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Viscosity prediction of Pongamia pinnata (Karanja) oil by molecular dynamics simulation using GAFF and OPLS force field
Journal of Molecular Graphics and Modelling ( IF 2.9 ) Pub Date : 2020-09-24 , DOI: 10.1016/j.jmgm.2020.107764
E Sneha 1 , Amjesh Revikumar 2 , Jaykumar Y Singh 3 , Ananthan D Thampi 1 , S Rani 1
Affiliation  

The increasing concern on the harmful effects caused by mineral oil-based lubricants towards the environment has given impetus to the evolution of green-lubricants. Vegetable oils are highly biodegradable, renewable, and possesses good lubricating property. In the present study Pongamia pinnata, non-edible vegetable oil, also known as Karanja Oil (KO) was used as the base oil for a lubricant. The preliminary properties, such as fatty acid profile and viscosity, which has a vital role in governing the performance of lubricants were evaluated experimentally as per international standards. The shear viscosity of KO which constitutes 8 major fatty acids were predicted using non-equilibrium molecular dynamics (NEMD) and periodic perturbation (PP) method using Optimised Potentials for Liquid Simulations (OPLS) and Generalized Amber Force Field (GAFF). The shear viscosities were evaluated at temperatures ranging from 313K to 373 K and pressure P = 0.1 MPa. The experimental and simulation data of KO shear viscosity are in line with each other using OPLS. The kinematic viscosities were calculated using the shear viscosities and densities obtained from simulation. The variation between experimental and simulation data is less while using OPLS, while GAFF force fields resulted in higher deviations.



中文翻译:

利用GAFF和OPLS力场的分子动力学模拟预测樟子松(Kongnja)油的粘度

人们越来越关注矿物油基润滑剂对环境造成的有害影响,从而推动了绿色润滑剂的发展。植物油是高度可生物降解的,可再生的,并具有良好的润滑性能。在本研究中,Pongamia pinnata,一种不可食用的植物油,也称为Karanja油(KO),被用作润滑剂的基础油。根据国际标准,通过实验评估了在控制润滑剂性能中起着至关重要作用的初步性能,例如脂肪酸分布和粘度。使用非平衡分子动力学(NEMD)和周期性扰动(PP)方法(使用液体模拟的最佳电位(OPLS)和广义琥珀色力场(GAFF))预测了构成8种主要脂肪酸的KO的剪切粘度。在313K至373 K的温度和压力P = 0.1 MPa的条件下评估剪切粘度。使用OPLS的KO剪切粘度的实验和模拟数据相互一致。使用从模拟获得的剪切粘度和密度来计算运动粘度。使用OPLS时,实验数据和模拟数据之间的差异较小,而GAFF力场导致的偏差较大。

更新日期:2020-10-05
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