Thick-walled composite tubes for offshore applications : an example of stress and failure analysis for filament-wound multi-layered pipes

Peer reviewed

Description of an 8 MW reference wind turbine

An 8 MW wind turbine is described in terms of mass distribution, dimensions, power curve, thrust curve, maximum design load and tower configuration. This turbine has been described as part of the EU FP7 project LEANWIND in order to facilitate research into logistics and naval architecture efficiencies for future offshore wind installations. The design of this 8 MW reference wind turbine has been checked and validated by the design consultancy DNV-GL. This turbine description is intended to bridge the gap between the NREL 5 MW and DTU 10 MW reference turbines and thus contribute to the standardisation of research and development activities in the offshore wind energy industry.

Predicting the performance of passenger ships using computer simulation

When designing a new passenger ship or naval vessel or modifying an existing design, how do we ensure that the proposed design is safe from an evacuation point of view? In the wake of major maritime disasters such as the Herald of Free Enterprise and the Estonia and in light of the growth in the numbers of high density, high-speed ferries and large capacity cruise ships, issues concerned with the evacuation of passengers and crew at sea are receiving renewed interest. In the maritime industry, ship evacuation models are now recognised by IMO through the publication of the Interim Guidelines for Evacuation Analysis of New and Existing Passenger Ships including Ro-Ro. This approach offers the promise to quickly and efficiently bring evacuation considerations into the design phase, while the ship is "on the drawing board" as well as reviewing and optimising the evacuation provision of the existing fleet. Other applications of this technology include the optimisation of operating procedures for civil and naval vessels such as determining the optimal location of a feature such as a casino, organising major passenger movement events such as boarding/disembarkation or restaurant/theatre changes, determining lean manning requirements, location and number of damage control parties, etc. This paper describes the development of the maritimeEXODUS evacuation model which is fully compliant with IMO requirements and briefly presents an example application to a large passenger ferry.

Projeto e Construção de um Veleiro Autónomo, utilizando materiais compósitos, Impressão a 3D e Aprendizagem Máquina

Este trabalho consistiu no projeto e construção de um veleiro autónomo de pequena escala. No início do trabalho, é feito um estudo acerca dos diferentes tipos de veículos autónomos, dando mais enfase aos veleiros. Em seguida, é iniciado o projeto do casco do veleiro, aplicando conceitos básicos de Arquitetura Naval. A forma do casco é desenhada com recurso ao programa DELFT Ship Free, onde são realizados estudos hidrodinâmicos do mesmo. Posteriormente é retratado a construção do casco projetado, com recurso a materiais compósitos e impressão 3D de componentes do veleiro. São ainda descritos os sensores, controladores, atuadores e programação desenvolvida para o veleiro. É também realizado um estudo sumário da estimativa de consumos e autonomia do sistema. No final, encontram-se os resultados obtidos das provas de mar efetuadas ao veleiro.

L’épave du Machault, 1760 : entre tradition et innovation.

Cette étude s’intéresse à l’industrie de la construction navale pour le milieu du XVIIIe siècle en France dans la région de Bayonne. L’objectif est de documenter la relation qu’entretiennent les pratiques de construction traditionnelles et innovatrices à cette période. L’architecture de la frégate le Machault est au cœur de cette analyse. Construit en 1757 à Bayonne et perdu en 1760, le Machault a été fouillé, documenté et parallèlement récupéré par les archéologues de Parcs Canada entre 1969 et 1972 à Ristigouche dans la baie des Chaleurs, Québec. Cette étude constitue la première analyse architecturale approfondie menée sur ces vestiges. L’analyse est réalisée en trois temps qui correspondent aux trois grandes étapes de la chaine opératoire de la construction du navire. Tout d’abord, il est question d’aborder l’aspect de la foresterie afin de saisir la nature de la ressource forestière mobilisée pour la construction de la frégate. Ensuite, ce mémoire se penche sur la conception architecturale des navires qui renvoie à un aspect plus théorique, car il relève de la façon dont les formes du navire ont été « pensées ». Enfin, la charpenterie est abordée afin de saisir la séquence d’assemblage du navire. Ensemble, ces trois grands aspects dressent un portrait général de la construction navale pour la région de Bayonne au milieu du XVIIIe siècle.

