Strong Nonlinear Coupling Research Articles

Symmetric second-harmonic generation in sub-wavelength periodically poled thin film lithium niobate

Second-harmonic generation (SHG) extensively employs periodically poled nonlinear crystals through forward quasi-phase-matching to achieve efficient frequency conversion. As poling periods approach sub-micrometers, backward quasi-phase-matching has also been demonstrated, albeit by utilizing pulsed laser drives. The realization of symmetric second-harmonic generation, characterized by counterpropagating pumps, however, has remained elusive despite theoretical predictions. The main challenge lies in achieving strong nonlinear coupling with the poling period below half the wavelength of the second-harmonic light. The recent emergence of high-quality ferroelectric lithium niobate thin films provides an opportunity for achieving precise domain control at submicron dimensions. In this paper, we demonstrate reliable control of ferroelectric domains in a thin film lithium niobate waveguide with a poling period down to 370 nm, thereby realizing highly efficient continuous-wave pumped symmetric SHG. This demonstration not only validates the feasibility of achieving subwavelength periodic poling on waveguides but could also enable submicron ferrolectric domain structures to be leveraged in integrated photonics and nonlinear optics research.

A novel time-series probabilistic forecasting method for multi-energy loads

Due to the strong nonlinearity, stochasticity, and high coupling of multi-energy loads, this paper proposes a time-series probabilistic forecasting method based on radial basis function network-based autoregressive with exogenous inputs (RBF-ARX) and Gaussian mixture model (GMM). First, due to the nature of the nonlinear coupling between multi-energy loads, the degree of correlation is quantitatively evaluated by introducing the maximum information coefficient with the optimal feature method. Second, the Fourier transform method is used to categorize the load data into deterministic and stochastic components. For the deterministic component, the RBF-ARX method is used for forecasting, which can realize multiple inputs and multiple outputs. It only requires less training data with faster computation speed than other benchmark methods. For the stochastic component, a GMM with an improved Markov Monte Carlo sampling is used to generate the time-series stochastic component, which can accurately characterize the probabilistic properties. This paper compares the forecasting accuracy and computation time under 4 seasons using the proposed model with benchmark models. Results show that the proposed model can improve forecasting accuracy of multi-energy loads by at least 50 % and the computational efficiency can be improved by at least 30 %, which means a superior performance.

Optical manipulation of the charge-density-wave state in RbV3Sb5.

Broken time-reversal symmetry in the absence of spin order indicates the presence of unusual phases such as orbital magnetism and loop currents1-4. The recently discovered kagome superconductors AV3Sb5 (where A is K, Rb or Cs)5,6 display an exotic charge-density-wave (CDW) state andhave emerged as astrong candidate for materialshosting a loop currentphase. The idea that the CDW breaks time-reversal symmetry7-14 is, however, being intensely debated due to conflicting experimental data15-17. Here we use laser-coupled scanning tunnelling microscopy to study RbV3Sb5. By applying linearly polarized light along high-symmetry directions, we show that the relative intensities of the CDW peaks can be reversibly switched, implying a substantial electro-striction response, indicative of strong nonlinear electron-phonon coupling. A similar CDW intensity switching is observed with perpendicular magnetic fields, which implies an unusual piezo-magnetic response that, in turn, requires time-reversal symmetry breaking. We show that the simplest CDW that satisfies these constraints is an out-of-phase combination of bond charge order and loop currents that we dub a congruent CDW flux phase. Our laser scanning tunnelling microscopy data open the door to the possibility of dynamic optical control of complex quantum phenomenon in correlated materials.

A Review of Somatic Design for Soft Robotic Grippers: From Parts Integration to Functional Synergy

Somatic design is crucial for soft grippers to emulate the embodied intelligence of their biological counterparts, due to the blurry boundaries among material, structure, and function. There are five critical parts in somatic design, including morphology, material, fabrication, actuation, and variable stiffness. The strong nonlinear coupling among these factors often leads to mutual influence and functional compromise after integration. Herein, methods and strategies harnessed in these parts for performance improvement of soft grippers are systematically reviewed, particularly clarifying how to exert the coupling enhancement effects of the somatic parts for functional synergy. Finally, the remaining challenges and the future development directions of soft grippers for organism‐like intelligence and wide‐range applications are discussed.

