709 research outputs found

    Density measurements of endothermic hydrocarbon fuels at temperatures from 235 k to 353 k and pressures up to 4.09 mpa

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    The densities of endothermic hydrocarbon fuels were measured covering the temperature from (235.0 to 353.0) K at pressures of (0.68,1.47, 3.06 and 4.09) MPa. The densitometer is based on the attenuation theory of gamma ray with a count rate mode. When the intensity of a gamma beam passes through fuels, it decreases exponentially. According to Beer−Lambert’s law, densities of fuel were calculated through the different count rates and densities of referenced fluid. Pure hexane and a binary mixture of n-heptane and n-octane were adopted respectively to validate the reliability and accuracy of the densitometer. Results showed that the average absolute deviation(AAD) was lower than 0.32 % and the maximum absolute deviation(MAD) was within 0.67 %.Papers presented at the 13th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, Portoroz, Slovenia on 17-19 July 2017 .International centre for heat and mass transfer.American society of thermal and fluids engineers

    PSO-based Parameter Estimation of Nonlinear Kinetic Models for β-Mannanase Fermentation

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    Particle swarm optimization (PSO), as a novel evolutionary algorithm involved in social interaction for global space search, was firstly used in kinetic parameter estimation. Based on three developed nonlinear kinetic equations for bacterial cell growth, total sugar utilization and β-mannanase production by Bacillus licheniformis under the support of a batch fermentation process, various PSO algorithms as well as gene algorithms (GA) were developed to estimate kinetic parameters. The performance comparison among these algorithms indicates the improved PSO (Trelea 1) is most suitable for kinetic parameter estimation of β-mannanase fermentation. In order to find the physical-chemical-meanings of kinetic parameters from many optimized results, multiobjective optimization with a normalized weight method was adopted. The 9 desired parameters in equations were obtained by the Trelea 1 type PSO with two batches fermentation data, and the results predicted by the models were also in good agreement with the experimental observations

    The energy spectrum of all-particle cosmic rays around the knee region observed with the Tibet-III air-shower array

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    We have already reported the first result on the all-particle spectrum around the knee region based on data from 2000 November to 2001 October observed by the Tibet-III air-shower array. In this paper, we present an updated result using data set collected in the period from 2000 November through 2004 October in a wide range over 3 decades between 101410^{14} eV and 101710^{17} eV, in which the position of the knee is clearly seen at around 4 PeV. The spectral index is -2.68 ±\pm 0.02(stat.) below 1PeV, while it is -3.12 ±\pm 0.01(stat.) above 4 PeV in the case of QGSJET+HD model, and various systematic errors are under study now.Comment: 12 pages, 7 figures, accepted by Advances in space researc

    Moon Shadow by Cosmic Rays under the Influence of Geomagnetic Field and Search for Antiprotons at Multi-TeV Energies

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    We have observed the shadowing of galactic cosmic ray flux in the direction of the moon, the so-called moon shadow, using the Tibet-III air shower array operating at Yangbajing (4300 m a.s.l.) in Tibet since 1999. Almost all cosmic rays are positively charged; for that reason, they are bent by the geomagnetic field, thereby shifting the moon shadow westward. The cosmic rays will also produce an additional shadow in the eastward direction of the moon if cosmic rays contain negatively charged particles, such as antiprotons, with some fraction. We selected 1.5 x10^{10} air shower events with energy beyond about 3 TeV from the dataset observed by the Tibet-III air shower array and detected the moon shadow at 40σ\sim 40 \sigma level. The center of the moon was detected in the direction away from the apparent center of the moon by 0.23^\circ to the west. Based on these data and a full Monte Carlo simulation, we searched for the existence of the shadow produced by antiprotons at the multi-TeV energy region. No evidence of the existence of antiprotons was found in this energy region. We obtained the 90% confidence level upper limit of the flux ratio of antiprotons to protons as 7% at multi-TeV energies.Comment: 13pages,4figures; Accepted for publication in Astroparticle Physic

    Electric charge quantization and the muon anomalous magnetic moment

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    We investigate some proposals to solve the electric charge quantization puzzle, which simultaneously explain the recent measured deviation on the muon anomalous magnetic moment. For this we assess extensions of the Electro-Weak Standard Model spanning modifications on the scalar sector only. It is interesting to verify that one can have modest extensions which easily account for the solution for both problems.Comment: 20 pages, 1 figures, needs macro axodraw.st

