Using a side-branched volume to tune the acoustic field in a looped-tube travelling-wave thermoacoustic engine with a RC load

Travelling-wave thermoacoustic engine utilises a compact acoustic network to obtain a right time-phasing between the acoustic velocity and pressure oscillations within the regenerator to force gas parcels to experience a Stirling-like thermodynamic cycle. As such, thermal energy can be converted to mechanical work (i.e., high-intensity pressure waves). It is therefore crucial to control the time-phasing carefully to improve the performance of thermoacoustic engines. Various ways have been proposed and demonstrated for adjusting time-phasing, including both passive and active methods. The aim of this study is to introduce a new passive phase tuning method (i.e., a side-branched acoustic volume) to tune the time-phasing within a looped-tube travelling wave thermoacoustic engine. The proposed concept has been investigated both numerically and experimentally in this research. An experimental rig was simulated and designed using DeltaEC software (Design Environment for Low-amplitude ThermoAcoustic Energy Conversion). It was then constructed according to the obtained theoretical model. The result of this study showed a qualitative agreement between experimental measurement and numerical simulations, demonstrating that the proposed technique can effectively adjust the phase angle between the acoustic velocity and pressure oscillations within the loop-tube thermoacoustic engines, and improve its performance

A Combined Organic Rankine Cycle-Heat Pump System for Domestic Hot Water Application

This paper investigates a novel system to improve the efficiency of using natural gas for domestic heating. The exhaust from a gas burner powers a small-scale Organic Rankine Cycle (ORC) system using hexane as the working fluid, which is used to directly drive the compressor of a heat pump, using R134a as the working fluid. Water is heated from ambient by passing it through three heat exchangers, the condenser of the Heat Pump, the condenser of the ORC, and the secondary heat exchanger that is heated by the hot flue gas from the burner after it transfers the heat to the evaporator of the ORC subsystem. By using the heat generated from the burning of gas in a burner in this way, a fuel-to-usable-heat efficiency of up to 160% is projected, outperforming the other technologies discussed, giving it the potential to significantly reduce energy demand and carbon emissions. This paper investigates the effect of varying ambient conditions upon the cycle, namely the temperature of ambient air, which has a strong effect on the performance of the heat pump

Bayesian adaptive lasso quantile regression

Recently, variable selection by penalized likelihood has attracted much research interest. In this paper, we propose adaptive Lasso quantile regression (BALQR) from a Bayesian perspective. The method extends the Bayesian Lasso quantile regression by allowing different penalization parameters for different regression coefficients. Inverse gamma prior distributions are placed on the penalty parameters. We treat the hyperparameters of the inverse gamma prior as unknowns and estimate them along with the other parameters. A Gibbs sampler is developed to simulate the parameters from the posterior distributions. Through simulation studies and analysis of a prostate cancer dataset, we compare the performance of the BALQR method proposed with six existing Bayesian and non-Bayesian methods. The simulation studies and the prostate cancer data analysis indicate that the BALQR method performs well in comparison to the other approaches

Prior elicitation in Bayesian quantile regression for longitudinal data

© 2011 Alhamzawi R, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original auhor and source are credited.This article has been made available through the Brunel Open Access Publishing Fund.In this paper, we introduce Bayesian quantile regression for longitudinal data in terms of informative priors and Gibbs sampling. We develop methods for eliciting prior distribution to incorporate historical data gathered from similar previous studies. The methods can be used either with no prior data or with complete prior data. The advantage of the methods is that the prior distribution is changing automatically when we change the quantile. We propose Gibbs sampling methods which are computationally efficient and easy to implement. The methods are illustrated with both simulation and real data.This article is made available through the Brunel Open Access Publishing Fund

Verifying collision avoidance behaviours for unmanned surface vehicles using probabilistic model checking

Collision avoidance is an essential safety requirement for unmanned surface vehicles (USVs). Normally, its practical verification is non-trivial, due to the stochastic behaviours of both the USVs and the intruders. This paper presents the probabilistic timed automata (PTAs) based formalism for three collision avoidance behaviours of USVs in uncertain dynamic environments, which are associated with the crossing situation in COLREGs. Steering right, acceleration, and deceleration are considered potential evasive manoeuvres. The state-of-the-art prism model checker is applied to analyse the underlying models. This work provides a framework and practical application of the probabilistic model checking for decision making in collision avoidance for USVs

Incremental eigenpair computation for graph Laplacian matrices: theory and applications

The smallest eigenvalues and the associated eigenvectors (i.e., eigenpairs) of a graph Laplacian matrix have been widely used for spectral clustering and community detection. However, in real-life applications, the number of clusters or communities (say, K) is generally unknown a priori. Consequently, the majority of the existing methods either choose K heuristically or they repeat the clustering method with different choices of K and accept the best clustering result. The first option, more often, yields suboptimal result, while the second option is computationally expensive. In this work, we propose an incremental method for constructing the eigenspectrum of the graph Laplacian matrix. This method leverages the eigenstructure of graph Laplacian matrix to obtain the Kth smallest eigenpair of the Laplacian matrix given a collection of all previously compute

Nonexistence of Entanglement Sudden Death in High NOON States

We study the dynamics of entanglement in continuous variable quantum systems (CVQS). Specifically, we study the phenomena of Entanglement Sudden Death (ESD) in general two-mode-N-photon states undergoing pure dephasing. We show that for these states, ESD never occurs. These states are generalizations of the so-called High NOON states, shown to decrease the Rayleigh limit of lambda to lambda/N, which promises great improvement in resolution of interference patterns if states with large N are physically realized. However, we show that in dephasing NOON states, the time to reach V_crit, critical visibility, scales inversely with N^2. On the practical level, this shows that as N increases, the visibility degrades much faster, which is likely to be a considerable drawback for any practical application of these states.Comment: 4 pages, 1 figur