Modelling of chloride transport in non-saturated concrete : from microscale to macroscale

A model for chloride transport in concrete is proposed. The model accounts for transport several transport mechanisms such as diffusion, advection, migration, etc. This work shows the chloride transport equations at the macroscopic scale in non-saturated concrete. The equations involve diffusion, migration, capillary suction, chloride combination and precipitation mechanisms. The material is assumed to be infinitely rigid, though the porosity can change under influence of chloride binding and precipitation. The involved microscopic and macroscopic properties of the materials are measured by standardized methods. The variables which must be imposed on the boundaries are temperature, relative humidity and chloride concentration. The output data of the model are the free, bound, precipitated and total chloride ion concentrations, as well as the pore solution content and the porosity. The proposed equations are solved by means of the finite element method (FEM) implemented in MATLAB (classical Galerkin formulation and the streamline upwind Petrov-Galerkin (SUPG) method to avoid spatial instabilities for advection dominated flows).

Desarrollo de un sistema basado en el conocimiento para la definición de líneas de montaje aeronáuticas en la fase conceptual : Aplicación a una aeroestructura aeronáutica

La presente Tesis persigue la definición y el desarrollo de un sistema basado en el conocimiento que permita la generación de modelos de líneas de montaje durante la fase conceptual de definición de una aeroestructura aeronáutica. Para ello, se propone la definición de un modelo formal del proceso en concurrencia asociado al diseño de líneas de montaje en la fase conceptual, y de un modelo de la estructura de datos básica para soportar dicho proceso. Ambos modelos sirven de base para el desarrollo de una aplicación de prueba de concepto en el entorno del sistema comercial CAX-PLM CATIA v5. Los modelos de línea generados integran las tres estructuras básicas definidas en el modelo propuesto: producto, procesos y recursos. Los modelos generados son estructuras “de montaje”, basadas en estructuras de producto “de fabricación” a su vez derivadas de estructuras “de diseño”. Cada modelo generado se evalúa en términos de cuatro estimaciones básicas: dimensiones máximas del nodo producto, distancia de transporte y medio a utilizar, tiempo total de ejecución y coste total. La generación de modelos de línea de montaje se realiza en concurrencia con la función diseño de producto, teniendo por tanto la oportunidad de influir en la misma e incluir requerimientos de fabricación y montaje al producto en las primeras fases de su ciclo de vida, lo que proporciona una clara ventaja competitiva. El desarrollo propuesto en esta Tesis permite sentar las bases para realizar desarrollos con objeto de asistir a los diseñadores durante la fase conceptual de generación de diseños de líneas de montaje. La aplicación prototipo desarrollada demuestra la viabilidad de la propuesta conceptual que se realiza en la Tesis. ABSTRACT The current thesis proposes the definition and development of a knowledge-based system to generate aircraft components assembly line models during the conceptual phase of the product life cycle. With this objective, the definition of a formal activity model to represent the design of assembly lines during the conceptual phase is proposed; such model considers the concurrence with the product design process. Associated to the activity model, a data structure model is defined to support such process. Both models are the basis for the development of a proof of concept application within the environment of the commercial CAX-PLM system CATIA v5. The generated assembly line models integrate the three basic structures defined in the proposed model: product, processes and resources. The generated models are “As Prepared” structures based on “As Planned” structures derived from “As Designed” structures. Each generated model is evaluated in terms of four basic estimates: maximum dimensions of the product node, transport distance and transport mean to be used, total execution time and total cost. The assembly line models generation is made in concurrence with the product design function. Therefore, it provides the opportunity to influence on it and allows including manufacturing and assembly requirements early in the product life cycle, which gives a clear competitive advantage. The development proposed in this Thesis allows setting the foundation to carry out further developments with the aim of assisting designers during the conceptual phase of the assembly line design process. The developed prototype application shows the feasibility of the conceptual proposal presented in the Thesis.

Data Mining with Enhanced Neural Networks-CMMSE

Abstract This paper presents a new method to extract knowledge from existing data sets, that is, to extract symbolic rules using the weights of an Artificial Neural Network. The method has been applied to a neural network with special architecture named Enhanced Neural Network (ENN). This architecture improves the results that have been obtained with multilayer perceptron (MLP). The relationship among the knowledge stored in the weights, the performance of the network and the new implemented algorithm to acquire rules from the weights is explained. The method itself gives a model to follow in the knowledge acquisition with ENN.

