Analysis of coupling between two sub-machines in co-axis dual-mechanical-port flux-switching PM machine for fuel-based extended range electric vehicles

The permanent magnet (PM) field coupling between inner and outer machines of co-axis dual-mechanical-port flux-switching PM (CADMP-FSPM) machines is investigated. Firstly, the relationships between the inner and outer stator teeth are analytically evaluated, with three key stator teeth types defined, i.e. series, parallel, and independent teeth. Secondly, the negative effects of PM field coupling, including high even-order electromotive force (EMF) harmonics, three-phase EMFs asymmetry and DC bias component in flux-linkages, are investigated and verified by two CADMP-FSPM machines, namely, 5/6–12/22, and 5/6–18/42 structures. It is found that for avoiding the negative effects of PM field coupling, all inner and outer stator teeth types should be the same, thus, a 10/12–12/22 structure CADMP-FSPM machine is introduced for analysis. Thirdly, the performance of the 10/12–12/22, 5/6–12/22, and 5/6–18/42 structures, featured by PM field distributions, d-axis flux-linkage ripples, cogging torques, electromagnetic torques, losses and efficiencies, are comparatively analysed by finite element (FE) analysis. The results indicate that the 10/12–12/22 structure exhibits the lowest PM field coupling level and the best performance. Moreover, the 10/12–12/22 structure can avoid all the negative effects of PM field couplings. A prototyped 10/12–12/22 CADMP-FSPM machine is built and tested to verify the FE predicted results.</p

Genomic selection to improve husk tightness based on genomic molecular markers in maize

IntroductionThe husk tightness (HTI) in maize plays a crucial role in regulating the water content of ears during the maturity stage, thereby influencing the quality of mechanical grain harvesting in China. Genomic selection (GS), which employs molecular markers, offers a promising approach for identifying and selecting inbred lines with the desired HTI trait in maize breeding. However, the effectiveness of GS is contingent upon various factors, including the genetic architecture of breeding populations, sequencing platforms, and statistical models.MethodsAn association panel of maize inbred lines was grown across three sites over two years, divided into four subgroups. GS analysis for HTI prediction was performed using marker data from three sequencing platforms and six marker densities with six statistical methods.ResultsThe findings indicate that a loosely attached husk can aid in the dissipation of water from kernels in temperate maize germplasms across most environments but not nessarily for tropical-origin maize. Considering the balance between GS prediction accuracy and breeding cost, the optimal prediction strategy is the rrBLUP model, the 50K sequencing platform, a 30% proportion of the test population, and a marker density of r2=0.1. Additionally, selecting a specific SS subgroup for sampling the testing set significantly enhances the predictive capacity for husk tightness.DiscussionThe determination of the optimal GS prediction strategy for HTI provides an economically feasible reference for the practice of molecular breeding. It also serves as a reference method for GS breeding of other agronomic traits

Presentation_1_Genomic selection to improve husk tightness based on genomic molecular markers in maize.pdf

IntroductionThe husk tightness (HTI) in maize plays a crucial role in regulating the water content of ears during the maturity stage, thereby influencing the quality of mechanical grain harvesting in China. Genomic selection (GS), which employs molecular markers, offers a promising approach for identifying and selecting inbred lines with the desired HTI trait in maize breeding. However, the effectiveness of GS is contingent upon various factors, including the genetic architecture of breeding populations, sequencing platforms, and statistical models.MethodsAn association panel of maize inbred lines was grown across three sites over two years, divided into four subgroups. GS analysis for HTI prediction was performed using marker data from three sequencing platforms and six marker densities with six statistical methods.ResultsThe findings indicate that a loosely attached husk can aid in the dissipation of water from kernels in temperate maize germplasms across most environments but not nessarily for tropical-origin maize. Considering the balance between GS prediction accuracy and breeding cost, the optimal prediction strategy is the rrBLUP model, the 50K sequencing platform, a 30% proportion of the test population, and a marker density of r2=0.1. Additionally, selecting a specific SS subgroup for sampling the testing set significantly enhances the predictive capacity for husk tightness.DiscussionThe determination of the optimal GS prediction strategy for HTI provides an economically feasible reference for the practice of molecular breeding. It also serves as a reference method for GS breeding of other agronomic traits.</p