Application of Machine Learning Techniques for Classification of Cavity Flow and Resonance

Srivastava, Ankur; Meade, Andrew

doi:10.2514/1.c032282

Cited by 5 publications

(7 citation statements)

References 15 publications

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“…SFA was developed from mesh-free finite element research but shares similarities with the boosting [16] and matching pursuit [17] algorithms. It was later used to provide kernel based solutions to regression [9] and classification problems [6,7,10]. We start our approximation of utilizing the RBF ( ):…”

Section: Active Learning For Classification Problemsmentioning

confidence: 99%

“…In a related work, the authors demonstrated how machine learning tools could be used with Design of Experiments (DOEs) to steer the experiment by investigating input parameter sensitivities to the classification of the cavity flow type [6]. The authors used a Galerkin-derived adaptive implementation of artificial neural networks called sequential function approximation (SFA) [6][7][8][9][10] to predict the cavity flow type with or without acoustic resonance as a function of lengthto-depth ratio ( /ℎ), width-to-depth ratio ( /ℎ), and the freestream Mach number ( ∞ ). The authors treated this problem as a multiclass classification problem and justified the selection of SFA by comparison against state-of-the-art classification tools.…”

Section: Introductionmentioning

confidence: 99%

“…However, the work presented in [6] could only steer the cavity experiment by input parameter selection with respect to cavity flow type classification. It could not take part in the data collection procedure given a set of input parameters.…”

Section: Introductionmentioning

confidence: 99%

“…With centered at * , minimize(8) to solve for the optimization parameter with . Calculate the coefficients and from(6) and(7), respectively. Update the residual vector = −1 − ( + ).…”

mentioning

confidence: 99%

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Use of Active Learning to Design Wind Tunnel Runs for Unsteady Cavity Pressure Measurements

Srivastava

Meade

2014

International Journal of Aerospace Engineering

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Wind tunnel tests to measure unsteady cavity flow pressure measurements can be expensive, lengthy, and tedious. In this work, the feasibility of an active machine learning technique to design wind tunnel runs using proxy data is tested. The proposed active learning scheme used scattered data approximation in conjunction with uncertainty sampling (US). We applied the proposed intelligent sampling strategy in characterizing cavity flow classes at subsonic and transonic speeds and demonstrated that the scheme has better classification accuracies, using fewer training points, than a passive Latin Hypercube Sampling (LHS) strategy.

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Section: Active Learning For Classification Problemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…With centered at * , minimize(8) to solve for the optimization parameter with . Calculate the coefficients and from(6) and(7), respectively. Update the residual vector = −1 − ( + ).…”

mentioning

confidence: 99%

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Use of Active Learning to Design Wind Tunnel Runs for Unsteady Cavity Pressure Measurements

Srivastava

Meade

2014

International Journal of Aerospace Engineering

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“…When the cavity is deep, it is known as an open cavity flow, and the shear layer bridges the cavity. The normal mode or the feedback loop is responsible for strong self-sustained oscillations [4][5][6][7][8][9], which give rise to structural loading problems and acoustic noise. In the case of shallow (or closed) cavity flow, two distinct separation regions exist downstream of the front face and upstream of the rear face.…”

mentioning

confidence: 99%