Today we are proud to announce the release of Wolfram SystemModeler 4.1. We will present some of the news in blog posts, beginning with this one, in which we will highlight the new reliability functionality.
We will illustrate this with an example, and you can try it out by downloading a trial version of SystemModeler and this example model, and a trial of the Wolfram Hydraulic library.
Most people probably have experiences with things they bought and liked, but that then suddenly failed for some reason. During the last few years we have both experienced this problem, including a complete engine breakdown in Johan’s car (the engine had to be replaced), and Jan’s receiver, which suddenly went completely silent (the receiver had to be sent in for repair and have its network chip replaced).
In both cases it caused problems for the customers (us) as well as for the producer. These are just a couple of examples, and we’re sure you have your own.
February 11, 2015 — Johan Rhodin, Kernel Developer
Modelica is the object-oriented modeling language used in SystemModeler to model components and systems. When I first learned Modelica, I read all books available about the language (there are not that many!) and found the book Introduction to Physical Modeling with Modelica by Michael Tiller to be the best out there.
In 2012, when Michael started a Kickstarter campaign to fund the development of a Creative Commons licensed book about Modelica, I was the first person to back it, and Wolfram Research became one of the gold sponsors of the book. A new key feature in SystemModeler 4.0 is the full Modelica by Example book included in the product. This makes it much easier to get started learning Modelica.
I had the opportunity to ask Michael a couple of questions about the new book and Modelica.
January 6, 2015 — Mikael Forsgren, Wolfram MathCore
Mathematical modeling is not just used for understanding and designing new products and drugs; modeling can also be used in health care, and in the future, your doctor might examine your liver with a mathematical model just like the one researchers at AstraZeneca have developed.
The liver is a vital organ, and currently there isn’t really a way to compensate for loss of liver function in the long term. The liver performs a wide range of functions, including detoxification, protein synthesis, and secretion of compounds necessary for digestion, just to mention a few. In the US and Europe, up to 15 % of all acute liver failure cases are due to drug-induced liver injury, and the risk of injuring the liver is of major concern in testing new drug candidates. So in order to safely monitor the impact of a new drug candidate on the liver, researchers at the pharmaceutical company AstraZeneca have recently published a method for evaluating liver function that combines magnetic resonance imaging (MRI) and mathematical modeling—potentially allowing for early identification of any reduced liver function in humans.
Last year, Wolfram MathCore and AstraZeneca worked together on a project where we investigated some modifications of AstraZeneca’s modeling framework. We presented the promising results at the ISMRM-ESMRMB Joint Annual Meeting, which is the major international magnetic resonance conference. In this blog post, I’ll show how the Wolfram Language was used to calculate liver function and how more complex models of liver function can be implemented in Wolfram SystemModeler.
December 23, 2014 — Robert Palmer, Applications Engineer
In April this year, I attended the 7th Noordwijkerhout Symposium on Pharmacokinetics,
Pharmacodynamics and Systems Pharmacology in the Netherlands. The conference focuses on the use of mathematical modeling in pharmacology and pharmaceutical R&D, and this year, the main topic was the emerging concept of systems pharmacology.
In general terms, systems pharmacology can be seen as the combination of pharmacometrics and systems biology, with one of its key principles being the integration of biological data and mathematical models describing several different levels of biological complexity—spanning from the molecular or cellular level to that of a whole organism or population. Usually, such integration of data and models is referred to as multilevel, or multiscale, modeling, and has the important benefit of allowing us to translate information on disease and drug effects from the biochemical level—where the effects originate—to changes on the whole body or population level, which are more important from a clinical and pharmacological point of view.
In this blog post, I thought we would take a closer look at what a systems pharmacology approach might look like. Specifically, I’ll focus on some of the practical aspects of building complex, multilevel biological models, and how these can be dealt with using Wolfram SystemModeler.
