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AIAA Aviation Forum

作者： Thomas, Jeffrey P. Dowell, Earl H. Duke Univ Dept Mech Engn & Mat Sci Durham NC 27708 USA

ISBN: (数字)9781624105982

ISBN: (纸本)9781624105982

We present the details of two primary research topics in the area of aeroelastic optimization that we are currently working on. The first topic is a new discrete adjoint aeroelastic design optimization solution methodology, which has a computational cost that is independent of the number of structural degrees-of-freedom of the aeroelastic model. In recent years, we have developed a discrete adjoint nonlinear harmonic balance frequency-domain unsteady aeroelastic design optimization methodology, which is able to compute aeroelastic geometric design gradients, that can then be used with an optimization solver to compute an alternate geometric design, which will delay the onset of flutter. This methodology has proven to be robust, however it can be computationally costly since the computational cost is proportional to the number of structural degrees-of-freedom. The new methodology we present does not have that issue, and only a single adjoint solution computation is required when design gradients are required by whatevera* optimization algorithm one happens to be using. The reason this is possible is that we have recently developed a new nonlinear harmonic balance frequency-domain based aeroelastic solution technique where both the aeroelastic solver and computational flow solver can be marched to convergence simultaneously. With our original aeroelastic solution methodology, we run the aeroelastic solver and flow solver sequentially, one after the other. With the ability to march the aeroelastic and flow solvers simultaneously, we automatically satisfy the constraints that the aeroelastic residual and flow solver residual are zero once the combined aeroelastic solver and flow solver is converged. The second topic discussed in this abstract are new transonic aeroelastic flutter and transonic buffet design optimization results that are computed using a constrained optimization solver. Previously, our transonic aeroelastic flutter and transonic buffet design optimization

关键词： Transonic Aeroelasticity Lift Over Drag Ratio Harmonic Balance Frequency Domain optimization algorithm Computing Angle of Attack Freestream Mach Number NACA 0012 Wind Tunnels

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Optimized Bezier-curve-based command generation and robust inverse optimal control for attitude tracking of spacecraft

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AEROSPACE SCIENCE AND TECHNOLOGY 2022年第0期121卷 107183-107183页

作者： Wang, Peng Zhang, Xiaobing Nanjing Univ Sci & Technol Sch Energy & Power Engn Nanjing Peoples R China

In this paper we address the optimized attitude command generation and robust inverse optimal control design for the spacecraft attitude tracking with large angle slews. Firstly, with the reference attitude command described in a Bezier curve through the spherical linear interpolation, an optimized reference command is generated to meet the principles of small energy consumption and short turning time. The applied optimization combines the particle swarma* optimization algorithm and genetic algorithm based on information entropy to guarantee the diversity of the population and improve the global search capabilities. Next, a robust inverse optimal control is developed for the attitude tracking of the obtained optimized reference attitude command, consisting of an inverse optimal Lyapunov approach for performance guarantee and an arctangent-based integral sliding mode control for the disturbance attenuation. A stronger generality of the control Lyapunov function is obtained for the inverse optimality. Furthermore, the developed control efforts are of smaller amplitudes due to introducing the auxiliary angular velocity for the smooth transition to the reference attitude command. Finally, simulation results are included to show the performance of the proposed scheme. (c) 2021 Elsevier Masson SAS. All rights reserved.

关键词： Attitude control Inverse optimal control optimization algorithm Bezier curve Arctangent-based sliding mode control

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A Framework of gradient-based shape optimization using feature-based CAD parameterization

A Framework of gradient-based shape optimization using featu...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Sun, Liang Yao, Weigang Robinson, Trevor T. Marques, Simao Armstrong, Cecil G. Queens Univ Belfast Sch Mech & Aerosp Engn Belfast BT9 5AH Antrim North Ireland Univ Surrey Dept Mech Sci Guildford GU2 7XH Surrey England

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

This paper presents a framework of performing aerodynamic shape optimization using the feature-based CAD system, which is widely used in aerospace industry. An interface is set up to communicate between the system installs the CAD package and the system installs the CFD solver. Mesh is adapted to fit the new CAD model after each parametric update by performing surface mesh deformation and volume mesh deformation in sequence. The proposed framework can be easily integrated into the industrial product design and analysis process.

