A novel sound quality simulation approach was proposed to optimize the acoustic performance of a four-cylinder diesel engine.Finite element analysis,single-input and multiple-output technology,flexible multi-body dyna...A novel sound quality simulation approach was proposed to optimize the acoustic performance of a four-cylinder diesel engine.Finite element analysis,single-input and multiple-output technology,flexible multi-body dynamics,and boundary element codes were used to acquire the hexahedron-element model,experimental modal frequencies,vibration velocities,and structurally radiated noise of the block,respectively.The simulated modal frequencies and vibration velocities agreed well with the experimental data,which validated the finite-element block.The acoustic response showed that considerable acoustic power levels existed in 1500-1900 Hz and 2300-2800 Hz as the main frequency ranges to optimize the block acoustics.Then,the optimal block is determined in accordance with the novel approach,which reduces the overall value,high-frequency amplitudes,and peak values of acoustic power;thus,the loudness,sharpness,and roughness decline to make the sound quieter,lower-pitched,and smoother,respectively.Finally,the optimal block was cast and bench-tested.The results reveal that the sound quality of the optimal-block engine is substantially improved as numerically expected,which verifies the effectiveness of the research approach.展开更多
For too many state features are used in the diesel engine state evaluation and fault diagnosis,it is not easy to obtain the rational eigenvalues.In the paper,the cylinder subassembly of diesel engine is used to search...For too many state features are used in the diesel engine state evaluation and fault diagnosis,it is not easy to obtain the rational eigenvalues.In the paper,the cylinder subassembly of diesel engine is used to search for the method of establishing state feature system and optimal approach.The signal of diesel engine has been collected when the piston ring and airtight ring are working at different states,then with the Bootstrap method and Genetic Algorithm(GA),an optimum parameter combination is received.Example shows this method is simple and efficient for establishing diesel engine state feature system,Thus,this method is valuable for the virtual state evaluation of similar complex system.展开更多
A multi-objective improved genetic algorithm is constructed to solve the train operation simulation model of urban rail train and find the optimal operation curve.In the train control system,the conversion point of op...A multi-objective improved genetic algorithm is constructed to solve the train operation simulation model of urban rail train and find the optimal operation curve.In the train control system,the conversion point of operating mode is the basic of gene encoding and the chromosome composed of multiple genes represents a control scheme,and the initial population can be formed by the way.The fitness function can be designed by the design requirements of the train control stop error,time error and energy consumption.the effectiveness of new individual can be ensured by checking the validity of the original individual when its in the process of selection,crossover and mutation,and the optimal algorithm will be joined all the operators to make the new group not eliminate on the best individual of the last generation.The simulation result shows that the proposed genetic algorithm comparing with the optimized multi-particle simulation model can reduce more than 10%energy consumption,it can provide a large amount of sub-optimal solution and has obvious optimization effect.展开更多
针对柴油发动机推进特性下的中高负荷工况出现的NO_(x)排放峰值现象,以及燃油价格日益上涨带来降低油耗率的迫切需求,本研究通过调节柴油/甲醇组合燃烧(diesel/methanol compound combustion,DMCC)发动机多种控制参数,在保证动力性前提...针对柴油发动机推进特性下的中高负荷工况出现的NO_(x)排放峰值现象,以及燃油价格日益上涨带来降低油耗率的迫切需求,本研究通过调节柴油/甲醇组合燃烧(diesel/methanol compound combustion,DMCC)发动机多种控制参数,在保证动力性前提下,实现NO_(x)排放和有效燃油消耗率(brake specific fuel consumption,BSFC)的同步下降。为避免大规模试验带来的成本增加,首先基于高斯过程回归建立DMCC发动机排放的NO_(x)体积分数、BSFC和指示功率预测模型;然后将所建模型与第二代非支配排序遗传算法(non-dominated sorting genetic algorithm-Ⅱ,NSGA-Ⅱ)结合,对NO_(x)的体积分数和BSFC进行优化,并将Pareto前沿解集代入逼近理想解排序法(the technique for order preference by similarity to an ideal solution,TOPSIS)寻找最优控制参数组合;最后将最优控制参数组合标定至电子控制单元,与原机数据进行对比分析。结果表明:基于高斯过程回归建立的预测模型的拟合优度大于0.95,均方根误差小于1,具有良好的一致性和准确性;使用NSGA-Ⅱ获取的最佳控制参数与优化前(原机工况)的相比,NO_(x)的排放量下降74.5%,仅为3.47 g/(kW·h),BSFC平均下降6.7%,仅为203.5 g/(kW·h)。展开更多
