749,882 research outputs found
Evolution of Compact Stars and Dark Dynamical Variables
This work is aimed to explore the dark dynamical effects of modified
gravity theory on the dynamics of compact celestial star. We have taken the
interior geometry as spherical star which is filled with imperfect fluid
distribution. The modified field equations are explored by taking a particular
form of model, i.e., . These equations are
then utilized to formulate the well-known structure scalars under the dark
dynamical effects of this higher order gravity theory. Also, the evolution
equations for expansion and shear are formulated with the help of these scalar
variables. Further, all this analysis have been made under the condition of
constant and . We found a crucial significance of dark source terms and
dynamical variables on the evolution and density inhomogeneity of compact
objects.Comment: 18 pages, 4 figures, version accepted for publication in European
Physical Journal
Influence of Models on the Existence of Anisotropic Self-Gravitating Systems
This paper aims to explore some realistic configurations of anisotropic
spherical structures in the background of metric gravity, where is
the Ricci scalar. The solutions obtained by Krori and Barua are used to examine
the nature of particular compact stars with three different modified gravity
models. The behavior of material variables is analyzed through plots and the
physical viability of compact stars is investigated through energy conditions.
We also discuss the behavior of different forces, equation of state parameter,
measure of anisotropy and Tolman-Oppenheimer-Volkoff equation in the modeling
of stellar structures. The comparison from our graphical representations may
provide evidences for the realistic and viable gravity models at both
theoretical and astrophysical scale.Comment: 23 pages, 13 figures, version accepted for publication in European
Physical Journal
Hybrid iterative learning control of a flexible manipulator
This paper presents an investigation into the development of a hybrid control scheme with iterative learning for input tracking and end-point vibration suppression of a flexible manipulator system. The dynamic model of the system is derived using the finite element method. Initially, a collocated proportional-derivative (PD) controller using hub angle and hub velocity feedback is developed for control of rigid-body motion of the system. This is then extended to incorporate a non-collocated proportional-integral-derivative (PID) controller with iterative learning for control of vibration of the system. Simulation results of the response of the manipulator with the controllers are presented in the time and frequency domains. The performance of the hybrid iterative learning control scheme is assessed in terms of input tracking and level of vibration reduction in comparison to a conventionally designed PD-PID control scheme. The effectiveness of the control scheme in handling various payloads is also studied
Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects
Bayesian neural network (BNN) approach is employed to improve the nuclear
mass predictions of various models. It is found that the noise error in the
likelihood function plays an important role in the predictive performance of
the BNN approach. By including a distribution for the noise error, an
appropriate value can be found automatically in the sampling process, which
optimizes the nuclear mass predictions. Furthermore, two quantities related to
nuclear pairing and shell effects are added to the input layer in addition to
the proton and mass numbers. As a result, the theoretical accuracies are
significantly improved not only for nuclear masses but also for single-nucleon
separation energies. Due to the inclusion of the shell effect, in the unknown
region, the BNN approach predicts a similar shell-correction structure to that
in the known region, e.g., the predictions of underestimation of nuclear mass
around the magic numbers in the relativistic mean-field model. This manifests
that better predictive performance can be achieved if more physical features
are included in the BNN approach.Comment: 15 pages, 4 figures, and 3 table
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