This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapi...This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered accordingly.Secondly,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation space.Finally,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,respectively.The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results.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
考虑到当前梯级水库蓄水调度研究尚未开展碳减排调度,基于碳排放因子法提出了梯级水库蓄水期水碳多目标调度模型,制定了梯级水库提前蓄水策略,并以防洪风险最小化、发电量最大化和温室气体排放量最小化为调度目标,采用NSGA-II求解调度...考虑到当前梯级水库蓄水调度研究尚未开展碳减排调度,基于碳排放因子法提出了梯级水库蓄水期水碳多目标调度模型,制定了梯级水库提前蓄水策略,并以防洪风险最小化、发电量最大化和温室气体排放量最小化为调度目标,采用NSGA-II求解调度模型推求了梯级水库蓄水期优化调度方案,在金沙江中下游6座水库与三峡水库组成的梯级水库开展了实例研究。结果表明:相较于现行调度方案,优化调度方案集在防洪库容占用率为0~4.92%的情况下,发电量提升了7.23~40.26亿kW·h/a(0.65%~3.60%),弃水量减少了15.82~55.03亿m^(3)/a(6.45%~22.43%),温室气体排放量降低了38.55~45.63 Gg CO_(2e)/a(8.33%~9.85%),碳排放强度降低了0.39~0.47 kg CO_(2e)/(MW·h)(9.49%~11.44%),显著提升了梯级水库的发电量、抗旱供水能力并减少了温室气体排放。研究成果为实现梯级水库蓄水期水碳协同调度提供了技术支撑。展开更多
基金the National Natural Science Foundation of China(Grant No.42274119)the Liaoning Revitalization Talents Program(Grant No.XLYC2002082)+1 种基金National Key Research and Development Plan Key Special Projects of Science and Technology Military Civil Integration(Grant No.2022YFF1400500)the Key Project of Science and Technology Commission of the Central Military Commission.
文摘This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered accordingly.Secondly,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation space.Finally,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,respectively.The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results.
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)。
文摘考虑到当前梯级水库蓄水调度研究尚未开展碳减排调度,基于碳排放因子法提出了梯级水库蓄水期水碳多目标调度模型,制定了梯级水库提前蓄水策略,并以防洪风险最小化、发电量最大化和温室气体排放量最小化为调度目标,采用NSGA-II求解调度模型推求了梯级水库蓄水期优化调度方案,在金沙江中下游6座水库与三峡水库组成的梯级水库开展了实例研究。结果表明:相较于现行调度方案,优化调度方案集在防洪库容占用率为0~4.92%的情况下,发电量提升了7.23~40.26亿kW·h/a(0.65%~3.60%),弃水量减少了15.82~55.03亿m^(3)/a(6.45%~22.43%),温室气体排放量降低了38.55~45.63 Gg CO_(2e)/a(8.33%~9.85%),碳排放强度降低了0.39~0.47 kg CO_(2e)/(MW·h)(9.49%~11.44%),显著提升了梯级水库的发电量、抗旱供水能力并减少了温室气体排放。研究成果为实现梯级水库蓄水期水碳协同调度提供了技术支撑。
