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Delamination detection in composite laminates using improved surrogate-assisted optimization

COMPOSITE STRUCTURES 2021年 277卷 114622-114622页

作者： Tong, Huawei Pan, Jingwen Singh, Hemant Kumar Luo, Weili Zhang, Zhifang Hui, David Guangzhou Univ Sch Civil Engn Guangzhou 510006 Peoples R China Guangzhou Univ Res Ctr Wind Engn & Engn Vibrat Guangzhou 510006 Peoples R China Univ New South Wales Sch Engn & Informat Technol Canberra ACT 2600 Australia Univ New Orleans Dept Mech Engn New Orleans LA 70148 USA

Vibration-based delamination detection can be posed as an optimization problem, wherein the discrepancy of the frequency shifts obtained from a numerical model with assumed delamination parameters is minimized from target values of the frequency shifts. Unlike cracks, delamination is characterized by a mix of continuous and discrete parameters. The continuous variables comprise the in-plane locations and sizes, whereas discrete parameters correspond to the interface where the damage occurs. While the recent studies have demonstrated the effectiveness of surrogate-assisted optimization in determining the locations and sizes of the delamination, the prediction of interface has received scarce attention. To improve on this aspect, in this paper, individual surrogate models are built corresponding to each of the interfaces. Furthermore, we also attempt to improve the underlying optimization method by using multiple populations instead of one in order to reduce the likelihood of being trapped in local minima. The performance of the proposed approach is numerically investigated by considering various factors, such as the different number of samples for training the surrogate model, the combinations of different modes of frequency shifts used as input for damage detection, and the addition of artificial noise to the numerical frequency shifts to simulate the unavoidable measurement errors. Experimental investigations are also conducted on six delaminated beam specimens and one intact specimen. The proposed approach showed significant improvements in identifying the damage parameters, and in particular improving the prediction accuracy of the damage interface.

关键词： Damage detection Vibration analysis Composite structure Surrogate model optimization algorithm

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Short-term wind speed time series forecasting based on a hybrid method with multiple objective optimization for non-convex target

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ENERGY 2021年第PartB期215卷 119180-119180页

作者： Dong, Yunxuan Wang, Jing Xiao, Ling Fu, Tonglin Univ Macau Dept Elect & Comp Engn Macau Peoples R China Lanzhou Univ Technol Sch Law Lanzhou 730050 Gansu Peoples R China Chongqing Univ Posts & Telecommun Sch Econ & Management Chongqing 400065 Peoples R China LongDong Univ Sch Math & Stat Qingyang Gansu Peoples R China Chinese Acad Sci Shapotou Desert Res & Expt Stn Northwest Inst Ecoenvironm & Resources Lanzhou 730000 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Accurate and reliable wind speed forecasting (WSF) is crucial for wind power systems. As one of the effective forecast methods, machine learning (ML) methods are employed for wind speed time series forecasting because the excellent ability in fitting the relationship between data and cost function. However, the cost functions with non-convexity make the whole problem poor interpretability and poor robustness. In this paper, a novel hybrid supervised approach is proposed to solve the above problems. The proposed approach has adopted local convolutional neural networks (LCNNs) for convexity preserving of the cost function, in this way, a non-convex problem can be transformed as a convex problem so that heuristic optimization algorithms is adopted to find optimal parameters, and it helps to construct a more stable model. Highway Gate (HG) algorithm is adopted to decrease the computation complexity of the proposed model. The numerical simulation results indicate that the proposed method is not only effective for solving convergence problem cost by non-convexity, but also beneficial to improve accuracy and stability of the traditional ML for wind speed time series forecasting. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Wind speed forecasting Convolutional neural networks Hybrid forecast approach optimization algorithm

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Effects of optimized interaction of short-term hypergravity stimulation and nitrate-deficient cultivation in maize root using genetic-immunological algorithms

