Search

Purpose: To improve the simulation accuracy of the static characteristics of rubber under multiaxial loading, this study proposed a method to determine the parameters of the constitutive model of rubber under such conditions. Methods: Initially, a multiaxial loading test platform was developed to verify the effect of multiaxial loading on the static properties of rubber. Subsequently, considering both the interval uncertainty of the applied loads and the stochastic uncertainty of measurement noise, a new method combining Bayesian inference, interval analysis, and response surface modeling was used to develop a Mooney-Rivlin constitutive parameter identification model. The constitutive parameters C01 and C10 were successfully predicted using this method, validated using finite element simulations and experiments, and the errors were calculated. Finally, the effects of multiaxial loading (Z-direction preloading and X/Y-direction working loading) and measurement noise on the identification process were discussed. Results: The results indicated that the static stiffness rises in all axial directions as the preload level increased. The intrinsic parameters identified by considering preload ensured that the error in the experimental and simulated stiffness values remained within 15% in all directions compared to the case without considering preload. It was found that considering preload significantly improves the accuracy of static stiffness prediction. [ABSTRACT FROM AUTHOR]

A novel time-dependent deterministic SEIRS model, extended with vaccination, hospitalization, and vital dynamics, is introduced. Time-varying basic and effective reproduction numbers associated with this model are defined, which are crucial metrics in understanding epidemic dynamics. Furthermore, a parameter identification approach has been used to develop a numerical method to compute these numbers for long-term epidemics. We analyze the actual COVID-19 data from the USA, Italy, and Bulgaria to solve appropriate inverse problems and gain an understanding of the time evolution behavior of the basic and effective reproduction numbers. Moreover, an insightful comparison of key coronavirus data and epidemiological parameters across these countries has been conducted. For this purpose, while the basic and effective reproduction numbers provide insights into the virus transmission potential, we propose data-driven criteria for assessing the actual realization of the transmission potential of the SARS-CoV-2 virus and the effectiveness of the applied restrictive measures. To obtain these results, we conduct a mathematical analysis to demonstrate various biological properties of the new differential model, including non-negativity, boundedness, existence, and uniqueness of the solution. The new model and the associated numerical simulation tools proposed herein could be applied to COVID-19 data in any country worldwide and hold a promising potential for the transmission capacity and impact of the virus. [ABSTRACT FROM AUTHOR]

Machines with highly saturated magnetic circuits are utilized to maximize drive efficiency. However, their control is non-trivial due to highly non-linear characteristics, and therefore, an accurate parameter identification procedure is crucial. This paper presents and validates a comprehensive flux linkage identification procedure. Several steps needed for accurate parameter identification and their influence on the estimation results are described. The impact of all main parts of the electrical drive, i.e., dead times introduced between switching off and switching on the power transistors, phase delay from the current low-pass filters, delay from the control system, and misalignment of the d – axis angle due to the machine asymmetries is considered and partially eliminated. An improved identification procedure with the look-up table-based dead time compensation and estimation of equivalent circuit resistance is applied to estimate the parameters of the highly saturated PMSM traction machine from the fourth generation of the Toyota Prius, and the obtained results are compared with a Finite Element Method-based model of this machine. Finally, dynamic test results are conducted to prove the proposed approach's accuracy. [ABSTRACT FROM AUTHOR]

Due to the merits of high flexibility, large driving force, and light weight, the pneumatic artificial muscle (PAM) has been widely applied in robot. However, the inherent asymmetric hysteresis nonlinear characteristics affect the control performance of PAM seriously. This paper presents an improved generalized Prandtl–Ishlinskii (GPI-M) model for the asymmetric hysteresis characterization of PAM, which adopts the simplified Magic Formula (SMF) as the envelop function instead of hyperbolic tangent function (GPI) or arc tangent function (GPI-A). With regard to the effect of parameters identification, the Levenberg-Marquardt (LM) algorithm, genetic algorithm (GA), particle swarm optimization (PSO) algorithm, and differential evolution (DE) algorithm are studied, which shows the LM algorithm is far superior to the other three intelligent algorithms in terms of computational efficiency and identification accuracy. Furthermore, a crosswise comparative study on three models and two envelop functions investigating modeling accuracy reveals that the newly introduced GPI-M model exhibits better performance than the modified symmetric generalized Prandtl-Ishlinskii (MSGPI) model and the GPI-A model presented previously. Meanwhile, the influence of the operator numbers on the modeling accuracy of three models is discussed, which finds that the GPI-M model possess the best accuracy with the least operator numbers. [ABSTRACT FROM AUTHOR]

Accurate parameters identification of photovoltaic(PV) models is essential for state assessment of PV systems, as well as for supporting maximum power point tracking and system control, thus holding significant importance. To precisely identify parameters of different PV models, this paper proposes an improved JAYA algorithm based on self-adaptive method, termed Sjaya. Sjaya incorporates three position update strategies, all utilizing adaptive factors, automatically transitioning from explorative to exploitative behaviors, enhancing the population’s ability to escape local optima in the solution space and avoiding premature convergence. The first strategy involves learning towards the best and worst individuals in the population, with the individual iteration direction perturbed by adaptive and normal distribution probability factors to enhance population exploration. The second strategy entails learning towards superior and inferior subgroups, effectively leveraging information from the population, with the ranges of these two subgroups continuously evolving throughout the iteration process. In the third strategy, a novel individual selection mechanism is devised, allocating selection probabilities to individuals based on the exploration phase. Individual updates entail learning from three selected individuals within the population, thereby enhancing population diversity. The proposed Sjaya method is employed to address the parameters identification problem of single diode, double diode, and photovoltaic module models of various photovoltaic types. In numerical experiments, each algorithm was tested 30 times. The average root mean square error (RMSE) of Sjaya for the single diode model and double diode model of RTC France were 9.86022E-04 and 9.849674E-04, respectively. In addition, we use three PV modules to detect Sjaya and competing algorithms. The RMSE of Sjaya on the Photo Watt-PWP 201 module, STM6-40/36 module and STP6-120/36 module is 2.431177E-03, 1.772275E-03 and 1.568231E-02 respectively. The synthesis of experimental findings and analysis indicates that Sjaya outperforms other methods in terms of competitiveness, while also demonstrating high effectiveness and robustness. [ABSTRACT FROM AUTHOR]

This paper deals with the parameter identification of induction machine rotor and stator windings. These two magnetically coupled circuits are described and analyzed to explain the novel method that can identify the set of substitute parameters (not equivalent circuit parameters) which can be used for some analysis of transient processes in a stationary induction machine. The parameters are: inductive leakage factor, substitute rotor resistance, substitute rotor inductance, and substitute mutual inductance. The method belongs to the group of so-called off-line methods, i.e., it is performed on a machine in a standstill, and it assumes a star-connected induction motor. The correctness of the method is verified by calculation as well as by measurement. Moreover, the practical use of the method is shown in an example of how to determine the rotor winding temperature rise, and the results of the measurement on an induction motor with a power of 0.75 kW are presented. The method can be performed even when the rotor circuit is hidden inside the completely closed rotor construction, as in the case of a squirrel cage induction motor, i.e., without access to the rotor circuit. [ABSTRACT FROM AUTHOR]

