A Nonlinear Approach for Re-entry Guidance of Reusable Launch Vehicles Using Model Predictive Static Programming

A nonlinear suboptimal guidance scheme is developed for the reentry phase of the reusable launch vehicles. A recently developed methodology, named as model predictive static programming (MPSP), is implemented which combines the philosophies of nonlinear model predictive control theory and approximate dynamic programming. This technique provides a finite time nonlinear suboptimal guidance law which leads to a rapid solution of the guidance history update. It does not have to suffer from computational difficulties and can be implemented online. The system dynamics is propagated through the flight corridor to the end of the reentry phase considering energy as independent variable and angle of attack as the active control variable. All the terminal constraints are satisfied. Among the path constraints, the normal load is found to be very constrictive. Hence, an extra effort has been made to keep the normal load within a specified limit and monitoring its sensitivity to the perturbation.

Bond graph model of doubly fed three phase induction motor using the Axis Rotator element for frame transformation

Induction motor is a typical member of a multi-domain, non-linear, high order dynamic system. For speed control a three phase induction motor is modelled as a d–q model where linearity is assumed and non-idealities are ignored. Approximation of the physical characteristic gives a simulated behaviour away from the natural behaviour. This paper proposes a bond graph model of an induction motor that can incorporate the non-linearities and non-idealities thereby resembling the physical system more closely. The model is validated by applying the linearity and idealities constraints which shows that the conventional ‘abc’ model is a special case of the proposed generalised model.

Performance evaluation of elitist-mutated multi-objective particle swarm optimization for integrated water resources management

Optimal allocation of water resources for various stakeholders often involves considerable complexity with several conflicting goals, which often leads to multi-objective optimization. In aid of effective decision-making to the water managers, apart from developing effective multi-objective mathematical models, there is a greater necessity of providing efficient Pareto optimal solutions to the real world problems. This study proposes a swarm-intelligence-based multi-objective technique, namely the elitist-mutated multi-objective particle swarm optimization technique (EM-MOPSO), for arriving at efficient Pareto optimal solutions to the multi-objective water resource management problems. The EM-MOPSO technique is applied to a case study of the multi-objective reservoir operation problem. The model performance is evaluated by comparing with results of a non-dominated sorting genetic algorithm (NSGA-II) model, and it is found that the EM-MOPSO method results in better performance. The developed method can be used as an effective aid for multi-objective decision-making in integrated water resource management.

DMT of Multi-hop Cooperative Networks-Part II: Layered and Multi-Antenna Networks

We consider single-source, single-sink (ss-ss) multi-hop relay networks, with slow-fading Rayleigh links. This two part paper aims at giving explicit protocols and codes to achieve the optimal diversity-multiplexing tradeoff (DMT) of two classes of multi-hop networks: K-parallel-path (KPP) networks and Layered networks. While single-antenna KPP networks were the focus of the first part, we consider layered and multi-antenna networks in this second part. We prove that a linear DMT between the maximum diversity d(max). and the maximum multiplexing gain of 1 is achievable for single-antenna fully-connected layered networks under the half-duplex constraint. This is shown to be equal to the optimal DMT if the number of relaying layers is less than 4. For the multiple-antenna case, we provide an achievable DMT, which is significantly better than known lower bounds for half duplex networks. Along the way, we compute the DMT of parallel MIMO channels in terms of the DMT of the component channel. For arbitrary ss-ss single-antenna directed acyclic networks with full-duplex relays, we prove that a linear tradeoff between maximum diversity and maximum multiplexing gain is achievable using an amplify-and-forward (AF) protocol. Explicit short-block-length codes are provided for all the proposed protocols. Two key implications of the results in the two-part paper are that the half-duplex constraint does not necessarily entail rate loss by a factor of two as previously believed and that simple AN protocols are often sufficient to attain the best possible DMT.

