Characteristic analysis of hydraulic hybrid vehicle based on limit cycle

The theory of limit cycles was applied to hydraulic hybrid vehicle (HHV) to analyze the dynamic characteristics of the system. The exact mathematical models based on configuration diagram of HHV were built to study on equilibrium points, nonexistence of limit cycle and stability of equilibrium points. The analysis showed that if the Young’s modulus of fluid is neglected, the equilibrium points of the system will be distributed on both sides of the initial function. In addition, there is a unique equilibrium point according to the practical signification of the system parameters. The nonexistence analysis showed that there is no limit cycle for the system, no matter how the viscosity coefficient B changes. The stability analysis of equilibrium points showed that the system is asymptotically stable about the equilibrium point at B⩾0 and the equilibrium point is the center point of the system at B=0. Finally, the phase diagrams of global topological structure of HHV system were entirely described according to qualitative analysis of the singular points at infinity

Optimization Research of Heterogeneous 2D-Parallel Lattice Boltzmann Method Based on Deep Computing Unit

Currently, research on the lattice Boltzmann method mainly focuses on its numerical simulation and applications, and there is an increasing demand for large-scale simulations in practical scenarios. In response to this situation, this study successfully implemented a large-scale heterogeneous parallel algorithm for the lattice Boltzmann method using OpenMP, MPI, Pthread, and OpenCL parallel technologies on the “Dongfang” supercomputer system. The accuracy and effectiveness of this algorithm were verified through the lid-driven cavity flow simulation. The paper focused on optimizing the algorithm in four aspects: Firstly, non-blocking communication was employed to overlap communication and computation, thereby improving parallel efficiency. Secondly, high-speed shared memory was utilized to enhance memory access performance and reduce latency. Thirdly, a balanced computation between the central processing unit and the accelerator was achieved through proper task partitioning and load-balancing strategies. Lastly, memory access efficiency was improved by adjusting the memory layout. Performance testing demonstrated that the optimized algorithm exhibited improved parallel efficiency and scalability, with computational performance that is 4 times greater than before optimization and 20 times that of a 32-core CPU

Nondestructive quality assessment and maturity classification of loquats based on hyperspectral imaging

Abstract The traditional method for assessing the quality and maturity of loquats has disadvantages such as destructive sampling and being time-consuming. In this study, hyperspectral imaging technology was used to nondestructively predict and visualise the colour, firmness, and soluble solids content (SSC) of loquats and discriminate maturity. On comparison of the performance of different feature variables selection methods and the calibration models, the results indicated that the multiple linear regression (MLR) models combined with the competitive adaptive reweighting algorithm (CARS) yielded the best prediction performance for loquat quality. Particularly, CARS-MLR models with optimal prediction performance were obtained for the colour (R 2 P = 0.96, RMSEP = 0.45, RPD = 5.38), firmness (R 2 P = 0.87, RMSEP = 0.23, RPD = 2.81), and SSC (R 2 P = 0.84, RMSEP = 0.51, RPD = 2.54). Subsequently, distribution maps of the colour, firmness, and SSC of loquats were obtained based on the optimal CARS-MLR models combined with pseudo-colour technology. Finally, on comparison of different classification models for loquat maturity, the partial least square discrimination analysis model demonstrated the best performance, with classification accuracies of 98.19% and 97.99% for calibration and prediction sets, respectively. This study demonstrated that the hyperspectral imaging technique is promising for loquat quality assessment and maturity classification

Ground access behaviour of air-rail passengers: A case study of Dalian ARIS

Passenger ground access behaviour greatly impacts the promotion and development of air-rail integrated service (ARIS). However, little is known about people's actual choice behaviours with combined ARIS alternatives. To further understand the decision mechanisms of air-rail passengers, this paper specifically analyses the ground access behaviour of ARIS passengers and identifies key influential factors at the fostering stage when the air and rail services are not integrated. We first introduce the development of ARIS in China, including types of air-rail network synchronization, their development stages and the structure of the total ground access time. Using revealed preference panel data from ARIS passengers at Dalian Airport, a mixed multinomial logit model with unbalanced panel data was estimated while considering group characteristics and the random distribution of the total local dwelling time. It is found that passengers tend to make choices about ARIS alternatives from the perspective of ground access time and not from a perspective of high-speed scheduling. Passengers generally prefer to maintain a longer safety margin time when choosing ARIS alternatives

