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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Wang, Zhenbo Wei, Peng Sun, Liang Univ Tennessee Dept Mech Aerosp & Biomed Engn Knoxville TN 37996 USA George Washington Univ Dept Mech & Aerosp Engn Washington DC 20052 USA New Mexico State Univ Dept Mech & Aerosp Engn Las Cruces NM 88003 USA

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

In this paper, we propose a novel approach to trajectory optimization of electric vertical takeoff and landing (eVTOL) vehicles for urban air mobility (UAM) missions such as passenger transportation and cargo delivery. Our particular interest is to develop efficient algorithms that facilitate fast generation of optimal cruise, descent, and landing trajectories for eVTOL vehicles under operational constraints. As a preliminary study, we focus on the formulation of an optimal control problem with fixed time of flight and choose the control effort as the performance measure for a multirotor eVTOL vehicle. The main contribution of this work is the convexification of the formulated optimal control problem to better enable real-time trajectory optimization with required time of arrival, which is of key importance for future eVTOL operations. The convexification process is presented in this paper. The EHang 184 eVTOL vehicle is used in the simulations, and preliminary results of a UAM transportation case are provided to demonstrate the effectiveness of the proposed approach.

关键词： Urban Air Mobility Nonlinear Programming Trajectory optimization Vertical Takeoff and Landing Multirotor Airspeed Volocopter Unmanned Aerial Vehicle Aerodynamic Drag optimization algorithm

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Under-expanded jet noise prediction using surrogate models based on artificial neural networks 28

Under-expanded jet noise prediction using surrogate models b...

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28th AIAA/CEAS Aeroacoustics Conference

作者： Centracchio, Francesco Meloni, Stefano Jawahar, Hasan Kamliya Azarpeyvand, Mandi Camussi, Roberto Iemma, Umberto Roma Tre Univ Dept Engn Via Vito Volterra 62 I-00146 Rome Italy Univ Bristol Dept Aerosp Engn Bristol BS8 1TR Avon England Univ Bristol Dept Mech Aerosp Bristol BS8 1TR Avon England

ISBN: (数字)9781624106644

ISBN: (纸本)9781624106644

The use of surrogate models in recent times plays a key role in the design process and, over the past few years, machine learning algorithms are adapting to the use of active metamodelling techniques. In the present study, Artificial Neural Networks formulation is used as a data-driven nonlinear model aimed at describing the dynamics of an experimental test campaign related to the noise emitted by a single-stream jet in under-expanded conditions. The architecture of the neural network is selected employing a deterministic optimization algorithm, coupled with data informed tuning of the input parameters. The data set explored here was acquired in the state-of-the-art aeroacoustic facility at the University of Bristol. Both the near-and far-field acoustic measurements were carried out for a cold under-expanded jet for Mach numbers ranging from 1.1 to 1.4. The predictions by the metamodel are in good agreement with the experimental data, and the results demonstrate the capability of metamodels as a reliable tool to estimate jet noise in under-expanded flow conditions for a wide range of Mach numbers and near-field locations.

关键词： Surrogate Model Aircraft Noise Artificial Neural Network Mach Numbers Flow Conditions optimization algorithm Acoustic Measurement Near and Far Field Machine Learning Sound Pressure Level

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An optimization iterative algorithm based on nonnegative constraint with application to Allan variance analysis technique

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ADVANCES IN SPACE RESEARCH 2014年第5期53卷 836-844页

作者： Lv, Hanfeng Zhang, Liang Wang, Dingjie Wu, Jie Natl Univ Def Technol Coll Aerosp Sci & Engn Staff Room Flight Dynam & Control Changsha 410073 Hunan Peoples R China

It is well known that inertial integrated navigation systems can provide accurate navigation information. In these systems, inertial sensor random error often becomes the limiting factor to get a better performance. So it is imperative to have accurate characterization of the random error. Allan variance analysis technique has a good performance in analyzing inertial sensor random error, and it is always used to characterize various types of the random error terms. This paper proposes a new method named optimization iterative algorithm based on nonnegative constraint applied to Allan variance analysis technique to estimate parameters of the random error terms. The parameter estimates by this method are nonnegative and optimal, and the estimation process does not have matrix nearly singular issues. Testing with simulation data and the experimental data of a fiber optical gyro, the parameters estimated by the presented method are compared against other excellent methods with good agreement;moreover, the objective function has the minimum value. (C) 2014 COSPAR. Published by Elsevier Ltd. All rights reserved.

