Optimization of the Design of Complex Energy Conversion Systems Using Mathematical Programing and Genetic Algorithms

2006 ◽  
Author(s):  
Turang Ahadi-Oskui ◽  
George Tsatsaronis
Keyword(s):  
Genetic Algorithms ◽  
Energy Conversion ◽  
Optimization Methods ◽  
Process Variable ◽  
Optimization Method ◽  
Combined Cycle ◽  
Optimization Approach ◽  
Advantages And Disadvantages ◽  
Starting Point ◽  
Energy Conversion Systems

The paper presents two different optimization methods for the cost-effective design of energy conversion systems. Starting point of the optimization is a complex superstructure that allows several alternative design specifications for a combined-cycle-based cogeneration plant to be studied. Depending on the user specified demands for electricity and process steam, the optimization algorithm performs a simultaneous structural and process variable optimization of the design, to minimize the levelized total costs of the plant products. Mathematical programming and specialized genetic algorithms are used as optimization algorithms. These do not only differ largely in their optimization approach but also have different requirements for the modeling of the superstructure. Several optimization cases are presented to examine the applicability of both algorithms on the present optimization problem. A concluding comparison reveals the advantages and disadvantages of each optimization method.

Genetic Algorithms for Optimization of Building Envelopes and the Design and Control of HVAC Systems

2003 ◽  
Vol 125 (3) ◽  
pp. 343-351 ◽  
Author(s):  
L. G. Caldas ◽  
L. K. Norford
Keyword(s):  
Genetic Algorithms ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimization Method ◽  
Hvac Systems ◽  
Design Problems ◽  
Building Walls ◽  
Building Form ◽  
And Control ◽  
Future Work

Many design problems related to buildings involve minimizing capital and operating costs while providing acceptable service. Genetic algorithms (GAs) are an optimization method that has been applied to these problems. GAs are easily configured, an advantage that often compensates for a sacrifice in performance relative to optimization methods selected specifically for a given problem, and have been shown to give solutions where other methods cannot. This paper reviews the basics of GAs, emphasizing multi-objective optimization problems. It then presents several applications, including determining the size and placement of windows and the composition of building walls, the generation of building form, and the design and operation of HVAC systems. Future work is identified, notably interfaces between a GA and both simulation and CAD programs.

Development of a Multi-Objective Preliminary Design Optimization Approach for Axial Flow Compressors

2018 ◽  
Author(s):  
Youwei He ◽  
Jinju Sun ◽  
Peng Song ◽  
Xuesong Wang ◽  
Da Xu
Keyword(s):  
Design Optimization ◽  
Axial Flow ◽  
Optimization Methods ◽  
Optimization Method ◽  
Optimization Approach ◽  
Preliminary Design ◽  
Stringent Requirement ◽  
Stall Margin ◽  
Multi Objective ◽  
Axial Flow Compressors

A preliminary design optimization approach of axial flow compressors is developed. Loss correlations associated with airfoil geometry are introduced to relax the stringent requirement for the designer to prescribe the stage efficiency. In face of the preliminary design complexity resulted from the large number of design variables together with their stringent variation ranges and multiple design goals, the multi-objective optimization algorithm is incorporated. With such a developed preliminary design optimization method, the design space can be then explored extensively and the optimum designs of both high level overall efficiency and wide stall margin can be readily achieved. The preliminary design optimization method is validated in two steps. Firstly, an existing 5-stage compressor is redesigned without optimization. The obtained geometries and flow parameters are compared to the existing data and a good consistency is achieved. Then, the redesigned compressor is used as initial design and optimized by the developed multi-objective preliminary design optimization method, and significant performance gains are obtained, which demonstrates the effectiveness of the developed optimization methods.

