Joseph O'Connor | Searcharxiv

arXiv2025

Real-scale Smoothed Particle Hydrodynamics Tsunami Runup Modelling, with application to 3-D tsunami urban flows in Cilacap, South Java, Indonesia

The risk posed by tsunami waves is currently modelled over bare-earth representations by tsunami models. The complex flows around buildings and structures are crucial to represent the true state of the tsunami wave elevation, speed and forces exerted on buildings. Such 3D simulations have been unachievable for real scale modelling at a reasonable computational cost. We present here for the first time the use of Smoothed Particle Hydrodynamics (SPH) for tsunami simulation in a real setting of large scale (around 1 km). Our illustration is for Cilacap, Indonesia as constitutes a blueprint for future scenarios in South Java. SPH allows for the efficient modelling of shocks and complex interations of the flows with the structures. We also offer a range of test cases of increasing complexity and realism to tune and validate such realistic simulations, including the well known simplified beach of Seaside, Oregon at scale 1:50. We are able to reproduce realistic wave heights, velocities and even observed eddies. We provide guidance and discuss the various choices in terms of flow parametrisations, boundary conditions, and the trade-off of fidelity. computational cost. As a result, Probabilistic Tsunami Risk Assessments (PTRA) will become possible by making use a of combination of regional modelling of tsunamis with depth-average models (generation and propagation) as well as coastal modelling using SPH.

physics.flu-dyn↗

arXiv2022

Investigating Bayesian optimization for expensive-to-evaluate black box functions: Application in fluid dynamics

Bayesian optimization provides an effective method to optimize expensive-to-evaluate black box functions. It has been widely applied to problems in many fields, including notably in computer science, e.g. in machine learning to optimize hyperparameters of neural networks, and in engineering, e.g. in fluid dynamics to optimize control strategies that maximize drag reduction. This paper empirically studies and compares the performance and the robustness of common Bayesian optimization algorithms on a range of synthetic test functions to provide general guidance on the design of Bayesian optimization algorithms for specific problems. It investigates the choice of acquisition function, the effect of different numbers of training samples, the exact and Monte Carlo based calculation of acquisition functions, and both single-point and multi-point optimization. The test functions considered cover a wide selection of challenges and therefore serve as an ideal test bed to understand the performance of Bayesian optimization to specific challenges, and in general. To illustrate how these findings can be used to inform a Bayesian optimization setup tailored to a specific problem, two simulations in the area of computational fluid dynamics are optimized, giving evidence that suitable solutions can be found in a small number of evaluations of the objective function for complex, real problems. The results of our investigation can similarly be applied to other areas, such as machine learning and physical experiments, where objective functions are expensive to evaluate and their mathematical expressions are unknown.

cs.LG↗