L’épave du Machault, 1760 : entre tradition et innovation

Cette étude s’intéresse à l’industrie de la construction navale pour le milieu du XVIIIe siècle en France dans la région de Bayonne. L’objectif est de documenter la relation qu’entretiennent les pratiques de construction traditionnelles et innovatrices à cette période. L’architecture de la frégate le Machault est au cœur de cette analyse. Construit en 1757 à Bayonne et perdu en 1760, le Machault a été fouillé, documenté et parallèlement récupéré par les archéologues de Parcs Canada entre 1969 et 1972 à Ristigouche dans la baie des Chaleurs, Québec. Cette étude constitue la première analyse architecturale approfondie menée sur ces vestiges. L’analyse est réalisée en trois temps qui correspondent aux trois grandes étapes de la chaine opératoire de la construction du navire. Tout d’abord, il est question d’aborder l’aspect de la foresterie afin de saisir la nature de la ressource forestière mobilisée pour la construction de la frégate. Ensuite, ce mémoire se penche sur la conception architecturale des navires qui renvoie à un aspect plus théorique, car il relève de la façon dont les formes du navire ont été « pensées ». Enfin, la charpenterie est abordée afin de saisir la séquence d’assemblage du navire. Ensemble, ces trois grands aspects dressent un portrait général de la construction navale pour la région de Bayonne au milieu du XVIIIe siècle.

Rudimentary dictionary of terms used in architecture, civil, architecture, naval, building and construction, early and ecclesiastical art, engineering, civil, engineering, mechanical, fine art, mining, surveying, etc., to which are added explanatory observations on numerous subjects connected with practical art and science.

Issued in four parts.

The Specialized Mappings Architecture

A probabilistic, nonlinear supervised learning model is proposed: the Specialized Mappings Architecture (SMA). The SMA employs a set of several forward mapping functions that are estimated automatically from training data. Each specialized function maps certain domains of the input space (e.g., image features) onto the output space (e.g., articulated body parameters). The SMA can model ambiguous, one-to-many mappings that may yield multiple valid output hypotheses. Once learned, the mapping functions generate a set of output hypotheses for a given input via a statistical inference procedure. The SMA inference procedure incorporates an inverse mapping or feedback function in evaluating the likelihood of each of the hypothesis. Possible feedback functions include computer graphics rendering routines that can generate images for given hypotheses. The SMA employs a variant of the Expectation-Maximization algorithm for simultaneous learning of the specialized domains along with the mapping functions, and approximate strategies for inference. The framework is demonstrated in a computer vision system that can estimate the articulated pose parameters of a human’s body or hands, given silhouettes from a single image. The accuracy and stability of the SMA are also tested using synthetic images of human bodies and hands, where ground truth is known.

An Active Pattern Recognition Architecture for Mobile Robots

An active, attentionally-modulated recognition architecture is proposed for object recognition and scene analysis. The proposed architecture forms part of navigation and trajectory planning modules for mobile robots. Key characteristics of the system include movement planning and execution based on environmental factors and internal goal definitions. Real-time implementation of the system is based on space-variant representation of the visual field, as well as an optimal visual processing scheme utilizing separate and parallel channels for the extraction of boundaries and stimulus qualities. A spatial and temporal grouping module (VWM) allows for scene scanning, multi-object segmentation, and featural/object priming. VWM is used to modulate a tn~ectory formation module capable of redirecting the focus of spatial attention. Finally, an object recognition module based on adaptive resonance theory is interfaced through VWM to the visual processing module. The system is capable of using information from different modalities to disambiguate sensory input.

Fusion Artmap: A Neural Network Architecture for Multi-Channel Data Fusion and Classification

Fusion ARTMAP is a self-organizing neural network architecture for multi-channel, or multi-sensor, data fusion. Single-channel Fusion ARTMAP is functionally equivalent to Fuzzy ART during unsupervised learning and to Fuzzy ARTMAP during supervised learning. The network has a symmetric organization such that each channel can be dynamically configured to serve as either a data input or a teaching input to the system. An ART module forms a compressed recognition code within each channel. These codes, in turn, become inputs to a single ART system that organizes the global recognition code. When a predictive error occurs, a process called paraellel match tracking simultaneously raises vigilances in multiple ART modules until reset is triggered in one of them. Parallel match tracking hereby resets only that portion of the recognition code with the poorest match, or minimum predictive confidence. This internally controlled selective reset process is a type of credit assignment that creates a parsimoniously connected learned network. Fusion ARTMAP's multi-channel coding is illustrated by simulations of the Quadruped Mammal database.

ART-EMAP: A Neural Network Architecture for Learning and Prediction by Evidence Accumulation

This paper introduces ART-EMAP, a neural architecture that uses spatial and temporal evidence accumulation to extend the capabilities of fuzzy ARTMAP. ART-EMAP combines supervised and unsupervised learning and a medium-term memory process to accomplish stable pattern category recognition in a noisy input environment. The ART-EMAP system features (i) distributed pattern registration at a view category field; (ii) a decision criterion for mapping between view and object categories which can delay categorization of ambiguous objects and trigger an evidence accumulation process when faced with a low confidence prediction; (iii) a process that accumulates evidence at a medium-term memory (MTM) field; and (iv) an unsupervised learning algorithm to fine-tune performance after a limited initial period of supervised network training. ART-EMAP dynamics are illustrated with a benchmark simulation example. Applications include 3-D object recognition from a series of ambiguous 2-D views.

