arXiv subjects

Chayanon Wichitrnithed

Publications and source records attributed to Chayanon Wichitrnithed.

5 recordsLinked to original sources

arXiv2026

Investigating Forecast Proficiency of Hurricane-Induced Compound Flooding With a Discontinuous Galerkin Shallow Water Equation Solver

Recent severe storms on the U.S. Gulf coast have demonstrated the challenges presented by compound flooding, such as the interactions between rainfall runoff and storm surge. Historically, many studies have neglected these nonlinear interactions, but we propose to use a discontinuous Galerkin shallow water equation solver, which allows for incorporation of rainfall inputs directly onto the finite element mesh. In this work, we analyze the use of parametric rainfall for forecasting scenarios, using Hurricane Beryl (2024) as a case study. Beryl led to extensive flooding due to rainfall and storm surge along the Gulf. We use a collection of the National Oceanic and Atmospheric Administration's short-term advisories along with the best track data to demonstrate the efficacy of the parametric rainfall model for forecasting. Results show that the parametric rainfall input allowed for much more accurate inundation. Areas with heavy rainfall and low surge were affected the most, with many areas peaking over 50 cm above the baseline surge model. Almost none of the available high water marks from Beryl were captured by the standard models, but the compound flooding models capture many of them, the majority of which show relative errors under 10 percent. Results from the advisory forecast simulations were shown to be much closer to the best track hindcast simulation when rainfall forcing was used, even while early forecasts predicted the storm's trajectory much less accurately. The advisory simulations improved even further as Beryl neared the Texas coast, with sampled peak elevations most closely approximating the best track at Advisory 38, a few hours before landfall.

cs.CE↗

arXiv2026

Learning the Exact Flux: Neural Riemann Solvers with Hard Constraints

Godunov-type methods, which obtain numerical fluxes through local Riemann problems at cell interfaces, are among the most fundamental and widely used numerical methods in computational fluid dynamics. Exact Riemann solvers faithfully solve the underlying equations, but can be computationally expensive due to the iterative root-finding procedures they often require. Consequently, most practical computations rely on classical approximate Riemann solvers, such as Rusanov and Roe, which trade accuracy for computational speed. Neural networks have recently shown promise as an alternative for approximating exact Riemann solvers, but most existing approaches are data-driven or impose weak constraints. This may result in problems with maintaining balanced states, symmetry breaking, and conservation errors when integrated into a Godunov-type scheme. To address these issues, we propose a hard-constrained neural Riemann solver (HCNRS) and enforce five constraints: positivity, consistency, mirror symmetry, Galilean invariance, and scaling invariance. Numerical experiments are carried out for the shallow water and ideal-gas Euler equations on standard benchmark problems. In the absence of hard constraints, violations of the well-balanced property, mass conservation, and symmetry are observed. Notably, in the Euler implosion problem, the exact Riemann solver with MUSCL-Hancock captures the jet structure well, whereas the Rusanov flux is too diffusive and smears it out. HCNRS accurately reproduces the solution obtained by the exact Riemann solver. In contrast, an unconstrained neural formulation lacks mirror symmetry, which makes the solution depend on the choice of flux normal direction. As a result, the jet is either shifted or lost, along with diagonal symmetry.

physics.comp-ph↗

arXiv2026

Coupled Continuous-Discontinuous Galerkin Finite Element Solver for Compound Flood Simulations

Several recent tropical cyclones, e.g., Hurricane Harvey (2017), have lead to significant rainfall and resulting runoff. When the runoff interacts with storm surge, the resulting floods can be greatly amplified and lead to effects that cannot be correctly modeled by simple superposition of its distinctive sources. In an effort to develop accurate numerical simulations of runoff, surge, and compounding floods, we develop a locally conservative coupled DG-CG discretization of the shallow water equations and integrate it into the Advanced Circulation Model (ADCIRC). We also modify the continuity equation to include spatially and temporally variable rainfall into the model using parametric rainfall models. We demonstrate the capabilities of the scheme though a sequence of physically relevant numerical tests, including small scale test cases based on laboratory measurements and large scale experiments with Hurricane Harvey in the Gulf of Mexico. The results highlight the conservation properties and robustness of the developed method and show the potential of compound flood modeling using our approach.

physics.comp-ph↗

arXiv2025

GPU-acceleration of the Discontinuous Galerkin Shallow Water Equations Model (DG-SWEM) with OpenACC

This paper presents a porting of {DG-SWEM}, a first-order discontinuous Galerkin solver for storm surge based on the Advanced Circulation Model (ADCIRC), to NVIDIA GPUs. Time-explicit discontinuous Galerkin methods contain a large number of degrees of freedom but have been shown to exhibit a large amount of data parallelism due to the loose coupling between elements, and thus are naturally mapped to the GPU architecture. A previous framework in porting DG-SWEM to GPUs required converting subroutines from Fortran to C++ to be used with CUDA C++. By using OpenACC and Unified Memory, we simplify the porting process and maintain a single codebase for both CPU and GPU versions. We test the code using a large Hurricane Harvey scenario on NVIDIA's Grace Hopper chip, and compare the GPU code's performance on multiple H200 nodes to the CPU version on the same amount of Grace CPU nodes.

physics.comp-ph↗

arXiv2023

A Discontinuous Galerkin Finite Element Model for Compound Flood Simulations

Recent tropical cyclones, e.g., Hurricane Harvey (2017), have lead to significant rainfall and resulting runoff with accompanying flooding. When the runoff interacts with storm surge, the resulting floods can be greatly amplified and lead to effects that cannot be modeled by simple superposition of its distinctive sources. In an effort to develop accurate numerical simulations of runoff, surge, and compounding floods, we develop a local discontinuous Galerkin method for modified shallow water equations. In this modification, nonzero sources to the continuity equation are included to incorporate rainfall into the model using parametric rainfall models from literature as well as hindcast data. The discontinuous Galerkin spatial discretization is accompanied with a strong stability preserving explicit Runge Kutta time integrator. Hence, temporal stability is ensured through the CFL condition and we exploit the embarrassingly parallel nature of the developed method using MPI parallelization. We demonstrate the capabilities of the developed method though a sequence of physically relevant numerical tests, including small scale test cases based on laboratory measurements and large scale experiments with Hurricane Harvey in the Gulf of Mexico. The results highlight the conservation properties and robustness of the developed method and show the potential of compound flood modeling using our approach.

cs.CE↗