Modelling uncertainty of flood quantile estimation at ungauged sites by Bayesian networks

Prediction at ungauged sites is essential for water resources planning and management. Ungauged sites have no observations about the magnitude of floods, but some site and basin characteristics are known. Regression models relate physiographic and climatic basin characteristics to flood quantiles, which can be estimated from observed data at gauged sites. However, these models assume linear relationships between variables Prediction intervals are estimated by the variance of the residuals in the estimated model. Furthermore, the effect of the uncertainties in the explanatory variables on the dependent variable cannot be assessed. This paper presents a methodology to propagate the uncertainties that arise in the process of predicting flood quantiles at ungauged basins by a regression model. In addition, Bayesian networks were explored as a feasible tool for predicting flood quantiles at ungauged sites. Bayesian networks benefit from taking into account uncertainties thanks to their probabilistic nature. They are able to capture non-linear relationships between variables and they give a probability distribution of discharges as result. The methodology was applied to a case study in the Tagus basin in Spain.

Modelling and Control of Stepper Motors for High Accuracy Positioning Systems Used in Radioactive Environments

Hybrid Stepper Motors are widely used in open-loop position applications. They are the choice of actuation for the collimators in the Large Hadron Collider, the largest particle accelerator at CERN. In this case the positioning requirements and the highly radioactive operating environment are unique. The latter forces both the use of long cables to connect the motors to the drives which act as transmission lines and also prevents the use of standard position sensors. However, reliable and precise operation of the collimators is critical for the machine, requiring the prevention of step loss in the motors and maintenance to be foreseen in case of mechanical degradation. In order to make the above possible, an approach is proposed for the application of an Extended Kalman Filter to a sensorless stepper motor drive, when the motor is separated from its drive by long cables. When the long cables and high frequency pulse width modulated control voltage signals are used together, the electrical signals difer greatly between the motor and drive-side of the cable. Since in the considered case only drive-side data is available, it is therefore necessary to estimate the motor-side signals. Modelling the entire cable and motor system in an Extended Kalman Filter is too computationally intensive for standard embedded real-time platforms. It is, in consequence, proposed to divide the problem into an Extended Kalman Filter, based only on the motor model, and separated motor-side signal estimators, the combination of which is less demanding computationally. The efectiveness of this approach is shown in simulation. Then its validity is experimentally demonstrated via implementation in a DSP based drive. A testbench to test its performance when driving an axis of a Large Hadron Collider collimator is presented along with the results achieved. It is shown that the proposed method is capable of achieving position and load torque estimates which allow step loss to be detected and mechanical degradation to be evaluated without the need for physical sensors. These estimation algorithms often require a precise model of the motor, but the standard electrical model used for hybrid stepper motors is limited when currents, which are high enough to produce saturation of the magnetic circuit, are present. New model extensions are proposed in order to have a more precise model of the motor independently of the current level, whilst maintaining a low computational cost. It is shown that a significant improvement in the model It is achieved with these extensions, and their computational performance is compared to study the cost of model improvement versus computation cost. The applicability of the proposed model extensions is demonstrated via their use in an Extended Kalman Filter running in real-time for closed-loop current control and mechanical state estimation. An additional problem arises from the use of stepper motors. The mechanics of the collimators can wear due to the abrupt motion and torque profiles that are applied by them when used in the standard way, i.e. stepping in open-loop. Closed-loop position control, more specifically Field Oriented Control, would allow smoother profiles, more respectful to the mechanics, to be applied but requires position feedback. As mentioned already, the use of sensors in radioactive environments is very limited for reliability reasons. Sensorless control is a known option but when the speed is very low or zero, as is the case most of the time for the motors used in the LHC collimator, the loss of observability prevents its use. In order to allow the use of position sensors without reducing the long term reliability of the whole system, the possibility to switch from closed to open loop is proposed and validated, allowing the use of closed-loop control when the position sensors function correctly and open-loop when there is a sensor failure. A different approach to deal with the switched drive working with long cables is also presented. Switched mode stepper motor drives tend to have poor performance or even fail completely when the motor is fed through a long cable due to the high oscillations in the drive-side current. The design of a stepper motor output fillter which solves this problem is thus proposed. A two stage filter, one devoted to dealing with the diferential mode and the other with the common mode, is designed and validated experimentally. With this ?lter the drive performance is greatly improved, achieving a positioning repeatability even better than with the drive working without a long cable, the radiated emissions are reduced and the overvoltages at the motor terminals are eliminated.

