection caused by dropping the same assembly, they would likely need to use a transient model to capture the associated inertial effects, because the application time of the load is much faster and time-dependent effects must be captured.

The same real-world event is not always equal in the FEA world depending on the desired outcome of the analysis. It’s important to always keep in mind the real-world stressors the object will likely face and how those stressors could affect the component of interest. Inputting these nuances properly will result in an analysis that is accurate, valid and actionable.

Finite element analysis is an endless world and you will keep facing unique problems day by day. We believe that these suggestions will differ depending upon the type of your analysis and what you want to achieve. However, we still believe that these basic and fundamental improvements will have a significant effect on your solution time.

References

1- ansys.com/blog

2-For rabbit and snail, feaforall.com

Finite element solutions are becoming vital for complex engineering systems that are not simple to analyze by using old traditional methods. Comparing to theoretical approaches, FE models are capable to deal with much more intricate multiphysics problems. However, two crucial steps, meshing and its quality play an important role in your results.

]]>Finite element solutions are becoming vital for complex engineering systems that are not simple to analyze by using old traditional methods. Comparing to theoretical approaches, FE models are capable to deal with much more intricate multiphysics problems. However, two crucial steps, meshing and its quality play an important role in your results.

Finite element preprocessors have come a long way over the years, to the point where users with minimal training can create meshes that appear “good”. But, how can you really know if the mesh is good enough for your analysis? Meshes that are “good enough” are ones that produce results with an acceptable level of accuracy, assuming that all other inputs to the model are accurate.

Mesh density is a significant metric used to control accuracy (element type and shape also affect accuracy). Assuming no singularities are present, a high-density mesh will produce results with high accuracy. However, if a mesh is too dense, it will require a large amount of computer memory and long run times, especially for multiple-iteration runs that are typical of nonlinear and transient analyses.

There are several ways to check your mesh quality;

- Software mesh control features (you can check the global mesh control in Ansys here)
- Mesh Metrics
- Convergence Analysis
- Test data or to theoretical values

Unfortunately, test data and theoretical results are often not available. So, other means of evaluating mesh quality are needed.

“Mesh Metrics” is one of the most useful features in determining the correct shape and size of the elements. You can find a range of criteria for quality check of your mesh, however, in this article, we will only explain, Aspect Ratio, Jacobian Ratio and Skewness.

**Aspect Ratio**

The Aspect Ratio quantifies the quality of the elements, **where 1 is a perfectly shaped tetrahedral element and the element shape is worse with a higher Aspect Ratio.** You can see this in the image to the left. The aspect ratio is defined as the ratio of the shortest length of the element to the longest length of the element.

**Jacobian**

Jacobian (also called Jacobian Ratio) is a measure of the deviation of a given element from an ideally shaped element. **The jacobian value ranges from -1.0 to 1.0, where 1.0 represents a perfectly shaped element.**

**Skewness**

Skewness is the Angular Measure of Element quality with respect to the Angles of Ideal Element Types. It is one of the Primary Qualities Measures of FE Mesh. Skewness determines how close to ideal (i.e., equilateral or equi-angular) a face or cell is. **The acceptable range of skewness is “0 to 0.5”.**

The most fundamental and accurate method for evaluating mesh quality is to refine the mesh until a critical result, such as the maximum stress in a specific location converges (i.e. it doesn’t change significantly with each refinement). An example is shown in Figure 1, where a 2D bracket model is constrained at its top end and subjected to a shear load at the edge on the lower right. This generates a peak stress in the fillet, as shown. The curve shows that as the mesh density increases, the peak stress in the fillet increases. Ultimately, increasing the mesh density further produces only minor increases in peak stress. In this case, an increase from 1134 elements per unit area to 4483 elements per unit area yields only a 1.5% increase in stress.

The problem with this method is that it requires multiple remeshing and re-solving operations. While this method is fine for simple models, it can be very time-consuming for complex models. However, in Ansys you can easily perform this operation automatically by using Convergence tool option.

Basically, the convergence tool increases the mesh density and checks the results between each step. You can easily see how your results change depending on the element quantity.

