High Nonlinearity Research Articles

Distribution Grid Topology and Parameter Estimation Using Deep-Shallow Neural Network With Physical Consistency

To better monitor and control distribution grids, the exact knowledge of system topology and parameters is a fundamental requirement. However, topology information is usually incomplete due to limited sensors in the grid. Therefore, estimating the system parameters using partial data is a critical topic for distribution systems. Due to the high nonlinearity of unobservable system quantities and noises, Deep Neural Networks (DNNs) are widely utilized for accurate estimation. While traditional approaches either treat DNNs as a black box or embed little physical knowledge into DNNs, they cannot guarantee that the DNN model is consistent with physical equations and hence lack accuracy and interpretability. Therefore, we propose a Deep-Shallow neural Network (DSN) for distribution system estimation. The key is to create virtual nodes to represent nodes without sensors in the system, and denote a DNN to approximate missing quantities at virtual nodes. Then, the Power Flow (PF) equations can be estimated via a shallow neural network, achieving physical consistency. Isolated by virtual nodes, the whole system is decomposed into a set of reduced graphs with approximate PF equations. Likewise, we introduce a Reinforcement Learning-based search algorithm to connect the reduced grids into one connected system. Correspondingly, the DSN is fine-tuned to achieve consistency with the PF equations of the connected graph. Finally, the paper illustrates the superiority of the proposed DSN due to its physical consistency. Specifically, comprehensive experiments demonstrate the high performances of our DSN over other methods in distribution grids.

An Iterative Equal Area Criterion for Transient Stability Analysis of Grid-Tied Converter Systems With Varying Damping

With the increasing penetration of renewable energy generators, the stability issues of grid-tied converter systems become much more important. However, due to the high nonlinearity and varying damping of converter systems, conventional transient stability analysis methods are not applicable, which may bring to conservativeness or misjudgment on stability assessment. In this paper, the transient stability of grid-tied converter systems with varying damping is investigated to provide stability boundary estimation. Firstly, considering the frequency mutation of the phase-locked loop (PLL) caused by various perturbations, a modified swing equation model of grid-tied converter systems is built, which greatly improves the mathematical model accuracy under transient disturbances. To evaluate the impacts of varying damping and frequency mutation, an iterative equal area criterion (ITEAC) method is proposed with the iterative calculation of the accelerating and decelerating area, which renders stability boundaries with high accuracy. Moreover, the impacts of controller and system parameters on stability boundaries are quantitatively analyzed. Eventually, simulation and hardware-in-loop experiments are performed to verify the effectiveness and superiority of the proposed ITEAC.

Fabrication of a Laminated Actuator with Excellent Linearity Using Ground Potassium Sodium Niobate-Based Ceramic Sheets

Linearity is an important factor that affects actuator accuracy. However, the high nonlinearity of KNN piezoelectric ceramics restricts their application in actuators. In this study, we used grinding stress to improve the linearity of ceramic chips, and used them to fabricate a laminated actuator. The ceramic sheets were ground to a thickness of 0.5 mm. During grinding, some areas of the ceramic changed from tetragonal to orthorhombic, owing to the grinding stress. The piezoelectric constant (d33) increased from 198 to 268 pC/N. Notably, the linearity of the ceramics improved. Seven pieces of ground ceramics were bound, to fabricate a laminated multilayer actuator with a total thickness of 3.5 mm. A DC voltage was applied to the actuator, and the displacement was measured. The displacement reached 0.73 μm under a low driving voltage of 200 V. A linear regression analysis of the displacement–voltage relationship was performed, obtaining the regression equation of the actuator. The linearity correlation coefficient was approximately 0.9903, implying that the actuator exhibits a high accuracy. The grinding stress improved the linearity, together with the piezoelectric properties of the ceramic chips, thus improving the actuator accuracy. This research will promote the application of KNN piezoelectric ceramics in actuators.

Modeling and Simulation of Parametric Nonlinear Focused Ultrasound in Three-Dimensional Bubbly Liquids with Axial Symmetry by a Finite-Element Model

This paper presents the development of a numerical model able to track in time the behavior of nonlinear focused ultrasound when interacting with tiny gas bubbles in a liquid. Our goal here is to analyze the frequency components of the waves by developing a model that can easily be adapted to the geometrical restrictions and complexities that come out in several application frameworks (sonochemistry, medicine, and engineering). We thus model the behavior of nonlinear focused ultrasound propagating in a liquid with gas bubbles by means of the finite-element method in an axisymmetric three-dimensional domain and the generalized-α method in the time domain. The model solves a differential system derived for the nonlinear interaction of acoustic waves and gas bubble oscillations. The high nonlinearity and dispersion of the bubbly medium hugely affect the behavior of the finite-amplitude waves. These characteristics are used here to generate frequency components of the signals that do not exist at the source through nonlinear mixing (parametric antenna). The ability of the model to work with complex geometries, which is the main advantage of the method, is illustrated through the simulation of nonlinear focused ultrasound in a medium excited from two spherical sources in opposite directions.

