183 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
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
Fast buffet onset prediction and optimization method based on a pre-trained flowfield prediction model
The transonic buffet is a detrimental phenomenon occurs on supercritical
airfoils and limits aircraft's operating envelope. Traditional methods for
predicting buffet onset rely on multiple computational fluid dynamics
simulations to assess a series of airfoil flowfields and then apply criteria to
them, which is slow and hinders optimization efforts. This article introduces
an innovative approach for rapid buffet onset prediction. A machine-learning
flowfield prediction model is pre-trained on a large database and then deployed
offline to replace simulations in the buffet prediction process for new airfoil
designs. Unlike using a model to directly predict buffet onset, the proposed
technique offers better visualization capabilities by providing users with
intuitive flowfield outputs. It also demonstrates superior generalization
ability, evidenced by a 32.5% reduction in average buffet onset prediction
error on the testing dataset. The method is utilized to optimize the buffet
performance of 11 distinct airfoils within and outside the training dataset.
The optimization results are verified with simulations and proved to yield
improved samples across all cases. It is affirmed the pre-trained flowfield
prediction model can be applied to accelerate aerodynamic shape optimization,
while further work still needs to raise its reliability for this
safety-critical task.Comment: 44 pages, 20 figure
Incorporating Riemannian Geometric Features for Learning Coefficient of Pressure Distributions on Airplane Wings
The aerodynamic coefficients of aircrafts are significantly impacted by its
geometry, especially when the angle of attack (AoA) is large. In the field of
aerodynamics, traditional polynomial-based parameterization uses as few
parameters as possible to describe the geometry of an airfoil. However, because
the 3D geometry of a wing is more complicated than the 2D airfoil,
polynomial-based parameterizations have difficulty in accurately representing
the entire shape of a wing in 3D space. Existing deep learning-based methods
can extract massive latent neural representations for the shape of 2D airfoils
or 2D slices of wings. Recent studies highlight that directly taking geometric
features as inputs to the neural networks can improve the accuracy of predicted
aerodynamic coefficients. Motivated by geometry theory, we propose to
incorporate Riemannian geometric features for learning Coefficient of Pressure
(CP) distributions on wing surfaces. Our method calculates geometric features
(Riemannian metric, connection, and curvature) and further inputs the geometric
features, coordinates and flight conditions into a deep learning model to
predict the CP distribution. Experimental results show that our method,
compared to state-of-the-art Deep Attention Network (DAN), reduces the
predicted mean square error (MSE) of CP by an average of 8.41% for the DLR-F11
aircraft test set
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
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