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Active learning Kriging approach for creep-fatigue reliability assessment of turbine disk

STRUCTURAL AND MULTIDISCIPLINARY optimization 2025年第4期68卷 1-22页

作者： Huang, Ying Zhang, Jianguo Wang, Bowei Song, Lukai Wei, Yanxu Zhang, Wei Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China Hong Kong Polytech Univ Dept Mech Engn Hong Kong 100872 Peoples R China Beihang Univ Res Inst Aero Engine Beijing 102206 Peoples R China Taiyuan Univ Technol Coll Mech & Vehicle Engn Taiyuan 030024 Peoples R China

The active metamodel is hard to represent the creep-fatigue failure, which hinders the application of efficient active metamodeling technique in creep-fatigue reliability estimation. To improve the computing efficiency and accuracy of creep-fatigue reliability assessment for turbine disk involving complex coupling of multi-layer, multi-disciplinary, and multi-uncertainty, the efficient active metamodeling technique is first pushed deep into complex creep-fatigue reliability evaluation. By integrating the synergic surrogate strategy into the active metamodeling, a multi-layer surrogate control-based synergic enhanced Kriging (MSC-SEK) approach is proposed: Firstly, to precisely describe the complicated creep-fatigue strong-coupling relationships, a synergic enhanced Kriging (SEK) is established by organically synergizing multiple Kriging models, where a multi-colony multi-mutation artificial bee colony algorithm is designed to enhance the Kriging surrogate quality;further, to obtain high-quality modeling dataset, a novel MSC learning function is developed by synthetically considering multi-surrogate entropy and reliability-sensitive information. The superiority of MSC-SEK is validated by studying the creep-fatigue reliability evaluation of a typical aeroengine turbine disk. The current efforts open up an effective way to achieve high-accuracy and high-efficiency engineering creep-fatigue reliability evaluation.

关键词： Reliability evaluation Creep-fatigue Kriging Active metamodeling Turbine disk optimization algorithm

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GVP-RRT: a grid based variable probability Rapidly-exploring Random Tree algorithm for AGV path planning

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COMPLEX & INTELLIGENT SYSTEMS 2024年第6期10卷 8273-8286页

作者： Zhou, Yaozhe Lu, Yujun Lv, Liye Zhejiang Sci Tech Univ Sch Mech Engn Hangzhou 310018 Peoples R China Zhejiang Sci Tech Univ Longgang Inst Wenzhou Peoples R China

In response to the issues of low solution efficiency, poor path planning quality, and limited search completeness in narrow passage environments associated with Rapidly-exploring Random Tree (RRT), this paper proposes a Grid-based Variable Probability Rapidly-exploring Random Tree algorithm (GVP-RRT) for narrow passages. The algorithm introduced in this paper preprocesses the map through gridization to extract features of different path regions. Subsequently, it employs random growth with variable probability density based on the features of path regions using various strategies based on grid, probability, and guidance to enhance the probability of growth in narrow passages, thereby improving the completeness of the algorithm. Finally, the planned route is subjected to path re-optimization based on the triangle inequality principle. The simulation results demonstrate that the planning success rate of GVP-RRT in complex narrow channels is increased by 11.5-69.5% compared with other comparative algorithms, the average planning time is reduced by more than 50%, and the GVP-RRT has a shorter average planning path length.

关键词： Path planning AGV optimization algorithm RRT

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A multi modal fusion coal gangue recognition method based on IBWO-CNN-LSTM

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SCIENTIFIC REPORTS 2024年第1期14卷 1-21页

作者： Hao, Wenchao Jiang, Haiyan Song, Qinghui Song, Qingjun Sun, Shirong Shandong Univ Sci & Technol Coll Intelligent Equipment Tai An 271000 Shandong Peoples R China

Accurate identification of coal and gangue is a crucial guarantee for efficient and safe mining of top coal caving face. This article proposes a coal-gangue recognition method based on an improved beluga whalea* optimization algorithm (IBWO), convolutional neural network, and long short-term memory network (CNN-LSTM) multi-modal fusion model. First, the mutation and memory library mechanisms are introduced into the beluga whale optimization to explore the solution space fully, prevent falling into local optimum, and accelerate the convergence process. Subsequently, the image mapping of the audio signal and vibration signal is performed to extract Mel-Frequency Cepstral Coefficients (MFCC) features, generating rich sample data for CNN-LSTM. Then the multi-head attention mechanism is introduced into CNN-LSTM to speed up the training speed and improve the classification accuracy. Finally, the IBWO-CNN-LSTM coal-gangue recognition model is constructed by the optimal hyperparameter combination obtained by IBWO to realize the automatic recognition of coal-gangue. The benchmark function proves that IBWO is superior to othera* optimization algorithms. By building an experimental platform for the impact of coal and gangue falling on the tail beam of hydraulic support, multiple experimental data collection is carried out. The experimental results show that the proposed coal-gangue recognition model has better performance than other recognition models, and the accuracy rate reaches 95.238%. The multi-modal fusion strategy helps to improve the accuracy and robustness of coal-gangue recognition.