Advanced Exergoeconomic Analysis Applied to a Complex Energy Conversion System

2010 ◽  
Author(s):  
F. Petrakopoulou ◽  
G. Tsatsaronis ◽  
T. Morosuk ◽  
A. Carassai
Keyword(s):  
Environmental Impact ◽  
Energy Conversion ◽  
Gas Turbine ◽  
Combined Cycle ◽  
Exergy Destruction ◽  
Steam Turbines ◽  
Pressure Steam ◽  
Energy Conversion Systems ◽  
Exergoeconomic Analysis ◽  
The Cost

Exergy-based analyses are important tools for studying and evaluating energy conversion systems. While conventional exergy-based analyses provide us with important information, further insight on the potential for improving plant components and the overall plant as well as on the interactions among components of energy conversion systems are significant when optimizing a system. This necessity led to the development of advanced exergy-based analyses, in which the exergy destruction, as well as the associated costs and environmental impact are split into avoidable/unavoidable and endogenous/exogenous parts. Based on the avoidable parts of the exergy destruction, costs and environmental impact, the potential for improvement and related strategies are revealed. This paper presents the application of an advanced exergoeconomic analysis to a combined cycle power plant. The largest parts of the unavoidable cost rates are calculated for the components constituting the gas turbine system and the low-pressure steam turbine. The combustion chamber has the second highest avoidable investment cost, while it has the highest avoidable cost of exergy destruction. In general, most of the investment costs are unavoidable, with the exception of some heat exchangers of the plant. Similarly, most of the cost of exergy destruction is unavoidable with the exception of the expander in the gas turbine system and the high-pressure and intermediate-pressure steam turbines. In general, the advanced exergoeconomic analysis reveals high endogenous values, which suggest improvement of the total plant by improving the design of the components primarily in isolation, and lower exogenous values, which suggest that the component interactions are of lower significance for this plant.

scholarly journals Genetic Algorithms as Computational Methods for Finite-Dimensional Optimization

2021 ◽  
pp. 5-14
Author(s):  
Nataliya Gulayeva ◽  
Volodymyr Shylo ◽  
Mykola Glybovets
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Numerical Methods ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimization Method ◽  
Direct Search ◽  
Mathematical Programming Problem ◽  
Combinatorial Optimization Problems

Introduction. As early as 1744, the great Leonhard Euler noted that nothing at all took place in the universe in which some rule of maximum or minimum did not appear [12]. Great many today’s scientific and engineering problems faced by humankind are of optimization nature. There exist many different methods developed to solve optimization problems, the number of these methods is estimated to be in the hundreds and continues to grow. A number of approaches to classify optimization methods based on various criteria (e.g. the type of optimization strategy or the type of solution obtained) are proposed, narrower classifications of methods solving specific types of optimization problems (e.g. combinatorial optimization problems or nonlinear programming problems) are also in use. Total number of known optimization method classes amounts to several hundreds. At the same time, methods falling into classes far from each other may often have many common properties and can be reduced to each other by rethinking certain characteristics. In view of the above, the pressing task of the modern science is to develop a general approach to classify optimization methods based on the disclosure of the involved search strategy basic principles, and to systematize existing optimization methods. The purpose is to show that genetic algorithms, usually classified as metaheuristic, population-based, simulation, etc., are inherently the stochastic numerical methods of direct search. Results. Alternative statements of optimization problem are given. An overview of existing classifications of optimization problems and basic methods to solve them is provided. The heart of optimization method classification into symbolic (analytical) and numerical ones is described. It is shown that a genetic algorithm scheme can be represented as a scheme of numerical method of direct search. A method to reduce a given optimization problem to a problem solvable by a genetic algorithm is described, and the class of problems that can be solved by genetic algorithms is outlined. Conclusions. Taking into account the existence of a great number of methods solving optimization problems and approaches to classify them it is necessary to work out a unified approach for optimization method classification and systematization. Reducing the class of genetic algorithms to numerical methods of direct search is the first step in this direction. Keywords: mathematical programming problem, unconstrained optimization problem, constrained optimization problem, multimodal optimization problem, numerical methods, genetic algorithms, metaheuristic algorithms.

Comparative Study of Different Optimization Algorithms Used for Obtaining Diesel-Biodiesel Blends

2019 ◽  
Vol 70 (6) ◽  
pp. 1893-1896
Author(s):  
Stefan Sandru ◽  
Ion Onutu
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithms ◽  
Linear Programming ◽  
Classical Method ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Optimization Method ◽  
Biodiesel Blends ◽  
Dual Simplex ◽  
Linear Programming Problems

The purpose of this paper is to compare two different optimization methods, used in acquiring diesel-biodiesel blends. There were used five types of samples in order to enable the optimization of the final blend: there were chosen two types of hydrofined diesel fuel and there were synthesized three original types of biodiesel. The first optimization method used, dual simplex, is a classical method being used in solving linear programming problems. The second optimization method, the genetic algorithms, falls in the type of artificial intelligence algorithms, being an evolutionary method used when the problem requires searching an optimal solution in a great variety of valid solutions.