In this review, we successively present the methods for phenomenological modeling of the evolution of reported and unreported cases of COVID-19, both in the exponential phase of growth and then in a complete epidemic wave. After the case of an isolated wave, we present the modeling of several successive waves separated by endemic stationary periods. Then, we treat the case of multi-compartmental models without or with age structure. Eventually, we review the literature, based on 260 articles selected in 11 sections, ranging from the medical survey of hospital cases to forecasting the dynamics of new cases in the general population. This review favors the phenomenological approach over the mechanistic approach in the choice of references and provides simulations of the evolution of the number of observed cases of COVID-19 for 10 states (California, China, France, India, Israel, Japan, New York, Peru, Spain and United Kingdom). • Social changes influencing the contact rate transmission. • Phenomenological model and predictions for epidemic outbreaks. • Time-dependent parameters identification in epidemic models. • Environmental changes influencing the contact rate of transmission. • Public health policies and mutation dynamics changing the conditions of contagion. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Xi'an Jiaotong University is the property of Editorial Office of Journal of Xi'an Jiaotong University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The growing electric vehicle (EV) sector, while tackling climate issues, also encounters obstacles concerning resource usage and a rise in EV disposal. This study examines the circularity framework in the EV sector to address these problems, emphasizing the importance of reusing and upcycling resources for sustainability. Moreover, numerous nations have implemented recycling and R&D policies to offer legal and policy backing for the advancement of recycling technology. This report will investigate and contrast various recycling technologies. Furthermore, it involves an examination of key figures in the EV sector to identify deficiencies in the EV materials supply chain and tactics for minimizing waste. Conversations with a variety of EV stakeholders will provide perspectives on the creative methods businesses are implementing to tackle these obstacles. By embracing the principles of a circular economy, the EV industry can act as a model for a sustainable future, decreasing its environmental footprint and encouraging a more efficient use of resources. [ABSTRACT FROM AUTHOR]

In the current electromechanical model of cantilevered piezoelectric energy harvesters, the assumption of uniform electric field strength within the piezoelectric layer is commonly made. This uniform electric field assumption seems reasonable since the piezoelectric layer looks like a parallel-plate capacitor. However, for a piezoelectric bender, the strain distribution along the thickness direction is not uniform, which means the internal electric field generated by the spontaneous polarization cannot be uniform. In the present study, a non-uniform electric field in the piezoelectric layer is resolved using electrostatic equilibrium equations. Based on these, the traditional distributed parameter electromechanical model is corrected and simplified to a practical single mode one. Compared with a traditional model adopting a uniform electric field, the bending stiffness term involved in the electromechanical governing equations is explicitly corrected. Through comparisons of predicted power output with two-dimensional finite element analysis, the results show that the present model can better predict the power output performance compared with the traditional model. It is found that the relative corrections to traditional model have nothing to do with the absolute dimensions of the harvesters, but only relate to three dimensionless parameters, i.e., the ratio of the elastic layer's to the piezoelectric layer's thickness; the ratio of the elastic modulus of the elastic layer to the piezoelectric layer; and the piezoelectric materials' electromechanical coupling coefficient squared, k 31 2 . It is also found that the upper-limit relative corrections are only related to k 31 2 , i.e., the higher k 31 2 is, the larger the upper-limit relative corrections will be. For a PZT-5 unimorph harvester, the relative corrections of bending stiffness and corresponding resonant frequency are up to 17.8% and 8.5%, respectively. An inverse problem to identify the material parameters based on experimentally obtained power output performance is also investigated. The results show that the accuracy of material parameters identification is improved when considering a non-uniform electric field. [ABSTRACT FROM AUTHOR]

Solar energy is widely regarded as an environmentally friendly and sustainable source of power. It reduces greenhouse gas emissions and dependency on fossil fuels, contributing to a cleaner environment. It also provides cost savings and enhances energy security. However, technical challenges persist. Poor installation, inadequate maintenance, and aging can degrade photovoltaic (PV) systems, leading to failures or faults. These issues increase the risk of power losses, electrical shocks, and fires. Direct Current (DC) arcs, in particular, pose a significant fire hazard in PV systems due to their unpredictability and high potential for damage. However, accurately defining parameters for real-world DC arc faults is difficult. Developing computational models of electric arcs is essential for simulating, analyzing, and detecting these faults. In that sense, this work provides a comprehensive overview of the prominent black-box arc models documented in the scientific literature, along with various methods for parameter identification, to facilitate the investigation of arc-related incidents within PV systems.

