27,213 research outputs found
Analysis of process variables via CFD to evaluate the performance of a FCC riser
Feedstock conversion and yield products are studied through a 3D model simulating the main reactor of the fluid catalytic cracking (FCC) process. Computational fluid dynamic (CFD) is used with Eulerian-Eulerian approach to predict the fluid catalytic cracking behavior. The model considers 12 lumps with catalyst deactivation by coke and poisoning by alkaline nitrides and polycyclic aromatic adsorption to estimate the kinetic behavior which, starting from a given feedstock, produces several cracking products. Different feedstock compositions are considered. The model is compared with sampling data at industrial operation conditions. The simulation model is able to represent accurately the products behavior for the different operating conditions considered. All the conditions considered were solved using a solver ANSYS CFX 14.0. The different operation process variables and hydrodynamic effects of the industrial riser of a fluid catalytic cracking (FCC) are evaluated. Predictions from the model are shown and comparison with experimental conversion and yields products are presented; recommendations are drawn to establish the conditions to obtain higher product yields in the industrial process
Pseudospectral Model Predictive Control under Partially Learned Dynamics
Trajectory optimization of a controlled dynamical system is an essential part
of autonomy, however many trajectory optimization techniques are limited by the
fidelity of the underlying parametric model. In the field of robotics, a lack
of model knowledge can be overcome with machine learning techniques, utilizing
measurements to build a dynamical model from the data. This paper aims to take
the middle ground between these two approaches by introducing a semi-parametric
representation of the underlying system dynamics. Our goal is to leverage the
considerable information contained in a traditional physics based model and
combine it with a data-driven, non-parametric regression technique known as a
Gaussian Process. Integrating this semi-parametric model with model predictive
pseudospectral control, we demonstrate this technique on both a cart pole and
quadrotor simulation with unmodeled damping and parametric error. In order to
manage parametric uncertainty, we introduce an algorithm that utilizes Sparse
Spectrum Gaussian Processes (SSGP) for online learning after each rollout. We
implement this online learning technique on a cart pole and quadrator, then
demonstrate the use of online learning and obstacle avoidance for the dubin
vehicle dynamics.Comment: Accepted but withdrawn from AIAA Scitech 201
Monitoring luminous yellow massive stars in M33: new yellow hypergiant candidates
The evolution of massive stars surviving the red supergiant (RSG) stage
remains unexplored due to the rarity of such objects. The yellow hypergiants
(YHGs) appear to be the warm counterparts of post-RSG classes located near the
Humphreys-Davidson upper luminosity limit, which are characterized by
atmospheric instability and high mass-loss rates. We aim to increase the number
of YHGs in M33 and thus to contribute to a better understanding of the
pre-supernova evolution of massive stars. Optical spectroscopy of five
dust-enshrouded YSGs selected from mid-IR criteria was obtained with the goal
of detecting evidence of extensive atmospheres. We also analyzed BVI photometry
for 21 of the most luminous YSGs in M33 to identify changes in the spectral
type. To explore the properties of circumstellar dust, we performed SED-fitting
of multi-band photometry of the 21 YSGs. We find three luminous YSGs in our
sample to be YHG candidates, as they are surrounded by hot dust and are
enshrouded within extended, cold dusty envelopes. Our spectroscopy of star 2
shows emission of more than one H component, as well as emission of
CaII, implying an extended atmospheric structure. In addition, the long-term
monitoring of the star reveals a dimming in the visual light curve of amplitude
larger than 0.5 mag that caused an apparent drop in the temperature that
exceeded 500 K. We suggest the observed variability to be analogous to that of
the Galactic YHG Cas. Five less luminous YSGs are suggested as post-RSG
candidates showing evidence of hot or/and cool dust emission. We demonstrate
that mid-IR photometry, combined with optical spectroscopy and time-series
photometry, provide a robust method for identifying candidate YHGs. Future
discovery of YHGs in Local Group galaxies is critical for the study of the late
evolution of intermediate-mass massive stars.Comment: 24 pages, 12 figures, 7 Tables. A&A in pres
Comparison of the extended linear sigma model and chiral perturbation theory
The pion-nucleon scattering amplitudes are calculated in tree approximation
with the use of the extended linear sigma model (ELSM) as well as heavy baryon
chiral perturbation theory (HBPT), and the non-relativistic forms of the
ELSM results are compared with those of HBPT. We find that the amplitudes
obtained in ELSM do not agree with those derived from the more fundamental
effective approach, HBPT.Comment: 7 page
Cirrus cloud properties derived from coincident GOES and lidar data during the 1986 FIRE Cirrus Intensive Field Observations (IFO)
