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AIAA SciTech Forum and Exposition, 2024

作者： Navarro, Ester Velázquez Solano-López, Pablo Balbin, Ines Uriol Aerospace Systems and Transport Research Group Universidad Rey Juan Carlos Fuenlabrada28942 Spain Delft University of Technology South Holland Delft2629 HS Netherlands

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

The demand for pressurized, large structures in space, such as habitats or fuel deposits, is increasing as the space industry grows. The limited payload volume of launch vehicles, combined with their highly constrained shape, presents a significant challenge for these structures. Origami-inspired deployable structures have emerged as a potential solution for this problem. This work aims to design and optimize an origami-inspired deployable structure for use as a technology demonstrator in a 12U CubeSat. The choice of pattern, deployment mechanism, and material considerations are discussed as they are relevant in the initial configuration of the structural prototype. By combining two different types of printable materials, it is possible to create a pattern that is more flexible without the use of mechanical hinges. Then two different modeling methods are analyzed in order to study their physical behavior. Once all the desired configurations are computed, an optimization process is applied in order to obtain the most suitable one under the pre-defined requirements. © 2024 by Ester Velázquez Navarro, Pablo Solano-López, Inés Uriol Balbin.

关键词： Deployable Structures Satellites Design Optimization Space Industry Launch Vehicles Thermoplastic Polyurethane Numerical Analysis multi objective genetic algorithm Stress Distribution Space Structures

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Optimised Propeller Geometries for Low Broadband Noise

Optimised Propeller Geometries for Low Broadband Noise

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AIAA Aviation Forum

作者： Karimian, Amin Paruchuri, Chaitanya Joseph, Phillip Univ Southampton Inst Sound & Vibrat Res Southampton SO17 1BJ Hants England

ISBN: (数字)9781624107047

ISBN: (纸本)9781624107047

This research conducts an experimental investigation of the far-field acoustic properties of propellers with different pitch values. Discussions were held regarding the underlying physical principles and how pitch value affects the noise source mechanism in propellers. The classical propeller Broadband noise model was validated and utilised as an objective input for Optimisation using a genetic algorithm in order to evaluate the propeller turbulent Boundary Layer-Trailing Edge interaction Broadband noise. Optimisation were conducted on two blade section profiles in order to reduce broadband noise and boundary layer thickness while constraining the aerodynamic properties to be within 10% increase compared to the baseline propeller blade. A low noise propeller geometry profile was achieved with minimal aerodynamic deviations from the baseline propeller. Broadband noise reduction is found to be significant in the mid frequency region (100 Hz to 3 KHz), while shifting the peak of the Broadband spectrum to higher frequencies. The optimised propeller's overall sound power level was reduced by 3 [dB] as compared to the baseline. This was achieved by altering the upper and lower airfoil surfaces geometrically, reducing the Boundary Layer thickness and wall shear stresses at the Trailing Edge.

关键词： Propeller Performance Trailing Edges Boundary Layer Transition Aerodynamic Characteristics Shear Stress multi objective genetic algorithm Airfoil Profiles Acoustic Properties Aerodynamic Performance Turbulence Length Scale

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Mixed-Flow Assembly Line Balancing with Uncertain Assembly times in Remanufacturing 10th

Mixed-Flow Assembly Line Balancing with Uncertain Assembly t...

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10th IFAC Triennial Conference on Manufacturing Modelling, Management and Control (MIM)

作者： Liu, Qingtao Song, Jiayao Lv, Jingxiang Tang, Ou Zhang, Zeyu Changan Univ Key Lab Rd Construct Technol & Equipment MOE Xian 710064 Peoples R China Linkoping Univ Dept Management & Engn Div Prod Econ SE-58183 Linkoping Sweden