In order to reduce the influence of motor parameters variation on the current harmonic suppression and improve the accuracy of harmonic compensation voltage, a current harmonic suppression method considering parameters variation is proposed to suppress the current harmonic for interior permanent magnet synchronous motors (IPMSM) control system in this paper. Based on the current harmonic suppression method, a forgetting factor recursive least square method (FFRLS) is introduced to build an adaptive current harmonic suppression loop. The FFRLS can identify the IPMSM parameters, and the identification results of parameters variation are introduced into the steady-state harmonic voltage equation to dynamically update the harmonic compensation voltage. Therefore, the influence of motor parameters variation on the harmonic compensation voltage generation is reduced, and the effect of current harmonic suppression is improved. Finally, the effectiveness of the proposed method is verified by the simulation and experimental results. [ABSTRACT FROM AUTHOR]

There is a strong dynamic coupling between the base of free-floating space robot and the space manipulator. In order to reduce the interference of the movement of the space manipulator on the robot's base, the trajectory of the space manipulator needs to be optimized. In this paper, the joint path of space manipulators is optimized by using the pseudo-spectral method to reduce the impact of the arm movement on the base. For space robots, parameters identification is the premise of trajectory planning. A parameters identification method is proposed, which only needs to measure the base's angular speed. Numerical simulation results verify the validity of the proposed method. The attitude angle of floating base returns to the initial state after maneuvers of space manipulators. [ABSTRACT FROM AUTHOR]

Inter-area frequency modes of electromechanical oscillations are decisive factors that influence the performance of electrical power systems. However, the accurate estimation of these frequencies remains a challenge due to the non-linear behavior of the response system following a disturbance and the strong coupling among the frequencies. This work describes a curve fitting and electromechanical frequency estimation algorithm, called Multi-method Piecewise Identification (MPI), based on a combination of the Matrix Pencil Method (MPM), the Prony method, and Subspace Identification (SSI), choosing the best approximation of an interval basis according to the model accuracy index (MAI) and the mean square error (MSE). The advantages and limitations of MPI were analyzed by computational analyses that were carried out with Python-based code that estimates the low frequencies for several ringdown signals, including oscillating signals from Australian and Brazilian systems that arise after large disturbances. To ensure robustness to random and noisy conditions, the method was tested with a synthetic signal with Gaussian white noise and time-variant frequencies, and compared to the methods mentioned above, the Hilbert–Huang Transform (HHT), and Eigensystem Realization Algorithm (ERA). The MPI has shown robust results and is capable of obtaining a better fitting for the signal than the other methods.

Machines with highly saturated magnetic circuits are utilized to maximize drive efficiency. However, their control is non-trivial due to highly non-linear characteristics, and therefore, an accurate parameter identification procedure is crucial. This paper presents and validates a comprehensive flux linkage identification procedure. Several steps needed for accurate parameter identification and their influence on the estimation results are described. The impact of all main parts of the electrical drive, i.e., dead times introduced between switching off and switching on the power transistors, phase delay from the current low-pass filters, delay from the control system, and misalignment of the d–axis angle due to the machine asymmetries is considered and partially eliminated. An improved identification procedure with the look-up table-based dead time compensation and estimation of equivalent circuit resistance is applied to estimate the parameters of the highly saturated PMSM traction machine from the fourth generation of the Toyota Prius, and the obtained results are compared with a Finite Element Method-based model of this machine. Finally, dynamic test results are conducted to prove the proposed approach’s accuracy.

To describe materials deformation with viscoplastic characteristics, the Anand model is usually used. For a certain material, the classical method based on the saturation stress of material deformation is usually adopted to identify the parameters in its Anand model. However, for some materials, the saturation stress can not be obtained in the measurable strain range of general experiment equipment, and the classical method should be modified for this situation. Hence in this paper, a method used to identify the saturation stress of material from the insaturation state of deformation is proposed. For the proposed method, the identification of saturation stress is transferred to a problem of obtaining the solution of an objective function, which is derived from the stress-strain curves of insaturation state. By combining the proposed method, the classical Anand parameters identification method can be used in a wider range than before. [ABSTRACT FROM AUTHOR]

