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Design, analysis analysis and optimization of anisogrid composite lattice conical shells

COMPOSITES PART B-ENGINEERING 2018年 150卷 184-195页

作者： Belardi, Valerio G. Fanelli, Pierluigi Vivio, Francesco Univ Roma Tor Vergata Dept Enterprise Engn Via Politecn 1 I-00133 Rome Italy Univ Tuscia DEIM I-01100 Viterbo Italy

A methodology for structural analysis and optimal design of conical anisogrid composite lattice shell structures subject to different external loads concurrently applied and multiple stiffness constraints is presented. The critical buckling load of the anisogrid lattice conical structure is exactly assessed, independently of the buckling failure mode, by means of a discrete approach. The method makes use of a full FE parametric modeling technique able to manage all the geometrical parameters of the anisogrid composite lattice structure. Additionally, the genetic algorithm NSGA-II is employed to set up an optimization procedure which allows to analyze different sets of geometrical variables, both continuous and discrete, to reach the optimal solution in terms of mass amount and fulfilling of structural and stiffness requirements, aiming at the preliminary design of an actual structure. Numerical case -studies are outlined in order to demonstrate the practical usefulness and versatility of the proposed procedure to industrial cases where the anisogrid lattice conical structure undergoes multiple external loads and various stiffness constraints must be satisfied.

关键词： Carbon fiber Buckling Finite element analysis (FEA) Filament winding genetic algorithm optimization

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optimization of a novel multifunctional reactor containing m-xylene hydrodealkylation and naphtha reforming

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2019年第39期44卷 21882-21895页

作者： Shakeri, Mandi Iranshahi, Davood Naderifar, Abbas Amirkabir Univ Technol Dept Chem Engn Tehran Polytech 424 Hafez Ave Tehran 15914 Iran

In the present study, efforts have been made to improve the performance of the naphtha reforming process. To this end, the conventional reactors in the naphtha reforming process have been replaced with the new multifunctional reactors. These proposed reactors consist of hydrodealkylation and reforming sides separated by a solid wall and also permeation side that is separated from the reforming side by a palladium membrane wall. The naphtha reforming exchanges the heat with the hydrodealkylation of m-xylene, while this process exchanges the hydrogen gas with the permeation side. This new configuration will lead to an improvement in the production yield of aromatic in the catalytic reforming. According to the simulation results, the production rate of aromatic in this new suggestion increases by about 11% in comparison to the conventional process. The effects of design parameters such as the inlet temperatures of naphtha and hydrodealkylation feeds, the inlet molar flow rate of the hydrodealkylation side and, also the membrane thickness on the system performance have been studied. At the end of the work, the genetic algorithm optimization has been used to determine the best values of varies adjustable parameters in this system. (C) 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Naphtha reforming process Hydrogen and aromatics production Multifunctional reactor Hydrodealkylation of m-xylene Hydrogen permeation genetic algorithm optimization

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Grindstone4Spam: An optimization toolkit for boosting e-mail classification

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JOURNAL OF SYSTEMS AND SOFTWARE 2012年第12期85卷 2909-2920页

作者： Mendez, Jose R. Reboiro-Jato, M. Diaz, Fernando Diaz, Eduardo Fdez-Riverola, Fiorentino Univ Vigo Escuela Super Ingn Informat Orense Spain Univ Valladolid Escuela Univ Informat Segovia Spain Ultreia Comunicac SL Vigo Spain Univ Vigo Dept Comp Sci Vigo Spain Univ Valladolid Dept Comp Sci E-47002 Valladolid Spain

Resulting from the huge expansion of Internet usage, the problem of unsolicited commercial e-mail (UCE) has grown astronomically. Although a good number of successful content-based anti-spam filters are available, their current utilization in real scenarios is still a long way off. In this context, the SpamAssassin filter offers a rule-based framework that can be easily used as a powerful integration and deployment tool for the fast development of new anti-spam strategies. This paper presents Grindstone4Spam, a publicly available optimization toolkit for boosting SpamAssassin performance. Its applicability has been verified by comparing its results with those obtained by the default SpamAssassin software as well as four well-known anti-spam filtering techniques such as Naive Bayes, Flexible Bayes, Adaboost and Support Vector Machines in two different case studies. The performance of the proposed alternative clearly outperforms existing approaches working in a cost-sensitive scenario. (C) 2012 Elsevier Inc. All rights reserved.