关键词： Shape optimization Aerodynamic Shape optimization CAD System Aerospace Industry Lift Coefficient Mesh Generation Open Source Software Pressure Coefficient Linear Elasticity optimization algorithm

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Using Boundary Condition and Topology optimization to Design an Airplane Component

Using Boundary Condition and Topology Optimization to Design...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Persson, Johan A. Wiberg, Anton Linkoping Univ Management & Engn S-58183 Linkoping Sweden

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

This paper demonstrates a method that can be used to combine topology optimization with optimization of the boundary conditions. The method utilizes design of experiments and surrogate models to model how the boundary conditions affect the potential mass of the component. A demonstration of the method is made by applying it to design an airplane component and comparing the result to other approaches. The best design is then manufactured using additive manufacturing to verify that it is feasible.

关键词： Topology optimization Aeroplane Surrogate Model Design of Experiments Structural optimization optimization algorithm Finite Element Method Shape optimization Trusses Fuel Consumption

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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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An artificial hummingbird algorithm based localization with reduced number of reference nodes for wireless sensor networks

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PHYSICAL COMMUNICATION 2022年 55卷

作者： Bhat, Soumya J. Santhosh, K. V. Manipal Acad Higher Educ MAHE Manipal Inst Technol Ctr Cyber Phys Syst Dept Instrumentat & Control Engn Manipal 576104 Karnataka India Shri Madhwa Vadiraja Inst Technol & Management Dept Comp Sci & Engn Bantakal 574115 Karnataka India

Wireless Sensor Networks (WSN) are widely used in recent years due to the advancements in wireless and sensor technologies. Many of these applications require to know the location information of nodes. This information is useful to understand the collected data and to act on them. Existing localization algorithms make use of a few reference nodes for estimating the locations of sensor nodes. But, the positioning and utilization of reference nodes increase the cost and complexity of the network. To reduce the dependency on reference nodes, in this paper, we have developed a novel optimization based localization method using only two reference nodes for the localization of the entire network. This is achieved by reference nodes identifying a few more nodes as reference nodes by the analysis of the connectivity information. The sensor nodes then use the reference nodes to identify their locations in a distributive manner using Artificial Hummingbird algorithm (AHA). We have observed that the localization performance of the reported algorithm at a lower reference node ratio is comparable with other algorithms at higher reference node ratios. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Artificial hummingbird algorithm Hop based localization optimization algorithm Range free Reference nodes Wireless sensor network

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Efficient Multi-Information Source Multi-Objective Bayesian optimization

Efficient Multi-Information Source Multi-Objective Bayesian ...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Khatamsaz, Danial Peddareddygari, Lalith Friedman, Samuel Allaire, Douglas Texas A&M Univ J Mike Walker 66 Dept Mech Engn College Stn TX 77843 USA

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

Multi-objective optimization is often a difficult task owing to the need to balance competing objectives. A typical approach to handling this is to estimate a Pareto frontier in objective space by identifying non-dominated points. This task is typically computationally demanding owing to the need to incorporate information of high enough fidelity to be trusted in design and decision-making processes. In this work, we present a multi-information source framework for enabling efficient multi-objective optimization. The framework allows for the exploitation of all available information and considers both potential improvement and cost. The framework includes ingredients of model fusion, expected hypervolume improvement, and intermediate Gaussian process surrogates. The approach is demonstrated on two test problems and aerostructural wing design problem.

关键词： Bayesian optimization Cumulative Distribution Function Pareto Frontier Surrogate Model optimization algorithm Aerodynamic Performance Finite Element Analysis Bréguet Range Equation Aircraft Wing Design Open Source Software

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Modified artificial bee colony algorithm with differential evolution to enhance precision and convergence performance

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期198卷 1页

作者： Ustun, Deniz Toktas, Abdurrahim Erkan, Ugur Akdagli, Ali Engn Fac Dept Comp Engn TR-33400 Tarsus Mersin Turkey Karamanoglu Mehmetbey Univ Engn Fac Dept Elect Elect Engn TR-70200 Karaman Turkey Karamanoglu Mehmetbey Univ Engn Fac Dept Comp Engn TR-70200 Karaman Turkey Mersin Univ Engn Fac Dept Elect Elect Engn TR-33343 Mersin Turkey

