高转速浮装式机械密封端面变形影响因素分析及优化

Floating mechanical seals play an important part in the high-speed rotating machine,and its face deformation will lead to seal failure,also directly affects the device operation performance and service life.In this paper,based on the finite element method,a two-dimensional model of the thermal coupling numerical analysis high speed floating mechanical seal was established,and the influence of different parameters such as rotating speed,pressure,temperature and axial compression force on the deformation of seal face is analyzed.It is found that the dynamic and static face deformation increases exponentially with the increase of rotational speed.At high speed,with the increase of working pressure and temperature,the sealing face deformation increases linearly.When the working pressure reaches 8MPa,the sealing face is in dynamic balance,and no further deformation occurs.Under the condition of high speed and negative temperature difference,the deformation of the sealing end face is positive,with the increase of the axial compression force,the end face shrinked inward,and the deformation rate sudden decrease when the force reaches 4MPa.On the contrary,while the temperature difference is positive,the deformation of the seal end face is negative,and the end face expands outward,meanwhile the expansion of deformation are posi-tively correlated with the axial compression force.According to the analysis results,the control optimization method of the end face deformation is put forward,and the accuracy of the numerical analysis results is verified by the high-speed floating mechanical seal test platform,which provides theoretical guidance for the design and use of high-speed floating sealing ring.

Optimal Frequency Regulation Based on Characterizing the Air Conditioning Cluster by Online Deep Learning

The air conditioning cluster(ACC)is a potential candidate to provide frequency regulation reserves.However,the effective assessment of the ACC willing reserve capacity is often an obstacle for existing demand response(DR)programs,influenced by incentive prices,temperatures,etc.In this paper,the complex relationship between the ACC willing reserve capacity and its key influence factors is defined as a demand response characteristic(DRC).To learn about DRC along with real-time frequency regulation,an online deep learning-based DRC(ODLDRC)modeling methodology is designed to continuously retrain the deep neural network-based model.The ODL-DRC model trained by incoming new data does not require massive historical training data,which makes it more time-efficient.Then,the coordinate operation between ODL-DRC modeling and optimal frequency regulation(OFR)is presented.A robust decentralized sliding mode controller(DSMC)is designed to manage the ACC response power in primary frequency regulation against any ACC response uncertainty.An ODL-DRC model-based OFR scheme is formulated by taking the learning error into consideration.Thereby,the ODL-DRC model can be applied to minimize the total operational cost while maintaining frequency stability,without waiting for a well-trained model.The simulation cases validate the superiority of the OFR based on characterizing the ACC by online learning,which can capture the real DRC and simultaneously optimize the regulation performance with strong robustness against any ACC response uncertainty and learning error.

Learning-based adaptive optimal output regulation of linear and nonlinear systems:an overview

This paper reviews recent developments in learning-based adaptive optimal output regulation that aims to solve the problem of adaptive and optimal asymptotic tracking with disturbance rejection.The proposed framework aims to bring together two separate topics—output regulation and adaptive dynamic programming—that have been under extensive investigation due to their broad applications in modern control engineering.Under this framework,one can solve optimal output regulation problems of linear,partially linear,nonlinear,and multi-agent systems in a data-driven manner.We will also review some practical applications based on this framework,such as semi-autonomous vehicles,connected and autonomous vehicles,and nonlinear oscillators.

Application of the hybrid genetic particle swarm algorithm to design the linear quadratic regulator controller for the accelerator power supply

Purpose The purpose of this paper is to study a new method to improve the performance of the magnet power supply in the experimental ring of HIRFL-CSR.Methods A hybrid genetic particle swarm optimization algorithm is introduced,and the algorithm is applied to the optimal design of the LQR controller of pulse width modulated power supply.The fitness function of hybrid genetic particle swarm optimization is a multi-objective function,which combined the current and voltage,so that the dynamic performance of the closed-loop system can be better.The hybrid genetic particle swarm algorithm is applied to determine LQR controlling matrices Q and R.Results The simulation results show that adoption of this method leads to good transient responses,and the computational time is shorter than in the traditional trial and error methods.Conclusions The results presented in this paper show that the proposed method is robust,efficient and feasible,and the dynamic and static performance of the accelerator PWM power supply has been considerably improved.

NaMYB8 regulates distinct,optimally distributed herbivore defense traits

When herbivores attack, plants specifically reconfigure their metabolism. Herbivory on the wild tobacco Nicotiana attenuata strongly induces the R2R3 MYB transcriptional activator MYB8, which was reported to specifically regulate the accumulation of phenolamides(PAs). We discovered that transcriptional regulation of trypsin protease inhibitors(TPIs) and a threonine deaminase(TD) also depend on MYB8 expression. Induced distributions of PAs, TD and TPIs all meet predictions of optimal defense theory: their leaf concentrations increase with the fitness value and the probability of attack of the tissue. Therefore, we suggest that these defensive compounds have evolved to be co-regulated by MYB8.