Nonlinear Hybrid Research Articles

SMT based parameter identifiable combination detection for non-linear continuous and hybrid dynamics

Parameter identifiability is an important aspect of parameter estimation of dynamic system modelling. Several methods exist to determine identifiability of parameter sets using the model definition and analysis of experimental data. There is also the possibility of some parameters being independently unidentifiable but forming identifiable parameter combinations. These identifiable parameter combinations are useful in model reparameterisation to estimate parameters experimentally. Multiple numerical and algebraic methods exist to detect identifiable parameter combinations of dynamic system models represented as ordinary differential equations (ODE). Local identifiability analysis of hybrid system models are available in the literature. However, methods for structural identifiability analysis and identifiable combination detection for hybrid systems are not explored. Here, we have developed a parameter identifiable combination detection method for non-linear hybrid systems along with ODE systems using an SMT based parameter space exploration method. For higher dimensional systems and larger parameter space, SMT based approaches may easily become computationally intractable. This problem has been mitigated to a large extent by heuristically limiting the parameter space to be explored, using Gaussian process regression and gradient based approaches. The developed method has been demonstrated for some simple hybrid models, biochemical models of ODE systems and non-linear hybrid systems.

An Innovative and Efficient Implementation of Matrix-Free Newton Krylov Method for Neutronics/Thermal-hydraulics Coupling Simulation

The core physical behavior of reactors is essentially the result of multi-physical fields coupling feedback. High-fidelity neutronics/thermal-hydraulics (N/TH) analysis can simulate and predict nuclear reactor core phenomena realistically, providing advanced and reliable technical means during the design and safety analysis of nuclear reactor. In this work, an efficient and robustness coupling method using power density as the coupling parameter, Matrix-Free Newton Krylov (MFNK) method, is successfully developed and innovatively implemented in HNET for high-fidelity N/TH coupling simulation. To enhance the efficiency and stability, the multi-level generalized equivalence theory-based CMFD (ML-gCMFD) iterative acceleration method and ML-gCMFD coupling acceleration method are proposed. In addition, the nonlinear preconditioning and hybrid perturbation size formula are implemented to further improve the convergence. Finally, to evaluate the numerical accuracy, convergence, efficiency and stability of MFNK method, a series of representative problems, including a three-dimensional (3D) single fuel pin problem, VERA Benchmark Problem 6, and VERA Benchmark Problem 7, are analyzed by comparing with the current N/TH coupling methods. Numerical results indicate that MFNK method can obtain strong stability, high convergence performance, and relatively high computational efficiency while ensuring high accuracy. It demonstrates that MFNK method has significant performance advantages and potential for high-fidelity N/TH coupling simulation.

A new short-term wind power prediction methodology based on linear and nonlinear hybrid models

Fast and accurate wind power prediction is of great significance for grid planning. However, wind power dataset tends to be highly stochastic and volatile, while showing more stable seasonal and cyclical changes, which is a linear and nonlinear superposition features, and it is difficult to use a single linear or nonlinear model to make accurate predictions. Considering these essential features of wind power data, a short-term wind power prediction method based on linear and nonlinear hybrid models is proposed in this paper. First, the complete ensemble empirical mode decomposition (CEEMD) is used to decompose and reconstruct the wind speed and direction in the original dataset to reduce the volatility. Then, to capture the linear features in the dataset, the autoregressive integrated moving average model with exogenous input variables (AIRMAX) is used for linear modeling, and the sparrow search algorithm optimized gated recurrent unit (SSA-GRU) is used to model the nonlinear features. At the same time, to improve the efficiency of model training, federated learning (FL) is utilized for distributed model training. Finally, the ARIMAX and SSA-GRU models are weighted and summed the prediction results to gain the final prediction results using equal weighted averaging (EWA), minimum sum of squares error (MSSE), and maximum grey relational analysis (MGRA), which are commonly used to compute the weights of the combined models, respectively. The wind power dataset from a wind farm in northwest China from January 1, 2022 to October 31, 2022 are used for experiments and analysis, and the results show that the root mean square error (RMSE), R-square (R2), and accuracy (ACC) of the proposed method are 11.944, 0.987, and 0.613, respectively, which are better than all the compared models. The main contribution of this paper is twofold, on the one hand, it indirectly proves that the wind power dataset is formed by the superposition of linear and nonlinear features, and on the other hand, it puts forward a theoretical model and research paradigm for predicting short-term wind power and presents a scientific basis for grid scheduling.