3D hybrid modeling approach combining the finite and discrete element methods: Validation based on masonry shear wall tests

The masonry structures represent a remarkable portion of the world’s built heritage. The preservation of these buildings requires a multidisciplinary diagnostic approach that includes a structural assessment to estimate their stability. However, this task remains difficult because of the high nonlinearity behavior of the masonry material and the complexity of the geometries of this type of building. For this reason, several robust modeling strategies have been developed. Among these strategies, block-based models (BBM) based on the discrete element method and more specifically the NSCD “Non Smooth Contact Dynamics” allows to take into account the actual geometrical texture of masonry. However, in this type of approach, blocks are generally rigid or elastic and nonlinearity appears only at the interfaces between blocks governed by cohesive frictional laws. In this study, a new hybrid approach is proposed. It consists of integrating a model combining (i) a frictional cohesive zone model “FCZM” describing the block-mortar interface and (ii) a quasi-brittle damage model associated with the block while (iii) the elasticity of mortar joints is explicitly taken into account in the FCZM model by adding a linear spring in both normal and tangential directions. Thus, avoiding the use of extended blocks and corresponding homogeneous properties. The validation of this numerical approach is carried out on the basis of an experimental campaign on masonry shear wall test subjected to compression.

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Structure optimization of large-solid-core photonic crystal fibers based on Ge\(_{20}\)Sb\(_{5}\)Se\(_{75}\) for optical applications

This paper presents a new design of Ge20Sb5Se75 large-solid-core photonic crystal fiber (PCF) with a 1st-ring-removed square lattice. Using the full vector finite element method for anisotropic perfectly matched layers, we numerically examine the dispersion characteristics of the PCF in the wavelength range spanning from 1.5 µm to 6.0 µm. The results reveal that photonic crystal fibers exhibit a variety of dispersion properties, including all-normal and anomalous dispersion, featuring one or two zero dispersion wavelengths (ZDWs). We propose two designs with optimal dispersion characteristics based on our numerical simulations. These designs have small lattice constants (Ʌ = 1.0 µm; Ʌ = 1.5 µm) and low fill factors (d/Ʌ = 0.3; d/Ʌ = 0.35). Furthermore, these selected fibers offer high nonlinearity and low confinement loss, making them excellent candidates for a wide range of optical applications.

Superior optical limiting performance of lead-free metal halide perovskites nano-crystals embedded chalcogenide glass

The nonlinear optical properties of metal halide perovskites (MHP) are highly attractive, making them an emerging hotspot in the field of perovskite photonics. In this study, the third-order optical nonlinear properties of lead-free MHP (CsSnCl3) nano-crystals (NCs) embedded in a chalcogenide glass (GeS2-Sb2S3) substrate were characterized using the Z-scan technique. The spectral range examined was from 750 to 900 nm. Experimental results revealed that the incorporation of CsSnCl3 NCs enhanced the nonlinear refraction and nonlinear absorption behavior of the GeS2-Sb2S3 glass. Furthermore, the magnitude of these effects increased with the growth size of the CsSnCl3 NCs. Importantly, the MHP glasses exhibited strong two-photon absorption due to the large multiple-absorption cross-section of MHP crystals and the intrinsic high optical nonlinearity of the chalcogenide glass. The superior optical limiting performance induced by this process was successfully demonstrated.

Maximum Torque per Ampere Technique for Switched Reluctance Motor Drive

Numerous researchers have investigated switched reluctance motors (SRMs) as a competitor to other electrical motors for usage in electric and hybrid vehicle appliances. SRMs are fault-tolerant and have a broad operating speed range. However, they have numerous drawbacks, such as high nonlinearity in machine models, complex control, and torque ripple. This study aims to improve the SRM drive system to attain maximum torque per ampere in a wide speed range. In the design and control of SRMs, maximizing torque per ampere (MTPA) is a crucial objective. MTPA refers to the strategy of achieving the maximum possible torque output for a given current input (amperes). This is essential for enhancing the efficiency and performance of the motor. The paper focuses on achieving MTPA in an SRM drive system-based commutation angle control. Utilizing a bacterial foraging optimization algorithm (BFOA), the suitable commutation angles at each operation point are determined. The intended technique is straightforward to realize and does not require complex algorithms. Simulation comparisons on an 8/6 SRM are also presented to validate the intended control technique. The results indicate that the MTPA has been enhanced by 6.961% compared to the conventional method.

