Optimizing near-carbon-free nuclear energy systems:advances in reactor operation digital twin through hybrid machine learning algorithms for parameter identification and state estimation

Accurate and efficient online parameter identification and state estimation are crucial for leveraging digital twin simulations to optimize the operation of near-carbon-free nuclear energy systems.In previous studies,we developed a reactor operation digital twin(RODT).However,non-differentiabilities and discontinuities arise when employing machine learning-based surrogate forward models,challenging traditional gradient-based inverse methods and their variants.This study investigated deterministic and metaheuristic algorithms and developed hybrid algorithms to address these issues.An efficient modular RODT software framework that incorporates these methods into its post-evaluation module is presented for comprehensive comparison.The methods were rigorously assessed based on convergence profiles,stability with respect to noise,and computational performance.The numerical results show that the hybrid KNNLHS algorithm excels in real-time online applications,balancing accuracy and efficiency with a prediction error rate of only 1%and processing times of less than 0.1 s.Contrastingly,algorithms such as FSA,DE,and ADE,although slightly slower(approximately 1 s),demonstrated higher accuracy with a 0.3%relative L_2 error,which advances RODT methodologies to harness machine learning and system modeling for improved reactor monitoring,systematic diagnosis of off-normal events,and lifetime management strategies.The developed modular software and novel optimization methods presented offer pathways to realize the full potential of RODT for transforming energy engineering practices.

Dynamics Modeling and Parameter Identification for a Coupled-Drive Dual-Arm Nursing Robot

A dual-arm nursing robot can gently lift patients and transfer them between a bed and a wheelchair.With its lightweight design,high load-bearing capacity,and smooth surface,the coupled-drive joint is particularly well suited for these robots.However,the coupled nature of the joint disrupts the direct linear relationship between the input and output torques,posing challenges for dynamic modeling and practical applications.This study investigated the transmission mechanism of this joint and employed the Lagrangian method to construct a dynamic model of its internal dynamics.Building on this foundation,the Newton-Euler method was used to develop a dynamic model for the entire robotic arm.A continuously differentiable friction model was incorporated to reduce the vibrations caused by speed transitions to zero.An experimental method was designed to compensate for gravity,inertia,and modeling errors to identify the parameters of the friction model.This method establishes a mapping relationship between the friction force and motor current.In addition,a Fourier series-based excitation trajectory was developed to facilitate the identification of the dynamic model parameters of the robotic arm.Trajectory tracking experiments were conducted during the experimental validation phase,demonstrating the high accuracy of the dynamic model and the parameter identification method for the robotic arm.This study presents a dynamic modeling and parameter identification method for coupled-drive joint robotic arms,thereby establishing a foundation for motion control in humanoid nursing robots.

A transfer learning enhanced physics-informed neural network for parameter identification in soft materials

Soft materials,with the sensitivity to various external stimuli,exhibit high flexibility and stretchability.Accurate prediction of their mechanical behaviors requires advanced hyperelastic constitutive models incorporating multiple parameters.However,identifying multiple parameters under complex deformations remains a challenge,especially with limited observed data.In this study,we develop a physics-informed neural network(PINN)framework to identify material parameters and predict mechanical fields,focusing on compressible Neo-Hookean materials and hydrogels.To improve accuracy,we utilize scaling techniques to normalize network outputs and material parameters.This framework effectively solves forward and inverse problems,extrapolating continuous mechanical fields from sparse boundary data and identifying unknown mechanical properties.We explore different approaches for imposing boundary conditions(BCs)to assess their impacts on accuracy.To enhance efficiency and generalization,we propose a transfer learning enhanced PINN(TL-PINN),allowing pre-trained networks to quickly adapt to new scenarios.The TL-PINN significantly reduces computational costs while maintaining accuracy.This work holds promise in addressing practical challenges in soft material science,and provides insights into soft material mechanics with state-of-the-art experimental methods.