Artificial bee colony (ABC) and differential evolution (DE) are the most powerful and operative meta-heuristic algorithms inspired by the nature. Although both algorithms are successful, their successes vary from phase to phase, i.e. while ABC is better in the exploration ability, DE is well in the exploitation capability. Because the diversity of mutation and exponential crossover operators is prominently better than that of onlooker bee;in this study, the exploitation ability of ABC is enhanced by replacing the onlooker bee operator with those of mutation and the crossover phases of DE in order to increase the accuracy and speed up the convergence. We hereby introduce a novel modified algorithm denoted "modified ABC by DE" (mABC). The precision performance of mABC is verified through 20 classical benchmark functions and CEC 2014 test suit by a comprehensive comparison with recent ABC variants and hybrids for 30 and 50 dimensions. The results are interpreted using various statistical evaluations such as Wilcoxon, Friedman, and Nemenyi tests. Moreover, mABC is comparatively examined over convergence plots. In concise, the mean ranks of mABC are 1.4 and 2.3 for classical benchmark functions and CEC 2014, respectively. mABC outperforms the other variants averagely for 14 of 20 classical benchmark functions and 24 of 30 CEC 2014 functions. The results manifest that the proposed mABC is a robust and reliable algorithm as well as better than the existing ABC variants and hybrids with regard to high optimization performance like precision and convergence.

关键词： Artificial bee colony (ABC) Differential evolution (DE) optimization algorithm Modified ABC Modified algorithm Hybrid algorithm

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Flow Distortion Based S-Duct optimization using Adjoint Methodology

Flow Distortion Based S-Duct Optimization using Adjoint Meth...

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AIAA Aviation Forum

作者： Kamat, Vinayak Rao, Vinod Xu, Min Saxena, Swati ANSYS Software Pvt Ltd 34-1 Rajiv Gandhi Infotech Pk Pune 411057 Maharashtra India ANSYS Inc 10 Cavendish Court Lebanon NH 03766 USA ANSYS Inc 2645 Zanker Rd San Jose CA 95139 USA ANSYS Inc Pune Maharashtra India

ISBN: (数字)9781624105982

ISBN: (纸本)9781624105982

In the present work, a duct geometry is optimized to reduce the flow distortion using adjoint method. The S-duct geometry is from AIAA Propulsion and Aerodynamics Workshop (PAW [1]). ANSYS Fluent Adjoint solver is used to run the design optimization study. The workflow involves a 4-step process: CFD run, adjoint calculation, design calculation based on the sensitivity data and environment constraints, and mesh update. This process is repeated until the optimum geometry is obtained. The variance of total pressure at the Aerodynamic Interface Plane (AIP) is chosen as the quantity of interest. It correlates well with the circumferential and radial distortion index (CDI & RDI) as defined in [2], which indicates flow distortion at the inlet/exit plane. After 3 design iterations, the variance of total pressure and the average CDI at AIP was reduced by 20.91% and 8.36% respectively, while the maximum CDI and RDI showed a drop of 2.15% and 12.37% respectively.

关键词： ANSYS Computational Fluid Dynamics Aerodynamics Shape optimization Static Pressure Flow Separation Propulsion System Conjugate Gradient Method optimization algorithm Steepest Descent Method

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Robust Adaptive Fault Reconfiguration for Micro-gas Turbine Based on Optimized T-S Fuzzy Model and Nonsingular TSMO

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2020年第7期22卷 2204-2222页

作者： Ma, Leiming Xiao, Lingfei Meng, Zhongxiang Huang, Xinhao Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Nanjing 210016 Peoples R China

This paper presents a novel robust fault reconfiguration scheme based on optimized Takagi-Sugeno (T-S) fuzzy model and nonsingular terminal sliding mode observer (NTSMO) with adaptive law for micro-gas turbine (MGS). An optimized T-S fuzzy model is introduced because it can approximate any nonlinear model with arbitrary precision, and an improved imperial competition algorithm (ICA) using adaptive reform probability is proposed to improve the accuracy of the model. A linear transformation method is introduced to decouple the fault and disturbance of the system. The nonsingular terminal sliding mode observer is designed to reconstruct actuator fault and disturbance with unknown upper bound of a change rate, in which an adaptive law is introduced to update the sliding mode gain in real-time to eliminate the influence of fault, disturbance and modeling uncertainty. Simulations in Matlab/Simulink show high reconfiguration accuracy and high-speed of the proposed method despite of the presence of fault, disturbance and modeling uncertainty.

关键词： Micro-gas turbine Takagi-Sugeno fuzzy model Adaptive nonsingular terminal sliding mode Robust fault reconfiguration optimization algorithm

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