Since installing solid oxide fuel cells (SOFCs)-based systems suffers from high expenses, accurate and reliable modeling is heavily demanded to detect any design issue prior to the system establishment. However, such mathematical models comprise certain unknowns that should be properly estimated to effectively describe the actual operation of SOFCs. Accordingly, due to their recent promising achievements, a tremendous number of metaheuristic optimizers (MHOs) have been utilized to handle this task. Hence, this effort targets providing a novel thorough review of the most recent MHOs applied to define the ungiven parameters of SOFCs stacks. Specifically, among over 300 attempts, only 175 articles are reported, where thirty up-to-date MHOs from the last five years are comprehensively illustrated. Particularly, the discussed MHOs are classified according to their behavior into; evolutionary-based, physics-based, swarm-based, and nature-based algorithms. Each is touched with a brief of their inspiration, features, merits, and demerits, along with their results in SOFC parameters determination. Furthermore, an overall platform is constructed where the reader can easily investigate each algorithm individually in terms of its governing factors, besides, the simulation circumstances related to the studied SOFC test cases. Over and above, numerical simulations are also introduced for commercial SOFCs’ stacks to evaluate the proposed MHOs-based methodology. Moreover, the mathematical formulation of various assessment criteria is systematically presented. After all, some perspectives and observations are provided in the conclusion to pave the way for further analyses and innovations. [ABSTRACT FROM AUTHOR]

The growing electric vehicle (EV) sector, while tackling climate issues, also encounters obstacles concerning resource usage and a rise in EV disposal. This study examines the circularity framework in the EV sector to address these problems, emphasizing the importance of reusing and upcycling resources for sustainability. Moreover, numerous nations have implemented recycling and R&D policies to offer legal and policy backing for the advancement of recycling technology. This report will investigate and contrast various recycling technologies. Furthermore, it involves an examination of key figures in the EV sector to identify deficiencies in the EV materials supply chain and tactics for minimizing waste. Conversations with a variety of EV stakeholders will provide perspectives on the creative methods businesses are implementing to tackle these obstacles. By embracing the principles of a circular economy, the EV industry can act as a model for a sustainable future, decreasing its environmental footprint and encouraging a more efficient use of resources.

In recent years, the demand for photovoltaic (PV) energy has increased parallel to scientific research on PV cells, including electrical modeling, characterization, and extraction of unknown parameters. Moreover, our main contribution in this paper focuses on experimental investigation of the effect of solar radiation and temperature on the performance of a small photovoltaic plant years after its installation in the Ouargla region. As in many parts of the world, this studied area has experienced rising temperatures due to climate change, affecting system outputs. Therefore, in the first phase, we offer to characterize PV modules in various conditions and analyze the electrical parameters’ performance. The results indicate solar radiation and temperature influence PV modules’ electrical parameters. Moreover, the temperature influences the open-circuit voltage, while solar radiation positively impacts the short-circuit current. On the other side, we determine the optimal parameters of these modules and develop an accurate PV model using the bald eagle search algorithm (BES), gradian-based optimizer algorithm (GBO), and whale optimization Algorithm (WOA) based on a single diode model. The achievements show that the BES and GBO algorithms give good results for the optimum estimation of the PV model compared to WOA, where the best fitness was recorded at 0.015608 with the lowest deviation of 0.012565 and 0.039588, respectively. However, the BES has generated the minimum error values and with minimum iteration number, which indicates that this technique is more stable and robust for PV module parameter extraction. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for identifying the parameters of the multispecies Lotka-Volterra model based on sampled data. The conditions (nonhyperplanarity, recurrence) under which the "Stripe" algorithm achieves the identification goals are found. The relation between nonhyperplanar recurrent trajectories and the persistent excitation condition for systems of general form is studied. [ABSTRACT FROM AUTHOR]

The vibration response of the plates attached with the free viscoelastic damping layer is investigated to predict the performance of free surface damping treatment. The frequency-dependent dynamic properties of the viscoelastic layer are expressed by fractional Kelvin-Voigt model. A simple and efficient identification approach based on the gradient-based optimization principle is employed to evaluate the viscoelastic model parameters from the experimental data. Both classical and fractional Kelvin-Voigt models are employed to describe the viscoelasticity, and the corresponding model parameters are identified. Further, the application of these identified parameters in the modeling and analyzing of real viscoelastic composite structures is demonstrated through quantitative examples. Step load-induced transient responses of the viscoelastic structures modeled using the classical and fractional model parameters are investigated and compared, to demonstrate the consequences of unknowingly using the classical Kelvin-Voigt model. The effects of the viscoelastic layer thickness on the transient response of the two-layered plate are also studied. [ABSTRACT FROM AUTHOR]

