Accelerated Airfoil Design Using Neural Network Approaches

• URL: http://arxiv.org/abs/2503.24052v1
• Date: Mon, 31 Mar 2025 13:14:14 GMT
• Title: Accelerated Airfoil Design Using Neural Network Approaches
• Authors: Anantram Patel, Nikhil Mogre, Mandar Mane, Jayavardhan Reddy Enumula, Vijay Kumar Sutrakar,
• Abstract summary: prediction of airfoil shape from targeted pressure distribution (suction and pressure sides) and vice versa is demonstrated.<n> dataset is generated for 1600 airfoil shapes.<n>Five different CNN and DNN models are developed depending on the input/output parameters.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, prediction of airfoil shape from targeted pressure distribution (suction and pressure sides) and vice versa is demonstrated using both Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) techniques. The dataset is generated for 1600 airfoil shapes, with simulations carried out at Reynolds numbers (Re) ranging from 10,000 and 90,00,000 and angles of attack (AoA) ranging from 0 to 15 degrees, ensuring the dataset captured diverse aerodynamic conditions. Five different CNN and DNN models are developed depending on the input/output parameters. Results demonstrate that the refined models exhibit improved efficiency, with the DNN model achieving a multi-fold reduction in training time compared to the CNN model for complex datasets consisting of varying airfoil, Re, and AoA. The predicted airfoil shapes/pressure distribution closely match the targeted values, validating the effectiveness of deep learning frameworks. However, the performance of CNN models is found to be better compared to DNN models. Lastly, a flying wing aircraft model of wingspan >10 m is considered for the prediction of pressure distribution along the chordwise. The proposed CNN and DNN models show promising results. This research underscores the potential of deep learning models accelerating aerodynamic optimization and advancing the design of high-performance airfoils.

Related papers

• Toward Routing River Water in Land Surface Models with Recurrent Neural Networks [0.0]
  We study the performance of recurrent neural networks (RNNs) for river routing in land surface models (LSMs) Instead of observed precipitation, the LSM-RNN uses instantaneous runoff calculated from physics-based models as an input. We train the model with data from river basins spanning the globe and test it using historical streamflow measurements.
  arXiv  Detail & Related papers  (2024-04-22T14:21:37Z)
• AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction [40.58819011476455]
  This paper presents a novel approach named Physics guided Neural Network for Air Quality Prediction (AirPhyNet) We leverage two well-established physics principles of air particle movement (diffusion and advection) by representing them as differential equation networks. Experiments on two real-world benchmark datasets demonstrate that AirPhyNet outperforms state-of-the-art models for different testing scenarios.
  arXiv  Detail & Related papers  (2024-02-06T07:55:54Z)
• Graph Neural Networks for Pressure Estimation in Water Distribution Systems [44.99833362998488]
  Pressure and flow estimation in Water Distribution Networks (WDN) allows water management companies to optimize their control operations. We combine physics-based modeling and Graph Neural Networks (GNN), a data-driven approach, to address the pressure estimation problem. Our GNN-based model estimates the pressure of a large-scale WDN in The Netherlands with a MAE of 1.94mH$$O and a MAPE of 7%.
  arXiv  Detail & Related papers  (2023-11-17T15:30:12Z)
• Enhancing Deep Neural Network Training Efficiency and Performance through Linear Prediction [0.0]
  Deep neural networks (DNN) have achieved remarkable success in various fields, including computer vision and natural language processing. This paper aims to propose a method to optimize the training effectiveness of DNN, with the goal of improving model performance.
  arXiv  Detail & Related papers  (2023-10-17T03:11:30Z)
• Machine learning enhanced real-time aerodynamic forces prediction based on sparse pressure sensor inputs [7.112725255953468]
  This paper presents a data-driven aerodynamic force prediction model based on a small number of pressure sensors. The model is tested on numerical and experimental dynamic stall data of a 2D NACA0015 airfoil, and numerical simulation data of dynamic stall of a 3D drone.
  arXiv  Detail & Related papers  (2023-05-16T06:15:13Z)
• A predictive physics-aware hybrid reduced order model for reacting flows [65.73506571113623]
  A new hybrid predictive Reduced Order Model (ROM) is proposed to solve reacting flow problems. The number of degrees of freedom is reduced from thousands of temporal points to a few POD modes with their corresponding temporal coefficients. Two different deep learning architectures have been tested to predict the temporal coefficients.
  arXiv  Detail & Related papers  (2023-01-24T08:39:20Z)
• Boosted Dynamic Neural Networks [53.559833501288146]
  A typical EDNN has multiple prediction heads at different layers of the network backbone. To optimize the model, these prediction heads together with the network backbone are trained on every batch of training data. Treating training and testing inputs differently at the two phases will cause the mismatch between training and testing data distributions. We formulate an EDNN as an additive model inspired by gradient boosting, and propose multiple training techniques to optimize the model effectively.
  arXiv  Detail & Related papers  (2022-11-30T04:23:12Z)
• An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions [73.4962254843935]
  We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history. This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
  arXiv  Detail & Related papers  (2022-04-18T23:42:18Z)
• Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery [62.997667081978825]
  We propose an enhanced version of the physics-constrained deep neural network (PCDNN) approach to provide high-accuracy voltage predictions. The ePCDNN can accurately capture the voltage response throughout the charge--discharge cycle, including the tail region of the voltage discharge curve.
  arXiv  Detail & Related papers  (2022-03-03T19:56:24Z)
• A Battle of Network Structures: An Empirical Study of CNN, Transformer, and MLP [121.35904748477421]
  Convolutional neural networks (CNN) are the dominant deep neural network (DNN) architecture for computer vision. Transformer and multi-layer perceptron (MLP)-based models, such as Vision Transformer and Vision-Mixer, started to lead new trends. In this paper, we conduct empirical studies on these DNN structures and try to understand their respective pros and cons.
  arXiv  Detail & Related papers  (2021-08-30T06:09:02Z)
• ANNETTE: Accurate Neural Network Execution Time Estimation with Stacked Models [56.21470608621633]
  We propose a time estimation framework to decouple the architectural search from the target hardware. The proposed methodology extracts a set of models from micro- kernel and multi-layer benchmarks and generates a stacked model for mapping and network execution time estimation. We compare estimation accuracy and fidelity of the generated mixed models, statistical models with the roofline model, and a refined roofline model for evaluation.
  arXiv  Detail & Related papers  (2021-05-07T11:39:05Z)
• A Compact Deep Architecture for Real-time Saliency Prediction [42.58396452892243]
  Saliency models aim to imitate the attention mechanism in the human visual system. Deep models have a high number of parameters which makes them less suitable for real-time applications. Here we propose a compact yet fast model for real-time saliency prediction.
  arXiv  Detail & Related papers  (2020-08-30T17:47:16Z)
• Wind speed prediction using multidimensional convolutional neural networks [5.228711636020665]
  This paper introduces a model based on convolutional neural networks (CNNs) for wind speed prediction tasks. We show that compared to classical CNN-based models, the proposed model is able to better characterise the wind data.
  arXiv  Detail & Related papers  (2020-07-04T20:48:41Z)
• A deep convolutional neural network model for rapid prediction of fluvial flood inundation [0.0]
  Deep convolutional neural network (CNN) method is presented for rapid prediction of fluvial flood inundation. CNN model is trained using outputs from a 2D hydraulic model (i.e. LISFLOOD-FP) to predict water depths. CNN model is highly accurate in capturing flooded cells as indicated by several quantitative assessment matrices.
  arXiv  Detail & Related papers  (2020-06-20T11:37:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.