Integration of a data acquisition system based on FlexRIO technology with EPICS

EPICS (Experimental Physics and Industrial Control System) lies in a set of software tools and applications which provide a software infrastructure for building distributed data acquisition and control systems. Currently there is an increase in use of such systems in large Physics experiments like ITER, ESS, and FREIA. In these experiments, advanced data acquisition systems using FPGA-based technology like FlexRIO are more frequently been used. The particular case of ITER (International Thermonuclear Experimental Reactor), the instrumentation and control system is supported by CCS (CODAC Core System), based on RHEL (Red Hat Enterprise Linux) operating system, and by the plant design specifications in which every CCS element is defined either hardware, firmware or software. In this degree final project the methodology proposed in Implementation of Intelligent Data Acquisition Systems for Fusion Experiments using EPICS and FlexRIO Technology Sanz et al. [1] is used. The final objective is to provide a document describing the fulfilled process and the source code of the data acquisition system accomplished. The use of the proposed methodology leads to have two diferent stages. The first one consists of the hardware modelling with graphic design tools like LabVIEWFPGA which later will be implemented in the FlexRIO device. In the next stage the design cycle is completed creating an EPICS controller that manages the device using a generic device support layer named NDS (Nominal Device Support). This layer integrates the data acquisition system developed into CCS (Control, data access and communication Core System) as an EPICS interface to the system. The use of FlexRIO technology drives the use of LabVIEW and LabVIEW FPGA respectively. RESUMEN. EPICS (Experimental Physics and Industrial Control System) es un conjunto de herramientas software utilizadas para el desarrollo e implementación de sistemas de adquisición de datos y control distribuidos. Cada vez es más utilizado para entornos de experimentación física a gran escala como ITER, ESS y FREIA entre otros. En estos experimentos se están empezando a utilizar sistemas de adquisición de datos avanzados que usan tecnología basada en FPGA como FlexRIO. En el caso particular de ITER, el sistema de instrumentación y control adoptado se basa en el uso de la herramienta CCS (CODAC Core System) basado en el sistema operativo RHEL (Red Hat) y en las especificaciones del diseño del sistema de planta, en la cual define todos los elementos integrantes del CCS, tanto software como firmware y hardware. En este proyecto utiliza la metodología propuesta para la implementación de sistemas de adquisición de datos inteligente basada en EPICS y FlexRIO. Se desea generar una serie de ejemplos que cubran dicho ciclo de diseño completo y que serían propuestos como casos de uso de dichas tecnologías. Se proporcionará un documento en el que se describa el trabajo realizado así como el código fuente del sistema de adquisición. La metodología adoptada consta de dos etapas diferenciadas. En la primera de ellas se modela el hardware y se sintetiza en el dispositivo FlexRIO utilizando LabVIEW FPGA. Posteriormente se completa el ciclo de diseño creando un controlador EPICS que maneja cada dispositivo creado utilizando una capa software genérica de manejo de dispositivos que se denomina NDS (Nominal Device Support). Esta capa integra la solución en CCS realizando la interfaz con la capa EPICS del sistema. El uso de la tecnología FlexRIO conlleva el uso del lenguaje de programación y descripción hardware LabVIEW y LabVIEW FPGA respectivamente.

Global research alliance modelling platform (GRAMP): an open web platform for modelling greenhouse gas emissions from agro-ecosystems

Carbon (C) and nitrogen (N) process-based models are important tools for estimating and reporting greenhouse gas emissions and changes in soil C stocks. There is a need for continuous evaluation, development and adaptation of these models to improve scientific understanding, national inventories and assessment of mitigation options across the world. To date, much of the information needed to describe different processes like transpiration, photosynthesis, plant growth and maintenance, above and below ground carbon dynamics, decomposition and nitrogen mineralization. In ecosystem models remains inaccessible to the wider community, being stored within model computer source code, or held internally by modelling teams. Here we describe the Global Research Alliance Modelling Platform (GRAMP), a web-based modelling platform to link researchers with appropriate datasets, models and training material. It will provide access to model source code and an interactive platform for researchers to form a consensus on existing methods, and to synthesize new ideas, which will help to advance progress in this area. The platform will eventually support a variety of models, but to trial the platform and test the architecture and functionality, it was piloted with variants of the DNDC model. The intention is to form a worldwide collaborative network (a virtual laboratory) via an interactive website with access to models and best practice guidelines; appropriate datasets for testing, calibrating and evaluating models; on-line tutorials and links to modelling and data provider research groups, and their associated publications. A graphical user interface has been designed to view the model development tree and access all of the above functions.

