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.
September 30, 2013 — Johan Rhodin, Kernel Developer
What is the cost of extending a warranty for a car? I’d be interested to know, since my car broke down just past the 100,000 mile marker on a road trip through America. With Mathematica 9 comes complete functionality for reliability analysis that can help us analyze systems like cars. I thought it might be worthwhile to take Mathematica for a spin and look at how some technical systems can be modeled and analyzed.
June 11, 2013 — Mikael Forsgren, Wolfram MathCore
Wolfram SystemModeler ships with model libraries for a large selection of domains such as electronics, mechanics, and biochemistry. Now I am pleased to present a new library in the family, the SystemDynamics library by François E. Cellier and Stefan Fabricius. System dynamics, a methodology developed by Jay Forrester in the ’60s and ’70s, is well suited for understanding the dynamics of large-scale systems with diverse components. It has been famously applied by the Club of Rome to investigate the limits of human growth; other applications include production management, life sciences, and economics (some showcases of the methodology can be found here).
December 11, 2012 — Mikael Forsgren, Wolfram MathCore
Yesterday, the Nobel Prize in Chemistry was awarded to Robert J. Lefkowitz and Brian K. Kobilka for having mapped how a family of cellular receptors called G-protein-coupled receptors (GPCRs) work. The Nobel Prize winners’ research has proven to be very important in the development of novel therapeutic drugs—about 40–50% of all therapeutic drugs in use today are centered on GPCRs. The real beauty of GPCR-based response systems is that they include components that are used over and over again for the response to external signals in many kinds of cellular functions throughout our bodies. Sight, smell, and the adrenaline response are examples of these GPCR-mediated responses with physiologically important functions.
Identifying new targets for therapeutic drug intervention includes analysis of the complex webs of signaling pathways and feedback systems in our cells, extending beyond the first event of a signal connecting with the GPCR on the cell surface, which is non-trivial. Lately the cost-effective practice of using mathematical models as an initial step for finding those elusive new targets, and also as a tool for understanding how other reactions of a cell might be affected by a new drug, has been growing. In this blog post we are going to use modeling and simulation in order to illustrate how the GPCR-based cellular response to an external signal can be modified. And by performing this analysis, I thought we should also see how we can find promising targets for therapeutic drug design, which are then aimed at either increasing or decreasing the response. Since the first two steps in the pathways are identical in most of the GPCR-based signal responses in a cell, we can freely choose a representative model. One such well understood signal response pathway that uses GPCR is the mating pheromone response in yeast, which we are here going to explore using Mathematica and Wolfram SystemModeler.
October 25, 2012 — Robert Palmer, Applications Engineer
During the last decades, the development and use of therapeutic monoclonal antibodies (mAbs) have grown rapidly. Today, more than 30 different mAbs are successfully used in the clinic—playing important roles in treating complex diseases such as cancers and auto-immune disorders—and more than 200 are in clinical trials.
The history of mAbs has, however, not been without problems. In 2006, a first-in-human clinical trial of an mAb, aimed at treating leukemia and rheumatoid arthritis, went terribly wrong. Although the trial was run according to an approved protocol, all volunteers receiving the drug had severe inflammatory reactions and multiple organ failure. The tragic event shocked the medical community and highlighted a very important issue: how do you select a safe starting dose in first-in-human trials?
Now, as you may guess, the complete answer to this question is not an easy one. It’s also beyond the scope of this blog post. However, as a consequence of the dramatic happenings in 2006, the European Medicines Agency (EMEA) recently published new guidelines to address the issue of starting dose selection in first-in-human trials. Interestingly, the guidelines recommend that the use of modeling and simulation should play an integral part in the selection process, and in this post I thought we would study what such an approach might look like using Wolfram SystemModeler and Mathematica.