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The impact energy analysis by genetic algorithm and response surface methods to study the plastic composite, compatibilizer, and recycled poly effects

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JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY 2020年第1期141卷 421-433页

作者： Chen, Zhixiong Shahrajabian, Hamzeh Bagherzadeh, Seyed Amin Maleki, Alireza Quang-Vu Bach Shanghai Univ Engn Sci Sch Air Transportat Shanghai 201620 Peoples R China Islamic Azad Univ Dept Mech Engn Najafabad Branch Najafabad Iran Ton Duc Thang Univ Fac Environm & Labour Safety Sustainable Management Nat Resources & Environm R 19 Nguyen Huu ThoDist 7 Ho Chi Minh City Vietnam

This study attempts to develop a high-performance wood plastic composite (WPC) in terms of impact strength, tensile strength, and tensile modulus. For this purpose, a new methodology is offered to find the optimum parameters of WPC including the content of wood flour, compatibilizer (MAPE), and recycled polyethylene terephthalate (RP) for optimizing the impact strength. The WPC samples with different contents of wood flour (30-40 mass%), MAPE (4-12 phr), and RP (0-35 phr) were prepared by melting technique in corotating twin screw extruder. To determine the tensile strength, tensile modulus, and impact strength, the tensile and impact tests were conducted. The significance of each input factor on the outputs was estimated by analysis of variance. The maximization of impact strength considering the tensile strength and modulus as constrains was done by genetic algorithm coupled with the response surface method. The optimum content of wood flour, MAPE, and RP were obtained as 30 mass%, 10 phr, and 25 phr, respectively.

关键词： genetic algorithm optimization Composites Multi-constrained Impact energy RSM

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A Multi-Entity Field Approximation to determine the source location of multiple atmospheric contaminant releases

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ATMOSPHERIC ENVIRONMENT 2012年 62卷 593-604页

作者： Annunzio, Andrew J. Young, George S. Haupt, Sue Ellen Natl Ctr Atmospher Res Appl Res Lab Boulder CO 80307 USA Penn State Univ Dept Meteorol University Pk PA 16802 USA

In the event of an accidental or intentional contaminant release, it is imperative to locate the source of the contaminant for use in hazard prediction models. In some situations more than a single contaminant release will be present, which becomes a complicating factor when contaminants from these releases significantly overlap. Here we present a Lagrangian approach to determine the source locations of multiple contaminant releases. For this approach, we assume that the concentration field is approximated by a superposition of contaminant entities, where an entity is a discrete object;namely a puff for an instantaneous release and a plume for a continuous release. The state of each entity is inferred from surface observations of the contaminant, and extrapolation of each entity's state provides an estimate of the contaminant source locations. We call this method a Multi-Entity Field Approximation (MEFA) because together the entities' concentration fields sum to approximate the observed concentration field. In this work, we outline the MEFA process for both instantaneous and continuous contaminant releases using data from two FUSION Field Trial 2007 (FFT07) Trials where contaminant fields from multiple contaminant releases overlap close to the source location. Published by Elsevier Ltd.

关键词： Source term estimation Multi-Entity Field Approximation Entity Atmospheric transport and dispersion FUSION Field Trial 2007 genetic algorithm optimization

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Prediction surface roughness of 3D printed parts using genetic algorithm optimized hybrid learning model

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JOURNAL OF THERMOPLASTIC COMPOSITE MATERIALS 2024年第7期37卷 2225-2245页

作者： Akgun, Gazi Ulkir, Osman Marmara Univ Dept Mechatron Engn RTE Maltepe CampusIdealtepe Yolu 15 Istanbul Turkiye Mus Alparslan Univ Dept Elect & Energy Mus Turkiye

The final product of additive manufacturing (AM) or 3D printing critically depends on the surface quality. An experimental study on the 3D printed intake manifold flange using acrylonitrile butadiene styrene (ABS) material was executed by varying the four process parameters. A fused deposition modeling (FDM) based 3D printer was used to fabricate the flanges. The association between the parameters and the surface roughness of printed ABS flanges was investigated. A feed forward neural network (FFNN) model trained on particle swarm optimization (PSO) optimized with a genetic algorithm (GA) was used to estimate the surface roughness. A Box-Behnken design (BBD) with printing parameters at three levels was used, and 25 parts were fabricated. The suggested model demonstrated a coefficient of determination (R2) of 0.9865 on test values, mean of root-mean-square-error (RMSE) of 0.1231 after 500 times training for generalization. And also mean of overfitting factor is 0.7110. This means that the suggested system could generalize. Comparing the results from the suggested model and ANN, the suggested hybrid model outperformed ANN in predicting the surface roughness values with no overfitting. This suggests that GA optimized PSO based FFNN may be a more suitable method for estimating product quality in terms of surface roughness.