In order to solve the friction and wear problem of the sliding electric contact of the pantograph of electrified railway, the wear characteristics of the pantograph slide were studied by using the self-made sliding electric contact tester of the pantograph slide, and the variation law of the wear rate with the contact current, sliding speed, pressure fluctuation amplitude and fluctuation frequency was obtained. Based on this, the mathematical model expression of the wear rate is constructed through the study of the proportional relationship between no-current-carrying wear, pressure load fluctuation wear, total wear rate under current-carrying friction and wear rate under pure mechanical friction by using Archard wear model. The combination of general global optimization algorithm and McQuart algorithm is used to optimize the parameter identification of the wear rate model expression, and the accuracy of the wear rate model is proved. The qualitative analysis of the wear rate is transformed into the quantitative analysis of the wear rate, which has certain guiding significance for further research on the wear of the arch mesh system. [ABSTRACT FROM AUTHOR]

An approach to identifying the parameters of the motion model for a surface ship subject to external disturbances is proposed. It uses the measurements of heading, yaw rate, speed through the water, and global satellite navigation system (GNSS) data. The model structure is set in the state space. We use the criterion of how close is the real vehicle response to a given control input to its motion model response under the same disturbances. It is proposed to apply the Kalman filter with the state vector including the disturbances, and an iterated procedure for estimating parameters by minimizing the criterion. It is shown that this ensures stable identification of model parameters under different disturbances. Simulation results are presented to evaluate the quality of identification. The approach was validated in full-scale tests of a high-speed boat. [ABSTRACT FROM AUTHOR]

The precision and stability of the Robot-Assisted Percutaneous Puncture (RAPP) system have become increasingly crucial with the widespread integration of robotic technology in the field of medicine. The accurate calibration of the RAPP system prior to surgery significantly influences target positioning performance. This study proposes a novel system calibration method that simultaneously addresses system hand–eye calibration and robot kinematic parameters calibration, thereby enhancing the surgery success rate and ensuring patient safety. Initially, a Closed-loop Hand–eye Calibration (CHC) method is employed to rapidly establish transformation relationships among system components. These CHC results are then integrated with nominal robot kinematic parameters to preliminarily determine the system calibration parameters. Subsequently, a hybrid algorithm, combining the regularized Levenberg–Marquardt (LM) algorithm and a particle filtering algorithm, is utilized to accurately estimate the system calibration parameters in stages. Numerical simulations and puncture experiments were conducted using the proposed system calibration method and other comparative methods. The experimental results revealed that, among several comparative methods, the approach presented in this paper yields the greatest improvement in the puncture accuracy of the RAPP system, demonstrating the accuracy and effectiveness of this method. In conclusion, this calibration method significantly contributes to enhancing the precision, operational capability, and safety of the RAPP system in practical applications. [ABSTRACT FROM AUTHOR]

Temperature rise of the hub motor in distributed drive electric vehicles (DDEVs) under long-time and overload operating conditions brings parameter drift and degrades the performance of the motor. A novel online parameter identification method based on improved teaching-learning-based optimization (ITLBO) is proposed to estimate the stator resistance, ��-axis inductance, ��-axis inductance, and flux linkage of the hub motor with respect to temperature rise. The effect of temperature rise on the stator resistance, ��-axis inductance, ��-axis inductance, and magnetic flux linkage is analysed. The hub motor parameters are identified offline. The proposed ITLBO algorithm is introduced to estimate the parameters online. The Gaussian perturbation function is employed to optimize the TLBO algorithm and improve the identification speed and accuracy. The mechanisms of group learning and low-ranking elimination are established. After that, the proposed ITLBO algorithm for parameter identification is employed to identify the hub motor parameters online on the test bench. Compared with other parameter identification algorithms, both simulation and experimental results show the proposed ITLBO algorithm has rapid convergence and a higher convergence precision, by which the robustness of the algorithm is effectively verified. Keywords: parameters identification, teaching–learning-based optimization, hub motor, temperature rise.

