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Optimal control of high harmonics for the generation of double attosecond pulses by using a chirped laser pulse

JOURNAL OF MODERN OPTICS 2017年第20期64卷 2117-2122页

作者： Navid, H. A. Keshavarzi, M. Univ Bonab Dept Laser & Opt Engn Bonab Iran

An efficient scheme for the optimization of ultrashort femtosecond pulse shapes interacting with an atom to control high harmonics spectrum and double attosecond pulse generation is presented. The time-dependent Schrodinger equation of one-dimensional hydrogen atom is solved numerically to obtain electric field emission. The genetic algorithm optimization method is used to control the phase and amplitude of ultrashort excitation laser pulses to generate the desired attosecond-shaped pulses. An appropriate cost function is introduced for genetic algorithm optimization of double attosecond pulse generation. It is shown that the relative intensity of two generated pulses, their delay time and duration can be controlled in this approach. Finally, the parameters of the optimized emitted attosecond pulse are compared with those of desired pulses, and the underlying physical mechanisms are discussed in detail.

关键词： Chirped pulse double attosecond pulses high harmonic generation optimal control theory genetic algorithm optimization

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Composite material structure optimization design and aeroelastic analysis on forward swept wing

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2019年第13期233卷 4679-4695页

作者： Xue Rongrong Ye Zhengyin Ye Kun Wang Gang Northwestern Polytech Univ Sch Aeronaut Youyi West Rd 127 Xian 710072 Shaanxi Peoples R China

The static aeroelastic torsion divergence problem is the main obstacle to bring forward swept wing into massive applications. The aeroelastic tailoring technique-based radial basis function neural networks (RBFNNs) and genetic algorithm (GA) optimization in MATLAB considering the material orientation, thickness, and lay-up are elucidated in the present work. RBFNNs are used to build a surrogate model between the composite parameters and structure displacement, which is proved robust and accurate. Then an optimal structure is obtained by GA global search based on RBFNNs model with the weight constrain. The displacements of the forward swept wing caused by an approximate aerodynamic load are decreased 32.5% through finite element method (FEM) static structural analysis. The modal analysis illustrates that the first mode frequency increases by 33.0% and the second mode increases by 37.9%. A computational aeroelasticity approach is developed by in-house Hybrid Unstructured Reynolds-Averaged Navier-Stokes solver associating an open source FEM code - Calculix. The results of coupling calculations show effectiveness of aeroelastic tailoring optimization of composite forward swept wing without weight penalty. The results obtained demonstrate that for the forward swept wing, the most violent situation appears around Mach Number 1.0 where the aeroelastic tailoring optimization could decrease the torsion angle by nearly 70.0%. The torsion of forward swept wing will increase at subsonic and decrease at supersonic with the increase of velocity.

关键词： Composite material forward swept wing aeroelastic tailoring radial basis function neural networks genetic algorithm optimization

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A new approach of GA-based type reduction of interval type-2 fuzzy model for nonlinear MIMO system: Application in methane oxidation process

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2017年 167卷 152-160页

作者： Tayyebi, Shokoufe Soltanali, Saeed Res Inst Petr Ind POB 14665-137 Tehran Iran

The aim of this paper is the application of a type-2 fuzzy model to a nonlinear MIMO system. In the suggested approach, genetic algorithm is employed to find the optimal type reduction of the interval type-2 fuzzy model for model prediction. To verify its performance, the proposed method is used in prediction of the methane conversion and the formic acid selectivity of the methane oxidation process with H2O2. In this process, the input variables are temperature, pressure, time on stream, catalyst content, H2O2 concentration and stirring speed. Interval type-2 fuzzy logic model has designed with reduced rules number compared with the type-1 fuzzy model. The results confirm that the proposed method based on type reduction interval type-2 fuzzy model outperforms the other strategies with an R-2 value of greater than 0.988.

关键词： Interval type-2 fuzzy logic genetic algorithm optimization Type reduction Modeling Methane oxidation

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On efficient modeling of drain current for designing high-power GaN HEMT-based circuits

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JOURNAL OF COMPUTATIONAL ELECTRONICS 2024年第6期23卷 1355-1367页

作者： Jarndal, Anwar Rakib, Famin Rahman Alim, Mohammad Abdul Univ Sharjah Elect Engn Dept Sharjah U Arab Emirates Univ Chittagong Elect & Elect Engn Dept Chittagong Bangladesh

In this paper, different modeling approaches to the drain current, including analytical and artificial neural network (ANN) modeling, are investigated. The adopted models address the inherent self-heating and kink effects, especially in high-power GaN-based high electron mobility transistors (HEMTs). Different optimization algorithms were demonstrated for extracting the model parameters, including genetic algorithm optimization (GAO), gray wolf optimization (GWO), growth optimization (GO), and particle swarm optimization (PSO). The modeling approaches are applied to DC IV measurements of 1-mm, 4-mm, and 2-mm GaN HEMTs on SiC and Si substrates. An improved optimization procedure was applied to the analytical models to find the main parameters responsible for fitting the general nonlinear behavior of the device. Then, the thermal or self-heating parameters are tuned for best fitting in the high-power dissipation region. The kink effect has been counted by adding another factor to the analytical formula to characterize the voltage dependency of this effect. The ANN modeling provides an efficient and cost-effective solution to accurately simulate the IV characteristics with less effort. In this technique, there is no need for a predefined closed formula or a complicated fitting parameter extraction process. Also, the model training was enhanced by using a genetic algorithm augmented backpropagation technique. The investigated analytical and ANN techniques were demonstrated by modeling the IV characteristics of the considered GaN HEMTs. The results obtained confirm the advantages of using ANN modeling for solving such problems and large-signal modeling applications.

关键词： GaN HEMT Large-signal modeling Artificial neural networks genetic algorithm optimization Gray wolf optimization Growth optimization Particle swarm optimization Kink effect Self-heating effect

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Rolling bearing fault detection using continuous deep belief network with locally linear embedding

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COMPUTERS IN INDUSTRY 2018年 96卷 27-39页

作者： Shao, Haidong Jiang, Hongkai Li, Xingqiu Liang, Tianchen Northwestern Polytech Univ Sch Aeronaut Xian 710072 Shaanxi Peoples R China

Rolling bearing fault detection is of crucial significance to enhance the availability, the reliability and the security of rotating machinery. In this paper, a novel method called continuous deep belief network with locally linear embedding is proposed for rolling bearing fault detection. Firstly, a new comprehensive feature index is defined based on locally linear embedding to quantify rolling bearing performance degradation. Secondly, a continuous deep belief network (CDBN) is constructed based on a series of trained continuous restricted Boltzmann machines (CRBMs) to model vibration signals. Finally, the key parameters of the continuous deep belief network are optimized with genetic algorithm (GA) to adapt to the signal characteristics. The proposed method is applied to analyze the experimental bearing signals. The results demonstrate that the proposed method is more superior in stability and accuracy to the traditional methods. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Continuous deep belief network Rolling bearing Fault detection Comprehensive feature index genetic algorithm optimization

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Co-optimization of operating parameters, fuel composition, and piston bowl geometry of gasoline compression ignition (GCI) engine at high loads

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INTERNATIONAL JOURNAL OF ENGINE RESEARCH 2023年第9期24卷 4044-4062页

作者： Zhang, Pengwei Duan, Huiquan Xu, Guangfu Li, Yaopeng Jia, Ming Dalian Univ Technol Key Lab Ocean Energy Utilizat & Energy Conservat Minist Educ Dalian Peoples R China Dalian Univ Technol Key Lab Ocean Energy Utilizat & Energy Conservat Minist Educ Dalian 116024 Peoples R China

Focusing on the gasoline compression ignition (GCI) combustion mode, the operating parameters, fuel composition, and piston bowl geometry were collaboratively optimized at high loads to improve engine performance. A meliorative genetic algorithm coupled with a three-dimensional computational fluid dynamics (CFD) program was adopted as the optimization tool. The optimal GCI combustion strategy is summarized based on the optimization results, and the key parameters affecting engine performance at high loads are further analyzed. The results show that the employment of the open-type piston bowl, high-reactivity fuel, and late start of injection (SOI) is desirable for GCI engines at high loads. Under this strategy, fuel burns in multiple stages, which can reduce the heat release rate and advance the combustion phase. The width of the piston bowl is the geometric parameter with the most notable impact on GCI combustion. Due to the weaker turbulent kinetic energy and the smaller surface area, the larger piston bowl width is conducive to diminishing the heat transfer losses of the engine and promoting the oxidation of unburned hydrocarbon (HC) emissions. Relative to the optimal case, the strategy with the open-type piston bowl, high-reactivity fuel, and early SOI can reduce fuel consumption but results in a higher in-cylinder pressure rise rate and increased ringing intensity (RI). On the contrary, the re-entrant type piston bowl, low-reactivity fuel, and late SOI can significantly reduce the pressure rise rate and RI while deteriorating thermal efficiency.

关键词： Gasoline compression ignition (GCI) genetic algorithm optimization collaborative optimization fuel composition piston bowl geometry

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Predictive modelling and optimization of carbon fiber mechanical properties through high temperature furnace

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APPLIED THERMAL ENGINEERING 2017年 125卷 1539-1554页

作者： Khayyam, Hamid Fakhrhoseini, Seyed Mousa Church, Jeffrey S. Milani, Abbas S. Bab-Hadiashar, Alireza Jazar, Reza N. Naebe, Minoo RMIT Univ Sch Engn Melbourne Vic 3000 Australia Deakin Univ Inst Frontier Mat Carbon Nexus Geelong Vic 3216 Australia Commonwealth Sci & Ind Res Org Mfg Waurn Ponds Vic 3216 Australia Univ British Columbia Sch Engn Composites Res Network Vancouver BC V1V 1V7 Canada

Developments in carbon fiber mechanical properties are directly dependent on its manufacturing process, especially the thermal process of carbonization through a High Temperature (HT) furnace. These mechanical properties cannot be straightforwardly predicted experimentally since the thermal process involves complex parameters such as: (i) zones temperature, (ii) applied fiber tension, (iii) the N2 gas flow rate which is influenced by uncertain disturbances. The aim of this paper is to optimize the carbon fiber modulus based on the given range of tensile strength using a predictive modelling technique. The data collection of the carbonization thermal process is significantly costly and time consuming, hence we used a novel DOE method to minimize the number of required experiments. The carbon fiber with the highest and lowest tensile and modulus properties were initially analyzed by X-ray diffraction and Raman Spectroscopy, but no significant differences were observed that could account for the differences in measured mechanical and physical properties, thus a surrogate modelling approach was adopted. Four different mathematical methods were employed for modelling and compared with one another. These are (i) non-linear multivariable regression, (ii) Levenberg-Marquardt neural network algorithm, (iii) thin plate splines, and (iv) a convex hull technique. The results clearly showed that the convex hull technique has less error compared to the other tested methods. This method was selected to optimize the carbon fiber modulus based on the given range of tensile strength. Using the convex hull technique along with an evolutionary search (genetic algorithm) method, it is shown that the modulus of carbon fiber can be optimized for any given range of tensile strength, providing an optimum set of control parameters are chosen during the process. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： HT furnace Thermal process genetic algorithm optimization Convex hulls

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Short-term reforecasting of power output from a 48 MWe solar PV plant

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SOLAR ENERGY 2015年第Feb.期112卷 68-77页

作者： Chu, Yinghao Urquhart, Bryan Gohari, Seyyed M. I. Pedro, Hugo T. C. Kleissl, Jan Coimbra, Carlos F. M. Univ Calif San Diego Dept Mech & Aerosp Engn Jacobs Sch Engn Ctr Renewable Resource Integrat La Jolla CA 92093 USA Univ Calif San Diego Dept Mech & Aerosp Engn Jacobs Sch Engn Energy Res Ctr La Jolla CA 92093 USA

