215 research outputs found
Airfoil's Aerodynamic Coefficients Prediction using Artificial Neural Network
Figuring out the right airfoil is a crucial step in the preliminary stage of
any aerial vehicle design, as its shape directly affects the overall
aerodynamic characteristics of the aircraft or rotorcraft. Besides being a
measure of performance, the aerodynamic coefficients are used to design
additional subsystems such as a flight control system, or predict complex
dynamic phenomena such as aeroelastic instability. The coefficients in question
can either be obtained experimentally through wind tunnel testing or, depending
upon the accuracy requirements, by numerically simulating the underlying
fundamental equations of fluid dynamics. In this paper, the feasibility of
applying Artificial Neural Networks (ANNs) to estimate the aerodynamic
coefficients of differing airfoil geometries at varying Angle of Attack, Mach
and Reynolds number is investigated. The ANNs are computational entities that
have the ability to learn highly nonlinear spatial and temporal patterns.
Therefore, they are increasingly being used to approximate complex real-world
phenomenon. However, despite their significant breakthrough in the past few
years, ANNs' spreading in the field of Computational Fluid Dynamics (CFD) is
fairly recent, and many applications within this field remain unexplored. This
study thus compares different network architectures and training datasets in an
attempt to gain insight as to how the network perceives the given airfoil
geometries, while producing an acceptable neuronal model for faster and easier
prediction of lift, drag and moment coefficients in steady state,
incompressible flow regimes. This data-driven method produces sufficiently
accurate results, with the added benefit of saving high computational and
experimental costs
CNN-based flow control device modelling on aerodynamic airfoils
Wind energy has become an important source of electricity generation, with the aim of achieving a cleaner and more sustainable energy model. However, wind turbine performance improvement is required to compete with conventional energy resources. To achieve this improvement, flow control devices are implemented on airfoils. Computational fluid dynamics (CFD) simulations are the most popular method for analyzing this kind of devices, but in recent years, with the growth of Artificial Intelligence, predicting flow characteristics using neural networks is becoming increasingly popular. In this work, 158 different CFD simulations of a DU91W(2)250 airfoil are conducted, with two different flow control devices, rotating microtabs and Gurney flaps, added on its Trailing Edge (TE). These flow control devices are implemented by using the cell-set meshing technique. These simulations are used to train and test a Convolutional Neural Network (CNN) for velocity and pressure field prediction and another CNN for aerodynamic coefficient prediction. The results show that the proposed CNN for field prediction is able to accurately predict the main characteristics of the flow around the flow control device, showing very slight errors. Regarding the aerodynamic coefficients, the proposed CNN is also capable to predict them reliably, being able to properly predict both the trend and the values. In comparison with CFD simulations, the use of the CNNs reduces the computational time in four orders of magnitude.The authors are thankful to the government of the Basque Country for the ELKARTEK21/10 KK-2021/00014 and ITSAS-REM IT1514-22 research programs, respectively
Flowfield prediction of airfoil off-design conditions based on a modified variational autoencoder
Airfoil aerodynamic optimization based on single-point design may lead to
poor off-design behaviors. Multipoint optimization that considers the
off-design flow conditions is usually applied to improve the robustness and
expand the flight envelope. Many deep learning models have been utilized for
the rapid prediction or reconstruction of flowfields. However, the flowfield
reconstruction accuracy may be insufficient for cruise efficiency optimization,
and the model generalization ability is also questionable when facing airfoils
different from the airfoils with which the model has been trained. Because a
computational fluid dynamic evaluation of the cruise condition is usually
necessary and affordable in industrial design, a novel deep learning framework
is proposed to utilize the cruise flowfield as a prior reference for the
off-design condition prediction. A prior variational autoencoder is developed
to extract features from the cruise flowfield and to generate new flowfields
under other free stream conditions. Physical-based loss functions based on
aerodynamic force and conservation of mass are derived to minimize the
prediction error of the flowfield reconstruction. The results demonstrate that
the proposed model can reduce the prediction error on test airfoils by 30%
compared to traditional models. The physical-based loss function can further
reduce the prediction error by 4%. The proposed model illustrates a better
balance of the time cost and the fidelity requirements of evaluation for cruise
and off-design conditions, which makes the model more feasible for industrial
applications
Airfoil GAN: Encoding and Synthesizing Airfoils forAerodynamic-aware Shape Optimization
The current design of aerodynamic shapes, like airfoils, involves
computationally intensive simulations to explore the possible design space.
