检索结果-内蒙古大学图书馆

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Mechanical characterization and optimization of delamination factor in drilling bidirectional jute fibre-reinforced polymer biocomposites

引用

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2020年第7-8期111卷 2073-2094页

作者： Belaadi, Ahmed Boumaaza, Messaouda Amroune, Salah Bourchak, Mostefa Univ 20 Aout 1955 Fac Technol Dept Genie Mecan BP 26 Route El Hadaiek Skikda 21000 Algeria Univ 08 Mai 1945 Lab Silicates Polymeres & Nano Composites LSPN Guelma 24000 Algeria Univ 8 Mai 1945 Lab Genie Civil & Hydraul LGCH Guelma 24000 Algeria Univ Msila Dept Genie Mecan Msila 166 BP Algeria Univ Msila Lab Mat & Mecan Struct LMMS Msila Algeria King Abdulaziz Univ Aerosp Engn Dept Jeddah Saudi Arabia

In this comprehensive investigation, we evaluate the mechanical properties and the effect of drilling parameters (spindle speed, 355, 710 and 1400 rev/min;feed rate, 50, 108 and 190 mm/min;and the tool diameter, 5, 7 and 10 mm) as a function of the cutting process on the delamination factor when drilling jute fabrics reinforced epoxy matrix biocomposites. The study is carried out using different drill geometries and material (HSS_TiN, HSS and brad and spur drill bits). The design of experiment is developed by the application analysis of variance (ANOVA). The response surface methodology (RSM) and artificial neural networks (ANN) are applied to validate the results obtained during the experiment and to predict the behaviour of the structure under any cutting condition. Result analysis showed the superiority of the ANN model over the RSM model, while the feed rate had a significant contribution on spindle speed and diameter. The optimum conditions obtained for the F-d factor were a feed rate of 51 mm/min, a spindle speed of 1160 rev/min and a drilling diameter of 5 mm.

关键词： Industrial fabrics textiles Composite delamination Mechanical properties Statistical properties methods Machining RSM ANN genetic algorithm optimization

来源：评论

学校读者我要写书评

暂无评论

Thermal energy management optimization of solar thermal energy system based on small parabolic trough collectors for bitumen maintaining on heat process

引用

SOLAR ENERGY 2020年 211卷 1403-1421页

作者： Ghazouani, Mokhtar Bouya, Mohsine Benaissa, Mohammed Anoune, Kamal Ghazi, Mohamed Mohammed V Univ Rabat Fac Sci Lab Matiere Condensee & Sci Interdisciplinaires L POB 1014 Rabat Morocco Int Univ Rabat Lab Energies Renouvelables & Mat Avances LERMA POB 11100 Sala Al Jadida Morocco

Solar energy integration in heat consuming processes is a promising replacement for fossil energy. We are interested in a small Solar Parabolic Trough Collectors field combined with a sensible heat storage reservoir. Unfortunately, such a system has a higher cost than fossil energy and is challenging to optimize. Therefore, this work's main objective is to provide an optimized design and management to minimize energy cost (EC) and maximize renewable energy utilization fraction (REF). This study is done by optimizing the sizing and the thermal energy flow management of a hybrid solar-fossil fuel energy system. The optimization problem is formulated based on mass and energy conservation equations, heat transfer phenomena modeling, correlations, and economic data. We resolved it by combining trial and error method, multi-node calculation procedure, and genetic algorithm. To evaluate our method, we studied a hybrid solar-fossil fuel energy system used for the bitumen heat maintaining process in Rabat region-Morocco. The evaluation considered different thermal storage capacities. Using our optimization, we reached REF >= 40% and EC < 0.05US$/kWh for small thermal capacities and REF >= 85% and EC less than 0.2US$/kWh for large thermal capacities. This is equivalent to saving, respectively, more than 0.75 and 1.5 tons of CO2 emissions/year/kW for small and large thermal storage capacities. Other important recommendations are also provided in this work.

