Based on the PB Neural Network of Optimization Design in Lubricant Additives

2011 ◽  
Vol 311-313 ◽  
pp. 218-222
Author(s):  
Zhuo Jun Chen ◽  
Long Long Feng
Keyword(s):  
Neural Network ◽  
Optimization Design ◽  
Lubricant Additive ◽  
Lubricant Additives ◽  
Base Oil ◽  
Mass Percent ◽  
Forecast Precision ◽  
Lubricant Formulation ◽  
Validation Experiment ◽  
P Type

This article use the Sulphide Isobutene, Five Sulfides Dialkyl, and Star of Phosphorus as the additives, Neopentyl Polyol Ester (NPE) as base oil for screening lubricant formulation. The purpose of this article is screening the lubricant additives formula. Apply the BP neural network method in optimization design. Through the optimization of lubricant additive formula select the best formula for experiment. The selected best formula is Sulphide Isobutene 0.8%(mass percent), Five Sulfides Dialkyl 1.2%(mass percent) , Star of Phosphorus 1.6%(mass percent), relative error is 0.089.After validation experiment，it is conclusion that S-type blends with P-type additive use will acquire good result, and the method of optimal convergence faster, the forecast precision test is satisfied.

scholarly journals Usage of Biomass-based Carbon Materials as Lubricant Additive: Effects on Rheological and Tribological Properties

2021 ◽  
Vol 10 (4) ◽  
pp. 2861-2868
Keyword(s):  
Surface Engineering ◽  
Carbon Materials ◽  
Viscosity Index ◽  
Lubricant Additive ◽  
Internal Resistance ◽  
Lubricant Additives ◽  
Thermochemical Conversion ◽  
Base Oil ◽  
Conversion Unit ◽  
Lubrication Characteristics

Lubricating oils are thick and sticky fluids used for greasing moving parts of machines and engines. This paper expresses key advances in surface engineering and the use of biomass materials as lubricant additives. In order to enhance the lubrication characteristics of base oil, the biochar lubricant additives were successfully prepared. The tribological behaviors of biochar (biomass and hybrid) lubricant additives in two types of base oils (SN500 and SN900) were evaluated. In the current study, biomass-based carbon materials (biochar and hybrid) from a thermochemical conversion unit were employed as a lubricant additive. The rheological and tribological behavior of the base oil modified with biochar additives were experimentally determined. Surface analyses via SEM confirmed the surface enhancement of the worn exterior plane via the effect produced by the biochar additives. It was also observed that the biomass biochar using SN900 improved the kinematic viscosities of the base oil more than the hybrid biochar. This may be attributed to the chars' fundamental composition, which makes the fluid’s internal resistance flow under gravitational force. With SN500, the viscosity index improves with the biochar from 106 to108 but is reduced for SN900 from 102 to 97.09.

scholarly journals Preparation of TiO2/Ti3C2Tx hybrid nanocomposites and their tribological properties as base oil lubricant additives

RSC Advances ◽  
2017 ◽  
Vol 7 (8) ◽  
pp. 4312-4319 ◽  
Author(s):  
Maoquan Xue ◽  
Zhiping Wang ◽  
Feng Yuan ◽  
Xianghua Zhang ◽  
Wei Wei ◽  
...  
Keyword(s):  
Liquid Phase ◽  
Tribological Properties ◽  
Lubricant Additives ◽  
Hybrid Nanocomposites ◽  
Phase Synthesis ◽  
Base Oil

TiO2/Ti3C2Tx hybrid nanocomposites were successfully prepared by a liquid phase synthesis technology. The hybrid nanocomposites improve the tribological properties of base oil by mending the surface and formation a uniform tribofilm on the surface.

scholarly journals Automatic Design System with Generative Adversarial Network and Convolutional Neural Network for Optimization Design of Interior Permanent Magnet Synchronous Motor

2021 ◽  
Author(s):  
Yuki Shimizu ◽  
Shigeo Morimoto ◽  
Masayuki Sanada ◽  
Yukinori Inoue
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Permanent Magnet ◽  
Optimization Design ◽  
Design System ◽  
Automatic Design ◽  
Wide Range ◽  
Interior Permanent Magnet ◽  
Long Time

The optimal design of interior permanent magnet synchronous motors requires a long time because finite element analysis (FEA) is performed repeatedly. To solve this problem, many researchers have used artificial intelligence to construct a prediction model that can replace FEA. However, because the training data are generated by FEA, it takes a very long time to obtain a sufficient amount of data, making it impossible to train a large-scale prediction model. Here, we propose a method for generating a large amount of data from a small number of FEA results using machine learning. An automatic design system with a deep generative model and a convolutional neural network is then constructed. With its sufficient data, the proposed system can handle three topologies and three motor parameters in a wide range of current vector regions. The proposed system was applied to multi-objective optimization design, with the optimization completed in 13-15 seconds.

scholarly journals Automatic Design System with Generative Adversarial Network and Convolutional Neural Network for Optimization Design of Interior Permanent Magnet Synchronous Motor

2021 ◽  
Author(s):  
Yuki Shimizu ◽  
Shigeo Morimoto ◽  
Masayuki Sanada ◽  
Yukinori Inoue
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Permanent Magnet ◽  
Optimization Design ◽  
Design System ◽  
Automatic Design ◽  
Wide Range ◽  
Interior Permanent Magnet ◽  
Long Time

The optimal design of interior permanent magnet synchronous motors requires a long time because finite element analysis (FEA) is performed repeatedly. To solve this problem, many researchers have used artificial intelligence to construct a prediction model that can replace FEA. However, because the training data are generated by FEA, it takes a very long time to obtain a sufficient amount of data, making it impossible to train a large-scale prediction model. Here, we propose a method for generating a large amount of data from a small number of FEA results using machine learning. An automatic design system with a deep generative model and a convolutional neural network is then constructed. With its sufficient data, the proposed system can handle three topologies and three motor parameters in a wide range of current vector regions. The proposed system was applied to multi-objective optimization design, with the optimization completed in 13-15 seconds.

