1,026 research outputs found
Modified flower pollination algorithm for global optimization
In this paper, a modified flower pollination algorithm (MFPA) is proposed to improve the performance of the classical algorithm and to tackle the nonlinear equation systems widely used in engineering and science fields. In addition, the differential evolution (DE) is integrated with MFPA to strengthen its exploration operator in a new variant called HFPA. Those two algorithms were assessed using 23 well-known mathematical unimodal and multimodal test functions and 27 well-known nonlinear equation systems, and the obtained outcomes were extensively compared with those of eight well-known metaheuristic algorithms under various statistical analyses and the convergence curve. The experimental findings show that both MFPA and HFPA are competitive together and, compared to the others, they could be superior and competitive for most test cases
A space-time discontinuous Galerkin finite element method for two-fluid problems
A space-time discontinuous Galerkin finite element method for two fluid flow problems is presented. By using a combination of level set and cut-cell methods the interface between two fluids is tracked in space-time. The movement of the interface in space-time is calculated by solving the level set equation, where the interface geometry is identified with the 0-level set. To enhance the accuracy of the interface approximation the level set function is advected with the interface velocity, which for this purpose is extended into the domain. Close to the interface the mesh is locally refined in such a way that the 0-level set coincides with a set of faces in the mesh. The two fluid flow equations are solved on this refined mesh. The procedure is repeated until both the mesh and the flow solution have converged to a reasonable accuracy.\ud
The method is tested on linear advection and Euler shock tube problems involving ideal gas and compressible bubbly magma. Oscillations around the interface are eliminated by choosing a suitable interface flux
State-of-the-art in aerodynamic shape optimisation methods
Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners
Hybrid Optimal Theory and Predictive Control for Power Management in Hybrid Electric Vehicle
This paper presents a nonlinear-model based hybrid optimal control technique
to compute a suboptimal power-split strategy for power/energy management in a
parallel hybrid electric vehicle (PHEV). The power-split strategy is obtained
as model predictive control solution to the power management control problem
(PMCP) of the PHEV, i.e., to decide upon the power distribution among the
internal combustion engine, an electric drive, and other subsystems. A
hierarchical control structure of the hybrid vehicle, i.e., supervisory level
and local or subsystem level is assumed in this study. The PMCP consists of a
dynamical nonlinear model, and a performance index, both of which are
formulated for power flows at the supervisory level. The model is described as
a bi-modal switched system, consistent with the operating mode of the electric
ED. The performance index prescribing the desired behavior penalizes vehicle
tracking errors, fuel consumption, and frictional losses, as well as sustaining
the battery state of charge (SOC). The power-split strategy is obtained by
first creating the embedded optimal control problem (EOCP) from the original
bi-modal switched system model with the performance index. Direct collocation
is applied to transform the problem into a nonlinear programming problem. A
nonlinear predictive control technique (NMPC) in conjunction with a sequential
quadratic programming solver is used to compute suboptimal numerical solutions
to the PMCP. Methods for approximating the numerical solution to the EOCP with
trajectories of the original bi-modal PHEV are also presented in this paper.
The usefulness of the approach is illustrated via simulation results on several
case studies
On modelling effects in the battery and thermal storage scheduling problem
The growing use of intermittent renewable energy sources requires an increased amount of storage capacity to match uncertain generation with uncertain demand. A possible solution is the use of thermal and electrical storages. This paper compares several model formulations: mixed integer linear programs (MILPs), nonlinear programs (NLPs), mixed integer nonlinear programs (MINLPs) for optimizing the operation of a multi-modal home energy system comprising heating and electricity subsystems. The respective optimization problems are then resolved within a model predictive control scheme and the final solutions are compared in terms of runtime and optimality. The results indicate that a thermocline-based thermal storage model leads to the overall lowest costs while not significantly impeding computing times. Additionally, the results show that a continuous heat pump model leads to reduced computing times without affecting the modelling accuracy
Comparing Numerical Methods for Isothermal Magnetized Supersonic Turbulence
We employ simulations of supersonic super-Alfvenic turbulence decay as a
benchmark test problem to assess and compare the performance of nine
astrophysical MHD methods actively used to model star formation. The set of
nine codes includes: ENZO, FLASH, KT-MHD, LL-MHD, PLUTO, PPML, RAMSES, STAGGER,
and ZEUS. We present a comprehensive set of statistical measures designed to
quantify the effects of numerical dissipation in these MHD solvers. We compare
power spectra for basic fields to determine the effective spectral bandwidth of
the methods and rank them based on their relative effective Reynolds numbers.
We also compare numerical dissipation for solenoidal and dilatational velocity
components to check for possible impacts of the numerics on small-scale density
statistics. Finally, we discuss convergence of various characteristics for the
turbulence decay test and impacts of various components of numerical schemes on
the accuracy of solutions. We show that the best performing codes employ a
consistently high order of accuracy for spatial reconstruction of the evolved
fields, transverse gradient interpolation, conservation law update step, and
Lorentz force computation. The best results are achieved with divergence-free
evolution of the magnetic field using the constrained transport method, and
using little to no explicit artificial viscosity. Codes which fall short in one
or more of these areas are still useful, but they must compensate higher
numerical dissipation with higher numerical resolution. This paper is the
largest, most comprehensive MHD code comparison on an application-like test
problem to date. We hope this work will help developers improve their numerical
algorithms while helping users to make informed choices in picking optimal
applications for their specific astrophysical problems.Comment: 17 pages, 5 color figures, revised version to appear in ApJ, 735,
July 201
A path-following algorithm for linear programming using quadratic and logarithmic penalty functions
Cover title.Includes bibliographical references (p. 29-32).Research partially supported by the U.S. Army Research Office (Center for Intelligent Control Systems) DAAL03-86-K-0171by Paul Tseng
Splitting Methods for Convex Clustering
Clustering is a fundamental problem in many scientific applications. Standard
methods such as -means, Gaussian mixture models, and hierarchical
clustering, however, are beset by local minima, which are sometimes drastically
suboptimal. Recently introduced convex relaxations of -means and
hierarchical clustering shrink cluster centroids toward one another and ensure
a unique global minimizer. In this work we present two splitting methods for
solving the convex clustering problem. The first is an instance of the
alternating direction method of multipliers (ADMM); the second is an instance
of the alternating minimization algorithm (AMA). In contrast to previously
considered algorithms, our ADMM and AMA formulations provide simple and unified
frameworks for solving the convex clustering problem under the previously
studied norms and open the door to potentially novel norms. We demonstrate the
performance of our algorithm on both simulated and real data examples. While
the differences between the two algorithms appear to be minor on the surface,
complexity analysis and numerical experiments show AMA to be significantly more
efficient.Comment: 37 pages, 6 figure
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