Usually, such design relies on the prior definition of design parameters and
places restrictions on synthesizing novel shapes. In this work, we propose a
data-driven shape encoding and generating method, which automatically learns
representations from existing airfoils and uses the learned representations to
generate new airfoils. The representations are then used in the optimization of
synthesized airfoil shapes based on their aerodynamic performance. Our model is
built upon VAEGAN, a neural network that combines Variational Autoencoder with
Generative Adversarial Network and is trained by the gradient-based technique.
Our model can (1) encode the existing airfoil into a latent vector and
reconstruct the airfoil from that, (2) generate novel airfoils by randomly
sampling the latent vectors and mapping the vectors to the airfoil coordinate
domain, and (3) synthesize airfoils with desired aerodynamic properties by
optimizing learned features via a genetic algorithm. Our experiments show that
the learned features encode shape information thoroughly and comprehensively
without predefined design parameters. By interpolating/extrapolating feature
vectors or sampling from Gaussian noises, the model can automatically
synthesize novel airfoil shapes, some of which possess competitive or even
better aerodynamic properties comparing with training airfoils. By optimizing
shape on learned features via a genetic algorithm, synthesized airfoils can
evolve to have specific aerodynamic properties, which can guide designing
aerodynamic products effectively and efficiently
Machine Learning in Aerodynamic Shape Optimization
Machine learning (ML) has been increasingly used to aid aerodynamic shape optimization (ASO), thanks to the availability of aerodynamic data and continued developments in deep learning. We review the applications of ML in ASO to date and provide a perspective on the state-of-the-art and future directions. We first introduce conventional ASO and current challenges. Next, we introduce ML fundamentals and detail ML algorithms that have been successful in ASO. Then, we review ML applications to ASO addressing three aspects: 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, such as interactive design optimization. Practical large-scale design optimizations remain a challenge because of the high cost of ML training. Further research on coupling ML model construction with prior experience and knowledge, such as physics-informed ML, is recommended to solve large-scale ASO problems
A Convolutional Neural Network Model based on Multiscale Structural Similarity for the Prediction of Flow Fields
We have seen the emerging applications of deep neural networks for flow field predictions in the past few years. Most of the efforts rely on the increased complexity of the model itself or take advantage of novel network architectures, such as convolutional neural networks (CNN). However, reaching low prediction error cannot guarantee the quality of the predicted flow fields in terms of the perceived visual quality. This work introduces the multi-scale structural similarity (MS-SSIM) index method for flow field prediction. First, we train CNN models using the commonly used root mean squared error (RMSE) loss function as the reference. Then we introduce the SSIM loss function to capture the high-level features. Furthermore, we investigate the effects of the MS-SSIM weights on the predictive performance. Our results show that while the pixel-wise prediction error of RMSE-based models is as low as 1.3141 x 10−2, the perceived visual quality of the predicted flow fields, such as contour-line smoothness, is poorly represented. In contrast, the MS-SSIM models significantly improve the perceived visual quality with an SSIM loss value as low as 7.370 x 10−3, although having a slightly higher prediction error of 1.3912x10−2 . These values are 41.7% lower in the SSIM loss and 5.9% higher in the RMSE than the best RMSE model. In particular, we report that a weight combination of 0.3 and 0.7 for the MS-SSIM loss function provides the best predictive performance in our case. Our study has pointed out a possible future endeavor to invent a quality metric based on structural similarity, which should excel in flow-field-related approximations
DDSL: Deep Differentiable Simplex Layer for Learning Geometric Signals
We present a Deep Differentiable Simplex Layer (DDSL) for neural networks for
geometric deep learning. The DDSL is a differentiable layer compatible with
deep neural networks for bridging simplex mesh-based geometry representations
(point clouds, line mesh, triangular mesh, tetrahedral mesh) with raster images
(e.g., 2D/3D grids). The DDSL uses Non-Uniform Fourier Transform (NUFT) to
perform differentiable, efficient, anti-aliased rasterization of simplex-based
signals. We present a complete theoretical framework for the process as well as
an efficient backpropagation algorithm. Compared to previous differentiable
renderers and rasterizers, the DDSL generalizes to arbitrary simplex degrees
and dimensions. In particular, we explore its applications to 2D shapes and
illustrate two applications of this method: (1) mesh editing and optimization
guided by neural network outputs, and (2) using DDSL for a differentiable
rasterization loss to facilitate end-to-end training of polygon generators. We
are able to validate the effectiveness of gradient-based shape optimization
with the example of airfoil optimization, and using the differentiable
rasterization loss to facilitate end-to-end training, we surpass state of the
art for polygonal image segmentation given ground-truth bounding boxes
- …