Browsing by Author "Choudhari, Meelan M."

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    Evolution of an Acoustic Disturbance to Transition in the Boundary Layer on an Airfoil
    Kanner, Howard S. (Virginia Tech, 1999-02-22)
    An experiment has been conducted to examine the generation and subsequent evolution of boundary-layer disturbances on a two-dimensional airfoil up through transition to turbulent flow. The experiment was conducted at the NASA Langley Research Center "2 ft by 3 ft Low Speed Wind Tunnel Facility." The primary objective of the experiment was to generate a comprehensive database that includes the effect of the external disturbance environment on the transition process and can be used as a benchmark for future transition prediction tools. The airfoil used for this experiment was custom designed. The model was a 6% thick, 4-ft chord unswept symmetric wing. A description of the design procedure, along with the theoretical stability characteristics of the airfoil will be presented in this paper. The experiment consisted of establishing the mean flow conditions, forcing two-dimensional Tollmien-Schlichting (T-S) waves in the boundary layer using modulated acoustic bursts in the free-stream, and acquiring the mean boundary-layer data and fluctuating disturbance data using hot-wire probes. The acoustic receptivity due to surface roughness near Branch I has been examined. The surface roughness consisted of two-dimensional strips of tape applied at and symmetrically spaced about Branch I. Repeated roughness elements were spaced one wavelength apart based upon the wavelength of the primary forcing frequency as determined by linear-stability theory. The test conditions consisted of mean flow velocities of 15 and 20 m/s, which correspond to chord Reynolds numbers of 1.25 and 1.68 million, respectively. Boundary-layer disturbance profiles and constant boundary-layer height chordwise traverses were acquired and examined at individual frequencies and in total energy amplitude / broadband forms. The experimental results match well with linear stability theory and linear parabolized stability equations, indicating breakdown of disturbances between N-factors of 7 and 11 with surface roughness on the model. It was observed that when the flow physics change, differences between linear-stability theory and experiment are strongly apparent. An amplitude-based breakdown criterion was defined for the developing boundary-layer responses, which were burst-type packets like the acoustic forcing signal. A criterion was defined for the breakdown of both maxima of the T-S-like disturbance profile. Overall, the effects of surface roughness and free-stream acoustic forcing on boundary-layer receptivity and stability were examined in a well-documented disturbance environment. These results will be used to validate and refine non-linear flow theories as well as help to provide an improved understanding and improved methods to control flow transition.
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    Machine Learning Approaches to Data-Driven Transition Modeling
    Zafar, Muhammad-Irfan (Virginia Tech, 2023-06-15)
    Laminar-turbulent transition has a strong impact on aerodynamic performance in many practical applications. Hence, there is a practical need for developing reliable and efficient transition prediction models, which form a critical element of the CFD process for aerospace vehicles across multiple flow regimes. This dissertation explores machine learning approaches to develop transition models using data from computations based on linear stability theory. Such data provide strong correlation with the underlying physics governed by linearized disturbance equations. In the proposed transition model, a convolutional neural network-based model encodes information from boundary layer profiles into integral quantities. Such automated feature extraction capability enables generalization of the proposed model to multiple instability mechanisms, even for those where physically defined shape factor parameters cannot be defined/determined in a consistent manner. Furthermore, sequence-to-sequence mapping is used to predict the transition location based on the mean boundary layer profiles. Such an end-to-end transition model provides a significantly simplified workflow. Although the proposed model has been analyzed for two-dimensional boundary layer flows, the embedded feature extraction capability enables their generalization to other flows as well. Neural network-based nonlinear functional approximation has also been presented in the context of transport equation-based closure models. Such models have been examined for their computational complexity and invariance properties based on the transport equation of a general scalar quantity. The data-driven approaches explored here demonstrate the potential for improved transition prediction models.
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    Recurrent neural network for end-to-end modeling of laminar-turbulent transition
    Zafar, Muhammad I.; Choudhari, Meelan M.; Paredes, Pedro; Xiao, Heng (Cambridge University Press, 2021-06-29)
    Accurate prediction of laminar-turbulent transition is a critical element of computational fluid dynamics simulations for aerodynamic design across multiple flow regimes.Traditional methods of transition prediction cannot be easily extended to flow configurations where the transition process depends on a large set of parameters. In comparison, neural network methods allow higher dimensional input features to be considered without compromising the efficiency and accuracy of the traditional data-driven models. Neural network methods proposed earlier follow a cumbersome methodology of predicting instability growth rates over a broad range of frequencies, which are then processed to obtain the N-factor envelope, and then, the transition location based on the correlatingN-factor. This paper presents an end-to-end transition model based on a recurrent neural network, which sequentially processes the mean boundary-layer profiles along the surface of the aerodynamic body to directly predict the N-factor envelope and the transition locations over a two-dimensional airfoil. The proposed transition model has been developed and assessed using a large database of 53 airfoils over a wide range of chord Reynolds numbers and angles of attack. The large universe of airfoils encountered in various applications causes additional difficulties. As such, we provide further insights on selecting training datasets from large amounts of available data.Although the proposed model has been analyzed for two-dimensional boundary layers in this paper, it can be easily generalized to other flows due to embedded feature extraction capability of convolutional neural network in the model.