关键词： SpamAssassin framework genetic algorithm optimization Naive Bayes Content-based techniques Spam detection

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Experimental investigation and optimization of delamination factors in the drilling of jute fiber-reinforced polymer biocomposites with multiple estimators

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2021年第9-10期116卷 2885-2907页

作者： Adda, Bachir Belaadi, Ahmed Boumaaza, Messaouda Bourchak, Mostefa Univ 20 Aout 1955 Dept Genie Mecan Fac Technol Skikda Algeria Univ 20 Aout 1955 Lab LGMM Skikda Algeria Univ 8 Mai 1945 Lab LSPN Guelma Algeria Univ 8 Mai 1945 Lab Genie Civil & Hydraul LGCH Guelma Algeria King Abdulaziz Univ Aerosp Engn Dept Jeddah Saudi Arabia

Currently, the manufacture of composite structures often requires material removal operations using a cutting tool. Indeed, since biocomposites are generally materials that do not conduct electricity, electro-erosion cannot be utilized. As a result, the processes that can be used not only the unconventional method of abrasive water jet but also conventional machining, such as drilling. Delamination factors (F-d) are widely recognized for controlling the damaged area (delamination) induced by drilling in industry. As discussed in the literature, several approaches are available to evaluate and quantify the delamination surrounding a hole. In this context, the objective of this study is to compare the three F-d evaluation methods that have been most frequently used in previous investigations. To this end, three spindle and feed speeds and three Brad and Spur drills (BSD) tool diameters were selected (L-27) for drilling 155-g/m(2) density jute fabric-reinforced polyester biocomposites. The desirability function (DF) was further made to optimize the drilling parameters. The response surface methodology (RSM) and artificial neural networks (ANNs) were applied to validate the results obtained experimentally as well as to predict the behavior of the structure depending on the cutting conditions.

关键词： Drilling machining Composite delamination Statistical properties methods RSM ANN genetic algorithm optimization

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An investigation on the effects of silica and copper oxide nanoparticles on rheological and fluid loss property of drilling fluids

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JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 2020年第1期10卷 91-101页

作者： Medhi, Srawanti Chowdhury, Satyajit Gupta, Dharmender Kumar Mazumdar, Aryab Univ Petr & Energy Studies Dehra Dun India

The increase in hydrocarbon production from problematic production zones having high fluid loss and formation damage has led to the emergence of non-damaging drilling fluids (NDDF). Recently, nanotechnology has found a wide array of applications in the oil and gas industry. Most applications of nanotechnology and enhancement in properties of drilling fluids are restricted to bentonite, xanthan gum and a few oil-based mud. In this study, the effects of silica and copper oxide nanoparticles on polyamine-based NDDF and conventional bentonite-based drilling fluids (BDF) were investigated. Silica nanoparticles were prepared using sol-gel method, and copper oxide nanoparticles were synthesized using co-precipitation method. Nano-based drilling fluids were prepared by dispersing nanoparticles in concentrations of 0.5%, 0.8% and 1% by weight. Furthermore, testing of these nano-based drilling fluids was conducted by measuring specific gravity, pH, rheological properties and filtrate loss at surface temperature (room temperature) and then aging it at bottom-hole temperature (80 degrees C). The addition of silica and copper oxide nanoparticles to both the drilling fluids did not show much effect on pH and specific gravity. Addition of 0.5% concentration of silica nanoparticles in NDDF showed least degradation in rheological properties compared to other fluids. It showed reduction in filtrate loss by 31%. Moreover, silica nanoparticles in conjunction with BDF acted as a mud thinner showing a decrease in viscosity and yield point. On the contrary, when used with NDDFs, silica nanoparticles acted as a mud thickener. Copper oxide nanoparticles behaved as a thinner in both the drilling fluids with a highest reduction in plastic viscosity of 24% for 0.8% of copper oxide nanoparticle in BDF. Thinning properties were enhanced as the doping concentrations of copper oxide nanoparticles increased;however, the fluid loss controlling ability decreased except for 0.5% concentration by 31% a