关键词： Additive manufacturing surface roughness fused deposition modeling particle swarm optimization learning artificial neural network genetic algorithm optimization

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Thermodynamic assessment of the dry-cooling supercritical Brayton cycle in a direct-heated solar power tower plant enabled by CO2-propane mixture

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RENEWABLE ENERGY 2023年第1期203卷 649-663页

作者： Ma, Ning Meng, Fugui Hong, Wenpeng Li, Haoran Niu, Xiaojuan Northeast Elect Power Univ Sch Energy & Power Engn Jilin 132012 Peoples R China

This paper proposes an improved recompression combined cycle configuration to explore the potential of using CO2-propane in a direct-heating solar power tower (SPT) plant. The effects of critical parameters on overall performance were detailly analyzed. The exergy distribution of CO2 and CO2-propane cycles was compared respectively after being optimized by a genetic algorithm. The results show that the exergy loss of regenerators was reduced, and the entire exergy efficiency of the SPT plant was improved by configuring two high -temperature regenerators. The cycle performance of propane, serving as the second additive, becomes more significant when the cooling temperature is increased. The overall thermal efficiency of the CO2-propane-configured SPT plant is 2.08% higher than using CO2 alone at a condensation temperature of 50 degrees C. Meanwhile, the exergy efficiency of the SPT plant with CO2-propane cycle decreases less (1.67%) than CO2 (2.02%) when the condensation temperature rises from 42 degrees C to 50 degrees C. However, the temperature difference of the receiver would decrease when using CO2-propane. This study provides a valuable reference for the application of CO2-propane mixture in the dry-cooling Brayton cycle of the direct-heated SPT plant.

关键词： Supercritical Brayton cycle Carbon dioxide Binary mixture Solar power tower plant Thermodynamic analysis genetic algorithm optimization

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An optimal neural network plasma model: a case study

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2001年第1期56卷 39-50页

作者： Kim, B Park, S Sejong univ Dept Elect Engn Keangjin Gu Seoul 143747 South Korea Chonnam Natl Univ Dept Elect Engn Buk Ku Kwangju 500757 South Korea

Artificial neural networks, particularly backpropagation neural network (BPNN), have recently been applied to model various plasma processes. Developing BPNN model, however, is complicated by the presence of several adjustable factors whose optimal values are initially unknown. These may include initial weight distribution, hidden neurons, gradient of neuron activation function, and training tolerance. A methodology is presented to optimize various factor effects, which was accomplished hy implementing genetic algorithm (Gh) on the best models. Particular emphasis was placed on a qualitative measure of initial weight distribution, whose magnitude and directionality were varied. Interactions between factors were examined by means of a 2(4) factorial experiment. Parametric effect analysis revealed the dissimilarity between the best and average prediction characteristics. Both gradient and initial weight distribution exerted a conflicting effect on both average and best performances. GA-optimized models exhibited about 20% improvement over the experimentally chosen best models. Further improvement of more than 30% was achieved with respect to statistical response surface models. Plasma modeled is an inductively coupled plasma, whose experimental data were collected with Langmuir probe from an etch equipment capable of processing 200-mm wafers. (C) 2001 Published by Elsevier Science B.V.

关键词： plasma modeling backpropagation neural networks genetic algorithm optimization Langmuir probe

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Design of photovoltaic powered reverse osmosis desalination systems considering membrane fouling caused by intermittent operation

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RENEWABLE ENERGY 2019年 135卷 108-121页

作者： Freire-Gormaly, M. Bilton, A. M. Univ Toronto Water & Energy Res Lab Dept Mech & Ind Engn 5 Kings Coll Rd Toronto ON M5S 3G7 Canada

Photovoltaic powered reverse osmosis systems are stand-alone water purification systems with the potential to provide a secure drinking water supply to remote off-grid communities. These systems are often operated intermittently with extended shut-down periods to accommodate the variability of renewable energy. This work presents a novel design tool for photovoltaic powered reverse osmosis installations which considers membrane fouling during intermittent operation to ensure more reliable and long-lasting systems. The tool uses a genetic algorithm coupled to a simulation model composed of a physical system model and a cost model to evaluate the system cost. The physical model determines the local energy production. The water system model uses an experimentally validated membrane fouling model to determine the water production. Several case studies (variable location, water production, and reliability) were used to demonstrate the method. In conclusion, the results show that incorporating the fouling model was essential to design reliable and cost-optimal systems. The method showed for smaller daily water demand, the system configuration did not vary with location, indicating they can be mass-produced. The design framework will aid in the configuration of low-cost photovoltaic powered reverse osmosis systems that are reliable throughout the system life. (C) 2018 Published by Elsevier Ltd.

关键词： Photovoltaic Renewable energy powered desalination Intermittent operation genetic algorithm optimization Simulation Solar energy

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Optimized fuzzy rule-based energy management for a battery-less PV/wind-BWRO desalination system

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ENERGY 2018年 159卷 216-228页

作者： Ben Ali, I. Turki, M. Belhadj, J. Roboam, X. Univ Tunis El Manor Lab Syst Elect Ecole Natl Ingn Tunis LR 11 ES 15BP 371002 Tunis Belvedere Tunis Tunisia Univ Jendouba Ecole Super Ingn Medjez El Bab P5 Jendouba 9070 Tunisia Univ Tunis Ecole Natl Super Ingn Tunis BP 56 Montfleury 1008 Tunisia Univ Toulouse LAPLACE Lab Plasma & Convers Energie UMR CNRSINPUPSENSEEIHT 2 Rue Camichel F-31071 Toulouse France

Coupling water desalination processes with Renewable Energy Sources (RESs) can be a sustainable and ecological approach to the global water/energy supply crisis. In this regard, small-scale standalone battery-less Brackish Water Reverse Osmosis (BWRO) desalination system powered by a hybrid PV/Wind RES is expected to meet freshwater demand of a small isolated community. One particularity of the proposed architecture deals with the absence of electrochemical storage;only taking benefit of hydraulic storage in water tanks when RE is available. This study puts forward the prime importance of Water/Power flows management optimization. For this purpose, an online Fuzzy Logic-based Energy Management Strategy (FLEMS) is proposed. Firstly, a Hand-Made Fuzzy Inference System (HMFIS) was designed to identify the instantaneous power sharing between the system hydro-mechanical processes (well pumping, water storage and desalination processes). Secondly, an offline genetic algorithm optimization was applied on the HMFIS design in order to optimize the power sharing factor and maximize freshwater production. Then, when applying unknown power profile, the optimized FLEMS demonstrated its performance to improve the system energy efficiency and enhance the brackish water (from 16.7% in autumn to 63% in summer) and freshwater production (to 3.3% during autumn for instance) compared with the HMFIS-based EMS. (C) 2018 Elsevier Ltd, All rights reserved.

关键词： Water/energy production Renewable energy sources Energy management Fuzzy logic genetic algorithm optimization

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Flexible High Throughput QC-LDPC Decoder With Perfect Pipeline Conflicts Resolution and Efficient Hardware Utilization

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2020年第12期67卷 5454-5467页

作者： Petrovic, Vladimir L. Markovic, Milos M. Mezeni, Dragomir M. El Saranovac, Lazar V. Radosevic, Andreja Univ Belgrade Sch Elect Engn Belgrade 11120 Serbia Tannera LLC Belgrade 11111 Serbia

