MATLAB vs. SimScale

MATLAB really does best for solving computational problems in math and engineering. Especially when you have to use a lot of functions in your solving process, or if you have a nonlinear equation that must be iteratively solved. [MATLAB] can also perform things like integration and derivation on your equations that you put into it.

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For a small company, SimScale is a great fit. It allows me to gain the use and support of higher-end software analysis on demand without the traditional high cost. This is super convenient for a company that does occasional design and analysis. Since the computing is cloud-based it also allows me to process large and detailed models without the high hardware cost that can be required. One potential drawback is that the design information is stored on someone else's server. For some industries and companies this would be a deal-breaker.

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• It has a very user friendly library which helps users learn this software fairly quickly in a short span of time.
• The graphical user interface provided by the software is really good.
• The code that a person writes allows options for debugging.
• One can visualize the flow of control of their code inside MATLAB.

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• Material non-linear analysis.
• Geometric non-linear analysis.
• Meshing.

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• MatLab is pricier than most of its competitors and because of this reason, many organizations are moving towards cheaper alternatives - mostly Python.
• MatLab is inefficient when it comes to performing a large number of iterations. It gets laggy and often crashes. Python is better in this regard.
• There is a limited number of hardware options (mostly NI) that can be connected directly to the data acquisition toolbox.

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• Learning from tutorials is great, but limiting. The online help is a must.
• Some simulations take a lot of time, but at least it's not on your PC.
• Sometimes the software times out due to the server.

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I had a great experience with SimScale but we only used the software for a short period of time to complete the current design requirements. It is not a software that we would continually subscribe to. Next subscription event would occur when there is a design requirement to do so.

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MATLAB is pretty easy to use. You can extend its capabilities using the programming interface. Very flexible capabilities when it comes to graphical presentation of your data (so many different kinds of options for your plotting needs). Anytime you are working with large data sets, or with matrices, MATLAB is likely to be very helpful.

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The built-in search engine is not as performing as I wish it would be. However, the YouTube channel has a vast library of informative video that can help understanding the software. Also, many other software have a nice bridge into MATLAB, which makes it very versatile. Overall, the support for MATLAB is good.

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How MATLAB compares to its competition or similar open access tools like R (programming language) or SciLab is that it's simply more powerful and capable. It embraces a wider spectrum of possibilities for far more fields than any other environment. R, for example, is intended primarily for the area of statistical computing. SciLab, on the other hand, is a similar open access tool that falls very short in its computing capabilities. It's much slower when running larger scripts and isn't documented or supported nearly as well as MATLAB.

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SimScale is much faster to spin up on than OpenFOAM. Ansys CFD tools are much more expensive! Both tools require expensive computing equipment to run, which SimScale does not!

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• MATLAB helps us quickly sort through large sets of data because we keep the same script each time we run an analyzation, making it very efficient to run this whole process.
• The software makes it super easy for us to create plots that we can then show to investors or clients to display our data.
• We are also looking to create an app for our product, and we will not be able to do that on MATLAB, therefore creating a limiting issue and a new learning curve for a programming language.

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• It has allowed us to increase our service offering which has lead to additional work.

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info