Design Optimization of Power Plants by Considering Multiple Partial Load Operation Points

2007 ◽  
Author(s):  
Marc Ju¨des ◽  
George Tsatsaronis
Keyword(s):  
Design Optimization ◽  
Power Plants ◽  
Optimization Procedure ◽  
Optimization Methods ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Optimization Task ◽  
Operation Conditions ◽  
Partial Load ◽  
Conventional Optimization

The design optimization of complex energy conversion systems requires the consideration of typical operation conditions. Due to the complex optimization task, conventional optimization methods normally take into account only one operation point that is, in the majority of cases, the full load case. To guarantee good operation at partial loads additional operation conditions have to be taken into account during the optimization procedure. The optimization task described in this article considers altogether four different operation points of a cogeneration plant. Modelling requirements, such as the equations that describe the partial load behavior of single components, are described as well as the problems that occur, when nonlinear and nonconvex equations are used. For the solution of the resulting non-convex mixed-integer nonlinear programming (MINLP) problem, the solver LaGO is used, which requires that the optimization problem is formulated in GAMS. The results of the conventional optimization approach are compared to the results of the new method. It is shown, that without consideration of different operation points, a flexible operation of the plant may be impossible.

scholarly journals Multidisciplinary Aircraft Conceptual Design Optimization Considering Fidelity Uncertainties

2021 ◽  
Author(s):  
Daniel J. Neufeld
Keyword(s):  
Design Optimization ◽  
Case Studies ◽  
Conceptual Design ◽  
Optimization Methods ◽  
Optimization Approach ◽  
Approximate Analysis ◽  
Reliability Based Design ◽  
Analysis Methods ◽  
Conventional Optimization ◽  
Aircraft Conceptual Design

Aircraft conceptual design traditionally utilizes simplified analysis methods and empirical equations to establish the basic layout of new aircraft. Applying optimization methods to aircraft conceptual design may yield solutions that are found to violate constraints when more sophisticated analysis methods are introduced. The designer's confidence that proposed conceptual designs will meet their performance target is limited when conventional optimization approaches are utilized. Therefore, there is a need for an optimization approach that takes into account the uncertainties that arise when traditional analysis methods are used in aircraft conceptual design optimization. This research introduces a new aircraft conceptual design optimization approach that utilizes the concept of Reliability Based Design Optimization (RBDO). RyeMDO, a framework for multi-objective, multi-disciplinary RBDO was developed for this purpose. The performance and effectiveness of the RBDO-MDO approaches implemented in RyeMDO were evaluated to identify the most promising approaches for aircraft conceptual design optimization. Additionally, an approach for quantifying the errors introduced by approximate analysis methods was developed. The approach leverages available historical data to quantify the uncertainties introduced by approximate analysis methods in two engineering case studies: the conceptual design optimization of an aircraft wing box structure and the conceptual design optimization of a commercial aircraft. The case studies were solved with several of the most promising RBDO-MDO integrated approaches. The proposed approach yields more conservative solutions and estimates the risk associated with each solution, enabling designers to reduce the likelihood that conceptual aircraft designs will fail to meet objectives later in the design process.

scholarly journals Multidisciplinary Aircraft Conceptual Design Optimization Considering Fidelity Uncertainties

2021 ◽  
Author(s):  
Daniel J. Neufeld
Keyword(s):  
Design Optimization ◽  
Case Studies ◽  
Conceptual Design ◽  
Optimization Methods ◽  
Optimization Approach ◽  
Approximate Analysis ◽  
Reliability Based Design ◽  
Analysis Methods ◽  
Conventional Optimization ◽  
Aircraft Conceptual Design

Aircraft conceptual design traditionally utilizes simplified analysis methods and empirical equations to establish the basic layout of new aircraft. Applying optimization methods to aircraft conceptual design may yield solutions that are found to violate constraints when more sophisticated analysis methods are introduced. The designer's confidence that proposed conceptual designs will meet their performance target is limited when conventional optimization approaches are utilized. Therefore, there is a need for an optimization approach that takes into account the uncertainties that arise when traditional analysis methods are used in aircraft conceptual design optimization. This research introduces a new aircraft conceptual design optimization approach that utilizes the concept of Reliability Based Design Optimization (RBDO). RyeMDO, a framework for multi-objective, multi-disciplinary RBDO was developed for this purpose. The performance and effectiveness of the RBDO-MDO approaches implemented in RyeMDO were evaluated to identify the most promising approaches for aircraft conceptual design optimization. Additionally, an approach for quantifying the errors introduced by approximate analysis methods was developed. The approach leverages available historical data to quantify the uncertainties introduced by approximate analysis methods in two engineering case studies: the conceptual design optimization of an aircraft wing box structure and the conceptual design optimization of a commercial aircraft. The case studies were solved with several of the most promising RBDO-MDO integrated approaches. The proposed approach yields more conservative solutions and estimates the risk associated with each solution, enabling designers to reduce the likelihood that conceptual aircraft designs will fail to meet objectives later in the design process.

Aerodynamic Design Optimization of the Last Stage of a Multi-Stage Compressor by Using an Adjoint Method

2016 ◽  
Author(s):  
Jing Yang ◽  
Jiaqi Luo ◽  
Juntao Xiong ◽  
Feng Liu
Keyword(s):  
Design Optimization ◽  
Adjoint Method ◽  
Guide Vane ◽  
Inlet Guide Vane ◽  
Optimization Approach ◽  
Operation Condition ◽  
Operation Conditions ◽  
Multi Stage ◽  
Last Stage ◽  
Continuous Adjoint

The paper presents the aerodynamic optimization of the last row of a 4.5-stage compressor by means of a gradient-based optimization method that utilizes the continuous adjoint approach. An adjoint mixing-plane formulation is used to allow for the computation of adjoint solutions for multistage turbomachinery problems. Firstly, a conventional one-dimensional design method and empirical correlations are used to produce a base design of a 4.5-stage low-speed, low compression ratio compressor with an inlet guide vane to adjust to different operation conditions. Then the last stage is redesigned by the adjoint method to reduce the flow losses at the operation condition near stall through modifying the aerodynamic shape and stagger angle of the stator blade. The cost function is defined as a weighted sum of the entropy production and a constraint on the mass flow rate. Finally, a multi-point design optimization approach by using the adjoint method is employed to improve the performance of the last stage at two different operation conditions. The results demonstrate that the stator shape modifications improve the aerodynamic performance of the stage and illustrate the functionality of the adjoint-based multi-stage optimization system.

Integration of an Automatic Optimizer Functionality Into the Design Process of Industrial Steam Turbines

2012 ◽  
Author(s):  
Dimitri Drapkin ◽  
Franz Kores ◽  
Thomas Polklas
Keyword(s):  
Design Process ◽  
Optimization Methods ◽  
Simultaneous Optimization ◽  
Design Parameters ◽  
Optimization Approach ◽  
Steam Turbines ◽  
Particle Swarm Optimizer ◽  
Optimization Task ◽  
Wide Range ◽  
Optimization Parameters

Industrial steam turbines are mostly tailor made machinery, varying in a wide range of specifications. This feature introduces high requirements on the design process which has to be flexible, efficient and fast at the same time. Given live steam and design parameters as input, the geometry corresponding to the valid design scheme can be calculated together with the required thermodynamic, aerodynamic and mechanical characteristics. By variation of design parameters a design may be achieved which optimizes both, efficiency and cost. The optimization task is formulated mathematically, e.g. crucial optimization parameters, criteria for evaluation of different designs and other required constraints are selected. The structure of the resulting optimization problem is analyzed. Based on this analysis a modular optimization system design is proposed. The choice of Genetic Algorithms and Adaptive Particle Swarm Optimizer as optimization methods is discussed, recommendations for their proper use are given. A bicriterial optimization approach for a simultaneous optimization of efficiency and cost is developed.

scholarly journals Partitioning and solving large-scale tactical harvest scheduling problems for industrial plantation forests

2020 ◽  
Vol 50 (8) ◽  
pp. 811-818
Author(s):  
Pedro Bellavenutte ◽  
Woodam Chung ◽  
Luis Diaz-Balteiro
Keyword(s):  
Large Scale ◽  
Optimization Procedure ◽  
Mixed Integer ◽  
Forest Planning ◽  
Planning Problem ◽  
Optimization Approach ◽  
Scheduling Problems ◽  
Harvest Scheduling ◽  
Plantation Forests ◽  
Industrial Plantation

Spatially explicit, tactical forest planning is a necessary but challenging task in the management of plantation forests. It involves harvest scheduling and planning for road access and log transportation over time and space. This combinatorial problem can be formulated into the fixed-charge transportation problem (FCTP), in which the sum of fixed and variable costs is minimized while meeting harvest volume requirements and allowing necessary road maintenance and log hauling activities. The problem can be solved using general optimization methods such as mixed-integer linear programming (MILP), but the computational efficiency of the MILP-based approach quickly drops as the size and complexity of the problem increases. We developed a new optimization procedure that partitions the large planning problem into smaller subproblems. We applied a hybrid optimization approach using both MILP and heuristic rules to efficiently solve the large FCTP that otherwise may not be solvable using traditional methods. We applied our approach to an industrial plantation forest in Brazil. Our applications demonstrate the performance of the new optimization procedure and the benefit of solving large forest planning problems that integrate harvest scheduling with road access and transportation.

Optimization Methods in Modeling the Mechanical Properties of Heavy Steel Plates / Metody Optymalizacyjne W Modelowaniu Własności Mechanicznych Blach Grubych

2012 ◽  
Vol 57 (4) ◽  
pp. 971-979
Author(s):  
A.Z. Grzybowski
Keyword(s):  
Mechanical Properties ◽  
Steel Production ◽  
Optimization Methods ◽  
Linear Constraints ◽  
Steel Plates ◽  
Chance Constrained Programming ◽  
Optimization Approach ◽  
Industrial Manufacturing ◽  
Optimization Task ◽  
Constrained Programming

The paper is devoted to an optimization approach to a problem of statistical modeling of mechanical properties of heavy steel plates during a real industrial manufacturing process. The approach enables the manufacturer to attain a specific set of the final product properties by optimizing the alloying composition within the grade specifications. Because this composition has to stay in the agreement with earlier indicated specifications, it leads to the large system of linear constraints, and the problem itself can be expressed in the form of linear programming (LP) task. It turns out however, that certain of the constraints contain the coefficients which have to be estimated on the base of the data gathered in the production process and as such they are uncertain. Consequently, the initial optimization task should be modeled as so-called Chance Constrained Programming problem (CCP), which is a special class within the stochastic programming problems. The paper presents mathematical models of the optimization problem that result from both approaches and indicates differences which are important for the decision makers in the production practice. Some examples illustrating the differences in solutions resulting from LP and CCP models are presented as well. Although the statistical analysis presented in this paper is based on the data gathered in the ISD Czestochowa Steelworks, the proposed approach can be adopted in any other process of steel production.

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.

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 Simulation-Optimization Approach for the Logistics Network Design of Biomass Co-Firing with Coal at Power Plants

2018 ◽  
Vol 10 (11) ◽  
pp. 4299 ◽  
Author(s):  
Maria Aranguren ◽  
Krystel Castillo-Villar ◽  
Mario Aboytes-Ojeda ◽  
Marcio Giacomoni
Keyword(s):  
Simulation Model ◽  
Power Plants ◽  
Agricultural Land ◽  
Programming Model ◽  
Ghg Emissions ◽  
Mixed Integer ◽  
Weather Data ◽  
United States Department ◽  
Optimization Approach ◽  
Biomass Supply

This work proposes a hybrid scheme that combines a simulation model and a mathematical programming model for designing logistic networks for co-firing biomass, specifically switchgrass, in conventional coal-fired power plants. The advantages of co-firing biomass include: (1) the creation of green jobs; (2) the efficient use of current power plant infrastructure; (3) fostering the penetration of renewable energy into power networks; and, (4) the reduction of greenhouse gas (GHG) emissions. The novelty of this work lies in the inclusion of (1) the inherent variability of biomass supply at the parcel level, and (2) the effects of climate change on future biomass supply when designing a feedstock logistic network. The design optimization is conducted at the farm/parcel level (most, if not all, previous works have used county level average data) and integrates the crop growth predictions employing United States Department of Agriculture’s (USDA’s) Agricultural Land Management with Numerical Assessment Criteria (ALMANAC) simulation model; the output of the simulations is input into the mixed integer linear programming (MILP) hub-and-spoke model to minimize the overall cost of the logistic network. Specifically, the MILP-based model selects the parcels and depot locations as well as biomass transportation flows by taking into consideration different types of soil, land cover characteristics, and predicted yields, which account for both historical and forecasted weather data. The hybrid methodology was tested by solving realistic situations, which considered varying weather conditions. The gross results indicate that the optimized logistic network enabled meeting a 20% biomass co-firing rate demand, which reduced 1,158,867 Mg per year in GHG emissions by co-firing with biomass.

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