基于动量叶素理论,推导同半径对旋风力机功率与前、后级轴向速度诱导因子的关系,结果与经典模型吻合;将单级风力机Wilson设计法发展为考虑级间干扰的对旋风机设计法,实现以环形微元盘面功率系数最大为目标,前、后级轴向和环向诱导因子...基于动量叶素理论,推导同半径对旋风力机功率与前、后级轴向速度诱导因子的关系,结果与经典模型吻合;将单级风力机Wilson设计法发展为考虑级间干扰的对旋风机设计法,实现以环形微元盘面功率系数最大为目标,前、后级轴向和环向诱导因子为变量的优化设计,并完成2 k W单级和对旋风力机设计实例。最后采用CFD方法验证对旋风力机实验结果和设计结果,数值计算表明,设计工况下,对旋风机的风能利用率(46.32%)高于单级风力机(41.44%),并分析了数值预测的风能利用率与理论最佳值(64.00%)和(59.26%)的差异。展开更多
As the simplest hydrogen-bonded alcohol,liquid methanol has attracted intensive experimental and theoretical interest.However,theoretical investigations on this system have primarily relied on empirical intermolecular...As the simplest hydrogen-bonded alcohol,liquid methanol has attracted intensive experimental and theoretical interest.However,theoretical investigations on this system have primarily relied on empirical intermolecular force fields or ab initio molecular dynamics with semilocal density functionals.Inspired by recent studies on bulk water using increasingly accurate machine learning force fields,we report a new machine learning force field for liquid methanol with a hybrid functional revPBE0 plus dispersion correction.Molecular dynamics simulations on this machine learning force field are orders of magnitude faster than ab initio molecular dynamics simulations,yielding the radial distribution functions,selfdiffusion coefficients,and hydrogen bond network properties with very small statistical errors.The resulting structural and dynamical properties are compared well with the experimental data,demonstrating the superior accuracy of this machine learning force field.This work represents a successful step toward a first-principles description of this benchmark system and showcases the general applicability of the machine learning force field in studying liquid systems.展开更多
文摘基于动量叶素理论,推导同半径对旋风力机功率与前、后级轴向速度诱导因子的关系,结果与经典模型吻合;将单级风力机Wilson设计法发展为考虑级间干扰的对旋风机设计法,实现以环形微元盘面功率系数最大为目标,前、后级轴向和环向诱导因子为变量的优化设计,并完成2 k W单级和对旋风力机设计实例。最后采用CFD方法验证对旋风力机实验结果和设计结果,数值计算表明,设计工况下,对旋风机的风能利用率(46.32%)高于单级风力机(41.44%),并分析了数值预测的风能利用率与理论最佳值(64.00%)和(59.26%)的差异。
基金supported by the CAS Project for Young Scientists in Basic Research(YSBR-005)the National Natural Science Foundation of China(22325304,22221003 and 22033007)We acknowledge the Supercomputing Center of USTC,Hefei Advanced Computing Center,Beijing PARATERA Tech Co.,Ltd.,for providing high-performance computing services。
文摘As the simplest hydrogen-bonded alcohol,liquid methanol has attracted intensive experimental and theoretical interest.However,theoretical investigations on this system have primarily relied on empirical intermolecular force fields or ab initio molecular dynamics with semilocal density functionals.Inspired by recent studies on bulk water using increasingly accurate machine learning force fields,we report a new machine learning force field for liquid methanol with a hybrid functional revPBE0 plus dispersion correction.Molecular dynamics simulations on this machine learning force field are orders of magnitude faster than ab initio molecular dynamics simulations,yielding the radial distribution functions,selfdiffusion coefficients,and hydrogen bond network properties with very small statistical errors.The resulting structural and dynamical properties are compared well with the experimental data,demonstrating the superior accuracy of this machine learning force field.This work represents a successful step toward a first-principles description of this benchmark system and showcases the general applicability of the machine learning force field in studying liquid systems.