]]>Choosing the appropriate contact type depends on the type of problem you are trying to solve. If modelling the ability of bodies to separate or open slightly is important and/or obtaining the stresses very near a contact interface is important, consider using one of the nonlinear contact types (Frictionless, Rough, Frictional), which can model gaps and more accurately model the true area of contact. However, using these contact types usually results in longer solution times and can have possible convergence problems due to the contact nonlinearity. If convergence problems arise or if determining the exact area of contact is critical, consider using a finer mesh on the contact faces or edges.

Continue reading Ansys Contact Types and Explanations at Mechead.com.

]]>Choosing the appropriate contact type depending on the type of problem you are trying to solve is often tricky. Particularly, a number of new Ansys users are very confused about the differences among the contact types; such as **Frictionless**, **Rough**, **Frictional**, **No separation**. In this article, we will explain the general features of contact behaviours with illustrations.

This is the default configuration and applies to all contact regions (surfaces, solids, lines, faces, edges). If contact regions are bonded, then no sliding or separation between faces or edges is allowed. Think of the region as glued. This type of contact allows for a linear solution since the contact length/area will not change during the application of the load. If contact is determined on the mathematical model, any gaps will be closed and any initial penetration will be ignored. [Not supported for Rigid Dynamics. Fixed joint can be used instead.]

No separation contact: Once the contact is detected, then the target and contact surface are tied up for the rest of the analysis. Slide is possible, but the nodes in contact are bonded to the target surface in normal direction.

This setting models standard unilateral contact; that is, normal pressure equals zero if separation occurs. Thus gaps can form in the model between bodies depending on the loading. This solution is nonlinear because the area of contact may change as the load is applied. A zero coefficient of friction is assumed, thus allowing free sliding. The model should be well constrained when using this contact setting. Weak springs are added to the assembly to help stabilize the model in order to achieve a reasonable solution.

In this setting, the two contacting geometries can carry shear stresses up to a certain magnitude across their interface before they start sliding relative to each other. This state is known as “sticking.” The model defines an equivalent shear stress at which sliding on the geometry begins as a fraction of the contact pressure. Once the shear stress is exceeded, the two geometries will slide relative to each other. The coefficient of friction can be any nonnegative value. [Not supported for Rigid Dynamics. Forced Frictional Sliding should be used instead.]

Similar to the frictionless setting, these setting models perfectly rough frictional contact where there is no sliding. It only applies to regions of faces (for 3D solids) or edges (for 2D plates). By default, no automatic closing of gaps is performed. This case corresponds to an infinite friction coefficient between the contacting bodies. [Not supported for Explicit Dynamics analyses.]

You use a Remote Point as a scoping mechanism for remote boundary conditions. Remote points are a way of abstracting a connection to a solid model, be it a vertex, edge, face, body, or node, to a point in space (specified by Location). The solver uses multipoint constraint (MPC) equations to make these connections. With remote points, you can easily apply a boundary condition from outside of your design. For instance, one of the basic examples of the engineering problems, a torsion condition applied to a cantilever beam can be designed with remote points. Figure 1 Cantilever problem If you would like to design this FE model in Ansys, you do not need to build all the model in order to apply the torsion force which is connected by a lever on the edge-top of the beam. Remote points allow you applying boundary conditions by defining points in the selected coordinate systems. Particularly, this feature help users to create submodels without dealing with big-scale result files. Remote Points are akin to the various remote loads available in the Mechanical application. Remote boundary conditions create remote points in space behind the scenes, or, internally, whereas the Remote Point objects define a …

Continue reading Remote Points in Ansys Workbench at Mechead.com.

]]>You use a Remote Point as a scoping mechanism for remote boundary conditions. Remote points are a way of abstracting a connection to a solid model, be it a vertex, edge, face, body, or node, to a point in space (specified by Location). The solver uses multipoint constraint (MPC) equations to make these connections.

With remote points, you can easily apply a boundary condition from outside of your design. For instance, one of the basic examples of the engineering problems, a torsion condition applied to a cantilever beam can be designed with remote points.