ART-EMAP: A Neural Network Architecture for Object Recognition by Evidence Accumulation

A new neural network architecture is introduced for the recognition of pattern classes after supervised and unsupervised learning. Applications include spatio-temporal image understanding and prediction and 3-D object recognition from a series of ambiguous 2-D views. The architecture, called ART-EMAP, achieves a synthesis of adaptive resonance theory (ART) and spatial and temporal evidence integration for dynamic predictive mapping (EMAP). ART-EMAP extends the capabilities of fuzzy ARTMAP in four incremental stages. Stage 1 introduces distributed pattern representation at a view category field. Stage 2 adds a decision criterion to the mapping between view and object categories, delaying identification of ambiguous objects when faced with a low confidence prediction. Stage 3 augments the system with a field where evidence accumulates in medium-term memory (MTM). Stage 4 adds an unsupervised learning process to fine-tune performance after the limited initial period of supervised network training. Each ART-EMAP stage is illustrated with a benchmark simulation example, using both noisy and noise-free data. A concluding set of simulations demonstrate ART-EMAP performance on a difficult 3-D object recognition problem.

A Neural Network Architecture for Autonomous Learning, Recognition, and Prediction in a Nonstationary World

In a constantly changing world, humans are adapted to alternate routinely between attending to familiar objects and testing hypotheses about novel ones. We can rapidly learn to recognize and narne novel objects without unselectively disrupting our memories of familiar ones. We can notice fine details that differentiate nearly identical objects and generalize across broad classes of dissimilar objects. This chapter describes a class of self-organizing neural network architectures--called ARTMAP-- that are capable of fast, yet stable, on-line recognition learning, hypothesis testing, and naming in response to an arbitrary stream of input patterns (Carpenter, Grossberg, Markuzon, Reynolds, and Rosen, 1992; Carpenter, Grossberg, and Reynolds, 1991). The intrinsic stability of ARTMAP allows the system to learn incrementally for an unlimited period of time. System stability properties can be traced to the structure of its learned memories, which encode clusters of attended features into its recognition categories, rather than slow averages of category inputs. The level of detail in the learned attentional focus is determined moment-by-moment, depending on predictive success: an error due to over-generalization automatically focuses attention on additional input details enough of which are learned in a new recognition category so that the predictive error will not be repeated. An ARTMAP system creates an evolving map between a variable number of learned categories that compress one feature space (e.g., visual features) to learned categories of another feature space (e.g., auditory features). Input vectors can be either binary or analog. Computational properties of the networks enable them to perform significantly better in benchmark studies than alternative machine learning, genetic algorithm, or neural network models. Some of the critical problems that challenge and constrain any such autonomous learning system will next be illustrated. Design principles that work together to solve these problems are then outlined. These principles are realized in the ARTMAP architecture, which is specified as an algorithm. Finally, ARTMAP dynamics are illustrated by means of a series of benchmark simulations.

Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps

A new neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors. The architecture, called Fuzzy ARTMAP, achieves a synthesis of fuzzy logic and Adaptive Resonance Theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Fuzzy ARTMAP also realizes a new Minimax Learning Rule that conjointly minimizes predictive error and maximizes code compression, or generalization. This is achieved by a match tracking process that increases the ART vigilance parameter by the minimum amount needed to correct a predictive error. As a result, the system automatically learns a minimal number of recognition categories, or "hidden units", to met accuracy criteria. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy logic play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Improved prediction is achieved by training the system several times using different orderings of the input set. This voting strategy can also be used to assign probability estimates to competing predictions given small, noisy, or incomplete training sets. Four classes of simulations illustrate Fuzzy ARTMAP performance as compared to benchmark back propagation and genetic algorithm systems. These simulations include (i) finding points inside vs. outside a circle; (ii) learning to tell two spirals apart; (iii) incremental approximation of a piecewise continuous function; and (iv) a letter recognition database. The Fuzzy ARTMAP system is also compared to Salzberg's NGE system and to Simpson's FMMC system.

A blackboard architecture for a hybrid CBR system for scientific software

This paper describes the use of a blackboard architecture for building a hybrid case based reasoning (CBR) system. The Smartfire fire field modelling package has been built using this architecture and includes a CBR component. It allows the integration into the system of qualitative spatial reasoning knowledge from domain experts. The system can be used for the automatic set-up of fire field models. This enables fire safety practitioners who are not expert in modelling techniques to use a fire modelling tool. The paper discusses the integrating powers of the architecture, which is based on a common knowledge representation comprising a metric diagram and place vocabulary and mechanisms for adaptation and conflict resolution built on the Blackboard.