Modelling of chloride penetration into non-saturated concrete: case study application for real marine offshore structures

The aim of this paper is to explain the chloride concentration profiles obtained experimentally from control samples of an offshore platform after 25 years of service life. The platform is located 12 km off the coast of the Brazilian province Rio Grande do Norte, in the north-east of Brazil. The samples were extracted at different orientations and heights above mean sea level. A simple model based on Fick’s second law is considered and compared with a finite element model which takes into account transport of chloride ions by diffusion and convection. Results show that convective flows significantly affect the studied chloride penetrations. The convection velocity is obtained by fitting the finite element solution to the experimental data and seems to be directly proportional to the height above mean sea level and also seems to depend on the orientation of the face of the platform. This work shows that considering solely diffusion as transport mechanism does not allow a good prediction of the chloride profiles. Accounting for capillary suction due to moisture gradients permits a better interpretation of the material’s behaviour

Structuring research methods and data with the research object model: genomics workflows as a case study

Background: One of the main challenges for biomedical research lies in the computer-assisted integrative study of large and increasingly complex combinations of data in order to understand molecular mechanisms. The preservation of the materials and methods of such computational experiments with clear annotations is essential for understanding an experiment, and this is increasingly recognized in the bioinformatics community. Our assumption is that offering means of digital, structured aggregation and annotation of the objects of an experiment will provide necessary meta-data for a scientist to understand and recreate the results of an experiment. To support this we explored a model for the semantic description of a workflow-centric Research Object (RO), where an RO is defined as a resource that aggregates other resources, e.g., datasets, software, spreadsheets, text, etc. We applied this model to a case study where we analysed human metabolite variation by workflows. Results: We present the application of the workflow-centric RO model for our bioinformatics case study. Three workflows were produced following recently defined Best Practices for workflow design. By modelling the experiment as an RO, we were able to automatically query the experiment and answer questions such as “which particular data was input to a particular workflow to test a particular hypothesis?”, and “which particular conclusions were drawn from a particular workflow?”. Conclusions: Applying a workflow-centric RO model to aggregate and annotate the resources used in a bioinformatics experiment, allowed us to retrieve the conclusions of the experiment in the context of the driving hypothesis, the executed workflows and their input data. The RO model is an extendable reference model that can be used by other systems as well.

Determination of the JWL constants for ANFO and emulsion explosives from cylinder test data

The Jones-Wilkins-Lee (JWL) equation of state parameters for ANFO and emulsion-type explosives have been obtained from cylinder test expansion measurements. The calculation method comprises a new radial expansion function, with a non-zero initial velocity at the onset of the expansion in order to comply with a positive Gurney energy at unit relative volume, as the isentropic expansion from the CJ state predicts. The equations reflecting the CJ state conditions and the measured expansion energy were solved for the JWL parameters by a non-linear least squares scheme. The JWL parameters of thirteen ANFO and emulsion type explosives have been determined in this way from their cylinder test expansion data. The results were evaluated through numerical modelling of the tests with the LS-DYNA hydrocode; the expansion histories from the modelling were compared with the measured ones, and excellent agreement was found.

A linked data approach to sentiment and emotion analysis of twitter in the financial domain

Sentiment analysis has recently gained popularity in the financial domain thanks to its capability to predict the stock market based on the wisdom of the crowds. Nevertheless, current sentiment indicators are still silos that cannot be combined to get better insight about the mood of different communities. In this article we propose a Linked Data approach for modelling sentiment and emotions about financial entities. We aim at integrating sentiment information from different communities or providers, and complements existing initiatives such as FIBO. The ap- proach has been validated in the semantic annotation of tweets of several stocks in the Spanish stock market, including its sentiment information.

Data consistency management in an open smart home management platform

In this paper, the authors introduce a novel mechanism for data management in a middleware for smart home control, where a relational database and semantic ontology storage are used at the same time in a Data Warehouse. An annotation system has been designed for instructing the storage format and location, registering new ontology concepts and most importantly, guaranteeing the Data Consistency between the two storage methods. For easing the data persistence process, the Data Access Object (DAO) pattern is applied and optimized to enhance the Data Consistency assurance. Finally, this novel mechanism provides an easy manner for the development of applications and their integration with BATMP. Finally, an application named "Parameter Monitoring Service" is given as an example for assessing the feasibility of the system.