关键词： Coal-gangue recognition Multi-modal fusion model optimization algorithm Audio signal Vibration signal

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Joint ABS Deployment and TBS Antenna Downtilt optimization for Coverage Maximization

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IEEE WIRELESS COMMUNICATIONS LETTERS 2022年第7期11卷 1329-1333页

作者： Li, Huan Zhai, Daosen Zhang, Ruonan Wang, Chen Tang, Xiao Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

In this letter, we consider an air-and-ground cooperative network, where several aerial base stations (ABS) help terrestrial base stations (TBS) for coverage enhancement. In this network, we first quantify the space-time coverage ratio (STCR) by fully considering the antenna models and the dynamic of the ABS, and then formulate a joint ABS deployment and TBS antenna downtilt optimization problem with the objective to maximize the STCR of the concerned area. The objective function involves many control variables and judgement operations, which make the problem very complex. To solve the problem effectively, we first adopt the genetic algorithm (GA). Using the solutions of the GA as training samples, we propose a deep neural network architecture to further reduce the computational time. Simulation results indicate that the proposed GA significantly improves the coverage ratio and the deep neural network (DNN) architecture achieves orders of magnitude acceleration in computational time with acceptable performance.

关键词： Antennas Genetic algorithms Interference Three-dimensional displays Wireless communication Signal to noise ratio optimization Cooperative communication unmanned aerial vehicle network coverage optimization algorithm

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High-dimensional efficient global optimization using both random and supervised embeddings

High-dimensional efficient global optimization using both ra...

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AIAA Aviation Forum

作者： Priem, Rainy Diouane, Youssef Bartoli, Nathalie Dubreuil, Sylvain Saves, Paul DGA Maitrise Informat Aeronaut Syst Tech Unit Bruz France Polytech Montreal Math & Ind Engn Dept Montreal PQ Canada Univ Toulouse DTIS ONERA Informat Proc & Syst Dept Toulouse France Univ Toulouse ISAE SUPAERO F-31055 Toulouse 4 France

ISBN: (数字)9781624107047

ISBN: (纸本)9781624107047

Bayesian optimization (BO) is one of the most powerful strategies to solve expensive blackbox optimization problems. However, BO methods are conventionally used for optimization problems of small dimension because of the curse of dimensionality. In this paper, to solve high dimensional optimization problems, we propose to incorporate linear embedding subspaces of small dimension to efficiently perform the optimization. An adaptive learning strategy for these linear embeddings is carried out in conjunction with the optimization. The resulting BO method, named EGORSE, combines in an adaptive way both random and supervised linear embeddings. EGORSE has been compared to state-of-the-art algorithms and tested on academic examples with a number of design variables ranging from 10 to 600. The obtained results show the high potential of EGORSE to solve high-dimensional black-box optimization problems, both in terms of CPU time and number of calls to the expensive black-box.

关键词： Black Box Cumulative Distribution Function Bayesian optimization Python Surrogate Model Robots optimization algorithm Information Processing Path Planning Artificial Intelligence

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Analysis of Flow Around an Aerial Tactical Pod carried by the A29B Aircraft for Viscous Drag optimization

Analysis of Flow Around an Aerial Tactical Pod carried by th...

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AIAA Aviation Forum

作者： Anguisaca, Jose N. Castellanos, Santiago Sanchez, Xavier Ctr Res & Dev Ecuadorian Air Force Sangolqui 171103 Pichincha Ecuador Ctr Res & Dev Ecuadorian Air Force Mech Engn Dept Mfg Proc Lab Sangolqui 171103 Pichincha Ecuador Armed Forces Univ ESPE Mech Engn Dept Composite Mat Lab Sangolqui 171103 Pichincha Ecuador

ISBN: (数字)9781624107047

ISBN: (纸本)9781624107047

The goal of this study is to identify the best geometry for a tactical pod to reduce viscous drag. To accomplish this, the body's geometry has been mathematically described and controlled by a set of shape parameters. The baseline geometry can then be changed to produce different iterations of the pod. Then, a simple method is used to qualitatively predict the drag due to viscosity based on the momentum deficit of the boundary layer at the body's trailing edge. This technique has been improved by incorporating dynamic boundary layer transition calculation when evaluating different geometries. The viscous drag prediction method and a (1+1)-Evolution Strategya* optimization algorithm are used to iteratively alter the geometry by perturbing the shape parameters until a shape with the least drag is achieved. At the final stage, the results from the optimization process are compared to the solution of the Transition-SST viscous model for both the baseline and optimized geometries, from which it is confirmed that the viscous drag calculation method based on the momentum deficit of the boundary layer can be effectively used to optimize an axisymmetric body and that including the dynamic calculation of the boundary layer transition is a valid improvement to the method that could lead to better results during the optimization process.

