Manifold Model Research Articles

Localization of Signals in the Near-Field of an Antenna Array

The problem of locating signals transmitted in the proximity of an antenna array has been studied extensively in the signal processing literature. In this paper, we consider the standard array manifold models used in these works and show that they differ, sometimes significantly, from the model based on electromagnetic theory. In particular, the standard models do not correspond to the equations governing an electromagnetic field near an antenna or an array. They also do not take into account the characteristics of the near-field source, such as the type and orientation of the transmitting antenna, which may have a profound impact on the signals received by the array. We use selected numerical examples based on a numerical electromagnetic code to illustrate the various issues raised herein.

Hyperspectral Anomaly Detection via Global and Local Joint Modeling of Background

Anomaly detection is a hot topic in hyperspectral signal processing. The key point of hyperspectral anomaly detection is the modeling of the background. In this paper, we propose a novel anomaly detection method via global and local joint modeling of background. Based on the observation that the local three-dimensional patch belonging to the background in hyperspectral image (HSI) usually lies in a low dimensional manifold, we propose to reconstruct the background part of a HSI from its subsample by scalable low dimensional manifold modeling (SLDMM). Thus, the background of HSI can be well characterized in both global and local aspects. Taking into consideration that the SLDMM reconstructs the background part at a low sampling ratio, we propose a multiple random sampling reconstruction strategy to further improve the detection accuracies and robustness. The final background is generated by the mean of backgrounds reconstructed from the multiple random sampling and the anomalies are contained in the residual between the observed HSI and the mean background. Experimental results on three real datasets demonstrate that the proposed anomaly detection method outperforms other state-of-the-art hyperspectral anomaly detection methods.

Towards optimal path computation in a simplicial complex

Computing optimal path in a configuration space is fundamental to solving motion planning problems in robotics and autonomy. Graph-based search algorithms have been widely used to that end, but they suffer from drawbacks. We present an algorithm for computing the shortest path through a metric simplicial complex that can be used to construct a piece-wise linear discrete model of the configuration manifold. In particular, given an undirected metric graph, G, which is constructed as a discrete representation of an underlying configuration manifold (a larger “continuous” space typically of dimension greater than one), we consider the Rips complex, , associated with it. Such a complex, and hence shortest paths in it, represent the underlying metric space more closely than what the graph does. Our algorithm requires only a local connectivity-based description of an abstract graph, , and a cost/length function, , as inputs. No global information such as an embedding or a global coordinate chart is required. The local nature of the proposed algorithm makes it suitable for configuration spaces of arbitrary topology, geometry, and dimension. We not only develop the search algorithm for computing shortest distances, but we also present a path reconstruction algorithm for explicitly computing the shortest paths through the simplicial complex. The complexity of the presented algorithm is comparable with that of Dijkstra’s search, but, as the results presented in this paper demonstrate, the shortest paths obtained using the proposed algorithm represent the geodesic paths in the original metric space significantly more closely.

Lyapunov-based low-energy low-thrust transfers to the Moon

This paper investigates the numerical computation of low-fuel low-thrust Earth-Moon transfers in a full ephemeris model incorporating the gravitational influence of the Sun, the Moon and all planets of the solar system plus the solar radiation pressure perturbation based on a sample spacecraft area and mass. First, an initial velocity increment to be provided by the launcher is computed. This initial impulse puts the spacecraft on the counterpart in the full ephemeris model of a stable invariant manifold defined in the Sun-Earth Circular Restricted Three-Body Problem. Then, after a coast arc, a closed-loop thrust law is applied to bring the spacecraft to the target lunar orbit. This control law is based on Lyapunov control theory. More precisely, a control-Lyapunov function is defined as the weighted quadratic distance between the first five equinoctial orbital elements of the spacecraft in a Moon-centered reference frame and those defining the target lunar orbit. The control is computed in such a way so as to make the time derivative of the control-Lyapunov function as negative as possible. Numerical results are provided first for a transfer with constant maximum thrust. Then, it is shown that unlike in the case of an open loop control, concentrating the thrust in the vicinity of the perilune and the apolune increases the transfer duration but without reducing the fuel consumption. This is largely due to the uncontrolled effect of the perturbations acting on the spacecraft during the coast arcs. Finally, the robustness of the guidance law against unexpected engine shutdown events is demonstrated.

