This paper describes the use of a quadratic manifold for the model order reduction of structural dynamics problems featuring geometric nonlinearities. The manifold is tangent to a subspace spanned by the most relevant vibration modes, and its curvature is provided by modal derivatives obtained by sensitivity analysis of the eigenvalue problem, or its static approximation, along the vibration mo...

We construct a three-dimensional topological sigma model which is induced from a generalized complex structure on a target generalized complex manifold. This model is constructed from maps from a three-dimensional manifold X to an arbitrary generalized complex manifold M . The theory is invariant under the diffeomorphism on the world volume and the b-transformation on the generalized complex st...

We show any Riemannian curvature model can be geometrically realized by a manifold with constant scalar curvature. We also show that any pseudo-Hermitian curvature model, para-Hermitian curvature model, hyperpseudo-Hermitian curvature model, or hyper-para-Hermitian curvature model can be realized by a manifold with constant scalar and ⋆-scalar curvature.

Data obtained from Nary calibration tests of an 18-cylinder, two-row, radial engine of 8350-cubic-inch displacement and a 14-cylinder, two-row, radial engine of 2600-cubic-inch displacement {carburetor types) were analyzed to show the correlation between the air consumption of these engines and the parameters that evaluate the air consumption from intake-manifold temperature and pressure, exhau...

Many high dimensional data sets are characterized by a small number of degrees of freedom. Standard manifold-based dimensionality reduction techniques include the bias that all data lies on (or near) a single global manifold. Here we consider data sampled from multiple manifolds. We demonstrate a straightforward technique for multiple manifold embedding: the data is clustered, and each cluster ...

the starting behavior of a supersonic axisymmetric mixed compression air intake was numerically investigated. the code solves reynolds-averaged navier–stokes equations using an explicit finite volume method in a structured grid by the roe flux difference splitting scheme. further, it uses the baldwin–lomax algebraic model to compute the turbulent viscosity coefficient. the correct method of sur...

In this paper, we present a novel Bayesian model for manifold learning, suitable for data that are comprised of multiple modes of observations. Our data are assumed to be lying on a non-linear, low-dimensional manifold, modelled as a locally linear structure. The manifold local structure and the manifold coordinates are latent stochastic variables that are estimated from a training set. Through...

A strategy based on motion planning is proposed for airpath control of turbocharged diesel engines equipped with exhaust gas recirculation. The objective is to manage the air and burned gas masses in the cylinder. The model considered uses simple balance equations for the intake manifold. The fully actuated dynamics are easily inverted, yielding straightforward open-loop control laws. This appr...

In this paper, we constrain faces to points on a manifold within the parameter space of a linear statistical model. The manifold is the subspace of faces which have maximally likely distinctiveness and different points correspond to unique identities. We provide a detailed empirical validation for the chosen manifold. We show how the Log and Exponential maps for a hyperspherical manifold can be...

Abstract. In this paper, we present a new cosmological model using fractal manifold. We prove that a space defined by this kind of manifold is an expanding space. This model provides us with consistent arguments pertaining to the relationship between variation of geometry and movement of matter. This study leads to the existence of new fundamental principles. A clear picture is then portrayed a...