Parametric study on the water impacting of a free-falling symmetric wedge based on the extended von Karman's momentum theory

The present study is concerned with the peak acceleration azmax occurring during the water impact of a symmetric wedge. This aspect can be important for design considerations of safe marine vehicles. The water-entry problem is firstly studied numerically using the finite-volume discretization of the incompressible Navier-Stokes equations and the volume-of-fluid method to capture the air-water interface. The choice of the mesh size and time-step is validated by comparison with experimental data of a free fall water-entry of a wedge. The key original contribution of the article concerns the derivation of a relationship for azmax (as well as the correlated parameters when azmax occurs), the initial velocity, the deadrise angle and the mass of the wedge based on the transformation of von Karman momentum theory which is extended with the inclusion of the pile-up effect. The pile-up coefficient, which has been proven dependent on the deadrise angle in the case of water-entry with a constant velocity, is then investigated for the free fall motion and the dependence law derived from Dobrovol'skaya is still valid for varying deadrise angle. Reasonable good theoretical estimates of the kinematic parameters are provided for a relatively wide range of initial velocity, deadrise angle and mass using the extended von Karman momentum theory which is the combination of the original von Karman method and Dobrovol'skaya's solution and this theoretical approach can be extended to predict the kinematic parameters during the whole impacting phase.Comment: arXiv admin note: text overlap with arXiv:2207.1041

Effects of wave parameters on load reduction performance for amphibious aircraft with V-hydrofoil

An investigation of the influence of the hydrofoil on load reduction performance during an amphibious aircraft landing on still and wavy water is conducted by solving the Unsteady Reynolds-Averaged Navier-Stokes equations coupled with the standard $k-\omega$ turbulence model in this paper. During the simulations, the numerical wave tank is realized by using the velocity-inlet boundary wave maker coupled with damping wave elimination technique on the outlet, while the volume of fluid model is employed to track the water-air interface. Subsequently, the effects of geometric parameters of hydrofoil have been first discussed on still water, which indicates the primary factor influencing the load reduction is the static load coefficient of hydrofoil. Furthermore, the effects of descent velocity, wave length and wave height on load reduction are comprehensively investigated. The results show that the vertical load reduces more than 55$\%$ at the early stage of landing on the still water through assembling the hydrofoil for different descent velocity cases. Meanwhile, for the amphibious aircraft with high forward velocity, the bottom of the fuselage will come into close contact with the first wave when landing on crest position, and then the forebody will impact the next wave surface with extreme force. In this circumstance, the load reduction rate decreases to around 30$\%$, which will entail a further decline with the increase of wave length or wave height

Decreased Netrin-1 and Correlated Th17/Tregs Balance Disorder in Aβ1–42 Induced Alzheimer’s Disease Model Rats

There is increasing evidence indicating that inflammation represents a key pathological component of Alzheimer’s disease (AD). A possible factor that may contribute to this process is netrin-1, a neuronal guidance molecule. This molecule has been shown to exert an unexpected immunomodulatory function. However, the potential changes and correlations of netrin-1 with T helper 17/regulatory T cells (Th17/Tregs) as related to inflammation in AD has yet to be examined. In this study, netrin-1 and Th17/Tregs balance were investigated, and the relationship among netrin-1, Th17/Tregs and cognitive function were analyzed in a rat model of AD. In this model, a bilateral intracerebroventricular administration of Amyloid β1-42 (Aβ1–42) was used to produce spatial learning and memory deficits, as well as increased neuronal apoptosis, which were detected 7 days after injection for AD7d group and 14 days for AD14d group. Netrin-1 concentrations were significantly down regulated in both serum and cerebrospinal fluid (CSF) of these AD rats, effects which were strongly correlated with cognitive deficits. Increased levels of interleukin (IL)-17 and deceased IL-10 were observed in both the circulation and CSF and were also correlated with the percent of time spent in the target quadrant of AD in these rats. These changes resulted in netrin-1 concentrations being negatively correlated with IL-17 but positively correlated with IL-10 concentrations in the serum and CSF. We also found that the Th17/Tregs balance was disrupted in these AD rats. Collectively, these findings reveal that the reduction in netrin-1 and the correlated disruption of Th17/Tregs balance in AD rats may diminish the immunosuppressive effect of netrin-1 on Th17/Tregs in AD pathogenesis