Effects of turbulence-chemistry interactions on auto-ignition and flame structure for n-dodecane spray combustion

The Engine Combustion Network (ECN) spray A under diesel engine conditions is investigated with a non-adiabatic 5D Flamelet Generated Manifolds (FGM) model with the consideration of detailed chemical kinetic mechanisms. The enthalpy deficit due to droplet vapourisation is considered by employing an additional controlling parameter in the FGM library. In this FGM model, β-PDF is used for the PDF integration over the control variable space. Validation results in non-reacting conditions indicate relatively good agreement between the predicted and experimental data in terms of liquid and vapour penetrations and mixture fraction spatial distribution. In reacting conditions, the effects of variance of mixture fraction and progress variable were examined. The ignition delay time and the quasi-steady flame structure are both affected by the variances. The variance of mixture fraction delays the ignition process and the variance of progress variable accelerates it. For mixture fraction, the ignition process is quicker at any stage in the case of neglecting variance. While things are more complex for progress variable, the ignition process is advanced in the case of neglecting variance at early times, but surpassed by the case of β-PDF later and until auto-ignition. When variance of mixture fraction is considered, the OH mass fraction shows a wide spatial distribution. While if not, a very thin flame is observed with a higher peak in OH, and a very large lift-off length. The variance of progress variable has little impact on the global flame structure, but makes the flame lift-off length much shorter. This study confirms the general observation, that the variance of mixture fraction is of higher importance in high temperature non-premixed combustion, however, we found that the variance of progress variable is far from negligible.

Effect of Tube Size on Flame and Pressure Wave Propagation in a Tube Closed at One End: A Numerical Study

ABSTRACT Deflagration of fire and explosion caused by ignition of a methane-air mixture is a major safety concern in industrial settings, such as the mining and petrochemical industries. The aim of this study is to investigate numerically the effect of tube size on the flame and pressure wave propagations for methane-air deflagration in a tube closed at one end. A partially premixed combustion model that avoids the need to specify the flame speed is deployed based upon the Flamelet Generated Manifold (FGM) model. Good agreement is achieved between the predicted results and the benchmark experimental data collected using a large-scale detonation tube (L/D = 66). Subsequently, the explosion behaviour immediately after the ignition of methane-air mixtures and the propagation characteristics of the flame front and pressure wave through the tube are examined, covering a broad range of L/D, that is from 26 to 526, in which the diameter is changed and the length is kept fixed. The results show that the pressure wave propagates significantly faster in narrower tubes and hence decouples from the flame front shortly after ignition, which in turn results in a low overpressure at the flame front. Moreover, abrupt changes in gas properties are observed in narrow tubes with L/D ≥ 132. The peak overpressure increases as the tube diameter increases; however, the local maximum pressure decreases substantially in large tubes when approaching the tube vent but remains almost constant throughout in the narrow tubes. Similarly, the flame propagates faster in narrower tubes. A correlation that estimates the distance the flame propagates in the exponential acceleration stage is proposed as a function of tube size and time. Deviations of less than 7% are obtained when comparing the predicted results using the correlation against the experimental data. The results provide local information that aids the theoretical interpretation of experimental observations and the understanding of the fuel combustion and explosion phenomena in different sized tubes.

The uniform conditional state model for turbulent reacting flows

A novel model for the source term in RANS/LES of turbulent combustion, the Uniform Conditional State (UCS) model, is derived. The UCS model can be viewed as both a generalization and a specialization of Conditional Moment Closure (CMC): a generalization in that the number of conditioning variables is arbitrary, but a specialization in that conditionally-filtered fields are assumed to be independent of position. As a consequence of the latter property, distinct physical and conditional domains exist, and fields defined on the two domains are only loosely coupled. The central conditional transport equation in UCS takes the same form as that in the Multidimensional Flamelet Manifold (MFM) model, but is based on different starting assumptions and therefore does not require a “flamelet” assumption in turbulent flows. Like the MFM, the model is compatible with any diffusive flux law, and remains theoretically valid for any flame on the premixed-non-premixed continuum.The UCS model is tested on the Sandia flame series using mixture fraction and progress variable as conditioning variables and an experimental PDF for post-processing. Predicted scalar profiles show good agreement with experiment, with the notable exception of NO. Scalar profiles generally show modest sensitivity to the conditional scalar dissipation model, but the source of progress variable and mass fraction of the soot precursor C2H2 show greater sensitivity.