Remanufacturing has in recent decades become an alternative way for sustainable development to cope with the increasingly resource crisis and environmental pollution problems. The line balancing of remanufacturing is more complex than that of traditional manufacturing due to the uncertainty in the remanufacturing process, especially as the assembly time is uncertain. As a result, the line balancing of the remanufacturing assembly has become a critical issue. This paper evaluates the uncertain assembly time in each station based on an approach of Fuzzy Graphical Evaluation and Review Technology (FGERT) network. A remanufacturing multi-objective mixed-flow assembly model is established by optimizing the cycle time, assembly line smoothing coefficient and balance time loss rate. Then an adaptive bilayer genetic algorithm is proposed to solve the optimization problem. Finally, an example was given to demonstrate the effectiveness of the proposed method. Results show that the production rhythm balance of remanufacturing assembly is significantly improved, and the idle time in each assembly station has been reduced as well. The cycle time of the remanufacturing has reduced from 344s to 336s, with the smoothing coefficient and balance time loss rate of being 8.73 and 1.69% compared to that without assembly line balancing. The proposed method provides a useful tool for improving mixed-flow remanufacturing assembly lines in an electromechanics industry. Copyright (C) 2022 The Authors.

关键词： Remanufacturing Mixed-model assembly line Assembly line balancing multi objective genetic algorithm

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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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Coupled Boundary-Layer Suction and Airfoil Optimization for Hybrid Laminar Flow Control

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AIAA JOURNAL 2021年第12期59卷 5158-5173页

作者： Sudhi, Anand Elham, Ali Badrya, Camli Tech Univ Carolo Wilhelmina Braunschweig Inst Fluid Mech Cluster Excellence Sustainable & Energy Efficient Hermann Blenk Str 37 D-38108 Braunschweig Germany Tech Univ Carolo Wilhelmina Braunschweig Aircraft Conceptual Design Inst Aircraft Design & Lightweight Struct Cluster Excellence Sustainable & Energy Efficient Hermann Blenk Str 37 D-38108 Braunschweig Germany

The present work incorporates the effects of boundary-layer suction in designing airfoils for hybrid laminar flow control application. The overall airfoil shape, the suction velocity distribution, and its location are optimized for minimum total drag while sustaining laminar flow over 80% of the airfoil top surface. Nondominated Sorting genetic algorithm II is used with the aerodynamic load computations from XFOILSUC and transition prediction from the improved Van Ingen e(N) method. The airfoil is designed for short-range subsonic conditions for three design lift-coefficient scenarios: 1) 0.4, 2) in the range of 0.3-0.7, and 3) in the range of 0.3-0.7 with suction onset at midchord. The optimized airfoil for scenario 3, with suction onset at midchord, results in a thicker airfoil with natural laminar flow (NLF) on the upper surface comparable to NLF airfoils. The total drag reduces by one-third due to the effects of suction. On the other hand, without limiting the suction onset (scenario 2), a thinner airfoil with longer NLF on the bottom surface and lower total drag is obtained. Optimum suction onset location is predicted to be at 30% of the chord. Suction is also observed to improve the drag bucket at off-design conditions.

关键词： Boundary Layer Thickness Airfoil Profiles Hybrid Laminar Flow Control Natural Laminar Flow Aerodynamic Efficiency multi objective genetic algorithm Reynolds Averaged Navier Stokes Velocity Profiles Skin Friction Coefficient Blended Wing Body

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Structural Optimization of a Large-Scale 3D-Printed High-Altitude Propeller Blade with Experimental Validation

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JOURNAL OF AIRCRAFT 2024年第6期61卷 1586-1599页

作者： Malim, Ahmed Mourousias, Nikolaos Marinus, Benoit G. De Troyer, Tim Royal Mil Acad B-1000 Brussels Belgium Vrije Univ Brussel B-1050 Brussels Belgium Royal Mil Acad Dept Mech Engn Ave Renaissance 30 B-1000 Brussels Belgium Vrije Univ Brussel Dept Engn Technol Pl Laan 2 B-1050 Brussels Belgium Joint Framework Royal Mil Acad RMA & Vrije Univ Br Dept Mech Engn Ave Renaissance 30 Brussels Belgium Joint Framework RMA & VUB Dept Mech Engn Ave Renaissance 30 Brussels Belgium