关键词： Non damaging drilling fluids Silica nanoparticle Copper oxide nanoparticle Rheological properties Fluid loss control genetic algorithm optimization

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Development of a Generalized Mathematical Model for Slider-Crank Mechanism Based on Multiobjective Concurrent Engineering with Application

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021年第8期46卷 8037-8053页

作者： Dang, Hoang Minh Bui, Van Phuong Phung, Van Binh Thom, Do Van Van Minh, Phung Gavriushin, Sergey Sergeevich Duc, Nguyen Viet Ind Univ Ho Chi Minh City Ho Chi Minh City Vietnam Bauman Moscow State Tech Univ Moscow Russia Le Quy Don Tech Univ Hanoi Vietnam Russian Acad Sci Mech Engn Res Inst Moscow Russia Thuyloi Univ 175 Tay Son Hanoi Vietnam

A design optimization of a slider-crank mechanism (SCM) based upon multiobjective concurrent engineering is presented in this paper. The proposed generalized mathematical model includes geometric equations, kinetic-dynamic expressions, fatigue strength and stability requirements and allows to take into consideration the manufacturing capabilities available. The model also allows for describing the actual mechanism characteristics and can be used directly for the detailed design stage of SCMs, in contrast to the simplified physical models available for preliminary design. Based on this approach, the multiobjective model of SCM which includes 11 variables, seven constrained expressions and the three most important objective functions (total mass, required power and maximum dynamic reaction) was developed. The closed-form analytical expressions were established to the relationship between objective functions, constraints and variables. The correctness of the model was checked by using the finite element method. By using the developed model, the optimization design of the main transmission of a fruit/vegetable washer was carried out. The genetic algorithm optimization has yielded 73 Pareto optimal solutions, and the three most suitable solutions according to the specific manufacturing capability were determined by employing a decision-making process based on the method of successive concessions. Further, a comparative study showed that the solutions obtained by the developed model have excelled the one obtained by means of the experience-based approach. It is noteworthy that the generalized mathematical model and the problem-solving approach adopted in this work can also be used for the SCM synthesis of other mechanical systems.

关键词： Slider-crank mechanism synthesis Multiobjective optimization model Concurrent engineering Decision-making process genetic algorithm optimization Fruit vegetable washer

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A genetic algorithm-optimized backpropagation neural network model for predicting soil moisture content using spectral data

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JOURNAL OF SOILS AND SEDIMENTS 2024年第7期24卷 2816-2828页

作者： Wang, Jiawei Wu, Yongyi Zhang, Yulu Wang, Honghao Yan, Hong Jin, Hua Taiyuan Univ Technol Coll Water Conservancy Sci & Engn Taiyuan 030024 Peoples R China Taiyuan Bilan Water Conservancy Engn Design Co Ltd Taiyuan 030024 Peoples R China Taiyuan Univ Technol Coll Mat Sci & Engn Taiyuan 030024 Peoples R China

Purpose Accurate assessment of soil moisture content (SMC) is crucial for applications in climate science, hydrology, ecology, and agriculture. However, conventional SMC characterization and measurement are expensive, time-consuming, and have negative effects on soil. Recently, the application of multispectral technology provides a new idea for SMC accurate detection. The objective of this study was to develop and compare regression and machine learning algorithms to estimate SMC from multispectral images. Materials and methods A multispectral sensor was used to collect spectral images of 125 soil samples from five distinct soil textures in Shanxi province at varying degrees of soil moisture, ranging from arid to fully saturated. A set of seven spectral parameters was derived from images, and predictive relationships were developed against laboratory-measured SMC. A linear regression (LR) model and a backpropagation neural network model based on genetic algorithm optimization (GA-BP) were compared in this study to predict SMC. Results and discussion The results showed that (1) the spectral reflectance and SMC exhibit a clear negative correlation, and the lower the SMC, the larger the spectral reflectance is. (2) The GA-BP neural network model exhibits higher prediction accuracy and performance (R-2 = 0.978 similar to 0.990, RMSE = 0.366 similar to 0.799%, MAE = 0.360 similar to 0.890%). (3) The GA-BP model exhibits the excellent inversion precision for the fine sand soil (R-2 = 0.990, RMSE = 0.518%, MAE = 0.360%). Conclusions This study introduces an effective methodology for accurate estimation of SMC using multispectral remote sensing technology. It further underscores the significant effectiveness of employing backpropagation neural networks and genetic algorithms in SMC prediction, providing a rapid, precise, non-intrusive, and practical approach towards precision agriculture.

