Spreadsheets have now been joined by multi-purpose modelling environments such as Mathematica/Wolfram System Modeler (Wolfram Research, USA), Matlab/Simulink (Math-Works, USA), and dozens others. Combining flexibility with great power, these can model mathematical functions, process plants, mechanical devices and electrical systems. The open source Modelica modelling language, for instance, allows users to create and link blocks of equations describing, say, individual items on a flowsheet.
From Desktop to Smartphone
In turn, Modelica can be used with a number of commercial front-ends including Wolfram System Modeler and SimulationX (ITI, Germany). One reviewer wrote recently that for modelling a fuel cell application, the open source Scil-ab/Xcos environment has 80–90% the power of the commercial package Matlab/Simulink, which costs several thousand dollars. As with much open source software, Scil-ab/Xcos offers an active user community but only little documentation. For the most difficult tasks, multi-purpose simulators can run on high-performance computing (HPC) clusters, often taking advantage of each server’s graphics processing unit (GPU) as well as the main floating point processor (CPU).
At the other end of the scale, capable equation solvers and graphing packages are available on smartphones. The most powerful multi-purpose simulators offer ideal platforms for the new trend towards multi-scale modelling (see below).
Insights in Molecular Modelling
Harnessing computing power to a knowledge of atomic properties and chemical bonding can help chemists predict the shapes and chemical properties of complex molecules. This has many applications in life sciences, from fundamental research to the development of new drugs. “Computational chemistry” is also increasingly used in materials research to help design new products including catalysts, polymers, electrodes for high-performance batteries, and thermal insulators, and to understand reaction kinetics.
Dozens of programs are available in this demanding field. Some model single atoms (“molecular mechanics”), while others take account of electrons too (“quantum models”). Some are preferred for investigating existing molecules, others target the design of new substances. Some can handle a large range of structures, while others are more specialised: for instance, a whole class of software is available for modelling nanostructures such as carbon nanotubes and graphene. Some use HPC to target cutting-edge problems, while others yield useful results on modest PCs.
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