CFD of the Future: Year 2025 and Beyond

Runchal, Akshai K.; Rao, Madhukar M.

doi:10.1007/978-981-15-2670-1_22

Cited by 16 publications

(15 citation statements)

References 68 publications

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“…Further research is expected to improve the simulation fidelity in the design optimization of industrial shapes such as aircraft and wind turbines. Some parameters in CFD solvers such as the relaxation parameter are adjustable, and it has been shown that tuning them using ML yields faster convergence while retaining stability [422]. Discacciati et al [423] designed an ANN to estimate artificial viscosity in discontinuous Galerkin schemes and integrated it into a Runge-Kutta solver.…”

Section: Numerical Simulation Accelerationmentioning

confidence: 99%

Machine Learning in Aerodynamic Shape Optimization

Li¹,

Martins²

2022

Preprint

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Large volumes of experimental and simulation aerodynamic data have been rapidly advancing aerodynamic shape optimization (ASO) via machine learning (ML), whose effectiveness has been growing thanks to continued developments in deep learning. In this review, we first introduce the state of the art and the unsolved challenges in ASO. Next, we present a description of ML fundamentals and detail the ML algorithms that have succeeded in ASO. Then we review ML applications contributing to ASO from three fundamental perspectives: compact geometric design space, fast aerodynamic analysis, and efficient optimization architecture. In addition to providing a comprehensive summary of the research, we comment on the practicality and effectiveness of the developed methods. We show how cutting-edge ML approaches can benefit ASO and address challenging demands like interactive design optimization. However, practical large-scale design optimizations remain a challenge due to the costly ML training expense. A deep coupling of ML model construction with ASO prior experience and knowledge, such as taking physics into account, is recommended to train ML models effectively.

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Section: Numerical Simulation Accelerationmentioning

confidence: 99%

Machine Learning in Aerodynamic Shape Optimization

Li¹,

Martins²

2022

Preprint

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“…In addition to regression for property modeling of porous media, image‐to‐image transformation can be classified as regression if implemented in a deterministic manner (Hennigh, 2017) despite a category of stochastic transformations that lead to a randomized different realization in each prediction(Isola et al., 2017). A major application of image‐to‐image transformation in porous material research is to predict velocity and pressure fields during pore‐scale fluid flow without attending regular numerical simulations like finite volume, finite element, or Lattice Boltzmann which are computationally intensive (Runchal & Rao, 2020). One of the first exemplary applications of this transformation has been presented by Hennigh (2017).…”

Section: Data Regressionmentioning

confidence: 99%

“…A major application of image-to-image transformation in porous material research is to predict velocity and pressure fields during pore-scale fluid flow without attending regular numerical simulations like finite volume, finite element, or Lattice Boltzmann which are computationally intensive (Runchal & Rao, 2020).…”

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

Review of Data Science Trends and Issues in Porous Media Research With a Focus on Image‐Based Techniques

Rabbani

Fernando

Shams

et al. 2021

Water Resources Research

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“…Mekov et al [113] has predicted that the performance of CFD in respiratory diseases, as with other areas of its application, will be advanced by deep machine learning (ML) [114][115]. The improvement will include simulation speed, accuracy of results, and the user-friendliness of the CFD software [116][117][118]. Precisely, computer programmer are currently using machine learning to develop automatic classifiers information from different networks which produce signals that are going to be used for performing certain tasks.…”

Section: Prospectsmentioning

confidence: 99%

Computational Fluid Dynamics Modeling in Respiratory Airways Obstruction: <i>Current Applications and Prospects</i>

Ayodele¹,

Oluwatosin²,

Taiwo³

et al. 2021

IJBSE

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Breathing conditions pertaining to nasal obstruction, obstructive sleep apnea, and airflow resistance in the human lower airways have been investigated extensively by researchers over the years. Due to the availability of advanced computer numerical models, such as computational fluid dynamics (CFD), researchers have made progressive studies of airflow characteristic, especially the effects of airflow pressure, velocity and wall shear stress in human obstructive airways. Studies utilizing CFD have enhanced clinical understanding of the physiology and pathophysiology of the respiratory system through the concept of three-dimensional models that facilitate airflow simulation. The main objective of this article is to review recent CFD literature on nasal airflow and lower airway obstruction. The review covers the role of segmentation threshold in the outcome of airflow simulation in the nasal cavity, and results of fluid structure interaction (FSI) and computational fluid dynamics in nasal obstruction and airway collapse in obstructive sleep apnea were also correlated. For models of the lower airways, we evaluated the effect of extra-thoracic airway (ETA) on downstream airflow during simulation against the popular Weibel's model. In the concluding section, we discussed the advantages, limitations, and prospects (precisely with deep machine learning) of computational fluid dynamics in the clinical assessment and investigation of respiratory diseases.

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CFD of the Future: Year 2025 and Beyond

Cited by 16 publications

References 68 publications

Machine Learning in Aerodynamic Shape Optimization

Machine Learning in Aerodynamic Shape Optimization

Review of Data Science Trends and Issues in Porous Media Research With a Focus on Image‐Based Techniques

Computational Fluid Dynamics Modeling in Respiratory Airways Obstruction: <i>Current Applications and Prospects</i>

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CFD of the Future: Year 2025 and Beyond

Cited by 16 publications

References 68 publications

Machine Learning in Aerodynamic Shape Optimization

Machine Learning in Aerodynamic Shape Optimization

Review of Data Science Trends and Issues in Porous Media Research With a Focus on Image‐Based Techniques

Computational Fluid Dynamics Modeling in Respiratory Airways Obstruction: &lt;i&gt;Current Applications and Prospects&lt;/i&gt;

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Computational Fluid Dynamics Modeling in Respiratory Airways Obstruction: <i>Current Applications and Prospects</i>