Browsing by Author "Van't Wout, Elwin"

Now showing 1 - 5 of 5
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    An efficient boundary element solver for trans-abdominal high-intensity focused ultrasound treatment planning
    (2017) Gelat, Pierre; Seyyed Reza Haqshenas; Betcke, Timo; Van't Wout, Elwin; Saffari, Nader
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    Building integral equation methods with the open-source library BEM++
    (IEEE, 2016) Van't Wout, Elwin; Betcke, Timo; Scroggs, Matthew
    Surface Integral Equations are often used to model electromagnetic scattering phenomena. Large-scale problems can efficiently be solved with Boundary Element Methods (BEM) of which the Method of Moments (MoM) has found widespread use in the computational electromagnetics community. The framework of integral equations allows for the design of many different formulations for a wide range of scattering problems. Most of them are a clever combination of the basic electric and magnetic field integral operators. In this paper, the open-source library BEM++ will be used as a powerful tool to build different integral equation formulations and preconditioners.
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    Classifying bubble cavitation with machine learning trained on physical models
    (2024) Gatica González, Trinidad; Van't Wout, Elwin; Pontificia Universidad Católica de Chile. Escuela de Ingeniería
    When an air filled bubble inside a liquid is excited by an acoustic signal the oscillations may cause cavitation where the bubble collapses and disintegrates into multiple smaller bubbles. This is a crucial phenomenon in various engineering applications, such as underwater noise and biomedical engineering. Accurately modeling cavitation is essential for optimal design of acoustical systems. In this study, our objective is to develop a machine learning model capable of classifying bubble oscillation as stable or transient cavitation. We implemented numerical solvers for the Rayleigh-Plesset, Keller-Miksis, and Gilmore differential equations to describe bubble oscillations with different mathematical models. These models takes the bubble radius, acoustic pressure, frequency, and temperature as input variables and provide a time series of the bubble radius. We used multiple thresholds to distinguish between stable and transient cavitation, based on variables such as the maximum radius, maximum velocity, acoustic emission, inertial and pressure functions for each model. We created a training dataset on a range of relevant physical scenarios. The machine learning algorithm combines different thresholds and models to predict the expected cavitation type. Our machine learning approach allows for fast predictions and uncertainty estimates of cavitation type on a wide range of scenarios. This approach offers greater robustness compared to numerical solution methods, leveraging four thresholds and three equations for enhanced accuracy.
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    Software frameworks for integral equations in electromagnetic scattering based on Calderon identities
    (2017) Scroggs, Matthew W.; Betcke, Timo; Burman, Erik; Smigaj, Wojciech; Van't Wout, Elwin
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    Stability analysis of the marching-on-in-time boundary element method for electromagnetics
    (2016) Van't Wout, Elwin; Van Der Heul, Duncan R.; Van Der Ven, Harmen; Vuik, Cornelis