Improved Particle Swarm Optimization for Parameter Identification of Permanent Magnet Synchronous Motor

In the process of identifying parameters for a permanent magnet synchronous motor,the particle swarm optimization method is prone to being stuck in local optima in the later stages of iteration,resulting in low parameter accuracy.This work proposes a fuzzy particle swarm optimization approach based on the transformation function and the filled function.This approach addresses the topic of particle swarmoptimization in parameter identification from two perspectives.Firstly,the algorithm uses a transformation function to change the form of the fitness function without changing the position of the extreme point of the fitness function,making the extreme point of the fitness function more prominent and improving the algorithm’s search ability while reducing the algorithm’s computational burden.Secondly,on the basis of themulti-loop fuzzy control systembased onmultiplemembership functions,it is merged with the filled function to improve the algorithm’s capacity to skip out of the local optimal solution.This approach can be used to identify the parameters of permanent magnet synchronous motors by sampling only the stator current,voltage,and speed data.The simulation results show that the method can effectively identify the electrical parameters of a permanent magnet synchronous motor,and it has superior global convergence performance and robustness.

Modal Parameter Identification Method of Jacket Platform Structure Based on AFDD and Optimized FBFFT

Offshore platforms are susceptible to structural damage due to prolonged exposure to random loads,such as wind,waves,and currents.This is particularly true for platforms that have been in service for an extended period.Identifying the modal parameters of offshore platforms is crucial for damage diagno sis,as it serves as a prerequisite and foundation for the process.Therefore,it holds great significance to prioritize the identification of these parameters.Aiming at the shortcomings of the traditional Fast Bayesian Fast Fourier Transform(FBFFT) method,this paper proposes a modal parameter identification method based on Automatic Frequency Domain Decomposition(AFDD) and optimized FBFFT.By introducing the AFDD method and Powell optimization algorithm,this method can automatically identify the initial value of natural frequency and solve the objective function efficiently and simply.In order to verify the feasibility and effectiveness of the proposed method,it is used to identify the modal parameters of the IASC-ASCE benchmark model and the j acket platform structure model,and the Most Probable Value(MPV) of the modal parameters and their respective posterior uncertainties are successfully identified.The identification results of the IASC-ASCE benc hmark model are compared with the identification re sults of the MODE-ID method,which verifies the effectivene ss and accuracy of the proposed method for identifying modal parameters.It provides a simple and feasible method for quantifying the influence of uncertain factors such as environmental parameters on the identification results,and also provide s a reference for modal parameter identification of other large structures.

Robot Zero-Moment Control Algorithm Based on Parameter Identification of Low-Speed Dynamic Balance

This paper proposes a zero-moment control torque compensation technique.After compensating the gravity and friction of the robot,it must overcome a small inertial force to move in compliance with the external force.The principle of torque balance was used to realise the zero-moment dragging and teaching function of the lightweight collaborative robot.The robot parameter identification based on the least square method was used to accurately identify the robot torque sensitivity and friction parameters.When the robot joint rotates at a low speed,it can approximately satisfy the torque balance equation.The experiment uses the joint position and the current motor value collected during the whole moving process under the low-speed dynamic balance as the excitation signal to realise the parameter identification.After the robot was compensated for gravity and static friction,more precise torque control was realised.The zero-moment dragging and teaching function of the robot was more flexible,and the drag process was smoother.

Double Update Intelligent Strategy for Permanent Magnet Synchronous Motor Parameter Identification

