Multi-Objective Optimization of the Microchannel Heat Sink Used for Combustor of the Gas Turbine

This research presents a surrogate model and computational fluid dynamic analysis-based multi-objective optimization approach for microchannel heat sinks. The Non-dominated Sorting Genetic Algorithm is suggested to obtain the optimal solution set, and the Kriging model is employed to lower the simulation’s computational cost. The physical model consists of a coolant chamber, a mainstream chamber, and a solid board equipped with staggered Zigzag cooling channels. Five design variables are examined in relation to the geometric characteristics of the microchannel heat sinks: the length of inlet of the cooling channels, the width of the cooling channels, the length of the “zigzag”, the height of the cooling channels, and the periodic spanwise width. The optimal geometry is established by choosing the averaged cooling effectiveness and coolant mass flow rate which enters the mainstream chamber through the microchannel heat sinks as separate objectives. From the Pareto front, the optimal microchannel heat sinks structures are obtained. Three optimized results are studied, including the maximum cooling effectiveness, minimum coolant mass flow rate, and a compromise between the both objectives; a reference case using the median is compared as well. Numerical assessments corresponding to the four cases are performed, and the flow and cooling performance are compared. Furthermore, an analysis is conducted on the mechanisms that impact the ideal geometric parameters for cooling performance

Anti-occlusion multi-object surveillance based on improved deep learning approach and multi-feature enhancement for unmanned smart grid safety

Unattended substations are the basis of intelligent substations, which require remote surveillance and control. Limited by the number of visual sensors, remote manual monitoring is incomplete and inefficient. Onsite workers and intruders are easily hidden by the smart grid facility, which affects the safety surveillance of personnel and equipment. The traditional kernelized correlation filter (KCF) method has a poor ability to adapt to the practical environment. This paper presents an anti-occlusion framework on the basis of imaging techniques to solve the problem of optimized surveillance. The novelty is further strengthened by its more practical Deep Learning model and tracking methods. Firstly, a multi-feature fusion model of the HOG feature and color feature is proposed to enhance target characteristics as the target is severely blocked. Secondly, a target classifier training and fast detection method based on improved CNN is introduced. Lastly, to overcome the drawbacks of the KCF tracking algorithm, such as its inability to scale adaptive and blind updates, a new adaptive learning rate strategy is proposed for occlusion tracking. The effects on the OTB-2013 dataset demonstrate that the improved technique has better accuracy and robustness when compared to KCF methods

Multi-objective tracking for smart substation onsite surveillance based on YOLO Approach and AKCF

The onsite surveillance plays an important role in the smart substation since the smart substation is unattended. All the sites and operation staff should be supervised throughout the process since a series of risks exist on the working sites. KCF (Kernel Correlation Filter) is an effective method to track a moving object for safety surveillance. However, the occlusion and shape changes worsen the performance of KCF, especially on the occasion of multi-objective detection. This paper proposes a comprehensive method for improving the precision and robustness of detection. Firstly, all the moving objects are detected by the YOLO method. In the tracking part, an AKCF (Augmented Kernel Correlation Filter) is proposed for the heavily occluded object, and the Kalman Filter (KF) serves as a supplementary output. Moreover, in the target association section, based on priority matching and rematching based on motion estimation, a two-stage target association method is proposed. Test outcomes indicate that the proposed algorithm is accurate and robust for tracking workers’ trajectories and conducting surveillance

Genetic Mapping and Analysis of a Compact Plant Architecture and Precocious Mutant in Upland Cotton

With the promotion and popularization of machine cotton-picking, more and more attention has been paid to the selection of early-maturity varieties with compact plant architecture. The type of fruit branch is one of the most important factors affecting plant architecture and early maturity of cotton. Heredity analysis of the cotton fruit branch is beneficial to the breeding of machine-picked cotton. Phenotype analysis showed that the types of fruit branches in cotton are controlled by a single recessive gene. Using an F2 population crossed with Huaxin102 (normal branch) and 04N-11 (nulliplex branch), BSA (Bulked Segregant Analysis) resequencing analysis and GhNB gene cloning in 04N-11, and allelic testing, showed that fruit branch type was controlled by the GhNB gene, located on chromosome D07. Ghnb5, a new recessive genotype of GhNB, was found in 04N-11. Through candidate gene association analysis, SNP 20_15811516_SNV was found to be associated with plant architecture and early maturity in the Xinjiang natural population. The GhNB gene, which is related to early maturity and the plant architecture of cotton, is a branch-type gene of cotton. The 20_15811516_SNV marker, obtained from the Xinjiang natural population, was used for the assisted breeding of machine-picked cotton varieties