Gas Flows in Microsystems

International audienc

Probabilistic Linear Discriminant Analysis for Acoustic Modeling

Acoustic models using probabilistic linear discriminant analysis (PLDA) capture the correlations within feature vectors using subspaces which do not vastly expand the model. This allows high dimensional and correlated feature spaces to be used, without requiring the estimation of multiple high dimension covariance matrices. In this letter we extend the recently presented PLDA mixture model for speech recognition through a tied PLDA approach, which is better able to control the model size to avoid overfitting. We carried out experiments using the Switchboard corpus, with both mel frequency cepstral coefficient features and bottleneck feature derived from a deep neural network. Reductions in word error rate were obtained by using tied PLDA, compared with the PLDA mixture model, subspace Gaussian mixture models, and deep neural networks

Support the Underground: Characteristics of Beyond-Mainstream Music Listeners

Music recommender systems have become an integral part of music streaming services such as Spotify and Last.fm to assist users navigating the extensive music collections offered by them. However, while music listeners interested in mainstream music are traditionally served well by music recommender systems, users interested in music beyond the mainstream (i.e., non-popular music) rarely receive relevant recommendations. In this paper, we study the characteristics of beyond-mainstream music and music listeners and analyze to what extent these characteristics impact the quality of music recommendations provided. Therefore, we create a novel dataset consisting of Last.fm listening histories of several thousand beyond-mainstream music listeners, which we enrich with additional metadata describing music tracks and music listeners. Our analysis of this dataset shows four subgroups within the group of beyond-mainstream music listeners that differ not only with respect to their preferred music but also with their demographic characteristics. Furthermore, we evaluate the quality of music recommendations that these subgroups are provided with four different recommendation algorithms where we find significant differences between the groups. Specifically, our results show a positive correlation between a subgroup's openness towards music listened to by members of other subgroups and recommendation accuracy. We believe that our findings provide valuable insights for developing improved user models and recommendation approaches to better serve beyond-mainstream music listeners.Comment: Accepted for publication in EPJ Data Science - link to published version will be adde

Design, Fabrication, and Testing of a Micro Fuel Injection Swirler for Lean Premixed Combustion in Gas Turbine Engines

Due to growing energy demands and the need for increased fuel consumption efficiency, environmental protection agencies are imposing more stringent emissions regulations on gas turbine combustion systems with emphasis on NOx emissions reduction. Emerging technological combustion schemes to reduce NOx commonly employ lean premixed combustion. Decreases in NOx are globally obtained by flame temperature decrease and locally improved by homogeneity of the reacting mixture. A new micro fuel injection swirler, capable of providing efficient and rapid mixing over a short distance, is presented in this thesis. The conception of the Micro Fuel Injection Swirler (MFIS) was motivated by the need for enhanced mixing devices in lean premixed combustion and the capabilities of a micro manufacturing technique developed at Louisiana State University in conjunction with Mezzo Technologies. The MFIS uses a circular array of porous panels manufactured with an internal fluid cavity which allows for micro scale fuel distribution. The fuel is injected perpendicular to the blade opposing the oncoming stream of air which produces a highly turbulent swirling flow to enhance combustion stability at ultra lean operation necessary to reduce NOx emissions. A process was developed to fabricate and assemble the MFIS economically and reliably while ensuring dimensional stability. A benchmark swirler was also manufactured with similar dimensions as the MFIS but none of the inherent geometry characterizing the advantages of the MFIS. A combustion chamber was designed and fabricated to provide testing infrastructure for verifying the performance of the MFIS. Combustion results indicated that the MFIS was capable of achieving relatively lower equivalence ratios at LBO compared to benchmark cases tested. At a set equivalence ratio, the MFIS produced higher flame temperatures, higher heat release rates, and comparable NOx emissions. At equivalent operating temperatures, the MFIS produced nearly equal NOx emissions compared to the perfectly premixed case. Hydrogen testing showed that the lean blowout limits could be extended with hydrogen addition, providing further reduction in NOx while allowing stable combustion. In summary, the MFIS was capable of providing efficient air/fuel mixing over a short premixing distance, affirming its effectiveness in lean premixed combustion systems

Identifiable and interpretable nonparametric factor analysis

Factor models have been widely used to summarize the variability of high-dimensional data through a set of factors with much lower dimensionality. Gaussian linear factor models have been particularly popular due to their interpretability and ease of computation. However, in practice, data often violate the multivariate Gaussian assumption. To characterize higher-order dependence and nonlinearity, models that include factors as predictors in flexible multivariate regression are popular, with GP-LVMs using Gaussian process (GP) priors for the regression function and VAEs using deep neural networks. Unfortunately, such approaches lack identifiability and interpretability and tend to produce brittle and non-reproducible results. To address these problems by simplifying the nonparametric factor model while maintaining flexibility, we propose the NIFTY framework, which parsimoniously transforms uniform latent variables using one-dimensional nonlinear mappings and then applies a linear generative model. The induced multivariate distribution falls into a flexible class while maintaining simple computation and interpretation. We prove that this model is identifiable and empirically study NIFTY using simulated data, observing good performance in density estimation and data visualization. We then apply NIFTY to bird song data in an environmental monitoring application.Comment: 50 pages, 17 figure

DEVELOPMENT AND TESTING OF A PRE-PROTOTYPE MACH 2 RAMGEN ENGINE

The research and development effort of a new kind of combustion engine is presented. The engine is designed to convert the thrust from ramjet modules into shaft torque, which in turn can be used for electrical power generation or mechanical drive applications. An aggressive test program was undertaken that included evaluation of the existing engine, as well as incorporation of novel improvements to the thrust modules and supporting systems. Fuel mixing studies with Vortex Generators and bluff body flame holders illuminated the importance of increasing the shear-layer area and spreading angle to augment flame volume. Evaluation of flame-holding configurations (with variable fuel injection methods) concluded that the heat release zone, and therefore combustion efficiency, could be manipulated by judicious selection of bluff body geometry, and is less influenced by fuel injection distribution. Air film cooling studies demonstrated that acceptable combustor life could be achieved with optimized air film distribution patterns and thermal barrier coatings

Index to 1985 NASA Tech Briefs, volume 10, numbers 1-4

Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1985 Tech Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences