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Machining stability enhancement in multi-axis milling of titanium hollow blade by introducing multiple damping and rigid supporters

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JOURNAL OF MANUFACTURING PROCESSES 2021年 64卷 198-208页

作者： Hou, Junming Zhao, Zhengcai Fu, Yucan Qian, Ning Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 210016 Peoples R China Nanjing Inst Technol Ind Ctr Nanjing 211167 Peoples R China

Hollow blades, which are important components of aeroengines, have thin-walled structures and are subjected to vibrations in the machining process owing to its low rigidity and damping. This paper proposes an approach to enhance the machining stability of titanium hollow blades by introducing multiple damping and rigid supporters to the blade machining system in multi-axis milling process. A theoretical prediction model, considering the influence of original system, transmission and supporter, is established to predict the dynamic parameters of blade with a single damping or rigid supporter. Then, the model is applied in multi-supporter system and an optimization algorithm is used to determine reasonable positions of multiple supporters. The stability lobe diagrams of blade system with no supporter, optimized damping supporters and optimized rigid supporters are analysed and validated by blade machining experiment. The conclusion can be achieved that the sound signal is suppressed up to 40 % by adding optimized supporters and the machining stability is enhanced effectively.

关键词： Hollow blade Dynamic parameters optimization algorithm Multiple supporters

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Enhancing residential energy access with optimized stand-alone hybrid solar-diesel-battery systems in Buea, Cameroon

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SCIENTIFIC REPORTS 2024年第1期14卷 1-19页

作者： Amoussou, Isaac Paddy, Eriisa Yiga Agajie, Takele Ferede Ibrahim, Fuseini Seidu Agajie, Elsabet Ferede Nsanyuy, Wirnkar Basil Bajaj, Mohit Mohammadi, Shir Ahmad Dost Univ Buea Fac Engn & Technol Dept Elect & Elect Engn POB 63 Buea Cameroon Debre Markos Univ Sch Elect & Comp Engn Debre Markos Ethiopia Graph Era Deemed Univ Dept Elect Engn Dehra Dun 248002 India Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman Jordan Graph Era Hill Univ Dehra Dun 248002 India Alberoni Univ Fac Engn Dept Elect & Elect Kohistan Kapisa Afghanistan

This study examined the optimal size of an autonomous hybrid renewable energy system (HRES) for a residential application in Buea, located in the southwest region of Cameroon. Two hybrid systems, PV-Battery and PV-Battery-Diesel, have been evaluated in order to determine which was the better option. The goal of this research was to propose a dependable, low-cost power source as an alternative to the unreliable and highly unstable electricity grid in Buea. The decision criterion for the proposed HRES was the cost of energy (COE), while the system's dependability constraint was the loss of power supply probability (LPSP). The crayfish optimization algorithm (COA) was used to optimize the component sizes of the proposed HRES, and the results were contrasted to those obtained from the whale optimization algorithm (WOA), sine cosine algorithm (SCA), and grasshopper optimization algorithm (GOA). The MATLAB software was used to model the components, criteria, and constraints of this single-objective optimization problem. The results obtained after simulation for LPSP of less than 1% showed that the COA algorithm outperformed the other three techniques, regardless of the configuration. Indeed, the COE obtained using the COA algorithm was 0.06%, 0.12%, and 1% lower than the COE provided by the WOA, SCA, and GOA algorithms, respectively, for the PV-Battery configuration. Likewise, for the PV-Battery-Diesel configuration, the COE obtained using the COA algorithm was 0.065%, 0.13%, and 0.39% lower than the COE provided by the WOA, SCA, and GOA algorithms, respectively. A comparative analysis of the outcomes obtained for the two configurations indicated that the PV-Battery-Diesel configuration exhibited a COE that was 4.32% lower in comparison to the PV-Battery configuration. Finally, the impact of the LPSP reduction on the COE was assessed in the PV-Battery-Diesel configuration. The decrease in LPSP resulted in an increase in COE owing to the nominal capacity of the diesel gene

关键词： Hybrid renewable energy COE LPSP optimization algorithm Diesel generator

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Modeling and optimal control of fast filling process of hydrogen to fuel cell vehicle

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JOURNAL OF ENERGY STORAGE 2021年 35卷

作者： Bai, Yunfeng Zhang, Caizhi Duan, Hao Jiang, Shangfeng Zhou, Zhiming Grouset, Didier Zhang, Mingjun Ye, Xuefeng Chongqing Univ Sch Automot Engn Chongqing 400044 Peoples R China Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Chongqing Univ Chongqing Automot Collaborat Innovat Ctr Chongqing 400044 Peoples R China Zhengzhou Yutong Bus Co Ltd Yutong Ind PkYutong Rd Zhengzhou 450061 Henan Peoples R China IMT Mines Albi CNRS UMR 5302 RAPSODEE Campus Jarlard F-81013 Albi 09 France Beijing PERIC Hydrogen Technol Co Ltd Damei Ctr Qingnian Rd Beijing 100123 Peoples R China Chongqing Changan New Energy Vehicle Technol Co L Prop Res Inst Chongqing 400000 Peoples R China

Due to the rapid compression of hydrogen and the Joule-Thompson effect specific to hydrogen during the fast filling process, the internal temperature of the cylinder rises sharply which may lead to hidden safety hazards. In this study, a high-pressure hydrogen filling process is considered, and a simple mathematical model of a cascade storage system of a hydrogen refilling station is developed to analyze the temperature rise in hydrogen cylinders under different working conditions. The results show the pressure switching coefficient has a great impact on the filling time, and the pre-cooling of hydrogen has a significant impact on the temperature rise and the states of charge (SOC) within cylinder. Herein, a multi-objective iterative optimization algorithm is proposed to calculate the above two controllable variables (pressure switching coefficient, pre-cooling temperature of hydrogen) with the objectives of faster refueling, lower energy consumption and higher SOC within cylinders in cascade hydrogen refueling. Besides, this method could significantly decrease energy consumption, improve SOC and allow acceptable refueling time.

关键词： Cascade storage system Fast filling Pressure switching coefficient Pre-cooling temperature optimization algorithm

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Measurement of four main catechins content in green tea based on visible and near-infrared spectroscopy using optimized machine learning algorithm

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JOURNAL OF FOOD COMPOSITION AND ANALYSIS 2025年 138卷

作者： Luo, Wei Li, Wenyoujia Liu, Shuling Li, Qicheng Huang, Haihua Zhang, Hailiang East China Jiaotong Univ Coll Elect & Automat Engn Nanchang 330013 Peoples R China Jiangxi Inst Sci & Technol Informat Nanchang 330046 Peoples R China

The catechin content is critical for assessing the nutritional value and commercial worth of green tea, making detection methods valuable for its quality evaluation. This study utilized visible and near-infrared spectroscopy (Vis-NIRS, 400-2500 nm) combined with an optimized machine learning model, integrating the global search whale optimization algorithm (GSWOA) and kernel extreme learning machine (KELM), to predict the content of four main catechins (EC, ECG, EGC, and EGCG) in fresh tea leaves. Multiple preprocessing and effective wavelength selection methods were applied to improve prediction accuracy. The results showed that the distribution of effective wavelengths verified the importance of the visible spectrum. The GSWOA-KELM demonstrated superior performance, optimizing the modeling capabilities of KELM and outperforming other models, including partial least squares regression (PLSR) and random forest (RF), with R2P values exceeding 0.98 and RMSEP values below 3.3 for all catechins. Notably, even without additional data processing, the model maintained good effects, achieving a minimum R2p of 0.9751 and a maximum RMSEP of 3.3654. Therefore, the proposed approach proved to be both accurate and rapid for non-destructive, online catechin analysis and had practical value for tea appreciation.

关键词： Visible and near-infrared spectroscopy Green tea Catechin content Machine learning optimization algorithm

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Trimming Analysis Method of Quad Tilt Rotor Based on Aerodynamic Interference Model

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JOURNAL OF AIRCRAFT 2021年第2期58卷 253-265页

作者： Wang, Zhigang Wang, Qiannan Yu, Hongrui Duan, Dengyan Ding, Zhiwei Li, Jianbo Yangzhou Collaborat Innovat Res Inst Shenyang Aircraft Design & Res Inst Yangzhou 225000 Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Natl Key Lab Rotorcraft Aeromech Nanjing 210016 Peoples R China

The quad tilt rotor (QTR) has complex aerodynamic characteristics and strong aerodynamic interference. The flight dynamic model is the basis of control. To effectively account for the aerodynamic interference between the multiple aerodynamic surfaces of the QTR unmanned aerial vehicle, a hybrid optimization trimming analysis method based on Immune algorithm/Levenberg-Marquardt (IA/LM) and lattice Boltzmann method (LBM) is proposed. The aerodynamic characteristics between the wing and the rotor calculated by the LBM are compensated into the flight dynamics model. The IA/LM algorithm is used to reduce the dependence on the initial trimming value. The effectiveness of the hybrid optimal trimming analysis method is verified through the analysis of trimming simulation results.

关键词： Tiltrotor Aircraft Aerodynamic Characteristics LBM UAV Aircraft Flight Dynamics Fuselages Helicopters CFD optimization algorithm Lift to Drag Ratio

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Calculation Method of Probability Integration Method Parameters Based on MIV-GP-BP Model

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2021年第1期28卷 160-168页

作者： Chi, Shenshen Yu, Xuexiang Wang, Lei Anhui Univ Sci & Technol Sch Earth & Environm Huainan 232001 Peoples R China Anhui Univ Sci & Technol Sch Geomat Huainan 232001 Peoples R China Anhui Univ Sci & Technol Key Lab Aviat Aerosp Ground Cooperat Monitoring & KLAHEI KLAHEI18015 Huainan 232001 Peoples R China Anhui Univ Sci & Technol Coal Ind Engn Res Ctr Min Area Environm & Disaste Huainan 232001 Peoples R China

In order to guarantee the precision of the parameters of the probability integral method (PIM), starting from optimizing input and improving algorithm an algorithm integrating the genetic algorithm (GA) and particle swarm optimization (PSO) was put forward to optimize the prediction model of BP neural network and the mean impact value algorithm (MIV) was applied to optimize the input of BP neural network. The mean impact value algorithm (MIV) was applied to optimize the input of BP neural network. The measured data of 50 working faces were chosen as the training and testing sets to build the MIV-GP-BP model. The results showed that among the five parameters, the RMSE was between 0.0058 and 1.1575, the MaxRE of q, tan beta, b and theta was less than 5.42%, and the MeaRE was less than 2.81%. The RMSE of s/H did not exceed 0.0058, the MaxRE was less than 9.66% and the MeaRE was less than 4.31% (the parameters themselves were small). The optimized neural network model had higher prediction accuracy and stability.

关键词： BP neural network MIV algorithm mining subsidence optimization algorithm PIM underground mining

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Application of a novel grey Bernoulli model to predict the global consumption of renewable energy

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ENERGY REPORTS 2021年 7卷 7200-7211页

作者： Duan, Huiming Wang, Siqi He, Chenglin Huang, Jiangbo Chongqing Univ Posts & Telecommun Sch Sci Chongqing 400065 Peoples R China

Accurate prediction of renewable energy can provide an important basis for national energy security and the government to formulate policies. Therefore, according to the non-linearity of energy prediction system, this paper proposes a new nonlinear grey Bernoulli optimization model by the grey Bernoulli extended model, which is a nonlinear grey prediction model, and studies the properties of the new model. The order and nonlinear coefficient of the new model are optimized by Particle Swarm optimization. Then, through the consumption of global renewable energy, such as solar, wind and hydropower as empirical analyses, the results of the four evaluation indicators show that the new model works better than the original model, which has higher prediction accuracy than before and makes the prediction model more applicable. At the same time, the model results were compared with the weighted grey model, Verhulst and the discrete grey model, and the new model has the highest accuracy. Finally, the new model is used to forecast the global consumption of wind, solar and hydropower energy in 2019-2023. The results will provide important forecasting information for global energy conservation and emission reduction policies. (C) 2021 The Authors. Published by Elsevier Ltd.

关键词： Grey prediction model Bernoulli model Renewable energy consumption optimization algorithm Forecasting

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Semi-Analytical Sensitivity Analysis for Multibody System Dynamics Described by Differential-Algebraic Equations

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AIAA JOURNAL 2021年第3期59卷 893-904页

作者： Peng, Haijun Zhang, Mengru Zhang, Lidan Dalian Univ Technol Dept Engn Mech State Key Lab Struct Anal Ind Equipment Dalian 116024 Peoples R China Delft Univ Technol Dept Precis & Microsyst Engn NL-2600 AA Delft Netherlands

A large number of deployable space structures involve multibody system dynamics, and in order to effectively analyze and optimize dynamic performance, the sensitivity information of multibody systems is often required. At present, the sensitivity analysis methods of multibody system dynamics, which have been widely used, are mainly finite difference method, direct differentiation method, and adjoint variable method. Among them, the finite difference method is an approximate method;the direct differentiation method and the adjoint variable method are analytical methods. Based on the dynamic problems of the multibody system in the form of differential-algebraic equations, the semi-analytical sensitivity analysis method for multibody system dynamics is proposed in this paper, which combines the simplicity of the finite difference method with the accuracy of the analytical methods. It includes the local semi-analytical method based on the element level and the global semi-analytical method based on the system level, of which the latter has higher computational efficiency. Through two numerical examples, the effectiveness and numerical stability of the method are verified. This method not only retains the accuracy and efficiency of the analytical methods, but also simplifies the derivation and coding of analytical formulas by combining with the existing programs. It has stronger versatility and is beneficial to the sensitivity calculation of large-scale complex multibody systems.

关键词： Sensitivity Analysis Multibody Dynamics Numerical Integration Finite Difference Method Space Structures Trusses Mathematical Models Structural optimization optimization algorithm Finite Element Method

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Uncertainty reduction in quantitative precipitation prediction by tuning ofKain-Fritchscheme input parameters in theWRFmodel using the simulated annealing optimization method

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METEOROLOGICAL APPLICATIONS 2020年第4期27卷 e1919-e1919页

作者： Afshar, Mohaddeseh A. Azadi, Majid Rezazadeh, Maryam Univ Hormozgan Bandar Abbas Iran Atmospher Sci & Meteorol Res Ctr ASMERC Tehran Iran

In the study, using the simulated annealing (SA) optimization method, tried to reduce the uncertainty of the prediction of the Weather Research and Forecasting (WRF) numerical model in convective rainfall forecasts. To this end, three parametersP(d),P(e)andP(h)in Kain-Fritch convective scheme that's related to downdraft mass flux, entrainment mass flux and starting height of downdraft above updraft source layer, respectively, are optimized using SA algorithm to achieve better values. Two nested domain were used in the study with 30 and 10 km resolution which inner domain cover southern coasts of the Caspian Sea for the study area. Runtime of the model was 36 hr with the first 12 hr spin-up time. Study period selected October 12, 2012 for training algorithm and October 8, 2015 for test run. After 100 iteration of the algorithm, 1, 1 and 50 was obtained forP(d),P(e)andP(h)respectively, while default values of these parameters was 0, 0, and 150. Results show that in both cases, model with default values underestimates rainfall amount and after optimization, model performance improves. Also, spatial distribution of the model precipitation forecast was less than observations and after optimization, spatial distribution improves. Statistical analysis of results indicated Mean Bias (MB) and root mean square error (RMSE) in training case were -4.6 and 17.1 in model with default values and became -3.7 and 14.3 in optimized one, respectively. Also, MB and RMSE for test case increased to -8.4 and 23.8, respectively, in model result with optimized parameters from -10.6 to 29.4 in model with default parameters.

关键词： convective rainfall prediction Iran optimization algorithm simulated annealing method Weather Research and Forecasting (WRF) model

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AG-SGD: Angle-Based Stochastic Gradient Descent

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IEEE ACCESS 2021年 9卷 23007-23024页

作者： Song, Chongya Pons, Alexander Yen, Kang Florida Int Univ Dept Elect & Comp Engn Miami FL 33174 USA

In the field of neural network, stochastic gradient descent is often employed as an effective method of accelerating the result's convergence. Generating the new gradient from the past gradient is a common method adopted by many existing optimization algorithms. Since the past gradient is not computed based on the most updated stochastic gradient descent state, it can introduce a deviation to the new gradient computation, negatively impacting its rate of convergence. To resolve this problem, we propose an algorithm that quantifies this deviation based on the angle between the past and the current gradients, which is then applied to calibrate these two gradients, generating a more accurate new gradient. To demonstrate the broad applicability of the algorithm, the proposed method is implemented into a neural network and a logistic regression classifier which are evaluated on the datasets MNIST and NSL-KDD, respectively. An in-depth analysis is performed to compare our algorithm with nine optimization algorithms in two experiments, demonstrating the advantages in the cost and the error rate reductions from adopting the proposed method.

关键词： Neural networks optimization Classification algorithms Trajectory Rendering (computer graphics) Reliability Object recognition Stochastic gradient descent optimization algorithm gradient deviation neural network

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