Investigations into the Airside Cooling of a Heat Exchanger

In this study we investigate the air-side cooling of a flat-plate fin and tube heat transfer condenser with numerical simulations. A new design is proposed which utilises vortex generators to direct the flow in such a way as to remove some of the stagnant heated air that collects in the wake of the pipes. A comparative study of the proposed design and a standard tube and fin condenser is conducted by varying the air side entrance velocities. The Shear Stress Tension, SST $\kappa - \omega$ 2-equation turbulent model is used to solve the RANS model in ANSYS Fluent 18. The results show an improved heat transfer gain with the proposed model at the expense of a greater pressure drop. We explain various analysis of the results concluding that the gains in heat transfer at higher air side face velocities are greater than the expense of power and that the proposed model is predicted to yield a flat-plate fin and pipe condenser that is more compact and energy efficient than the standard design. We predict the improvement of ideal power consumption over typical operating conditions on our portion of the condenser to be between 7\% and 15\%

The quest for information retrieval on the semantic web

Semantic search has been one of the motivations of the Semantic Web since it was envisioned. We propose a model for the exploitation of ontology-based KBs to improve search over large document repositories. The retrieval model is based on an adaptation of the classic vector-space model, including an annotation weighting algorithm, and a ranking algorithm. Semantic search is combined with keyword-based search to achieve tolerance to KB incompleteness. Our proposal has been tested on corpora of significant size, showing promising results with respect to keyword-based search, and providing ground for further analysis and research

Using TREC for cross-comparison between classic IR and ontology-based search models at a Web scale

The construction of standard datasets and benchmarks to evaluate ontology-based search approaches and to compare then against baseline IR models is a major open problem in the semantic technologies community. In this paper we propose a novel evaluation benchmark for ontology-based IR models based on an adaptation of the well-known Cranfield paradigm (Cleverdon, 1967) traditionally used by the IR community. The proposed benchmark comprises: 1) a text document collection, 2) a set of queries and their corresponding document relevance judgments and 3) a set of ontologies and Knowledge Bases covering the query topics. The document collection and the set of queries and judgments are taken from one of the most widely used datasets in the IR community, the TREC Web track. As a use case example we apply the proposed benchmark to compare a real ontology-based search model (Fernandez, et al., 2008) against the best IR systems of TREC 9 and TREC 2001 competitions. A deep analysis of the strengths and weaknesses of this benchmark and a discussion of how it can be used to evaluate other ontology-based search systems is also included at the end of the paper

Using historical data to enhance rank aggregation

Rank aggregation is a pervading operation in IR technology. We hypothesize that the performance of score-based aggregation may be affected by artificial, usually meaningless deviations consistently occurring in the input score distributions, which distort the combined result when the individual biases differ from each other. We propose a score-based rank aggregation model where the source scores are normalized to a common distribution before being combined. Early experiments on available data from several TREC collections are shown to support our proposal

Explicit relevance models in intent-oriented information retrieval diversification

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, http://dx.doi.org/10.1145/2348283.2348297.The intent-oriented search diversification methods developed in the field so far tend to build on generative views of the retrieval system to be diversified. Core algorithm components in particular redundancy assessment are expressed in terms of the probability to observe documents, rather than the probability that the documents be relevant. This has been sometimes described as a view considering the selection of a single document in the underlying task model. In this paper we propose an alternative formulation of aspect-based diversification algorithms which explicitly includes a formal relevance model. We develop means for the effective computation of the new formulation, and we test the resulting algorithm empirically. We report experiments on search and recommendation tasks showing competitive or better performance than the original diversification algorithms. The relevance-based formulation has further interesting properties, such as unifying two well-known state of the art algorithms into a single version. The relevance-based approach opens alternative possibilities for further formal connections and developments as natural extensions of the framework. We illustrate this by modeling tolerance to redundancy as an explicit configurable parameter, which can be set to better suit the characteristics of the IR task, or the evaluation metrics, as we illustrate empirically.This work was supported by the national Spanish projects TIN2011-28538-C02-01 and S2009TIC-1542