IGFT-MHCNN: An intelligent diagnostic model for motor compound faults based decoupling and denoising of multi-source vibration signals

In view of the insufficient representation of single source signals for multi-point compound fault and the nonlinear strong coupling of vibration signals in motor transmission system, the inverse graph Fourier transform and improved multi-head convolutional neural network (IGFT-MHCNN) diagnostic model–based decoupling and denoising of multi-source vibration signals is proposed. The MHDCNN is constructed by improved multi-head convolution network and multi-label decoupling classifier, in which they extract multi-source signal features and decouple compound fault label information, respectively. Due to strong noise in compound fault and mapping capability of graphic signal processing method for complex data, a signal reconstruction method based on IGFT is established to remove residual noise from multi-source signals and enhance the main feature components. Two experimental cases show that the constructed model can effectively reduce vibration noise and achieve multi-label decoupling and identification of motor compound faults with strong coupling.

Large nonlinear optical magnetoelectric response in a noncentrosymmetric magnetic Weyl semimetal

Weyl semimetals resulting from either inversion (P) or time-reversal (T) symmetry breaking have been revealed to show the record-breaking large optical response due to intense Berry curvature of Weyl-node pairs. Different classes of Weyl semimetals with both P and T symmetry breaking potentially exhibit optical magnetoelectric (ME) responses, which are essentially distinct from the previously observed optical responses in conventional Weyl semimetals, leading to the versatile functions such as directional dependence for light propagation and gyrotropic effects. However, such optical ME phenomena of (semi)metallic systems have remained elusive so far. Here, we show the large nonlinear optical ME response in noncentrosymmetric magnetic Weyl semimetal PrAlGe, in which the polar structural asymmetry and ferromagnetic ordering break P and T symmetry. We observe the giant second harmonic generation (SHG) arising from the P symmetry breaking in the paramagnetic phase, being comparable to the largest SHG response reported in Weyl semimetal TaAs. In the ferromagnetically ordered phase, it is found that interference between this nonmagnetic SHG and the magnetically induced SHG emerging due to both P and T symmetry breaking results in the magnetic field switching of SHG intensity. Furthermore, such an interference effect critically depends on the light-propagating direction. The corresponding magnetically induced nonlinear susceptibility is significantly larger than the prototypical ME material, manifesting the existence of the strong nonlinear dynamical ME coupling. The present findings establish the unique optical functionality of P- and T-symmetry broken ME topological semimetals.

Capture mechanism of a multi-dimensional wave energy converter with a strong coupling parallel drive

Wave energy is a promising renewable energy source. How to improve wave energy capture efficiency is a key challenge of wave energy generation. A multi-dimensional wave energy converter (MDWEC) with a strong coupling parallel drive is proposed. The MDWEC can efficiently absorb wave energy through a multi-dimensional moving body driven by six parallel hydraulic cylinders. Based on the Lagrangian approach, high nonlinear strong coupling dynamic models of the MDWEC are established. The hydraulic cylinder force acting on the converter is deduced according to the principle of virtual work. The nonlinear hydrostatic restoring force for different submerged body geometry shapes is derived. The vertical restoring force caused by pitch and roll, the pitch and roll restoring torque caused by deviating from the equilibrium position, the coupled radiation force, and the coupled inertial force between pitch and surge are considered. The hydrodynamic performance, the motion response, and the transient power behavior are investigated. The upper and lower platform hinge point radius, the hinge point center angle, and conversion parameters are optimized based on a genetic algorithm. Wave power capture ability comparison with different design parameters, shapes, and wave states is presented. As the significant wave height decreases from 3 m to 1 m, the capture efficiency increases from 68.3% to 79.1%. The capture ability of MDWEC with semiellipsolid is the highest. Compared with a heaving cone converter, the MDWEC improves the capture efficiency by 47.5% with a significant wave height of 1 m and an energy period of 4 s.

