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In the field of mechanical engineering, inclined rubber bearings reduce vertical stiffness through tilted arrangement to effectively isolate environmental vibrations. When applied to large-scale structural engineering, however, further attention must be paid to their vertical hysteretic performance under large deformation, so as to provide a basis for three-dimensional seismic isolation analysis of structures. Traditional seismic design often simplifies the vertical constitutive model of bearings as linear, while tests have shown that the vertical behavior of inclined rubber bearings exhibits significant asymmetric hysteretic characteristics, which cannot be accurately described by existing symmetric constitutive models. In this paper, vertical performance tests are further conducted on inclined rubber bearing specimens, and a modified hysteretic polynomial model is proposed to adapt it to the theoretical description of asymmetric vertical hysteretic behavior of inclined rubber bearings. Through parameter modification, device testing, and comparative analysis of results, the accuracy and effectiveness of the model are verified, providing a theoretical basis for its engineering application.

This paper investigates the properties of a mass−attached piezoelectric stack actuator and analyzes its sensitivity, which is defined as the spectrum of the driving force (the output) caused by a single−frequency voltage (the input). The force spectrum is utilized because of the nonlinear hysteresis effect of the piezoelectric stack. The sensitivity analysis shows that the nonlinear dynamics of the actuator can be interpreted as a cascade of two subsystems: a nonlinear hysteresis subsystem and a linear mechanical subsystem. Analytical solutions of the nonlinear differential equations are proposed, which show that the nonlinear transformation can be described by a steady−state mapping of a single−frequency voltage input to a multiple−frequency driving force at the driving frequency and its odd harmonics. The steady−state sensitivity is then determined by the response of the mechanical subsystem to the line spectrum of the driving force. The maximum sensitivity can be achieved by setting the frequency of the input voltage close to the natural frequency of the mechanical subsystem. The analytical model is also validated by a numerical model and experimental results and it may be used for the analysis and design of piezoelectric actuators with different structural configurations.

The hysteresis model can be used to accurately predict the magnetic hysteresis characteristics of ferromagnetic materials. Incorporating the hysteresis model into finite element calculations enables precise prediction of field distributions, voltage or current variations in circuits, and losses, which is essential for electromagnetic transient analysis involving remanent magnetization. When incorporating the hysteresis model into finite element analysis, prohibitively small time-steps are required to resolve hysteresis loops, leading to excessive simulation times compared to simplified BH curve approaches. Furthermore, numerical instabilities arise near zero-crossing points of magnetic flux density, where erroneous negative differential reluctivity values may lead to the divergence of the nonlinear solving process. A finer time resolution needs to be utilized to ensure the convergence of the nonlinear solver. This leads to more time-steps and longer computational time. This work proposes a localized stabilization strategy for regulating the differential reluctivity in instability-prone regions of the hysteresis loop, which can stabilize the nonlinear iteration while avoiding the local refinement of time resolution and thus reduce the overall computation time.

Fast steering mirrors (FSMs), driven by piezoelectric ceramics, are usually used as actuators for high-precision beam control. A FSM generally contains four ceramics that are distributed in a crisscross pattern. The cooperative movement of the two ceramics along one radial direction generates the deflection of the FSM in the same orientation. Unlike the hysteresis nonlinearity of a single piezoelectric ceramic, which is symmetric or asymmetric, the FSM exhibits complex hysteresis characteristics. In this paper, a systematic way of modeling the hysteresis nonlinearity of FSMs is proposed using a Madelung’s rules based symmetric hysteresis operator with a cascaded neural network. The hysteresis operator provides a basic hysteresis motion for the FSM. The neural network modifies the basic hysteresis motion to accurately describe the hysteresis nonlinearity of FSMs. The wiping-out and congruency properties of the proposed method are also analyzed. Moreover, the inverse hysteresis model is constructed to reduce the hysteresis nonlinearity of FSMs. The effectiveness of the presented model is validated by experimental results.

A significant event happened for electrical engineering in 2008, when researchers at HP Labs announced that they had found “the missing memristor,” a fourth basic circuit element that was postulated nearly four decades earlier by Dr. Leon Chua, who was also instrumental in developing the mathematical theories of memristive, memcapacitive, and meminductive systems, resulting in an entire class of “mem-models” that are the foundation of the present work. By applying well-known mechanical–electrical analogies, the mathematics of mem-models may be transferred to the setting of engineering mechanics, creating the mechanical counterparts of memristors, memcapacitors, etc. However, this transfer is nontrivial; for example, a new concept and state variable called “absement,” the time integral of deformation, emerge. We study these mem-models, which are characterized by a “zero-crossing” property that has interesting implications for nonlinear constitutive modeling, particularly hysteresis, and we identify some examples of “mem-dashpots” and “mem-springs,” which include displacement-dependent and variable dampers, the superelasticity found in shape-memory alloys, and the pinched hysteresis loops associated with self-centering structures. This work adds to the fast-growing body of literature on elements and systems labeled with “mem,” which is a basic branch of study in nonlinear dynamics.

