Nonextensive statistics: Theoretical, experimental and computational evidences and connections

The domain of validity of standard thermodynamics and Boltzmann-Gibbs statistical mechanics is discussed and then formally enlarged in order to hopefully cover a variety of anomalous systems. The generalization concerns {\it nonextensive} systems, where nonextensivity is understood in the thermodynamical sense. This generalization was first proposed in 1988 inspired by the probabilistic description of multifractal geometries, and has been intensively studied during this decade. In the present effort, after introducing some historical background, we briefly describe the formalism, and then exhibit the present status in what concerns theoretical, experimental and computational evidences and connections, as well as some perspectives for the future. In addition to these, here and there we point out various (possibly) relevant questions, whose answer would certainly clarify our current understanding of the foundations of statistical mechanics and its thermodynamical implicationsComment: 15 figure

Optimal Allocation of Distributed Generation with the Presence of Photovoltaic and Battery Energy Storage System Using Improved Barnacles Mating Optimizer

This paper proposes an improved version of Barnacles mating optimizer (BMO) for solving the optimal allocation problem of distribution generator (DGs) in radial distribution systems (RDSs). BMO is a recent bioinspired optimization algorithm that mimics the intelligence behavior of Barnacles\u27 mating. However, like with any metaheuristic optimization approach, it may face issues such as local optima trapping and low convergence rate. Hence, an improved BMO is adopted based on the quasi oppositional (QOBMO) and the chaos maps theories (CQOBMO). The two improvement methods are applied to increase the convergence performance of the conventional BMO. To prove the efficiency of the improved QOBMO and CQOBMO algorithms, 23 benchmark functions are used, and the accomplished results are compared with the conventional BMO. Then, the improved algorithm is applied to minimize the total power and energy losses in the distribution systems considering the uncertainty of DG power generation and time‐varying load demand. The uncertainty of DG is represented using photovoltaic‐based DG (PVDG). The improved method is employed to find the optimal power scheduling of PVDG and battery energy storage (BES) during 24 h. Two standard IEEE RDS (IEEE 33‐bus and IEEE 69‐bus) are used to simulate the case studies. Finally, the obtained results show that significant loss reductions (LRs) are achieved using the improved BMO where LRs reach 65.26%, and 68.86% in IEEE 33‐bus and 69‐bus, respectively, in the case of PVDG integration. However, using PVDG and BES the energy loss reductions reach 64% and 67.80% in IEEE 33‐bus and 69‐bus, respectively, which prove the efficiency of the improved BMO algorithm in finding the optimal solutions obtained so far

Uncertainty quantification in coastal aquifers using the multilevel Monte Carlo method

We consider a class of density-driven flow problems. We are particularly interested in the problem of the salinization of coastal aquifers. We consider the Henry saltwater intrusion problem with uncertain porosity, permeability, and recharge parameters as a test case. The reason for the presence of uncertainties is the lack of knowledge, inaccurate measurements, and inability to measure parameters at each spatial or time location. This problem is nonlinear and time-dependent. The solution is the salt mass fraction, which is uncertain and changes in time. Uncertainties in porosity, permeability, recharge, and mass fraction are modeled using random fields. This work investigates the applicability of the well-known multilevel Monte Carlo (MLMC) method for such problems. The MLMC method can reduce the total computational and storage costs. Moreover, the MLMC method runs multiple scenarios on different spatial and time meshes and then estimates the mean value of the mass fraction. The parallelization is performed in both the physical space and stochastic space. To solve every deterministic scenario, we run the parallel multigrid solver ug4 in a black-box fashion. We use the solution obtained from the quasi-Monte Carlo method as a reference solution.Comment: 24 pages, 3 tables, 11 figure

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations

In recent algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature- inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field

Sequentially Modified Gravitational Search Algorithm for Image Enhancement

Gravitational Search Algorithm (GSA) is based on the acceleration trend feature of objects with a mass towards each other and includes many interdependent parameters. The gravitational constant among these parameters influences the speeds and positions of the agents, meaning that the search capability depends on the largescale gravitational constant. The proposed new algorithm, which was obtained with the use of two operators at different times of the call and sequentially doing works, was named as Sequentially Modified ‎ Gravitational Search Algorithm (SMGSA). SMGSA is applied to 10 basic and 6 composite benchmark functions. Each function is run 30 times and the best, mean and median values are obtained. The achieved results are compared with the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and GSA among the heuristic optimization algorithms. Between GSA and the operator for each function convergence speed, standard deviation and graphical comparisons are included. Beside this, by using the Wilcoxon signed rank test, the comparison of the averages of the data as two dependent groups of GSA and the new operators is performed. It is seen that the obtained results provided better results than the other methods. Additionally, in this study, SMGSA was applied to the transformation function among image enhancement techniques which are engineering applications. The success of this method has been increased by optimizing the parameters of the transformation function used. Effective improvement has been achieved in terms of both visual and information quality

Articles indexats publicats per investigadors del Campus de Terrassa: 2017

Aquest informe recull els 241 treballs publicats per 222 investigadors/es del Campus de Terrassa en revistes indexades al Journal Citation Report durant el 2017Postprint (published version

Numerical simulations of die casting with uncertainty quantification and optimization using neural networks

Die casting is one type of metal casting in which liquid metal is solidified in a reusable die. In such a complex process, measuring and controlling the process parameters is difficult. Conventional deterministic simulations are insufficient to completely estimate the effect of stochastic variation in the process parameters on product quality. In this research, a framework to simulate the effect of stochastic variation together with verification, validation, uncertainty quantification and design optimization is proposed. This framework includes high-speed numerical simulations of solidification, micro-structure and mechanical properties prediction models along with experimental inputs for calibration and validation. In order to have a better prediction of product quality, both experimental data and stochastic variations in process parameters with numerical modeling are employed. This enhances the utility of traditional numerical simulations used in die casting. OpenCast, a novel and comprehensive computational framework to simulate solidification problems in materials processing is developed. Heat transfer, solidification and fluid flow due to natural convection are modeled. Empirical relations are used to estimate the microstructure parameters and mechanical properties. The fractional step algorithm is modified to deal with the numerical aspects of solidification by suitably altering the coefficients in the discretized equation to simulate selectively only in the liquid and mushy zones. This brings significant computational speed up as the simulation proceeds. Complex domains are represented by unstructured hexahedral elements. The algebraic multigrid method, blended with a Krylov subspace solver is used to accelerate convergence. Multiple case studies are presented by coupling surrogate models such as polynomial chaos expansion (PCE) and neural network with OpenCast for uncertainty quantification and optimization. The effects of stochasticity in the alloy composition, boundary and initial conditions on the product quality of die casting are analyzed using PCE. Further, a high dimensional stochastic analysis of the natural convection problem is presented to model uncertainty in the material properties and boundary conditions using neural networks. In die casting, heat extraction from molten metal is achieved by cooling lines in the die which impose nonuniform boundary temperatures on the mold wall. This boundary condition along with the initial molten metal temperature affect the product quality quantified in terms of micro-structure parameters and yield strength. Thus, a multi-objective optimization problem is solved to demonstrate a procedure for improvement of product quality and process efficiency