Efficient computation of volume fractions for multi-material cell complexes in a grid by slicing

The paper presents a novel slicing based method for computation of volume fractions in multi-material solids given as a B-rep whose faces are triangulated and shared by either one or two materials. Such objects occur naturally in geoscience applications and the said computation is necessary for property estimation problems and iterative forward modeling. Each facet in the model is cut by the planes delineating the given grid structure or grid cells. The method, instead of classifying the points or cells with respect to the solid, exploits the convexity of triangles and the simple axis-oriented disposition of the cutting surfaces to construct a novel intermediate space enumeration representation called slice-representation, from which both the cell containment test and the volume-fraction computation are done easily. Cartesian and cylindrical grids with uniform and non-uniform spacings have been dealt with in this paper. After slicing, each triangle contributes polygonal facets, with potential elliptical edges, to the grid cells through which it passes. The volume fractions of different materials in a grid cell that is in interaction with the material interfaces are obtained by accumulating the volume contributions computed from each facet in the grid cell. The method is fast, accurate, robust and memory efficient. Examples illustrating the method and performance are included in the paper.

Studies of solar and stellar flares using multi-instrument data

Solar flares were first observed by plain eye in white light by William Carrington in England in 1859. Since then these eruptions in the solar corona have intrigued scientists. It is known that flares influence the space weather experienced by the planets in a multitude of ways, for example by causing aurora borealis. Understanding flares is at the epicentre of human survival in space, as astronauts cannot survive the highly energetic particles associated with large flares in high doses without contracting serious radiation disease symptoms, unless they shield themselves effectively during space missions. Flares may be at the epicentre of man s survival in the past as well: it has been suggested that giant flares might have played a role in exterminating many of the large species on Earth, including dinosaurs. Having said that prebiotic synthesis studies have shown lightning to be a decisive requirement for amino acid synthesis on the primordial Earth. Increased lightning activity could be attributed to space weather, and flares. This thesis studies flares in two ways: in the spectral and the spatial domain. We have extracted solar spectra using three different instruments, namely GOES (Geostationary Operational Environmental Satellite), RHESSI (Reuven Ramaty High Energy Solar Spectroscopic Imager) and XSM (X-ray Solar Monitor) for the same flares. The GOES spectra are low resolution obtained with a gas proportional counter, the RHESSI spectra are higher resolution obtained with Germanium detectors and the XSM spectra are very high resolution observed with a silicon detector. It turns out that the detector technology and response influence the spectra we see substantially, and are important to understanding what conclusions to draw from the data. With imaging data, there was not such a luxury of choice available. We used RHESSI imaging data to observe the spatial size of solar flares. In the present work the focus was primarily on current solar flares. However, we did make use of our improved understanding of solar flares to observe young suns in NGC 2547. The same techniques used with solar monitors were applied with XMM-Newton, a stellar X-ray monitor, and coupled with ground based Halpha observations these techniques yielded estimates for flare parameters in young suns. The material in this thesis is therefore structured from technology to application, covering the full processing path from raw data and detector responses to concrete physical parameter results, such as the first measurement of the length of plasma flare loops in young suns.

Selective dissemination of XML documents based on genetically learned user model and Support Vector Machines

Extensible Markup Language ( XML) has emerged as a medium for interoperability over the Internet. As the number of documents published in the form of XML is increasing, there is a need for selective dissemination of XML documents based on user interests. In the proposed technique, a combination of Adaptive Genetic Algorithms and multi class Support Vector Machine ( SVM) is used to learn a user model. Based on the feedback from the users, the system automatically adapts to the user's preference and interests. The user model and a similarity metric are used for selective dissemination of a continuous stream of XML documents. Experimental evaluations performed over a wide range of XML documents, indicate that the proposed approach significantly improves the performance of the selective dissemination task, with respect to accuracy and efficiency.

Vector evaluated particle swarm optimization (VEPSO) for multi-objective design optimization of composite structures

We present a generic method/model for multi-objective design optimization of laminated composite components, based on vector evaluated particle swarm optimization (VEPSO) algorithm. VEPSO is a novel, co-evolutionary multi-objective variant of the popular particle swarm optimization algorithm (PSO). In the current work a modified version of VEPSO algorithm for discrete variables has been developed and implemented successfully for the, multi-objective design optimization of composites. The problem is formulated with multiple objectives of minimizing weight and the total cost of the composite component to achieve a specified strength. The primary optimization variables are - the number of layers, its stacking sequence (the orientation of the layers) and thickness of each layer. The classical lamination theory is utilized to determine the stresses in the component and the design is evaluated based on three failure criteria; failure mechanism based failure criteria, Maximum stress failure criteria and the Tsai-Wu failure criteria. The optimization method is validated for a number of different loading configurations - uniaxial, biaxial and bending loads. The design optimization has been carried for both variable stacking sequences, as well fixed standard stacking schemes and a comparative study of the different design configurations evolved has been presented. (C) 2007 Elsevier Ltd. All rights reserved.