Multidisciplinary design optimization under correlated uncertainties

2017 ◽  
Vol 25 (3) ◽  
pp. 262-275 ◽  
Author(s):  
Huanwei Xu ◽  
Wei Li ◽  
Liudong Xing ◽  
Shun-Peng Zhu
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Optimization Methods ◽  
Optimization Method ◽  
Quantitative Model ◽  
Collaborative Optimization ◽  
Multidisciplinary Design ◽  
Engineering System ◽  
Optimization Approach ◽  
Complex Mechanical Systems

Uncertainty analysis is a hot research topic in multidisciplinary design optimization for complex mechanical systems. Existing multidisciplinary design optimization works typically assume that uncertainties are uncorrelated of each other. In real-world engineering systems, however, correlations do exist between different uncertainties. The multidisciplinary design optimization methods without considering correlations between uncertainties may cause inaccuracy and thus misleading optimization results. In this article, we make contributions by proposing a new multidisciplinary design optimization approach based on the ellipsoidal set theory to investigate the characteristics of correlated uncertainties and incorporate their effects in the multidisciplinary design optimization through an advanced collaborative optimization method, where the quantitative model of correlated uncertainties is transformed into constrains of subsystems. Both a mathematical example and a case study of an engineering system are provided to illustrate feasibility and validity of the proposed method.

scholarly journals Representation and estimation of the power coefficient in wind energy conversion systems

2019 ◽  
Vol 28 (50) ◽  
pp. 77-90
Author(s):  
José Genaro González-Hernández ◽  
Rubén Salas-Cabrera
Keyword(s):  
Wind Energy ◽  
Energy Conversion ◽  
Power Coefficient ◽  
University Professors ◽  
Fourth Degree ◽  
Wind Energy Conversion ◽  
Advantages And Disadvantages ◽  
Wind Energy Conversion Systems ◽  
State Observers ◽  
Energy Conversion Systems

This paper aims at summarizing various methods used for representing and estimating the power coefficient in wind turbines, such as exponential, sinusoidal and polynomial models, as well as mathematical tools known as state observers. We present an exhaustive bibliographic review of the models used to calculate the power coefficient, given that this type of studies are scarce nowadays. In addition, we propose models that can be satisfactorily used for various analyzes of wind energy conversion systems, such as the representation by a polynomial function of fourth degree and the models based on the stochastic probability function. The relevance of this work is supported by the advantages and disadvantages of the various models and estimators of the power coefficient, which are presented at the end of the article in a comparative table with the purpose of offering to the reader a general summary. Ultimately, this review aims at helping researchers, students, university professors and those who wish to venture into this field, even though they do not have much experience, to establish a quick synthesized understanding of the different models and representations of the power coefficient.

scholarly journals An Overall Uniformity Optimization Method of the Spherical Icosahedral Grid Based on the Optimal Transformation Theory

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1516
Author(s):  
Fuli Luo ◽  
Xuesheng Zhao ◽  
Wenbin Sun ◽  
Yalu Li ◽  
Yuanzheng Duan
Keyword(s):  
Transportation Cost ◽  
Optimization Methods ◽  
Optimization Method ◽  
Cell Decomposition ◽  
Optimization Approach ◽  
Transformation Theory ◽  
Grid Deformation ◽  
Centroidal Voronoi Tessellations ◽  
Grid Optimization ◽  
Simulation Errors