Prediction and visualization map for physicochemical indices of kiwifruits by hyperspectral imaging

Soluble solid content (SSC), firmness, and color (L*, a*, and b*) are important physicochemical indices for assessing the quality and maturity of kiwifruits. Therefore, this research aimed to realize the nondestructive detection and visualization map for the physicochemical indices of kiwifruits at different maturity stages by hyperspectral imaging coupled with the chemometrics. To further improve the detection accuracy and working efficiency of the models, competitive adaptive reweighted sampling (CARS) and successive projection algorithm were employed to choose feature wavelengths for predicting the physicochemical indices of kiwifruits. Multiple linear regression (MLR) was designed to develop simplified detection models based on feature wavelengths for determining the physicochemical indices of kiwifruits. The results showed that 32, 18, 26, 29, and 32 feature wavelengths were extracted from 256 full wavelengths to predict the SSC, firmness, L*, a*, and b*, respectively, with the CARS algorithm. Not only was the working efficiency of the CARS-MLR model improved, but the prediction accuracy of the CARS-MLR model for determining the physicochemical indices was also at its relative best. The residual predictive deviations of the CARS-MLR model for determining the SSC, firmness, L*, a*, and b* were 3.09, 2.90, 2.32, 2.74, and 2.91, respectively, which were all above 2.3. Compared with the model based on the full spectra, the CARS-MLR model could be used to predict the physicochemical indices of kiwifruits. Finally, the visualization map for the physicochemical indices of kiwifruits at different maturity stages was generated by calculating the spectral response of each pixel on the kiwifruit samples with the CARS-MLR model. This made the detection for the physicochemical indices of kiwifruits more intuitive. This study demonstrates that hyperspectral imaging coupled with the chemometrics is promising for the nondestructive detection and visualization map for the physicochemical indices of kiwifruits, and also provides a novel theoretical basis for the nondestructive detection of kiwifruit quality

Statistical Characteristics of Mesoscale Convective Systems Initiated over the Tibetan Plateau in Summer by Fengyun Satellite and Precipitation Estimates

In order to investigate the key characteristics of mesoscale convective systems (MCSs) initiated over the Tibetan Plateau (TP) in recent years and the main differences in circulation and environmental factors between different types of MCSs, an automatic MCS identification and tracking method was applied based on the data from China’s Fengyun satellite and precipitation estimates. In total, 8820 MCSs were found to have been initiated over the TP during the summers from 2013 to 2019, and a total of 9.3% of them were able to move eastward out of the TP (EO). The number of MCSs showed a monthly variation, with a maximum in July and a minimum in June, while most EOs occurred in June. Compared with other types of MCSs, EOs usually had a lower cloud-top temperature, a greater rainfall intensity, a longer life duration, more rapid development, larger areas of rainfall and convective clouds, longer tracks and a wider influence range, indicating that EOs are more vigorous than the other types of MCSs. The movement of MCSs is mainly due to the mid- to high-level dynamic conditions, and moisture is an essential factor in their development and maintenance

Heterogeneous Parallel Implementation of Large-Scale Numerical Simulation of Saint-Venant Equations

Large-scale floods are one of the major events that impact the national economy and people’s livelihood every year during the flood season. Predicting the factors of flood evolution is a worldwide problem. We use the two-dimensional Saint-Venant equations as an example and for high-performance computing in modelling the flood behavior. Discretization of the two-dimensional Saint-Venant equations with initial and boundary conditions with the finite difference method in the explicit leapfrog scheme is carried out. Afterwards, we employed a large-scale heterogeneous parallel solution on the “SunRising-1” supercomputer system using MPI, OpenMP, Pthread, and OpenCL runtime libraries. On this basis, we applied communication/calculation overlapping and the local memory acceleration to optimize the performance. Finally, various performance tests of the parallel scheme are carried out from different perspectives. We have found this method is efficient and recommend this approach be used in solving systems of partial differential equations similar to the Saint-Venant equations