Modern communication standards, such as 5G new radio (5G NR), require a high speed decoder for highly irregular quasi-cyclic low density parity check (QC-LDPC) codes. A widely used approach in QC-LDPC decoders is a layered decoding schedule which processes the parity check matrix in parts, thus providing faster convergence. However, pipelined layered decoding architecture suffers from data hazards that reduce the throughput. This paper presents a novel architecture, which can facilitate any QC-LDPC decoding without stall cycles caused by pipeline hazards. The decoder conveniently incorporates both the layered and the flooding schedules in cases when hazards occur. The paper also presents the genetic algorithm based optimization of the decoding schedule for better signal-to-noise ratio (SNR) performance. The proposed architecture enables insertion of a large number of pipeline stages, thus providing high operating frequency. As a case study, the FPGA implementation for WiMAX, DVB-S2X, and 5G NR provided coded throughput of up to 1.77 Gbps, 4.32 Gbps, and 4.92 Gbps at 10 iterations, respectively. The results show a strong throughput increase of 30%-109% compared with the conventional layered decoder for 5G NR for the same SNR performance. The decoder provides highly efficient utilization of resources when compared with the state-of-the-art solutions.

关键词： Decoding Throughput Schedules Pipelines Signal to noise ratio Iterative decoding 5G new radio genetic algorithm optimization high throughput layered decoding low density parity check (LDPC) codes pipeline quasi cyclic (QC) LDPC

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Electric vehicle battery-ultracapacitor hybrid energy storage system and drivetrain optimization for a real-world urban driving scenario

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2021年第5期43卷 1-15页

作者： Silva, Ludmila C. A. Eckert, Jony J. Lourenco, Maria A. M. Silva, Fabricio L. Correa, Fernanda C. Dedini, Franco G. Univ Campinas UNICAMP Campinas Integrated Syst Lab LabSIn Campinas Brazil Fed Univ Technol Parana UTFPR Ponta Grossa Parana Brazil

A battery has normally a high energy density with low power density, while an ultracapacitor has a high power density but a low energy density. Therefore, this paper has been proposed to associate more than one storage technology generating a hybrid energy storage system (HESS), which has battery and ultracapacitor, whose objective is to improve the electric vehicle (EV) driving range. The HESS parameters have been evaluated in a configuration of EV powered by two in-wheel electric motors, coupled straight into the front wheels, and by a unique EM, connected to a differential transmission to drive the rear wheels. Moreover, this paper considers a real-world drive cycle based on the urban driving behavior of Campinas city, one of the most populous cities in Brazil. Aiming to minimize the HESS size and enhance the EV driving range, an optimization problem was formulated and solved using a genetic algorithm technique, in which the EV drivetrain parameters and HESS components and control are optimized. Finally, the obtained Pareto frontier defines the optimum EV configurations, in which the best-selected configurations were able to perform up to 188 km with a 418 kg HESS (maximum drive range solution), or 82.75 km with a 146.58 kg HESS (minimum HESS solution) and 319 km with a 188.43 kg HESS (best trade-off solution), without presenting performance losses.

关键词： Electric vehicle (EV) Hybrid energy storage system (HESS) genetic algorithm optimization Real-world driving cycle

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Optimal seismic retrofit design method for asymmetric soft first-story structures

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STRUCTURAL ENGINEERING AND MECHANICS 2022年第6期81卷 677-689页

作者： Dereje, Assefa Jonathan Kim, Jinkoo Sungkyunkwan Univ Dept Global Smart City Suwon South Korea

Generally, the goal of seismic retrofit design of an existing structure using energy dissipation devices is to determine the optimum design parameters of a retrofit device to satisfy a specified limit state with minimum cost. However, the presence of multiple parameters to be optimized and the computational complexity of performing non-linear analysis make it difficult to find the optimal design parameters in the realistic 3D structure. In this study, genetic algorithm-based optimal seismic retrofit methods for determining the required number, yield strength, and location of steel slit dampers are proposed to retrofit an asymmetric soft first-story structure. These methods use a multi-objective and single-objective evolutionary algorithms, each of which varies in computational complexity and incorporates nonlinear time-history analysis to determine seismic performance. Pareto-optimal solutions of the multi-objective optimization are found using a non-dominated sorting genetic algorithm (NSGAII). It is demonstrated that the developed multi-objective optimization methods can determine the optimum number, yield strength, and location of dampers that satisfy the given limit state of a three-dimensional asymmetric soft first-story structure. It is also shown that the single-objective distribution method based on minimizing plan-wise stiffness eccentricity turns out to produce similar number of dampers in optimum locations without time consuming nonlinear dynamic analysis.

关键词： genetic algorithm optimization multi objective optimization seismic retrofit slit dampers soft first-story

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