A smart, real-time reforecast method is applied to the intra-hour prediction of power generated by a 48 MWe photovoltaic (PV) plant. This reforecasting method is developed based on artificial neural network (ANN) optimization schemes and is employed to improve the performance of three baseline prediction models;(1) a physical deterministic model based on cloud tracking techniques;(2) an auto-regressive moving average (ARMA) model;and (3) a k-th Nearest Neighbor (kNN) model. Using the measured power data from the PV plant, the performance of all forecasts is assessed in terms of common error statistics (mean bias, mean absolute error and root mean square error) and forecast skill over the reference persistence model. With the reforecasting method, the forecast skills of the three baseline models are significantly increased for time horizons of 5, 10, and 15 min. This study demonstrates the effectiveness of the optimized reforecasting method in reducing learnable errors produced by a diverse set of forecast methodologies. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Real-time reforecasting Artificial neural networks genetic algorithm optimization PV generation

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Gear shifting multi-objective optimization to improve vehicle performance, fuel consumption, and engine emissions

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MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES 2018年第2期46卷 238-253页

作者： Eckert, Jony Javorski Santiciolli, Fabio Mazzariol Bertoti, Elvis Costa, Eduardo dos Santos Correa, Fernanda Cristina de Alkmin e Silva, Ludmila Correa Dedini, Franco Giuseppe Univ Estadual Campinas Integrated Syst Dept DSI Integrated Syst Lab LabSIn Campinas SP Brazil Univ Tecnol Fed Parana Dept Elect Engn Ponta Grossa PR Brazil

In the present work, the Adaptive-Weight genetic algorithm was employed in order to determine the gear shifting strategies that allow an automobile to work in the best compromise among fuel consumption, engine emissions, and vehicle performance. For the assessment of each of the three objective functions, a simulation model based on engine data and on the well-established equations of the longitudinal dynamics was developed. The driving cycle chosen for the calculations was the FTP-75, which takes into account both cold and hot starts, meaning that the transient operation during the warm-up of the catalyst is also considered.

关键词： Engine emissions fuel consumption gear shifting strategy genetic algorithm optimization vehicle dynamics

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Piston-Seals Friction Modeling Using a Modified Maxwell Slip Formation and genetic Identification algorithm

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IEEE ACCESS 2022年 10卷 126516-126524页

作者： Mustafa, Rashad Abdo, Ali Siam, Jamal Kucukay, Ferit Birzeit Univ Dept Mech & Mechatron Engn Birzeit 9730000 Palestine Birzeit Univ Dept Elect & Comp Engn Birzeit 9730000 Palestine Tech Univ Carolo Wilhelmina Braunschweig Inst Automot Engn D-38106 Braunschweig Germany

Improving spontaneity, shifting control, and efficiency are the main goals of actuators for hydraulic automated transmissions. Friction losses of piston-seals play an essential role in achieving these goals. Therefore, modeling the complex friction behavior of piston seals leads to a better understanding of the determinant factors of energy losses and, consequently, the realization of more efficient transmission actuators. This paper proposes a piston-seal friction model based on the Generalized Maxwell-Slip model. The proposed model introduces an additional hydraulic-pressure dependency that emulates the influence of cylinder-pressure on the displacement variable while accounting for various piston-seal structures. A genetic algorithm is also applied to identify and optimize the parameters of the proposed friction model. Simulations with O-Ring, D-Ring, and Bonded Piston seals were developed to show the validity of the proposed model in practical scenarios. The results were also compared with the original Generalized Maxwell Slip friction model to show the superiority of the proposed model in representing the experimental data.

关键词： Friction Mathematical models optimization Pistons Force Behavioral sciences genetic algorithms Friction modeling generalized Maxwell model genetic algorithm optimization modified generalized Maxwell model piston seal proportional-integral observer

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