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Numerically-informed neural networks for degree adaptive unsteady incompressible flow simulations / VALERIA RAMUDO

Swansea University Author: VALERIA RAMUDO

  • E-Thesis – open access under embargo until: 11th February 2026

DOI (Published version): 10.23889/SUthesis.69401

Abstract

The need for transient incompressible flow simulations in science and engineering has driven the demand for high-order methods over conventional low-order finite element and finite volume approaches. High-order methods offer greater accuracy and efficiency in capturing the complex, time-dependent be...

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Published: Swansea, Wales, UK 2025
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
Supervisor: Sevilla, Ruben ; Hassan, Oubay
URI: https://cronfa.swan.ac.uk/Record/cronfa69401
first_indexed 2025-05-01T15:11:22Z
last_indexed 2025-05-02T04:25:11Z
id cronfa69401
recordtype RisThesis
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spelling 2025-05-01T16:22:34.5906904 v2 69401 2025-05-01 Numerically-informed neural networks for degree adaptive unsteady incompressible flow simulations e80396909adaf6cf5de499580295c056 VALERIA RAMUDO VALERIA RAMUDO true false 2025-05-01 The need for transient incompressible flow simulations in science and engineering has driven the demand for high-order methods over conventional low-order finite element and finite volume approaches. High-order methods offer greater accuracy and efficiency in capturing the complex, time-dependent behaviour of fluid systems because of the lower dissipation and dispersion of high-order approximations. Traditional low-order methods often require highly refined meshes to achieve comparable accuracy, leading to higher computational costs.This thesis focuses on problems where flow features such as vortices or gust pertur-bations need to be propagated over long distances. These flow features can be more accurately propagated using high-order methods, but their localised nature suggests that incorporating degree adaptive schemes can lead to significantly more efficient sim-ulations by only employing high-order approximations where needed. Discontinuous Galerkin methods have gained significant popularity and provide an easy-to-implement framework for degree adaptivity. In particular, the hybridisable discontinuous Galerkin is adopted in this work and implemented in Fortran 90.This thesis provides two original scientific contributions. First, a conservative projec-tion scheme has been developed and implemented to enable efficient degree adaptive simulations for transient incompressible flows. The proposed scheme is found to remove all the numerical artefacts shown by a standard adaptive process due to the violation of the free-divergence condition when projecting a solution from a space of polynomials of a given degree to a space of polynomials with a lower degree. Second, a novel degree adaptive procedure is designed by using a trained artificial neural network to predict the solution at a future time from the solution at the current time. The procedure is shown to perform the degree adaptivity in places where flow features will travel in the future and prevents the traditional requirement to perform degree adaptivity cycles within a time step. E-Thesis Swansea, Wales, UK transient incompressible flow, high-order methods, hybridisable discontinuous Galerkin, degree adaptivity, artificial neural network 11 2 2025 2025-02-11 10.23889/SUthesis.69401 COLLEGE NANME COLLEGE CODE Swansea University Sevilla, Ruben ; Hassan, Oubay Doctoral Ph.D EPSRC DTA; IHPC Singapore EPSRC DTA; IHPC Singapore 2025-05-01T16:22:34.5906904 2025-05-01T16:02:00.1207385 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering VALERIA RAMUDO 1 Under embargo Under embargo 2025-05-01T16:17:20.8320505 Output 55298786 application/pdf E-Thesis – open access true 2026-02-11T00:00:00.0000000 Copyright: The Author, Valeria Agustina Felipe Ramudo, 2025. true eng
title Numerically-informed neural networks for degree adaptive unsteady incompressible flow simulations
spellingShingle Numerically-informed neural networks for degree adaptive unsteady incompressible flow simulations
VALERIA RAMUDO
title_short Numerically-informed neural networks for degree adaptive unsteady incompressible flow simulations
title_full Numerically-informed neural networks for degree adaptive unsteady incompressible flow simulations
title_fullStr Numerically-informed neural networks for degree adaptive unsteady incompressible flow simulations
title_full_unstemmed Numerically-informed neural networks for degree adaptive unsteady incompressible flow simulations
title_sort Numerically-informed neural networks for degree adaptive unsteady incompressible flow simulations
author_id_str_mv e80396909adaf6cf5de499580295c056
author_id_fullname_str_mv e80396909adaf6cf5de499580295c056_***_VALERIA RAMUDO
author VALERIA RAMUDO
author2 VALERIA RAMUDO
format E-Thesis
publishDate 2025
institution Swansea University
doi_str_mv 10.23889/SUthesis.69401
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
document_store_str 0
active_str 0
description The need for transient incompressible flow simulations in science and engineering has driven the demand for high-order methods over conventional low-order finite element and finite volume approaches. High-order methods offer greater accuracy and efficiency in capturing the complex, time-dependent behaviour of fluid systems because of the lower dissipation and dispersion of high-order approximations. Traditional low-order methods often require highly refined meshes to achieve comparable accuracy, leading to higher computational costs.This thesis focuses on problems where flow features such as vortices or gust pertur-bations need to be propagated over long distances. These flow features can be more accurately propagated using high-order methods, but their localised nature suggests that incorporating degree adaptive schemes can lead to significantly more efficient sim-ulations by only employing high-order approximations where needed. Discontinuous Galerkin methods have gained significant popularity and provide an easy-to-implement framework for degree adaptivity. In particular, the hybridisable discontinuous Galerkin is adopted in this work and implemented in Fortran 90.This thesis provides two original scientific contributions. First, a conservative projec-tion scheme has been developed and implemented to enable efficient degree adaptive simulations for transient incompressible flows. The proposed scheme is found to remove all the numerical artefacts shown by a standard adaptive process due to the violation of the free-divergence condition when projecting a solution from a space of polynomials of a given degree to a space of polynomials with a lower degree. Second, a novel degree adaptive procedure is designed by using a trained artificial neural network to predict the solution at a future time from the solution at the current time. The procedure is shown to perform the degree adaptivity in places where flow features will travel in the future and prevents the traditional requirement to perform degree adaptivity cycles within a time step.
published_date 2025-02-11T06:46:52Z
_version_ 1851284010331799552
score 11.090362

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