关键词： Allan variance Parameter estimation optimization algorithm Inertial navigation Random error model

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Review of the performance optimization of parallel manipulators

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MECHANISM AND MACHINE THEORY 2022年 170卷

作者： Yang, Chao Ye, Wei Li, Qinchuan Jiaxing Univ Coll Mech & Elect Engn Jiaxing 314001 Peoples R China Zhejiang Sci Tech Univ Mechatron Inst Hangzhou 310018 Zhejiang Peoples R China

Performance optimization of parallel manipulators (PMs) have attracted adequate attention in recent years. It mainly concerns performance indices, optimization algorithms and optimization methods. This paper presents an in-depth, comprehensive, reasoned overview for the three basic issues. The research status and existing problems of these issues along with the distinctive approaches that have been explored are reported. Advantages, disadvantages, application scenarios and recommendations of various performance indices, optimization algorithms and methods are discussed. Focus is also placed on future research trends. The results are useful for researchers and engineers to properly select performance indices, optimization algorithms, and optimization methods when designing a PM.

关键词： Parallel manipulator Performance index optimization algorithm optimization method

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Hybrid Energy Storage System sizing model based on load recurring pattern identification

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JOURNAL OF ENERGY STORAGE 2024年 91卷

作者： Lucas, Alexandre Golmaryami, Sara Carvalhosa, Salvador INESC Technol & Sci INESC TEC Porto Portugal

Hybrid Energy Storage Systems (HESS) have attracted attention in recent years, promising to outperform single batteries in some applications. This can be in decreasing the total cost of ownership, extending the combined lifetime, having higher versatility in providing multiple services, and reducing the physical hosting location. The sizing of hybrid systems in such a way that proves to optimally replace a single battery is a challenging task. This is particularly true if such a tool is expected to be a practical one, applicable to different inputs and which can provide a range of optimal solutions for decision makers as a support. This article provides exactly that, presenting a technology -independent sizing model for Hybrid Energy Storage Systems. The model introduces a three-step algorithm: the first block employs a clustering of time series using Dynamic Time Warping (DTW), to analyze the most recurring pattern. The second block optimizes the battery dispatch using Linear Programming (LP). Lastly, the third block identifies an optimal hybridization area for battery size configuration (H indicator), and offers practical insights for commercial technology selection. The model is applied to a real dataset from an office building to verify the tool and provides viable and non-viable hybridization sizing examples. For validation, the tool was compared to a full optimization approach and results are consistent both for the single battery sizing, as well as for confirming the hybrid combination dimensioning. The optimal solution potential (H) in the example provided is 0.13 and the algorithm takes a total of 30s to run a full year of data. The model is a Pythonbased tool, which is openly accessible on GitHub, to support and encourage further developments and use.

关键词： Hybrid Energy Storage System Hybridization model Sizing methodology Recurring pattern analysis optimization algorithm

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Development of ANN-based metaheuristic models for the study of the durability characteristics of high-volume fly ash self-compacting concrete with silica fume

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JOURNAL OF BUILDING ENGINEERING 2024年 94卷

作者： Kumar, Shashikant Kumar, Divesh Ranjan Wipulanusat, Warit Keawsawasvong, Suraparb Muzaffarpur Inst Technol Dept Civil Engn Muzaffarpur India Thammasat Univ Fac Engn Thammasat Sch Engn Dept Civil EngnRes Unit Data Sci & Digital Transf Pathum Thani Thailand Thammasat Univ Fac Engn Thammasat Sch Engn Dept Civil EngnRes Unit Sci & Innovat Technol Civ Pathum Thani Thailand