Hyperspectral image classification based on local feature decoupling and hybrid attention SpectralFormer network

ABSTRACT Classification of hyperspectral image (HSI) is an essential step in various remote sensing tasks. Currently, transformer-based models have made significant contributions to HSI classification due to their remarkable ability to model the relation among long-range sequence data. However, HSI data exhibit strong nonlinear coupling and strong correlation, leading to a decrease in the classification performance of transformer-based models. The SpectralFormer network has achieved excellent performance in HSI classification, but its cross-layer adaptive fusion mechanism cannot effectively highlight critical information, affecting the representation ability of spectral-spatial features. To address the above issues, this paper proposes an HSI classification method based on local feature decoupling and SpectralFormer with a hybrid attention module (HAMSF) network. Specifically, this paper first uses the histogram of oriented gradient algorithm to preprocess HSI coupled data and achieve preliminary nonlinear decoupling of HSI data. Secondly, the hybrid attention module is embedded in a cross-layer adaptive fusion mechanism, generating refined neighbouring information before each information fusion to improve the spectral-spatial feature representation ability. Finally, the processed data are input into the HAMSF network for the final decoupling and classification of HSI. The experiment is carried out on three benchmark hyperspectral datasets, and the experimental results confirm that the proposed method has better classification performance compared to several transformer-based and classical HSI classification methods.

Isotope effects on transport characteristics of edge and core plasmas heated by neutral beam injection (NBI) in an inward shifted configuration at the Large Helical Device

Isotope effects have been investigated in Neutral Beam Injection (NBI) heated plasmas on the Large Helical Device with similar operational parameters between Hydrogen (H) and Deuterium (D) plasmas. Experimental results show that the global energy confinement has no significant dependence on the isotope mass under similar discharge conditions with nearly the same heating power, line-averaged density ( nˉe ) and magnetic field. For both electron and ion energy transport, the transport coefficients, which are obtained based on local power balance analysis, have analogous profiles between H and D dominant plasmas. For neoclassical χe and χi values, they are almost equal between H and D dominant plasmas in low nˉe discharges, whereas in high nˉe cases they are lower in H plasmas than those in D ones. At low nˉe , the electron and ion thermal transport in both H and D plasmas are dominated by neoclassical transport at a certain zone ( ρ≈0.6−0.85 ), while the anomalous transport process has primary effects in the remaining area, and the density fluctuations exhibit ion temperature gradient mode nature. With increase of nˉe , the anomalous transport becomes prevailing and the density fluctuations propagate along electron diamagnetic drift direction. Bispectral analysis reveals that the H plasma has stronger nonlinear coupling in edge density fluctuations in both low and high density discharges, which is probably due to that the D plasma has stronger damping rate for the nonlinear interaction of turbulence. For a comparative study, the present results have been compared with those observed in the ECRH discharges (Tanaka et al 2019 Nucl. Fusion 59 126040). The reasons for the similarities and dissimilarities between these two different heating manners are not clear yet. To unravel the underlying physics, essential inputs from theories and simulations are required.

Bursting oscillation behaviors of a multi-time scales pumped storage power station with governor subsystem nonlinearity

Pumped-storage power station (PSPS) play a crucial role in supporting the grid integration of intermittent energy and require frequent regulation to balance fluctuations. Eliminating potential stability risks under the strong nonlinear and complex multi-scale coupling characteristics to ensure the stability of PSPS is crucial for new power systems. Although governor nonlinear elements have been studied in previous studies, the effects and potential risks of their non-smooth characteristics on the stability of different subsystems have not been reported. In this study, a mathematical model of PSPS considering the non-smooth characteristics of governor is established by introducing a segmentation function. Then the influence laws of their effect on important variables in different subsystems are studied through the time waveforms and phase trajectories. Additionally, sensitivity analysis is employed to identify key parameters affecting stability at various stages. Finally, the risk of bursting oscillation caused by the nonlinear elements is revealed. The results show that the nonlinear elements have less effect on the hydraulic subsystem, but their non-smooth characteristic induces state switching in mechanical subsystems, which poses a risk for frequency oscillations. Adjusting the deadband has an inhibiting effect on both oscillations caused by nonlinear elements and improves stability in hydraulic and mechanical subsystems. This work provides fresh perspectives and theoretical basis for minimizing the risk of bursting oscillations while improving Pumped-storage plant stability.

