The first report of Plasmopara halstedii race 337 in the Russian Federation

Sunflower downy mildew caused by Plasmopara halstedii (Farl.) Berl. et de Toni is a destructive and widespread disease. More than 50 races of P. halstedii have been recorded worldwide. In 2020, in the Russian Federation (Zernogradsky district, Rostov region), a globally new race 337 was identified for the first time. The pathogen was identified on the plants of a foreign sunflower hybrid bearing the resistance gene Pl₆. According to the five-digit racial nomenclature, its virulence profile was determined as 337 53. It is the first P. halstedii race recorded in the Russian Federation that simultaneously infects all differential lines of the 3^rd triplet, i.e., HA-R4, HA-R5 and HA-335. The sunflower lines RHA-274, 803-1, PSC8, RHA-419 and RHA-340 were resistant to it. All the collected isolates of the new race were susceptible to the fungicide mefenoxam.

Performance of textured spherical thrust hybrid bearing operating with shear thinning and piezoviscous lubricants

Improving the lubricating performance of tribo-pairs using engineering textured surfaces has been the main focus of tribology research in recent years. The use of a suitably designed micro-texture on the bearing surface may have a beneficial effect on the performance of fluid film bearings. In the present paper, a mathematical model of a hybrid spherical thrust bearing is developed considering the effect of shear thinning and piezoviscous behaviour of a lubricant. The modified Reynolds equation for a hybrid spherical thrust bearing configuration together with a restrictor flow equation for a capillary restrictor is solved using a finite-element method. In this work, the effect of various micro-textures shapes (spherical, circular, conical and square) and non-Newtonian lubricant behaviour having shear thinning and piezoviscous effects are analysed. The numerically simulated result shows a strong dependence on the combined effect of shear thinning and piezoviscous lubricant behaviour and a chosen geometric shape of a texture. The frictional power loss is seen to reduce nearly by 24.05%, and the stiffness gets enhanced by 11.08%.

Effect of Temperature and Surface Roughness on the Tribological Behavior of Electric Motor Greases for Hybrid Bearing Materials

Greased bearings in electric motors (EMs) are subject to a wide range of operational requirements and corresponding micro-environments. Consequently, greases must function effectively in these conditions. Here, the tribological performance of four market-available EM greases was characterized by measuring friction and wear of silicon nitride sliding on hardened 52100 steel. The EM greases evaluated had similar viscosity grades but different combinations of polyurea or lithium thickener with mineral or synthetic base oil. Measurements were performed at a range of temperature and surface roughness conditions to capture behavior in multiple lubrication regimes. Results enabled direct comparison of market-available products across different application-relevant metrics, and the analysis methods developed can be used as a baseline for future studies of EM grease performance.

Process chain for the manufacture of hybrid bearing bushings

The current study presents a novel Tailored Forming process chain developed for the production of hybrid bearing bushings. In a first step, semi-finished products in the form of locally reinforced hollow profiles were produced using a new co-extrusion process. For this purpose, a modular tool concept was developed in which a steel tube made of a case-hardening steel, either C15 (AISI 1015) or 20MnCr5 (AISI 5120), is fed laterally into the tool. Inside the welding chamber, the steel tube is joined with the extruded aluminum alloy EN AW-6082. In the second step, sections from the compound profiles were formed into hybrid bearing bushings by die forging. In order to set the required forming temperatures for each material—aluminum and steel—simultaneously, a tailored heating strategy was developed, which enabled successful die forging of the hybrid workpiece to the desired bearing bushing geometry. Using either of the case-hardening steels in combination with aluminum, this novel process chain made it possible to produce intact hybrid bearing bushings, which showed both macroscopically and microscopically intimate material contact inside the compound zone.

Control of hybrid electromagnetic bearing and elastic foil gas bearing under deep learning

The hybrid electromagnetic and elastic foil gas bearing is explored based on the radial basis function (RBF) neural network in this study so as to improve its stabilization in work. The related principles and structure of hybrid electromagnetic and elastic foil gas bearings is introduced firstly. Then, the proportional, integral, and derivative (PID) bearing controller is introduced and improved into two controllers: IPD and CPID. The controllers and hybrid bearing system are controlled based on the RBF neural network based on deep learning. The characteristics of the hybrid bearing system are explored at the end of this study, and the control simulation research is developed based on the Simulink simulation platform. The effects of the PID, IPD, and CIPD controllers based on the RBF neural network are compared, and they are also compared based on the traditional particle swarm optimization (PSO). The results show that the thickness, spread angle, and rotation speed of the elastic foil have great impacts on the bearing system. The proposed CIPD bearing control method based on RBF neural network has the shortest response time and the best control effect. The controller parameter tuning optimization starts to converge after one generation, which is the fastest iteration. It proves that RBF neural network control based on deep learning has high feasibility in hybrid bearing system. Therefore, the results provide an important reference for the application of deep learning in rotating machinery.

Numerical investigations regarding a novel process chain for the production of a hybrid bearing bushing

This contribution deals with the numerical investigations to develop a novel process chain for hybrid solid components using Tailored Forming. For manufacturing a hybrid bearing bushing, co-extrusion is the first step to produce hybrid semi-finished workpieces followed by a die forging process, machining processes and hardening. Combining aluminium with steel, compounds with wear-resistant functional surfaces and reduced weight are realised. Numerical simulations are a decisive part of the process chain design, for example to determine suitable process parameters for the co-extrusion process and to predict the thickness of intermetallic phases in the joining zone using a macroscopic phenomenological model. A numerical design including a tool analysis of the die forging process was carried out taking the experimentally determined material properties and the temperature profile after inductive heating into account. Additionally, the damage and fatigue behaviour of the polycrystalline material of the joining zone are modelled at the microstructure level. Moreover, a new discretization scheme, namely the virtual element method, which is more efficient at grain level, is developed regarding a crystal plasticity framework. Numerical simulations are used to develop inductive heating strategies for the forming process and for the design of the inductive hardening of the functional surface at the end of the process chain. In order to investigate the performance of this hybrid machine element under application-oriented conditions, a contact simulation is linked with a statistical damage model to calculate the bearing fatigue. In this study, a general overview of the individual process steps is given and results of the respective models are presented.