The construction of durable and sustainable infrastructure requires the use of industrial byproducts such as fly ash (FA) and silica fume (SF) to enhance strength and durability. This study introduces novel machine learning models to forecast the results of rapid chloride penetration test (RCPT) for self-compacting concrete (SCC) containing high volumes of FA and SF. This study assessed the effects of supplementary cementitious materials (SCMs) and elevated temperature curing on the RCPT outcomes for SCC. Various metaheuristic algorithms including teaching-learning-based optimization (TLBO), ant colony optimization (ACO), the imperialist competitive algorithm (ICA), and shuffled complex evolution (SCE)-optimize the learning rate, weights, and biases of artificial neural network (ANN) models. A dataset of 360 experimental RCPT data points with seven input parameters was used to train and test the hybrid models. The accuracy of these models was assessed using eight performance indices, and the results were further analyzed through rank analysis, scatter plots, and error matrices. The training and testing sets for the AI models, specifically ANN-TLBO, exhibit a strong correlation between the experimental and predicted RCPT values, with R2 values of 0.9962 for training and 0.9676 for testing, compared to the R2 values of the other proposed models. Consequently, the results of this study suggest that the ANN-TLBO model is the optimal hybrid ANN model for predicting RCPT values in SCC. Verified experimental results and external validation indicate that the ANN-TLBO model is an effective alternative for accurately predicting real-time RCPT in SCC.

关键词： High-volume fly ash Self-compacting concrete Rapid chloride penetration test Artificial neural network optimization algorithm

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A Multi-objective approach for Robust Structural Topology optimization

A Multi-objective approach for Robust Structural Topology Op...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Nathan Palar, Pramudita Satria Zuhal, Lavi Rizki Fac Mech & Aerosp Engn Inst Teknol Bandung Bandung 40132 West Java Indonesia

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

This paper presents a robust multi-objective approach for structural topology optimization. The performance and the stability are two conflicting objectives, hence, it is sometimes preferable to treat them as multiple objectives to gain important insight regarding various optimal topologies. The optimization problem is formulated as a simultaneous minimization of expectation and standard deviation of compliance in the presence of uncertainties in loading magnitude, boundary conditions, or material properties. It is imperative to obtain a good representation of the Pareto front with as few finite element simulations as possible. This paper proposes the combination of topology optimization with Polynomial Chaos Expansion (PCE) and Pareto Optimal Tracing (POT) to seek diverse Pareto optimal solutions efficiently. A benchmark test case with two and three random loads is used to test efficacy of the proposed method and the results are compared to the deterministic optimization. We studied the impact of the PCE to reduce the number of realizations and POT to reduce the number of iterations on the estimation of the Pareto front with various combinations of weights assigned to the expectation and the standard deviation. Results show that robust topology optimization with POT and PCE yielded better and diverse topologies than the conventional approach, which restarts the search from the uniform distributed density as the baseline design.

关键词： Topology optimization Standard Deviation Material Properties Poisson's Ratio Simply Supported Beams Structural optimization optimization algorithm Monte Carlo Simulation Stiffness Matrices Collocation Method

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Fast Path Planning for Unmanned Aerial Vehicles by Self-Correction Based on Q-Learning

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2021年第4期18卷 203-211页

作者： Wang, Ziyang Yang, Hongbing Wu, Qingsong Zheng, Jiafei Soochow Univ Sch Mech & Elect Engn Suzhou 215006 Jiangsu Peoples R China

This paper addresses a path planning problem for unmanned aerial vehicles with correcting position errors through correction-point navigation, which requires a rapid response when determining the flight path. A two-layer nested iterative hybrid algorithm based on Q learning is proposed to achieve multiobjective optimization by minimizing path lengths and correction times while reducing the complexity of the algorithm and improving the efficiency of path planning. The lower-layer algorithm uses a Q-learning framework based on the experience playback mechanism and exploration/exploitation mechanism. The rewards and punishments for the Q-table values in the lower-layer algorithm are innovatively managed with the unit "path." The upper-layer algorithm is based on the Pareto multiobjective algorithm. The Pareto frontier is continuously updated with the solutions from the lower-layer algorithm, which timely provides the lower-layer algorithm feedback. Finally, simulation experiments are conducted to evaluate the effectiveness of the algorithm, and the results show that the proposed algorithm outperforms particle swarm optimization.