If you would like to design this FE model in Ansys, you do not need to build all the model in order to apply the torsion force which is connected by a lever on the edge-top of the beam. Remote points allow you applying boundary conditions by defining points in the selected coordinate systems. Particularly, this feature help users to create submodels without dealing with big-scale result files.

Remote Points are akin to the various remote loads available in the Mechanical application. Remote boundary conditions create remote points in space behind the scenes, or, internally, whereas the Remote Point objects define a specific point in space only. As a result, the external Remote Point can be associated to a portion of geometry that can have multiple boundary conditions scoped to it. This single remote association avoids overconstraint conditions that can occur when multiple remote loads are scoped to the same geometry. The overconstraint occurs because multiple underlying contact elements are used for the individual remote loads when applied as usual to the geometry. When the multiple remote loads are applied to a single remote point, scoped to the geometry, the possibility of overconstraint is greatly reduced.

Remote Points are a powerful tool for working with and controlling the Degrees of Freedom (DOF) of a body. Remote Points provide a property, DOF Selection, which gives you a finer control over the active DOF’s used to connect the Remote Point location to the body.

Furthermore, Remote Points can be used independently, without being scoped to a boundary condition. Remote Point creates MPC equations and therefore can be used to model phenomena, such as coupling a set of nodes so that they have the same DOF solution.

Another capability of Remote Points is that they are also a scoping mechanism for the Constraint Equation object. The equation relates to the degrees of freedom (DOF) of one or more remote points

A Remote Point or multiple remote points work in tandem with the boundary conditions listed below.

- Point Mass
- Thermal Point Mass
- Joints
- Spring
- Bearing
- Beam Connection
- Remote Displacement
- Remote Force
- Moment

In early 2021, Ferrari shared very interesting news or Ferrari collectors, a project named Air Painting that brings together wind tunnel models, airflows represented by CFD and hand brushstrokes of a painter. This is absolutely unique and very exciting not only for Ferrari collectors also for those who are a huge fan of aerodynamic visualizations.

Continue reading Air Flow Painting from Ferrari at Mechead.com.

]]>Recently, Ferrari has shared very interesting news for Ferrari collectors, a project named **Air Painting **that brings together wind tunnel models, airflows represented by CFD and hand brushstrokes of a painter. This is absolutely unique and very exciting not only for Ferrari collectors also for those who are a huge fan of aerodynamic visualizations.

Luca Zanetti, Head of Global Sales explains that ‘It all began four years ago with a drawing, a simple visualisation of an airflow in the wind tunnel around a car bodywork, generated by CFD (Computational Fluid Dynamics) software. It was fascinating, the visualisation of speed: we asked if it were possible to give it a shape, to create a physical representation of the air that flows along the surfaces of the car.’

The answer comes in seven models of a Ferrari 488 Pista, exact reproductions of the full-scale cars used in aerodynamic wind tunnel tests. As the Speedform name suggests, the project gives a form to speed itself.

‘When we are inside the wind tunnel we are able to measure the effect of the movement of the car in the air, thanks to dozens of sensors positioned inside the model and upon its surface,’ explains Biancalana. By using CFD software these airflows can be visualised, which is fundamental for the aerodynamic technicians to understand and to control the factors which generate the aerodynamic forces.

‘Lines and colours of differing intensities show the behaviour of the speed, and of the air pressure upon the model. The technicians are able to choose which of these parameters to visualise,’ says Cattabriga. ‘The possible aerodynamic representations are extremely varied, with dozens of nuances. It was not simple to choose which to utilise for this project.’

‘When we are inside the wind tunnel we are able to measure the effect of the movement of the car in the air, thanks to dozens of sensors positioned inside the model and upon its surface,’ explains Biancalana. By using CFD software these airflows can be visualised, which is fundamental for the aerodynamic technicians to understand and to control the factors which generate the aerodynamic forces.

‘Lines and colours of differing intensities show the behaviour of the speed, and of the air pressure upon the model. The technicians are able to choose which of these parameters to visualise,’ says Cattabriga. ‘The possible aerodynamic representations are extremely varied, with dozens of nuances. It was not simple to choose which to utilise for this project.’

Transferring the vectorial quantities from the digital space to the models was done by hand-held paintbrush. The spirit of Ferrari is very much evident in all this: using advanced technology to create something, but relying upon an expert hand to complete the work and to render it unique. ‘The coloured lines along the models are one hundred per cent generated by the CFD,’ confirms Zanetti. ‘The painters paint them one by one with a millimetric precision, affording the models another material dimension.’

The design and the processes underpinning the Speedform have been patented by Ferrari, in order to protect this means of using shape and colour to represent a decisive moment in the birth of each new model. A moment in which the design of the car becomes evaluated by its most severe critic, the wind.

The Speedform models are quite imposing, almost two metres in length, all with a significant dynamic strength from the colour scale and the fluidity of the lines. They are built in carbon using rapid prototyping around an aluminium structure to recreate perfectly the shape of the car, to a scale of forty or fifty per cent. There are five CDF visualisations, each realised in eight, numbered, pieces.

This innovative project is aimed exclusively at Ferrari clients who desire to have objects in their collections that come with individual certification and which are part of an extremely limited series. Speedforms are truly a unique gift: not only are they an aesthetically attractive object, but also real, tangible, and accurate examples of the technological processes that lead to the creation of a Ferrari road car.

To the never-ending story of the legend of the automobile, Ferrari has added a new chapter.

Source: https://magazine.ferrari.com/en/passion/2021/01/20/news/air-painting-speedform-models-93680/

]]>We often see linear and nonlinear analysis in academic studies, however, how well do you know about it?

Continue reading What is Linear and NonLinear Analysis? at Mechead.com.

]]>We often see linear and nonlinear analysis in academic studies, however, how well do you know about it?

A linear static analysis is an analysis where a linear relation holds between applied forces and displacements. In practice, this is applicable to structural problems where stresses remain in the linear elastic range of the used material. In a linear static analysis the model’s stiffness matrix is constant, and the solving process is relatively short compared to a nonlinear analysis on the same model. Therefore, for a first estimate, the linear static analysis is often used prior to performing a full nonlinear analysis.

A nonlinear analysis is an analysis where a nonlinear relation holds between applied forces and displacements. Nonlinear effects can originate from geometrical nonlinearity’s (i.e. large deformations), material nonlinearity’s (i.e. elasto-plastic material), and contact. These effects result in a stiffness matrix which is not constant during the load application. This is opposed to the linear static analysis, where the stiffness matrix remained constant. As a result, a different solving strategy is required for the nonlinear analysis and therefore a different solver.

Modern analysis software makes it possible to obtain solutions to nonlinear problems. However, experienced skill is required to determine their validity and these analyses can easily be inappropriate. Care should be taken to specify appropriate model and solution parameters. Understanding the problem, the role played by these parameters and a planned and logical approach will do much to ensure a successful solution.

The source of this nonlinearity can be attributed to multiple system properties, for example, materials, geometry, nonlinear loading and constraints. Here are some examples…

**2.1 Geometric Nonlinearity**

In analyses involving geometric nonlinearity, changes in geometry as the structure deforms are considered in formulating the constitutive and equilibrium equations. Many engineering applications such as metal forming, tire analysis, and medical device analysis require the use of large deformation analysis based on geometric nonlinearity. Small deformation analysis based on geometric nonlinearity is required for some applications, like analysis involving cables, arches and shells. This is often controlled by Large Deflection in Ansys

**Material Nonlinearity**

Material nonlinearity involves the nonlinear behavior of a material based on a current deformation, deformation history, rate of deformation, temperature, pressure, and so on. Examples of nonlinear material models are large strain (visco) elasto-plasticity and hyperelasticity (rubber and plastic materials).

**Constraint and Contact Nonlinearity**

Constraint nonlinearity in a system can occur if kinematic constraints are present in the model. The kinematic degrees-of-freedom of a model can be constrained by imposing restrictions on its movement.