Preliminary experimental comparison and feasibility analysis of CO2/R134a mixture in Organic Rankine Cycle for waste heat recovery from diesel engines

This paper presents results of a preliminary experimental study of the Organic Rankine Cycle (ORC) using CO2/R134a mixture based on an expansion valve. The goal of the research was to examine the feasibility and effectiveness of using CO2 mixtures to improve system performance and expand the range of condensation temperature for ORC system. The mixture of CO2/R134a (0.6/0.4) on a mass basis was selected for comparison with pure CO2 in both the preheating ORC (P-ORC) and the preheating regenerative ORC (PR-ORC). Then, the feasibility and application potential of CO2/R134a (0.6/0.4) mixture for waste heat recovery from engines was tested under ambient cooling conditions. Preliminary experimental results using an expansion valve indicate that CO2/R134a (0.6/0.4) mixture exhibits better system performance than pure CO2. For PR-ORC using CO2/R134a (0.6/0.4) mixture, assuming a turbine isentropic efficiency of 0.7, the net power output estimation, thermal efficiency and exergy efficiency reached up to 5.30 kW, 10.14% and 24.34%, respectively. For the fitting value at an expansion inlet pressure of 10 MPa, the net power output estimation, thermal efficiency and exergy efficiency using CO2/R134a (0.6/0.4) mixture achieved increases of 23.3%, 16.4% and 23.7%, respectively, versus results using pure CO2 as the working fluid. Finally, experiments showed that the ORC system using CO2/R134a (0.6/0.4) mixture is capable of operating stably under ambient cooling conditions (25.2–31.5 °C), demonstrating that CO2/R134a mixture can expand the range of condensation temperature and alleviate the low-temperature condensation issue encountered with CO2. Under the ambient cooling source, it is expected that ORC using CO2/R134a (0.6/0.4) mixture will improve the thermal efficiency of a diesel engine by 1.9%

A new small satellite sunspot triggering recurrent standard- and blowout-coronal jets

In this paper,we report a detailed analysis of recurrent jets originated from a location with emerging, canceling and converging negative magnetic field at the east edge of NOAA active region AR11166 from 2011 March 09 to 10. The event presented several interesting features. First, a satellite sunspot appeared and collided with a pre-existing opposite polarity magnetic field and caused a recurrent solar jet event. Second, the evolution of the jets showed blowout-like nature and standard characteristics. Third, the satellite sunspot exhibited a motion toward southeast of AR11166 and merged with the emerging flux near the opposite polarity sunspot penumbra, which afterward, due to flux convergence and cancellation episodes, caused recurrent jets. Fourth, three of the blowout jets associated with coronal mass ejections (CMEs), were observed from field of view of the Solar Terrestrial Relations Observatory. Fifth, almost all the blowout jet eruptions were accompanied with flares or with more intense brightening in the jet base region, while almost standard jets did not manifest such obvious feature during eruptions. The most important, the blowout jets were inclined to faster and larger scale than the standard jets. The standard jets instead were inclined to relative longer-lasting. The obvious shearing and twisting motions of the magnetic field may be interpreted as due to the shearing and twisting motions for a blowout jet eruption. From the statistical results, about 30% blowout jets directly developed into CMEs. It suggests that the blowout jets and CMEs should have a tight relationship.Comment: ApJ 18 pages, 7 figure

NMF-Based Comprehensive Latent Factor Learning with Multiview da

Multiview representations reveal the latent information of the data from different perspectives, consistency and complementarity. Unlike most multiview learning approaches, which focus only one perspective, in this paper, we propose a novel unsupervised multiview learning algorithm, called comprehensive latent factor learning (CLFL), which jointly exploits both consistent and complementary information among multiple views. CLFL adopts a non-negative matrix factorization based formulation to learn the latent factors. It learns the weights of different views automatically which makes the representation more accurate. Experiment results on a synthetic and several real datasets demonstrate the effectiveness of our approach

Microstructure and Fe-vacancy ordering in the KFexSe2 superconducting system

Structural investigations by means of transmission electron microscopy (TEM) on KFexSe2 with 1.5 \leq x \leq 1.8 have revealed a rich variety of microstructure phenomena, the KFe1.5Se2 crystal often shows a superstructure modulation along the [310] zone-axis direction, this superstructure can be well interpreted by the Fe-vacancy order within the a-b plane. Increase of Fe-concentration in the KFexSe2 materials could not only result in the appearance of superconductivity but also yield clear alternations of microstructure. Structural inhomogeneity, the complex superstructures and defect structures in the superconducting KFe1.8Se2 sample have been investigated based on the high-resolution TEM.Comment: 13 pages, 4 figure

Reversible Embedding to Covers Full of Boundaries

In reversible data embedding, to avoid overflow and underflow problem, before data embedding, boundary pixels are recorded as side information, which may be losslessly compressed. The existing algorithms often assume that a natural image has little boundary pixels so that the size of side information is small. Accordingly, a relatively high pure payload could be achieved. However, there actually may exist a lot of boundary pixels in a natural image, implying that, the size of side information could be very large. Therefore, when to directly use the existing algorithms, the pure embedding capacity may be not sufficient. In order to address this problem, in this paper, we present a new and efficient framework to reversible data embedding in images that have lots of boundary pixels. The core idea is to losslessly preprocess boundary pixels so that it can significantly reduce the side information. Experimental results have shown the superiority and applicability of our work