We consider the optimal scheduling of hydropower plants in a hydrothermal interconnected system. This problem, of outmost importance for large-scale power systems with a high proportion of hydraulic generation, requir...
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We consider the optimal scheduling of hydropower plants in a hydrothermal interconnected system. This problem, of outmost importance for large-scale power systems with a high proportion of hydraulic generation, requires a detailed description of the so-called hydro unit production function. In our model, we relate the amount of generated hydropower to nonlinear tailrace levels;we also take into account hydraulic losses, turbine-generator efficiencies, as well as multiple 0-1 states associated with forbidden operation zones. Forbidden zones are crucial to avoid nasty phenomena such as mechanical vibrations in the turbine, cavitation, and low efficiency levels. The minimization of operating costs subject to such detailed constraints results in a large-scale mixed-integer nonlinear programming problem. By means of Lagrangian Relaxation, the original problem is split into a sequence of smaller and easy-to-solve subproblems, coordinated by a dual master program. In order to deal better with the combinatorial aspect introduced by the forbidden zones, we derive three different decomposition strategies, applicable to various configurations of hydro plants (with few or many units, which can be identical or different). We use a sequential quadratic programming algorithm to solve nonlinear subproblems. We assess our approach on a real-life hydroelectric configuration extracted from the south sub region of the Brazilian hydrothermal power system.
This work presents an interactive computer tool for the optimal design of helical extension springs. The developed algorithm incorporates all design considerations used in the manufacturing industry and specialized de...
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This work presents an interactive computer tool for the optimal design of helical extension springs. The developed algorithm incorporates all design considerations used in the manufacturing industry and specialized design manuals. All these considerations are used to create a novel optimum design formulation. The flexibility of this software allows the user to characterize a particular design problem, through the selection of the right constraints for each case. The optimization model includes sixteen constraints (linear and nonlinear) derived from different failure criteria and designs and manufacture recommendations. This nonlinear problem is solved using sequential quadratic programming in Matlab. The characteristics of this tool are showed through development of a benchmark problem. The free software developed in this work is available at http://***/optimun/resortes/.
This paper presents the work done in designing a morphing wing concept for a small experimental unmanned aerial vehicle to improve the vehicle's performance over its intended speed range. The wing is designed with...
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This paper presents the work done in designing a morphing wing concept for a small experimental unmanned aerial vehicle to improve the vehicle's performance over its intended speed range. The wing is designed with a multidisciplinary design optimization tool, in which an aerodynamic shape optimization code coupled with a structural morphing model is used to obtain a set of optimal wing shapes for minimum drag at different flight speeds. The optimization procedure is described as well as the structural model. The aerodynamic shape optimization code, that uses a viscous two-dimensional panel method formulation coupled with a nonlinear lifting-line algorithm and a sequential quadratic programming optimization algorithm, is suitable for preliminary wing design optimization tasks. The morphing concept, based on changes in wing-planform shape and wing-section shape achieved by extending spars and telescopic ribs, is explained in detail. Comparisons between optimized fixed wing performance, optimal morphing wing performance, and the performance of the wing obtained from the coupled aerodynamic-structural solution are presented. Estimates for the performance enhancements achieved by the unmanned aerial vehicles when fitted with this new morphing wing are also presented. Some conclusions on this concept are addressed with comments on the benefits and drawbacks of the morphing mechanism design.
Numerically discretized dynamic optimization problems having active inequality and equality path constraints that along with the dynamics induce locally high index differential algebraic equations often cause the opti...
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Numerically discretized dynamic optimization problems having active inequality and equality path constraints that along with the dynamics induce locally high index differential algebraic equations often cause the optimizer to fail in convergence or to produce degraded control solutions. In many applications, regularization of the numerically discretized problem in direct transcription schemes by perturbing the high index path constraints helps the optimizer to converge to useful control solutions. For complex engineering problems with many constraints it is often difficult to find effective nondegenerate perturbations that produce useful solutions in some neighborhood of the correct solution. In this paper we describe a numerical discretization that regularizes the numerically consistent discretized dynamics and does not perturb the path constraints. For all values of the regularization parameter the discretization remains numerically consistent with the dynamics and the path constraints specified in the original problem. The regularization is quantifiable in terms of time step size in the mesh and the regularization parameter. For fully regularized systems the scheme converges linearly in time step size. The method is illustrated with examples.
A structural optimization method of a three-axis force sensor for robot fingers is proposed. To achieve dexterous hands like human hands, it is important to measure the forces loaded on robot fingers. We have been dev...
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ISBN:
(纸本)9781424447749
A structural optimization method of a three-axis force sensor for robot fingers is proposed. To achieve dexterous hands like human hands, it is important to measure the forces loaded on robot fingers. We have been developing three-axis force sensors that can detect three-axis forces simultaneously with strain gauges. This sensor is small and can be produced inexpensively. However, because the proposed sensor has been newly devised, there is neither a design basis for it nor an accumulation of know-how. Therefore, we suggest a method to optimize the sensor structure using design of experiments and finite element analysis. In the proposed method, the approximated expressions of the objective function and the constraints are generated by the response surface method and FEA. The sequential quadratic programming method is used to optimize the design variables. As a result, a force sensor is designed the robot fingers that is more effective than the prototype sensor, and the effectiveness of the proposed method is verified.