PLANT STRESS

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PLANT STRESS 2024年 14卷

作者： Concepcion Ii, Ronnie Relano, R. -Jay Janairo, Adrian Genevie Francisco, Kate Garcia, Lance Montanvert, Hugo De La Salle Univ Dept Mfg Engn & Management Manila Philippines De La Salle Univ Ctr Engn & Sustainabil Dev Res Intelligent Syst Res Unit Manila Philippines De La Salle Univ Ctr Nat Sci & Environm Res Plant & Soil Hlth Res Unit Manila Philippines Univ Clermont Auvergne Clermont Ferrand France

Nitrate is a macronutrient substantial for plant root and shoot growth, however, the availability of nitrate within soil-based and soilless cultivation environments is not consistently optimal, presenting a significant challenge for plant growth and development. Traditional seed stimulation includes scarification, soaking, hormone application and microbial application but they are all invasive. This study pioneered an experimental approach to address the challenges posed by nutrient deficiency in hydroponic environment by integrating Multigene Genetic Programming (MGGP) with immunological computation algorithms, namely Clonal Selection algorithm (CSA), Ant Colony optimization algorithm (ACOA), and COVID optimization algorithm (COVIDOA) in determining the exact optimal time exposure to 2 g hypergravity that can induced the growth of three maize genotypes (PSB 92-97, NSIC CN 302, and NSIC CN 282). Through varying dry seed exposure times to hypergravity (6, 12, and 24 h), labeled models gCSA, gACOA, and gCOVIDOA converged to 20.120 h, 22.466, and 19.700 h, respectively, based on the formulated 2-gene model of root-to-shoot ratio as a function of exposure time. Exposure time between 20 and 24 h increased the root-to-shoot ratio (R/S) by at least a factor of 2.631 and the seedling's dry weight by 13.430 g while between 10 and 15 h of exposure reduced the overall biomass. gACOA-treated seedings exhibited an R/S of 3.732 +/- 0.067 having the highest uniformity among the control, gCSA, and gCOVIDOA treatments. gACOA-treated seedlings have healthier root hair compared to unexposed seeds after 14 days and revealed the highest rate of increase in metaxylem, xylem, phloem, and radicle diameters with a factor of 3.651 mu m/hr, 1.440 mu m/hr, 0.872 mu m/hr, and 71.602 mu m/hr of exposure in 2 g hypergravity. This study implies that stimulating corn seeds using hypergravity can help lessen the introduction of nutrient fertilizers in the long run which could help in reducing the fa

关键词： Abiotic stress Biostimulant Complex environment Digital agriculture Evolutionary computing optimization algorithm Seed germination Simulated gravity Space agriculture Sustainable agriculture

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optimization of Connect6 based on Principal Variation Search and Transposition Tables algorithms 32

Optimization of Connect6 based on Principal Variation Search...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Qi, Zhiyang Huang, Xiaosong Shen, Yu Shi, Jiyun Beijing Inst Technol Sch Comp Sci Beijing 100081 Peoples R China

ISBN: (纸本)9781728158556

Connect6 is an intuitive and fair game with no first-hand advantage. Because of the large search tree, the search depth is often lower, to sacrifice strength of chess to improve search efficiency. In this case, this paper proposes an optimization algorithm for pruning Connect6 search tree based on the principal variation search and transposition tables algorithms. The optimization algorithm obtained PV node through searching, narrowed the pruning interval, and stored the key value of the situation into the transposition table through searching, and then directly returned the evaluation value when the same chess game was searched again, which greatly improved the search efficiency. Through a large number of repeated experiments, the effectiveness of the algorithm can be proved.

关键词： Connect6 PVS Transposition tables optimization algorithm

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Least Squares Approximation-based Polynomial Chaos Expansion for Uncertainty Quantification and Robust optimization in Aeronautics

Least Squares Approximation-based Polynomial Chaos Expansion...