The parameters of permanent magnet synchronous motor(PMSM)affect the performance of vector control servo system.Because of the complexity of nonlinear model of PMSM,it is very difficult to identify the parameters of PMSM.Aiming at the problems of large amount of data calculation,low identification accuracy and poor robustness in the process of multi parameter identification of permanent magnet synchronous motor,this paper proposes a weighted differential evolutionary particle swarm optimization algorithm based on double update strategy.By introducing adaptive judgment factor to control the proportion of weighted difference evolution(WDE)algorithm and particle swarm optimization(PSO)algorithm in each iteration process,and consider using PSO algorithm or WDE algorithm to update individuals according to the probability law.The individuals obtained from WDE operation are used to guide the individual evolution process in PSO operation through the information exchangemechanism.The proposed WDEPSO algorithm can ensure the diversity and effectiveness of the individual evolution of the population.The algorithm is applied to parameter identification of PMSMdrive system.The simulation results show that the proposed algorithm has better convergence performance and has strong robustness,parameter identification of permanent magnet synchronous motor based on proposed method does not need to rely on more data sheet on the motor design value,can motor stator resistance identification at the same time,the rotor flux linkage,d/q-axis inductance and electrical parameters,and can effectively track the parameters value.

PARAMETER IDENTIFICATION OF LUGRE FRICTION MODEL FOR FLIGHT SIMULATION SERVO SYSTEM BASED ON ANT COLONY ALGORITHM

In light of the high nonlinearity of LuGre friction model, a novel method based on ant colony algorithm(ACA) for identifying the friction parameters of flight simulation servo system is proposed. ACA is a parallelized bionic optimization algorithm inspired from the behavior of real ants, and a kind of positive feedback mechanism is adopted in ACA. On the basis of brief introduction of LuGre friction model, a method for identifying the static LuGre friction parameters and the dynamic LuGre friction parameters using ACA is derived. Finally, this new friction parameter identification scheme is applied to a electric-driven flight simulation servo system with high precision. Simulation and application results verify the feasibility and the effectiveness of the scheme. It provides a new way to identify the friction parameters of LuGre model.

FREE VIBRATION ANALYSIS AND PHYSICAL PARAMETER IDENTIFICATION OF NON-UNIFORM BEAM CARRYING SPRING-MASS SYSTEMS

To analyze a multibody system composed of non-uniform beam and spring-mass subsystems, the model discretization is carried on by utilizing the finite element method（FEM）, the dynamic model of non-uniform beam is developed by using the transfer matrix method of multibody system（MS-TMM）, the transfer matrix of non-u- niform beam is derived, and the natural frequencies are computed. Compared with the numerical assembly method （NAM）, the results by MS-TMM have good agreement with the results by FEM, and are better than the results by NAM. When using the high precision method, the global dynamic equations of the complex multibody system are not needed and the orders of involved system matrices are decreased greatly. For the investigation on the re- verse problem of the physical parameter identification of multibody system, MS-TMM and the optimization tech- nology based on genetic algorithms（GAs） are combined and extended. The identification problem is exchanged for an optimization problem, and it is formulated as a global minimum solution of the objective function with respect to natural frequencies of multibody system. At last, the numerical example of non-uniform beam with attach- ments is discussed, and the identification results indicate the feasibility and the effectivity of the proposed aop- proach.

Parameter identification of hysteretic model of rubber-bearing based on sequential nonlinear least-square estimation

In order to evaluate the nonlinear performance and the possible damage to rubber-bearings （RBs） during their normal operation or under strong earthquakes, a simplified Bouc-Wen model is used to describe the nonlinear hysteretic behavior of RBs in this paper, which has the advantages of being smooth-varying and physically motivated. Further, based on the results from experimental tests performed by using a particular type of RB （GZN 110） under different excitation scenarios, including white noise and several earthquakes, a new system identification method, referred to as the sequential nonlinear least- square estimation （SNLSE）, is introduced to identify the model parameters. It is shown that the proposed simplified Bouc- Wen model is capable of describing the nonlinear hysteretic behavior of RBs, and that the SNLSE approach is very effective in identifying the model parameters of RBs.

Parameter identification and global sensitivity analysis of Xin'anjiang model using meta-modeling approach

Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on （1） a screening method （Morris） for qualitative ranking of parameters, and （2） a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model （RSMSobol）. First, the Morris screening method was used to qualitatively identify the parameters＇ sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model （RSM） were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.