Water-Inflow Forecast of Submarine Tunnel Based on BP Neural Networks

2011 ◽  
Vol 90-93 ◽  
pp. 2173-2177
Author(s):  
Chen Cai ◽  
Tao Huang ◽  
Xun Li ◽  
Yun Zhen Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Water Inflow ◽  
Water Volume ◽  
Network Algorithm ◽  
Forecast Precision ◽  
Neural Network Algorithm ◽  
Computation Analysis

The submarine tunnel water-inflow question has many kinds of factor synthesis influences, has highly the complexity and the misalignment, This article used the BP neural network algorithm to establish the submarine tunnel welling up water volume forecast model and to carry on the computation analysis, The result indicated that this model restraining performance is good, the forecast precision is high and simple feasible. This method has provided a new mentality for the submarine tunnel welling up water volume's forecast.

scholarly journals Effect of Ionicity of Three Protic Ionic Liquids as Neat Lubricants and Lubricant Additives to a Biolubricant

Coatings ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 713 ◽  
Author(s):  
Hong Guo ◽  
Angela Rina Adukure ◽  
Patricia Iglesias
Keyword(s):  
Ionic Liquid ◽  
Ionic Liquids ◽  
Friction And Wear ◽  
Cost Effective ◽  
Lubricant Additive ◽  
Lubricant Additives ◽  
Environmental Damage ◽  
Protic Ionic Liquids ◽  
Protic Ionic Liquid ◽  
Wear Process

Friction and wear of sliding surfaces are responsible for important energy losses and negative environmental effects. The use of environmentally friendly and cost-effective protic ionic liquids as neat lubricants and lubricant additives has the potential to increase the efficiency and durability of mechanical components without increasing the environmental damage. In this work, three halogen-free protic ionic liquids with increasing extent of ionicity, 2-hydroxyethylammonium 2-ethylhexanoate, 2-hydroxymethylammonium 2-ethylhexancate, and 2-hydroxydimethylammonium 2-ethylhexanoate, were synthesized and studied as neat lubricants and additives to a biodegradable oil in a steel–steel contact. The results show that the use of any protic ionic liquid as a neat lubricant or lubricant additive reduced friction and wear with respect to the biodegradable oil. The ionic liquid with the lowest ionicity reached the highest wear reduction. The one possessing the highest ionicity presented the poorest friction and wear behaviors as a neat lubricant, probably due to the more ionic nature of this liquid, which promoted tribocorrosion reactions on the steel surface. This ionic liquid performed better as an additive, showing that a small addition of this liquid in a biodegradable oil is enough to form protective layers on steel surfaces. However, it is not enough to accelerate the wear process with detrimental tribocorrosion reactions.

Aerodynamic Optimization Design of Compressor Cascade Based on Parallel Multi-Objective Genetic Algorithm and Artificial Neural Network

2011 ◽  
Vol 138-139 ◽  
pp. 534-539
Author(s):  
Li Hai Chen ◽  
Qing Zhen Yang ◽  
Jin Hui Cui
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Optimization Design ◽  
Compressor Cascade ◽  
Aerodynamic Optimization ◽  
Navier Stokes Equation ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Artificial Neural

Genetic algorithm (GA) is improved with fast non-dominated sort approach and crowded comparison operator. A new algorithm called parallel multi-objective genetic algorithm (PMGA) is developed with the support of Massage Passing Interface (MPI). Then, PMGA is combined with Artificial Neural Network (ANN) to improve the optimization efficiency. Training samples of the ANN are evaluated based on the two-dimensional Navier-Stokes equation solver of cascade. To demonstrate the feasibility of the hybrid algorithm, an optimization of a controllable diffusion cascade is performed. The optimization results show that the present method is efficient and trustiness.

BPNN–QSTR Modeling to Develop Isosteres as Sulfur-Free, Anti-Wear Lubricant Additives

2018 ◽  
Vol 141 (1) ◽  
Author(s):  
Xinlei Gao ◽  
Zhan Wang ◽  
Tingting Wang ◽  
Ze Song ◽  
Kang Dai ◽  
...  
Keyword(s):  
Energy Dispersive Spectrometer ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Lubricant Additives ◽  
Thiazole Ring ◽  
Wear Performance ◽  
Base Oil ◽  
Wide Range ◽  
Electron Microscope Analysis ◽  
Branched Chain

The principle of isosterism was employed to design low- or zero-sulfur anti-wear lubricant additives. Thiobenzothiazole compounds and 2-benzothiazole-S-carboxylic acid esters were employed as templates. Sulfur in the thiazole ring or in the branched chain was exchanged with oxygen, CH2, or an NH group. Similarly, the template's benzimidazole ring was replaced with a quinazolinone group. Quantitative structure tribo-ability relationship (QSTR) models by back propagation neural network (BPNN) method were used to study correlations between additive structures and their anti-wear performance. The features of rubbing pairs with different additives were identified by energy dispersive spectrometer-scanning electron microscope analysis. A wide range of samples showed that sulfur substitution in additive molecules was found to be reasonable and feasible. Combined effects of the anti-wear additive and the base oil were able to improve anti-wear performance.

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