This paper presents a structural optimization of a 3D-printed thermoplastic-core propeller blade for high-altitude unmanned aerial vehicles using a genetic algorithm. It aims to minimize the weight of the blade by creating longitudinal holes and spars and to minimize the blade tip deflection during operation at an altitude of 16 km. The objective is to check the validity of a 3D-printed material modeling approach for large-scale and more complex 3D-printed parts, such as hollow twisted blades. The approach is based on attentively selecting the printing parameters that reduce the anisotropy of the 3D-printed parts. The linear isotropic behavior of 3D-printed Polylactic Acid and Acrylonitrile Butadiene Styrene M30 tensile samples, which have been tested at ambient and low temperatures (-60 degrees C), is utilized to numerically predict the experimental deformation and natural frequencies of 3D-printed substitute and twisted full blades with good accuracy. The validated linear isotropic model of each material is then used to evaluate the objectives and the constraints of the two-objective optimization process using one-way fluid-structure interaction. Four optimized blades have been selected from the Pareto fronts as candidates, printed, and tested in bending and vibration. The numerical and experimental results of the candidate blades in terms of deformation and natural frequencies are in good agreement, proving the validity of the present macroscale approach for large-scale hollow twisted blades.

关键词： Marine Propellers Structural Design and Development Fluid Structure Interaction multi objective genetic algorithm Finite Element Analysis Numerical Simulation Ultimate Tensile Strength Structural Optimization Additive Manufacturing 3D-printed Material Modeling

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Optimisation Framework for Distinctive Vertical Axis Wind Turbine Blade Generation Using Hybrid multi-objective genetic algorithms and Deep Neural Networks

Optimisation Framework for Distinctive Vertical Axis Wind Tu...

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AIAA Aviation Forum

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

ISBN: (数字)9781624105982

ISBN: (纸本)9781624105982

There has been a considerable impetus to curb global climate change brought about by the deleterious environmental impact of fossil fuels. This has driven the use of renewable energy, especially wind energy, as it is amongst the most abundant forms of renewable sources available to mankind. However, harnessing the wind has been challenging, not only due to its unpredictable and intermittent nature but also due to variations in wind conditions from one location to another. These challenges result in significantly longer design times for developing wind turbines while still yielding sub-optimal designs due to the varying nature of the operating conditions. Furthermore, commonly used blade design routines are heavily dependent on empirical models, which do not account for the wake interactions between the different blades of the turbine. Therefore, to circumvent these problems, a radically new design methodology has been formulated, wherein the latest computational methods and machine learning algorithms have been used. This paper delves into this problem by establishing a computational design framework that can be used to develop blade profiles for vertical axis wind turbines (VAWT) with the aim of maximizing efficiency at the given operating conditions using hybrid optimisation methods. This framework realises genetic algorithms by invasive weed optimisation (IWO), and multi-objective implementation using non-dominated sorting (NSGA-II) while utilizing machine learning and artificial deep neural networks in function approximation to reduce the overall computational cost. The optimisation framework has been validated using an extensive array of known test functions and further by comparatively testing an optimised blade design for a set of operating conditions with commercially prevalent blades under similar design constraints.

