Research on force model and characteristics of large wind turbine pitch system based on SCADA data

In large-scale wind turbines, the force state of the pitch system greatly influences safe operation and service life. This paper provides a novel method to estimate blade pitch load, bearing friction torque, and motor pitch torque. In this method, the force equilibrium equations are established by investigating the force of the pitch system under multiple operating conditions. The multidimensional BIN method is employed to classify the supervisory control and data acquisition (SCADA) data of wind turbines into several intervals. The multidimensional scatter data is processed in a single-valued way. Then, the estimating model of the pitch system forces is established by combining the obtained data and the equilibrium equations. Taking a 2 MW wind turbine as an example, the variation characteristics of blade pitch load, bearing friction torque, and motor pitch torque under multiple operating conditions are analyzed. Some interesting and valuable conclusions are obtained. For example, when the wind speed increases, the blade pitch load increases significantly in the maximum wind energy tracking region, but there is no obvious change in the observed constant power output region. The wind speed and azimuth have little effect on the bearing friction torque. The variation trend of motor pitch torque is consistent with that of blade pitch load in the maximum wind energy tracking region

UnifiedVisionGPT: Streamlining Vision-Oriented AI through Generalized Multimodal Framework

In the current landscape of artificial intelligence, foundation models serve as the bedrock for advancements in both language and vision domains. OpenAI GPT-4 has emerged as the pinnacle in large language models (LLMs), while the computer vision (CV) domain boasts a plethora of state-of-the-art (SOTA) models such as Meta's SAM and DINO, and YOLOS. However, the financial and computational burdens of training new models from scratch remain a significant barrier to progress. In response to this challenge, we introduce UnifiedVisionGPT, a novel framework designed to consolidate and automate the integration of SOTA vision models, thereby facilitating the development of vision-oriented AI. UnifiedVisionGPT distinguishes itself through four key features: (1) provides a versatile multimodal framework adaptable to a wide range of applications, building upon the strengths of multimodal foundation models; (2) seamlessly integrates various SOTA vision models to create a comprehensive multimodal platform, capitalizing on the best components of each model; (3) prioritizes vision-oriented AI, ensuring a more rapid progression in the CV domain compared to the current trajectory of LLMs; and (4) introduces automation in the selection of SOTA vision models, generating optimal results based on diverse multimodal inputs such as text prompts and images. This paper outlines the architecture and capabilities of UnifiedVisionGPT, demonstrating its potential to revolutionize the field of computer vision through enhanced efficiency, versatility, generalization, and performance. Our implementation, along with the unified multimodal framework and comprehensive dataset, is made publicly available at https://github.com/LHBuilder/SA-Segment-Anything.Comment: 9 pages, 29 figure

Comparative metabolomics analysis of milk components between Italian Mediterranean buffaloes and Chinese Holstein cows based on LC-MS/MS technology

Buffalo and cow milk have a very different composition in terms of fat, protein, and total solids. For a better knowledge of such a difference, the milk metabolic profiles and characteristics of metabolites was investigated in Italian Mediterranean buffaloes and Chinese Holstein cows were investigated by liquid chromatography tandem-mass spectrometry (LC-MS/MS) in this study. Totally, 23 differential metabolites were identified to be significantly different in the milk from the two species of which 15 were up-regulated and 8 down-regulated in Italian Mediterranean buffaloes. Metabolic pathway analysis revealed that 4 metabolites (choline, acetylcholine, nicotinamide and uric acid) were significantly enriched in glycerophospholipid metabolism, nicotinate and nicotinamide metabolism, glycine, serine and threonine metabolism, as well as purine metabolism. The results provided further insights for a deep understanding of the potential metabolic mechanisms responsible for the different performance of Italian Mediterranean buffaloes’ and Chinese Holstein cows’ milk. The findings will offer new tools for the improvement and novel directions for the development of dairy industry

Experimental Research on the Effect of Particle Parameters on Dynamic Stall Characteristics of the Wind Turbine Airfoil

