Molecular dynamics simulation model for the quantitative assessment of tool wear during single point diamond turning of cubic silicon carbide

Silicon carbide (SiC) is a material of great technological interest for engineering applications concerning hostile environments where silicon-based components cannot work (beyond 623 K). Single point diamond turning (SPDT) has remained a superior and viable method to harness process efficiency and freeform shapes on this harder material. However, it is extremely difficult to machine this ceramic consistently in the ductile regime due to sudden and rapid tool wear. It thus becomes non trivial to develop an accurate understanding of tool wear mechanism during SPDT of SiC in order to identify measures to suppress wear to minimize operational cost.

In this paper, molecular dynamics (MD) simulation has been deployed with a realistic analytical bond order potential (ABOP) formalism based potential energy function to understand tool wear mechanism during single point diamond turning of SiC. The most significant result was obtained using the radial distribution function which suggests graphitization of diamond tool during the machining process. This phenomenon occurs due to the abrasive processes between these two ultra hard materials. The abrasive action results in locally high temperature which compounds with the massive cutting forces leading to sp3–sp2 order–disorder transition of diamond tool. This represents the root cause of tool wear during SPDT operation of cubic SiC. Further testing led to the development of a novel method for quantitative assessment of the progression of diamond tool wear from MD simulations.

Invariant Information Local Sub-map Filter (IILSF) for Efficient Simultaneous Localisation and Mapping of Large Environments

This paper presents an Invariant Information Local Sub-map Filter (IILSF) as a technique for consistent Simultaneous Localisation and Mapping (SLAM) in a large environment. It harnesses the benefits of sub-map technique to improve the consistency and efficiency of Extended Kalman Filter (EKF) based SLAM. The IILSF makes use of invariant information obtained from estimated locations of features in independent sub-maps, instead of incorporating every observation directly into the global map. Then the global map is updated at regular intervals. Applying this technique to the EKF based SLAM algorithm: (a) reduces the computational complexity of maintaining the global map estimates and (b) simplifies transformation complexities and data association ambiguities usually experienced in fusing sub-maps together. Simulation results show that the method was able to accurately fuse local map observations to generate an efficient and consistent global map, in addition to significantly reducing computational cost and data association ambiguities.

Formal calibration methodology for CFD models of naturally ventilated indoor environments

Well planned natural ventilation strategies and systems in the built environments may provide healthy and comfortable indoor conditions, while contributing to a significant reduction in the energy consumed by buildings. Computational Fluid Dynamics (CFD) is particularly suited for modelling indoor conditions in naturally ventilated spaces, which are difficult to predict using other types of building simulation tools. Hence, accurate and reliable CFD models of naturally ventilated indoor spaces are necessary to support the effective design and operation of indoor environments in buildings. This paper presents a formal calibration methodology for the development of CFD models of naturally ventilated indoor environments. The methodology explains how to qualitatively and quantitatively verify and validate CFD models, including parametric analysis utilising the response surface technique to support a robust calibration process. The proposed methodology is demonstrated on a naturally ventilated study zone in the library building at the National University of Ireland in Galway. The calibration process is supported by the on-site measurements performed in a normally operating building. The measurement of outdoor weather data provided boundary conditions for the CFD model, while a network of wireless sensors supplied air speeds and air temperatures inside the room for the model calibration. The concepts and techniques developed here will enhance the process of achieving reliable CFD models that represent indoor spaces and provide new and valuable information for estimating the effect of the boundary conditions on the CFD model results in indoor environments. © 2012 Elsevier Ltd.

Difference equation approach to two-thermocouple sensor characterization in constant velocity flow environments

Thermocouples are one of the most popular devices for temperature measurement due to their robustness, ease of manufacture and installation, and low cost. However, when used in certain harsh environments, for example, in combustion systems and engine exhausts, large wire diameters are required, and consequently the measurement bandwidth is reduced. This article discusses a software compensation technique to address the loss of high frequency fluctuations based on measurements from two thermocouples. In particular, a difference equation (DE) approach is proposed and compared with existing methods both in simulation and on experimental test rig data with constant flow velocity. It is found that the DE algorithm, combined with the use of generalized total least squares for parameter identification, provides better performance in terms of time constant estimation without any a priori assumption on the time constant ratios of the thermocouples.