In the current electromechanical model of cantilevered piezoelectric energy harvesters, the assumption of uniform electric field strength within the piezoelectric layer is commonly made. This uniform electric field assumption seems reasonable since the piezoelectric layer looks like a parallel-plate capacitor. However, for a piezoelectric bender, the strain distribution along the thickness direction is not uniform, which means the internal electric field generated by the spontaneous polarization cannot be uniform. In the present study, a non-uniform electric field in the piezoelectric layer is resolved using electrostatic equilibrium equations. Based on these, the traditional distributed parameter electromechanical model is corrected and simplified to a practical single mode one. Compared with a traditional model adopting a uniform electric field, the bending stiffness term involved in the electromechanical governing equations is explicitly corrected. Through comparisons of predicted power output with two-dimensional finite element analysis, the results show that the present model can better predict the power output performance compared with the traditional model. It is found that the relative corrections to traditional model have nothing to do with the absolute dimensions of the harvesters, but only relate to three dimensionless parameters, i.e., the ratio of the elastic layer’s to the piezoelectric layer’s thickness; the ratio of the elastic modulus of the elastic layer to the piezoelectric layer; and the piezoelectric materials’ electromechanical coupling coefficient squared, k312. It is also found that the upper-limit relative corrections are only related to k312, i.e., the higher k312 is, the larger the upper-limit relative corrections will be. For a PZT-5 unimorph harvester, the relative corrections of bending stiffness and corresponding resonant frequency are up to 17.8% and 8.5%, respectively. An inverse problem to identify the material parameters based on experimentally obtained power output performance is also investigated. The results show that the accuracy of material parameters identification is improved when considering a non-uniform electric field.

We present an inertia-augmented relaxed micromorphic model that enriches the relaxed micromorphic model previously introduced by the authors via a term Curl P · in the kinetic energy density. This enriched model allows us to obtain a good overall fitting of the dispersion curves while introducing the new possibility of describing modes with negative group velocity that are known to trigger negative refraction effects. The inertia-augmented model also allows for more freedom on the values of the asymptotes corresponding to the cut-offs. In the previous version of the relaxed micromorphic model, the asymptote of one curve (pressure or shear) is always bounded by the cut-off of the following curve of the same type. This constraint does not hold anymore in the enhanced version of the model. While the obtained curves' fitting is of good quality overall, a perfect quantitative agreement must still be reached for very small wavelengths that are close to the size of the unit cell. [ABSTRACT FROM AUTHOR]

Ultra-precise actuation at extremely low speeds over a broad range is a major challenge for advanced manufacturing. A novel two-axis differential micro-feed system (TDMS) has been proposed recently to overcome the low-speed crawling of the worktable. However, due to the diversity of the force states of the TDMS, the methods for identification identifyingof friction parameters traditionally (like the all -components identification method, ACIM) didn't did not perform well. And many studies on the performance of the pre-sliding phase of the TDMS are missing. Therefore, a novel whole-system identification method (WSIM) based on the TDMS was proposed in this paper to precisely identify the friction parameters under different states of motion. The generalized Maxwell sliding (GMS) friction model was also applied to improve the accurate description of the pre-sliding. A novel corrected Stribeck curve based on the TDMS (TDMSSC) was proposed under the uniqueness of the TDMS structure. Control experiments showedn that the WSIM has higher precision and stability rather thancompared torather than the ACIM, and the correction of the Stribeck curve for the TDMS makes a contribution to the performance. This method significantly improves the accuracy and stability of the machine tool drive system. [ABSTRACT FROM AUTHOR]

