The main purpose of this research is the second-order modeling of flow and turbulent heat flux in nonpremixed methane-air combustion.A turbulent stream of non-premixed combustion in a stoichiometric condition,is numer...The main purpose of this research is the second-order modeling of flow and turbulent heat flux in nonpremixed methane-air combustion.A turbulent stream of non-premixed combustion in a stoichiometric condition,is numerically analyzed through the Reynolds averaged Navier-Stokes(RANS) equations.For modeling radiation and combustion,the discrete ordinates(DO) and eddy dissipation concept model have been applied.The Reynolds stress transport model(RSM) also was used for turbulence modeling.For THF in the energy equation,the GGDH model and high order algebraic model of HOGGDH with simple eddy diffusivity model have been applied.Comparing the numerical results of the SED model(with the turbulent Prandtl 0.85) and the second-order heat flux models with available experimental data follows that applying the second-order models significantly led to the modification of predicting temperature distribution and species mass fraction distribution in the combustion chamber.Calculation of turbulent Prandtl number in the combustion chamber shows that the assumption of Pr_(t) of 0.85 is far from reality and Pr_(t) in different areas varies from 0.4 to 1.2.展开更多
机器人和数控机床等高端机械用位置伺服系统的定位性能易受摩擦力矩等干扰的影响,对此提出了一种基于改进粒子群算法(particle swarm optimization algorithm with improved particle velocity and position update formula,IPSO-VP)的...机器人和数控机床等高端机械用位置伺服系统的定位性能易受摩擦力矩等干扰的影响,对此提出了一种基于改进粒子群算法(particle swarm optimization algorithm with improved particle velocity and position update formula,IPSO-VP)的伺服系统摩擦参数辨识及前馈补偿方法。首先,分析并建立基于Stribeck的摩擦模型,在传统粒子群算法(PSO)的基础上,提出了一种基于改进粒子群算法(IPSO-VP)的摩擦参数辨识方法,该方法采用一种新的基于粒子维度信息的位置和速度自适应更新策略,以及一种新的基于Logistic混沌非线性变化惯性权重对模型参数进行辨识;其次,基于辨识获得的摩擦力矩值,将其前馈补偿到伺服系统交轴电流上以补偿摩擦力矩。为了验证算法的有效性,搭建系统进行了测试,结果表明相较于基于传统粒子群算法(PSO)的参数辨识方法,采用基于改进粒子群算法(IPSO-VP)的系统,其参数的辨识精度和迭代收敛速度更高,从而提高了机器人和数控机床等用伺服系统的跟踪控制性能和鲁棒性。展开更多
文摘The main purpose of this research is the second-order modeling of flow and turbulent heat flux in nonpremixed methane-air combustion.A turbulent stream of non-premixed combustion in a stoichiometric condition,is numerically analyzed through the Reynolds averaged Navier-Stokes(RANS) equations.For modeling radiation and combustion,the discrete ordinates(DO) and eddy dissipation concept model have been applied.The Reynolds stress transport model(RSM) also was used for turbulence modeling.For THF in the energy equation,the GGDH model and high order algebraic model of HOGGDH with simple eddy diffusivity model have been applied.Comparing the numerical results of the SED model(with the turbulent Prandtl 0.85) and the second-order heat flux models with available experimental data follows that applying the second-order models significantly led to the modification of predicting temperature distribution and species mass fraction distribution in the combustion chamber.Calculation of turbulent Prandtl number in the combustion chamber shows that the assumption of Pr_(t) of 0.85 is far from reality and Pr_(t) in different areas varies from 0.4 to 1.2.
文摘机器人和数控机床等高端机械用位置伺服系统的定位性能易受摩擦力矩等干扰的影响,对此提出了一种基于改进粒子群算法(particle swarm optimization algorithm with improved particle velocity and position update formula,IPSO-VP)的伺服系统摩擦参数辨识及前馈补偿方法。首先,分析并建立基于Stribeck的摩擦模型,在传统粒子群算法(PSO)的基础上,提出了一种基于改进粒子群算法(IPSO-VP)的摩擦参数辨识方法,该方法采用一种新的基于粒子维度信息的位置和速度自适应更新策略,以及一种新的基于Logistic混沌非线性变化惯性权重对模型参数进行辨识;其次,基于辨识获得的摩擦力矩值,将其前馈补偿到伺服系统交轴电流上以补偿摩擦力矩。为了验证算法的有效性,搭建系统进行了测试,结果表明相较于基于传统粒子群算法(PSO)的参数辨识方法,采用基于改进粒子群算法(IPSO-VP)的系统,其参数的辨识精度和迭代收敛速度更高,从而提高了机器人和数控机床等用伺服系统的跟踪控制性能和鲁棒性。