The performance of concrete exposed to marine environments: predictive modelling and use of laboratory/on site test methods

This paper reports an approach by which laboratory based testing and numerical modelling can be combined to predict the long term performance of a range of concretes exposed to marine environments. Firstly, a critical review of the test methods for assessing the chloride penetration resistance of concrete is given. The repeatability of the different test results is also included. In addition to the test methods, a numerical simulation model is used to explore the test data further to obtain long-term chloride ingress trends. The combined use of testing and modelling is validated with the help of long-term chloride ingress data from a North Sea exposure site. In summary, the paper outlines a methodology for determining the long term performance of concrete in marine environments.

Calibrated CFD simulation to evaluate thermal comfort in a highly-glazed naturally ventilated room

Natural ventilation is a sustainable solution to maintaining healthy and comfortable environmental conditions in buildings. However, the effective design, construction and operation of naturally ventilated buildings require a good understanding of complex airflow patterns caused by the buoyancy and wind effects.The work presented in this article employed a 3D computational fluid dynamics (CFD) analysis in order to investigate environmental conditions and thermal comfort of the occupants of a highly-glazed naturally ventilated meeting room. This analysis was facilitated by the real-time field measurements performed in an operating building, and previously developed formal calibration methodology for reliable CFD models of indoor environments. Since, creating an accurate CFD model of an occupied space in a real-life scenario requires a high level of CFD expertise, trusted experimental data and an ability to interpret model input parameters; the calibration methodology guided towards a robust and reliable CFD model of the indoor environment. This calibrated CFD model was then used to investigate indoor environmental conditions and to evaluate thermal comfort indices for the occupants of the room. Thermal comfort expresses occupants' satisfaction with thermal environment in buildings by defining the range of indoor thermal environmental conditions acceptable to a majority of occupants. In this study, the thermal comfort analysis, supported by both field measurements and CFD simulation results, confirmed a satisfactory and optimal room operation in terms of thermal environment for the investigated real-life scenario. © 2013 Elsevier Ltd.

Towards Automated Data-Driven Model Creation for Cloud Computing Simulation

The increasing complexity and scale of cloud computing environments due to widespread data centre heterogeneity makes measurement-based evaluations highly difficult to achieve. Therefore the use of simulation tools to support decision making in cloud computing environments to cope with this problem is an increasing trend. However the data required in order to model cloud computing environments with an appropriate degree of accuracy is typically large, very difficult to collect without some form of automation, often not available in a suitable format and a time consuming process if done manually. In this research, an automated method for cloud computing topology definition, data collection and model creation activities is presented, within the context of a suite of tools that have been developed and integrated to support these activities.

Oil hydrocarbon-bacteria interactions in coastal environments

The ability of microorganisms to use oil hydrocarbons as a source of carbon and energy is crucial for environmental oil detoxification. However, there is still a lack of knowledge on fundamental aspects of this process on specific habitats and under different climate scenarios. In the first phase of this work, the culturable fraction of the oil hydrocarbon (OH) degrading bacteria from the sea surface microlayer (SML) of the estuarine system Ria de Aveiro was characterized. In the second phase, the impact of oil contamination on the active bacterial community was studied under climate change scenarios. Pseudomonas emerged as the prevailing genera among OH degrading bacteria in the SML. Moreover, culture-independent methods revealed that the relative abundance and diversity of Gammaproteobacteria, in which Pseudomonas is included, varies along an estuarine gradient of contamination. In order to access the impact of oil contamination on microbial communities under climate change scenarios, an experimental life support system for microcosm experiments (ELLS) was developed and validated for simulation of climate change effects on microbial communities. With the ELSS it is possible to simulate, in controlled conditions, fundamental parameters of the dynamics of coastal and estuarine systems while maintaining community structure in terms of the abundance of the most relevant members of the indigenous bacterial community. A microcosm experiment in which the independent and combined impact of ultraviolet radiation, ocean acidification and oil contamination on microbial communities was conducted. The impact on bacterial communities was accessed with a 16S RNA (cDNA) based barcode pyrosequencing approach. There was a drastic decrease of Desulfobacterales relative abundance after oil contamination under the reduced pH value estimated for 2100, when compared to present values. Since members of this order are known OH degraders, such a significant decrease may have consequences on OH detoxification of contaminated environments under the pH levels of the ocean expected for the future. Metagenome predictions based on the 16S RNA database indicated that several degradation pathways of OH could be affected under oil contamination and reduced water pH. Taken together, the results from this work bring new information on the dynamics of OH degrading bacteria in coastal and estuarine environments under present and future climate scenarios.

