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Results for “CLVisc”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Longitudinal fluctuations and decorrelations of anisotropic flows in relativistic heavy-ion collisions

We study the longitudinal decorrelations of elliptic, triangular and quadrangular flows in heavy-ion collisions at the LHC and RHIC energies. The event-by-event CLVisc (3+1)-dimensional hydrodynamics model, combined with the fully fluctuating AMPT initial conditions, is utilized to simulate the space-time evolution of the strongly-coupled quark-gluon plasma. Detailed analysis is performed for the longitudinal decorrelations of flow vectors, flow magnitudes and flow orientations. We find strong correlations between final-state longitudinal decorrelations of anisotropic flows and initial-state longitudinal structures and collision geometry: while the decorrelation of elliptic flow shows a non- monotonic centrality dependence due to initial elliptic geometry, typically the longitudinal flow decorrelations are larger in lower energy and less central collisions where the mean lengths of the string structure are shorter in the initial states.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Effects of dissipative baryon current in heavy-ion collisions at RHIC-BES energies

The CLVisc (3+1)D viscous hydrodynamic model is extended to include the equation of net baryon conservation and the Israel-Stewart-like equations for dissipative baryon current. Using the NEOSB equation of state, we simulate the dynamical evolution and collectivity of the quark-gluon plasma with finite chemical potential, assuming smooth energy density and net baryon density distributions at the initial proper time. Numerical results are shown for the impact of net-baryon dissipation on particle yields and p T spectra in heavy-ion collisions at beam energy scan energies.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Hydrodynamic description of D meson production in high-energy heavy-ion collisions *

The large values and constituent-quark-number scaling of the elliptic flow of low-p T D mesons imply that charm quarks, initially produced through hard processes, might be partially thermalized through strong interactions with quark-gluon plasma (QGP) in high-energy heavy-ion collisions. To quantify the degree of thermalization of low-p T charm quarks, we compare the D 0 meson spectra and elliptic flow from a hydrodynamic model to experimental data as well as transport model simulations. We use an effective charm chemical potential at the freeze-out temperature to account for the initial charm quark production from hard processes and assume that they are thermalized in the local comoving frame of the medium before freeze-out. D 0 mesons are sampled statistically from the freeze-out hyper-surface of the expanding QGP as described by the event-by-event (3+1)D viscous hydrodynamic model CLVisc. Both the hydrodynamic and transport models can describe the elliptic flow of D 0 mesons at P T < 3 GeV/c as measured in Au+Au collisions at $sqrts_{NN} = 200$ GeV. Though the experimental data on spectra are consistent with the hydrodynamic result at small p T ~ 1 GeV/c, they deviate from the hydrodynamic model at high transverse momentum, GeV/c. The diffusion and parton energy loss mechanisms in the transport model can describe the measured spectra reasonably well within the theoretical uncertainty. Finally, our comparative study indicates that charm quarks only approach local thermal equilibrium at small p T , even though they acquire sizable elliptic flow comparable to light-quark hadrons at both small and intermediate p T .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Interplaying mechanisms behind single inclusive jet suppression in heavy-ion collisions

The suppression factor for single inclusive jets in Pb + Pb collisions at the Large Hadron Collider (LHC) has a weak dependence on the transverse momentum $p_T$ and remains almost the same at two colliding energies, $\sqrt{s}$ = 2.76 and 5.02 TeV, though the central rapidity density of bulk hadrons increases by about 20%. Here, this phenomenon is investigated within the linear Boltzmann transport (LBT) model, which includes elastic and inelastic processes based on perturbative QCD for both jet shower and recoil medium partons as they propagate through a quark-gluon plasma (QGP). With the dynamic evolution of the QGP given by the 3 + 1 D CLVisc hydrodynamic model with event-by-event fully fluctuating initial conditions, single inclusive jet suppression in Pb + Pb collisions from LBT agrees well with experimental data. The weak $ \sqrt{s} $ and $p_T$ dependence of the jet suppression factor at LHC are found to result directly from the $ \sqrt{s} $ dependence of the initial jet $p_T$ spectra and slow $p_T$ dependence of the jet energy loss. Contributions from jet-induced medium response, influence of radial expansion, both of which depend on jet-cone size, and jet flavor composition all conjoin to give a slow $p_T$ dependence of jet energy loss and the single jet suppression factor R AA , their dependence on $\sqrt{s}$ , and jet-cone size. Single inclusive jet suppression at $\sqrt{s}$ = 200 GeV is also predicted that actually decreases slightly with p T in the $p_T$< 50 GeV/c range because of the steeper initial jet spectra though the $p_T$ dependence of the jet energy loss is weaker than that at LHC.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Classify QCD phase transition with deep learning

The state-of-the-art pattern recognition method in machine learning (deep convolution neural network) is used to identify the equation of state (EoS) employed in the relativistic hydrodynamic simulations of heavy ion collisions. High-level correlations of particle spectra in transverse momentum and azimuthal angle learned by the network act as an effective EoS-meter in deciphering the nature of the phase transition in QCD. The EoS-meter is model independent and insensitive to other simulation inputs including the initial conditions and shear viscosity for hydrodynamic simulations. Through this study we demonstrate that there is a traceable encoder of the dynamical information from the phase structure that survives the evolution and exists in the final snapshot of heavy ion collisions and one can exclusively and effectively decode these information from the highly complex final output with machine learning when traditional methods fail. Besides the deep neural network, the performance of traditional machine learning classifiers are also provided.

74 ATOMIC AND MOLECULAR PHYSICS↗