• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.6
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• pp.31-38
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• 2005

Aerodynamic influence coefficient linearly relates pressure with downwash in panel method for load analysis in which the viscosity of a flow is ignored and the compressibility cannot be taken into account in transonic region. Since the planform of an aerodynamic surface determines the coefficient, the panel method has a limit to the analysis of low Reynolds number flow. The accuracy of the pressure distribution can be improved by a direct correction to the pressure or a correction to the downwash, which is considered the change of camber or thickness, using the aerodynamic coefficients from wind tunnel test as constraints. A premultiplying correction method as well as a postmultiplying correction method is applied to a micro air vehicle to provide more accurate aerodynamic pressure for trim and load analyses. Theoretical aerodynamic pressure is obtained from the panel method. Correction factor matrix and correct pressure coefficient are computed for the conditions with two constraints in addition to single constraint. The postmultiplying correction method gives a better improvement in pressure distribution on micro air vehicle due to the flow characteristics on it.

• Journal of the Korean Society of Propulsion Engineers
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• v.3 no.3
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• pp.31-39
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• 1999

In this paper, numerical experiment is attempted to analyze and compare the combustion efficiency of the burning sprays due to OFO, FOF triplet / FOOF split doublet injectors. Preconditioned Wavier-Stokes equation system with low Reynolds number $\kappa$-$\varepsilon$ model for turbulence closure, is LU-SGS time-integrated. Spray processes are modeled by DSF analysis with experimentally determined injection characteristics. n-heptane/air global reaction model approximates the combustion for simplicity, and the influence of turbulence on the chemical reaction is included using eddy dissipation model. The results showed the FOF triplet injector of highest combustion efficiency, whereas the OFO type of poet performance. It was also observed that the droplet mean diameter and the average gas temperature due to the mixing efficiency, are the representative parameters for the performance design of combustion.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.5
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• pp.337-344
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• 2024

This study presents a deep learning-based framework to predict the aerodynamic performance of low Reynolds number airfoils. The framework employs a convolutional neural network (CNN) combined with a variational autoencoder (VAE) to efficiently handle large datasets. Moreover, the signed distance function is used as the network input to represent the airfoil configuration in the image data and parameterize the CNN. A novel generative model based on projection-based manifold learning is proposed to overcome the data mining limitation of computational fluid dynamics which may incur significant computational costs. The interpolation and extrapolation accuracy of the proposed framework is evaluated using the NACA 4-digit airfoil configuration.The results show improved accuracy via data augmentation performed by the proposed generative model.