Design, Simulations and Tests of a Novel Force and Moments Sensor for Instrumented Knee Implants.

Early identification of mechanical complications of total knee arthroplasties is of great importance to minimize the complexity and iatrogenicity of revision surgeries. There is therefore a critical need to use smart knee implants during intra or postoperative phases. Nevertheless, these devices are absent from commercialized orthopaedic implants, mainly due to their manufacturing complexity. We report the design, simulations and tests of a force and moments sensor integrated inside the tibial tray of a knee implant. By means of a "tray-pillar-membrane" arrangement, strain gauges and metal additive technology, our device facilitates the manufacturing and assembly steps of the complete system. We used finite element simulations to optimize the sensor and we compared the simulation results to mechanical measurements performed on a real instrumented tibial tray. With a low power acquisition electronics, the measurements corroborate with simulations for low vertical input forces. Additionally, we performed ISO fatigue testings and high force measurements, with a good agreement compared to simulations but high non-linearities for positions far from the tray centre. In order to estimate the center of pressure coordinates and the normal force applied on the tray, we also implemented a small-size artificial neural network. This work shows that relevant mechanical components acting on a tibial tray of a knee implant can be measured in an easy to assemble, leak-proof and mechanically robust design while offering relevant data usable by clinicians during the surgical or rehabilitation procedures. This work contributes to increase the technological readiness of smart orthopaedic implants.

Fuzzy Memory Sampled-Data Controller Design for PMSG-Based WECS With Stochastic Packet Dropouts

The fuzzy memory sampled-data control problem for permanent magnet synchronous generator (PMSG)-based wind energy conversion system (WECS) with stochastic packet dropouts is discussed in this paper. To do this, the PMSG-based WECS dynamics are firstly modelled by a set of fuzzy IF-THEN rules of the Takagi–Sugeno (T–S) fuzzy model because of high nonlinearity. And then, to minimize design conservatism, a suitable augmented looped-type Lyapunov functional along with a novel integral inequality is established by using the sampling mode information with signal transmission delay. Further, a fuzzy memory-based sampled-data controller is designed to obtain the asymptotic stability condition under the circumstances of packet dropouts, and simultaneously, disturbance reduction is confirmed. Thereafter, the associated fuzzy controller gains can be computed by solving a number of linear matrix inequalities (LMIs). Finally, the application of the suggested method to a real-world system is proved using PMSG-based WECS, and the mathematical Rossler's system demonstrates the effectiveness and superiority of the proposed technique over the existing method.

Utilizing Se vacancies as electronic traps to synergize impedance matching and dipole polarization with ultrathin strategy to boost Fe-Se electromagnetic wave absorption

The electronic structure, transport properties and magnetic properties of the material can be adjusted by regulating the formation and concentration of anionic vacancies. In particular, materials based on transition metal chulides (TMC) have high second-order nonlinearity and atomic thin thickness, and anionic vacancy engineering can effectively achieve the optimization of material properties. However, the impact of anion vacancy engineering on dipole polarization has not been fully resolved and clarified yet, and it cannot be solely relied upon semi-empirical methods. Therefore, it is of utmost importance to deeply understand the intrinsic mechanism between vacancies and electromagnetic (EM) parameters. This knowledge will provide crucial guidance for the development of advanced electromagnetic wave (EMW) absorption materials. In this study, a novel approach is proposed for the first time, which involves the creation of Se vacancies at the heterogeneous interface formed by Fe-Se compounds. Remarkably, the generation of Se vacancies leads to an increased electron transfer from Fe to Se atoms, resulting in the formation of numerous dipolarization centers. This effect significantly enhances the material’s polarization loss capacity. Furthermore, the presence of Se vacancies acts as traps for nearby free electrons, reducing the material’s conductivity, weakening the skin effect, and improving impedance matching. The precise modulation of Se vacancies in Fe-Se compounds has led to the achievement of ultrathin characteristics and significantly enhanced EM absorption capabilities. With a thickness of only 1.5 mm, the effective absorption bandwidth (EAB) reaches 5.42 GHz, covering the Ku band, which represents a remarkable increase of 53.5 % compared to unmodified Fe-Se compounds. At a thickness of 1.3 mm, the minimum reflection loss (RL) reaches −42.4 dB, demonstrating an impressive improvement of 41.3 %. Moreover, simulations of the radar cross section (RCS) indicate that the material possesses a robust microwave attenuation ability, underscoring its great potential for practical applications.