Double casing joints are flexible pipe joints used for connecting aircraft fuel pipelines, which can compensate for the displacement and corner of the connected pipes and have complex mechanical characteristics. However, it is difficult to use sensors to directly measure the mechanical connection parameters of flexible joints. In this paper, we construct a coupling dynamics model and parameter identification of a double casing joint. Firstly, we analyze the structure and working principle of double-layer casing joints and establish the dynamics model of a single-layer flexible joint based on the transfer matrix method. Then, we deduce the coupling matrix of the inner and outer pipeline according to the deformation coordination conditions combined with matrix dimension extension. We establish the coupling dynamics model of flow–solid coupling of double casing joints. Furthermore, parameters such as equivalent stiffness and damping of each motion of the double casing joint in the casing unit are identified using the force-state mapping (FSM) method, and an analytical solution in the frequency domain under hammering excitation is given by the dynamics model. Finally, the dynamics test bench of the double casing joint for aircraft fuel is set up, and the free mode test of the double casing joint assembly is carried out. The results show that under free boundary hammering excitation, the theoretical and experimental frequency-domain response results are well matched, both obtaining seven main resonance peaks, and the maximum error is 9.45%, which shows the validity of the pipeline dynamics modeling method with a double casing joint. [ABSTRACT FROM AUTHOR]

Selective catalytic reduction (SCR) technology is the most widely used denitrification method in industry. However, the calibration of its kinetic model is very problematic due to the large number of parameters involved and the nonlinear characteristics, especially for the undesired reactions such as formation-decomposition of ammonium nitrate (AN), ammonium sulfate (AS), ammonium bisulfate (ABS), etc. In this paper, the synthetic gas test bench and the SCR system of WD10 marine diesel engine burning high-sulfur fuel oil (HSFO) and low-sulfur fuel oil (LSFO) are taken as the research objects. Firstly, based on the experimental data obtained from the synthetic gas test bench, the pre-exponential multiplier and activation energy of each reaction were identified using the Discrete-Grid algorithm. In this work, considering that the production of N2O, SO3, AN, AS, and ABS in the undesired reactions is very low, not on the same order of magnitude as that of NO and NH3, it is easy to overlook their reaction characteristics in the optimization process. We have adopted an approach that amplifies the error between the predicted concentration and the observed concentration of the species to solve this problem. Secondly, a detailed and complete SCR kinetic model was constructed. Finally, with the parameter identification results obtained from the synthesis gas bench as initial values, all the kinetic parameters of the WD10 marine engine SCR model was calibrated simultaneously using the Discrete-Grid algorithm. The results showed that the SCR kinetic model derived from the synthetic gas test bench was suitable for the real marine engine SCR system when the range of temperature and space velocity (SV) of the synthetic gas bench test is similar to that of the marine SCR system. [ABSTRACT FROM AUTHOR]

It is of great significance to have research on the deformation characteristics and stress distribution of aortic wall. Reliable prediction of constitutive parameters requires an inverse process, which possesses challenges. This work proposes an inverse procedure to identify the constitutive parameters of aortic walls, which integrates nonlinear finite element method (FEM), random forest (RF) model and hybrid Random Search (RS) and Grid Search (GS) algorithm. FEM models are first established to simulate nonlinear deformation of aortic walls subjected to uniaxial tension tests. A dataset of nonlinear relationship between the engineering stress and main stretch of aortic walls is created using FEM models and the nonlinear relationship is learned through RF model. The hybrid RS&GS algorithms are used to adjust the major hyperparameters in RF. Then the optimized RF is utilized to predict constitutive parameters of aortic walls with the help of uniaxial tension tests. The prediction results show that the RF optimized by hybrid RS&GS (RF-RS&GS) approach is an effective and accurate approach to identify the constitutive parameters of aortic walls. The present RF-RS&GS model can be further extended for the predictions of constitutive parameters of other types of nonlinear soft materials. Additionally, the relative importance of constitutive parameters of aortic walls in Gasser–Ogden–Holzapfel (GOH) strain energy function is investigated. It is found that the parameters C 1 0 and κ in GOH are most intensive to the engineering stress of aortic walls. [ABSTRACT FROM AUTHOR]