An Advanced eLearning Environment Developed for Engineering Learners

Monitoring and evaluating engineering learners through computer-based laboratory exercises is a difficult task, especially under classroom conditions. A complete diagnosis requires the capability to assess both the competence of the learner to use the scientific software and the understanding of the theoretical principles. This monitoring and evaluation needs to be continuous, unobtrusive and personalized in order to be effective. This study presents the results of the pilot application of an eLearning environment developed specifically with engineering learners in mind. As its name suggests, the Learner Diagnosis, Assistance, and Evaluation System based on Artificial Intelligence (StuDiAsE) is an Open Learning Environment that can perform unattended diagnostic, evaluation and feedback tasks based on both quantitative and qualitative parameters. The base architecture of the system, the user interface and its effect on the performance of postgraduate engineering learners are being presented

Use of a probabilistic model to design energy transmission and distribution networks for low enthalpy geothermal multiple use schemes

A probabilistic model is suggested for the design of transmission and distribution network of geothermal energy to potential consumption sites, in cases where the development of various competitive or complementary non-electrical uses is probable, within the broader area of a field. The model can be used to find out (a) the optimum network that may offer the best economic results to the agent who will undertake the development of the field, and (b) the corresponding selling price at which the thermal fluids will be supplied to the end users who are assumed to be other than the above agent. Model input data can be collected in the frame of an appropriate market study, which is roughly specified in this work and commented according to relevant experience from a Greek geothermal field. Finally, applicability of the model is demonstrated through an indicative example.Geothermal energy Multiple use Probabilistic model Probability Location-allocation

Optimal economic thickness of various insulation materials for different orientations of external walls considering the wind characteristics

The economic optimum insulation thickness of various insulation materials for external walls of different topologies and orientations was determined, taking into account the heating and cooling period and the speed and direction of the wind. Annual heating and cooling transmission loads are being calculated based on transient heat flow through the external walls and by using hourly climatic data for an entire typical meteorological year of the city of Larnaca, Cyprus. The available wind speed and direction data have been statistically analysed for the assessment of the prevalent wind directions in the area. The optimisation is carried out using the Life Cycle Savings method. According to the results, the north-facing walls offer the greatest economic benefit compared to the corresponding wall types of different orientation, regardless of the insulation thickness. They also have the shortest payback period. The optimum insulation thickness calculated for any wall topology and orientation varies from 4.25 cm to 15.5 cm, and the payback period varies from 5.47 years to 12.11 years