October 25, 2016 — Patrik Ekenberg, Applications Engineer, Wolfram MathCore

Today I am excited to announce SystemModeler 4.3. This release focuses on three key areas: model analytics, collaboration and performance, which I will illustrate in this blog. You can see more on the What’s New page, or download a trial to try it yourself.

I’ll start by talking about our improvements in collaboration. I develop lots of models in SystemModeler, and when I do, I seldom develop them in a vacuum. Either I send a model to my colleagues for them to use, I receive one from them or models get sent back and forth while we work on them together. This is, of course, also true for novice users. A great way to learn how to use SystemModeler—or any product, for that matter—is to look at things other people have done, whether it be a coworker or other users online, and build upon that.

Whether you send your models to other people, receive models or send models between your own platforms, we want to make sure that you have everything you need to start using the model, straight out of the box.

As an example, I have built a model of an inverted pendulum using the PlanarMechanics library. It has a linear-quadratic regulator built using the Modelica Standard Library, and it also includes components from the ModelPlug library that connect to real-life hardware, such as actuators and sensors on an Arduino board (or any other board following the Firmata protocol).

September 1, 2016 — Håkan Wettergren, Applications Engineer, SystemModeler (MathCore)

## Background

Explore the contents of this article with a **free Wolfram SystemModeler trial**.Rolling bearings are one of the most common machine elements today. Almost all mechanisms with a rotational part, whether electrical toothbrushes, a computer hard drive or a washing machine, have one or more rolling bearings. In bicycles and especially in cars, there are a lot of rolling bearings, typically 100–150. Bearings are crucial—and their failure can be catastrophic—in development-pushing applications such as railroad wheelsets and, lately, large wind turbine generators. The Swedish bearing manufacturer SKF estimates that the global rolling bearing market volume in 2014 reached between 330 and 340 billion bearings.

Rolling bearings are named after their shapes—for instance, cylindrical roller bearings, tapered roller bearings and spherical roller bearings. Radial deep-groove ball bearings are the most common *rolling* bearing type, accounting for almost 30% of the world bearing demand. The most common *roller* bearing type (a subtype of a rolling bearing) is the tapered roller bearing, accounting for about 20% of the world bearing market.

With so many bearings installed every year, the calculations in the design process, manufacturing quality, operation environment, etc. have improved over time. Today, bearings often last as long as the product in which they are mounted. Not that long ago, you would have needed to change the bearings in a car’s gearbox or wheel bearing several times during that car’s lifetime. You might also have needed to change the bearings in a bicycle, kitchen fan or lawn mower.

For most applications, the basic traditional bearing design concept works fine. However, for more complex multidomain systems or more advanced loads, it may be necessary to use a more advanced design software. Wolfram SystemModeler has been used in advanced multidomain bearing investigations for more than 14 years. The accuracy of the rolling bearing element forces and Hertzian contact stresses are the same as the software from the largest bearing manufacturers. However, SystemModeler provides the possibilities to also model the dynamics of the nonlinear and multidomain surroundings, which give the understanding necessary for solving the problems of much more complex systems. The simulation time for models developed in SystemModeler is also shorter than comparable approaches.

March 7, 2016 — Håkan Wettergren, Applications Engineer, SystemModeler (MathCore)

Explore the contents of this article with a **free Wolfram SystemModeler trial**.One of the most common causes for vibrations in mechanical systems is imbalance in the rotating parts of a machine. Much effort has therefore gone into developing methods and devices for balancing rotating machines.

Balance is a requirement for many types of rotating machinery, such as electric motors, pumps, fans, turbines, generators, centrifugal compressors, and propellers. Many people know about the balance of their car wheels. If these systems are not properly balanced, the vibration will cause not only reduced efficiency and component fatigue but also disturbances for the environment, such as vibration and noise. The most common methods for balancing rotating machinery are the influence coefficient method and the modal balancing method. The car wheel balancing is, for instance, a subpart of the influence coefficient method.

Wolfram SystemModeler is used for modeling the rotor, and the Wolfram Language for the evaluation of the results. The workflow shows how powerful it is to combine these two softwares.

A disc with mass *m* is mounted on a shaft with stiffness *k*. The rotor rotates with the angular velocity *W*. The disc has an imbalance *u*. The unit for the imbalance is kg*m.

January 18, 2016 — Håkan Wettergren, Applications Engineer, SystemModeler (MathCore)

Explore the contents of this article with a **free Wolfram SystemModeler trial**.Wolfram SystemModeler is a tool for multidomain analysis. One area with many multidomain applications is hydraulics: fluid power systems. Fluid power is one of three main methods of transmitting power. The other two are mechanical transmission, via gears and shafts, and electrical transmission, via wires. In SystemModeler, all three can be used at the same time without any restrictions or simplification.

This blog describes how the SystemModeler hydraulic library can be used in education, but the focus is not only on the hydraulic part. The idea is also to show how to build up an interesting, real application where hydraulics play an essential role. In the model it is then possible to study the effects of filter locations, choose valves, adjust settings, study different oil grades, etc. This post may also give ideas to hydraulic engineers used to working with conventional software as to what more can be done with SystemModeler compared to the standard software.