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AIAA Aviation Forum

作者： Mura, Riccardo Ghisu, Tiziano Shahpar, Shahrokh Univ Cagliari Ind Engn I-09123 Cagliari Italy Univ Cagliari Dept Mech Chem & Mat Engn I-09123 Cagliari Italy Rolls Royce PLC Aerothermal Design Syst Derby DE24 8BJ England

ISBN: (数字)9781624105982

ISBN: (纸本)9781624105982

For many engineering problems, reliability and robustness are far more important than the nominal performance when it comes to the choice of a design over another. Availability of state-of-the-art simulation codes and trusted modeling approaches help in reducing the epistemic uncertainties, but what is needed for decision-making is ultimately the knowledge of the aleatory uncertainty for the system of interest, and methods capable of minimizing such uncertainty while limiting the impact on the expected performance. A reliable measure of performance is only achieved if all uncertainties affecting the system's geometry, physical properties and operational conditions can be propagated through the model equations to the quantities of interest. optimization in a deterministic sense, providing the best possible performance for nominal values of the design variables, is then replaced by Robust optimization, which aims at maximizing the expected performance while minimizing the probability of its degradation. Today, multi-physics problems and demanding high-fidelity simulations are common denominators in Aeronautics as well as in many other industrial fields. It is then necessary to employ techniques that are able to perform uncertainty quantification and propagation at reasonable costs, without requiring the development of dedicated solvers: Non-Intrusive Polynomial Chaos is a prime candidate, but not all of its variants are equally accurate and cost-effective. Efficiency and flexibility of Least Squares surrogates and gradient-enhanced Polynomial Chaos expansions for Uncertainty Quantification and Robust optimization are discussed in this paper employing an in-house framework. Advantages of the proposed methods over Collocation approaches are demonstrated by comparing the results for a selection of algebraic test cases and for the shape optimization of the NACA 0012 airfoil under transonic conditions.

关键词： Aeronautics NACA 0012 Optimal Control Theory Numerical Integration Computing Aerodynamic Shape optimization Probability Density Functions Robust Design Sensitivity Analysis optimization algorithm

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Path Planning and optimization of Unmanned Ground Vehicles (UGVs) in the Field 3

Path Planning and Optimization of Unmanned Ground Vehicles (...

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3rd International Conference on Unmanned Systems (ICUS)

作者： Chen, Zhiwei Hu, Jinwen Zhao, Chunhui Hou, Xiaolei Pan, Quan Xu, Zhao Jia, Caijuan Sch Northwestern Polytech Univ Xian Peoples R China Xian ASN Technol Grp GO Ltd Xian Peoples R China

ISBN: (纸本)9781728180250

Unmanned Ground Vehicle (UGV) can be as a replacement of human beings during a dangerous mission. It can also be applied to military and performs repetitive mechanical transportation. For the UGVs in the conventional layout and structured urban environment, the research of environment perception and path planning technology has been relatively mature. Compared with the flat ground, the control of the UGV in the field is unknown and more complex. This article revolves autonomous control of the UGV in the field, putting forward a kind of path planning and optimizing technology for the UGV under complex environment. Research content includes: Conduct field environment modeling with environment information. First, the environment is detected by the fusion of lidar and IMU. Then a more accurate environment model is obtained by training the RBF neural network. Use the obtained environmental model to conduct path planning for the UGV in the field. The path planning trajectory is obtained by introducing the distance between the starting point and target point, the environmental height and environmental gradient constraints to construct the cost function. Then Analyze different characteristics of the paths that are generated by modifying the constraint function. Optimize the smoothness of the generated path for the field UGV. The path is optimized from broken lines into a curve by using a 5th order polynomial trajectory.

