2,170 research outputs found
Numerical simulations of winds driven by radiation force from the corona above a thin disk
Observations show that winds can be driven from the innermost region (inside
a 50 Schwarschild radius) of a thin disk. It is interesting to study the winds
launched from the innermost region. A hot corona above the black hole (BH) thin
disk is irradiated by the disk. We perform two-dimensional hydrodynamical
simulations to study the winds driven by radiation force from the corona in the
innermost regions. The hard X-ray spectrum from active galactic nuclei (AGNs)
suggests that the corona temperature is about K, so that we mainly
analyze the properties of winds (or outflows) from the K corona. The
disk luminosity plays an important role in driving the outflows. The more
luminous the disk, the stronger the outflows. Mass outflow rate () at a 90 Schwarschild radius depends on disk luminosity, which can be
described as ( is the ratio
of the disk luminosity to the Eddington luminosity). In the case of high
luminosity (e.g. ), the supersonic outflows with maximum speed
Km s are launched at -- and
-- away from the pole axis. The Bernoulli parameter keeps
increasing with the outward propagation of outflows. The radiation force keeps
accelerating the outflows when outflows move outward. Therefore, we can expect
the outflows to escape from the BH gravity and go to the galactic scale. The
interaction between outflows and interstellar medium may be an important AGN
feedback process.Comment: 9 pages, 12 figures, accepted for publication in Ap
Two dimensional numerical simulations of Supercritical Accretion Flows revisited
We study the dynamics of super-Eddington accretion flows by performing
two-dimensional radiation-hydrodynamic simulations. Compared with previous
works, in this paper we include the component of the viscous
stress and consider various values of the viscous parameter . We find
that when is included, the rotational speed of the
high-latitude flow decreases, while the density increases and decreases at the
high and low latitudes, respectively. We calculate the radial profiles of
inflow and outflow rates. We find that the inflow rate decreases inward,
following a power law form of . The value of
depends on the magnitude of and is within the range of .
Correspondingly, the radial profile of density becomes flatter compared with
the case of a constant . We find that the density profile can be
described by , and the value of is almost same for a
wide range of ranging from to . The inward
decrease of inflow accretion rate is very similar to hot accretion flows, which
is attributed to the mass loss in outflows. To study the origin of outflow, we
analyze the convective stability of slim disk. We find that depending on the
value of , the flow is marginally stable (when is small) or
unstable (when is large). This is different from the case of
hydrodynamical hot accretion flow where radiation is dynamically unimportant
and the flow is always convectively unstable. We speculate that the reason for
the difference is because radiation can stabilize convection. The origin of
outflow is thus likely because of the joint function of convection and
radiation, but further investigation is required.Comment: 16 pages, 13 figures, accepted for publication in Ap
Computing resource allocation in three-tier IoT fog networks: a joint optimization approach combining Stackelberg game and matching
Fog computing is a promising architecture to
provide economical and low latency data services for future
Internet of Things (IoT)-based network systems. Fog computing
relies on a set of low-power fog nodes (FNs) that are located
close to the end users to offload the services originally targeting
at cloud data centers. In this paper, we consider a specific
fog computing network consisting of a set of data service operators
(DSOs) each of which controls a set of FNs to provide the
required data service to a set of data service subscribers (DSSs).
How to allocate the limited computing resources of FNs to all
the DSSs to achieve an optimal and stable performance is an
important problem. Therefore, we propose a joint optimization
framework for all FNs, DSOs, and DSSs to achieve the optimal
resource allocation schemes in a distributed fashion. In the
framework, we first formulate a Stackelberg game to analyze
the pricing problem for the DSOs as well as the resource allocation
problem for the DSSs. Under the scenarios that the DSOs
can know the expected amount of resource purchased by the
DSSs, a many-to-many matching game is applied to investigate
the pairing problem between DSOs and FNs. Finally, within the
same DSO, we apply another layer of many-to-many matching
between each of the paired FNs and serving DSSs to solve
the FN-DSS pairing problem. Simulation results show that our
proposed framework can significantly improve the performance
of the IoT-based network systems
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