Graphene-integrated hybrid plasmonic waveguide for Kerr nonlinear application

Graphene, with its broadband optical properties and high optical nonlinearity, shows promise as an ultrathin material compatible with CMOS technology for all-optical signal processing in integrated photonics. This paper presents simulation, analysis, and design of a novel graphene-integrated nonlinear hybrid plasmonic waveguide. Finite element simulations study the Kerr nonlinear effect of extra graphene layers. The enhanced nonlinear parameter of [Formula: see text][Formula: see text]W[Formula: see text][Formula: see text]Km[Formula: see text] and nonlinear figure-of-merit (FOM) of 2.10 achieved at 1,550[Formula: see text]nm exceed those of silicon-on-insulator (SOI) hybrid plasmonic waveguides. The proposed graphene-integrated hybrid plasmonic waveguide (GHPW) demonstrates high Kerr nonlinearity and FOM, indicating potential for nonlinear all-optical signal processing.

Transparent conductive oxides and low-loss nitride-rich silicon waveguides as building blocks for neuromorphic photonics

Fully CMOS-compatible photonic memory holding devices hold a potential in the development of ultrafast artificial neural networks. Leveraging the benefits of photonics such as high-bandwidth, low latencies, low-energy interconnect, and high speed, they can overcome the existing limits of electronic processing. To satisfy all these requirements, a photonic platform is proposed that combines low-loss nitride-rich silicon as a guide and low-loss transparent conductive oxides as an active material that can provide high nonlinearity and bistability under both electrical and optical signals.

Model-free optimal robust tracking control for the joint actuated by pneumatic artificial muscles with saturated air pressure input

At present, positioning and tracking the joint angle of the PAM-actuated system is still a challenging issue, due to the inherent hysteresis, high nonlinearities, and time-varying characteristics. Considering the problem of uncertain parameters, external disturbance, and saturated input, a model-free optimal robust tracking control scheme is proposed for the PAMs system in this paper. Also, a nonquadratic cost function is introduced to address the saturation air pressure input. Moreover, an integral reinforcement learning(IRL) algorithm is developed to solve the H∞ tracking Hamilton–Jacobi–Isaacs(HJI) equation. And the IRL algorithm is implemented on a critic–actor-disturbance neural network (NN) structure. The presented control scheme avoids the complete system dynamic and simultaneously compensates for the uncertain parameters, and external disturbance. Then, the effectiveness of the optimal robust tracking controller is verified via computational simulation and keeps the system stable. In addition, the tracking experiment is implemented on a hardware platform, indicating that the presented approach achieves satisfactory tracking performance.

Disturbance observer–based backstepping sliding mode cascade control of 2–degrees of freedom hydraulic tunneling robot

For the 2–degrees of freedom position tracking problem of the robotized hydraulic-driven roadheader with high nonlinearities and strong uncertainties, a practical disturbance observer–based backstepping sliding cascade control method together with an adaptive compensator is proposed and investigated. The presented methodology mainly includes a continuous nonsingular fast terminal sliding mode with the backstepping technique and the power reaching law with time-varying coefficients used to achieve satisfactory performance against the multi-source disturbances, two disturbance observers used to approximately estimate the unknown dynamics in the mechanical and hydraulic subsystem, respectively. Simultaneously, a continuous robustifying term is also utilized to compensate for the residual disturbances and enhance the robustness. The presented control method doesn’t need the precise model thanks to the auxiliary disturbance observer, and can ensure fast convergence and small tracking error thanks to the backstepping sliding cascade control and the adaptive robust compensator. Based on Lyapunov theory, stability of the overall closed-loop system is proved rigorously, and asymptotically bounded tracking performance of the robotic manipulator is guaranteed. Finally, 2–degrees of freedom trajectory tracking experiments are conducted, and comparative results effectively verify the superiorities of the proposed method.