关键词： Soil moisture content Spectral characteristics Neural network model genetic algorithm optimization

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A Waveguide Slot-Fed Patch Antenna Array

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IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 2023年第8期22卷 2027-2031页

作者： Hoseini, Mehri Foororaghi, Keyvan Abdolali, Ali Tarbiat Modares Univ Dept Elect & Comp Engn Tehran *** Iran Iran Univ Sci & Technol Dept Elect Engn Tehran *** Iran

This letter proposes an optimal patch based on the Haar function series and a genetic algorithm to design waveguide-fed traveling-wave arrays. A supercell is designed that provides better pattern characteristics. The conductance and susceptance equations are obtained in terms of offset and resonance length from simulation results. Using Elliot's design equations of the proposed structure, a waveguide slot-fed traveling-wave path antenna array is designed and fabricated for broadside radiation at 9.5 GHz frequency. The gain of the proposed array is 17.6 and 17.7 dB, and the side lobe level is -22.5 and -21 dB in the H-plane and E-plane, respectively. Near broadside radiation is achieved with good radiation efficiency in the high-frequency range.

关键词： Elliot's design equations genetic algorithm optimization Haar function series traveling-wave array waveguide-fed patch antenna array

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AN ADAPTIVE EVOLUTIONARY algorithm FOR UWB MICROSTRIP ANTENNAS optimization USING A MACHINE LEARNING TECHNIQUE

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2013年第8期55卷 1864-1868页

作者： Silva, Claudio R. M. Martins, Sinara R. Fed Univ Rio Grande Norte UFRN Dept Commun Engn BR-59072970 Natal RN Brazil

This article presents an application of a machine learning technique to enhance a multiobjective evolutionary algorithm to estimate fitness function behaviors from a set of experiments made in laboratory to analyze a microstrip antenna used in ultra wideband wireless devices. These function behaviors are related to three objectives: bandwidth, return loss, and central frequency deviation. Each objective is used inside an aggregate adaptive weighted fitness function that estimates the behavior in the algorithm. The machine learning technique enabled a dynamic estimation of an aggregated compound fitness function and made it possible to a prototype algorithm to learn and adapt with a set of experiments stored in a web system repository. The final results were then compared with the ones obtained with a similar antenna modeled in a simulator program and with the ones of a real prototype antenna built from the optimal values obtained after the optimization. (c) 2013 Wiley Periodicals, Inc. Microwave Opt Technol Lett 55:1864-1868, 2013

关键词： multiobjective evolutionary algorithms machine learning genetic algorithm optimization

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Large-Scale Inverse Design of a Planar On-Chip Mode Sorter

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ACS PHOTONICS 2022年第2期9卷 378-382页

作者： Di Domenico, Giuseppe Weisman, Dror Panichella, Annibale Roitman, Dolev Arie, Ady Tel Aviv Univ Sch Elect Engn Fleischman Fac Engn IL-69978 Tel Aviv Israel Delft Univ Technol Software Technol NL-2628 XE Delft Netherlands

Spatial modes of light can be used as carriers of information in classical optical communication or as an alphabet in quantum optical communication. In order to exploit the spatial domain, it is required to (de)multiplex different modes from a shared input channel into different output ports. Mode sorters have been employed in free-space and fiber systems but to date have not been realized for planar guided waves. Here we present a general method for compact on-chip sorting of different planar beams with a micrometric footprint and nanometric thickness. The designs were generated using a linkage-tree-based genetic algorithm and were experimentally demonstrated on a surface plasmon polariton platform by sorting of Hermite-Gaussian beams. The method used here can be readily applied to optimize complex, large-scale optical devices involving beam propagation methods.

关键词： integrated photonics mode (de)multiplexing dielectric metasurface genetic algorithm optimization subwavelength grating

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