Development of turbomachines for renewable energy systems and energy-saving applications

Abstract Turbomachines play a significant role in some key sectors as aircraft and marine propulsion, power production, heat ventilation and air conditioning and chemical processing. The success of dynamic machines is connected to the wide variety of demands that they can cover, together with their compactness, reliability and availability. In this respect, such machines are the favourite candidate to support an efficient exploitation of some renewable energy sources and the development of energy-saving systems. Innovative plants require machines which can work with new fluids (e.g. Organic Rankine Cycle systems) or in new operating conditions (e.g. high-flexibility or new pressure ratios) and it poses new challenging aspects in the preliminary machinery design. Moreover, another challenging aspect is how innovative techniques (e.g. high-integrated design systems, 3D printing) can be integrated in the design process and how much they can affect the machine development and final performance. Two case studies are presented to focus the attention on such aspects, discussing preliminary design and prototyping of "unconventional" turbomachines

How to Color a French Flag--Biologically Inspired Algorithms for Scale-Invariant Patterning

In the French flag problem, initially uncolored cells on a grid must differentiate to become blue, white or red. The goal is for the cells to color the grid as a French flag, i.e., a three-colored triband, in a distributed manner. To solve a generalized version of the problem in a distributed computational setting, we consider two models: a biologically-inspired version that relies on morphogens (diffusing proteins acting as chemical signals) and a more abstract version based on reliable message passing between cellular agents. Much of developmental biology research has focused on concentration-based approaches using morphogens, since morphogen gradients are thought to be an underlying mechanism in tissue patterning. We show that both our model types easily achieve a French ribbon - a French flag in the 1D case. However, extending the ribbon to the 2D flag in the concentration model is somewhat difficult unless each agent has additional positional information. Assuming that cells are are identical, it is impossible to achieve a French flag or even a close approximation. In contrast, using a message-based approach in the 2D case only requires assuming that agents can be represented as constant size state machines. We hope that our insights may lay some groundwork for what kind of message passing abstractions or guarantees, if any, may be useful in analogy to cells communicating at long and short distances to solve patterning problems. In addition, we hope that our models and findings may be of interest in the design of nano-robots

Artificial life meets computational creativity?

I review the history of work in Artificial Life on the problem of the open-ended evolutionary growth of complexity in computational worlds. This is then put into the context of evolutionary epistemology and human creativity

Neural networks and support vector machines based bio-activity classification

Classification of various compounds into their respective biological activity classes is important in drug discovery applications from an early phase virtual compound filtering and screening point of view. In this work two types of neural networks, multi layer perceptron (MLP) and radial basis functions (RBF), and support vector machines (SVM) were employed for the classification of three types of biologically active enzyme inhibitors. Both of the networks were trained with back propagation learning method with chemical compounds whose active inhibition properties were previously known. A group of topological indices, selected with the help of principle component analysis (PCA) were used as descriptors. The results of all the three classification methods show that the performance of both the neural networks is better than the SVM