One of the main difficulties in detecting cirrus clouds and determining their correct altitude using satellite measurements is their nonblackness. In the present algorithm (Rossow et al., 1985) used by the International Satellite Cloud Climatology Project (ISCCP), the cirrus cloud emissivity is estimated from the derived cloud reflectance using a theoretical model relating visible (VIS, 0.65 micron) optical depth to infrared (IR, 10.5 micron) emissivity. At this time, it is unknown how accurate this approach is or how the derived cloud altitude relates to the physical properties of the cloud. The First ISCCP Regional Experiment (FIRE) presents opportunities for determining how the observed radiances depend on the cloud properties. During the FIRE Cirrus Intensive Field Observations (IFO, see Starr, 1987), time series of cloud thickness, height, and relative optical densities were measured from several surface-based lidars. Cloud microphysics and radiances at various wavelengths were also measured simultaneously over these sites from aircraft at specific times during the IFO (October 19 to November 2, 1986). Satellite-observed radiances taken simultaneously can be matched with these data to determine their relationships to the cirrus characteristics. The first step is taken toward relating all of these variables to the satellite observations. Lidar-derived cloud heights are used to determine cloud temperatures which are used to estimate cloud emissivities from the satellite IR radiances. These results are then correlated to the observed VIS reflectances for various solar zenith angles
Massive Hyper-Kahler Sigma Models and BPS Domain Walls
With the non-Abelian Hyper-Kahler quotient by U(M) and SU(M) gauge groups, we
give the massive Hyper-Kahler sigma models that are not toric in the N=1
superfield formalism. The U(M) quotient gives N!/[M! (N-M)!] (N is a number of
flavors) discrete vacua that may allow various types of domain walls, whereas
the SU(M) quotient gives no discrete vacua. We derive BPS domain wall solution
in the case of N=2 and M=1 in the U(M) quotient model.Comment: 16 pages, 1 figure, contribution to the Proceedings of the
International Conference on "Symmetry Methods in Physics (SYM-PHYS10)" held
at Yerevan, Armenia, 13-19 Aug. 200
The entropy of randomized network ensembles
Randomized network ensembles are the null models of real networks and are
extensivelly used to compare a real system to a null hypothesis. In this paper
we study network ensembles with the same degree distribution, the same
degree-correlations or the same community structure of any given real network.
We characterize these randomized network ensembles by their entropy, i.e. the
normalized logarithm of the total number of networks which are part of these
ensembles.
We estimate the entropy of randomized ensembles starting from a large set of
real directed and undirected networks. We propose entropy as an indicator to
assess the role of each structural feature in a given real network.We observe
that the ensembles with fixed scale-free degree distribution have smaller
entropy than the ensembles with homogeneous degree distribution indicating a
higher level of order in scale-free networks.Comment: (6 pages,1 figure,2 tables
Condensates and quasiparticles in inflationary cosmology: mass generation and decay widths
During de Sitter inflation massless particles of minimally coupled scalar
fields acquire a mass and a decay width thereby becoming \emph{quasiparticles}.
For bare massless particles non-perturbative infrared radiative corrections
lead to a self-consistent generation of mass, for a quartic self interaction , and for a cubic self-interaction the mass is induced
by the formation of a non-perturbative \emph{condensate} leading to . These radiatively generated masses restore de Sitter
invariance and result in anomalous scaling dimensions of superhorizon
fluctuations. We introduce a generalization of the non-perturbative
Wigner-Weisskopf method to obtain the time evolution of quantum states that
include the self-consistent generation of mass and regulate the infrared
behavior. The infrared divergences are manifest as poles in
in the single particle self-energies, leading to a re-arrangement of the
perturbative series non-analytic in the couplings. A set of simple rules that
yield the leading order infrared contributions to the decay width are obtained
and implemented. The lack of kinematic thresholds entail that all particle
states acquire a decay width, dominated by the emission and absorption of
superhorizon quanta for cubic and quartic couplings respectively to
leading order in . The decay of single particle quantum states hastens as
their wavevectors cross the Hubble radius and their width is related to the
highly squeezed limit of the bi- or tri-spectrum of scalar fluctuations
respectively.Comment: 31 pages, 7 figures. Comments and references, matches published
versio
Detection of a Moving Rigid Solid in a Perfect Fluid
In this paper, we consider a moving rigid solid immersed in a potential
fluid. The fluid-solid system fills the whole two dimensional space and the
fluid is assumed to be at rest at infinity. Our aim is to study the inverse
problem, initially introduced in [3], that consists in recovering the position
and the velocity of the solid assuming that the potential function is known at
a given time. We show that this problem is in general ill-posed by providing
counterexamples for which the same potential corresponds to different positions
and velocities of a same solid. However, it is also possible to find solids
having a specific shape, like ellipses for instance, for which the problem of
detection admits a unique solution. Using complex analysis, we prove that the
well-posedness of the inverse problem is equivalent to the solvability of an
infinite set of nonlinear equations. This result allows us to show that when
the solid enjoys some symmetry properties, it can be partially detected.
Besides, for any solid, the velocity can always be recovered when both the
potential function and the position are supposed to be known. Finally, we prove
that by performing continuous measurements of the fluid potential over a time
interval, we can always track the position of the solid.Comment: 19 pages, 14 figure
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