    Does accelerating universe indicates Brans-Dicke theory

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    The evolution of universe in Brans-Dicke (BD) theory is discussed in this paper. Considering a parameterized scenario for BD scalar field ϕ=ϕ0aα\phi=\phi_{0}a^{\alpha} which plays the role of gravitational "constant" GG, we apply the Markov Chain Monte Carlo method to investigate a global constraints on BD theory with a self-interacting potential according to the current observational data: Union2 dataset of type supernovae Ia (SNIa), high-redshift Gamma-Ray Bursts (GRBs) data, observational Hubble data (OHD), the cluster X-ray gas mass fraction, the baryon acoustic oscillation (BAO), and the cosmic microwave background (CMB) data. It is shown that an expanded universe from deceleration to acceleration is given in this theory, and the constraint results of dimensionless matter density Ω0m\Omega_{0m} and parameter α\alpha are, Ω0m=0.2860.0390.047+0.037+0.050\Omega_{0m}=0.286^{+0.037+0.050}_{-0.039-0.047} and α=0.00460.01710.0206+0.0149+0.0171\alpha=0.0046^{+0.0149+0.0171}_{-0.0171-0.0206} which is consistent with the result of current experiment exploration, α0.132124\mid\alpha\mid \leq 0.132124. In addition, we use the geometrical diagnostic method, jerk parameter jj, to distinguish the BD theory and cosmological constant model in Einstein's theory of general relativity.Comment: 16 pages, 3 figure

    Observational constraint on generalized Chaplygin gas model

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    We investigate observational constraints on the generalized Chaplygin gas (GCG) model as the unification of dark matter and dark energy from the latest observational data: the Union SNe Ia data, the observational Hubble data, the SDSS baryon acoustic peak and the five-year WMAP shift parameter. It is obtained that the best fit values of the GCG model parameters with their confidence level are As=0.730.06+0.06A_{s}=0.73^{+0.06}_{-0.06} (1σ1\sigma) 0.09+0.09^{+0.09}_{-0.09} (2σ)(2\sigma), α=0.090.12+0.15\alpha=-0.09^{+0.15}_{-0.12} (1σ1\sigma) 0.19+0.26^{+0.26}_{-0.19} (2σ)(2\sigma). Furthermore in this model, we can see that the evolution of equation of state (EOS) for dark energy is similar to quiessence, and its current best-fit value is w0de=0.96w_{0de}=-0.96 with the 1σ1\sigma confidence level 0.91w0de1.00-0.91\geq w_{0de}\geq-1.00.Comment: 9 pages, 5 figure

    Combined constraints on modified Chaplygin gas model from cosmological observed data: Markov Chain Monte Carlo approach

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    We use the Markov Chain Monte Carlo method to investigate a global constraints on the modified Chaplygin gas (MCG) model as the unification of dark matter and dark energy from the latest observational data: the Union2 dataset of type supernovae Ia (SNIa), the observational Hubble data (OHD), the cluster X-ray gas mass fraction, the baryon acoustic oscillation (BAO), and the cosmic microwave background (CMB) data. In a flat universe, the constraint results for MCG model are, Ωbh2=0.022630.00162+0.00184\Omega_{b}h^{2}=0.02263^{+0.00184}_{-0.00162} (1σ1\sigma) 0.00195+0.00213^{+0.00213}_{-0.00195} (2σ)(2\sigma), Bs=0.77880.0723+0.0736B_{s}=0.7788^{+0.0736}_{-0.0723} (1σ1\sigma) 0.0904+0.0918^{+0.0918}_{-0.0904} (2σ)(2\sigma), α=0.10790.2539+0.3397\alpha=0.1079^{+0.3397}_{-0.2539} (1σ1\sigma) 0.2911+0.4678^{+0.4678}_{-0.2911} (2σ)(2\sigma), B=0.001890.00756+0.00583B=0.00189^{+0.00583}_{-0.00756} (1σ1\sigma) 0.00915+0.00660^{+0.00660}_{-0.00915} (2σ)(2\sigma), and H0=70.7113.142+4.188H_{0}=70.711^{+4.188}_{-3.142} (1σ1\sigma) 4.149+5.281^{+5.281}_{-4.149} (2σ)(2\sigma).Comment: 12 pages, 1figur
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