文摘A novel sound quality simulation approach was proposed to optimize the acoustic performance of a four-cylinder diesel engine.Finite element analysis,single-input and multiple-output technology,flexible multi-body dynamics,and boundary element codes were used to acquire the hexahedron-element model,experimental modal frequencies,vibration velocities,and structurally radiated noise of the block,respectively.The simulated modal frequencies and vibration velocities agreed well with the experimental data,which validated the finite-element block.The acoustic response showed that considerable acoustic power levels existed in 1500-1900 Hz and 2300-2800 Hz as the main frequency ranges to optimize the block acoustics.Then,the optimal block is determined in accordance with the novel approach,which reduces the overall value,high-frequency amplitudes,and peak values of acoustic power;thus,the loudness,sharpness,and roughness decline to make the sound quieter,lower-pitched,and smoother,respectively.Finally,the optimal block was cast and bench-tested.The results reveal that the sound quality of the optimal-block engine is substantially improved as numerically expected,which verifies the effectiveness of the research approach.
文摘For too many state features are used in the diesel engine state evaluation and fault diagnosis,it is not easy to obtain the rational eigenvalues.In the paper,the cylinder subassembly of diesel engine is used to search for the method of establishing state feature system and optimal approach.The signal of diesel engine has been collected when the piston ring and airtight ring are working at different states,then with the Bootstrap method and Genetic Algorithm(GA),an optimum parameter combination is received.Example shows this method is simple and efficient for establishing diesel engine state feature system,Thus,this method is valuable for the virtual state evaluation of similar complex system.
基金This work was supported by the Youth Backbone Teachers Training Program of Henan Colleges and Universities under Grant No.2016ggjs-287the Project of Science and Technology of Henan Province under Grant Nos.172102210124 and 202102210269.
文摘A multi-objective improved genetic algorithm is constructed to solve the train operation simulation model of urban rail train and find the optimal operation curve.In the train control system,the conversion point of operating mode is the basic of gene encoding and the chromosome composed of multiple genes represents a control scheme,and the initial population can be formed by the way.The fitness function can be designed by the design requirements of the train control stop error,time error and energy consumption.the effectiveness of new individual can be ensured by checking the validity of the original individual when its in the process of selection,crossover and mutation,and the optimal algorithm will be joined all the operators to make the new group not eliminate on the best individual of the last generation.The simulation result shows that the proposed genetic algorithm comparing with the optimized multi-particle simulation model can reduce more than 10%energy consumption,it can provide a large amount of sub-optimal solution and has obvious optimization effect.
文摘针对柴油发动机推进特性下的中高负荷工况出现的NO_(x)排放峰值现象,以及燃油价格日益上涨带来降低油耗率的迫切需求,本研究通过调节柴油/甲醇组合燃烧(diesel/methanol compound combustion,DMCC)发动机多种控制参数,在保证动力性前提下,实现NO_(x)排放和有效燃油消耗率(brake specific fuel consumption,BSFC)的同步下降。为避免大规模试验带来的成本增加,首先基于高斯过程回归建立DMCC发动机排放的NO_(x)体积分数、BSFC和指示功率预测模型;然后将所建模型与第二代非支配排序遗传算法(non-dominated sorting genetic algorithm-Ⅱ,NSGA-Ⅱ)结合,对NO_(x)的体积分数和BSFC进行优化,并将Pareto前沿解集代入逼近理想解排序法(the technique for order preference by similarity to an ideal solution,TOPSIS)寻找最优控制参数组合;最后将最优控制参数组合标定至电子控制单元,与原机数据进行对比分析。结果表明:基于高斯过程回归建立的预测模型的拟合优度大于0.95,均方根误差小于1,具有良好的一致性和准确性;使用NSGA-Ⅱ获取的最佳控制参数与优化前(原机工况)的相比,NO_(x)的排放量下降74.5%,仅为3.47 g/(kW·h),BSFC平均下降6.7%,仅为203.5 g/(kW·h)。
