摘要
针对船用低速机烟气余热回收(WHR)的超临界二氧化碳(S-CO_(2))再压缩布雷顿循环(SCRBC)参数优化,利用低速机台架的试验数据,建立了再压缩布雷顿循环一维仿真模型,基于神经网络模型拟合得到循环参数增压比、分流比与循环效率和净回收功的关系,通过多目标遗传算法优化(MOGA)和多准则决策方法(TOPSIS)确定低速机不同负荷下的最佳循环参数组合,提高了循环效率和净回收功.结果表明:在低速机100%负荷、分流比为0.117及增压比为1.804时,净回收功达到178.14 kW,布雷顿循环效率达到19.22%,此时,系统总效率提升了1.68%,燃油消耗率降低了6.43 g/(kW·h).通过对系统进行火用分析,冷却器火用损失最大为85 kW,火用损失效率为11.44%;换热器的火用损失为32 kW,火用损失效率为3.44%.完成了船舶低速机烟气余热S-CO_(2)再压缩布雷顿循环性能优化方法的研究,并可推广到其他低速机.
A one-dimensional simulation model of supercritical carbon dioxide(S-CO_(2))recompression Brayton cycle(SCRBC)was developed for the flue gas waste heat recovery(WHR)of a marine low-speed engine using the bench test data.The relationship between the pressure ratio and split ratio with the cycle efficiency and net recovery work was obtained by fitting a neural network model.A multi-objective genetic algorithm(MOGA)optimization with the technique for order preference by similarity to an ideal solution(TOPSIS)method was used to determine the optimal combination of cycle parameters for different loads of the low-speed engine,so that the cycle efficiency and the net recovered work can be optimized.The results show that at 100%load,when the split ratio is 0.117 and the pressure ratio is 1.804,the net recovered work reaches 178.14 kW and the Brayton cycle efficiency reaches 19.22%.This means that the total system efficiency is increased by 1.68%and the fuel consumption rate is reduced by 6.43 g/(kW·h).Exergy analysis result of the system shows that the maximum heat loss of the cooler is 85 kW and the heat loss efficiency is 11.44%.The heat exchanger heat loss is 32 kW and the heat loss efficiency is 3.44%.The performance optimization method of the S-CO_(2) recompression Brayton cycle for flue gas waste heat recovery could provide a useful measure for marine low-speed diesel engines.
作者
谢良涛
杨建国
杨欣
孙思聪
胡闹
范玉
Xie Liangtao;Yang Jianguo;Yang Xin;Sun Sicong;Hu Nao;Fan Yu(School of Naval Architecture,Ocean and Energy Power Engineering,Wuhan University of Technology,Wuhan 430063,China;Key Laboratory of Marine Power Engineering and Technology Granted by MOT,Wuhan 430063,China;Shanghai Marine Diesel Engine Research Institute,Shanghai 200090,China)
出处
《内燃机学报》
EI
CAS
CSCD
北大核心
2024年第3期253-260,共8页
Transactions of Csice
基金
国家高技术船舶科研资助项目(工信部联装函[2017]21号).
关键词
船用低速机
再压缩布雷顿循环
循环参数
神经网络模型
marine low-speed engine
recompression Brayton cycle
cycle parameters
neural network model