The improvement of overall uniformity and smoothness of spherical icosahedral grids, the basic framework of atmospheric models, is a key to reducing simulation errors. However, most of the existing grid optimization methods have optimized grid from different aspects and not improved overall uniformity and smoothness of grid at the same time, directly affecting the accuracy and stability of numerical simulation. Although a well-defined grid with more than 12 points cannot be constructed on a sphere, the area uniformity and the interval uniformity of the spherical grid can be traded off to enhance extremely the overall grid uniformity and smoothness. To solve this problem, an overall uniformity and smoothness optimization method of the spherical icosahedral grid is proposed based on the optimal transformation theory. The spherical cell decomposition method has been introduced to iteratively update the grid to minimize the spherical transportation cost, achieving an overall optimization of the spherical icosahedral grid. Experiments on the four optimized grids (the spring dynamics optimized grid, the Heikes and Randall optimized grid, the spherical centroidal Voronoi tessellations optimized grid and XU optimized grid) demonstrate that the grid area uniformity of our method has been raised by 22.60% of SPRG grid, −1.30% of HR grid, 38.30% of SCVT grid and 38.20% of XU grid, and the grid interval uniformity has been improved by 2.50% of SPRG grid, 2.80% of HR grid, 11.10% of SCVT grid and 11.00% of XU grid. Although the grid uniformity of the proposed method is similar with the HR grid, the smoothness of grid deformation has been enhanced by 79.32% of grid area and 24.07% of grid length. To some extent, the proposed method may be viewed as a novel optimization approach of the spherical icosahedral grid which can improve grid overall uniformity and smoothness of grid deformation.

scholarly journals A useful approach to sensitivity and predictability studies in geophysical fluid dynamics: conditional non-linear optimal perturbation

2019 ◽  
Vol 7 (1) ◽  
pp. 214-223 ◽  
Author(s):  
Qiang Wang ◽  
Mu Mu ◽  
Guodong Sun
Keyword(s):  
Initial Conditions ◽  
Optimal Parameter ◽  
Initial Perturbation ◽  
Optimization Method ◽  
Ocean Model ◽  
Optimization Approach ◽  
Optimal Perturbation ◽  
Advantages And Disadvantages ◽  
Non Linear ◽  
Computational Aspects

Abstract In atmospheric and oceanic studies, it is important to investigate the uncertainty of model solutions. The conditional non-linear optimal perturbation (CNOP) method is useful for addressing the uncertainty. This paper reviews the development of the CNOP method and its computational aspects in recent years. Specifically, the CNOP method was first proposed to investigate the effects of the optimal initial perturbation on atmosphere and ocean model results. Then, it was extended to explore the influences of the optimal parameter perturbation, model tendency perturbation and boundary condition perturbation. To obtain solutions to these optimal perturbations, four kinds of optimization approaches were developed: the adjoint-based method, the adjoint-free method, the intelligent optimization method and the unconstrained optimization method. We illustrate the calculation process of each method and its advantages and disadvantages. Then, taking the Zebiak–Cane model as an example, we compare the CNOPs related to initial conditions (CNOP-Is) calculated by the above four methods. It was found that the dominant structures of the CNOP-Is for different methods are similar, although some differences in details exist. Finally, we discuss the necessity and possible direction for designing a more effective optimization approach related to the CNOP in the future.

scholarly journals Simulation and Optimization of Control of Selected Phases of Gyroplane Flight

2017 ◽  
Author(s):  
Wienczyslaw Stalewski
Keyword(s):  
Flight Control ◽  
Numerical Optimization ◽  
Stokes Equations ◽  
Optimization Problems ◽  
Computational Simulation ◽  
Optimization Methods ◽  
Optimization Method ◽  
Optimization Approach ◽  
Simulation And Optimization ◽  
Control Procedures

The optimization methods are increasingly used to solve challenging problems of aeronautical engineering. Typically, the optimization methods are utilized in design of aircraft airframe or its structure. The presented study is focused on an improvement of aircraft-flight-control procedures through the numerical optimization approach. The optimization problems concern selected phases of flight of light gyroplane - a rotorcraft using an unpowered rotor in autorotation to develop lift and an engine-powered propeller to provide thrust. An original methodology of computational simulation of rotorcraft flight was developed and implemented. In this approach the aircraft-motion equations are solved step-by-step, simultaneously with the solution of the Unsteady Reynolds-Averaged Navier-Stokes equations, which is conducted to assess aerodynamic forces acting on the aircraft. As a numerical optimization method, the BFGS algorithm was adapted. The developed methodology was applied to optimize the flight-control procedures in selected stages of gyroplane flight in direct proximity of the ground, where properly conducted control of the aircraft is critical to ensure flight safety and performance. The results of conducted computational optimizations proved qualitative correctness of the developed methodology. The research results can be helpful in design of easy-to-control gyroplanes and also in the training of pilots of this type of rotorcraft.

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