Contact Force Surrogate Model and Its Application in Pantograph–Catenary Parameter Optimization

The significant increase in the speed of high-speed trains has made the optimization of pantograph–catenary parameters aimed at improving current collection quality become one of the key issues that urgently need to be addressed. In this paper, a method and solutions are proposed for optimizing multiple pantograph–catenary parameters, taking into account the speed levels and engineering feasibility, for pantograph–catenary systems that contain dozens of parameters and exhibit strong nonlinear coupling characteristics. Firstly, a surrogate model capable of accurately predicting the standard deviation of contact force based on speed and 14 pantograph–catenary parameters was constructed by using the pantograph–catenary finite element model and feedforward neural network. Secondly, sensitivity analysis and rating of the pantograph–catenary parameters under different speeds were conducted using the variance-based method and the surrogate model. Finally, by combining the sensitivity analysis results and the Selective Crow Search Algorithm, joint optimization of 10 combinations of the pantograph–catenary parameters across the entire speed range was performed, providing efficient pantograph–catenary parameter optimization solutions for various engineering conditions.

Longitudinal vibration analysis and active disturbance rejection decoupling control of marine anticorrosive coatings Hybrid Propulsion Screw

The traditional manual methods of mixing paint, the construction process is difficult to ensure the quality and efficiency of the construction due to construction instability and error. To address this issue, we propose a novel approach to automatically mix and discharge marine anti-corrosion coatings using a Hybrid Propulsion Screw. The longitudinal vibration of the Hybrid Propulsion Screw (HPS) during operation is a critical factor that affects the mixing and discharge stability of anti-corrosion coatings. To analyze the vibration characteristics and stability of HPS during operation, we established a three-degree-of-freedom longitudinal vibration model of the HPS using the concentrated mass method and numerically solved it using the Runge-Kutta algorithm, and investigated the effects of external excitation and frequency on the longitudinal vibration of the system. Moreover, to reduce the fatigue damage caused by vibration, we introduced the Active Disturbance Rejection Decoupling Control (ADRDC) considering the nonlinear strong coupling relationship in HPS. And finally, we used the Fibonacci series to adjust the parameters of the Extended State Observer (ESO), and the simulation results demonstrate that our proposed approach is effective for controlling the longitudinal vibration amplitude of the system, which is of great significance for developing efficient and reliable screw stirring devices.

Development of process design tools for extrusion-based bioprinting: From numerical simulations to nomograms through reduced-order modeling

The planning of a bioprinting procedure requires the definition of several process variables. In extrusion-based bioprinting these are, for instance, the printing pressure, the nozzle diameter, the target extrusion velocity and/or mass flow rate. They should be properly set in order to allow printability of the bio-ink, as well as to ensure high cell viability at the end of the process. In fact, printing procedures expose cells to shear and extensional stresses that can lead to mechanobiological damage mechanisms. Bioprinting planning is then a challenging task since process variables are closely interconnected each other through the physical response of bio-inks. Non-Newtonian characteristics of bio-inks, together with possible complex geometries of the extruding system, generally introduce a strong non-linear coupling among process variables. To date, the bioprinting planning in laboratory practice is generally performed via expensive and time-consuming trial-and-error procedures. The aim of this work is the development of novel methodological approaches for an informed definition of printing process variables such to guarantee target conditions of the outcome. The non-linear coupling among dominant process variables is described via a semi-analytical approach, calibrated through high-fidelity numerical solutions and defined via a reduced-order modeling strategy. A cell damage law depending on bioprinting conditions is also introduced, generalizing state-of-the-art approaches on the basis of available experimental evidence. The proposed framework allows to build operative nomograms, whose practical utility is confirmed via some exemplary applications. The latter address the prediction of extrusion velocity, mass flow rate and cell viability, when both the printing pressure and nozzle diameter vary within typically-adopted ranges. The analyzed case studies highlight soundness and effectiveness of such a modeling strategy in providing a clear and straight pathway for planning and setup of bioprinting processes.

