We are excited to announce the latest installment in the Wolfram SystemModeler series, Version 5.1, where our primary focus has been on pushing the scope of use for models of systems beyond the initial stages of development.
Since 2012, SystemModeler has been used in a wide variety of fields with an even larger number of goals—such as optimizing the fuel consumption of a car, finding the optimal dosage of a drug for liver disease and maximizing the lifetime of a battery system. The Version 5.1 update expands SystemModeler beyond its previous usage horizons to include a whole host of options, such as:
- Exporting models in a form that includes a full simulation engine, which makes them usable in a wide variety of tools
- Providing the right interface for your models so that they are easy for others to explore and analyze
- Sharing models with millions of users with the simulation core now included in the Wolfram Language
January 4, 2018 — Michael Gammon, Blog Administrator, Document and Media Systems
Whew! So much has happened in a year. Consider this number: we added 230 new functions to the Wolfram Language in 2017! The Wolfram Blog traces the path of our company’s technological advancement, so let’s take a look back at 2017 for the blog’s year in review.
Our goal with SystemModeler is to provide a state-of-the-art environment for modeling, simulation—and analytics—that leverages the Wolfram technology stack and builds on the Modelica standard for systems description (that we helped to develop).
SystemModeler is routinely used by the world’s engineering organizations on some of the world’s most complex engineering systems—as well as in fields such as life sciences and social science. We’ve been pursuing the development of what is now SystemModeler for more than 15 years, adding more and more sophistication to the capabilities of the system. And today we’re pleased to announce the latest step forward: SystemModeler 5.
April 7, 2017 — Håkan Wettergren, Applications Engineer, SystemModeler (MathCore)
Vibration measurement is an important tool for fault detection in rotating machinery. In a previous post, “How to Use Your Smartphone for Vibration Analysis, Part 1: The Wolfram Language,” I described how you can perform a vibration analysis with a smartphone and Mathematica. Here, I will show how this technique can be improved upon using the Wolfram Cloud. One advantage with this is that I don’t need to bring my laptop.
March 28, 2017 — Markus Dahl, Applications Engineer, SystemModeler (MathCore)
Industry 4.0, the fourth industrial revolution of cyber-physical systems, is on the way! With it come sensors and boards that are much cheaper than they used to be. All of these components are connected through some kind of network or cloud so that they are able to talk to each other. This is where the OPC Unified Architecture (OPC UA) comes in. OPC UA is a machine-to-machine communication protocol for industrial automation. It is designed to be the successor to the older OPC Classic protocol that is bound to the Microsoft-only process exchange COM/DCOM (if you are interested in the OPCClassic library for Wolfram SystemModeler, you can find it here).
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)
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.
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.