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In a broad sense, model reduction means producing a low-dimensional dynamical system that replicates either approximately, or more strictly, exactly and topologically, the output of a dynamical system. Model reduction has an important role in the study of dynamical systems and also with engineering problems. In many cases, there exists a good low-dimensional model for even very high-dimensional systems, even infinite dimensional systems in the case of a PDE with a low-dimensional attractor. The theory of global attractors approaches these issues analytically, and focuses on finding (depending on the question at hand), a slow-manifold, inertial manifold, or center manifold, on which a restricted dynamical system represents the interesting behavior of the dynamical system; the main issue depends on defining a stable invariant manifold in which the dynamical system is invariant. These approaches are analytical in nature, however, and are therefore not always appropriate for dynamical systems known only empirically through a dataset. Empirically, the collection of tools available are much more restricted, and are essentially linear in nature. Usually variants of Galerkin's method, project the dynamical system onto a function linear subspace spanned by modes of some chosen spanning set. Even the popular Karhunen–Loeve decomposition, or POD, method is exactly such a method. As such, it is forced to either make severe errors in the case that the invariant space is intrinsically a highly nonlinear manifold, or bypass low-dimensionality by retaining many modes in order to capture the manifold. In this work, we present a method of modeling a low-dimensional nonlinear manifold known only through the dataset. The manifold is modeled as a discrete graph structure. Intrinsic manifold coordinates will be found specifically through the ISOMAP algorithm recently developed in the Machine Learning community originally for purposes of image recognition.

The application of a flapping wing mechanism offers a vast range of development possibilities for unmanned aerial vehicles (UAVs) and autonomous underwater vehicles (AUVs). The influence of wake transitions on flapping wing mechanism’s capabilities is not fully understood particularly at low Reynolds numbers. The numerical investigation of a symmetric airfoil performing sinusoidal heaving oscillations is performed to explore the wake transitions. The influence of heaving parameters on wake transitions when exposed to a constant velocity flow is investigated. The existence of reverse von Karman vortex street, deflected wake and chaotic wake is observed. The wake deflection is found to switch its direction before transforming into a chaotic wake. The coherent structures and its evolution with the flow are presented using proper orthogonal decomposition (POD). The underlying structures and their interactions for different wake situations are identified. Correlations for the nondimensional maximum velocity in the wake in terms of frequency and amplitude is proposed. The wake dynamics is found to depend significantly on the leading edge vortices. The time-varying velocity fluctuations in the flow field are presented and discussed in detail. The velocity fluctuation contours are used to identify the regions of momentum transfer. The transient nature of the flow field is studied using the phase plot. A transition route from the periodic to chaotic regime though a quasi-periodic regime is established using time series analysis. The wake transitions are observed to be more sensitive towards heaving frequency than the heaving amplitude.