Data-driven aeroelastic analyses of structures in turbulent wind conditions using enhanced Gaussian Processes with aerodynamic priors

Recent advancements in data-driven aeroelasticity have been driven by the wealth of data available in the wind engineering practice, especially in modeling aerodynamic forces. Despite progress, challenges persist in addressing free-stream turbulence and incorporating physics knowledge into data-driven aerodynamic force models. This paper presents a hybrid Gaussian Process (GPs) methodology for non-linear modeling of aerodynamic forces induced by gusts and motion on bluff bodies. Building on a recently developed GP model of the motion-induced forces, we formulate a hybrid GP aerodynamic force model that incorporates both gust- and motion-induced angles of attack as exogenous inputs, alongside a semi-analytical quasi-steady (QS) model as a physics-based prior knowledge. In this manner, the GP model incorporates the absent physics of the QS model, and the non-dimensional hybrid formulation enhances its appeal from an aerodynamic perspective. We devise a training procedure that leverages simultaneous input signals of gust angles, based on random free-stream turbulence, and motion angles, based on random broadband signals. We verify the methodology through analytical linear aerodynamics of a flat plate and non-linear aerodynamics of a bridge deck using Computational Fluid Dynamics (CFD). The standout feature of the presented methodology is its applicability for aeroelastic buffeting analyses, showcasing robustness when handling broadband excitation. Importantly, the non-linear hybrid model preserves its capability to capture higher-order harmonics in the motion-induced forces and remains applicable for flutter analysis, while incorporating both motion and gust angles as input. Applications of the methodology are anticipated in the aeroelastic analysis and monitoring of slender line-like structures.

Delivery of 5-fluorouracil for cancer therapy using aptamer-based nonlinear hybridization chain reaction

5-Fluorouracil (5-FU) is a conventional nucleotide analogue used for cancer treatment. However, its clinical application faces challenges such as low stability and non-specific toxicity. With the remarkable advancements in DNA nanotechnology, DNA-based self-assembled nanocarriers have emerged as powerful tools for delivering nucleotide drugs. In this study, we have designed a non-linear hybrid chain reaction involving a fuel strand with AS1411 aptamer sequence to construct a dendritic structure capable of carrying 5-FU. This structure specifically targets cancer cells with overexpressed nucleolin on their surface, allowing the 5-FU to exert its anticancer effects and achieve therapeutic outcomes. Furthermore, we have also investigated the mechanistic action of this drug delivery system, aiming to establish a novel therapeutic platform for 5-FU treatment.

Nonlinear lower hybrid wave equations in collisional tokamak plasmas

A new set of coupled integro-differential nonlinear lower hybrid (LH) wave equations is derived within the framework of a kinetic theory coupled to the Maxwell equations to study the parametric instabilities (PIs) produced by LH waves in collisional tokamak plasma. Previous models of nonlinear LH wave equations have been significantly improved. The wave equations derived overcome the limits and incorrectness of the standard theory of the PI in inhomogeneous plasma. They allow us to treat the full spectrum in the parallel and poloidal wavenumber of the coupled LH power wave, diffraction effects and possible cascade phenomena, which are elements of the nonlinear LH physics ignored in the standard PI theory. Numerical solutions of the new nonlinear LH wave equations are proposed. The relevant LH frequency spectra produced by PI are calculated, exhibiting characteristic features of PI observed in LH experiments. It is shown that the LH sideband amplification can be overestimated by orders of magnitude by the standard theory of PI. A benchmark of the new model is provided for spatially homogeneous plasmas. The role of the collisions for PI has been assessed. We demonstrate that previous analyses significantly overestimated their stabilization effect.

Identification of switched gated recurrent unit neural networks with a generalized Gaussian distribution

Due to the limitations of the model itself, the performance of switched autoregressive exogenous (SARX) models will face potential threats when modeling nonlinear hybrid dynamic systems. To address this problem, a robust identification approach of the switched gated recurrent unit (SGRU) model is developed in this paper. Firstly, all submodels of the SARX model are replaced by gated recurrent unit neural networks. The obtained SGRU model has stronger nonlinear fitting ability than the SARX model. Secondly, this paper departs from the conventional Gaussian distribution assumption for noise, opting instead for a generalized Gaussian distribution. This enables the proposed model to achieve stable prediction performance under the influence of different noises. Notably, no prior assumptions are imposed on the knowledge of operating modes in the proposed switched model. Therefore, the EM algorithm is used to solve the problem of parameter estimation with hidden variables in this paper. Finally, two simulation experiments are performed. By comparing the nonlinear fitting ability of the SGRU model with the SARX model and the prediction performance of the SGRU model under different noise distributions, the effectiveness of the proposed approach is verified.

A Nonlinear Hybrid Modeling Method for Pump Turbines by Integrating Delaunay Triangulation Interpolation and an Improved BP Neural Network

In order to solve the problem that the data processing accuracy of the characteristic curves is not high, which affects the accuracy of the simulation of variable-speed pumped storage units, this paper proposes a nonlinear hybrid modeling method for pump turbines by integrating Delaunay triangulation interpolation and an improved back-propagation neural network. Firstly, the improved Sutter transform is used to preprocess the original curve, and the convex hull of the transformed curve is calculated. With the unknown point inside the convex hull, Delaunay triangulation interpolation is used to model the transformed curve. With the unknown point outside the convex hull, the back-propagation neural network is first established based on the input and output, and then the initial weights and thresholds of the network are determined using the mind evolution algorithm to complete the modeling and simulation of the curve. Simulation results show that the proposed method fully integrates the high-precision features of interpolation and the powerful nonlinear fitting ability of the neural network, which greatly improves the efficiency and accuracy of pump turbine modeling.

Intrinsic nonlinear geometric phase in SHG from zincblende crystal symmetry media

Abstract We demonstrate that AlGaAs thin films and metasurfaces generate a distinct intrinsic nonlinear geometric phase in their second harmonic signals, differing significantly from previous studies on nonlinear dielectric, plasmonic, or hybrid metasurfaces. Unlike conventional observations, our study reveals that the second harmonic phase remains unaffected by the linear optical response at both pump and harmonic wavelengths, introducing a novel realm of achievable phase functions yet to be explored. Furthermore, we explore the interplay between this intrinsic nonlinear geometric phase and the geometric phase induced by rotations of nanoresonators within metasurface arrangements. Our findings extend the capabilities of nonlinear wavefront shaping metasurfaces, exploiting phase manipulation to uncover unique phenomena exclusive to the nonlinear regime.

Electric field induced second harmonic generation in arsenic sulfide deposited by thermal evaporation

Electric field induced second harmonic (EFISH) measurements are performed on thin films of arsenic sulfide deposited on chromium coated fused silica substrates by thermal evaporation of amorphous As2S3 bulk material. EFISH allows to widely tune the second-order optical susceptibility (χ(2)). An observed shift of the minimum of the second harmonic generation (SHG) intensity away from 0 V reveals a non-EFISH bulk χ(2), which is unexpected for amorphous materials. Additional SHG measurements on as-deposited films for investigation of the non-EFISH bulk χ(2) show Maker fringe patterns. Analyzing them and calibrating the SHG response against beta barium borate (BBO) crystal allows us to extract the components of the χ(2) tensor. The main component of this non-EFISH bulk χ(2) has a value of ∼0.22 pm/V. By increasing the χ(2) by applying the dc electric field a maximum value of 3.8 pm/V was achieved in EFISH measurements. This value is comparable to the χ(2) of traditional nonlinear crystals, e.g., BBO, which makes arsenic sulfide an interesting candidate for nonlinear hybrid structures.

A Unified Approach and Related Fixed-Point Theorems for Suzuki Contractions

This paper aims to give an extended class of contractive mappings combining types of Suzuki contractions α-admissible mapping and Wardowski F-contractions in b-metric-like spaces. Our results cover and generalize many of the recent advanced results on the existence and uniqueness of fixed points and fulfill the Suzuki-type nonlinear hybrid contractions on various generalized metrics.

High-order discretization–based self-adaptive turbulence eddy simulation for supersonic base flow with PHengLEI software

PurposeThe purpose of the present study is to develop a new numerical framework that can predict the supersonic base flow more accurately, including the development of axisymmetrically separated shear layer and recompression shock. To this end, two aspects are improved and combined, i.e. a newly self-adaptive turbulence eddy simulation (SATES) turbulence modeling method and a high-order discretization numerical scheme. Furthermore, the performance of the new numerical framework within a general-purpose PHengLEI software is assessed in detail.Design/methodology/approachSatisfactory prediction of the supersonic separated shear layer with unsteady wake flow is quite challenging. By using a unified turbulence model called SATES combining high-order accurate discretization numerical schemes, the present study first assesses the performance of newly developed SATES for supersonic axisymmetric separation flows. A high-order finite differencing-based compressible computational fluid dynamics (CFD) code called PHengLEI is developed and several different numerical schemes are used to investigate the effects on shock-turbulence interactions, which include the monotonic upstream-centered scheme for conservation laws (MUSCL), weighted compact nonlinear scheme (WCNS) and hybrid cell-edge and cell-node dissipative compact scheme (HDCS).FindingsCompared with the available experimental data and the numerical predictions, the results of SATES by using high-order accurate WCNS or HDCS schemes agree better with the experiments than the results by using the MUSCL scheme. The WCNS and HDCS can also significantly improve the prediction of flow physics in terms of the instability of the annular shear layer and the evolution of the turbulent wake.Research limitations/implicationsThe small deviations in the recirculation region can be found between the present numerical results and experimental data, which could be caused by the inaccurate incoming boundary layer condition and compressible effects. Therefore, a proper incoming boundary layer condition with turbulent fluctuations and compressibility effects need to be considered to further improve the accuracy of simulations.Practical implicationsThe present study evaluates a high-order discretization-based SATES turbulence model for supersonic separation flows, which is quite valuable for improving the calculation accuracy of aeronautics applications, especially in supersonic conditions.Originality/valueFor the first time, the newly developed SATES turbulence modeling method combining the high-order accurate WCNS or HDCS numerical schemes is implemented on the PHengLEI software and successfully applied for the simulations of supersonic separation flows, and satisfactory results are obtained. The unsteady evolutions of the supersonic annular shear layer are analyzed, and the hairpin vortex structures are found in the simulation.

Refined Aircraft Positioning Based on Stochastic Hybrid Estimation with Adaptive Square-Root Unscented Particle Filtering

The positioning of civil aviation aircraft relative to a geographic reference point on Earth in a Cartesian frame is significant to detect the deviations from the desired path, especially for high-altitude airports or special airports based on performance-based navigation (PBN). To obtain these critical deviations during aircraft approach and landing, it is fundamental to estimate the continuous flight variables and discrete flight modes simultaneously with enough accuracy. With the coordinate conversion between the North, East, and Down (NED) frame and the geographic coordinate system based on World Geodetic System 1984 (WGS-84) considered, this study proposed a non-linear stochastic hybrid estimation algorithm with adaptive square-root unscented particle filtering (ASR-UPF) to estimate the true path. The probabilities of mode transition, represented by the normal cumulative density function of continuous states, determine whether to proceed with mode transitions. In addition, the adaptive update characterized by tracking variable noise and the importance sampling distributions based on the results of square-root unscented Kalman filtering (SR-UKF), as a comparative study of continuous system filtering, were used. The experiments illustrated the ASR-UPF is able to reduce the state estimation error more effectively, and more promptly track the error caused by incorrect mode estimation with adaptability compared to the SR-UKF. A further test with real flight data indicates that the proposed method gives the refined estimation of position and azimuth in NED frame.

Improving Output Power of a Torsional-Flutter Harvester in Stochastic Thunderstorms by Duffing - Van Der Pol Restoring Torque

Abstract Wind energy harvesters are usually designed to operate in the low wind speed range. They rely on smaller swept areas, as a complement to larger horizontal-axis wind turbines. A torsional-flutter-based apparatus is investigated herein to extract wind energy. A nonlinear hybrid restoring toque mechanism, installed at equally spaced supports, is used to produce energy through limit-cycle vibration. Energy conversion and storage from the wind flow are enabled by eddy currents. The apparatus is used during thunderstorm outflows to explore the efficiency in non-ideal wind conditions. The thunderstorm flow model accounts for both non-stationary turbulence and slowly varying mean speed, replicating thunderstorm's intensification and decay stages. This paper evolves from a recent study to examine stochastic stability. More Specifically, the output power is a random process that is derived numerically. Various thunderstorm features and variable apparatus configurations are evaluated. Numerical investigations confirm the detrimental effect of non-ideal, thunderstorms on harvester performance with, on average, an adverse increment of operational speed (about +30\%). Besides nonlinear damping, the benign flutter-prone effect is controlled by the square of the flapping angle. Since flapping amplitudes are moderate at sustained flutter, activation of the apparatus is delayed and exacerbated by the non-stationary outflow and aeroelastic load features. Finally, efficiency is carefully investigated by quantification of output power and “quality factor”.

Initial Value Problem for Hybrid Improved Conformable Fractional Differential Equations with Retardation and Anticipation

This manuscript is devoted to proving some results concerning the existence of solutions for a class of initial value problem for nonlinear implicit fractional Hybrid differential equations and improved conformable fractional derivative. The result is based on a fixed point theorem due to Dhage. Further, an example is provided for the justification of our main result.

Consensus analysis via impulsive control for nonlinear hybrid singular switched multi-agent systems with event-triggered mechanism

ABSTRACT Our research is focused on achieving consensus for non-linear singular switched multi-agent systems on arbitrary time scales. The hybrid control strategy that combines an impulsive controller with centralised and distributed event-triggered controllers is proposed. First, the Drazin-inverse approach is applied to transform the singular multi-agent switched system into a differential algebraic switched system. Then event-triggered schemes are applied to study the leader–follower consensus conditions. In the end, some simulation-based examples are given to verify the proposed results.

Trajectory Tracking Nonlinear Hybrid Control of Automated Guided Vehicles

Automated guided vehicles (AGVs), so necessary in industrial environments, require precise control of trajectory tracking to make accurate stops at logistics stations, such as loading stations, or to pick up or drop off trolleys, pallets, or racks. This paper proposes a hybrid control architecture for trajectory tracking of a hybrid tricycle-differential AGV. The control strategy combines conventional proportional integral derivative (PID) control with advanced nonlinear Lyapunov control (LPC). The LPC is used for trajectory tracking while the PID is used for speed control of the robot. The stability of the controller is demonstrated for any differentiable trajectory. When a PID optimized with genetic algorithms is compared with the proposed controller for several trajectories, the LPC outperforms it in all cases.

Scale-Aware Edge-Preserving Full Waveform Inversion with Diffusion Filter for Crosshole Sensor Arrays.

Full waveform inversion (FWI) is recognized as a leading data-fitting methodology, leveraging the detailed information contained in physical waveform data to construct accurate, high-resolution velocity models essential for crosshole surveys. Despite its effectiveness, FWI is often challenged by its sensitivity to data quality and inherent nonlinearity, which can lead to instability and the inadvertent incorporation of noise and extraneous data into inversion models. To address these challenges, we introduce the scale-aware edge-preserving FWI (SAEP-FWI) technique, which integrates a cutting-edge nonlinear anisotropic hybrid diffusion (NAHD) filter within the gradient computation process. This innovative filter effectively reduces noise while simultaneously enhancing critical small-scale structures and edges, significantly improving the fidelity and convergence of the FWI inversion results. The application of SAEP-FWI across a variety of experimental and authentic crosshole datasets clearly demonstrates its effectiveness in suppressing noise and preserving key scale-aware and edge-delineating features, ultimately leading to clear inversion outcomes. Comparative analyses with other FWI methods highlight the performance of our technique, showcasing its ability to produce images of notably higher quality. This improvement offers a robust solution that enhances the accuracy of subsurface imaging.

Entropy production on mixed convection stagnation point flow of nonlinear radiative fourth-grade hybrid nanofluid through a stretchable Riga surface

The purpose of the current study is to inspect entropy generation, mixed convective stagnation point flow, and thermal transfer features of nonlinear radiative fourth-grade hybrid nanofluid (NF) confined by a convectively heated Riga surface. The heat transport is examined with the existence of two disparate heat source modulations, variable thermal conductivity, and viscous dissipation. The original flow equations are first transmuted using appropriate transformations into non-dimensional ordinary differential equations, which are then solved via an overlapping grid-based spectral collocation scheme. The upshots of various pertinent parameters on velocity, temperature, entropy generation, and valuable engineering quantities are deliberated. Pivotal results obtained reveal that speedily flow of fluid and skin friction can be accelerated by strong magnetic force, rising mixed convection, and material parameters. Also, convective boundary conditions, along with nonlinear radiation and fluctuating thermal conductivity, are recommended for boosting fluid temperature, rates of entropy generation and heat transport. Thermal mechanism in hybrid NF is dominant over simple NF, which implies that performance of hybrid NF is better than that of simple NF. The outcomes of this study can be useful in enriching thermal performance of the working fluid, assisting in diagnosing causes of incompetency in thermal systems, and discovering suitable means of minimizing entropy generation with the intention of mitigating the loss of useful and scarce energy resources.

Nonlocal boundary value problems for functional hybrid differential equations involving generalized w-Caputo fractional operator

In this manuscript , we establish the existence and uniqueness of solutions for boundary value problems of nonlinear hybrid fractional differential equations involving generalized $\omega-$Caputo fractional derivatives. The proofs are based on Krasnoselskii fixed point theorem and some basic concept of $\omega-$Caputo fractional analysis. As application, an nontrivial example is given in the last part of this paper to illustrate our theoretical results.