On the suitability of intent spaces for IR diversification

This is an electronic version of the paper presented at the International Workshop on Diversity in Document Retrieval (DDR 2012), held in Seattle on 2012Recent developments in Information Retrieval diversity are based on the consideration of a space of information need aspects, a notion which takes different forms in the literature. The choice of a suitable aspect space for diversification is a critical issue when designing an IR diversification strategy, which has not been explicitly addressed to some depth in the literature. This paper aims to identify relevant properties of the aspect space which may help the system designer in making a suitable choice in selecting and configuring this space, and diagnosing malfunctions of the diversification algorithms. In particular, we identify the mutual information between aspects and documents as a meaningful magnitude, in terms of which anomalous cases can be characterized. We further seek to discern favorable cases through a combination of theoretic and empirical analysis.This work is supported by the Spanish Government (TIN2011-28538-C02-01), and the Government of Madrid (S2009TIC-1542)

Intent-oriented diversity in recommender systems

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, http://dx.doi.org/10.1145/2009916.2010124.Diversity as a relevant dimension of retrieval quality is receiving increasing attention in the Information Retrieval and Recommender Systems (RS) fields. The problem has nonetheless been approached under different views and formulations in IR and RS respectively, giving rise to different models, methodologies, and metrics, with little convergence between both fields. In this poster we explore the adaptation of diversity metrics, techniques, and principles from ad-hoc IR to the recommendation task, by introducing the notion of user profile aspect as an analogue of query intent. As a particular approach, user aspects are automatically extracted from latent item features. Empirical results support the proposed approach and provide further insights.This work is supported by the Spanish Government (TIN2008- 06566-C04-02), and the Government of Madrid (S2009TIC-1542)

Simulating Condensation in the Theory of Classical Nucleation

The scope of this thesis is to numerically investigate condensation that occurs in transonic and supersonic flows. Condensation shocks are a phenomenon that occurs within converging-diverging nozzles. There are many applications forconverging-diverging nozzles, such as thermovapour compressors (TVC), which are largely used for desalination. As water resources become more precious within the western United States there is a need to develop cost effective solutions for cleaning water. Steam generator power stations are another application where enhanced simulations and accuracy of design could improve efficiency and save on carbon emissions. Traditionally, numerical designs of transonic flows have been conducted with either an ideal gas equation of state (EoS), which ignores condensation, or a homogeneous equilibrium approach, which assumes the fluid to be in thermo-dynamic equilibrium. The classical theory of nucleation was developed through the kinetic theory of gases to explain the process of condensation in supersonic flows, which occurs in a metastable state. The theory has recently been implemented in commercial codes and enjoys some success, particularly in low pressure flows. Improvement of the theory of classical nucleation in both high pressure and low pressure flows can help to improve the design of a multitude of systems. Through a self developed code that simulates one-dimensional transonic vapour flow through a converging-diverging nozzle, a new isothermal correction was developed. This new isothermal correction brings greater accuracy to simulations of homogeneously condensing high pressure flows and proved to be more accurate than the present theory in some lower pressure simulations

Measuring vertex centrality in co-occurrence graphs for online social tag recommendation

Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) Proceedings of ECML PKDD (The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases) Discovery Challenge 2009, Bled, Slovenia, September 7, 2009.We present a social tag recommendation model for collaborative bookmarking systems. This model receives as input a bookmark of a web page or scientific publication, and automatically suggests a set of social tags useful for annotating the bookmarked document. Analysing and processing the bookmark textual contents - document title, URL, abstract and descriptions - we extract a set of keywords, forming a query that is launched against an index, and retrieves a number of similar tagged bookmarks. Afterwards, we take the social tags of these bookmarks, and build their global co-occurrence sub-graph. The tags (vertices) of this reduced graph that have the highest vertex centrality constitute our recommendations, whThis research was supported by the European Commission under contracts FP6-027122-SALERO, FP6-033715-MIAUCE and FP6-045032 SEMEDIA. The expressed content is the view of the authors but not necessarily the view of SALERO, MIAUCE and SEMEDIA projects as a whol

Inferring user intent in web search by exploiting social annotations

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, http://dx.doi.org/10.1145/1835449.1835636In this paper, we present a folksonomy-based approach for implicit user intent extraction during a Web search process. We present a number of result re-ranking techniques based on this representation that can be applied to any Web search engine. We perform a user experiment the results of which indicate that this type of representation is better at context extraction than using the actual textual content of the document.This research was partially supported by the Spanish Ministry of Science and Education (TIN2008-06566-C04-02) and the Regional Government of Madrid (S2009TIC-1542)