Efficient content pushing in IEEE 802.11p vehicular environments

Vehicular networking is a new field that is expected to be widely adopted in the near future. One of the key applications inherent to this novel communications paradigm is content delivery to on-board users. In this paper we focus specifically on broadcast-based content delivery. We propose a content delivery scheme that is optimized for performance in order to improve the maximum amount of data than can be delivered, while also reducing delivery time to a minimum. With this goal our study combines both analytical and simulation results to determine the optimal packet size for content delivery so as to achieve the maximum throughput possible at different distances, and considering both static and mobile receivers. Experimental results show that our optimizations provide efficient delivery of multimedia contents for distances up to 200 meters when relying on IEEE 802.lip based broadcasting. Copyright © 2010 ACM.

A simulation investigation into the effects of sea level rise on tidal propagation in fjords

Senior thesis written for Oceanography 445

MASCEM: electricity markets simulation with strategic agents

Electricity markets are complex environments, involving numerous entities trying to obtain the best advantages and profits while limited by power-network characteristics and constraints.1 The restructuring and consequent deregulation of electricity markets introduced a new economic dimension to the power industry. Some observers have criticized the restructuring process, however, because it has failed to improve market efficiency and has complicated the assurance of reliability and fairness of operations. To study and understand this type of market, we developed the Multiagent Simulator of Competitive Electricity Markets (MASCEM) platform based on multiagent simulation. The MASCEM multiagent model includes players with strategies for bid definition, acting in forward, day-ahead, and balancing markets and considering both simple and complex bids. Our goal with MASCEM was to simulate as many market models and player types as possible. This approach makes MASCEM both a short- and mediumterm simulation as well as a tool to support long-term decisions, such as those taken by regulators. This article proposes a new methodology integrated in MASCEM for bid definition in electricity markets. This methodology uses reinforcement learning algorithms to let players perceive changes in the environment, thus helping them react to the dynamic environment and adapt their bids accordingly.

Adaptive learning in agents behaviour: a framework for electricity markets simulation

Electricity markets are complex environments with very particular characteristics. A critical issue regarding these specific characteristics concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, which was performed so that the competitiveness could be increased, but it also had exponential implications in the increase of the complexity and unpredictability in those markets scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behaviour. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This dissertation presents ALBidS – Adaptive Learning strategic Bidding System, a multiagent system created to provide decision support to market negotiating players. This system is integrated with the MASCEM electricity market simulator, so that its advantage in supporting a market player can be tested using cases based on real markets’ data. ALBidS considers several different methodologies based on very distinct approaches, to provide alternative suggestions of which are the best actions for the supported player to perform. The approach chosen as the players’ actual action is selected by the employment of reinforcement learning algorithms, which for each different situation, simulation circumstances and context, decides which proposed action is the one with higher possibility of achieving the most success. Some of the considered approaches are supported by a mechanism that creates profiles of competitor players. These profiles are built accordingly to their observed past actions and reactions when faced with specific situations, such as success and failure. The system’s context awareness and simulation circumstances analysis, both in terms of results performance and execution time adaptation, are complementary mechanisms, which endow ALBidS with further adaptation and learning capabilities.

Adaptive learning in agents behaviour: A framework for electricity markets simulation

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.

