We are excited to release version 2.2.3 of the MATLAB® Toolbox! We are very appreciative of the customer feedback and have implemented lots of exciting new features as well as bug fixes. Keep reading to find out more about the top new features of the toolbox.
Extrapolation Post Processing
Extrapolation is an extremely useful tool which allows various useful outputs to obtained from a simple model without modelling excessively large 3D models. Extrapolation works by setting up a plane around the source and the pressure time history of each point on the extrapolation plane is stored and is treated like a point source. The contributions of each source are then summed around the extrapolation plane. This process allows data to be obtained from beyond the confines of what has been modelled. Various types of extrapolation can be calculated for any model created in the toolbox:
- Location Response – Calculates the time-domain pressure response at a point in space
- Radial Plot (a.k.a. Beam Pattern) – Calculates the pressure around a radius at a frequency of interest
- Field Plot (a.k.a. Beam Profile) – Calculates the pressure over a flat plane of interest at a frequency of interest
For more information on how to use extrapolation see How to Extrapolate Results the in-built help documentation.
OnScale includes several advanced attenuation models to simulate the damping in a system. Damping, which varies by material and frequency, can be separated into longitudinal and shear components. It is not feasible for OnScale to determine the damping characteristics at every frequency with complete accuracy. The damping tool enables you to visualize the damping that has been applied to the model and to make any changes to better match the damping of the actual material. You can enter all the critical variables used in OnScale attenuation routines such as damping mechanism, longitudinal and shear attenuation base values, and frequency coefficients and it displays in a graph the attenuations against frequency for the requested vs actual damping.
For more information on how to use the damping tool see How to Add Materials the in-built help documentation.
Data Array Visualization in Post Processor
Up until now, all post processing in the toolbox has been time history based. But now it is possible to output data arrays from the model and visualize them in post process. This allows you plot the acoustic pressure, velocity, displacement etc. at the final stage in the model. You can also calculate and plot the maximum and minimum of any of these data arrays.
For more information on how to plot data arrays see How to Plot Data Arrays the in-built help documentation.
Monte Carlo Class
Monte Carlo is a statistical method which uses simulation to model the probability of outcomes of a complex model whose behavior cannot be easily determined due to a vast number of variables. Monte Carlo studies can provide engineers with a full picture of a design space by running massive studies using random inputs and calculating key performance indicators (KPIs). The correlation between the inputs and KPIs is represented graphically so engineers can easily extract information.
The process of setting up Monte Carlo studies has been greatly simplified in the new toolbox update. There is now a Monte Carlo class which contains methods to set up, run and post process Monte Carlo studies. A study can be set up in just a few lines of code and all the plotting is handled.
Check out the PMUT Monte Carlo Study example in the in-built help documentation to help get you started.
Genetic Algorithm Class
Genetic Algorithm (GA) is a method which is used for design optimization. This random-based algorithm echoes the process of natural selection by selecting the fittest individuals to produce the next generation of offspring. GA starts with a random population which is made up of individuals, the algorithm then uses fittest individuals from that population to form another population and this process is repeated until an optimal result is reached.
The process of setting up Genetic Algorithm optimization has also been greatly simplified in the new toolbox update. There is now a Genetic Algorithm class which contains methods to set up and run Genetic Algorithm optimization. The class is seamlessly integrated with MATLAB®’s Global Optimization Toolbox so that optimization can be run in only a few lines of code.
Check out the Genetic Algorithm Optimization example the in-built help documentation to help get you started.
That’s it for the top new features of v2.2.3 of the MATLAB® Toolbox! Head over to our Help Center to get the latest version and start using the new features. For more information on the new functionality check out the toolbox capability list. We hope you enjoy the new updates! As always, please post any feedback on our forum.
Monte Carlo Analysis with OnScale and MATLAB® Toolbox
Join us on Tuesday 19th January for a webinar on how to run Monte Carlo studies with the new version of the MATLAB® toolbox for Cloud Engineering Simulation with OnScale. Click here to register!