A regularized deconvolution method for turbulent closure modeling in implicitly filtered large-eddy simulation

Turbulence closure models that are based on a regularized deconvolution method (RDM) are proposed for application to implicitly filtered Large-Eddy Simulation (LES). This method reconstructs sub-grid scale contributions by approximately recovering filtered quantities through an optimization approach. Physical principles on the boundedness and conservation of reconstructed quantities are preserved by constraining the optimization problem. To account for effects of scales that are not resolved in implicitly filtered LES, a reconstruction method is proposed that combines sub-grid projection and energy-similarity to enrich unresolved scales. With this method, closure models are developed for turbulent scalar fluxes and turbulence–chemistry interaction that are intrinsically consistent with the LES formulation. These closure models are evaluated in application to a piloted turbulent jet flame with inhomogeneous inlet composition, in which combustion is represented by a two-scalar manifold model. The RDM-formulation is benchmarked against simulations obtained using a presumed PDF method and eddy diffusivity closure model. In partially-premixed flame regions, it is observed that RDM improves the prediction of turbulent mixing, and faster grid convergence is observed for results with RDM compared to simulations employing a presumed PDF method.

An approach to computational fluid dynamic air-flow simulation in the internal combustion engine intake manifold

The subject of this paper is modeling of an intake manifold of a four-stroke IC engine using contemporary software tools. Virtual 3D CAD model of an intake manifold was designed based on a real intake manifold of a four-stroke IC engine. Based on the CAD model a 3D CFD model of the intake manifold was created. The modeling has been done with the purpose of simulation of the air flow inside the intake manifold in order to monitor values of the internal pressure during several seconds of the engine operation in three different operating points. Also, an experiment was conducted, which included measurements of intake manifold pressure in the same engine operating points in the course of a time interval of approximately the same duration. The results of both the simulation and the experimental measurements have been shown in the paper proving that the created model was good enough for the intended purpose.

Investigation of heat dissipation in exhaust manifold using computational fluid dynamics

ABSTRACT In the field of automobile industry, the emergent condition is to reduce the noise, emission etc. For reducing the same, a lot of studies are going on. In an IC engine, the inlet manifold is used for supplying energy and the exhaust manifold for exit of smoke after power stroke, a lot of surveys are going on recycling of exhaust gas and to save energy, temperature at the exhaust manifold is about 1200° F. It is a single pipe which collects all exhaust gases from all the cylinders. Cast iron is used for manufacturing the exhaust manifold, due to its unique properties. In the exhaust system, the exhaust manifold plays a vital role for safely sending away toxic gases, reducing noise and air pollution. During exhaust, high heat is dissipated; it is harmful for the environment. The main concern of this research article is to design an exhaust manifold and analyse the heat dissipation, mainly how much heat is dissipated and how to save energy. Heat produced has been entirely different, best coating can be used for further research. A detailed study is done in this research. Exhaust manifold modelling is done using the software Pro-E and Computational Fluid Dynamics is done by the software Ansys.

Cracking failure analysis of an engine exhaust manifold at high temperatures based on critical fracture toughness and FE simulation approach

D5S is an alloy used in the manufacture of exhaust manifold of the internal combustion (IC) engines. Considering that exhaust manifold is one of the engine components that are under intense thermal and mechanical loads, fracture failure analysis should be regarded for this engine part. Fracture toughness determination is the main criterion for structural fracture analysis. In this research, crack failure was analyzed based on the finite element method (FEM), upon which critical fracture toughness was determined. Using the FEM analysis, a crack was assigned on the exhaust manifold model in order to obtain its fracture toughness at the first mode (KI) under thermo-mechanical loads. According to the ASTM standard, four compact tension (CT) specimens with different notch lengths of 4 mm, 5 mm, 9 mm, and 12 mm were prepared. Then, using the tension test at a high temperature, critical fracture force was determined. CT specimens were simulated by the finite element ABAQUS software. Fracture force obtained from the tension test was exerted on the CT specimen in the simulation software. Then, critical fracture toughness (KIC) was gained by using the fracture analysis in FE simulation. Finally, KI was compared with KIC and crack propagation was discussed.