Load Control Timescales Simulation in a Multi-Agent Smart Grid Platform

Environmental concerns and the shortage in the fossil fuel reserves have been potentiating the growth and globalization of distributed generation. Another resource that has been increasing its importance is the demand response, which is used to change consumers’ consumption profile, helping to reduce peak demand. Aiming to support small players’ participation in demand response events, the Curtailment Service Provider emerged. This player works as an aggregator for demand response events. The control of small and medium players which act in smart grid and micro grid environments is enhanced with a multi-agent system with artificial intelligence techniques – the MASGriP (Multi-Agent Smart Grid Platform). Using strategic behaviours in each player, this system simulates the profile of real players by using software agents. This paper shows the importance of modeling these behaviours for studying this type of scenarios. A case study with three examples shows the differences between each player and the best behaviour in order to achieve the higher profit in each situation.

L’impact d’une simulation sur des dispositifs mobiles et en situation de collaboration sur la compréhension de l’effet photoélectrique au niveau collégial

L’innovation pédagogique pour elle-même s’avère parfois discutable, mais elle se justifie quand les enseignants se heurtent aux difficultés d’apprentissage de leurs étudiants. En particulier, certaines notions de physique sont réputées difficiles à appréhender par les étudiants, comme c’est le cas pour l’effet photoélectrique qui n’est pas souvent compris par les étudiants au niveau collégial. Cette recherche tente de déterminer si, dans le cadre d’un cours de physique, la simulation de l’effet photoélectrique et l’utilisation des dispositifs mobiles et en situation de collaboration favorisent une évolution des conceptions des étudiants au sujet de la lumière. Nous avons ainsi procédé à l’élaboration d’un scénario d’apprentissage collaboratif intégrant une simulation de l’effet photoélectrique sur un ordinateur de poche. La conception du scénario a d’abord été influencée par notre vision socioconstructiviste de l’apprentissage. Nous avons effectué deux études préliminaires afin de compléter notre scénario d’apprentissage et valider la plateforme MobileSim et l’interface du simulateur, que nous avons utilisées dans notre expérimentation : la première avec des ordinateurs de bureau et la seconde avec des ordinateurs de poche. Nous avons fait suivre à deux groupes d’étudiants deux cours différents, l’un portant sur une approche traditionnelle d’enseignement, l’autre basé sur le scénario d’apprentissage collaboratif élaboré. Nous leur avons fait passer un test évaluant l’évolution conceptuelle sur la nature de la lumière et sur le phénomène de l’effet photoélectrique et concepts connexes, à deux reprises : la première avant que les étudiants ne s’investissent dans le cours et la seconde après la réalisation des expérimentations. Nos résultats aux prétest et post-test sont complétés par des entrevues individuelles semi-dirigées avec tous les étudiants, par des enregistrements vidéo et par des traces récupérées des fichiers logs ou sur papier. Les étudiants du groupe expérimental ont obtenu de très bons résultats au post-test par rapport à ceux du groupe contrôle. Nous avons enregistré un gain moyen d’apprentissage qualifié de niveau modéré selon Hake (1998). Les résultats des entrevues ont permis de repérer quelques difficultés conceptuelles d’apprentissage chez les étudiants. L’analyse des données recueillies des enregistrements des séquences vidéo, des questionnaires et des traces récupérées nous a permis de mieux comprendre le processus d’apprentissage collaboratif et nous a dévoilé que le nombre et la durée des interactions entre les étudiants sont fortement corrélés avec le gain d’apprentissage. Ce projet de recherche est d’abord une réussite sur le plan de la conception d’un scénario d’apprentissage relatif à un phénomène aussi complexe que l’effet photoélectrique, tout en respectant de nombreux critères (collaboration, simulation, dispositifs mobiles) qui nous paraissaient extrêmement utopiques de réunir dans une situation d’apprentissage en classe. Ce scénario pourra être adapté pour l’apprentissage d’autres notions de la physique et pourra être considéré pour la conception des environnements collaboratifs d’apprentissage mobile innovants, centrés sur les besoins des apprenants et intégrant les technologies au bon moment et pour la bonne activité.