The transport properties of quark-gluon plasma created in relativistic heavy-ion collisions are quantified by an improved global Bayesian analysis using the CERN Large Hadron Collider Pb–Pb data at ...sNN=2.76 and 5.02 TeV. The results show that the uncertainty of the extracted transport coefficients is significantly reduced by including new sophisticated collective flow observables from two collision energies for the first time. This work reveals the stronger temperature dependence of specific shear viscosity, a lower value of specific bulk viscosity, and a higher hadronization switching temperature than in the previous studies. The sensitivity analysis confirms that the precision measurements of higher-order harmonic flow and their correlations are crucial in extracting accurate values of the transport properties.
Using the AdS/CFT correspondence, the effect of α′-correction on the value of Chiral Magnetic Effect (CME) is computed by adding a number of spinning probe D7-branes in the α′-corrected background. ...We numerically show that the magnitude of CME rises in the presence of α′-correction for massive solutions and this increase is more sensible at higher temperatures. However, this value does not change for massless solution. Although some of the D7-brane embeddings have no CME, after applying the α′-correction they find a non-zero value for the CME. We also show that the effect of α′-correction removes the singularity from some of the D7-brane embeddings.
Heavy-ion experiments provide a new opportunity to gain a deeper understanding of the structure of nuclei. To achieve this, it is crucial to identify observables under circumstances that are ...minimally affected by the process that leads to the initial state of heavy-ion collisions from nuclear wavefunction. In this study, we demonstrate that when assuming scale-invariance, the effect of this stage on the initial energy or entropy density moments in ultra-central symmetric collisions is negligible for nucleon sizes of approximately 0.7 fm or larger for large nuclei. By borrowing cluster expansion method from statistical physics and using scale-invariance assumption, we calculate the average ellipticity of initial density at the presence of short-range correlation. We compare our calculations to Monte Carlo studies and assess the accuracy of various methods of short-range correlation sampling. Additionally, we find that the isobar ratio can constrain the initial state parameters, in addition to deformation. Our study indicates that the isobar ratios in ultra-central collisions are especially sensitive to the fluctuation in the weight of the nuclei constituents and the two-body correlation among nucleons. This insight is crucial for drawing conclusions about nuclear deformations based on isobar ratios.
We study the angular correlation and the amount of top quark polarization in the production of a higgs boson in association with a single top quark in the
t
−channel at the LHC. We also study the ...effect of anomalous
W
t
b
couplings on the angular correlation and on the production cross section of the process. The cross section and angular correlation is almost insensitive to the variation of the Higgs boson mass within 3 GeV. The robustness of the angular correlation against the center-of-mass energy of the proton-proton collision, the variation of parton distribution functions, and the change of factorization scale is investigated. The sensitivity of this process to the anomalous couplings is examined.
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•GA is used to optimize RBF, MLP and NARX to predict the SOC of HCCI engine.•The best MLP is a 25-20-20 network with 1 and 2 delays for inputs and feedback.•Optimal number of neurons ...and bandwidth of Gaussian kernel for RBF are 0.2 and 800.•The best NARX is a 2-layer network with 10 and 5 neurons in the 1st and 2nd layers.•Optimized NARX has the best performance and least computational cost.
The combustion process in Homogeneous Charge Compression Ignition Engines (HCCI) is one of the new methods of futuristic combustion technologies. Since there is no direct operator for the start of the combustion (SOC) of these engines, air-fuel mixture properties at the moment of entering the combustion chamber, specifies the ignition timing. In HCCI engines, the ignition timing is the most crucial factor in determining other engine operating characteristics such as power output, pollution, and fuel consumption. To control SOC, there should be an accurate predictive model based on the entering air-fuel mixture properties. The Artificial Neural Networks (ANN) approach can be considered as a solution with less computational costs than traditional physics-based modeling. In this investigation, a multi-input single-output model was developed for predicting the SOC of the HCCI engine for a wide range of engine operation. Three popular architectures namely the Nonlinear Autoregressive Network with Exogenous Inputs (NARXNET), Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) were used, for this purpose. The networks were trained using experimental data taken from a one-cylinder Ricardo engine. The network architecture was optimized using a Genetic Algorithm (GA) method. By using GA, the proposed networks also have the optimum network structures, improved model predictive behaviors, and simulation costs of the learning process. After optimization, the regression ratio between the outputs of MLP and the corresponding experimental data was increased from 0.8965 to 0.96166. This value was improved from 0.7623 to 0.83991 for RBF. By using GA, the time needed to train the NARX was reduced from 3.12 s to 0.46 s. By comparing the model predictions with the experimental data, it was shown that the selected neural network architectures are powerful approaches for non-linear modeling the SOC of the HCCI engine.
Corrosion inhibition mechanism of two multifunctional nanostructured inhibitors, including graphene oxide/silver nanostructure (GO-Ag) and carbon quantum dots/copper nanoparticles (CQDs-Cu) on API ...5 L Grade X60 PSL2 surface (10 mm × 10 mm × 3 mm) was investigated by quantum chemical calculation and molecular dynamics (MD) simulations. Global reactivity parameters such as E
HOMO
, E
LUMO
, energy gap, etc., have been studied to investigate their relative corrosion inhibition performance. Local reactivity parameters of both inhibitor molecules have been analyzed through Mulliken population distribution and Fukui functions. Moreover, the adsorption behavior of the multifunctional nanostructured inhibitor’s molecule on the Fe (1 1 0) surface has been studied using MD simulations. The high E
HOMO
and low value of E
LUMO
indicated the existence of doner-acceptor sites on the inhibitor’s molecule, resulting in the high tendency of CQDs-Cu and GO-Ag to react with Fe and create a protective layer. The result of interaction energy, diffusion coefficient, and fractional free volume indicated the formation of a compact film on the surface of Fe by CQDs-Cu and GO-Ag, tackling the movement of corrosive particles towards the Fe surface. The corrosion inhibition mechanism of multifunctional nanostructured inhibitors was finally suggested based on experimental and theoretical studies.
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