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

Structural investigation of nucleophosmin interaction with the tumor suppressor Fbw7γ

Nucleophosmin (NPM1) is a multifunctional nucleolar protein implicated in ribogenesis, centrosome duplication, cell cycle control, regulation of DNA repair and apoptotic response to stress stimuli. The majority of these functions are played through the interactions with a variety of protein partners. NPM1 is frequently overexpressed in solid tumors of different histological origin. Furthermore NPM1 is the most frequently mutated protein in acute myeloid leukemia (AML) patients. Mutations map to the C-terminal domain and lead to the aberrant and stable localization of the protein in the cytoplasm of leukemic blasts. Among NPM1 protein partners, a pivotal role is played by the tumor suppressor Fbw7γ, an E3-ubiquitin ligase that degrades oncoproteins like c-MYC, cyclin E, Notch and c-jun. In AML with NPM1 mutations, Fbw7γ is degraded following its abnormal cytosolic delocalization by mutated NPM1. This mechanism also applies to other tumor suppressors and it has been suggested that it may play a key role in leukemogenesis. Here we analyse the interaction between NPM1 and Fbw7γ, by identifying the protein surfaces implicated in recognition and key aminoacids involved. Based on the results of computational methods, we propose a structural model for the interaction, which is substantiated by experimental findings on several site-directed mutants. We also extend the analysis to two other NPM1 partners (HIV Tat and CENP-W) and conclude that NPM1 uses the same molecular surface as a platform for recognizing different protein partners. We suggest that this region of NPM1 may be targeted for cancer treatment

An adaptive technique for content-based image retrieval

We discuss an adaptive approach towards Content-Based Image Retrieval. It is based on the Ostensive Model of developing information needs—a special kind of relevance feedback model that learns from implicit user feedback and adds a temporal notion to relevance. The ostensive approach supports content-assisted browsing through visualising the interaction by adding user-selected images to a browsing path, which ends with a set of system recommendations. The suggestions are based on an adaptive query learning scheme, in which the query is learnt from previously selected images. Our approach is an adaptation of the original Ostensive Model based on textual features only, to include content-based features to characterise images. In the proposed scheme textual and colour features are combined using the Dempster-Shafer theory of evidence combination. Results from a user-centred, work-task oriented evaluation show that the ostensive interface is preferred over a traditional interface with manual query facilities. This is due to its ability to adapt to the user's need, its intuitiveness and the fluid way in which it operates. Studying and comparing the nature of the underlying information need, it emerges that our approach elicits changes in the user's need based on the interaction, and is successful in adapting the retrieval to match the changes. In addition, a preliminary study of the retrieval performance of the ostensive relevance feedback scheme shows that it can outperform a standard relevance feedback strategy in terms of image recall in category search

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

An information-theoretic framework for semantic-multimedia retrieval

This article is set in the context of searching text and image repositories by keyword. We develop a unified probabilistic framework for text, image, and combined text and image retrieval that is based on the detection of keywords (concepts) using automated image annotation technology. Our framework is deeply rooted in information theory and lends itself to use with other media types. We estimate a statistical model in a multimodal feature space for each possible query keyword. The key element of our framework is to identify feature space transformations that make them comparable in complexity and density. We select the optimal multimodal feature space with a minimum description length criterion from a set of candidate feature spaces that are computed with the average-mutual-information criterion for the text part and hierarchical expectation maximization for the visual part of the data. We evaluate our approach in three retrieval experiments (only text retrieval, only image retrieval, and text combined with image retrieval), verify the framework’s low computational complexity, and compare with existing state-of-the-art ad-hoc models

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