When working with multibody systems, you may need to model a mechanism that transfers motion from one component to another. The mechanism used to implement this behavior, known as a cam-follower mechanism, plays an important role in many applications, including internal combustion engines, printing control mechanisms, textile weaving machines, and valves. You can easily model this type of mechanism with the Cam-Follower feature in the COMSOL® software. Let’s take a look at this feature in detail.

When a tuning fork is struck, and held against a tabletop, the peak frequency of the emitted sound doubles — a mysterious behavior that has left many people baffled. In this blog post, we explain the tuning fork mystery using simulation and provide some fun facts about tuning forks along the way.

In 1875, John Kerr placed current-carrying coils in holes on either side of a glass slab, which created an electric field. After a polarized beam of light passed through the slab, he noticed that the polarization was different. This difference is related to the change in the glass’ refractive index, which is proportional to the square of the electric field — a phenomenon called the Kerr effect. See how to model this effect and other linear and nonlinear phenomena.

Sweeps are very useful for characterizing a system and learning more about how different input values impact the results. You can perform several different types of sweeps in the COMSOL Multiphysics® software, including function, material, and parametric sweeps. However, precise and innovative simulation results also call for mathematical optimization. In this blog post, learn how to combine sweep studies with the built-in optimization functionality.

The algebraic multigrid (AMG) solver provides robust solutions for large CFD simulations. Available as of version 5.3a of the COMSOL Multiphysics® software, the AMG method only requires one mesh, in contrast to the geometric multigrid (GMG) solver, which requires at least one extra coarser mesh. This eliminates the hassle associated with creating coarse meshes for complex geometries with small details that are difficult to mesh unless a fine mesh is used.

In applications such as power transfer and consumer electronics, it may be critical to model electromagnetic heating of materials that are nonlinear in temperature; that is, the material’s electrical conductivity and thermal conductivity vary with temperature. When modeling these nonlinearities, even an experienced analyst can sometimes get quite unexpected results due to the combination of the nonlinear material properties, boundary conditions, and geometry. Let’s find out why this is in terms of a very simple example.

Stress corrosion is a type of degradation of a metal surface that is exposed to a corrosive environment and is subjected to mechanical stress, either residual or applied. This phenomenon can be difficult to predict and detect in underground pipelines, which could result in costly leaks and damage to the surrounding area. When modeling stress corrosion, a major challenge is coupling the mechanical and electrochemical interactions. Here, we look at how to overcome this challenge in the COMSOL Multiphysics® software.

Since high-speed communication is inevitable for evolving wireless systems, the demand for a higher data rate, higher frequency, larger spectrum, and wider bandwidth increases. When dealing with a wide bandwidth, multiple devices may have to be deployed in a wireless communication system to filter out unwanted noise and interfering signals, enhance the signal-to-noise ratio, and improve the sensitivity. A single tunable filter can replace these devices, reducing the system’s size and weight and the fabrication cost of multiple components.

When creating a simulation, you usually start by building the forward model, supplying various inputs, and then looking at the results. However, what if you have a set of results and want to find the input values that provide the same outcome? Here, we show how to use the Parameter Estimation study step, which helps you build an inverse model and solve for the optimal values of your model inputs.

We have already discussed different ways to create geometry objects of irregular shapes on the blog. In this blog post, we will demonstrate an incredibly powerful alternative approach by using interpolation functions. Instead of creating geometry objects, we will indirectly define the irregular shape by taking advantage of the spatial variation of a material property in the object we want to study.