x</sub> 排放峰值現(xiàn)象,以及燃油價(jià)格日益上漲帶來降低油耗率的迫切需求,本研究通過調(diào)節(jié)柴油/甲醇組合燃燒(diesel/methanol compoundcombustion,DMCC)發(fā)動(dòng)機(jī)多種控制參數(shù),在保證動(dòng)力性前提下,實(shí)現(xiàn) NO<sub>x</sub> 排放和有效燃油消耗率(brake specific fuel consumption,BSFC)的同步下降。為避免大規(guī)模試驗(yàn)帶來的成本增加,首先基于高斯過程回歸建立DMCC發(fā)動(dòng)機(jī)排放的 NO<sub>x</sub> 體積分?jǐn)?shù)、BSFC和指示功率預(yù)測模型;然后將所建模型與第二代非支配排序遺傳算法(non-dominated sorting genetic algorithm- I ,NSGA-II)結(jié)合,對(duì)NO<sub>x</sub> 的體積分?jǐn)?shù)和BSFC進(jìn)行優(yōu)化,并將Pareto前沿解集代入逼近理想解排序法(the technique fororder preference by similarity to an ideal solution,TOPSIS)尋找最優(yōu)控制參數(shù)組合;最后將最優(yōu)控制參數(shù)組合標(biāo)定至電子控制單元,與原機(jī)數(shù)據(jù)進(jìn)行對(duì)比分析。結(jié)果表明:基于高斯過程回歸建立的預(yù)測模型的擬合優(yōu)度大于0.95,均方根誤差小于1,具有艮好的一致性和準(zhǔn)確性;使用NSGA-Ⅱ獲取的最佳控制參數(shù)與優(yōu)化前(原機(jī)工況)的相比, NO<sub>x</sub> 的排放量下降 74.5% ,僅為 <img src="/qkimages/shhs/shhs202502/shhs20250215-1-l.jpg" with="114px" style="vertical-align: middle;"> ,BSFC平均下降 6.7% ,僅為 203.5g/(kW?h) 。-龍?jiān)雌诳W(wǎng)" />

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基于高斯過程回歸的船舶DMCC發(fā)動(dòng)機(jī)整機(jī)性能優(yōu)化

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中圖分類號(hào):U664.121;TP18 文獻(xiàn)標(biāo)志碼:A

Abstract: In response to the NOx emission peak phenomenon in medium and high load conditions under the propulsion characteristics in diesel engines,as well as the urgent need to reduce fuel consumption due to rising fuel prices,this study adjusts multiple control parameters of a diesel/methanol compound combustion(DMCC)engine to achieve simultaneous reductions in NOx emission and the brake specific fuel consumption (BSFC)under the premise of ensuring the power performance. To avoid the increased cost caused by large-scale experiments,predictive models for NOx volume fraction,BSFC,and indicated power of the DMCC engine are established based on Gaussian process regression. These models are then combined with the non-dominated sorting genetic algorithm- I (NSGA-II) to optimize NOx volume fractionand BSFC.The Pareto front solutions obtained are further analyzed using the technique for order preference by similarity to an ideal solution (TOPSIS)to find the optimal control parameter combination. Finally,the optimal control parameters are calibrated into the electronic control unit and compared with the original engine data.The results show that the predictive models based on Gaussan process regression achieve a goodness of fit greater than O.95 and a root mean square error of less than 1,indicating good consistency and accuracy. Compared to the parameters before optimization (the original engine conditions),using the optimal control parameters obtained by NSGA- I results in a 74.5% reduction in NOx emissions to just 3.47g/(kW?h) ,and an average BSFC reduction of 6.7% to 203.5g/(kW?h) :

Key words: marine diesel engine; diesel/methanol compound combustion; Gaussian process regression ; non-dominated sorting genetic algorithm- I (NSGA- II); technique for order preference by similarity to an ideal solution(TOPSIS)

0 引言

航運(yùn)業(yè)是化石燃料消耗和溫室氣體排放的主要來源之一,因此船用發(fā)動(dòng)機(jī)必須嚴(yán)格遵守日益嚴(yán)格的排放法規(guī)[1]。(剩余9886字)

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