The structure and working principle of a self-deigned high pressure electronic pneumatic pressure reducing valve (EPPRV) with slide pilot are introduced.The resistance value formulas and the relationship between the r...The structure and working principle of a self-deigned high pressure electronic pneumatic pressure reducing valve (EPPRV) with slide pilot are introduced.The resistance value formulas and the relationship between the resistance and pressure of three typical pneumatic resistances are obtained.Then,the method of static characteristics analysis only considering pneumatic resistances is proposed,the resistance network from gas supply to load is built up,and the mathematical model is derived from the flow rate formulas and flow conservation equations,with the compressibility of high pressure gas and temperature drop during the expansion considered in the model.Finally,the pilot spool displacement of 1.5 mm at an output pressure of 15MPa and the enlarging operating stroke of the pilot spool are taken as optimization targets,and the optimization is carried out based on genetic algorithm and the model mentioned above.The results show that the static characteristics of the EPPRV are significantly improved.The idea of static characteristics analysis and optimization based on pneumatic resistance network is valuable for the design of pneumatic components or system.展开更多
针对多目标狼群算法存在的搜索不充分、收敛性不足和多样性欠缺的问题,以及缺少对约束进行处理的问题,提出环境选择的双种群约束多目标狼群算法(multi-objective wolf pack algorithm for dual population constraints with environment...针对多目标狼群算法存在的搜索不充分、收敛性不足和多样性欠缺的问题,以及缺少对约束进行处理的问题,提出环境选择的双种群约束多目标狼群算法(multi-objective wolf pack algorithm for dual population constraints with environment selection,DCMOWPA-ES)。引入双种群约束处理方法给种群设置不同的搜索偏好,主种群运用可行性准则优先保留可行解,次种群通过ε约束探索不可行区域并将搜索结果传递给主种群,让算法能较好应对复杂的不可行区域,保障算法的可行性;提出维度选择的随机游走策略,使人工狼可自主选择游走方向,提高种群的全局搜索能力;设计精英学习的步长调整机制,人工狼通过向头狼学习的方式提升种群的局部搜索能力,确保算法的收敛性;采用环境选择的狼群更新策略,根据人工狼被支配的情况和所处位置的密度信息对其赋值,选择被支配数少且密度信息小的人工狼作为优秀个体,改善算法的多样性。为验证算法性能,将DCMOWPA-ES与六种新兴约束多目标优化算法在两组约束多目标测试集和汽车侧面碰撞设计问题上进行对比实验。实验结果表明,DCMOWPA-ES算法具备较好的可行性、收敛性和多样性。展开更多
基金Project(50575202) supported by the National Natural Science Foundation of China
文摘The structure and working principle of a self-deigned high pressure electronic pneumatic pressure reducing valve (EPPRV) with slide pilot are introduced.The resistance value formulas and the relationship between the resistance and pressure of three typical pneumatic resistances are obtained.Then,the method of static characteristics analysis only considering pneumatic resistances is proposed,the resistance network from gas supply to load is built up,and the mathematical model is derived from the flow rate formulas and flow conservation equations,with the compressibility of high pressure gas and temperature drop during the expansion considered in the model.Finally,the pilot spool displacement of 1.5 mm at an output pressure of 15MPa and the enlarging operating stroke of the pilot spool are taken as optimization targets,and the optimization is carried out based on genetic algorithm and the model mentioned above.The results show that the static characteristics of the EPPRV are significantly improved.The idea of static characteristics analysis and optimization based on pneumatic resistance network is valuable for the design of pneumatic components or system.
文摘针对多目标狼群算法存在的搜索不充分、收敛性不足和多样性欠缺的问题,以及缺少对约束进行处理的问题,提出环境选择的双种群约束多目标狼群算法(multi-objective wolf pack algorithm for dual population constraints with environment selection,DCMOWPA-ES)。引入双种群约束处理方法给种群设置不同的搜索偏好,主种群运用可行性准则优先保留可行解,次种群通过ε约束探索不可行区域并将搜索结果传递给主种群,让算法能较好应对复杂的不可行区域,保障算法的可行性;提出维度选择的随机游走策略,使人工狼可自主选择游走方向,提高种群的全局搜索能力;设计精英学习的步长调整机制,人工狼通过向头狼学习的方式提升种群的局部搜索能力,确保算法的收敛性;采用环境选择的狼群更新策略,根据人工狼被支配的情况和所处位置的密度信息对其赋值,选择被支配数少且密度信息小的人工狼作为优秀个体,改善算法的多样性。为验证算法性能,将DCMOWPA-ES与六种新兴约束多目标优化算法在两组约束多目标测试集和汽车侧面碰撞设计问题上进行对比实验。实验结果表明,DCMOWPA-ES算法具备较好的可行性、收敛性和多样性。