Effects of variable-volume Helmholtz resonator on air mass flow rate of intake manifold

The application of geometric modifications to the intake manifold to improve engine breathing provides positive results using different strategies. Helmholtz resonators (HR) are one of the most basic acoustic models and are widely used in engineering applications. Most of the works presented in the literature focus on the use of HR in the fields of acoustics. The present work experimentally investigates pulsating flow characteristics of the engine intake manifold and the effects of a Helmholtz resonator with variable internal volume. In this paper, acoustic studies were used to model the intake manifold and to develop a complex expression to determine the natural frequency of the system. The model included the primary and secondary intake pipes and the resonator. Experiments were conducted on a straight intake pipe connected to an engine cylinder head mounted on a test flow bench, especially designed for fluid dynamic studies of engine intake systems with unsteady flow conditions. Using electronic controls, the internal volume of the resonator was varied and adjusted according to the frequency of the valves as well as the natural frequency of the intake manifold. The natural frequency of the system was determined from a numerical model of the intake manifold. The results showed the internal volume of the resonator and frequency tuning affect the intake air mass flow rate at a wide range of camshaft velocities. For a fixed volume, the average increase of the mass flow rate was 17.8%, and when the volume was adjusted according to valve frequency, the average increase was 24.7%. The highest intake air mass flow rate was increased 31.5% when the resonator was tuned to the frequency of the system.

Optimal Landmark Point Selection Using Clustering for Manifold Modeling and Data Classification

As data volume and dimensions continue to grow, effective and efficient methods are needed to obtain the low dimensional features of the data that describe its true structure. Most nonlinear dimensionality reduction methods (NLDR) utilize the Euclidean distance between the data points to form a general idea of the data manifold structure. Isomap uses the geodesic distance between data points and then uses classical multidimensional scaling(cMDS) to obtain low dimensional features. As the data size increases Isomap becomes complex. To overcome this disadvantage, Landmark Isomap (L-Isomap) uses selected data points called landmark points and finds the geodesic distance from these points to all other non-landmark points. Traditionally, landmark points are randomly selected without considering any statistical property of the data manifold. We contend that the quality of the features extracted is dependent on the selection of the landmark points. In applications such as data classification, the net accuracy is dependent on the quality of the features selected, and hence landmark points selection might play a crucial role. In this paper, we propose a clustering approach to obtain the landmark points. These new points are now used to represent the data, and Fisher’s linear discriminants are used for classification. The proposed method is tested with different datasets to verify the efficacy of the approach.

Accuracy guarantee for determining the calorific value for dual gas sources

The gas mixing process can severely affect determination of the caloric value, which is a crucial step in natural gas energy metering. According to the technological process in a dual gas source distribution station, the mixing process of two natural gases with different components in manifold and T-pipe models was simulated through a computational fluid dynamics model. The mix efficiency and the calorific value of the two models were researched and compared. The results reveal that the quality of the gas mixture in the T-pipe model is poorer than that in the manifold model. Therefore, only the mix efficiency in the manifold model was optimized by adding a mixer. Employing an SK-type static mixer upstream of the straight pipe can greatly reduce the required length of straight pipe and significantly improve the quality of the gas mixture. These results can provide design references for the calorific value measurement for dual gas sources.

2-D DOA Estimation for Incoherently Distributed Sources Considering Mixed Circular and Noncircular Signals in Massive MIMO System

In practical environment, the received signals cannot be regarded as point sources due to the existence of signal scattering phenomena. Incoherently distributed source model is suitable for this realistic scenarios. The incoherently distributed DOA estimators based on noncircular property can provide a better performance. However, these estimators are not fit for the mixed circular and noncircular signals. In this paper, a conjugate generalized shift invariance algorithm is proposed for incoherently distributed sources comprising of circular and noncircular signals. The general array manifold (GAM) model of incoherently distributed source is established by one-order Taylor series approximation. To utilize the noncircular property, each incoherently distributed circular source is separated as two strictly incoherently distributed noncircular sources. A generalized shift invariance property is derived by dividing the whole uniform rectangular array (URA) into four overlapping subarrays. Then, a modified conjugate generalized least square (CGLS) ESPRIT algorithm is proposed to obtain the central DOAs, which can improve the estimation accuracy without any spectrum searching. In addition, a pair matching method and an angular spreads estimation algorithm are also designed based on the GAM model for this mixed sources scenario. Furthermore, the Cramer-Rao bound (CRB) of proposed algorithm is given and analyzed under the mixed incoherently distributed sources scenario. Numerical results illustrate the validity of the proposed algorithm in this realistic scenario.

