基于欧-Ⅵ排放法规,将柴油机整个工况区按照稳态试验循环(world harmonized stationary cycle,WHSC)和瞬态测试循环(world harmonized transient cycle,WHTC)试验循环的散点分布及世界非超限排放(world not to exceed,WNTE)法规要求等...基于欧-Ⅵ排放法规,将柴油机整个工况区按照稳态试验循环(world harmonized stationary cycle,WHSC)和瞬态测试循环(world harmonized transient cycle,WHTC)试验循环的散点分布及世界非超限排放(world not to exceed,WNTE)法规要求等划分为不同的区域,分区域选择合理且适量的工况点,寻找各工况其电控参数合适的边界,并基于V优选法进行试验设计;给定各区域拟达到的排放物控制范围,基于Matlab中基于模型标定(modelbased on calibration toolbox,MBC)工具箱对某高压共轨柴油机进行满足欧-Ⅵ排放法规要求且油耗最低的优化标定,获取各电控参数的全脉谱。并将其导入集成校准和应用工具(integrated calibration and application tools,INCA),且在试验台架上进行WHSC、WHTC及WNTE试验验证。试验结果表明:WHSC试验循环的氮氧化物(NO_x)排放和油耗率(有效燃油消耗率)与MBC模型预测值非常接近,偏差皆在1%以内,并满足欧-ⅥWHSC、WHTC及WNTE试验循环排放限值要求且油耗最低。展开更多
Sampling design(SD) plays a crucial role in providing reliable input for digital soil mapping(DSM) and increasing its efficiency.Sampling design, with a predetermined sample size and consideration of budget and spatia...Sampling design(SD) plays a crucial role in providing reliable input for digital soil mapping(DSM) and increasing its efficiency.Sampling design, with a predetermined sample size and consideration of budget and spatial variability, is a selection procedure for identifying a set of sample locations spread over a geographical space or with a good feature space coverage. A good feature space coverage ensures accurate estimation of regression parameters, while spatial coverage contributes to effective spatial interpolation.First, we review several statistical and geometric SDs that mainly optimize the sampling pattern in a geographical space and illustrate the strengths and weaknesses of these SDs by considering spatial coverage, simplicity, accuracy, and efficiency. Furthermore, Latin hypercube sampling, which obtains a full representation of multivariate distribution in geographical space, is described in detail for its development, improvement, and application. In addition, we discuss the fuzzy k-means sampling, response surface sampling, and Kennard-Stone sampling, which optimize sampling patterns in a feature space. We then discuss some practical applications that are mainly addressed by the conditioned Latin hypercube sampling with the flexibility and feasibility of adding multiple optimization criteria. We also discuss different methods of validation, an important stage of DSM, and conclude that an independent dataset selected from the probability sampling is superior for its free model assumptions. For future work, we recommend: 1) exploring SDs with both good spatial coverage and feature space coverage; 2) uncovering the real impacts of an SD on the integral DSM procedure;and 3) testing the feasibility and contribution of SDs in three-dimensional(3 D) DSM with variability for multiple layers.展开更多
文摘基于欧-Ⅵ排放法规,将柴油机整个工况区按照稳态试验循环(world harmonized stationary cycle,WHSC)和瞬态测试循环(world harmonized transient cycle,WHTC)试验循环的散点分布及世界非超限排放(world not to exceed,WNTE)法规要求等划分为不同的区域,分区域选择合理且适量的工况点,寻找各工况其电控参数合适的边界,并基于V优选法进行试验设计;给定各区域拟达到的排放物控制范围,基于Matlab中基于模型标定(modelbased on calibration toolbox,MBC)工具箱对某高压共轨柴油机进行满足欧-Ⅵ排放法规要求且油耗最低的优化标定,获取各电控参数的全脉谱。并将其导入集成校准和应用工具(integrated calibration and application tools,INCA),且在试验台架上进行WHSC、WHTC及WNTE试验验证。试验结果表明:WHSC试验循环的氮氧化物(NO_x)排放和油耗率(有效燃油消耗率)与MBC模型预测值非常接近,偏差皆在1%以内,并满足欧-ⅥWHSC、WHTC及WNTE试验循环排放限值要求且油耗最低。
基金funded by the Natural Science and Engineering Research Council (NSERC) of Canada (No. RGPIN-2014-04100)
文摘Sampling design(SD) plays a crucial role in providing reliable input for digital soil mapping(DSM) and increasing its efficiency.Sampling design, with a predetermined sample size and consideration of budget and spatial variability, is a selection procedure for identifying a set of sample locations spread over a geographical space or with a good feature space coverage. A good feature space coverage ensures accurate estimation of regression parameters, while spatial coverage contributes to effective spatial interpolation.First, we review several statistical and geometric SDs that mainly optimize the sampling pattern in a geographical space and illustrate the strengths and weaknesses of these SDs by considering spatial coverage, simplicity, accuracy, and efficiency. Furthermore, Latin hypercube sampling, which obtains a full representation of multivariate distribution in geographical space, is described in detail for its development, improvement, and application. In addition, we discuss the fuzzy k-means sampling, response surface sampling, and Kennard-Stone sampling, which optimize sampling patterns in a feature space. We then discuss some practical applications that are mainly addressed by the conditioned Latin hypercube sampling with the flexibility and feasibility of adding multiple optimization criteria. We also discuss different methods of validation, an important stage of DSM, and conclude that an independent dataset selected from the probability sampling is superior for its free model assumptions. For future work, we recommend: 1) exploring SDs with both good spatial coverage and feature space coverage; 2) uncovering the real impacts of an SD on the integral DSM procedure;and 3) testing the feasibility and contribution of SDs in three-dimensional(3 D) DSM with variability for multiple layers.