关键词： Path Planning UAV Particle Swarm optimization Pareto Frontier Reinforcement Learning Stochastic Dynamic Programming optimization algorithm Computing Performance Mathematical Models Genetic algorithm

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Efficient Flutter Prediction Using Reduced-Order Modeling

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AIAA JOURNAL 2021年第7期59卷 2670-2683页

作者： Lowe, Brandon M. Zingg, David W. Univ Toronto Inst Aerosp Studies 4925 Dufferin St Toronto ON M3H 5T6 Canada Univ Toronto Computat Aerodynam & Sustainable Aviat 4925 Dufferin St Toronto ON M3H 5T6 Canada Univ Toronto Ctr Res Sustainable Aviat 4925 Dufferin St Toronto ON M3H 5T6 Canada

Typical aerodynamic shape optimization and multidisciplinary optimization algorithms omit high-fidelity flutter predictions due to the associated computational costs. This paper presents a model order reduction framework as a step toward flutter-constrained aircraft optimization. The Euler equations linearized about a steady-state solution of the nonlinear Euler equations are used as the governing unsteady flow equations. Using a proper orthogonal decomposition approach, a reduced basis is constructed onto which the governing equations are projected. The result is a linear reduced-order model (ROM) with significantly fewer degrees of freedom capable of rapidly approximating aerodynamic forces. This ROM is coupled to a linear structural model to create a single monolithic aeroelastic system. The eigenvalues of the resulting system are analyzed for various flow conditions to determine the onset of flutter in the system. To ensure the stability of the ROM, the use of a stabilizing inner product is demonstrated. The flutter boundaries obtained for both a two-degree-of-freedom airfoil structure and the AGARD 445.6 wing model show good agreement with the full-order model and with the literature.

关键词： Primitive Variable Aerodynamic Shape optimization Structural Modeling Dynamic Aeroelastic Analysis Transonic Pitching Airfoil Model Order Reduction Unsteady Transonic Flow optimization algorithm Reynolds Averaged Navier Stokes Lift Coefficient

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optimization-based biomechanical lifting models for manual material handling: A comprehensive review

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART H-JOURNAL OF ENGINEERING IN MEDICINE 2022年第9期236卷 1273-1287页

作者： Zaman, Rahid Arefeen, Asif Quarnstrom, Joel Barman, Shuvrodeb Yang, James Xiang, Yujiang Oklahoma State Univ Sch Mech & Aerosp Engn 201 Gen Educ Bldg Stillwater OK 74078 USA Texas Tech Univ Dept Mech Engn Human Centr Design Res Lab Lubbock TX 79409 USA

Lifting is a main task for manual material handling (MMH), and it is also associated with lower back pain. There are many studies in the literature on predicting lifting strategies, optimizing lifting motions, and reducing lower back injury risks. This survey focuses on optimization-based biomechanical lifting models for MMH. The models can be classified as two-dimensional and three-dimensional models, as well as skeletal and musculoskeletal models. The optimization formulations for lifting simulations with various cost functions and constraints are reviewed. The corresponding equations of motion and sensitivity analysis are briefly summarized. Different optimization algorithms are utilized to solve the lifting optimization problem, such as sequential quadratic programming, genetic algorithm, and particle swarm optimization. Finally, the applications of the optimization-based lifting models to digital human modeling which refers to modeling and simulation of humans in a virtual environment, back injury prevention, and ergonomic safety design are summarized.

关键词： Manual material handling lifting optimization formulation human model optimization algorithm equations of motion digital human modeling lower back injury

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