Developing ontologies for representing data about key performance indicators

Multiple indicators are of interest in smart cities at different scales and for different stakeholders. In open environments, such as The Web, or when indicator information has to be interchanged across systems, contextual information (e.g., unit of measurement, measurement method) should be transmitted together with the data and the lack of such information might cause undesirable effects. Describing the data by means of ontologies increases interoperability among datasets and applications. However, methodological guidance is crucial during ontology development in order to transform the art of modeling in an engineering activity. In the current paper, we present a methodological approach for modelling data about Key Performance Indicators and their context with an application example of such guidelines.

Assessment of urban parameterizations in the WRF model for air quality modelling purposes in Madrid (Spain)

This study aims to assess the performance or multi-layer canopy parameterizations implemented in the mesoscale WRF model in order to understand their potential contribution to improve the description of energy fluxes and wind fields in the Madrid city. It was found that the Building Energy Model (BEP+BEM) parameterization yielded better results than the bulk standard scheme implemented in the Noah LSM, but very close to those of the Building Energy Parameterization (BEP). The later was deemed as the best option since data requirements and CPU time were smaller. Two annual runs were made to feed the CMAQ chemical-transport model to assess the impact of this feature in routinely air quality modelling activities.

A Semantic Approach to Problem-Based Learning with Conceptual Models

El aprendizaje basado en problemas se lleva aplicando con éxito durante las últimas tres décadas en un amplio rango de entornos de aprendizaje. Este enfoque educacional consiste en proponer problemas a los estudiantes de forma que puedan aprender sobre un dominio particular mediante el desarrollo de soluciones a dichos problemas. Si esto se aplica al modelado de conocimiento, y en particular al basado en Razonamiento Cualitativo, las soluciones a los problemas pasan a ser modelos que representan el compotamiento del sistema dinámico propuesto. Por lo tanto, la tarea del estudiante en este caso es acercar su modelo inicial (su primer intento de representar el sistema) a los modelos objetivo que proporcionan soluciones al problema, a la vez que adquieren conocimiento sobre el dominio durante el proceso. En esta tesis proponemos KaiSem, un método que usa tecnologías y recursos semánticos para guiar a los estudiantes durante el proceso de modelado, ayudándoles a adquirir tanto conocimiento como sea posible sin la directa supervisión de un profesor. Dado que tanto estudiantes como profesores crean sus modelos de forma independiente, estos tendrán diferentes terminologías y estructuras, dando lugar a un conjunto de modelos altamente heterogéneo. Para lidiar con tal heterogeneidad, proporcionamos una técnica de anclaje semántico para determinar, de forma automática, enlaces entre la terminología libre usada por los estudiantes y algunos vocabularios disponibles en la Web de Datos, facilitando con ello la interoperabilidad y posterior alineación de modelos. Por último, proporcionamos una técnica de feedback semántico para comparar los modelos ya alineados y generar feedback basado en las posibles discrepancias entre ellos. Este feedback es comunicado en forma de sugerencias individualizadas que el estudiante puede utilizar para acercar su modelo a los modelos objetivos en cuanto a su terminología y estructura se refiere. ABSTRACT Problem-based learning has been successfully applied over the last three decades to a diverse range of learning environments. This educational approach consists of posing problems to learners, so they can learn about a particular domain by developing solutions to them. When applied to conceptual modeling, and particularly to Qualitative Reasoning, the solutions to problems are models that represent the behavior of a dynamic system. Therefore, the learner's task is to move from their initial model, as their first attempt to represent the system, to the target models that provide solutions to that problem while acquiring domain knowledge in the process. In this thesis we propose KaiSem, a method for using semantic technologies and resources to scaffold the modeling process, helping the learners to acquire as much domain knowledge as possible without direct supervision from the teacher. Since learners and experts create their models independently, these will have different terminologies and structure, giving rise to a pool of models highly heterogeneous. To deal with such heterogeneity, we provide a semantic grounding technique to automatically determine links between the unrestricted terminology used by learners and some online vocabularies of the Web of Data, thus facilitating the interoperability and later alignment of the models. Lastly, we provide a semantic-based feedback technique to compare the aligned models and generate feedback based on the possible discrepancies. This feedback is communicated in the form of individualized suggestions, which can be used by the learner to bring their model closer in terminology and structure to the target models.