The frequent appearance of sandy and dusty weather in Northwest China impacts the wind turbine. Meanwhile, the non-constant phenomena, dynamic stall speed during the wind turbine operation, will lead to large load fluctuations and unsafe operation. However, few studies have been conducted at home and abroad on the effect of particle parameters on the dynamic stall of airfoils. This paper investigates the impact of particle parameters on the airfoil dynamic stall through numerical simulation of the coupling between the continuous phase and discrete phase by using the SST k-ω turbulence model for a two-dimensional NACA 0012 airfoil. The effect of particle parameters on the airfoil dynamic stall aerodynamic performance, the impact of the flow field around the airfoil, and the particles motion were studied, respectively. The investigation shows a reduction in the aerodynamic performance of the airfoil, due to the addition of particles. The effect is more prominent under a large angle of attack and less under a small angle of attack. When the angle of attack increases, the loss rate of lift coefficient in the windy and sandy environment gradually decreases, while irregular fluctuations emerge when the angle of attack decreases, and the overall rate of change increases more significantly, compared to the stage of the increasing angle of attack. For the particle diameter under 50 μm, the larger the particle diameter, the more significant the change of lift coefficient becomes, as well as the larger the vortex volume near the airfoil’s leading edge, and a large number of particles gather at the suction surface of the airfoil. For the particle diameter of 50 μm, the lift coefficient decreases at any angle of attack of the airfoil movement to the oscillation cycle, the vortex volume decreases, and a large number of particles gather at the pressure surface of the airfoil. However, for particle diameters above 50 μm, the lift coefficient gets reduced, followed by a decrease in the vortex volume near the airfoil leading edge with the increase of particle diameter, so that plenty of particles gather on the pressure surface of that airfoil. At the stage of increasing the airfoil angle of attack, with the increase of particle concentration, there is a gradual decrease of the peak lift coefficient and stall angle of attack of the airfoil, as well as a corresponding decrease of the drag coefficient divergence angle of attack and peak value. In contrast, when the airfoil angle of attack is decreased, the airflow reattachment process obviously lags behind that of the clean air. As the particle concentration increases, the airfoil separation point occurs earlier, and the higher the concentration, the earlier the separation point. The erosion maximum airfoil erosion rate increases with the particle concentration

Hazard Assessment for Biomass Gasification Station Using General Set Pair Analysis

Hazard assessment is critical for a biomass gasification station because it includes various hazardous factors. Set pair analysis (SPA) is a convenient and effective method for hazard assessment, but it has limitations, including (1) the inability to reflect the difference when the data belong to the same hazard grade and (2) the assessment results lack precision and accuracy. This study proposes an improved method designated as general set pair analysis (GSPA). Connection measure degree (CMD), which is based on the cosine function, as well as weighting deviation degree (WDD), relative membership degree (RMD), and comprehensive index (CI) were proposed in GSPA, and the algorithms were generated. The calculated results of these different methods can be utilized to overcome the shortcomings of SPA during hazard assessment. A case study of two biomass gasification stations in Shenyang City, Liaoning Province, Northeast China, was performed. The hazard assessment results of the GSPA were compared with those of SPA. The results showed that GSPA is a more effective, precise, and accurate method for hazard assessment of a biomass gasification station

A novel hazard assessment method for biomass gasification stations based on extended set pair analysis.

Biomass gasification stations are facing many hazard factors, therefore, it is necessary to make hazard assessment for them. In this study, a novel hazard assessment method called extended set pair analysis (ESPA) is proposed based on set pair analysis (SPA). However, the calculation of the connection degree (CD) requires the classification of hazard grades and their corresponding thresholds using SPA for the hazard assessment. In regard to the hazard assessment using ESPA, a novel calculation algorithm of the CD is worked out when hazard grades and their corresponding thresholds are unknown. Then the CD can be converted into Euclidean distance (ED) by a simple and concise calculation, and the hazard of each sample will be ranked based on the value of ED. In this paper, six biomass gasification stations are introduced to make hazard assessment using ESPA and general set pair analysis (GSPA), respectively. By the comparison of hazard assessment results obtained from ESPA and GSPA, the availability and validity of ESPA can be proved in the hazard assessment for biomass gasification stations. Meanwhile, the reasonability of ESPA is also justified by the sensitivity analysis of hazard assessment results obtained by ESPA and GSPA

Effects of marital status on overall and cancer-specific survival in laryngeal cancer patients: a population-based study

Abstract Marital status has long been recognized as an important prognostic factor for many cancers, however its’ prognostic effect for patients with laryngeal cancer has not been fully examined. We retrospectively analyzed 8834 laryngeal cancer patients in the Surveillance Epidemiology and End Results database from 2004 to 2010. Patients were divided into four groups: married, widowed, single, and divorced/separated. The difference in overall survival (OS) and cancer-specific survival (CSS) of the various marital subgroups were calculated using the Kaplan–Meier curve. Multivariate Cox regression analysis screened for independent prognostic factors. Propensity score matching (PSM) was also conducted to minimize selection bias. We included 8834 eligible patients (4817 married, 894 widowed, 1732 single and 1391 divorced/separated) with laryngeal cancer. The 5-year OS and CSS of married, widowed, single, and separated/divorced patients were examined. Univariate and multivariate analyses found marital status to be an independent predictor of survival. Subgroup survival analysis showed that the OS and CSS rates in widowed patients were always the lowest in the various American Joint Committee on Cancer stages, irrespective of sex. Widowed patients demonstrated worse OS and CSS in the 1:1 matched group analysis. Among patients with laryngeal cancer, widowed patients represented the highest-risk group, with the lowest OS and CSS