A Novel Framework to Automatically Generate IFML Models From Plain Text Requirements

User Interfaces (UI’s) are highly important in this era of web and mobile applications. Therefore, an efficient and accurate development of UI’s is desirable in early Software Development Life Cycle (SDLC) phases. To achieve this, Object Management Group (OMG) introduced Interaction Flow Modeling Language (IFML) standard in 2013. IFML provides the modeling of manifold UI’s for different applications like mobile, web and desktop. Although IFML is based on Model Driven Engineering (MDE) principle, the development of user interface models from initial requirements is still complex and time consuming task. Particularly, it requires domain expertise to understand several IFML concepts like domain model, view container etc. for the proper modeling of user interfaces. Consequently, there is a strong need of an approach to automate the development of IFML models from initial plain text requirements. This article presents a novel framework to automatically generate IFML models from textual requirements by utilizing the features of Natural Language Processing (NLP). Particularly, a set of NLP rules are developed to extract important IFML elements like View Components, Events etc. from textual requirements. Furthermore, a comprehensive algorithm is developed for the systematic execution of NLP rules in order to generate both IFML Domain and Core models. As a part of research, a sophisticated $T$ ext to IF ML ( T2IF ) tool is developed. The feasibility of proposed framework is demonstrated through movie manager and online bookstore case studies. The evaluation results prove that the proposed framework is capable of generating IFML models from textual requirements with high accuracy.

MRI Reconstruction From Sparse K-Space Data Using Low Dimensional Manifold Model

Recent compressed sensing (CS) approaches to utilize the similarity and redundancy of magnetic resonance (MR) image patches to enable reconstruction from sparse k-space measurements. In this paper, the patches' similarity and redundancy are exploited by applying the low-dimensional manifold model (LDMM). The basic assumption of the LDMM is that the image patches sample a manifold whose intrinsic dimensions are much lower than the high-dimensional ambient space. MR images also exhibit a low-dimensional patch-manifold structure. Based on this assumption, the dimension of the patch-manifold is used as a regularizer in a variational formulation. The MR image is then reconstructed by keeping the dimension of the patch manifold as small as possible. The proposed algorithm significantly increases the quality of the reconstructed images. The algorithm is evaluated on two datasets containing 100 MR images each. The reconstruction quality of the algorithm, gauged using three quality metrics: peak signal-to-noise ratio, structural similarity index measure, and normalized root-mean-square error, is better than the comparison methods.

A Slicing Optimization Method of Manifold Model for 3D Printing Based on Dual Inclinations Probability

A Slicing Optimization Method of Manifold Model for 3D Printing Based on Dual Inclinations Probability

Polarization Sensitivity of Antenna Arrays

The manifold of an array with identical antennas is generally assumed to be insensitive to the polarization of the impinging signal except for a complex scale factor which only affects the signal amplitude. Signal polarization is therefore considered in the literature in the context of diversely polarized arrays, which are specifically designed to be sensitive to polarization. In this paper, we show that the manifold of antenna arrays is generally sensitive to signal polarization even when the antennas have the same polarizations (e.g., identical antennas). We develop two measures which indicate the degree to which an array is sensitive to polarization. We show that direction-of-arrival estimation using an array manifold model which is independent of polarization may be biased due to model mismatch and suffer loss of resolution as the signal polarization varies. Numerical examples are presented to verify the analysis.

CT IMAGE RECONSTRUCTION ON A LOW DIMENSIONAL MANIFOLD.

The patch manifold of a natural image has a low dimensional structure and accommodates rich structural information. Inspired by the recent work of the low-dimensional manifold model (LDMM), we apply the LDMM for regularizing X-ray computed tomography (CT) image reconstruction. This proposed method recovers detailed structural information of images, significantly enhancing spatial and contrast resolution of CT images. Both numerically simulated data and clinically experimental data are used to evaluate the proposed method. The comparative studies are also performed over the simultaneous algebraic reconstruction technique (SART) incorporated the total variation (TV) regularization to demonstrate the merits of the proposed method. Results indicate that the LDMM-based method enables a more accurate image reconstruction with high fidelity and contrast resolution.