关键词： Freestream Velocity Aircrafts Laminar to Turbulent Transition Trailing Edges optimization algorithm Kinematic Viscosity Reynolds Averaged Navier Stokes Axisymmetric Flow Skin Friction Coefficient MATLAB

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Attitude Quaternion Estimation from Two Vector Observations

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2025年第3期48卷 700-706页

作者： Choukroun, Daniel Peng, Caitong Ben Gurion Univ Negev Pearlstone Ctr Aeronaut Engn Studies Mech Engn Dept POB 653 IL-84105 Beer Sheva Israel Ben Gurion Univ Negev Mech Engn Dept POB 653 IL-84105 Beer Sheva Israel

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A framework to reduce energy consumption in a press shop floor based on industrializable IIoT (I3oT)

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HELIYON 2024年第9期10卷 e29432页

作者： Peinado-Asensi, Ivan Montes, Nicolas Garcia, Eduardo CEU Univ Univ Cardenal Herrera CEU Dept Math Phys & Technol C San Bartolome 55 Valencia 46115 Spain Ford Spain Poligono Ind Ford S-N Valencia 46440 Spain

This article presents a framework to reduce energy consumption in a floor shop press based on Industrializable Industrial Internet of Things (I3oT). The I3oT proposes the development of IIoT tools using the information available in the system, without adding any additional sensors. Based on this philosophy, we proposed to develop the C360 criterion in our previous works, which allowed to extract all the information available in the stamping presses for the development of I3oT applications. In this article, we propose the development of a framework to optimize the parameters accessible from the C360 criterion for energy saving in the stamping process. Regarding the three parameters that can be modified and that affect energy consumption, that is, counterbalance pressure, tonnage and press speed, we will work with the first two in this paper. At the end of the article, the results obtained from the presses installed at Ford factory in Almussafes (Valencia) are shown based on their adjustment.

关键词： Industrializable IIoT(I3oT) Energy management optimization algorithm Automotive manufacturing Press shop

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Guaranteed Cost Gain-Scheduled Control of Typical Aeroelastic Section Model

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2025年第3期48卷 714-722页

作者： Pereira, Renan L. Gonzalez, Pedro J. Silvestre, Flavio J. Aeronaut Inst Technol Elect Engn Div BR-12228900 Sao Jose Dos Campos Brazil Tech Univ Berlin Flight Control & Aeroelast FMRA D-10587 Berlin Germany Tech Univ Berlin Flight Control & Aeroelast FMRA Marchstr 12 D-10587 Berlin Germany

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Application Of Chimp-based ANFIS Model For Forecasting The Compressive Strength Of The Improved High-performance Concrete

JOURNAL OF APPLIED SCIENCE AND ENGINEERING

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2024年第4期27卷 2295-2306页

作者： Yuan, Yan Zhengzhou Univ Sci & Technol Fac Civil & Architectural Engn Zhengzhou 450064 Henan Peoples R China

In order to assess the compressive strength (CS) of high-performance concrete (HPC) prepared with fly ash and blast furnace slag, several artificial-based analytics were applied. This study, it was employed the Chimp optimizer ( CO) to identify optimal values of determinative factors of Support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS), which could be adjusted to improve performance. The suggested approaches were established using 1030 tests, eight inputs (a primary component of mix designs, admixtures, aggregates, and curing age), and the CS as the forecasting objective. The outcomes were then contrasted with those found in the body of existing scientific literature. Calculation results point to the potential benefit of combining CO - SVR and CO - ANFIS study. When compared to the CO - SVR, the CO - ANFIS showed much higher R2 and lower Root means square error values. Comparing the findings shows that the created CO- ANFIS is superior to anything that has previously been published. In conclusion, the suggested CO - ANFIS analysis might be used to determine the proposed approach for estimating the CS of HPC augmented with blast furnace slag and fly ash.

关键词： High-performance concrete Compressive strength Support vector regression Adaptive neuro-fuzzy inference system optimization algorithm

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