Piezoelectric inkjet printing technology, known for its high precision and cost-effectiveness, has found extensive applications in various fields. However, the issue of residual vibration significantly limits its printing quality and efficiency. This paper presents a method for suppressing residual vibration based on the particle swarm optimization (PSO) algorithm. Initially, an improved PI model considering the nonlinear hysteresis characteristics of piezoelectric ceramics is established, and the model is identified through a strain gauge circuit to ensure its accuracy in describing the nonlinear hysteresis characteristics. Subsequently, a dynamic model of the piezoelectric inkjet printing system is constructed, with precise parameter identification achieved using the self-induction principle. This enables precise simulation of residual vibration. Finally, the driving waveform is optimized based on the PSO algorithm, with iterative calculations employed to find the optimal combination of driving waveform parameters, effectively suppressing residual vibration while ensuring sufficient injection energy. The results indicate that this method significantly reduces the amplitude of residual vibration, thereby effectively enhancing printing quality and stability. This research offers a novel solution for residual vibration suppression in piezoelectric inkjet printing technology, potentially advancing its applications in printing and biofabrication.

High performance of clutch control is essential for dual-clutch transmission (DCT) system to ensure good shifting smoothness of vehicle driving. The control performance of clutch in DCT driven by proportional solenoid valve depends on the output pressure control of the solenoid valve, while the output pressure of the solenoid valve is directly controlled by the energized current. Therefore, the relationship between working current and output pressure of the solenoid valve has significant impact on the clutch control and affects driving performance of the vehicle accordingly. However, the pressure-to-current (P/I) relationship of proportional solenoid valve has nonlinear hysteresis characteristic caused by magnetic materials, oil viscous friction, operation temperature, etc., which has negative effects on the accuracy and stability of solenoid valve pressure control. To cope with this problem, a machine learning model called long short-term memory (LSTM) for P/I of solenoid valve based on data mining is adopted in this paper, which is then used as feedforward compensation for closed-loop control of solenoid valve. The test result demonstrates that the machine learning model can effectively predict the output pressure at rising and falling phase of the same working current. Besides, this smart control method which has better applicability in engineering can effectively improve the control performance of proportional solenoid valve and further improve clutch control and vehicle driving performance.

Quantification of seismic damage to reinforced concrete (RC) members in terms of damages states is important for condition assessment and performance-based seismic design. This paper presents a classification for axially-loaded flexural-dominant well-detailed RC members into clearly-differentiated damage states based on change in failure mode, principles of mechanics, and loss in lateral load capacity. The paper considers extreme fiber compressive strain as the Engineering Response Parameter (ERP) for classification of the damage states. The identification of member damage states based on compressive strain can be very useful for seismic assessment of the existing buildings and performance-based design of new buildings. Published experimentally observed cyclic force–displacement relationships of RC columns have been numerically simulated and are used to develop statistics of compressive strain. These are further used to develop semi-empirical expressions for extreme fiber compressive strain in terms of RC member properties, viz. axial load ratio, longitudinal reinforcement ratio, and confinement reinforcement ratio. It is shown that the proposed expressions accurately predict experimentally observed compressive strain at different damage states. The fragility functions of damage states, that provide the probability of occurrence of the strain limits, are also presented. It is seen that the proposed expressions for strain and their limits accurately identify the damage states on the monotonic stress–strain relationship of confined concrete and validate the use of strain as an ERP. The present study also provides strain limits for different damage states with consistent level of conservatism.

In this work, experimental and numerical investigation on the deployment of solar panels with tape spring (TS) hinges showing complex nonlinear hysteresis behavior caused by the snap-through buckling was conducted. Subsequently, it was verified by comparing simulation results by multi-body dynamics (MBD) analysis with test results on ground-based deployment testing considering gravity compensation, termed zero-gravity (Zero-G) device. It has been difficult to predict the folding and unfolding behavior of TS hinges because their moment–rotation relationship showed a nonlinear hysteresis behavior. To realize this attribute, an algorithm that checks the sign of angular velocity of the revolute joints was used to distinguish folding from unfolding. The nonlinear hysteresis was implemented in terms of two path-dependent nonlinear moment–rotation curves with the aid of the expression function (a kind of user subroutine) in MBD software RecurDyn. Finally, it was found that the results of the deployment analysis were in excellent agreement with those of the test when the friction torques of the revolute joints were properly identified by an inverse analysis with the test frames, thus validating the MBD model.

To explore the effects of daily mean temperature (°C), average daily air pressure (hPa), humidity (%), wind speed (m/s), particulate matter (PM) 2.5 (μg/m3) and PM10 (μg/m3) on the admission rate of chronic kidney disease (CKD) patients admitted to the Second Affiliated Hospital of Harbin Medical University in Harbin and to identify the indexes and lag days that impose the most critical influence.The R language Distributed Lag Nonlinear Model (DLNM), Excel, and SPSS were used to analyze the disease and meteorological data of Harbin from 01 January 2010 to 31 December 2019 according to the inclusion and exclusion criteria.Meteorological factors and air pollution influence the number of hospitalizations of CKD to vary degrees in cold regions, and differ in persistence or delay. Non-optimal temperature increases the risk of admission of CKD, high temperature increases the risk of obstructive kidney disease, and low temperature increases the risk of other major types of chronic kidney disease. The greater the temperature difference is, the higher its contribution is to the risk. The non-optimal wind speed and non-optimal atmospheric pressure are associated with increased hospital admissions. PM2.5 concentrations above 40 μg/m3 have a negative impact on the results.Cold region meteorology and specific environment do have an impact on the number of hospital admissions for chronic kidney disease, and we can apply DLMN to describe the analysis.