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multi-objective Hidden Genes Genetic Algorithm for multigravity-Assist Trajectory Optimization

JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2022年第7期45卷 1269-1285页

作者： Ellithy, Ahmed Abdelkhalik, Ossama Englander, Jacob Iowa State Univ Aerospace Engn Dept 1200 Howe Hall Ames IA 50011 USA Iowa State Univ Dept Aerosp Engn 1200 Howe Hall Ames IA 50011 USA Johns Hopkins Appl Phys Lab Space Explorat Sect 11101 Johns Hopkins Rd Laurel MD 20723 USA

In multigravity-assist trajectory optimization, the size of the design space is a variable itself. The objective functions are usually replete with local minima. This paper presents a multi-objective hidden genes genetic algorithm (MOHGGA) for trajectory optimization. The length of the chromosome is selected large enough to enable modeling a given maximum number of swing-bys and maximum number of deep space maneuvers (DSMs). Binary tags are appended to those genes that control the swing-bys and DSMs. These binary tags are used to remove/add swing-bys and DSMs to a trajectory solution, and hence enable optimization among solutions of different sizes (different topologies). The MOHGGA generates Pareto fronts that have solutions of, in general, different number of swing-bys, swing-by planets, launch and arrival dates, and number of DSMs. Two objectives are considered in this paper: the total mission cost and total time of flight. An elitist nondominated sorting genetic algorithm is used. Local optimization is conducted on one objective function, holding the other objective function constant, to further improve the resulting Pareto front. Numerical results of four benchmark test cases for missions to Mars, Jupiter, Saturn, and Mercury are presented. The results demonstrate the capability of MOHGGA in searching for optimal trajectory topologies while optimizing two objectives.

关键词： Hidden Genes Genetic algorithms Trajectory Optimization Neptune Deep Space Maneuver Pericenter multi objective evolutionary algorithms Interplanetary Trajectories Lambert Algorithm Cassini Mission MATLAB

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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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multi-objective Cooperated Path Planning of multiple Unmanned Aerial Vehicles Based on Revisit Time

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2021年第12期18卷 919-932页

作者： Haghighi, Hassan Asadi, Davood Delahaye, Daniel Univ Toulouse Natl Sch Civil Aviat F-31400 Toulouse France Adana Alparslan Turkes Sci & Technol Univ Dept Aerosp Engn TR-01250 Adana Turkey

This paper investigates multi-objective optimization of coordinated patrolling flight of multiple unmanned aerial vehicles in the vicinity of terrain, while respecting their performance parameters. A new efficient modified A-star (A*) algorithm with a novel defined criterion known as individual revisit time cell value is introduced and extended to the whole area of the three-dimensional mountainous environment. As a contribution to solving tradeoffs in the optimization problem, revisit time is conjugated with other contrary costs effective in flight planning through Pareto analysis. By introducing the revisit time and applying a specific setup to mitigate computational complexity, the proposed algorithm efficiently revisits the desired zones, which are more important to be revisited during the patrolling mission. The results of the introduced modified A* algorithm are compared in various scenarios with two different algorithms: a complete and optimal algorithm known as Dijkstra, and an evolutionary algorithm known as the genetic algorithm. Simulation results demonstrate that the proposed algorithm generates faster and more efficient trajectories in complex multi-agent scenarios due to the introduced cell selection method and dynamic-based simplifications applied in this research.

关键词： UAV Path Planning multi objective evolutionary algorithms multi objective Genetic Algorithm Flight Planning Optimization Algorithm algorithms and Applications Flight Altitude Ailerons Markov Decision Process

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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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Integrated Shape and Trajectory Optimisation of Hypersonic Waveriders

Integrated Shape and Trajectory Optimisation of Hypersonic W...