关键词： Unmanned Ground Vehicles Environment Perception Path Planning RBF Neural Network optimization algorithm

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Using Genetic algorithms for Safe Swarm Trajectory optimization

Using Genetic Algorithms for Safe Swarm Trajectory Optimizat...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Rughani, Rahul Barnhart, David A. Univ Southern Calif Informat Sci Inst ISI Astronaut Engn Dept Marina Del Rey CA 90292 USA Univ Southern Calif Space Engn Res Ctr SERC Marina Del Rey CA 90292 USA

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

Satellite swarms are groups of spacecraft that operate cooperatively to perform a common task or goal in orbit. Examples of this are in-space assembly or manufacturing of structures, cooperative sensing platforms, on-orbit servicing, asteroid mining, etc. Swarms would be operating in relative-motion orbits, working around a common point in space, within a few kilometers of each other. Such swarms would share information between each member of the swarm, allowing them to have a shared situational awareness of the entire mission. This paper describes a novel approach to optimize relative-motion orbital trajectories for a spacecraft swarm of arbitrary size and function, using genetic algorithms. The use of genetic algorithms allows convergence to an optimal set or family of trajectories for each member of the swarm regardless of the size of the swarm or mission functions required, should a solution exist. These trajectories will be such that each spacecraft can perform their required individual actions while minimizing the fuel required for maneuvering and also avoiding conjunctions, to a prescribed probability of collision, for a given amount of time. Genetic algorithms have been used previously for optimization of low thrust orbit transfers, drone delivery networks, and control of self-driving cars, among other applications.

关键词： Genetic algorithm Satellite Swarm Trajectory optimization Asteroid Mining Drone Spacecraft Structures Micro Gravity Environment optimization algorithm Distributed Computing Orbital Mechanics

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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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optimization of an air turbostarter of a launch system for gas turbine engine

Optimization of an air turbostarter of a launch system for g...

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AIAA Propulsion and Energy Forum

作者： Zubanov, Vasilii M. Popov, Grigorii M. Volkov, Andrei A. Egorov, Igor Marchukov, Evgenii Samara Natl Res Univ Res & Educ Ctr Gas Dynam Studies Samara 443086 Russia Samara Natl Res Univ Dept Aircraft Engine Theory Samara 443086 Russia Moscow Aviat Inst Moscow 125993 Russia

ISBN: (数字)9781624106026

ISBN: (纸本)9781624106026

The paper describes the process of optimizing the blades of a starter air turbine for a gas turbine engine launch system. This task was initiated by the necessity to use an existing turbine when starting a new engine. During the study, it was found that the turbine, although it ensures the conditions for the joint operation with the auxiliary power unit turbine and meet the strength constraints, it does not allow to start the engine within the required time. As a result of studies using mathematical optimization methods involving commercial programs, a variant was found to modernize the baseline turbine, which provides an acceptable value of torque on the shaft with an adequate start-up time at all operating modes with minimal design changes.

关键词： Launch System Gas Turbine Engines Auxiliary Power Unit Mathematical optimization Mass Flow Rate optimization algorithm Air Flowing Atmospheric Conditions Air Mass Flow Rates Numerical Modeling

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The fractional accumulative time-delay GM (1, N) model and its application

The fractional accumulative time-delay GM (1, N) model and i...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Song Jiakang Hu Mingli Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing 210016 Peoples R China

ISBN: (纸本)9781665442077

Aiming at the problem of modeling small sample systems with time-delay cumulative effects, this paper introduces a time-delay coefficient control term and a fractional cumulative generation operator, and proposes a fractional cumulative time-delay GM (1, N) grey prediction model, using particle swarm optimization algorithm to determine the model's optimal time-lag effect control coefficient and the optimal fractional cumulative generation order. Finally, combine the total economic output value and fixed asset investment of Zhejiang Province from 2003 to 2011 to establish a forecast model, and compare the prediction results of the GM (1, 1)., GM(1,N) and time-delay GM(1,N) models shows that the fractional cumulative time-delay GM(1,N) model can better solve the small sample multivariate with cumulative time-delay characteristics system prediction problem.

关键词： Grey Model Fractional accumulation Time-delay Particle swarm optimization algorithm

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