Dynamic friction modelling and parameter identification for electromagnetic valve actuator

A new modified LuGre friction model is presented for electromagnetic valve actuator system.The modification to the traditional LuGre friction model is made by adding an acceleration-dependent part and a nonlinear continuous switch function.The proposed new friction model solves the implementation problems with the traditional LuGre model at high speeds.An improved artificial fish swarm algorithm(IAFSA)method which combines the chaotic search and Gauss mutation operator into traditional artificial fish swarm algorithm is used to identify the parameters in the proposed modified LuGre friction model.The steady state response experiments and dynamic friction experiments are implemented to validate the effectiveness of IAFSA algorithm.The comparisons between the measured dynamic friction forces and the ones simulated with the established mathematic friction model at different frequencies and magnitudes demonstrate that the proposed modified LuGre friction model can give accurate simulation about the dynamic friction characteristics existing in the electromagnetic valve actuator system.The presented modelling and parameter identification methods are applicable for many other high-speed mechanical systems with friction.

Parameter identification and pressure control of dynamic system in shield tunneling using least squares method

An estimation approach using least squares method was presented for identificationof model parameters of pressure control in shield tunneling.The state equation ofthe pressure control system for shield tunneling was analytically derived based on themass equilibrium principle that the entry mass of the pressure chamber from cutting headwas equal to excluding mass from the screw conveyor.The randomly observed noise wasnumerically simulated and mixed to simulated observation values of system responses.The numerical simulation shows that the state equation of the pressure control system forshield tunneling is reasonable and the proposed estimation approach is effective even ifthe random observation noise exists.The robustness of the controlling procedure is validatedby numerical simulation results.

Automatic modal parameter identification of high arch dams:feasibility verification

Modal parameters, including fundamental frequencies, damping ratios, and mode shapes, could be used to evaluate the health condition of structures. Automatic modal parameter identification, which plays an essential role in realtime structural health monitoring, has become a popular topic in recent years. In this study, an automatic modal parameter identification procedure for high arch dams is proposed. The proposed procedure is implemented by combining the densitybased spatial clustering of applications with noise(DBSCAN) algorithm and the stochastic subspace identification(SSI). The 210-m-high Dagangshan Dam is investigated as an example to verify the feasibility of the procedure. The results show that the DBSCAN algorithm is robust enough to interpret the stabilization diagram from SSI and may avoid outline modes. This leads to the proposed procedure obtaining a better performance than the partitioned clustering and hierarchical clustering algorithms. In addition, the errors of the identified frequencies of the arch dam are within 4%, and the identified mode shapes are in agreement with those obtained from the finite element model, which implies that the proposed procedure is accurate enough to use in modal parameter identification. The procedure is feasible for online modal parameter identification and modal tracking of arch dams.

Parameter identification of the fractional-order systems based on a modified PSO algorithm

In order to better identify the parameters of the fractional-order system,a modified particle swarm optimization(MPSO)algorithm based on an improved Tent mapping is proposed.The MPSO algorithm is validated with eight classical test functions,and compared with the POS algorithm with adaptive time varying accelerators(ACPSO),the genetic algorithm(GA),a d the improved PSO algorithm with passive congregation(IPSO).Based on the systems with known model structures a d unknown model structures,the proposed algorithm is adopted to identify two typical fractional-order models.The results of parameter identification show that the application of average value of position information is beneficial to making f 11 use of the information exchange among individuals and speeds up the global searching speed.By introducing the uniformity and ergodicity of Tent mapping,the MPSO avoids the extreme v^ue of position information,so as not to fall into the local optimal value.In brief the MPSOalgorithm is an effective a d useful method with a fast convergence rate and high accuracy.

A Technology for Online Parameter Identification of Permanent Magnet Synchronous Motor

Accuracy of the motor parameters is important in realizing high performance control of permanent magnet synchronous motor(PMSM).However,the inductance and resistance of motor winding vary with the change of temperature,rotor position and current frequency.In this paper,a technology based on circuit model is introduced for realizing online identification of the parameter of PMSM.In the proposed method,a set of nonlinear equations containing the parameters to be identified is established.Considering that it is very difficult to obtain the analytical solution of a nonlinear system of equations,Newton iterative method is used for solving the equations.Both the simulation and testing results confirm the effectiveness of the method presented.

Numerical and experimental evaluation on methods for parameter identification of thermal response tests

Several parameter identification methods of thermal response test were evaluated through numerical and experimental study.A three-dimensional finite-volume numerical model was established under the assumption that the soil thermal conductivity had been known in the simulation of thermal response test.The thermal response curve was firstly obtained through numerical calculation.Then,the accuracy of the numerical model was verified with measured data obtained through a thermal response test.Based on the numerical and experimental thermal response curves,the thermal conductivity of the soil was calculated by different parameter identification methods.The calculated results were compared with the assumed value and then the accuracy of these methods was evaluated.Furthermore,the effects of test time,variable data quality,borehole radius,initial ground temperature,and heat injection rate were analyzed.The results show that the method based on cylinder-source model has a low precision and the identified thermal conductivity decreases with an increase in borehole radius.For parameter estimation,the measuring accuracy of the initial temperature of the deep ground soil has greater effect on identified thermal conductivity.

Model-based parameter identification of comprehensive friction behaviors for giant forging press

A new experimental apparatus was set up to investigate the actual fi-iction characteristics on the basis of speed control of the serve system.A modified friction model was proposed due to real time varying deformation resistance.The approach to identify the parameters of comprehensive friction behaviors based on the modified model was proposed and applied to the forging press.The impacts on parameters which the external load had were also investigated.The results show that friction force decreases with velocity in the low velocity regime whereas the friction force increases with the velocity in the high velocity regime under no external load.It is also shown that the Coulomb friction force,the maximum static friction force and the vicious friction coefficient change linearly with the external load taking the velocity at which the magnitude of the steady state friction force becomes minimum as the critical velocity.

Synchronization and parameter identification of one class of realistic chaotic circuit

In this paper, the synchronization and the parameter identification of the chaotic Pikovsky-Rabinovich （PR） circuits are investigated. The linear error of the second corresponding variables is used to change the driven chaotic PR circuit, and the complete synchronization of the two identical chaotic PR circuits is realized with feedback intensity k increasing to a certain threshold. The Lyapunov exponents of the chaotic PR circuits are calculated by using different feedback intensities and our results are confirmed. The case where the two chaotic PR circuits are not identical is also investigated. A general positive Lyapunov function V, which consists of all the errors of the corresponding variables and parameters and changeable gain coefficient, is constructed by using the Lyapunov stability theory to study the parameter identification and complete synchronization of two nomidentical chaotic circuits. The controllers and the parameter observers could be obtained analytically only by simplifying the criterion dV/dt 〈 0 （differential coefficient of Lyapunov function V with respect to time is negative）. It is confirmed that the two non-identical chaotic PR circuits could still reach complete synchronization and all the unknown parameters in the drive system are estimated exactly within a short transient period.

Pattern-Moving-Based Parameter Identification of Output Error Models with Multi-Threshold Quantized Observations

This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm(M-AM-SGRPIA)for a class of single input single output(SISO)linear output error models with multi-threshold quantized observations.It proves the convergence of the designed algorithm.A pattern-moving-based system dynamics description method with hybrid metrics is proposed for a kind of practical single input multiple output(SIMO)or SISO nonlinear systems,and a SISO linear output error model with multi-threshold quantized observations is adopted to approximate the unknown system.The system input design is accomplished using the measurement technology of random repeatability test,and the probabilistic characteristic of the explicit metric value is employed to estimate the implicit metric value of the pattern class variable.A modified auxiliary model stochastic gradient recursive algorithm(M-AM-SGRA)is designed to identify the model parameters,and the contraction mapping principle proves its convergence.Two numerical examples are given to demonstrate the feasibility and effectiveness of the achieved identification algorithm.