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Innovative risk assessment approach for storm surge losses using adaptive models and machine learning algorithms
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Variational Bayesian model updating using normalizing flows
Full text linkWe investigate the use of normalizing flows to approximate transport maps from tractable reference densities to complex Bayesian posterior distributions for Bayesian model updating. A Gaus sian process (GP) surrogate with active sampling is used to provide a differentiable target density for optimizing the transport map. While results show normalizing flows can capture multimodal behavior in a simple example, further work is needed to refine the active sampling strategy and enable mode identification in the GP surrogate for robust multimodal density approximation
Optimal cubic nonlinear damping for seismic inter-story vibration isolation under critical double and multiple impulse inputs
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Accessibility risk assessment under Cascadia Subduction Zone earthquakes: case study on hospital accessibility in Portland metro area
Full text linkDoes resolution matter? Satellite imagery comparison for dynamic fuel mapping and wildfire risk assessment
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Experimental Analysis of Magnetic Focusing of the Plasma Arc of a Cutting Torch
Full text linkThis study aimed to verify the possibility of stabilizing and focusing a plasma column generated by a plasma cutter. The simulation performed by the COMSOL Multiphysics software is based on the actual configuration and geometry of the burner. This article presented a universal computational method based on FEM simulations, focusing on the deflection of the current of electrically charged particles in a magnetic field within the context of a plasma cutting torch. The simulations estimate the optimal shape and positioning of a focused electron beam for various magnetic lens positions and plasma stream energies, revealing that higher initial electron energies lead to a more even beam focus. Among the configurations tested, positioning the cathode 3 mm above the ring-shaped permanent magnet proved most effective, maintaining beam linearity and minimizing electron scattering, making it suitable for practical implementations
Exploring the Flow Dynamics of MHD Hybrid Nanofluid over a Non-Flat Porous Surface Using Neural Network Approach
Full text linkT he current work examined the heat transfer performance of TiO2 Al2O3/kerosene oil flowing over a permeable non-flat plate. The energy equation is modeled using Fourier’s law, and heating is supported by Joule heating and nonlinear thermal radiation. Moreover, the momentum equation is extended with mixed convection, magnetic field, and porous medium. We applied an artificial neural network (ANN) to the data obtained from the bvp4c solver to show the significance of AI techniques in predicting skin friction coefficient (SFC) and local Nusselt number (LNN) values. The ANN is developed with the Levenberg-Marquardt backpropagation algorithm to predict the values precisely, and its signif icance is assessed in terms of mean square error (MSE) between values obtained by the bvp4c solver and the predicted value neural network. T heoutcomes revealed that the heat transfer rate decayed with the rise in variable thermal conductivity. Moreover, when increasing the magnitude of the mixedconvectionparameter,thefluidvelocity constantly increases