Warranty, free service and rework policy for an imperfect manufacturing system with SAR sensitive demand under emission taxation

The aim of this study is to illustrate the impact of warranty/free service period, specific absorption rate (SAR) of the products, rework policy and emission taxation on the optimal business policy of an imperfect production inventory system in the current highly uncertain/intensified market situation. In this study, the customers’ demand is considered to be influenced by the warranty/free service period along with a permissible SAR of the items. As a result, the manufacturer invests in order to provide warranty period/free service period and fixing an appropriate SAR for the products which are shown in production cost, service cost and SAR reduction investment. Three distinct scenarios are analysed separately regarding the marketing assimilation of defective items. Each suggested scenario relying on the system is quantitatively validated using an example. Furthermore, manufacturers are bound to pay carbon emission tax due to the carbon emission during the production process. Due to high non-linearity of the objective function, a numerical example is solved using the artificial hummingbird algorithm (AHA). From the numerical experiment, it is observed that the optimal policy of the system is strongly influenced by the price and duration of the warranty, emission taxation rate and moderately affected by the SAR of the product. Finally, a sensitivity analysis is carried out, and its effect are shown graphically in order to provide guidance to the competent manager of the firm.

Super-flat coherent mid-infrared supercontinuum in all-normal dispersion As39Se61 chalcogenide photonic crystal fiber

We numerically demonstrated the generation of super-flat coherent mid-infrared supercontinuum in all-normal dispersion (ANDi) As39Se61 chalcogenide photonic crystal fiber (PCF). The PCF with flat-ANDi and high nonlinearities is obtained by optimizing the structural parameters of the fiber. When pumping a 50 fs pulse width and 6 kW peak power laser pulses at 3.35 μm, under 3dB spectral flatness the super-flat coherent mid-infrared supercontinuum extended from 2.17 to 7.47 μm is obtianed. Such coherent mid-infrared supercontinuum are enormously required for optical coherence tomography, biological imaging and frequency metrology.

River water temperature prediction using hybrid machine learning coupled signal decomposition: EWT versus MODWT

Accurate prediction of water temperature (Tw) will greatly help in improving our understanding of the overall thermal regime fluctuation, and it can help in making sound decisions. While great efforts have been devoted to the development of Tw models, further improvement in the prediction accuracy is challenging. Here, we propose a new hybrid machine learning (ML) models for predicting Tw from air temperature (Ta). First, two signal decomposition algorithms, i.e., the empirical wavelet transform (EWT) and maximum overlap discrete wavelet transform (MODWT) are used for decomposing the Ta into several subsequences. Second, the obtained subsequences are used as input variables for four ML models, i.e., the feedforward neural network (FFNN), the optimally pruned extreme learning machine (OPELM), the adaptive boosting (AdaBoost), and the bootstrap aggregating (Bagging). The development of the hybrid models is based on measured data form five measurement stations distributed over Poland. The experimental results show that, the new hybrid ML models achieved high predictive accuracies with Pearson correlation coefficient (R), Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE) and mean absolute error (MAE) of approximately ≈0.99, ≈0.98, ≈0.718 °C, and ≈0.599 °C, respectively. Finally, the experimental results have demonstrated that, the MAE and RMSE of the single models were reduced by 66.12% and 65.30%, while the R and NSE values were improved by 4.85% and 10.02%, respectively, showing a very promising prediction performance. Our new modelling approach proposed in the present study clearly highlight the high contribution of the EWT and MODWT in improving the Tw estimation that is most strongly influenced by Ta. The significant difference between single and hybrid models can be explained by the ability of the EWT and MODWT to capture the high nonlinearity between Ta and Tw. Our new approach is of interest for water resources management and for assessing the variability of Tw over time and space. Finally yet importantly, this is the first study in the literature for which EWT and MODWT were used for modelling Tw, through which we have demonstrated that, an important amount of information is available and cannot be captured using single ML models, and signal decomposition have helped to overcome this challenge.

Stochastic flutter analysis of a three-degree-of-freedom airfoil under vertical turbulence disturbance

This paper investigates the nonlinear stochastic dynamic response of a three-degree-of-freedom(3-DOF) airfoil with high substructural nonlinearity under vertical turbulent disturbances. Considering a two-dimensional flow field, the Dryden turbulence model is used to describe the vertical turbulent disturbance, and a nonlinear stochastic aerodynamic model based on the Theodorsen theory is established for the stochastic flutter problem of the wing. An analysis is conducted to reduce the dimensionality of the airfoil system and solve the critical flutter velocity of the system. The Monte-Carlo method is applied to analyze the stochastic P-bifurcation problem of the system, and the effects of parameters such as the incoming velocity, turbulence scale and intensity on the stochastic dynamics behavior of the system are clarified. It is shown that the amplitude of the steady-state oscillation of the system increases with the incoming velocity and turbulence intensity, whereas the critical flutter speed decreases with increases in these parameters.