Fernando M. Ramos

New Stochastic Algorithm for Design Optimization

A new stochastic algorithm for design optimization is introduced. Called generalized extremal optimization (GEO), it is intended to be used in complex optimization problems where traditional gradient-based methods may become inefficient, such as when applied to a nonconvex or disjoint design space, or when there are different kinds of design variables in it. The algorithm is easy to implement, does not make use of derivatives, and can be applied to unconstrained or constrained problems, and nonconvex or disjoint design spaces, in the presence of any combination of continuous, discrete, or integer variables. It is a global search metaheuristic, as are genetic algorithms (GAs) and simulated annealing (SA), but with the a priori advantage of having only one free parameter to adjust. The algorithm is presented in two implementations and its performance is assessed on a set of test functions. A simple application to the design of a glider airfoil is also presented. It is shown that the GEO algorithm is competitive in performance with the GA and SA and is an attractive tool to be used on applications in the aerospace field.

A comparison of some inverse methods for estimating the initial condition of the heat equation

In this work we analyze two explicit methods for the solution of an inverse heat conduction problem and we confront them with the least-squares method, using for the solution of the associated direct problem a classical finite difference method and a method based on an integral formulation. Finally, the Tikhonov regularization connected to the least-squares criterion is examined. We show that the explicit approaches to this inverse heat conduction problem will present disastrous results unless some kind of regularization is used.

Entropy- and tikhonov-based regularization techniques applied to the backwards heat equation☆

Abstract The goal of this paper is to analyze the performance of different regularization techniques for an inverse heat conduction problem (IHCP): the estimation of the initial condition. The inverse problem is formulated as a nonlinear constrained optimization problem, and a regularization term is added to the objective function with the help of a regularization parameter. Three classes of regularization methods have been considered: Tikhonov regularization, maximum entropy principle, and truncated singular value decomposition. Concerning the entropic methodology, two new techniques are introduced and good results were obtained using synthetic data corrupted with noise. The Morozovs discrepancy principle is used to find out the regularization parameter.

Experimental and theoretical investigation of a capillary pumped loop with a porous element in the condenser

Abstract A new CPL design is investigated experimentally and theoretically. In order to create a fixed physical interface between the liquid and the vapor phases inside the loop, the conventional tube condenser is replaced by a condenser containing a porous wick structure. The idea is to have a simple, light, and reliable system directed to applications where a high heat-transport capacity over long distances is needed, but a precise temperature control of the cold plate is not required. Experimental results, under different test conditions, are presented and illustrate the overall performance of the system. A CPL mathematical model, based on the nodal method, is described and validated experimentally.

Novel approaches to entropic regularization

In this work, two new entropic regularization techniques are introduced. They represent a generalization of the standard MaxEnt regularization method, and allow for a greater flexibility for introducing any prior information about the expected structure of the true physical model, or its derivatives, into the inversion procedure. The first technique is based on the minimization of the entropy of the vector of first-differences of unknown parameters. Adopting standard terminology, it is known as the minimum first-order entropy method (MinEnt-1). To illustrate the essential feature of the method, MinEnt-1 is applied to the reconstruction of two-dimensional geoelectric conductivity distributions from magnetotelluric data. The second technique is based on the maximization of the entropy of the vector of second-differences of the unknown parameters, and is denoted as the MaxEnt-2 method. The MaxEnt-2 method is applied to the retrieval of vertical profiles of temperature in the atmosphere from remote sensing data.

Non-extensive statistics and three-dimensional fully developed turbulence

In this paper, we present further evidence, based on new data from the Large Scale Biosphere Atmosphere Experiment in Amazonia (LBA), that the generalized thermostatistics provides a simple and accurate framework for modeling the statistical behavior of fully developed turbulence.

Identification of inherent optical properties and bioluminescence source term in a hydrologic optics problem

Abstract The estimation of the pair absorption–scattering of inherent optical properties (IOPs) and the bioluminescent source profile in natural waters is achieved from in situ irradiance data. This inverse problem is formulated as a nonlinear constrained optimization problem, assuming constant IOPs and that the unknown bioluminescent profile can be represented by a sum of distributed Gaussian sources. The objective function is defined as the squared Euclidean norm of the difference vector between experimental and computed data. The Hydrolight code, based on the invariant imbedding theory, is used for the direct problem. The methodology yielded good results using synthetic data for the joint estimation of IOPs and bioluminescence, performed in an alternate, step-by-step manner.

