COMSOL has the best multiphysical simulation capabilities in my experience. Technical support from Elisa at TECHNIC as well as the engineers at COMSOL has been great.
COMSOL is an important part of our research in plasma physics. We use it in the design of plasma systems and it helps us to obtain a greater understanding of the underlying physics. We have always valued the quick support from TECHNIC and COMSOL and it has been a pleasure to work with them.
Comsol has become a valuable part of our design and decision making process. The exceptional flexibility and access to the physics and solvers in Comsol has allowed us to have deeper understanding on thermomechanical solutions. Technic and Comsol have always been quick and helpful to resolve any issues and provide helpful advice on their products.
At Scion we use COMSOL Multiphysics to understand energy processes, such as the interplay of non-linear solid mechanics and heat & mass transfer during biomass compaction, to design new or more efficient processes.
We use COMSOL Multiphysics to design the customised muffler. With it, we can simulate the insertion loss at different spectrum with different muffler designs.
When running an uncertainty quantification study, you define a set of quantities of interest in terms of a COMSOL Multiphysics® model solution. In this way, the quantities of interest are functions of the input parameters.
In the case of a structural analysis, the quantities of interest can be the maximum displacement, stress, or deflection angle. For a heat transfer or CFD analysis, the quantities of interest may be maximum temperature, total heat loss, or the total fluid flow rate. For an electromagnetics simulation, they may be resistance, capacitance, or inductance. Since the Uncertainty Quantification Module is applicable to any physics model computed with the COMSOL Multiphysics® software, as well as any mathematical expression of various solved-for field quantities, the choices for what can be your quantity of interest are endless.
Any uncertain model input, whether it be a physics setting, geometric dimension, material property, or discretization setting, can be treated as an input parameter, and any model output can be used to define the quantities of interest.
The Screening, MOAT study type implements a lightweight global screening method that gives a qualitative measure of the importance of each input parameter. The method is purely sample based, using the Morris one-at-a-time (MOAT) method, and requires a relatively small number of COMSOL model evaluations. This makes it an ideal method when the number of input parameters is too large to allow more computationally expensive uncertainty quantification studies.
For each quantity of interest, this MOAT method computes the MOAT mean and MOAT standard deviation for each input parameter. These values are presented in a MOAT scatter diagram. The ranking of the MOAT mean and MOAT standard deviations gives the relative importance of the input parameters. A high value of the MOAT mean implies that the parameter is significantly influencing the quantity of interest. A high value of the MOAT standard deviation implies that the parameter is influential and that it is either strongly interacting with other parameters or that it has a nonlinear influence, or both.
The Sensitivity Analysis study type is used to compute how sensitive the quantities of interest are with respect to the input parameters. This study type includes two methods: the Sobol and correlation methods.
The Sobol method analyzes the entire input-parameter distribution and decomposes the variance of each quantity of interest into a sum of contributions from the input parameters and their interactions.
For each input parameter, the Sobol method computes the Sobol indices. The first-order Sobol index shows the variance of a quantity of interest attributed to the variance of each input parameters individually. The total Sobol index shows the variance of a quantity of interest attributed to the variance of each input parameters and its interaction with the other input parameters. The Sobol indices for each quantity of interest and all parameters are presented in a dedicated Sobol plot where the histograms are ordered by total Sobol index. The quantity of interest is most sensitive to the input parameter with the highest total Sobol index. The difference between the total Sobol index and first-order Sobol index for an input parameter measures the effect of the interaction between this input and others.
The Uncertainty Propagation study type is used to analyze how the uncertainties of input parameters propagate to each quantity of interest by estimating their probability density function (PDF). The underlying physics that maps the input parameters to the quantities of interest through COMSOL Multiphysics® model evaluations is for most applications impossible to compute analytically.
For this reason, a Monte Carlo analysis is necessary to approximate the PDFs. Similar to the Sobol method, a surrogate model is used to dramatically reduce the computational cost of the Monte Carlo analysis. For each quantity of interest, a kernel density estimation (KDE) is performed and visualized as a graph, as an approximation of the PDF. Furthermore, based on this analysis, a confidence interval table gives you, for each quantity of interest, the mean; standard deviation; minimum; maximum; and the lower and upper bound values corresponding to confidence levels of 90%, 95%, and 99%.
Compared to other uncertainty quantification study types, which investigate the overall uncertainty of the quantities of interest, the Reliability analysis, EGRA method addresses a more direct question. Given a nominal design and some specific uncertain inputs, what is the probability that the design fails? The failure can be a complete breakdown of the design, but it can also be phrased in terms of a quality criterion.
To ensure the reliability, the traditional approach of modeling and simulation is to use safety margins and worst-case scenarios. With a proper reliability analysis, it is possible to avoid overestimation and underestimation, since estimates of the actual probability can be made. A rough estimate can be drawn from the confidence interval table from the uncertainty propagation for each quantity of interest. But with reliability analysis, you can define a more sophisticated reliability criteria based on combinations of the quantities of interest and corresponding thresholds. The efficient global reliability analysis (EGRA) method used for the reliability analysis study efficiently directs the computational resources to the limit state that separates the failure and success of the design.
In order to fully evaluate whether or not the COMSOL Multiphysics® software will meet your requirements, you need to contact us. By talking to one of our sales representatives, you will get personalised recommendations and fully documented examples to help you get the most out of your evaluation and guide you to choose the best license option to suit your needs.
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