Visibility volumes for interactive path optimization

We describe a real-time system that supports design of optimal flight paths over terrains. These paths either maximize view coverage or minimize vehicle exposure to ground. A volume-rendered display of multi-viewpoint visibility and a haptic interface assists the user in selecting, assessing, and refining the computed flight path. We design a three-dimensional scalar field representing the visibility of a point above the terrain, describe an efficient algorithm to compute the visibility field, and develop visual and haptic schemes to interact with the visibility field. Given the origin and destination, the desired flight path is computed using an efficient simulation of an articulated rope under the influence of the visibility gradient. The simulation framework also accepts user input, via the haptic interface, thereby allowing manual refinement of the flight path.

Selective dissemination of XML documents based on genetically learned user model and Support Vector Machines

Extensible Markup Language ( XML) has emerged as a medium for interoperability over the Internet. As the number of documents published in the form of XML is increasing, there is a need for selective dissemination of XML documents based on user interests. In the proposed technique, a combination of Adaptive Genetic Algorithms and multi class Support Vector Machine ( SVM) is used to learn a user model. Based on the feedback from the users, the system automatically adapts to the user's preference and interests. The user model and a similarity metric are used for selective dissemination of a continuous stream of XML documents. Experimental evaluations performed over a wide range of XML documents, indicate that the proposed approach significantly improves the performance of the selective dissemination task, with respect to accuracy and efficiency.

Hybrid moving block bootstrap for stochastic simulation of multi-site multi-season streamflows

The Hybrid approach introduced by the authors for at-site modeling of annual and periodic streamflows in earlier works is extended to simulate multi-site multi-season streamflows. It bears significance in integrated river basin planning studies. This hybrid model involves: (i) partial pre-whitening of standardized multi-season streamflows at each site using a parsimonious linear periodic model; (ii) contemporaneous resampling of the resulting residuals with an appropriate block size, using moving block bootstrap (non-parametric, NP) technique; and (iii) post-blackening the bootstrapped innovation series at each site, by adding the corresponding parametric model component for the site, to obtain generated streamflows at each of the sites. It gains significantly by effectively utilizing the merits of both parametric and NP models. It is able to reproduce various statistics, including the dependence relationships at both spatial and temporal levels without using any normalizing transformations and/or adjustment procedures. The potential of the hybrid model in reproducing a wide variety of statistics including the run characteristics, is demonstrated through an application for multi-site streamflow generation in the Upper Cauvery river basin, Southern India. (C) 2004 Elsevier B.V. All rights reserved.

Designing a blended learning model for primary school language learning

In Finland, there is a desperate need for flexible, reliable and functional multi-e-learning settings for pupils aged 11-13. Southern Finland has several ongoing e-learning projects, but none that develop a multiple setting, with learning and teaching occurring between more than two schools. In 2006, internet connections were not broadband and data transfer was mainly audio data. Connections and technical problems occurred, which were an obstacle to multi-e-learning. Internet connections today enable web-based learning in major parts of
Lapland and by 2015, broadband will reach even the remotest villages up north. Therefore, it is important to research the possibilities of multi-e-learning and to build collaborative, learner-centred, versatile network models for primary school-aged pupils. The resulting model will facilitate distance learning to extend education to rural, sparsely populated areas, and it will give a model of using mobile devices in language portfolios. This will promote regional equality and prevent exclusion. Working with portfolios provides the opportunity to develop mobility from a pedagogical point of view. It is important to study the pros and cons of mobile devices in producing artefacts on portfolios in e-learning and language learning settings.
The current study represents a design-based research approach. The design research approach includes two important aspects concerning the current research: ‘a teacher as researcher’ aspect, which means there is the possibility to be strongly involved in developing processes and an obstacle-aspect, which means that problems while developing, are seen as a
promoter in evolving the designed model, as apposed to negative results.