关键词： Small parabolic trough collector Thermal energy storage Energy management Trial and error method Multi-node algorithms genetic algorithm optimization

来源：评论

学校读者我要写书评

暂无评论

A novel grey model based on traditional Richards model and its application in COVID-19

引用

CHAOS SOLITONS & FRACTALS 2021年 142卷 110480-110480页

作者： Luo, Xilin Duan, Huiming Xu, Kai Chongqing Univ Posts & Telecommun Sch Sci Chongqing 400065 Peoples R China Sichuan Int Studies Univ Sch Int Business Chongqing 400031 Peoples R China

In 2020, a new type of coronavirus is in the global pandemic. Now, the number of infected patients is increasing. The trend of the epidemic has attracted global attention. Based on the traditional Richards model and the differential information principle in grey prediction model, this paper uses the modified grey action quantity to propose a new grey prediction model for infectious diseases. This model weakens the dependence of the Richards model on single-peak and saturated S-shaped data, making Richards model more applicable, and uses genetic algorithm to optimize the nonlinear terms and the background value. To illustrate the effectiveness of the model, groups of slowly growing small-sample and large-sample data are selected for simulation experiments. Results of eight evaluation indexes show that the new model is better than the traditional GM(1,1) and grey Richards model. Finally, this model is applied to China, Italy, Britain and Russia. The results show that the new model is better than the other 7 models. Therefore, this model can effectively predict the number of daily new confirmed cases of COVID-19, and provide important prediction information for the formulation of epidemic prevention policies. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Grey prediction model COVID-19 Traditional Richards model genetic algorithm optimization GERM(1,1,e(at))

来源：评论

学校读者我要写书评

暂无评论

Systematic micro heat sink optimization based on hydrofoil shape pin fins

引用

CASE STUDIES IN THERMAL ENGINEERING 2021年 26卷

作者： Ismayilov, Fuad Akturk, Ali Peles, Yoav SIEMENS Energy 11842 Corp Blvd Orlando FL 32817 USA Univ Cent Florida 4000 Cent Florida Blvd Orlando FL 32816 USA

Multi-objective optimization of the hydrofoil shaped pin fins for micro heat sinks was studied. optimization tool utilized a multi objective genetic algorithm (MOGA) and a numerical model that was validated against previously obtained experimental single-phase heat transfer results on a hydrofoil shape micro scale pin fin. The numerical model was built using StarCCM + software. Several turbulence models were examined against single-phase experimental data and the Lag-EB k-epsilon turbulence model was chosen for validation. optimization tool varied the shape of the hydrofoil pin fin through flexible parametrization utilizing an ellipse and polynomials. An algorithm coupling pin fin parametrization code, validated the 3D CFD solutions and a MOGA interface was implemented. Subsequently, multi-objective optimization was performed to maximize the heat transfer coefficient while minimizing pressure drop. A broad range of novel geometries were obtained with a tradeoff between thermal and hydraulic performance for low Reynolds number. Novel pin fin shape, bird design, is proposed as a result of the study. Additional 3D steady state conjugate CFD simulations including the bird design pin fin were performed using already verified setup, and it was shown that the enhancement in the average heat transfer coefficient dominates over the pressure drop increase at higher Reynolds numbers.

关键词： Micro/mini-channel Heat sink Conjugate heat transfer modelling Hydrofoil pin fin genetic algorithm optimization Thermal performance enhancement

来源：评论

学校读者我要写书评

暂无评论

Optimizing displacement-based seismic design of mass timber rocking walls using genetic algorithm

引用

ENGINEERING STRUCTURES 2021年 229卷 111603-111603页

作者： Huang, Da Pei, Shiling Busch, Aleesha Colorado Sch Mines Dept Civil & Environm Engn Golden CO 80401 USA

This paper presents a rational procedure to optimize design for mass timber rocking wall systems utilizing a genetic algorithm (GA) beyond typical displacement-based design metrics for wood buildings. Traditional displacement-based design is limited to simple displacement targets, with optimization techniques (GA), it is feasibly to consider more performance chrematistics (e.g. costs, serviceability, etc.). By formulating drift targets and other structural design limit states within an elimination step of the GA optimization process, the method proposed here optimizes rocking wall design with multiple criteria that factor much more than lateral displacement targets in the seismic analysis. This type of optimization is difficult to perform using traditional manual trial-and-error approaches. An existing simplified nonlinear time-history simulation model (validated through full-scale shake table test data) of a wood rocking wall is employed in this process. The design for an example building in Seattle with a six-story rocking wall is presented using the proposed procedure. The results revealed that the optimization of the mass timber rocking wall lateral system can be achieved in a reasonable time frame using the proposed method. Even though the same drift limits were applied, final designs produced by GA varied, depending on the other optimization objectives. This demonstrates how the seismic design of wood rocking wall systems could be readily improved with computerized optimization tools that factor in other aspects of the design including cost.