The purpose of this research was to evaluate the effects of various concentrations of glucono-delta-lactone (GDL) and skim milk powder, as well as the addition of prebiotics, on the theology and problotic viabilities ...
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The purpose of this research was to evaluate the effects of various concentrations of glucono-delta-lactone (GDL) and skim milk powder, as well as the addition of prebiotics, on the theology and problotic viabilities of dairy tofu. Additionally, modern optimization techniques were applied to attempt to determine the optimal processing conditions and growth rate for the selected probiotics (Lactobacillus. acidophilus, L. casei, Bifidobacteria bifidum, and B. longum). There were 2 stages in this research to accomplish the goal. The 1st stage was to derive surface models using response surface methodology (RSM);the 2nd stage performed optimization on the models using sequential quadratic programming (SQP) techniques. The results were demonstrated to be effective. The most favorable production conditions of dairy tofu were 1% GDL, 0% peptides, 3% isomaltooligosaccharides (IMO), and 18% milk, as confirmed by subsequent verification experiments. Analysis of the sensory evaluation results revealed no significant difference between the probiotic dairy tofu and the GDL analog in terms of texture and appearance (P > 0.05). The viable numbers of probiotics were well above the recommended limit of 10(6) CFU/g for the probiotic dairy tofu throughout the tested storage period.
This paper presents an implementation of a sequential quadratic programming (SQP) algorithm for the solution of nonlinear programming (NLP) problems. In the proposed algorithm, a solution to the NLP problem is found b...
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This paper presents an implementation of a sequential quadratic programming (SQP) algorithm for the solution of nonlinear programming (NLP) problems. In the proposed algorithm, a solution to the NLP problem is found by minimizing the L1 exact penalty function. The search direction for the penalty function minimization is determined by solving a strictly convex quadraticprogramming (QP) problem. Here, we make the basic SQP algorithm more robust (i) by solving a relaxed, strictly convex, QP problem in cases where the constraints are inconsistent, (ii) by performing a non-monotone line search to improve efficiency, and (iii) by using second-order corrections to avoid the Maratos effect. The robustness of the algorithm is demonstrated via a C language implementation that is applied to numerous parameter optimization and optimal control problems that have appeared in the literature. The results obtained show that both non-monotone line searches and second-order corrections can significantly reduce the amount of work required to solve parameter optimization problems.
We present a multi-resolution-based approach for solving trajectory optimization problems. The original optimal control problem is solved using a direct method, thereby being transcribed into a nonlinear programming p...
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We present a multi-resolution-based approach for solving trajectory optimization problems. The original optimal control problem is solved using a direct method, thereby being transcribed into a nonlinear programming problem that is solved using standard nonlinear programming codes. The novelty of the proposed approach hinges on the automatic calculation of a suitable nonuniform grid over which the nonlinear programming problem is subsequently solved. This tends to increase numerical efficiency and robustness. Control and/or state constraints are handled with ease and without any additional computational complexity. The proposed algorithm is based on a simple and intuitive method to balance conflicting objectives, such as accuracy of the solution, convergence, and speed of computations. The benefits of the proposed algorithm over uniform grid implementations are demonstrated with the help of several nontrivial examples.
This paper presents an algorithm for the numerical solution of constrained parameter optimization problems. The solution strategy is based on a sequential quadratic programming (SQP) technique that uses the L-infinity...
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This paper presents an algorithm for the numerical solution of constrained parameter optimization problems. The solution strategy is based on a sequential quadratic programming (SQP) technique that uses the L-infinity exact penalty function. Unlike similar SQP algorithms the method proposed here solves only strictly convex quadratic programs to obtain the search directions. The global convergence properties of the algorithm are enhanced by the use of a nonmonotone line search and second-order corrections to avoid the Maratos effect. The paper also presents an ANSI C implementation of the algorithm. The effectiveness of the proposed method is demonstrated by solving numerous parameter optimization and optimal control problems that have appeared in the literature. (C) 2007 Elsevier Inc. All rights reserved.
A hybrid optimization technique, GA-SQP, is developed in which the genetic algorithm (GA) which is a stochastic method is combined with the sequential quadratic programming (SQP) method which is a deterministic method...
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A hybrid optimization technique, GA-SQP, is developed in which the genetic algorithm (GA) which is a stochastic method is combined with the sequential quadratic programming (SQP) method which is a deterministic method. This method was used to determine the kinetic parameters of the set of highly nonlinear hydrogenation reactions. Catalyst deactivation was also taken into account. The ability of GA and SQP in solving this type of problem was investigated. It was shown that although the SQP is fast, it is not able to solve this problem properly and is very sensitive to the choice of initial point. The GA was able to solve the problem after a large number of generations. It was shown that the new GA-SQP hybrid method is able to determine the final solution considerably faster than the GA while it is not sensitive to the initial point. (C) 2007 Elsevier Ltd. All rights reserved.
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