Selective Growth of Type‐II Weyl‐Semimetal and Van der Waals Stacking for Sensitive Terahertz Photodetection

AbstractThe emergence of novel topological semimetal materials, accompanied by exotic non‐equilibrium properties, not only provides a fertile playground for a fundamental level of interest but also opens exciting opportunities for inventing new applications by making use of different light‐induced effects such as nonlinear optics, optoelectronics, especially for the highly pursued terahertz (THz) technology due to the gapless electronic structures. Exploring type‐II Weyl semimetal endowed with the richness of quantum wavefunction and peculiar band structure, underlie strong nonlinear coupling with THz waves. Here, the selective growth of type‐II Weyl semimetal NbIrTe4 by means of a self‐flux approach is reported, which hosts strongly tilted Weyl cones and exotic Fermi arcs. The oscillating THz field induced by the antenna is engineered in terms of planar metal‐topological semimetal‐metal structure, along with van der Waals stacking, which allows for self‐powered photodetection at room temperature. The results elucidate the superior performance of NbIrTe4‐graphene heterostructure‐based photodetectors with responsivity up to 264.6 V W−1 at 0.30 THz, fast response of 1 µs as well as low noise equivalent power ˂0.28 nW Hz−0.5 is achieved, already exhibiting high‐quality imaging at THz frequency. The results promise superb impacts in exploring topological Weyl semimetals for efficient low‐energy photon harvesting.

Decoupling control of bearingless brushless DC motor using particle swarm optimized neural network inverse system

Neural network inverse system decoupling control can effectively solve the nonlinear strong coupling problem of bearingless brushless DC motor, but it has the problem of slow convergence and easy to fall into local extreme value. An Improved Particle Swarm Optimization (IPSO) algorithm has been employed to optimize the initial weights of the BP neural network inverse system. Comparisons between traditional inverse system decoupling control and the proposed method through simulations were conducted to confirm the efficacy and superiority of the proposed decoupling strategy. Furthermore, experiment research indicates that effective decoupling control can be achieved for both speed and radial displacement, as well as between the x and y-axis radial displacements, showcasing the good dynamic decoupling performance and stability of the proposed decoupling control strategy.

Research on aerodynamic shape optimization of reentry vehicle based on hybrid scale multi-fidelity neural network model

The mechanical expanded reentry vehicle has gained significant attention as a reliable solution for shuttle transportation and deep space exploration missions. However, the aerodynamic characteristics of such a reentry vehicle, including high dimensionality, strong nonlinearity, and parameter coupling, pose a challenge in achieving a balance between precision and efficiency in aerodynamic shape design. In this paper, we propose a hybrid scale multi-fidelity neural network (HS-MFNN) model by integrating low-fidelity and high-fidelity models with neural networks. This model is applied to optimize the aerodynamic shape of the reentry vehicle, addressing the challenge. The feasibility of applying the HS-MFNN model was validated through testing using the MBR function. The advantages of the HS-MFNN model were highlighted through a comparison with other models. Subsequently, the model was utilized in the aerodynamic optimization process of the reentry vehicle. The prediction error for the drag coefficient (Cd) was less than 1%, and for the head stagnation heat flux (QO), it was within 6%. Moreover, the computation time was reduced by three orders of magnitude, ensuring both computational accuracy and efficiency. After optimization, the Cd increased by 5.08%, while the QO decreased by 8.62%. These improvements demonstrate that the use of the HS-MFNN model significantly enhances the aerodynamic performance of the reentry vehicle. Consequently, the HS-MFNN model exhibits great potential for fast and efficient optimization processes.

Cycle Skipping-Induced Bifurcation Analysis of Single-Inductor Bipolar-Output DC–DC Converters With One-Cycle Control

Due to strong nonlinear coupling effect amongst input/output ports and special cycle skipping effect, single-inductor bipolar-output (SIBO) DC-DC converters with one-cycle control may exhibit various bifurcation phenomena with complex border collision. This brief aims to investigate the cycle skipping effect and explore the occurrence mechanism of these nonlinear behaviors for stability improvement. On account of the cycle skipping effect, a set of the discrete-time mathematical expressions are derived to describe the dynamic characteristics of the one-cycle controlled (OCC) SIBO converter in discontinuous state space. Then, bifurcation analysis is performed by numerical simulation and eigenvalue trajectories. It is shown that cycle skipping effect activates more multi-cycle modes except single-cycle modes. Interestingly, the same slow-scale bifurcations in different operation region present various oscillatory form, which leads to hybrid-scale instability. Especially, the stability boundaries are presented in multi-mode space to guide optimal design. Finally, some experiment results are provided to verify the theoretical analysis.

Preliminary stability design method and hybrid experimental validation of a floating platform for 10 MW wind turbine

Platform motions induced by wind and waves import strong nonlinear coupling effects in the aerodynamic characteristics of floating offshore wind turbines (FOWTs), which exacerbate the power instability of the wind turbine and should be considered during the preliminary platform design stage. For this reason, a platform motion-based method was proposed to estimate the power stability and successfully applied to the preliminary platform stability design in this paper. Firstly, a high-fidelity computational fluid dynamics (CFD) model of a 10 MW wind turbine was established using the oversetting grid and superposition motion technology. Then the influence laws of platform surge and pitch motions on the power characteristics of the wind turbine were summarized. Subsequently, a semi-submersible floating platform structure for supporting the 10 MW wind turbine was designed preliminarily. The power stability of the wind turbine and tilting stability of the platform were analyzed based on the hydrodynamic analysis results. Finally, a hybrid scaled model of the 10 MW FOWT was built, incorporating a six-degrees of freedom load actuation system based on multi-fans. The scaled model test was carried out in a wave basin, demonstrating that both the power stability and the tilting stability of the platform meet the proposed stability requirements.

A Knee-Guided Evolutionary Computation Design for Motor Performance Limitations of a Class of Robot With Strong Nonlinear Dynamic Coupling

Robots for high-speed manipulation require to produce motions beyond the performance limitations set by the traditional approaches. Recent results integrate the properties associated with dynamic coupling driving and structural mechanics to compute optimal smooth arm motions; however, when accelerating the convergence speed of potential solutions, those approaches cannot avoid premature convergence. In this article, we propose an autonomous motion planning method at the torque level for a class of robots considering multiple conflicting performance metrics. Specifically, we focus on the hyper dynamic manipulation of a golf swing robot using a knee-guided multiobjective optimization algorithm. Compared with traditional planning methods in position or velocity level, it can study motor performance limitations with strong nonlinear dynamic coupling beyond the motion limits designed by the manufacturers. First, the robot’s joint torque is approximated by the B-spline method using the solution at each iteration. Then, we transform the motion planning problem into a multiobjective optimization problem with soft constraints of torque limits and hard constraints of joint stops, and develop a knee-guided evolutionary algorithm to find the optimization solution with the quality tradeoffs between the scale of parameters and metrics. Finally, we conduct the simulation to demonstrate the dynamics performance of the golf swing robot. The results indicate that our approach can generate superior dynamics performance beyond limits with low energy consumption and high precision.

Distributed nonlinear model predictive control for cobalt removal process in zinc hydrometallurgy considering error compensation modelling

AbstractTo address the strong nonlinearity, uncertainty, and mutual coupling in the cobalt removal process of zinc hydrometallurgy, an algorithm based on an improved genetic algorithm (GA) backpropagation (BP) neural network combined with distributed nonlinear model predictive control (NMPC) is proposed. This method was applied to improve the quality of the purification solution and reduce the consumption of zinc powder, overcoming the challenges faced by the current cobalt removal process. First, a synergistic continuously stirred tank reactor (SCSTR) model was constructed for the dynamic cobalt removal process. Second, aiming at the problem that a single SCSTR model has difficulty describing the process accurately, based on the highly nonlinear mapping ability of data‐driven models, a method that organically integrates the SCSTR model and an error compensation model based on the GA‐BP neural network was proposed (GA‐BP‐SCSTR) to provide a more accurate online prediction of the process indicators. Then, a distributed NMPC architecture was developed using the GA‐BP‐SCSTR model, control vector parameterization (CVP) technique, and sequential quadratic programming (SQP) algorithm to achieve the coordinated control of the cobalt removal process. Finally, simulation results of an actual site showed that the prediction accuracy of the GA‐BP‐SCSTR model was higher than those of other models. The proposed predictive control method can maintain the outlet cobalt ion concentrations at the set values while achieving accurate control of the zinc powder addition. This approach can provide guidance for on‐site production and eliminate the blindness of manual experience control.