关键词： Displacement-based design genetic algorithm optimization Mass timber rocking wall Nonlinear time-history analysis

来源：评论

学校读者我要写书评

暂无评论

Multi-objective optimization design and control of plug-in hybrid electric vehicle powertrain for minimization of energy consumption, exhaust emissions and battery degradation

引用

ENERGY CONVERSION AND MANAGEMENT 2021年 234卷 113909-113909页

作者： da Silva, Samuel Filgueira Eckert, Jony Javorski Silva, Fabricio Leonardo Silva, Ludmila C. A. Dedini, Franco Giuseppe Univ Campinas UNICAMP Mech Engn Fac FEM Integrated Syst Lab Campinas SP Brazil

Plug-in hybrid electric vehicles (PHEV) offer an attractive alternative to achieve the ambitious goals set by strong policies focused on near-term air quality and fuel-efficient road transportation. This paper provides a comprehensive study regarding the PHEV?s optimum powertrain design, by means of a multi-criteria analysis carried out by the interactive adaptive-weight genetic algorithm approach. The optimization aims to simultaneously minimize the PHEV?s fuel consumption, exhaust emissions, electric powertrain size, battery state of health, charging time and costs. To achieve these objectives, several PHEV?s design parameters are optimized such as in-wheel electric motors? torque curves, battery voltage and capacity. The drivetrain is also optimized according to the determination of the best configuration of gearbox and differential gear ratios, taking into account constructive constraints. Furthermore, the fuzzy logic controllers responsible for the engine/electric motors power-split management and gear shifting control are included in the multi-objective optimization in order to define the best membership functions, rules and respective weights. To guarantee robust solutions, the PHEV is optimized under different driving conditions, which is given by the combination of the FTP-75, HWFET and US06 driving cycles. To evaluate the optimum PHEV performance, it is also simulated under the WLTC driving cycle and a realworld driving cycle based on the Campinas city. The best trade-off configuration results in 39.57% decrease in vehicle travel cost along with 43.39% carbon monoxide (CO), 45.13% unburned hydrocarbons (HC) and 72.64% nitrogen oxides (NOx) emissions reduction under the combined driving cycle.

关键词： Plug-in hybrid electric vehicles Fuel economy Exhaust emissions Battery state of health Fuzzy logic control genetic algorithm optimization

来源：评论

学校读者我要写书评

暂无评论

Biogas maximization using data-driven modelling with uncertainty analysis and genetic algorithm for municipal wastewater anaerobic digestion

引用

JOURNAL OF ENVIRONMENTAL MANAGEMENT 2021年 293卷 112875-112875页

作者： Asadi, Mohsen McPhedran, Kerry Univ Saskatchewan Dept Civil Geol & Environm Engn Saskatoon SK Canada

Anaerobic digestion processes create biogases that can be useful sources of energy. The development of datadriven models of anaerobic digestion processes via operating parameters can lead to increased biogas production rates, resulting in greater energy production, through process modification and optimization. This study assessed processed and unprocessed input operating parameter variables for the development of regression models with transparent structures ('white-box' models) to: (1) estimate biogas production rates from municipal wastewater treatment plant (MWTP) anaerobic digestors;(2) compare their performances to artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) models with opaque structures ('black-box' models) using Monte Carlo Simulation for uncertainty analysis;and (3) integrate the models with a genetic algorithm (GA) to optimize operating parameters for maximization of MWTP biogas production rates. The input variables were anaerobic digestion operating parameters from a MWTP including volatile fatty acids, total/fixed/volatile solids, pH, and inflow rate, which were processed via correlation tests and principal component analysis. Overall, the results indicated that the processed data did not improve regression model performances. Additionally, the developed non-linear regression model with the unprocessed inputs had the best performance based on values including R = 0.81, RMSE = 0.95, and IA = 0.89. However, this model was less accurate, but interestingly had less uncertainty, as compared to ANN and ANFIS models which indicates the compromise between model accuracy and uncertainty. Thus, all three models were coupled with GA optimization with maximum biogas production rate estimates of 22.0, 23.1, and 28.6 m3/min for ANN, ANFIS, and nonlinear regression models, respectively.

关键词： Municipal wastewater treatment Anaerobic digestion processes Data-driven modelling Monte Carlo simulation genetic algorithm optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：