Andreas Christoforou

This paper presents guidelines on the use of a characterization diagram for impact dynamics scaling and damage tolerance studies for aerospace structures. It is demonstrated that the diagram obtained for rigid impactors, can also be used for deformable impactors such as hail and bird strikes. By placing the impact response on the diagram useful information on the type of response and damage can be predicted prior to running experiments or simulations. It is expected that this methodology could be a very powerful tool to designers and experimentalists who work in the area of design and commissioning for damage resistance and tolerance especially with composite structures.

This paper presents a novel approach to improve the accuracy of classification models used for prediction purposes by integrating a Fuzzy Cognitive Map (FCM) to produce a hybrid model. The proposed methodology first uses the FCM to discover latent correlations that exist between the data in order to form a single variable. This variable is then fed in the classification model as part of the training and testing phases to enhance its accuracy. Experimental results using datasets describing two different problems suggested noteworthy improvements in the accuracy of various classification models.

Stick slip and bit bounce are common problems while drilling the production sections of oil wells with Polycrystalline Diamond Compact (PDC) bits. The production section often encounters different lithology including highly fractured limestone, sand/sandstone and reactive shale. In some cases, vibrations are so severe that they cause failure in drill string components, loss of expensive tools in the hole; such as bit and logging sources/equipment, bit damage, wellbore instability and reduction in the Rate of Penetration (ROP). The current paper presents a dynamic model and an active control strategy for reducing stick slip and bit bounce of a drill string with a top drive system. The model includes complete drive systems for both rotational and axial motions, as well as the hoisting system. Also included is the bit/formation interaction, which facilitates the inclusion of the effects of formation characteristics. First, the equations of axial and torsional motions are derived and solved numerically. The numerical results are validated with real data logs recorded while drilling the production sections in Kuwait fields. Second, feedback controllers are designed and incorporated into the model. Simulation results show that in certain conditions the drill string may experience high values of TOB (Torque on Bit) and WOB (Weight on Bit), which can damage sensitive tools within the drill string, such as, LWD (Logging While Drilling) tools and RSS (Rotary Steerable System). It is demonstrated that implementing only a torsional controller in the system is not effective in mitigating bit bounce phenomena, and an axial controller should be designed and implemented as well. Simulation results show that it is possible to minimize both stick slip and bit bounce and maintain desired drilling conditions with the axial and torsional controllers. It is expected that, the insight gained by the proposed model will be instrumental for determining and realizing optimal drilling conditions. Therefore, the cost related to drilling a specific well will be reduced, and the life of equipment used to drill the well, such as bit, motor, stabilizer, and drill collars, will be increased.

The fact that Cloud Computing is steadily becoming one of the most significant fields of Information and Communication Technology (ICT) has led many organizations to consider the benefits of migrating their business operations to the Cloud. Decision makers are facing strong challenges when assessing the feasibility of the adoption of Cloud Computing for their organizations. Cloud adoption is a multi-level decision which is influenced by a number of intertwined factors and concerns thus characterizing it as a complex and difficult to model real-world problem. In this paper we propose two decision support modeling approaches based on Influence Diagrams (ID) aiming to model the answer to the question “Adopt Cloud Services or Not?” Two models are developed and tested, the first is a generic ID with nodes interacting in a probabilistic manner, while the second is a more flexible version that utilizes Fuzzy Logic. Both models combine several factors that influence the decision to be taken, which were identified through literature review and input received from field experts. The proposed approaches are validated using five experimental scenarios, two synthetic and three real-world cases, and their performance suggests that they are highly capable of supporting the right decision.

As modern computing relies more and more on distributed solutions of services and resources over the cloud, the need of potential users to assess whether the transition from traditional software systems to the cloud would be to their benefit becomes even greater. Cloud vendors also seek ways to study beforehand the behavior of potential users with respect to their decision to adopt the cloud environment so as to take actions towards enhancing the positive side. Therefore, the study of the parameters forming the environment behind the cloud adoption decision is of paramount importance to both users and vendors. In this context the present paper proposes a multi-layer FCM approach which models a number of factors which play a decisive role to the cloud adoption issue and offers the means to study their influence. The factors are organized in different layers which focus on specific aspects of the cloud environment, something which, on one hand, enables tracking the causes for the decision outcome, and on the other offers the ability to study the dependencies between the leading determinants of the decision. The construction and analysis of the model is based on factors reported in the relevant literature and the utilization of experts' opinion. The efficacy and applicability of the proposed approach are demonstrated through four real-world experimental cases.