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AIAA SciTech Forum

作者： Carvalho, Jose Mooij, Erwin Delft Univ Technol Fac Aerosp Engn Astrodynam & Space Miss Delft Netherlands GNC Engn GMV Lisbon Portugal

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

This research performs a surrogate-assisted shape optimisation of hypersonic waveriders, where the trajectories of each shape are optimised with a multi-objective evolutionary algorithm for heat-load and cross-range. A study on the best evolutionary algorithm, node control strategy for angle of attack and bank angle profiles, and population size to use in the trajectory optimisation phase, are identified. The aerodynamics of the waveriders is computed with a new local surface inclination method blending the modified Newtonian and tangent wedge solutions, while the convective heat flux is computed for the leading edges using the Newton-Kays engineering model. Shape variability is introduced according to the layout of central composite designs, and analysis of variance is applied to identify the shape features driving the two objectives. Shock angle, leading edge radius and overall vehicle dimensions are the strongest drivers, while details on the planform shape are less relevant and should be left for posterior studies. The surrogates are a good approximation of the true fitness functions, so they were optimised in a single-objective framework, producing two optimal waverider designs.

关键词： Trajectory Optimization Lift Force Coefficient Leading Edge Bluntness Heat Flux Aerodynamic Performance Central Composite Designs multi objective evolutionary algorithms Waverider Design Pressure Coefficient Hypersonic Flight

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Surrogate-Assisted Differential Evolution Using Knowledge-Transfer-Based Sampling for Expensive Optimization Problems

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AIAA JOURNAL 2022年第5期60卷 3251-3266页

作者： Long, Teng Ye, Nianhui Shi, Renhe Wu, Yufei Tang, Yifan Beijing Inst Technol Sch Aerosp Engn Beijing Peoples R China Beijing Inst Technol Key Lab Dynam & Control Flight Vehicle Minist Educ Beijing 100081 Peoples R China Simon Fraser Univ Sch Mech Syst Engn Surrey BC V3T 0A3 Canada

To sufficiently reuse the knowledge from previous optimization efforts, a surrogate-assisted differential evolution using knowledge-transfer-based sampling (denoted as SADE-KTS) method is proposed for solving expensive black-box optimization problems. In SADE-KTS, a novel knowledge-transfer-based sampling method is integrated with the differential evolution framework to generate promising initial sample points. In this way, a least-squares support vector machine classifier is constructed based on the prior optimization knowledge database to calibrate the initial sample points adaptively, which improves the exploration performance via transferring the existed optimization efforts to the current optimization task. Moreover, the radial basis function and kriging surrogates are employed to replace the expensive simulation models for evolutionary operations, where the tailored differential evolution operators are cooperated with the sequential quadratic programming optimizer to lead the search to the global optimum efficiently. A number of numerical benchmarks are tested to illustrate the optimization capacity of SADE-KTS compared with several competitive optimization algorithms. Finally, SADE-KTS is applied to an airfoil aerodynamic knowledge-based optimization problem considering the existed optimization knowledge, which demonstrates the practicality and effectiveness of the proposed SADE-KTS in engineering practices.

关键词： Airfoil Sequential Quadratic Programming Support Vector Machine Black Box Numerical Simulation multi objective evolutionary algorithms Lagrange multipliers Lift Coefficient Finite Element Analysis multidisciplinary Design Optimization

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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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multi-objective multidisciplinary Design Optimization Approach for Partially Reusable Launch Vehicle Design

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JOURNAL OF SPACECRAFT AND ROCKETS 2020年第2期57卷 373-390页

作者： Brevault, Loic Balesdent, Mathieu Hebbal, Ali Off Natl Etud & Rech Aerosp F-91123 Palaiseau France Univ Paris Saclay DTIS Paris France Univ Paris Saclay Paris France Univ Lille CNRS Ctr Rech Informat Signal & Automat Lille Lille France

Reusability of the first stage of launch vehicles may offer new perspectives to lower the cost of payload injection into orbit if sufficient reliability and efficient refurbishment can be achieved. One possible option that may be explored is to design the vehicle first stage for both reusable and expendable uses, in order to increase the flexibility and adaptability to different target missions. This paper proposes a multilevel multidisciplinary design optimization (MDO) approach to design aerospace vehicles addressing multimission problems. The proposed approach is focused on the design of a family of launchers for different missions sharing commonalities using multi-objective MDO to account for the computational cost associated with the discipline simulations. The multimission problem addressed considers two missions: 1) a reusable configuration for a sun synchronous orbit with a medium payload range and recovery of the first stage using a gliding-back strategy;2) an expendable configuration for a medium payload injected into a geostationary transfer orbit. A dedicated MDO formulation introducing couplings between the missions is proposed in order to efficiently solve such a coupled problem while limiting the number of calls to the exact multidisciplinary analysis thanks to the use of Gaussian processes and multi-objective efficient global optimization.