December 5, 2014 — Malte Lenz, Wolfram MathCore
Could you fly using machine guns as the upward driving force? That’s the question asked in Randall Munroe’s What if? article, “Machine Gun Jetpack.” It turns out you could, because some machine guns have enough thrust to lift their own weight, and then some. In this post, I’ll explore the dynamics of shooting machine guns downward and study the actual forces, velocities, and heights that could be achieved. I’ll also repeat the warning from the What if? post: Please do not try this at home. That’s what we have modeling software for.
Machine gun with a squirrel on top
November 24, 2014 — Wolfram Blog Team
Thanksgiving is just around the corner, and that means you only have five weeks left to knock out your holiday shopping. Never fear, Wolfram is delivering amazing deals to customers across the globe, including North and South America, Australia, and parts of Asia and Africa to inspire a whole new year of computational creativity.
With the new, free ModelPlug library for Wolfram SystemModeler, you can connect Arduino boards to simulations in SystemModeler. Arduinos interface easily with input and output components, so you can integrate them into SystemModeler models, for example, to operate lights, run servos, and monitor sensors, switches, and potentiometers. With the ModelPlug library, you can freely mix hardware and software components in your simulations and use the Arduino as a data acquisition board.
If you want to follow along, you can download a trial of SystemModeler. It’s also available with a student license, or you can buy a home-use license. All hardware used in this blog post can be bought for less than $50.
Have you heard about the Boeing 747 Dreamlifter that flew to the wrong airport and was forced to land on too short of a runway? Luckily, that story had a happy ending, and no passengers were hurt. Still, it is a potentially dangerous scenario when the landing distance required (LDR) is longer than the runway, and there are other possible reasons for such a situation besides a pilot gone astray.
One potential cause of such a scenario is a flap system failure. Flaps are hinged devices located on the trailing edges of the wings, where their angular position can be adjusted to change the lift properties of the plane. For example, suitably adjusting the flap position can enable the plane to be flown at a lower speed while maintaining its lift, or allow it to be landed with a steeper angle of descent without any increase in speed. One of several resulting advantages is that the LDR becomes shorter. This makes me wonder: Could a small flap failure increase the LDR so much that the assigned runway is suddenly too short?
To answer such a question, you have to understand the effects that a failure on a component level have at a system level. How will the control system react to it? Can we somehow figure out how to detect it during a test procedure? Can we come up with a safety procedure to compensate for it, and what happens if the pilot or maintenance personnel for some reason fail to follow that procedure?
August 21, 2014 — Johan Rhodin, Kernel Developer
I’m an electrical engineer by training. In my first circuits class, all calculations were done by hand, and we could check solutions with unintuitive circuit simulators using the SPICE methodology. With SystemModeler I think it’s easier than ever to get started building virtual circuits and trying what-if scenarios for electrical circuits and systems. In this blog post, I’ll start from very basic circuits with components such as resistors and inductors and gradually add more complexity in the form of amplifiers and switching circuits.
Let’s start with the simplest electrical circuit I can think of:
Today we are proud to announce the release of Wolfram SystemModeler 4.
For SystemModeler 4, we have expanded the supported model libraries to cover many new areas. We’ve also improved workflows for everything from learning the software to developing models to analyzing and deploying them.
People have been using SystemModeler in an astonishing variety of areas. Many of those have been well supported by built-in libraries, but many are totally new domains where models typically need to be built from scratch.
For most applications, using existing model libraries gives a real boost to productivity, but developing a good library takes a lot of effort. There are many aspects to think of: the best structure for easy modeling, the right level of detail, the interfaces to other components, which components to include, documentation, etc. And you may very well have to refactor the library more than once before you’re done. Reusing components and interfaces from already tested and documented libraries not only speeds up development and learning, but also improves quality.
So we’ve made SystemModeler‘s already broad collection of built-in libraries even larger. For instance, we’ve added Digital, for digital electronics following the VHDL multivalued logic standard; QuasiStationary, for efficient approximate modeling of large analog circuits; and FundamentalWave, for modeling multiphase electrical machines. There are also many improvements to existing libraries, such as support for thermal ports in the Rotational and Translational mechanics libraries so that heat losses can be captured.