关键词： multi objective genetic algorithm Artificial Neural Network Turbine Blades Horizontal Axis Wind Turbine Wind Energy Computational Methods Renewable Energy Machine Learning Fossil Fuels Computational Fluid Dynamics

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A multi objective genetic algorithm for the facility layout problem based upon slicing structure encoding

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第12期39卷 10352-10358页

作者： Aiello, Giuseppe La Scalia, Giada Enea, Mario Univ Palermo Dipartimento Tecnol Meccan Prod & Ingn Gest I-90133 Palermo Italy

This paper proposes a new multi objective genetic algorithm (MOGA) for solving unequal area facility layout problems (UA-FLPs). The genetic algorithm suggested is based upon the slicing structure where the relative locations of the facilities on the floor are represented by a location matrix encoded in two chromosomes. A block layout is constructed by partitioning the floor into a set of rectangular blocks using guillotine cuts satisfying the areas requirements of the departments. The procedure takes into account four objective functions (material handling costs, aspect ratio, closeness and distance requests) by means of a Pareto based evolutionary approach. The main advantage of the proposed formulation, with respect to existing referenced approaches (e.g. bay structure), is that the search space is considerably wide and the practicability of the layout designs is preserved, thus improving the quality of the solutions obtained. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Facility layout problems multi objective genetic algorithm Slicing structure

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Design of Transonic Swept Wing for HLFC Application

Design of Transonic Swept Wing for HLFC Application

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AIAA Aviation Forum and Exposition

作者： Sudhi, Anand Radespiel, Rolf Badrya, Camli Inst Fluid Mech Hermann Blenk Str 37 D-38108 Braunschweig Germany

ISBN: (数字)9781624106101

ISBN: (纸本)9781624106101

This study presents an optimization platform to design a low drag airfoil for natural laminar flow (NLF) and hybrid laminar flow control (HLFC) applications at transonic flight conditions, at Mach number up to 0.78. NSGA II genetic algorithm-based optimizer coupled with Euler aerodynamic and boundary layer code solvers is capable to provide Pareto-optimal front solutions. For laminar-to-turbulent transition prediction, a correlated two N-factors method is used with local, linear stability computations. The Pareto front solutions trade between minimum pressure drag and maximum laminar regions. For NLF transonic airfoil natural laminar flow was extended until the shock, and the shock on the upper surface was pushed downstream. On the front lower surface undercut in the lower curvature is obtained. For a swept wing design (22.5 degrees), HLFC airfoil optimized with suction has 43% lower profile drag than airfoil optimized for NLF.

关键词： Swept Wing Hybrid Laminar Flow Control Supercritical Airfoils Laminar to Turbulent Transition Natural Laminar Flow Transport Aircraft multi objective genetic algorithm Lift Coefficient Unswept Wings Adverse Pressure Gradient

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Savalan: multi objective and homogeneous method for software modules clustering

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JOURNAL OF SOFTWARE-EVOLUTION AND PROCESS 2022年第1期34卷

作者： Arasteh, Bahman Fatolahzadeh, Ahmad Kiani, Farzad Istinye Univ Fac Engn & Nat Sci Dept Software Engn Istanbul Turkey Islamic Azad Univ Dept Comp Engn Tabriz Branch Tabriz Iran

Reverse engineering is used for extracting and understanding software architecture models from source code when the source code is the only available software product. Software module clustering is a reverse engineering method which decomposes software modules into several clusters (subsystems) by using module dependency graph. Finding the best clusters for the modules of software is a multi-objective and NP-hard problem;maximizing the cohesion among the modules, minimizing the coupling among different clusters, and maximizing the software modularization quality are considered as the main objectives of software module clustering. Some of these objectives, such as cohesion and coupling, are in contradiction with each other. Simultaneous improvement of all clustering objectives (cohesion, coupling, modularization quality, size, and number of clusters) is the main goal of this study. In this paper, by capitalizing on multi objective genetic algorithm and a new combination of objective functions, we proposed a homogeneous method, namely, Savalan, for clustering software modules. The proposed method generates high-quality clusters with strong cohesion within clusters and weak connections between clusters for the input source code. The results of conducted experiments on the 14 benchmark programs indicate that simultaneous improvement of all clustering objectives is the main merit of this method. According to the experimental results, the proposed algorithm was able to outperform the previous multi objective methods.

关键词： homogenous clustering module clustering multi objective genetic algorithm software maintenance

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