关键词： Expendable Launch Vehicle MDO Geostationary Transfer Orbit Sun Synchronous Orbit Probability Distribution Functions Solid Propellants Perigee Lift Coefficient Surrogate Model multi objective evolutionary algorithms

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Fast multi-objective Aerodynamic Optimization Using Sequential Domain Patching and multifidelity Models

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JOURNAL OF AIRCRAFT 2020年第3期57卷 388-398页

作者： Amrit, Anand Leifsson, Leifur Koziel, Slawomir Iowa State Univ Dept Aerosp Engn 537 Bissel Rd Ames IA 50011 USA Reykjavik Univ Engn Optimizat & Modeling Ctr Sch Sci & Engn Menntavegur 1 IS-102 Reykjavik Iceland

Exploration of design tradeoffs for aerodynamic surfaces requires solving of multi-objective optimization (MOO) problems. The major bottleneck here is the time-consuming evaluations of the computational fluid dynamics (CFD) model used to capture the nonlinear physics involved in designing aerodynamic surfaces. This, in conjunction with a large number of simulations necessary to yield a set of designs representing the best possible tradeoffs between conflicting objectives (referred to as a Pareto front), makes CFD-driven MOO very challenging. This paper presents a computationally efficient methodology aimed at expediting the MOO process for aerodynamic design problems. The extreme points of the Pareto front are obtained quickly using single-objective optimizations. Starting from these extreme points, identification of an initial set of Pareto-optimal designs is carried out using a sequential domain patching algorithm. Refinement of the Pareto front, originally obtained at the level of the low-fidelity CFD model, is carried out using local response surface approximations and adaptive corrections. The proposed algorithm is validated using a few multi-objective analytical problems and an aerodynamic problem involving MOO of two-dimensional transonic airfoil shapes where the figures of interest are the drag and pitching moment coefficients. A multifidelity model is constructed using CFD model and control points parameterizing the shape of the airfoil. The results demonstrate that an entire or a part of the Pareto front can be obtained at a low cost when considering up to eight design variables.

关键词： Aerodynamic Shape Optimization Morphing Airfoil CFD Simulation Surrogate Model Reynolds Averaged Navier Stokes multi objective evolutionary algorithms High Performance Computing multi objective Genetic Algorithm Sequential Quadratic Programming Lift Coefficient

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A Novel Framework for multi-objective Optimization of Airfoils Using Invasive Weed Optimization

A Novel Framework for Multi-Objective Optimization of Airfoi...

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AIAA Aviation Forum

作者： Pant, Pranshu Joseph, Joe Omkar, S. N. Indian Inst Sci Dept Aerosp Engn Bengaluru 560012 India

ISBN: (数字)9781624105982

ISBN: (纸本)9781624105982

Traditionally, airfoil design has been broadly limited to experimental and empirical methods. Over the years, computing power has grown exponentially and computational methods are becoming increasingly relevant. Still, generic airfoils continue to be used in most applications. Such airfoils yield sub-optimal performance and result in compromises in the overall design of the aircraft. With the advent of modern high-performance computing systems and metaheuristic optimization algorithms, optimizing airfoils for their specific use cases has become highly feasible. This paper presents a novel optimization framework for airfoils using the Invasive Weed Optimisation algorithm. The presented framework can be implemented using single or multiple objectives with the multi-objective functionality being realized through integration with NSGA-II. Additionally, this framework has the unique ability to operate in two fidelity modes in order to cater to a range of computational capabilities. The low fidelity mode is coupled with XFOIL while the high fidelity mode utilizes a RANS CFD solver on OpenFOAM. To depict the prowess of this framework, two test cases have been shown. The resulting optimized airfoils perform exceedingly well in their use case as compared to conventional airfoils, thereby validating the efficacy of this framework.

关键词： Airfoil Databases Computational Fluid Dynamics XFoil Reynolds Averaged Navier Stokes High Performance Computing Systems Optimization Algorithm Computational Methods Airfoil Geometry multi objective evolutionary algorithms Genetic Algorithm

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