Designing a blended learning model for primary school language learning

In Finland, there is a desperate need for flexible, reliable and functional multi-e-learning settings for pupils aged 11-13. Southern Finland has several ongoing e-learning projects, but none that develop a multiple setting, with learning and teaching occurring between more than two schools. In 2006, internet connections were not broadband and data transfer was mainly audio data. Connections and technical problems occurred, which were an obstacle to multi-e-learning. Internet connections today enable web-based learning in major parts of Lapland and by 2015, broadband will reach even the remotest villages up north. Therefore, it is important to research the possibilities of multi-e-learning and to build collaborative, learner-centred, versatile network models for primary school-aged pupils. The resulting model will facilitate distance learning to extend education to rural, sparsely populated areas, and it will give a model of using mobile devices in language portfolios. This will promote regional equality and prevent exclusion. Working with portfolios provides the opportunity to develop mobility from a pedagogical point of view. It is important to study the pros and cons of mobile devices in producing artefacts on portfolios in e-learning and language learning settings. The current study represents a design-based research approach. The design research approach includes two important aspects concerning the current research: ‘a teacher as researcher’ aspect, which means there is the possibility to be strongly involved in developing processes and an obstacle-aspect, which means that problems while developing, are seen as a promoter in evolving the designed model, as apposed to negative results.

Prediction of solid block masonry prism compressive strength using FE model

Masonry strength is dependent upon characteristics of the masonry unit,the mortar and the bond between them. Empirical formulae as well as analytical and finite element (FE) models have been developed to predict structural behaviour of masonry. This paper is focused on developing a three dimensional non-linear FE model based on micro-modelling approach to predict masonry prism compressive strength and crack pattern. The proposed FE model uses multi-linear stress-strain relationships to model the non-linear behaviour of solid masonry unit and the mortar. Willam-Warnke's five parameter failure theory developed for modelling the tri-axial behaviour of concrete has been adopted to model the failure of masonry materials. The post failure regime has been modelled by applying orthotropic constitutive equations based on the smeared crack approach. Compressive strength of the masonry prism predicted by the proposed FE model has been compared with experimental values as well as the values predicted by other failure theories and Eurocode formula. The crack pattern predicted by the FE model shows vertical splitting cracks in the prism. The FE model predicts the ultimate failure compressive stress close to 85 of the mean experimental compressive strength value.

Multi-criteria seismic hazard evaluation for Bangalore city, India

Different seismic hazard components pertaining to Bangalore city,namely soil overburden thickness, effective shear-wave velocity, factor of safety against liquefaction potential, peak ground acceleration at the seismic bedrock, site response in terms of amplification factor, and the predominant frequency, has been individually evaluated. The overburden thickness distribution, predominantly in the range of 5-10 m in the city, has been estimated through a sub-surface model from geotechnical bore-log data. The effective shear-wave velocity distribution, established through Multi-channel Analysis of Surface Wave (MASW) survey and subsequent data interpretation through dispersion analysis, exhibits site class D (180-360 m/s), site class C (360-760 m/s), and site class B (760-1500 m/s) in compliance to the National Earthquake Hazard Reduction Program (NEHRP) nomenclature. The peak ground acceleration has been estimated through deterministic approach, based on the maximum credible earthquake of M-W = 5.1 assumed to be nucleating from the closest active seismic source (Mandya-Channapatna-Bangalore Lineament). The 1-D site response factor, computed at each borehole through geotechnical analysis across the study region, is seen to be ranging from around amplification of one to as high as four times. Correspondingly, the predominant frequency estimated from the Fourier spectrum is found to be predominantly in range of 3.5-5.0 Hz. The soil liquefaction hazard assessment has been estimated in terms of factor of safety against liquefaction potential using standard penetration test data and the underlying soil properties that indicates 90% of the study region to be non-liquefiable. The spatial distributions of the different hazard entities are placed on a GIS platform and subsequently, integrated through analytical hierarchal process. The accomplished deterministic hazard map shows high hazard coverage in the western areas. The microzonation, thus, achieved is envisaged as a first-cut assessment of the site specific hazard in laying out a framework for higher order seismic microzonation as well as a useful decision support tool in overall land-use planning, and hazard management. (C) 2010 Elsevier Ltd. All rights reserved.