Numerical Modelling

Vyzikas, Thomas; Greaves, Deborah

doi:10.1002/9781119014492.ch8

Cited by 4 publications

(2 citation statements)

References 109 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In urban drainage, unstable, three-dimensional flow conditions are observed with randomness, implying turbulent flows. These conditions are related to frictional resistance, flow separation, transition from laminar to turbulent flow, extension of secondary flows, the propagation of jets and contrails, and rapid flow variations (Vyzikas and Greaves, 2018). Given the conditions of depth of the water sheet, the hydraulic characteristics of the flow may have a greater influence on the shear stresses than the disturbances in the flow around the grates.…”

Section: Numerical Modelmentioning

confidence: 99%

Three-dimensional Numerical Evaluation of Hydraulic Efficiency and Discharge Coefficient in Grate Inlets

Zambrano

González

Tequia

2022

EREM

View full text Add to dashboard Cite

Floods are one of the causes of ground movement and displacement, and due to rapid urbanization and urban growth may occur more frequently than before. The characteristics of an urban drainage system can define the occurrence and extent of flooding, where catchment elements have a determining role. This document presents the numerical investigation of the hydraulic inlet efficiency and the discharge coefficient of seven types of grate inlets. The FLOW-3D® simulator is used to test the gratings at a full scale, under flow rates of Q = 24, 34.1, 44, 100, 200 and 300 L/s, preserving the configuration of the experimental prototype with longitudinal slopes of 1.0%, 1.5% and 2.0% and a fixed cross slope, for a total of 126 models. Based on the results, hydraulic inlet efficiency curves and discharge coefficients are constructed for each type and a longitudinal slope condition. The results are adjusted with empirical formulations proposed in other investigations, serving to verify the results of physical testing of prototypes.

show abstract

Section: Numerical Modelmentioning

confidence: 99%

Three-dimensional Numerical Evaluation of Hydraulic Efficiency and Discharge Coefficient in Grate Inlets

Zambrano

González

Tequia

2022

EREM

View full text Add to dashboard Cite

show abstract

“…As such, physics-based numerical models are valuable tools for complex processes because they can generally capture high dimensionality with significant accuracy. Even still, due to the high complexity present in developing these models, numerical models are often simplified by adding tuning constants or simplifying the boundary conditions [11,12]. Additionally, due to the complexity of numerical models and the intense computational effort they require, they are generally suitable for applications of small spatial and temporal frames [13].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Coastal Engineering: Applications, Challenges, and Perspectives

Abouhalima,

das Neves,

Taveira-Pinto

et al. 2024

JMSE

View full text Add to dashboard Cite

The integration of machine learning (ML) techniques in coastal engineering marks a paradigm shift in how coastal processes are modeled and understood. While traditional empirical and numerical models have been stalwarts in simulating coastal phenomena, the burgeoning complexity and computational demands have paved the way for data-driven approaches to take center stage. This review underscores the increasing preference for ML methods in coastal engineering, particularly in predictive tasks like wave pattern prediction, water level fluctuation, and morphology change. Although the scope of this review is not exhaustive, it aims to spotlight recent advancements and the capacity of ML techniques to harness vast datasets for more efficient and cost-effective simulations of coastal dynamics. However, challenges persist, including issues related to data availability and quality, algorithm selection, and model generalization. This entails addressing fundamental questions about data quantity and quality, determining optimal methodologies for specific problems, and refining techniques for model training and validation. The reviewed literature paints a promising picture of a future where ML not only complements but significantly enhances our ability to predict and manage the intricate dynamics of coastal environments.

show abstract

On the improvements in nearshore wave height predictions using nested SWAN-SWASH modelling in the eastern coastal waters of India

Umesh

Behera

2021

Ocean Engineering

View full text Add to dashboard Cite

Numerical Modelling

Cited by 4 publications

References 109 publications

Three-dimensional Numerical Evaluation of Hydraulic Efficiency and Discharge Coefficient in Grate Inlets

Three-dimensional Numerical Evaluation of Hydraulic Efficiency and Discharge Coefficient in Grate Inlets

Machine Learning in Coastal Engineering: Applications, Challenges, and Perspectives

On the improvements in nearshore wave height predictions using nested SWAN-SWASH modelling in the eastern coastal waters of India

Contact Info

Product

Resources

About