Qwen Technical Report

Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans. In this work, we introduce Qwen, the first installment of our large language model series. Qwen is a comprehensive language model series that encompasses distinct models with varying parameter counts. It includes Qwen, the base pretrained language models, and Qwen-Chat, the chat models finetuned with human alignment techniques. The base language models consistently demonstrate superior performance across a multitude of downstream tasks, and the chat models, particularly those trained using Reinforcement Learning from Human Feedback (RLHF), are highly competitive. The chat models possess advanced tool-use and planning capabilities for creating agent applications, showcasing impressive performance even when compared to bigger models on complex tasks like utilizing a code interpreter. Furthermore, we have developed coding-specialized models, Code-Qwen and Code-Qwen-Chat, as well as mathematics-focused models, Math-Qwen-Chat, which are built upon base language models. These models demonstrate significantly improved performance in comparison with open-source models, and slightly fall behind the proprietary models.Comment: 59 pages, 5 figure

Multi-Physics Multi-Objective Optimal Design of Bearingless Switched Reluctance Motor Based on Finite-Element Method

The bearingless switched reluctance motor (BSRM) integrates the switched reluctance motor (SRM) with the magnetic bearings, which avoids mechanical bearings-loss and makes it promising in high-speed applications. In this paper, a comprehensive framework for the multi-physics multi-objective optimal design of BSRMs based on finite-element method (FEM) is proposed. At first, the 2-D electromagnetic model of a fabricated initial design prototype is built and solved by the open-source FEM software, Elmer. The iron loss model in Elmer based on the Fourier series is modified by a transient iron loss model with less computation time. Besides, a simplified lumped-parameter (LP) thermal model of the BSRM is applied to estimate the temperature rise of BSRM in the steady state. Then, the comprehensive framework for the multi-physics multi-objective optimal design of BSRMs based on FEM is proposed. The objectives, constraints, and decision variables for optimization are determined. The multi-objective genetic particle swarm optimizer is utilized to obtain the Pareto front of optimization. The electromagnetic performance of the final optimal design is compared with the initial design. Comparison results show that the average electromagnetic torque and the efficiency are significantly enhanced

Reactive Power Output Modeling of Synchronous Condenser in UHVDC Converter Station Based on Interlaced Superposition CNN-BiLSTM

Abstract To guarantee stable power system operation, a synchronous condenser (SC) is configured in an ultra-high voltage direct current (UHVDC) converter station to provide dynamic reactive power support to the power system. The research on the reactive power output modelling of a SC in an UHVDC converter station has important theoretical significance and practical value for the reactive power control of a SC in an UHVDC converter station. Focusing on the reactive power regulation system of the SC with strong coupling, multivariable, and nonlinear features, it is difficult for the universal analytic method to build the SC reactive power output model. A novel reactive power output model of the SC in the UHVDC converter station based on interlaced superposition convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) is proposed, in which a novel interlaced superposition CNN composed of convolution units with two different structures is built to increase the depth of the network and avoid over-fitting. Particularly the branch channels of convolution units with two different structures are connected by a convolution layer and a skip connection respectively. The interlaced superposition CNN-BiLSTM model is that the combination of the interlaced superposition CNN model and BiLSTM model for improving the model accuracy and generic capability. The Bayesian optimization method is used to optimize its hyperparameters. The application of interlaced Superposition CNN-BiLSTM in SC reactive power output modelling is a new technology. The excitation voltage and excitation current of the SC are used as inputs for the training and testing sampled data, and the reactive power of the SC is used as outputs for the training and testing sampled data. Therefore, the reactive power output model of a SC in an UHVDC converter station based on interlaced superposition CNN-BiLSTM is obtained, and it attains a low root mean square error (RMSE = 0.126750) and a high determination coefficient (R 2 = 0.999999)