Critical evaluation of Jet-A spray combustion using propane chemical kinetics in gas turbine combustion simulated by KIVA-2

Jet-A spray combustion has been evaluated in gas turbine combustion with the use of propane chemical kinetics as the first approximation for the chemical reactions. Here, the numerical solutions are obtained by using the KIVA-2 computer code. The KIVA-2 code is the most developed of the available multidimensional combustion computer programs for application of the in-cylinder combustion dynamics of internal combustion engines. The released version of KIVA-2 assumes that 12 chemical species are present; the code uses an Arrhenius kinetic-controlled combustion model governed by a four-step global chemical reaction and six equilibrium reactions. Researchers efforts involve the addition of Jet-A thermophysical properties and the implementation of detailed reaction mechanisms for propane oxidation. Three different detailed reaction mechanism models are considered. The first model consists of 131 reactions and 45 species. This is considered as the full mechanism which is developed through the study of chemical kinetics of propane combustion in an enclosed chamber. The full mechanism is evaluated by comparing calculated ignition delay times with available shock tube data. However, these detailed reactions occupy too much computer memory and CPU time for the computation. Therefore, it only serves as a benchmark case by which to evaluate other simplified models. Two possible simplified models were tested in the existing computer code KIVA-2 for the same conditions as used with the full mechanism. One model is obtained through a sensitivity analysis using LSENS, the general kinetics and sensitivity analysis program code of D. A. Bittker and K. Radhakrishnan. This model consists of 45 chemical reactions and 27 species. The other model is based on the work published by C. K. Westbrook and F. L. Dryer

Remarkably high abundance of presolar grains in interplanetary dust particles collected from the comet Grigg-Skjellerup dust stream

Isotopic studies of IDPs collected from the comet Grigg-Skjellerup dust stream reveal extremely high abundances of presolar grains in two of four IDPs. These abundances exceed those of any other extraterrestrial material analyzed and support a cometary origin for these IDPs

Strong disorder renormalization group on fractal lattices: Heisenberg models and magnetoresistive effects in tight binding models

We use a numerical implementation of the strong disorder renormalization group (RG) method to study the low-energy fixed points of random Heisenberg and tight-binding models on different types of fractal lattices. For the Heisenberg model new types of infinite disorder and strong disorder fixed points are found. For the tight-binding model we add an orbital magnetic field and use both diagonal and off-diagonal disorder. For this model besides the gap spectra we study also the fraction of frozen sites, the correlation function, the persistent current and the two-terminal current. The lattices with an even number of sites around each elementary plaquette show a dominant $\phi_0=h/e$ periodicity. The lattices with an odd number of sites around each elementary plaquette show a dominant $\phi_0/2$ periodicity at vanishing diagonal disorder, with a positive weak localization-like magnetoconductance at infinite disorder fixed points. The magnetoconductance with both diagonal and off-diagonal disorder depends on the symmetry of the distribution of on-site energies.Comment: 19 pages, 20 figure

Interaction effects and charge quantization in single-particle quantum dot emitters

We discuss a theoretical model of an on-demand single-particle emitter that employs a quantum dot, attached to an integer or fractional quantum Hall edge state. Via an exact mapping of the model onto the spin-boson problem we show that Coulomb interactions between the dot and the chiral quantum Hall edge state, unavoidable in this setting, lead to a destruction of precise charge quantization in the emitted wave-packet. Our findings cast doubts on the viability of this set-up as a single-particle source of quantized charge pulses. We further show how to use a spin-boson master equation approach to explicitly calculate the current pulse shape in this set-up.Comment: 5+5 pages, 3 figures, fixed typos, update Supplement Material and update figure

Finite-Dimensional Representations of the Quantum Superalgebra Uq_{q}[gl(2/2)]: II. Nontypical representations at generic qq

The construction approach proposed in the previous paper Ref. 1 allows us there and in the present paper to construct at generic deformation parameter $q$ all finite--dimensional representations of the quantum Lie superalgebra $U_{q}[gl(2/2)]$. The finite--dimensional $U_{q}[gl(2/2)]$-modules $W^{q}$ constructed in Ref. 1 are either irreducible or indecomposible. If a module $W^{q}$ is indecomposible, i.e. when the condition (4.41) in Ref. 1 does not hold, there exists an invariant maximal submodule of $W^{q}$, to say $I_{k}^{q}$, such that the factor-representation in the factor-module $W^{q}/I_{k}^{q}$ is irreducible and called nontypical. Here, in this paper, indecomposible representations and nontypical finite--dimensional representations of the quantum Lie superalgebra $U_{q}[gl(2/2)]$ are considered and classified as their module structures are analized and the matrix elements of all nontypical representations are written down explicitly.Comment: Latex file, 49 page

Applied analytical combustion/emissions research at the NASA Lewis Research Center

Emissions of pollutants from future commercial transports are a significant concern. As a result, the Lewis Research Center (LeRC) is investigating various low emission combustor technologies. As part of this effort, a combustor analysis code development program was pursued to guide the combustor design process, to identify concepts having the greatest promise, and to optimize them at the lowest cost in the minimum time

Wearable Sensor Data Based Human Activity Recognition using Machine Learning: A new approach

Recent years have witnessed the rapid development of human activity recognition (HAR) based on wearable sensor data. One can find many practical applications in this area, especially in the field of health care. Many machine learning algorithms such as Decision Trees, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, and Multilayer Perceptron are successfully used in HAR. Although these methods are fast and easy for implementation, they still have some limitations due to poor performance in a number of situations. In this paper, we propose a novel method based on the ensemble learning to boost the performance of these machine learning methods for HAR

Efficient ARQ retransmission schemes for two-way relay networks.

In this paper, we investigate different practical automatic repeat request (ARQ) retransmission protocols for two-way wireless relay networks based on network coding (NC). The idea of NC is applied to increase the achievable throughput for the exchange of information between two terminals through one relay. Using NC, throughput efficiency is significantly improved due to the reduction of the number of retransmissions. Particularly, two improved NC-based ARQ schemes are designed based on go-back-N and selective-repeat (SR) protocols. The analysis of throughput efficiency is then carried out to find the best retransmission strategy for different scenarios. It is shown that the combination of improved NC-based SR ARQ scheme in the broadcast phase and the traditional SR ARQ scheme in the multiple access phase achieves the highest throughput efficiency compared to the other combinations of ARQ schemes. Finally, simulation results are provided to verify the theoretical analysis