development of a bem cfd tool for vertical axis wind turbines based on the actuator disk model

Abstract The present work focuses on the numerical simulation of Vertical Axis Wind Turbines by means of an "in-house" BEM-based User Defined Function to be used 39ith the CFD code ANSYS Fluent. Typical VAWT unsteady and 3D phenomena, such as dynamic stall, flow curvature and tip losses, are taken into account by original and literature-based sub-models. The presence of the blades is mimicked by replacing them with suitable momentum sources. For the present work, the Actuator Cylinder Model has been employed. 3D analysis, of a SANDIA rotor, are carried out in order to assess the accuracy of our model against numerical simulations and experimental data. The current User Defined Function is able to give a satisfactory agreement with the reference cases especially from a qualitative point of view, with a significant computational time reduction to a factor of 10 compared to the case with the moving bodies. A typical wake behavior can be noticed in our simulations even though its recovery is strongly influenced by the lack of turbulence inherent to the chosen actuator model. The torque and the power coefficient of the turbines are also analyzed and compared against the reference cases, finding a remarkable agreement. The model has been successfully applied to predict the transient aerodynamic loads of an offshore 5 MW troposkein turbine subjected to the pitching motion of its platform. The operating conditions have been chosen in order to allow a qualitative comparison with a floating 5 MW horizontal axis turbine which performance under pitching motion is available in literature

Towards More Effective Techniques for Automatic Query Expansion

Techniques for automatic query expansion from top retrieved documents have recently shown promise for improving retrieval effectiveness on large collections but there is still a lack of systematic evaluation and comparative studies. In this paper we focus on term-scoring methods based on the differences between the distribution of terms in (pseudo-)relevant documents and the distribution of terms in all documents, seen as a complement or an alternative to more conventional techniques. We show that when such distributional methods are used to select expansion terms within Rocchio's classical reweighting scheme, the overall performance is not likely to improve. However, we also show that when the same distributional methods are used to both select and weight expansion terms the retrieval effectiveness may considerably improve. We then argue, based on their variation in performance on individual queries, that the set of ranked terms suggested by individual distributional methods can be combined to further improve mean performance, by analogy with ensembling classifiers, and present experimental evidence supporting this view. Taken together, our experiments show that with automatic query expansion it is possible to achieve performance gains as high as 21.34% over non-expanded query (for non-interpolated average precision). We also discuss the effect that the main parameters involved in automatic query expansion, such as query difficulty, number of selected documents, and number of selected terms, have on retrieval effectiveness

Looking at Vector Space and Language Models for IR using Density Matrices

In this work, we conduct a joint analysis of both Vector Space and Language Models for IR using the mathematical framework of Quantum Theory. We shed light on how both models allocate the space of density matrices. A density matrix is shown to be a general representational tool capable of leveraging capabilities of both VSM and LM representations thus paving the way for a new generation of retrieval models. We analyze the possible implications suggested by our findings.Comment: In Proceedings of Quantum Interaction 201

Towards a belief revision based adaptive and context sensitive information retrieval system

In an adaptive information retrieval (IR) setting, the information seekers' beliefs about which terms are relevant or nonrelevant will naturally fluctuate. This article investigates how the theory of belief revision can be used to model adaptive IR. More specifically, belief revision logic provides a rich representation scheme to formalize retrieval contexts so as to disambiguate vague user queries. In addition, belief revision theory underpins the development of an effective mechanism to revise user profiles in accordance with information seekers' changing information needs. It is argued that information retrieval contexts can be extracted by means of the information-flow text mining method so as to realize a highly autonomous adaptive IR system. The extra bonus of a belief-based IR model is that its retrieval behavior is more predictable and explanatory. Our initial experiments show that the belief-based adaptive IR system is as effective as a classical adaptive IR system. To our best knowledge, this is the first successful implementation and evaluation of a logic-based adaptive IR model which can efficiently process large IR collections

Rocchio Algorithm to Enhance Semantically Collaborative Filtering

International audienceRecommender system provides relevant items to users from huge catalogue. Collaborative filtering and content-based filtering are the most widely used techniques in personalized recommender systems. Collaborative filtering uses only the user-ratings data to make predictions, while content-based filtering relies on semantic information of items for recommendation. Hybrid recommendation system combines the two techniques. In this paper, we present another hybridization approach: User Semantic Collaborative Filtering. The aim of our approach is to predict users preferences for items based on their inferred preferences for semantic information of items. In this aim, we design a new user semantic model to describe the user preferences by using Rocchio algorithm. Due to the high dimension of item content, we apply a latent semantic analysis to reduce the dimension of data. User semantic model is then used in a user-based collaborative filtering to compute prediction ratings and to provide recommendations. Applying our approach to real data set, the MoviesLens 1M data set, significant improvement can be noticed compared to usage only approach, content based only approach

Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio