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Technique for Concept Selection Using Interactive Probabilistic multiple Attribute Decision Making

JOURNAL OF AEROSPACE COMPUTING INFORMATION AND COMMUNICATION 2009年第1期6卷 51-67页

作者： Biltgen, Patrick T. Mavris, Dimitri N. Georgia Inst Technol Sch Aerosp Engn ASDL Atlanta GA 30332 USA

Design is a decision-making process that depends on multiple attributes. Analysis of alternatives with respect to a single metric representing the "goodness" of the concept is difficult without resorting to subjective weightings on an overall evaluation criterion. The use of subjective factors in a decision-making process is often met with criticism, as the selection of a design may be traced to preferential decisions made on a certain day by a single individual. A methodology is needed that reduces the impact of uncertainty in the subjective weighting factors while retaining the traceability, defensibility, and rigor provided by traditional multiple attribute decision-making techniques. In this work, a standard process for systems engineering using the quality function deployment approach with a multiple attribute decision-making technique for concept selection is supplemented through the use of parametric slide bars to play "what-if" games and a probabilistic environment that plays all possible "what-if" games and summarizes the results. Using the modified process, families of concepts can be rapidly examined based on varying levels of subjective preferences. Decision makers, armed with a rapid parametric sensitivity analysis tool, can make more informed decisions about future concepts, policies, and acquisition decisions. An interactive graphical environment can be used to visualize the diverse sets of trades and understand non-intuitive answers by tracing customer needs to proposed solutions in real-time. In practice, the proposed process facilitates iteration between needs and concepts and fosters and increased understanding of the concept space between designer and decision maker.

关键词： Systems Engineering Zero Lift Drag Coefficient Sensitivity Analysis Probability Density Functions Missile Design Propellant Transport Aircraft multi objective evolutionary algorithms RADAR Space and Missile Systems Center

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Optimum Operating Parameters and Blade Setting of a High-Speed Propeller

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JOURNAL OF AIRCRAFT 2022年第2期59卷 484-501页

作者： Boulkeraa, Tayeb Ghenaiet, Adel Benini, Ernesto Polytech Mil Sch Fac Mech Engn & Proc Engn Lab Energy Convers Syst Bordj El Bahri 16046 Algeria Univ Sci & Technol Houari Boumediene Lab Turbomachinery Fluid Mech & Energet Dept Bab Ezzouar 16111 Algeria Univ Padua Dept Ind Engn Via Venezia 1 I-35131 Padua Italy

Traditionally, the design of propellers is accomplished on the bases of engineering experience and the availability of well-established experimental data. However, the emergence of computational fluid dynamics (CFD) tools coupled with advanced optimizers has made this task easier. This paper presents the essentials of an optimization-based CFD chain aimed at finding the optimal operating parameters and blade settings of a high-speed propeller. This fully automated procedure combines an improved hypercube sampling (IHS) for the initial design of experiment and a radial basis function (RBF) as the meta-model, hence leading to lesser CFD simulations. The coupling between IHS and RBF is judiciously implemented, and the sampling strategy and updates are thoroughly examined. The optimization procedure uses the multi-objective evolutionary algorithm nondominated sorting genetic algorithm to determine the operating parameters and blade setting that maximize the propeller efficiency and thrust. At the optimum point the blade operates with mild pressure gradients, whereas the transonic region is restricted to the upper corner of the blade tip.

关键词： High Speed Propellers Large Eddy Simulation multi objective evolutionary algorithms Genetic Algorithm Design of Experiments Freestream Conditions Reynolds Averaged Navier Stokes Blade Element Momentum Theory Aerodynamic Efficiency Optimization Algorithm

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Scheduling an Agile multipayload Earth-Observing Satellite

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JOURNAL OF SPACECRAFT AND ROCKETS 2024年第1期61卷 143-156页

作者： Zhang, Wenyuan Zheng, Gangtie Tsinghua Univ Sch Aerosp Engn Beijing 100084 Peoples R China

A highly integrated Earth-observing satellite can possess several maneuverable payloads to perform different missions simultaneously, which brings some challenges to the method of task scheduling. This paper addresses the selection and scheduling problem of an agile satellite with several independently maneuverable optical payloads. Some differences compared to the traditional scheduling problem of agile satellites are presented and considered in a constrained optimization model. A two-stage method is proposed to accomplish the scheduling of the satellite and payloads in different stages. Clusters are generated from preprocessed tasks by a clique partition algorithm, and their centers are used to calculate the pointing direction of the satellite in the first stage. A multiobjective local search algorithm is introduced to schedule tasks in each selected cluster in the second stage. Considering the time-dependent property of the transition time, the problem of determining the start observation time is transformed into linear programming in a proposed insertion operator that guarantees the feasibility of generated solutions. Two types of instances are created and tested to demonstrate the effectiveness of the proposed method, and some analyses are conducted based on the experimental results.

关键词： Search Algorithm Optimization Algorithm multi objective evolutionary algorithms Computing and Informatics Computer Graphics Earth Observation Satellite

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Optimization Alternatives for Robust Model-based Design of Synthetic Biological Circuits

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IFAC-PapersOnLine 2016年第7期49卷 821-826页

作者： Boada, Y. Pitarch, J.L. Vignoni, A. Reynoso-Meza, G. Picó, J. Universitat Politècnica de Valencia Camino de Vera S/N Valencia46022 Spain Systems Engineering and Control Department Universidad de Valladolid C/ Real de Burgos S/N Valladolid47011 Spain Center for Systems Biology Dresden and the Max Planck Institute of Molecular Cell Biology and Genetics Pfotenhauer str. 108 Dresden01307 Germany Pontifícia Universidade Catolica do Parana Imaculada Conceião 1155 CuritibaPR80215-901 Brazil

Synthetic biology is reaching the situation where tuning devices by hand is no longer possible due to the complexity of the biological circuits being designed. Thus, mathematical models need to be used in order, not only to predict the behavior of the designed synthetic devices; but to help on the selection of the biological parts, i.e., guidelines for the experimental implementation. However, since uncertainties are inherent to biology, the desired dynamics for the circuit usually requires a trade-off among several goals. Hence, a multi-objective optimization design (MOOD) naturally arises to get a suitable parametrization (or range) of the required kinetic parameters to build a biological device with some desired properties. Biologists have classically addressed this problem by evaluating a set of random Monte Carlo simulations with parameters between an operation range. In this paper, we propose solving the MOOD by means of dynamic programming using both a global multi-objective evolutionary algorithm (MOEA) and a local gradient-based nonlinear programming (NLP) solver. The performance of both alternatives is then checked in the design of a well-known biological circuit: a genetic incoherent feed-forward loop showing adaptive behavior. © 2016

关键词： Dynamic programming Computer aided design Economic and social effects evolutionary algorithms Genetic programming Intelligent systems Kinetic parameters Monte Carlo methods multiobjective optimization Nonlinear programming Timing circuits Adaptive behavior Biological parts Feedforward loops Local gradients multi objective evolutionary algorithms Parametrizations Robust modeling Synthetic biology

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Fast Solutions Enhancing using a Copula-based EDA and SVM for many-objective problems

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IFAC-PapersOnLine 2016年第12期49卷 781-786页

作者： Cheriet, Abdelhakim Cherif, Foudil Taleb-Ahmed, Abdelmalik LESIA Laboraroty University of Biskra Algeria LAMIH Electrical Engineering Department Valenciennes United States

In this paper we propose a new Copula-based Estimation of Distribution Algorithm, to solve Many-objective optimization problem and to get new optimal solutions in very court time. Our algorithm uses the proprieties of Copula and exploits their statistical properties to make new solutions using the founded optimal solutions through the estimation of their distribution. The first step of the proposed Copula-based Estimation of Distribution Algorithm (CEDA-SVM) is taking initial solutions offered by any MOEA (multi objective evolutionary Algorithm), and then creates Copulas to estimate their distribution, and we use Support Vector Machine (SVM) to learn the Pareto solutions model; those Copulas will be used to generate new solutions and SVM to avoid the expensive function evaluations. The idea of using the estimated distribution of the optimal solutions helps CEDA-SVM to avoid running the optimizer ( MOEA ) every time we need new alternatives solutions when the founded ones are not satisfactory. We tested CEDA-SVM on a set of many-objective benchmark problems traditionally used by the community, namely DTLZ (1, 2, 3, and 4) with different dimensions (3, 5, 8, 10, and 15). We used CEDA along with MOEA/D-Schy and MOEA/D-BI as two examples of MOEA thus resulting in two variants CEDA-MOAE/D-Scy and CEDA-MOEA/D-BI and compare them with MOEA/D-Schy and MOEA/D-BI. The results of our experiments show that, with both variants of CEDA-SVM, new solutions can be obtained in a very small time compared to the other algorithms. © 2016

关键词： evolutionary algorithms Distribution functions multiobjective optimization Optimal systems Support vector machines Bench mark problems Copula Estimation of distribution algorithms Initial solution Many objective optimizations multi objective evolutionary algorithms Optimal solutions Statistical properties

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multi-criteria multi-constrained Aerodynamic Optimization of Civil Tiltrotor Empennage Surfaces

Multi-criteria Multi-constrained Aerodynamic Optimization of...

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AIAA Aviation Forum and Exposition

作者： Battiston, A. Ponza, R. Benini, E. Bernini, G. Pecoraro, M. HIT09 Srl I-35127 Padua Italy Univ Padua Dept Ind Engn I-35131 Padua Italy Leonardo SpA Helicopter Div I-21017 Cascina Costa Di Samarat Italy

ISBN: (数字)9781624106101

ISBN: (纸本)9781624106101

The present work describes the overall optimization strategy that has been adopted for the enhancement of the aerodynamic performance of a civil tiltrotor empennage surfaces. The optimization process has been designed around GeDEA-II, a multi-objective evolutionary Algorithm developed at University of Padua. The optimization algorithm has been used in two different cases: a two-dimensional optimization of the empennage airfoil and a threedimensional optimization of the empennage winglets, patented by Leonardo Helicopters under the name of finlets. Results demonstrate the effectiveness of the optimization strategies for both the cases. A parametric study of the empennage planform has also been conducted with the aid of an artificial neural network, in order to assess the variations in aerodynamic performance for different geometries.

关键词： Tiltrotor Airframes Aerodynamic Performance Symmetric Airfoil Artificial Neural Network Optimization Algorithm multi objective evolutionary algorithms Leonardo Helicopters Aerodynamic Coefficients Aspect Ratio

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System Architecture Design Space Exploration: An Approach to Modeling and Optimization

System Architecture Design Space Exploration: An Approach to...

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AIAA Aviation Forum

作者： Bussemaker, J. H. Ciampa, P. D. Nagel, B. DLR German Aerosp Ctr Inst Syst Architectures Aeronaut Hamburg Germany

ISBN: (数字)9781624105982

ISBN: (纸本)9781624105982

Systematic modeling of architecture design spaces is needed when architecting complex systems, to support experts in making less biased decisions, and to formulate the optimization problem needed to explore the large combinatorial design space. Existing methods do not offer enough compatibility with the Model-Based Systems Engineering (MBSE) approaches, cannot model all needed design scenarios, or are not flexible enough when it comes to architecture evaluation. A new method is presented that provides a semantic representation of the architecture design space, modeled as the Architecture Design Space Graph (ADSG). The ADSG represents three types of architectural decisions: function-component mapping, component characterization, and component connection. The ADSG is constructed from a design space definition, and discrete architectural decisions are automatically inserted according to specified rules. Once decisions and metrics have been defined, the hierarchical, mixed-integer, multi-objective optimization problem can be formulated: decisions are mapped to design variables, and performance metrics are mapped to objectives or constraints. An application of the method to the Apollo mission architecting problem is presented.

关键词： Space Exploration Model Based System Engineering Apollo Missions Earth Orbit Rendezvous Optimization Algorithm Crew Member Earth Centrifugal Pump Turbofan multi objective evolutionary algorithms

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Effectiveness of Surrogate-Based Optimization algorithms for System Architecture Optimization

Effectiveness of Surrogate-Based Optimization Algorithms for...

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AIAA Aviation Forum and Exposition

作者： Bussemaker, Jasper H. Barton, Nathalie Lefebvre, Thierry Ciampa, Pier D. Nagel, Bjoern DLR German Aerosp Ctr Inst Syst Architectures Aeronaut Aircraft Design & Syst Integrat Hamburg Germany Univ Toulouse DTIS ONERA Informat Proc & Syst Dept Toulouse France DLR German Aerosp Ctr Inst Syst Architectures Aeronaut Hamburg Germany

ISBN: (数字)9781624106101

ISBN: (纸本)9781624106101

The design of complex system architectures brings with it a number of challenging issues, among others large combinatorial design spaces. Optimization can be applied to explore the design space, however gradient-based optimization algorithms cannot be applied due to the mixed-discrete nature of the design variables. It is investigated how effective surrogate-based optimization algorithms are for solving the black-box, hierarchical, mixed-discrete, multiobjective system architecture optimization problems. Performance is compared to the NSGAII multi-objective evolutionary algorithm. An analytical benchmark problem that exhibits most important characteristics of architecture optimization is defined. First, an investigation into algorithm effectiveness is performed by measuring how accurately a known Pareto-front can be approximated for a fixed number of function evaluations. Then, algorithm efficiency is investigated by applying various multi-objective convergence criteria to the algorithms and establishing the possible trade-off between result quality and function evaluations needed. Finally, the impact of hidden constraints on algorithm performance is investigated. The code used for this paper has been published.

关键词： Optimization Algorithm multi objective evolutionary algorithms Black Box algorithms Convergence Surrogate Model Gaussian Process Design of Experiments Computational Fluid Dynamics Systems Engineering Genetic Algorithm

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Design Optimization of multi-Stage Axial-Flow Compressor Using Machine Learning Models

Design Optimization of Multi-Stage Axial-Flow Compressor Usi...

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AIAA SciTech Forum

作者： Zhang, Xiawen Nichols, Andrew Thomos Zhang, Chuhua Liu, Feng Univ Calif Irvine Dept Mech & Aerosp Engn Irvine CA 92697 USA Xi An Jiao Tong Univ Sch Energy & Power Engn Xian 710049 Shaanxi Peoples R China

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

A rapid and accurate mean-line design optimization method for multi-stage axial-flow compressor is established and implemented on a 3.5-stage subsonic compressor. The accuracy of performance prediction using both empirical models and metamodels is compared. The aerodynamic matching of intra- and inter-stage compressor is investigated. The design variables are optimized for overall multi-stage performance improvement. The results show that the metamodels for loss coefficient, deviation angle and aerodynamic blockage exhibit better accuracy compared to the empirical models. For the investigated compressor, the design variables of stage 3 have dominant effects on the adiabatic efficiency. The pressure ratio on stage 3 should be less than the averaged distribution for higher efficiency. The stage inlet axial Mach number and flow angle have similar influence on the adiabatic efficiency. Increase of these two design variables results in increase of loading on stator 3 while increase of inter-stage hub radius results in increase of loading on rotor 3. After the optimization, higher adiabatic efficiency at design point and wider stall margin have been achieved.

关键词： Design Optimization multi Stage Compressors Machine Learning Aerodynamic Design Aerodynamic Performance Inlet Flow CFD simulation Aspect Ratio multi objective evolutionary algorithms Mass Flow Rate

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Linguistic Summarization of Time Series Data using Genetic algorithms

Linguistic Summarization of Time Series Data using Genetic A...

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7th Biannual Conference of the European-Society-for-Fuzzy-Logic-and-Technology (EUSFLAT)/17th Annual LFA Meeting

作者： Castillo-Ortega, Rita Marin, Nicolas Sanchez, Daniel Tettamanzi, Andrea G. B. Univ Granada Granada Spain European Ctr Soft Comp Mieres Asturias Spain Univ Milan Crema CR Italy

ISBN: (纸本)9789078677000

In this paper, the use of an evolutionary approach when obtaining linguistic summaries from time series data is proposed. We assume the availability of a hierarchical partition of the time dimension in the time series. The use of natural language allows the human users to understand the resulting summaries in an easy way. The number of possible final summaries and the different ways of measuring their quality has taken us to adopt the use of a multi objective evolutionary algorithm. We compare the results of the new approach with our previous greedy algorithms.

关键词： Linguistic Summarization multi objective evolutionary algorithms Time Series Dimensional Data Model Fuzzy Logic

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