On Application of Artificial Neural Network Methods in Large-eddy Simulations with Unresolved Urban Surfaces

Esau, Igor

doi:10.5539/mas.v4n8p3

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…Another area in wind technology that could benefit from ML is the optimisation of wind turbines and wind farms, which requires hundreds of simulations. For example, in [141], the researchers employed NN to predict the fluid-structure interaction in non-well-resolved surfaces accurately.…”

Section: Machine Learningmentioning

confidence: 99%

The Role of Computational Science in Wind and Solar Energy: A Critical Review

Drikakis

Dbouk

2022

Energies

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This paper concerns technology challenges for the wind and solar sectors and the role of computational science in addressing the above. Wind energy challenges include understanding the atmospheric flow physics, complex wakes and their interaction with wind turbines, aeroelastic effects and the associated impact on materials, and optimisation of wind farms. Concentrated solar power technologies require an optimal configuration of solar dish technology and porous absorber in the volumetric solar receiver for efficiency and durability and to minimise the convective heat losses in the receiver. Computational fluid dynamics and heat transfer have advanced in terms of numerical methods and physics-based models and their implementation in high-performance computing facilities. Despite this progress, computational science requires further advancement to address the technological challenges of designing complex systems accurately and efficiently, as well as forecasting the system’s performance. Machine Learning models and optimisation techniques can maximise the performance of simulations and quantify uncertainties in the wind and solar energy technologies. However, in a similar vein, these methods require further development to reduce their computational uncertainties. The need to address the global energy challenges requires further investment in developing and validating computational science methods and physics-based models for accurate and numerically efficient predictions at different scales.

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Section: Machine Learningmentioning

confidence: 99%

The Role of Computational Science in Wind and Solar Energy: A Critical Review

Drikakis

Dbouk

2022

Energies

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“…ANNs have also been used to optimize the design of structures, such as that of a yacht [139], a task which usually requires several time-consuming CFD simulations. Esau [140] used NNs to calculate fluid-structure interactions, even when the surface morphology is not well resolved.…”

Section: Surrogate Modellingmentioning

confidence: 99%

Machine-Learning Methods for Computational Science and Engineering

2020

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The re-kindled fascination in machine learning (ML), observed over the last few decades, has also percolated into natural sciences and engineering. ML algorithms are now used in scientific computing, as well as in data-mining and processing. In this paper, we provide a review of the state-of-the-art in ML for computational science and engineering. We discuss ways of using ML to speed up or improve the quality of simulation techniques such as computational fluid dynamics, molecular dynamics, and structural analysis. We explore the ability of ML to produce computationally efficient surrogate models of physical applications that circumvent the need for the more expensive simulation techniques entirely. We also discuss how ML can be used to process large amounts of data, using as examples many different scientific fields, such as engineering, medicine, astronomy and computing. Finally, we review how ML has been used to create more realistic and responsive virtual reality applications.

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“…For our purpose, the use of statistical learning to automate, at least partially, the development and evaluation of reduced order models, is most relevant. For turbulent flows, examples includes the development of a subgrid model for LES by [21], the optimal estimation of subgrid models for LES by [22], the parametrization of surface features in coarse LES by [23], and the use of neural networks to optimize the model constants of the k − ε turbulence model applied to simulations of data centres by [24]. Most recently, [25] have used statistical learning to determine the functional dependency of the closure terms for data generated by Spalart-Allmaras turbulence model, rather than full DNS, and [26] and [27] have used inverse modelling to obtain spatially distributed functional terms to aid closure modelling, instead of inferring model parameters directly.…”

Section: Introductionmentioning

confidence: 99%

Using statistical learning to close two-fluid multiphase flow equations for bubbly flows in vertical channels

Tryggvason

2016

International Journal of Multiphase Flow

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Data generated by direct numerical simulations (DNS) of bubbly up-flow in a periodic vertical channel is used to generate closure relationships for a simplified two-fluid model for the average flow. Nearly spherical bubbles, initially placed in a fully developed parabolic flow, are driven relatively quickly to the walls, where they increase drag and slowly reduce the flow rate. Once the flow rate has been decreased enough, some of the bubbles move back into the channel interior and the void fraction there approaches the value needed to balance the weight of the mixture and the imposed pressure gradient. A database is generated by averaging the DNS results over planes parallel to the walls, and a Model Averaging Neural Network (MANN) is used to find the relationships between unknown closure terms in a simple model equations for the average flow and the resolved variables. The closure relations are then tested, by following the evolution of different initial conditions, and it is found that the model predictions are in reasonably good agreement with DNS results.

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On Application of Artificial Neural Network Methods in Large-eddy Simulations with Unresolved Urban Surfaces

Abstract: Show more

Cited by 6 publications

References 24 publications

The Role of Computational Science in Wind and Solar Energy: A Critical Review

The Role of Computational Science in Wind and Solar Energy: A Critical Review

Machine-Learning Methods for Computational Science and Engineering

Using statistical learning to close two-fluid multiphase flow equations for bubbly flows in vertical channels

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