Guidelines for the LTO Noise Assessment of Future Civil Supersonic Aircraft in Conceptual Design

One of the most critical regulatory issues related to supersonic flight arises from limitations imposed by community noise acceptability. The most efficient way to ensure that future supersonic aircraft will meet low-noise requirements is the verification of noise emissions from the early stages of the design process. Therefore, this paper suggests guidelines for the Landing and Take-Off (LTO) noise assessment of future civil supersonic aircraft in conceptual design. The supersonic aircraft noise model is based on the semi-empirical equations employed in the early versions of the Aircraft NOise Prediction Program (ANOPP) developed by NASA, whereas sound attenuation due to atmospheric absorption has been considered in accordance with SAE ARP 866 B. The simulation of the trajectory leads to the prediction of the aircraft noise level on ground in terms of several acoustic metrics (LAmax, SEL, PNLTM and EPNL). Therefore, a dedicated validation has been performed, selecting the only available supersonic aircraft of the Aircraft Noise and Performance database (ANP), that is, the Concorde, through the matching with Noise Power Distance (NPD) curves for LAmax and SEL, obtaining a maximum prediction error of +/- 2.19%. At least, an application to departure and approach procedures is reported to verify the first noise estimations with current noise requirements defined by ICAO at the three certification measurement points (sideline, flyover, approach) and to draw preliminary considerations for future low-noise supersonic aircraft design

Using Ontologies for the Design of Data Warehouses

Obtaining an implementation of a data warehouse is a complex task that forces designers to acquire wide knowledge of the domain, thus requiring a high level of expertise and becoming it a prone-to-fail task. Based on our experience, we have detected a set of situations we have faced up with in real-world projects in which we believe that the use of ontologies will improve several aspects of the design of data warehouses. The aim of this article is to describe several shortcomings of current data warehouse design approaches and discuss the benefit of using ontologies to overcome them. This work is a starting point for discussing the convenience of using ontologies in data warehouse design.Comment: 15 pages, 2 figure

Ontology-based knowledge representation of experiment metadata in biological data mining

According to the PubMed resource from the U.S. National Library of Medicine, over 750,000 scientific articles have been published in the ~5000 biomedical journals worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to be able to assimilate and mine data from related investigations for purposes of meta-analysis. While computers have the potential for assisting investigators in the extraction, management and analysis of these data, information contained in the traditional journal publication is still largely unstructured, free-text descriptions of study design, experimental application and results interpretation, making it difficult for computers to gain access to the content of what is being conveyed without significant manual intervention. In order to circumvent these roadblocks and make the most of the output from the biomedical research enterprise, a variety of related standards in knowledge representation are being developed, proposed and adopted in the biomedical community. In this chapter, we will explore the current status of efforts to develop minimum information standards for the representation of a biomedical experiment, ontologies composed of shared vocabularies assembled into subsumption hierarchical structures, and extensible relational data models that link the information components together in a machine-readable and human-useable framework for data mining purposes

UK utility data integration: overcoming schematic heterogeneity

In this paper we discuss syntactic, semantic and schematic issues which inhibit the integration of utility data in the UK. We then focus on the techniques employed within the VISTA project to overcome schematic heterogeneity. A Global Schema based architecture is employed. Although automated approaches to Global Schema definition were attempted the heterogeneities of the sector were too great. A manual approach to Global Schema definition was employed. The techniques used to define and subsequently map source utility data models to this schema are discussed in detail. In order to ensure a coherent integrated model, sub and cross domain validation issues are then highlighted. Finally the proposed framework and data flow for schematic integration is introduced

Real-life performance of protocol combinations for wireless sensor networks

Wireless sensor networks today are used for many and diverse applications like nature monitoring, or process and wireless building automation. However, due to the limited access to large testbeds and the lack of benchmarking standards, the real-life evaluation of network protocols and their combinations remains mostly unaddressed in current literature. To shed further light upon this matter, this paper presents a thorough experimental performance analysis of six protocol combinations for TinyOS. During these protocol assessments, our research showed that the real-life performance often differs substantially from the expectations. Moreover, we found that combining protocols is far from trivial, as individual network protocols may perform very different in combination with other protocols. The results of our research emphasize the necessity of a flexible generic benchmarking framework, powerful enough to evaluate and compare network protocols and their combinations in different use cases

The Parametric Aircraft Noise Analysis Module - status overview and recent applications

The German Aerospace Center (DLR) is investigating aircraft noise prediction and noise reduction capabilities. The Parametric Aircraft Noise Analysis Module (PANAM) is a fast prediction tool by the DLR Institute of Aerodynamics and Flow Technology to address overall aircraft noise. It was initially developed to (1) enable comparative design studies with respect to overall aircraft ground noise and to (2) indentify promising low-noise technologies at early aircraft design stages. A brief survey of available and established fast noise prediction codes is provided in order to rank and classify PANAM among existing tools. PANAM predicts aircraft noise generated during arbitrary 3D approach and take-off flight procedures. Noise generation of an operating aircraft is determined by its design, the relative observer position, configuration settings, and operating condition along the flight path. Feasible noise analysis requires a detailed simulation of all these dominating effects. Major aircraft noise components are simulated with individual models and interactions are neglected. Each component is simulated with a separate semi-empirical and parametric noise source model. These models capture major physical effects and correlations yet allow for fast and accurate noise prediction. Sound propagation and convection effects are applied to the emitting noise source in order to transfer static emission into aircraft ground noise impact with respect to the actual flight operating conditions. Recent developments and process interfaces are presented and prediction results are compared with experimental data recorded during DLR flyover noise campaigns with an Airbus A319 (2006), a VFW-614 (2009), and a Boeing B737-700 (2010). Overall, dominating airframe and engine noise sources are adequately modeled and overall aircraft ground noise levels can sufficiently be predicted. The paper concludes with a brief overview on current code applications towards selected noise reduction technologies