'The project intends to develop a new Electric Machine Modelica Library with improving functionalities for an accurate design and simulation of motors and generators.
New Externally Excited Synchronous Machines, Permanent Magnet Synchronous Machines (PMSM and BLDC) and Asynchronous Induction Machines (IM) models in Modelica language including spatial harmonics and nonlinear saturation behavior, as well as improved thermal model for the thermal evaluation of the machine, are the main objectives of the proposed project, together new software tools with GUIs for guided model design from geometric motor considerations and torque and power demand profiles.
Generalized space phasor theory to m phases with arbitrary spatial angle of the coils, and arbitrary number of windings and winding factor of the coils will be considered for the new Library. Constructive harmonics and distortions of flux couplings are taken into account as well.
Also, non-linearity due to saturation and skin-effect on the resistances are considered in updated libraries. Moreover, thermal models able to calculate hotspots and asymmetric windings temperature distribution from the thermal losses into the machine are included in the Library.
A graphical guided Interface for directing the modeling of machines is included in the project.'
The challenge of climate change is real and growing. Every industrial sector must look deep into its operations and use every opportunity to play its part in reducing overall emissions. Euroenergest project aims is to reduce 10% of energy consumption in a specific areas of the automotive industry, such as HVAC.\nFor achieving this goal, an Intelligent Energy Management Systems (iEMS) will be developed. iEMS will be able to interact with industrial loads and available power sources with the objective of optimizing the demanded power and costs, as well as maximizing local and low-carbon energy sources (photovoltaics and CHP).\nIn the EuroEnergest project, production models for available power sources such as CHP and renewable are combined with load prediction models for industrial machinery and HVAC systems through an energy hub connection matrix with electricity and natural gas as the main power sources.\nThrough intensive use of artificial intelligence algorithms to apply a dynamic simulation to former models, together with some optimization based on energy prices and real production data it becomes possible to research and develop an intelligent EMS able to extract planning rules and optimized interconnections at energy hub level for energy consumptions and costs optimization. Industrial users and companies can take advantage of the individual modules, each of which can be used independently or all together linked to standardised Supervisory and Control Software.\nEuroenergest includes a deep analysis of CO2 emissions and process needed for measuring and modelling the manufacturing carbon footprint.\nA key part of this project is the enthusiastic involvement of one of Europe's most important automobile manufacturers, SEAT. This involvement gives EuroEnergest the opportunity to verify this groundbreaking system in a real world situation.
'Unexpected failure in an industrial production chain does not only involve the costs of failed parts replacement and the associated man-hour labour, but downtime costs have also have to be considered. To keep a machine functioning well it is a must to have good predictive maintenance, as it helps to reduce operating risk, avoids plant failures, provides reliable equipment, reduces operating costs, eliminates defects in operating plant and maximises production. Acoustic Emission (AE) is a phenomenon of transient elastic wave generation in materials under stress. When the material is subjected to stress at a certain level, a rapid release of strain energy takes place in the form of elastic wave which can be detected by transducers placed on it. Plastic deformation and growth of cracks are among the main sources of AE in metals. Though AE can came form any system under movement, the main source is doubtlessly from rotating machinery. Sources of AE in rotating machinery include impacting, cyclic fatigue cracks, friction, turbulence, material loss, cavitation, leakage, etc. In most cases the SMEs machine owner would be satisfied with a simple affordable device that is able to warn them from critical equipment failure.
Recent developments in sensing technology, microprocessors, and miniaturised radio transceivers has enabled a new generation of Wireless Sensors Networks. The future of these sensors is to have an ubiquitous sensing nodes that will autonomously report on operating conditions, and that this data will be used to facilitate structural health monitoring, embedded test & evaluation, and condition based maintenance of critical industrial rotating machinery without the use of expensive cabling. In addition, in order to provide sensing networks which are truly autonomous, chemical batteries must be eliminated from the sensor and some kind of energy harvesting has to be foreseen. Piezoelectric materials have demonstrated their ability to convert vibration energy from vibrating machinery and rotating structures into electrical energy for powering a wireless sensing node. Hence, an acoustic emission self-powered wireless sensor is one of the main objectives to be achieved in this project. The sensor will measure using frequency as opposed to time which is an advancement from the state of the art.'