A Time-Weighted Dynamic Time Warping Method for Land-Use and Land-Cover Mapping

This paper presents a time-weighted version of the dynamic time warping (DTW) method for land-use and land-cover classification using remote sensing image time series. Methods based on DTW have achieved significant results in time-series data mining. The original DTW method works well for shape matching, but is not suited for remote sensing time-series classification. It disregards the temporal range when finding the best alignment between two time series. Since each land-cover class has a specific phenological cycle, a good time-series land-cover classifier needs to balance between shape matching and temporal alignment. To that end, we adjusted the original DTW method to include a temporal weight that accounts for seasonality of land-cover types. The resulting algorithm improves on previous methods for land-cover classification using DTW. In a case study in a tropical forest area, our proposed logistic time-weighted version achieves the best overall accuracy of 87.32%. The accuracy of a version with maximum time delay constraints is 84.66%. A time-warping method without time constraints has a 70.14% accuracy. To get good results with the proposed algorithm, the spatial and temporal resolutions of the data should capture the properties of the landscape. The pattern samples should also represent well the temporal variation of land cover.

Heat Pipe Design Through Generalized Extremal Optimization

In this paper, an application of the Generalized Extremal Optimization (GEO) algorithm to the optimization of a heat pipe (HP) for a space application is presented. The GEO algorithm is a generalization of the Extremal Optimization (EO) algorithm, devised to be applied readily to a broad class of design optimization problems regardless of the design space complexity it would face. It is easy to implement, does not make use of derivatives, and can be applied to either unconstrained or constrained problems with continuous, discrete, or integer variables. The GEO algorithm has been tested in a series of test functions and shows to be competitive to other stochastic algorithms, such as the Genetic Algorithm. In this work, it is applied to the problem of minimizing the mass of an HP as a function of a desirable heat transport capability and a given temperature on the condenser. The optimal solutions were obtained for different heat loads, heat sink temperatures, and three working fluids: ammonia, methanol, and ethanol. The present design application highlights the GEO features of being easily implemented and efficient on tackling optimization problems when the objective function presents design variables with strong nonlinear interactions and is subject to multiple constraints.

Spacecraft thermal design with the Generalized Extremal Optimization Algorithm

This article describes an application of the Generalized Extremal Optimization (GEO) algorithm to the inverse design of a spacecraft thermal control system. GEO is a recently proposed global search meta-heuristic (Sousa, F.L. and Ramos, F.M., 2002, Function optimization using extremal dynamics. In: Proceedings of the 4th International Conference on Inverse Problems in Engineering (cd-rom), Rio de Janeiro, Brazil.; Sousa, F.L., Ramos, F.M., Paglione, P. and Girardi, R.M., 2003, New stochastic algorithm for design optimization. AIAA Journal, 41(9), 1808–1818.; Sousa, F.L., Ramos, F.M., Galski, R.L. and Muraoka, I., 2005, Chapter III. In: L.N. De Castro and F.J. Von Zuben (Eds) Generalized Extremal Optimization: A New Meta-heuristic Inspired by a Model of Natural Evolution, Accepted for publication in Recent Developments in Biologically Inspired Computing (Hershey, PA: Idea Group Inc.).) based on a model of natural evolution (Bak, P. and Sneppen, K., 1993, Punctuated equilibrium and criticality in a simple model of evolution. Physical Review Letters, 71(24), 4083–4086), and specially devised to be used in complex optimization problems (Sousa, F.L., Vlassov, V. and Ramos, F.M., 2002, Heat pipe design through generalized extremal optimization. In: Proceedings of the IX Brazilian Congress of Engineering and Thermal Sciences – ENCIT 2002, Caxambu, MG, Brazil.). GEO is easy to implement, has only one free parameter to adjust, does not make use of derivatives and can be applied to constrained or unconstrained problems, and to non-convex or even disjoint design spaces with any combination of continuous, discrete, or integer variables. The application reported here concerns the optimum design of a simplified configuration of the Brazilian Multi-mission Platform (in Portuguese, Plataforma Multi-missão, PMM) thermal control subsystem, comprising five radiators and one battery heater. The PMM is a multi-purpose space platform to be used in different types of missions such as Earth observation, scientific, or meteorological data collecting. The design procedure is tackled as a multiobjective optimization problem, considering two critical cases, operational hot case (HC) and cold case (CC). The results indicate the existence of non-intuitive, new and more efficient design solutions.