December 30, 2015 — Håkan Wettergren, Applications Engineer, SystemModeler (MathCore)

Explore the contents of this article with a **free Wolfram SystemModeler trial**.In 1869, Rankine extended Euler and Bernoulli’s century-old theory of lateral vibrations of bars to an understanding of rotating machinery that is out of balance. Classical dynamics had a new branch: rotor dynamics. Machine vibration caused by imbalance is one of the main characteristics of machinery in rotation.

All structures have natural frequencies. The critical speed of a rotating machine occurs when the rotational speed matches one of these natural frequencies, often the lowest. Until the end of the nineteenth century the primary way of improving performance, increasing the maximum speed at which a machine rotates without an unacceptable level of vibration, was to increase the lowest critical speed: rotors became stiffer and stiffer. In 1889, the famous Swedish engineer Gustaf de Laval pursued the opposite strategy: he ran a machine faster than the critical speed, finding that at speeds above the critical threshold, vibration decreased. The trick was to accelerate fast through the critical speed. Thirty years later in 1929, the American Henry Jeffcott wrote the equation for a similar system, a simple shaft supported at its ends. Such a rotor is now called the de Laval rotor or Jeffcott rotor and is the standard rotor model used in most basic equations describing various phenomena.

December 16, 2015 — Håkan Wettergren, Applications Engineer, SystemModeler (MathCore)

Explore the contents of this article with a **free Wolfram SystemModeler trial**.

#### Background

Today, many helicopters launch from and land on ships at sea. Some are conventional helicopters, both commercial and military, and some are drones. In Wolfram SystemModeler, we now have a system for simulating helicopter landings and launches that includes waves and ships. The models have been used for the design of mechanical parts, autopilots, landing criteria, and operational limits.

#### Major components of the system

The aim has been to develop a model with an accurate depiction of the waves, ship motion, and helicopters in such a way that the results can be used not only qualitatively but also quantitatively in real industrial applications.

The first task is to calculate the motion of the landing platform mounted on the ship’s deck. There is commercially available historical wave data for different seas and oceans. Since access to this data is expensive, we will instead describe the waves mathematically. A model of the forces on the ship’s hull was developed with classical analytical theory. With the waves and ship hull forces, the motion of the ship’s landing platform can be calculated. If we assume that the helicopter landing does not influence the landing platform motion, the system is simplified. We speed up the simulation by storing the motion in a database for the different wave heights, lengths, and directions, and the ship’s speed. Typically the database will include wave heights of 1, 2, 3, and 4 m; wave directions 0, 30, 60, 90, 120, 150, and 180 degrees; wave lengths 100, 150, and 200 m; and ship speeds of 5 and 10 knots. The helicopter was modeled with the MultiBody library. It includes mechanical parts such as rotors with gyroscopic effects and landing gear with hydraulic dampers. Friction models for wheel-deck interface and flexible beams for the rotor blades have been developed. We have also developed a simple autopilot where the landing algorithm is implemented and tested. For one application, the model has been run with the actual autopilot as hardware in the loop.

December 2, 2015 — Johan Rhodin, Kernel Developer

Explore the contents of this article with a **free Wolfram SystemModeler trial**.Today marks the release of Wolfram *SystemModeler* 4.2.

I’ll outline some of the new features and improvements we’ve done since Version 4.1. You could say that there are three main pieces to this release: usability, performance, and integration. Let’s take them one by one.

#### Usability

The first improvement you’ll notice as a user opening the product is that the diagram area is easier to understand, with crossing-line detection and joint connection points marked with solder dots:

November 30, 2015 — Wolfram Blog Team

It’s that time of year again and the holidays are upon us. Whatever your gifting traditions, Wolfram has perfect solutions for the tech lovers on your shopping list. From now until December 6, we are offering Cyber Week savings around the world, including North and South America, Australia, and parts of Asia and Africa.

November 24, 2015 — Håkan Wettergren, Applications Engineer, SystemModeler (MathCore)

Explore the contents of this article with a **free Wolfram SystemModeler trial**.

#### Background

Teachers and textbook authors often need to simplify a real-world problem to pinpoint a specific area to work with—for instance, the examples in a textbook. However, even in real-world engineering, simplifying a problem can bring clarity when our understanding might otherwise drown in a sea of details. In this blog, we will design the landing gear for a helicopter. I have chosen the example of landing gear because the simplification to one degree of freedom gives accurate results and is typically how the problem is treated in textbooks. The solution is attainable through hand calculation. But a more subtle understanding of the problem can be gained using the Wolfram Language and Wolfram *SystemModeler*.

August 26, 2015 — Patrik Ekenberg, Applications Engineer, Wolfram MathCore

Explore the contents of this article with a **free Wolfram SystemModeler trial**.Wouldn’t it be great if you could easily connect your simulation models to your existing infrastructure? Whether you are working in industries such as oil and gas, industrial energy, or life sciences, connecting to your processes in order to monitor and control them is vital.

The OPC (Object Linking and Embedding for Process Control) standard has been developed by industry and the OPC Foundation just for that purpose. OPC is a set of data transfer standards for multi-vendor, multi-platform, secure, and reliable interoperability in industrial automation: