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The indirect method for bridge modal identification based on the response of moving vehicles has attracted widespread attention in recent years. However, most existing studies only focus on the simply supported bridge, while continuous girder bridges are widely used in practical engineering. Therefore, this study proposes an indirect frequency identification method for continuous girder bridges. First, the mode shape formula of the equal-span continuous beam is deduced using the three-moment equations, and the analytical solution of the vehicle vibration response is deduced via the vehicle–bridge coupled vibration theory. Second, the derived analytical solution is verified through numerical analysis results and field test results. Finally, the effects of bridge and vehicle parameters on the frequency identification accuracy are analyzed. The results show that a reasonable frequency of the trailer should be selected to avoid the resonance between the vehicle and the bridge for better performance. The research findings can provide a reference for the indirect identification of continuous girder bridges based on the response of moving vehicles. [ABSTRACT FROM AUTHOR]

Non-viscous damping parameters can be identified approximately based on the frequency response function of structures. However, it is difficult or expensive to perform vibration test and layout enough amount measured points for structures. The stochastic subspace method can identify modal damping parameters only using responses data from partial measured points. This paper extends the stochastic subspace method from the viscous damping system to non-viscous damping system. In addition, the eigenvalues of the second-order form equation of motion are proved equally to the state space formulation. The identified procedure of the proposed method is given in detail. Finally, the method is validated using three numerical examples for assessing its accuracy. [ABSTRACT FROM AUTHOR]

It is essential for meeting the stringent real-time demands encountered in actual production scenarios. Employing the low computational complexity of recursive algorithms, some new schemes are developed for the parameter estimation of a class of time-varying systems. The temporal evolution of parameters is characterized through the autoregressive process, facilitating the construction of the identification model with regard to the autoregressive coefficients. Based on the computational efficiency of the gradient search, a parametric autoregression-based stochastic gradient algorithm is derived with an appropriate step size, achieving a compromise between the steepest descent and convergence rate. In order to address the limitation of the low estimation accuracy in gradient algorithms, a parametric autoregression-based multi-innovation stochastic gradient algorithm is explored by making use of the favorable information for corrections. The simulation results are given to demonstrate the effectiveness of the proposed algorithms. Therefore, for a class of time-varying systems whose parameters become the further insight through the autoregressive process, the proposed gradient methods can obtain the parameter estimates faster and more accurately while ensuring the real-time performance of time-varying systems. [ABSTRACT FROM AUTHOR]

The future power of photovoltaic systems (PVS) is gaining significant attention due to its rising potential. This has resulted in a substantial amount of research emphasizing the importance of optimizing the PVS efficiency. However, the identification of PV cell model parameters remains a challenging task, mainly due to the characteristics of PV cells and their dependence on varying meteorological conditions. In this work, we present a novel methodology based on an improved new multi objective particle swarm optimization (NMOPSO) algorithm for the PV cell parameters identification. The main goal is to minimize the root mean square error (RMSE) and to calculate the series resistance (Rs) by means of its non-linear equation form. The applied algorithm uses an evolving and adaptive search strategy to enhance both speed of convergence for the parameter identification process precision. Through extensive simulations, we demonstrate that proposed approach outperforms current methods in terms of accuracy, precision, and PV parameters extraction. [ABSTRACT FROM AUTHOR]

Structural damping, which has been widely used to quantify the energy dissipation of engineering structures, plays an important role in dynamic analysis during the design phase and the assessment of existing structures. However, the development of an accurate damping model remains an open question due to the unclear mechanism. In this study, structural damping was identified based on loss factors for beam structures, which were used for presenting the damping behavior during vibrations. The equation of motion for the bending deformation of a Timoshenko beam was derived, accounting for the material damping. To identify the loss factor of the beam from the response signals at a limited number of measurement points, an inverse algorithm based on wave coefficient estimation was developed, considering the numerical stability for data interpretation. Based on the proposed loss factor identification method, several beam models with different shapes of cross-sections were investigated, focusing on the feasibility of the method of varying cross-section cases. Comparing the spectral element model with the loss factor and the finite element model with Rayleigh damping, it was confirmed that the loss factor can better describe the actual energy dissipation behavior of the structure. It provides a more accurate theoretical model for estimating structural damping in practical engineering. [ABSTRACT FROM AUTHOR]

Energy harvesting technology, as an emerging energy technology, has contributed outstandingly to the application of online monitoring sensors in power systems. The accurate parameter identification of the harvesters is crucial for their power supply applications. This work aims to investigate the parameter identification of harvesters with complex equivalent circuit models, here taking a current transformer as an example. However, previous studies focused on optimizing algorithms rather than data sources. This paper demonstrates a vector impedance quantum genetic algorithm (QGA) parameter identification method to identify the amplitude and phase information of the impedance responses. By comparing the results of the proposed method with the genetic algorithm and PSO based on impedance responses and QGA based on load resistance responses, it is proved that the proposed vector impedance QGA identification method is optimal in terms of accuracy, speed, and robustness. The root mean square errors of the output voltage and the phase difference with the primary current for the proposed method are 5.884 × 10−5 V and 4.473 × 10−4 ms. Moreover, the practical applicability of this method is validated, demonstrating its effectiveness in real‐life and industrial settings. The proposed identification method in this paper changes the source data so that the sample data are small in volume and extensive in information, which enables faster and more accurate parameter identification. This study provides a new idea for parameter identification researches. [ABSTRACT FROM AUTHOR]

This paper presents a height control approach employing real‐time parameter identification for supersonic sea‐skimming missiles. Firstly, an integrated identification technique based on the Unscented Kalman filter was established for supersonic cruise missiles. The method facilitates the online identification of aerodynamic and thrust coefficients while simultaneously estimating state variables such as height and angle of attack. Secondly, a backstepping control method was designed to regulate the height of sea‐skimming missiles. Control commands were generated based on online identification and state estimation, as well as the additional angle of attack caused by wind being compensated. The simulation results show that the proposed height control method achieved a high level of precision when operating in the challenging conditions of ultralow‐height sea skimming, despite the presence of parameter uncertainties, measurement inaccuracies, sea wave disturbances, and wind effects. [ABSTRACT FROM AUTHOR]

Vitiligo is a skin disorder that is associated with a decreased risk of skin cancer, but it can lead to increased susceptibility to sunburn, psychological distress, and disruptions in daily life, consists of two primary subtypes: segmental and nonsegmental vitiligo, each with distinct underlying mechanisms. However, the reliable identification of diagnostic markers and the ability to differentiate between these subtypes have remained elusive challenges. This study aims to pioneer predictive algorithms for vitiligo diagnosis, harnessing the capabilities of AI (Artificial Intelligence) to amalgamate multisource data and uncover essential features for distinguishing vitiligo subtypes.An ensemble algorithm was thoughtfully developed for vitiligo diagnosis, utilizing a spectrum of machine learning techniques to evaluate the likelihood of vitiligo, whether segmental or nonsegmental. Diverse machine learning methodologies were applied to distinguish between healthy individuals and vitiligo patients, as well as to differentiate segmental from nonsegmental vitiligo. The ensemble algorithm achieved a remarkable AUC (Area Under the Curve) of 0.99 and an accuracy of 0.98 for diagnosing vitiligo. Furthermore, in predicting the development of segmental or nonsegmental vitiligo, the model exhibited an AUC of 0.79 and an accuracy of 0.73. Key parameters for vitiligo identification encompassed factors such as age, FBC (full blood count)-neutrophils, FBC-lymphocytes, LKF(liver and kidney function)-direct bilirubin, LKF-total bilirubin, and LKF-total protein levels. In contrast, vital indicators for monitoring the progression of segmental and nonsegmental vitiligo included FBC-B lymphocyte count, FBC-NK (Natural Killer) cell count, and LKF-alkaline phosphatase levels. This retrospective study underscores the potential of AI-driven analysis in identifying significant risk factors for vitiligo and predicting its subtypes at an early stage. These findings offer great promise for the development of effective diagnostic tools and the implementation of personalized treatment approaches in managing this challenging skin disorder. [ABSTRACT FROM AUTHOR]

A time domain rock mass parameter identification method considering the Rayleigh damping is proposed in this paper. Starting with a set of initial parameter values, the algorithm solves the finite-element (FE) equations updating the parameters in each iterative step until the result converges to a local optimum. Numerical FE analysis, displacement sensitivity analysis, and the truncated singular value decomposition method are integrated into the solution algorithm for parameter identification. The technique introduced is verified in numerical studies with in-situ experiments of rock mass structures. Our results show that the approach, compared with conventional methods, can reduce ambiguities caused by inadequate data and provide more accurate insights into the subsurface for rock mass quality evaluation. [ABSTRACT FROM AUTHOR]

The acquisition of bridge frequency by using the ubiquitous ordinary passenger vehicles and smartphones in cities has attracted an increasing amount of attention. This study proposes a bridge monitoring method and develops a vehicle–bridge crowdsourcing monitoring (VBCM) platform for public participation. It takes smartphones carried by vehicles as monitoring terminals for a long-term bridge monitoring task. To validate the feasibility of available smartphone brands as a signal collector, a small shake table test is carried out and the processing scheme for the smartphone sampling data is investigated. A smartphone sensor frequency gain technique is proposed to satisfy the fusion of multisensor data in different smartphones. Furthermore, a magnetic target identification technique is proposed to pick up and extract the bridge response signal segment corresponding to the vehicle’s entering and leaving, as well as to eliminate redundant data. To this end, the network of multiple smartphones is synchronized. For the sake of applicability and feasibility, a series of scaled experiments are conducted in the laboratory considering an adapted toy car driving over a simply supported steel bridge. The results demonstrated the practicality of the proposed methodology. The combination of easily accessible smartphones oriented to a crowdsourcing model and a driving vehicle lowers the threshold for the bridge monitoring process, which also pinpoints a potential for future structural health monitoring development. [ABSTRACT FROM AUTHOR]

The inhomogeneity between cells is the main cause of failure and thermal runaway in Lithium-ion battery packs. Electrochemical Impedance Spectroscopy (EIS) is a non-destructive testing technique that can map the complex reaction processes inside the battery. It can detect and characterise battery anomalies and inconsistencies. This study proposes a method for detecting impedance inconsistencies in Lithium-ion batteries. The method involves conducting a battery EIS test and Distribution of Relaxation Times (DRT) analysis to extract characteristic frequency points in the full frequency band. These points are less affected by the State of Charge (SOC) and have a strong correlation with temperature, charge/discharge rate, and cycles. An anomaly detection characteristic impedance frequency of 136.2644 Hz was determined for a cell in a Lithium-ion battery pack. Single-frequency point impedance acquisition solves the problem of lengthy measurements and identification of anomalies throughout the frequency band. The experiment demonstrates a significant reduction in impedance measurement time, from 1.05 h to just 54 s. The LOF was used to identify anomalies in the EIS data at this characteristic frequency. The detection results were consistent with the actual conditions of the battery pack in the laboratory, which verifies the feasibility of this detection method. The LOF algorithm was chosen due to its superior performance in terms of FAR (False Alarm Rate), MAR (Missing Alarm Rate), and its fast anomaly identification time of only 0.1518 ms. The method does not involve complex mathematical models or parameter identification. This helps to achieve efficient anomaly identification and timely warning of single cells in the battery pack. [ABSTRACT FROM AUTHOR]

In adaptive control, error models use system output error and adaptive laws to update controller parameters for control or identification tasks. Fractional-order calculus, involving non-integer-order derivatives and integrals, is increasingly important for modeling, estimation, and control due to its ability to generalize classical methods and offer improved robustness to disturbances. This paper addresses the gap in the literature where fractional-order gradient methods have not yet been extensively applied in identification and adaptive control schemes. We introduce a fractional-order error model with fractional-order gradient (FOEM1-FG), which integrates fractional gradient operators based on the Caputo fractional derivative. By using theoretical analysis and simulations, we confirm that FOEM1-FG maintains stability and ensures bounded output errors across a variety of input signals. Notably, the fractional gradient's performance improves as the order, β , increases with β > 1 , leading to faster convergence. Compared to existing integer-order methods, the proposed approach provides a more flexible and efficient solution in adaptive identification and control schemes. Our results show that FOEM1-FG offers superior stability and convergence characteristics, contributing new insights to the field of fractional calculus in adaptive systems. [ABSTRACT FROM AUTHOR]

The ability and rapid access to execution data and information in manufacturing workshops have been greatly improved with the wide spread of the Internet of Things and artificial intelligence technologies, enabling real-time unmanned integrated control of facilities and production. However, the widespread issue of data quality in the field raises concerns among users about the robustness of automatic decision-making models before their application. This paper addresses three main challenges relative to field data quality issues during automated real-time decision-making: parameter identification under measurement uncertainty, sensor accuracy selection, and sensor fault-tolerant control. To address these problems, this paper proposes a risk assessment framework in the case of continuous production workshops. The framework aims to determine a method for systematically assessing data quality issues in specific scenarios. It specifies the preparation requirements, as well as assumptions such as the preparation of datasets on typical working conditions, and the risk assessment model. Within the framework, the data quality issues in real-time decision-making are transformed into data deviation problems. By employing the Monte Carlo simulation method to measure the impact of these issues on the decision risk, a direct link between sensor quality and risks is established. This framework defines specific steps to address the three challenges. A case study in the steel industry confirms the effectiveness of the framework. This proposed method offers a new approach to assessing safety and reducing the risk of real-time unmanned automatic decision-making in industrial settings. [ABSTRACT FROM AUTHOR]

The development and validation of lateral control strategies for railway running gears with independently rotating driven wheels (IRDWs) are an active research area due to their potential to enhance straight-track centering, curve steering performance, and reduce noise and wheel–rail wear. This paper focuses on the practical application of theoretical models to a 1:5 scaled test rig developed by the German Aerospace Center (DLR), addressing the challenges posed by unmodeled phenomena such as hysteresis, varying damping and parameter identification. The theoretical model from prior work is adapted based on empirical measurements from the test rig, incorporating the varying open-loop stability of the front and rear wheel carriers, hysteresis effects, and other dynamic properties typically neglected in literature. A transparent procedure for identifying dynamic parameters is developed, validated through closed- and open-loop measurements. The refined model informs the design and tuning of a cascaded PI and PD controller, enhancing system stabilization by compensating for hysteresis and damping variations. The proposed approach demonstrates improved robustness and performance in controlling the lateral displacement of IRDWs, contributing to the advancement of safety-critical railway technologies. [ABSTRACT FROM AUTHOR]

The paper presents an approach to solving the problem of unknown motion parameters Bayesian identification for the stochastic dynamic system model with randomly delayed observations. The system identification and the object tracking tasks obtain solutions in the form of recurrent Bayesian relations for a posteriori probability density. These relations are not practically applicable due to the computational challenges they present. For practical implementation, we propose a conditionally minimax nonlinear filter that implements the concept of conditionally optimal estimation. The random delays model source is the area of autonomous underwater vehicle control. The paper discusses in detail a computational experiment based on a model that is closely aligned with this practical need. The discussion includes both a description of the filter synthesis features based on the geometric interpretation of the simulated measurements and an impact analysis of the effectiveness of model special factors, such as time delays and model unknown parameters. Furthermore, the paper puts forth a novel approach to the identification problem statement, positing a random jumping change in the motion parameters values. [ABSTRACT FROM AUTHOR]

Pipes are the main structures serving as the lifeline for oil and gas transportation. However, they are prone to cracks, holes and other damages due to harsh working environments, which can lead to leakage incidents and result in significant economic losses. Therefore, the development of structural health monitoring systems with advanced online diagnostic methods is of great importance for identifying local damages and assessing the safety state of pipe structures. These efforts can guide rapid repairs and ensure the continuous, efficient and cost-effective transportation of oil and gas resources. To address this problem, this paper proposes the development of a pipe monitoring system based on quasi-distributed fiber Bragg grating (FBG) sensing technology. The SSI-COV method is employed to process the sensor responses and extract the modal parameters of the structure. Based on this foundation, an enhanced damage identification index is proposed, which mitigates the effects of support and excitation positions on damage identification. The pipe structure can be regarded as a continuous super-statical beam, and based on its structural symmetry, a unit structure, specifically a stainless-steel pipe with fixed ends, is regarded as the experimental subject. Impact experiments have been conducted to analyze its behavior in both undamaged and damaged states. The research indicates that by using the proposed modal parameter identification method and the A S M D I damage index, A S M D I exhibits peak values at damage locations of the pipe structure. This allows for the identification of structural damage with high accuracy, fast processing efficiency and strong robustness. The study provides an effective and reliable damage diagnosis method, which can contribute to the refinement and visualization of pipe structural health monitoring systems. [ABSTRACT FROM AUTHOR]

The failure of bonded composite materials is accompanied by specific failure modes. These are specifically Mode I, Mode II, Mode III, and their combination (so-called mixed mode). These modes depend on the direction and type of loading. The mechanical properties describing the damage initiation and the damage evolution are unique according to the type of adhesive and present mode of failure. However, a few research studies have focused on an adhesive thicknesses greater than 0.2 mm. The main objective of this research is to investigate the mechanical properties of a bonded joint with large adhesive thickness loaded according to Modes I and II. The observed failure parameters, the cohesive and damage parameters, are identified by minimizing the difference between the force–displacement diagram obtained from the experimental data for both Mode I and Mode II. The finite element model is confronted with these parameters and is evaluated based on their agreement. Compared to other studies with a small adhesive layer thickness, the values of failure parameters are lower. The results show that the adhesive thickness has an influence on the values of cohesive and damage parameters and that these parameter values decrease significantly compared to a small adhesive thickness. The obtained parameters can be further used to predict the fracture toughness of other bonded joints loaded in any direction. [ABSTRACT FROM AUTHOR]

The paper deals with a source identification problem of the anomalous diffusion equations from nonlocal final data observations where the nonlinearity probably takes values in Hilbert scales. The existence and uniqueness results are proved by establishing some estimates for resolvent operators and using the embedding theorems. We also study regularity results for this equation in terms of the Hölder continuity of mild solutions. Finally, the multi-term fractional diffusion equations with polynomial nonlinearities and the ultra-slow diffusions are considered as illustrative applications. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Benezediols are widely used in different areas of industry, thus identification and quantification of benzenediols is of utmost importance due to their toxicity and high environmental abundance. In this work, benzenediol isomers (pyrocatechol, resorcinol, and hydroquinone) were investigated by using the Bray–Liebhafsky (BL) oscillatory reaction. All three isomers exhibit different behavior in the BL reaction, which renders the BL system applicable as a chemosensor. The period between the fifth and sixth oscillation, the amplitude of the sixth oscillation and in the case of hydroquinone, the emergence of a new oscillation in the BL reaction were selected as the parameters used for the identification and quantification of these isomers. Furthermore, electron paramagnetic resonance spectroscopy and DFT calculations were performed in order to provide insights into the mechanism of benzenediols reactions with the BL system. [ABSTRACT FROM AUTHOR]

The measurement of deep water gravity wave elevations using in situ devices, such as wave gauges, typically yields spatially sparse data due to the deployment of a limited number of costly devices. This sparsity complicates the reconstruction of the spatio-temporal extent of surface elevation and presents an ill-posed data assimilation problem, which is challenging to solve with conventional numerical techniques. To address this issue, we propose the application of a physics-informed neural network (PINN) to reconstruct physically consistent wave fields between two elevation time series measured at distinct locations within a numerical wave tank. Our method ensures this physical consistency by integrating residuals of the hydrodynamic nonlinear Schrödinger equation (NLSE) into the PINN's loss function. We first showcase a data assimilation task by employing constant NLSE coefficients predetermined from spectral wave properties. However, due to the relatively short duration of these measurements and their possible deviation from the narrow-band assumptions inherent in the NLSE, using constant coefficients occasionally leads to poor reconstructions. To enhance this reconstruction quality, we introduce the base variables of frequency and wavenumber, from which the NLSE coefficients are determined, as additional neural network parameters that are fine tuned during PINN training. Overall, the results demonstrate the potential for real-world applications of the PINN method and represent a step toward improving the initialization of deterministic wave prediction methods. [ABSTRACT FROM AUTHOR]

Utilizing thermal updrafts shows potential for enabling long-endurance cruising of fixed-wing unmanned aerial vehicles without energy consumption. This article presents a novel online method based on sparse identification of nonlinear dynamics (SINDy) approach to achievement identification of thermal sources in the atmosphere. Initially, the algorithm is incorporated into the upper-level planning system, interacting with the lower-level controller. Then, experiments are conducted through software-in-the-loop simulations (SITL) to validate the implementation of the proposed algorithm. It is found that direct observation of thermal sources through measurements using SINDy is unfeasible during straight and circular flight modes. Nevertheless, simulation analysis of the proposed approach indicates that under unobservable conditions, a portion of the parameters can still be identified. By comparing results obtained using the particle filter algorithm, this method is shown to accurately estimate the parameters with negligible errors under observability conditions. The novelty of this approach lies in its significant improvement of the localization accuracy of the thermal source, without the need for parameter adjustments in the algorithm. Finally, the proposed methods are integrated into commonly used hardware platforms, and their online feasibility is verified through hardware-in-the-loop simulations. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are widely applied in the form of new energy electric vehicles and large-scale battery energy storage systems to improve the cleanliness and greenness of energy supply systems. Accurately estimating the state of power (SOP) of lithium-ion batteries ensures long-term, efficient, safe and reliable battery operation. Considering the influence of the parameter identification accuracy on the results of state of power estimation, this paper presents a systematic review of model parameter identification and state of power estimation methods for lithium-ion batteries. The parameter identification methods include the voltage response curve analysis method, the least squares method and so on. On this basis, the methods used for modeling and estimating the SOP of battery cells and battery packs are classified and elaborated, focusing on summarizing the research progress observed regarding the joint estimation method for multiple states of battery cells. In conclusion, future methods for estimating the SOP of lithium-ion batteries and their improvement targets are envisioned based on the application requirements for the safe management of lithium-ion batteries. [ABSTRACT FROM AUTHOR]

Underwater soft manipulators are increasingly used for grasping underwater organisms and cultural relics due to their compliance and ability to protect delicate objects. Unlike rigid manipulators, these soft manipulators feature underwater soft bending actuators that deform under external forces, making their shape and output force challenging to predict. Consequently, classical beam theory is no longer applicable. To address these issues, this article models the underwater soft bending actuator as a cantilever beam and establishes its shape and output force using elliptic integral functions. Additionally, this article proposes a method for determining the unknown parameters of the driver shape and output force model using optimization techniques and introduces a solution process based on the particle swarm optimization framework. Given the role of tensile and bending stiffness in the actuator's performance, this paper employs a parameter identification method based on length and bending angle information. Tests demonstrate that the proposed method effectively estimates the deformation and output force of the underwater soft bending actuators, confirming its accuracy. [ABSTRACT FROM AUTHOR]

This paper addresses performance degradation in nonlinear flux linkage algorithms, arising from estimation errors in rotor flux linkage due to fluctuations in current and temperature. We introduce a parameter-adaptive flux linkage model using MRAS, which dynamically adjusts the rotor flux linkage, significantly minimizing estimation errors and improving control performance. When the rotor flux linkage of the motor undergoes sudden changes, the nonlinear flux linkage model shows a speed fluctuation of 5%, whereas the parameter-adaptive flux linkage model reduces the error to 0.6%. The algorithm demonstrates strong robustness when the stator resistance undergoes a sudden change. The control effectiveness under conditions such as load start and load mutation is excellent. Simulations and experiments demonstrate that the parameter-adaptive flux linkage model improves the estimation accuracy of the rotor position when motor parameters change. [ABSTRACT FROM AUTHOR]

Cable-hole transmission is widely applied in cable-driven mechanisms to reduce the mechanical size. However, the driving tension is attenuated with the cable threading through the hole caused by uncertain factors such as local deformation, friction, and other effects, and errors in cable-hole transmission occur. To improve the transmission accuracy of cable-driven mechanisms, a tension distribution model considering the cable lateral extrusion is established. Then, an analytical tension ratio of the cable-hole transmission is derived based on the perturbation method and tension distribution model. Parameters of the tension ratio are identified using a particle swarm optimization algorithm. An adaptive tension control method considering the cable lateral extrusion is designed and compared with the method excluding the cable lateral extrusion in the cable-hole transmission. Finally, a cable-hole transmission experimental device was constructed to verify the tension ratio, parameter identification, and servo control method of the cable-hole transmission. The results show the motion control accuracy of the cable-driven mechanism can be significantly improved with the tension ratio considering the cable lateral extrusion. Compared to the case excluding the cable lateral extrusion, the errors in cable-hole transmission considering the lateral extrusion are reduced by an order of magnitude, and the tension vibration is significantly weakened. [ABSTRACT FROM AUTHOR]

This paper proposes a procedurefor multi-criteria calibration of a thermo-mechanical model for numerical simulation of welding in the space vacuum. A finite-element model of a steel plate is created. Experimental and computational data are obtained. An inverse problem is formulated for the vector identification of five calibration parameters from the heat-flow model. They are evaluated for adequacy with controlled accuracy according to four criteria. An optimization problem is solved using a two-step interactive procedure. The parameter space studying method (PSI) has been applied to the study of multidimensional regions by means of quasi-uniform sounding. A Pareto-optimal set is defined. It is used to determine reduced ranked Pareto subsets by μ-selection. Salukvadze optimum is also determined. [ABSTRACT FROM AUTHOR]

Piezoelectric inkjet printing technology, known for its high precision and cost-effectiveness, has found extensive applications in various fields. However, the issue of residual vibration significantly limits its printing quality and efficiency. This paper presents a method for suppressing residual vibration based on the particle swarm optimization (PSO) algorithm. Initially, an improved PI model considering the nonlinear hysteresis characteristics of piezoelectric ceramics is established, and the model is identified through a strain gauge circuit to ensure its accuracy in describing the nonlinear hysteresis characteristics. Subsequently, a dynamic model of the piezoelectric inkjet printing system is constructed, with precise parameter identification achieved using the self-induction principle. This enables precise simulation of residual vibration. Finally, the driving waveform is optimized based on the PSO algorithm, with iterative calculations employed to find the optimal combination of driving waveform parameters, effectively suppressing residual vibration while ensuring sufficient injection energy. The results indicate that this method significantly reduces the amplitude of residual vibration, thereby effectively enhancing printing quality and stability. This research offers a novel solution for residual vibration suppression in piezoelectric inkjet printing technology, potentially advancing its applications in printing and biofabrication. [ABSTRACT FROM AUTHOR]

With the development and progress of Internet and data technology, the consensus control of multi-agent systems has been an important topic in nonlinear science. How to effectively achieve the consensus of leader–follower multi-agent systems at a low cost is a difficult problem. This paper analyzes the consensus control of complex financial systems. Firstly, the dynamic characteristics of the financial system are analyzed by the equilibrium points, bifurcation diagrams, and Lyapunov exponent spectra. The behavior of the financial system is discussed by different parameter values. Secondly, according to the Lyapunov stability theorem, the consensus of master–slave systems is proposed by linear feedback control, wherein the controllers are simple and low cost. And an adaptive control method for the consensus of master–slave systems is investigated based on financial systems with unknown parameters. In theory, the consensus of the leader–follower multi-agent systems is proved by the parameter identification laws and linear feedback control method. Finally, the effectiveness and reliability of the consensus of leader–follower multi-agent systems are verified through the experimental simulation results and circuit implementation. [ABSTRACT FROM AUTHOR]

With the gradual transformation of energy industries around the world, the trend of industrial reform led by clean energy has become increasingly apparent. As a critical link in the new energy industry chain, lithium-ion (Li-ion) battery energy storage system plays an irreplaceable role. Accurate estimation of Li-ion battery states, especially state of charge (SOC) and state of health (SOH), is the core to realize the safe and efficient utilization of energy storage systems. This paper presents a systematic and comprehensive evaluation and summary of the most advanced Li-ion battery state estimation methods proposed in the past 3 years, focusing on analyzing data-driven state estimation algorithms. At the same time, the latest Li-ion battery data sets and data selection methods are analyzed, and future research trends and possible challenges are proposed. This review will provide a valuable reference for future academic research in Li-ion battery state estimation. [ABSTRACT FROM AUTHOR]

A numerical model is proposed to investigate the electrodeposition coating process of open-porous polyurethane (PU) foams with nickel. The modelling approach is based on the mixture theory, which accounts for the multi-phase nature of the system comprising the porous foam structure and the electrolyte which consists of the deposition material in form of cations. The model takes into consideration various physical and electrochemical phenomena, including different ionic transport mechanisms, i.e. diffusion, convection and migration. By solving the governing equations numerically, the coating process and the relevant variables are predicted over time. The simulation results are compared with experimental data to assess the agreement between the model and the experimental results. The findings reveal that the numerical model provides valuable insights into the electrodeposition process and facilitates a deeper understanding of the underlying mechanisms and it can be used for optimizing the coating process parameters. [ABSTRACT FROM AUTHOR]

An accurate and efficient approach is required to identify the unknown parameters of solid oxide fuel cell (SOFC) mathematical model for a robust design of any energy system considering SOFC. This research study proposes a modified fire hawk algorithm (MFHA) to determine the values of SOFC model parameters. The performance evaluation of MFHA is tested on two case studies. Firstly, the performance of MFHA is tested on commercially available cylindrical cell developed by Siemens at four temperatures. Results reveal that the least value of sum of squared error (SSE) is 1.04E−05, 2.30E−05, 1.03E−05, and 1.60E−05 at 1073 K, 1173 K, 1213 K, and 1273 K respectively. Results obtained using MFHA have been compared with original fire hawk algorithm (FHA) and other well established and recent algorithms. Secondly, MFHA is implemented for estimating unknown parameters of a 5 kW dynamic tabular stack of 96 cells at various pressures and temperatures. The obtained value of SSE at different temperatures of 873 K, 923 K, 973 K, 1023 K and 1073 K is 1.18E−03, 6.12E−03, 2.21E−02, 5.18E−02, and 6.00E−02, respectively whereas, SSE at different pressures of 1 atm, 2 atm, 3 atm, 4 atm, and 5 atm is 6.05E−02, 6.11E−02, 5.53E−02, 5.11E−02, and 6.64E−02 respectively. [ABSTRACT FROM AUTHOR]

Reverse transcription–digital PCR (RT–dPCR) is attracting attention as a method that enables SI-traceable RNA quantification without calibration, but its accuracy and bias have not been thoroughly studied. In this study, the accurate quantification of RNA by the RT–dPCR method was investigated using NMIJ CRM 6204-b, an RNA certified reference material whose certified value was assigned by orthogonal chemical measurement methods. Moreover, a two-step RT–dPCR method was adopted to examine in detail the conditions for the RT reaction process, which was expected to be the major uncertainty component in the RT–dPCR measurement. Optimization experiments revealed that the type of reverse transcriptase, the concentration of template RNA, and the type and concentration of primers in the RT reaction affected the value quantified by RT–dPCR. Under the optimal conditions, the value quantified by RT–dPCR, 76.4 ng/μL ± 6.7 ng/μL (the quantified value ± expanded uncertainty (k = 2)), was consistent with the certified value, 68.2 ng/μL ± 5.8 ng/μL, of NMIJ CRM 6204-b RNA 1000-A within the expanded uncertainty. From the results of the uncertainty evaluation, the relative combined uncertainty of the RT–dPCR method was 4.42%, and the major uncertainty components in the RT–dPCR method were the preparation of RT solution (3.68%), the inter-day difference (1.80%), and the RT reaction (1.30%). Together, the results suggested that the contribution of the RT reaction process to the total uncertainty was greater than that of the dPCR process. [ABSTRACT FROM AUTHOR]

This study presents a novel Buyback Rail Freight Option (BRFO), leveraging Stackelberg game theory to enhance the strategic management of rail freight transactions. By integrating traditional buyback theory with a multi-phase trigeminal tree pricing model and parameter identification through a nonparametric Ito stochastic method, the research addresses key challenges of information asymmetry and market uncertainty. The proposed methodology emphasizes dynamic pricing strategies and market adaptation, constructing a Nash equilibrium framework within railway freight pricing. The findings suggest significant strategic benefits for railway enterprises, positioning BRFO as a crucial tool for improving competitiveness in the face of alternative transport options. [ABSTRACT FROM AUTHOR]

To properly simulate the laser directed energy deposition (L-DED) process, the heat source model is crucial as it represents the absorbed heat energy, which controls the thermal phenomena associated with L-DED. Goldak's double-ellipsoidal heat source model has been broadly used to represent energy distribution in a wide range of laser-based simulation processes. It is therefore important to accurately determine Goldak's model parameters for correct L-DED simulation. In this work, an original inverse identification procedure was proposed to determine Goldak's heat source parameters by coupling a 3D finite element thermal model with a genetic algorithm. The multi-objective Non-dominated Sorting Genetic Algorithm II (NSGA-II) was used by considering two objective functions simultaneously. The temperatures at specific points in the melt pool were extracted from the 3D finite element simulations and then used to construct objective functions for the optimization procedure. By minimizing the differences between the simulated and reference temperatures, Goldak's heat source parameters were identified based on experimental L-DED fabricated single-track deposits for different process conditions. The model accurately estimated the penetration depth and slightly overestimated the heat-affected zone (HAZ). The relative errors are reliable enough to reasonably predict Goldak's heat source model parameters. The inverse identification procedure proposed in this study can help reduce time and experimental costs to estimate heat source parameters for the simulation of the L-DED process. [ABSTRACT FROM AUTHOR]

Operational Modal Analysis (OMA) refers to the modal analysis with only output vibration signals of a structure in its operating state. Classic OMA has developed multiple recognition methods in both the time and frequency domains, where when the random excitation is unknown, the excitation chromaticity is usually treated as white color, which can often cause errors and affect the accuracy of identifying frequencies or damping ratios. In this article, the chromaticity recognition function is defined and a method Chromaticity Recognition Technology (CRT) for identifying noise chromaticity based on system response is proposed. Then, a simulation example is presented. The noise chromaticity is identified for the response of the system under four types of colored noise excitation, and the results of the identification of operational mode parameters with and without CRT are compared. Furthermore, the sensitivity of traditional OMA to different colored noise has been investigated. An experiment with a cantilever under base excitation of pink noise has been undertaken and the results demonstrate the feasibility of the proposed CRT in this paper. [ABSTRACT FROM AUTHOR]

As a critical component of substations, the substation frames are characterized by significant height and span, which presents substantial challenges and risks in conducting dynamic response tests using traditional sensors. To simplify these difficulties, this paper introduces an experimental method utilizing MEMS sensor-based vibration acquisition. In this approach, smartphones equipped with MEMS sensors are deployed on the target structure to collect vibration data under environmental excitation. This method was applied in a dynamic field test of a novel composite substation frame. During the test, the proposed MEMS-based vibration acquisition method was conducted in parallel with traditional ultra-low-frequency vibration acquisition methods to validate the accuracy of the MEMS data. The results demonstrated that the MEMS sensors not only simplified the testing process but also provided reliable data, offering greater advantages in testing convenience compared with traditional contact methods. The modal parameters of the substation frame, including modal frequencies, damping ratios, and mode shapes, were subsequently identified using the covariance-driven stochastic subspace identification method. The experimental methodology and findings presented in this paper offer valuable insights for structural dynamic response testing and the wind-resistant design of substation frames. [ABSTRACT FROM AUTHOR]

Since rail-to-ground insulation decreases, large-level direct currents (DCs) leak from railways and form metro stray currents, corroding the buried metal. To locate the rail-to-ground insulation deterioration area, a location method is proposed based on parameter identification methods and the monitored information including the station rail potentials, currents at the traction power substations (TPSs), and train traction currents and train positions. According to the monitoring information of two adjacent TPSs, the section location model of the metro line is proposed, in which the rail-to-ground conductances of the test section are equivalent to the lumped parameters. Using the rail resistivity and traction currents as the known information, the rail-to-ground conductances are calculated with the least square method (LSM). The rail-to-ground insulation deterioration sections are identified by comparing the calculated conductances with thresholds determined by the standard requirements and section lengths. Then, according to the section location results, a detailed location model of the degradation section is proposed, considering the location distance accuracy. Using the genetic algorithm (GA) to calculate the rail-to-ground conductances, degradation positions are located by comparing the threshold calculated with the standard requirements and location distance accuracy. The location method is verified by comparing the calculation results under different degradation conditions. Moreover, the applications of the proposed method to different degradation lengths and different numbers of degradation sections are analyzed. The results show that the proposed method can locate rail-to-ground insulation deterioration areas. [ABSTRACT FROM AUTHOR]

In the identification process of SSI–DATA method, the stabilization diagram is generally used to determine the order of the system. However, in the modal parameter identification of spatial grid structure, there are few stable points that meet the requirements, and the stability axis is not obvious. So, the location of the modal stability point cannot be determined. Furthermore, the QR decomposition of the Hankel matrix is computationally inefficient and difficult to use for online identification when analyzing the spatial structure. In order to solve the above problems, this paper proposes a time-saving random subspace method of spatial grid structure based on an improved stabilization diagram. On the basis of the traditional random subspace method, the projection of the Hankel matrix is transformed into the projection transformation of its R matrix, and then the subsequent calculation is carried out. Then, the frequency search domain is set and the auxiliary stability axis is made. According to the spectrum peak, stabilization diagram and the auxiliary stability axis, the position of the stability axis is comprehensively judged. The modal parameters of the structure can be identified more accurately, which improves the calculation efficiency and avoids modal omissions or misjudgments to a certain extent. Using a spatial structure model (upper chord 18 × 12m, lower chord 16 × 10m, grid 2 × 1.5m), the advantages of this method were compared and verified. The results indicate that the calculation time of the traditional SSI–DATA method is 29.077s, while the improved calculation time is 3.752s, resulting in an 87.1% improvement in calculation efficiency, while also preventing mode omission and misjudgment. By further applying this method to vibration monitoring of spatial grid structure hangars, its applicability, higher computational efficiency, and precision have been proven. [ABSTRACT FROM AUTHOR]

Introduction: Fuel cell technology is a harbinger of the future for generating electricity due to their high efficiency and low emissions achieved through the direct conversion of chemical energy into electrical energy without combustion. Methods: To optimize the design and performance, a fuel cell model is essential to predict its behaviour in different conditions. This technical note presents a novel physics-based approach, the Young's Double-slit Experiment Optimizer (YDEO), for identifying parameters in Proton Exchange Membrane Fuel Cells. A performance metric is established by formulating an objective function that relies on the summation of squared errors between experimental and estimated values. Results and discussion: The effectiveness of this approach is evaluated through the analysis of four benchmark test cases: Horizon 500 W, BCS500 W, NedstackPS6, and 250 W. The corresponding objective function values for these test cases are 0.011243, 2.065557, 0.011698, and 5.250849, respectively. The simulation results demonstrate the efficacy of the proposed YDEO algorithm when compared with other existing popular and contemporary algorithms in the literature. [ABSTRACT FROM AUTHOR]

Objective: The success rate of achieving seizure freedom after radiofrequency thermocoagulation surgery for patients with refractory focal epilepsy is about 20-40%. This study aims to enhance the prediction of surgical outcomes based on preoperative decisions through network model simulation, providing a reference for clinicians to validate and optimize surgical plans. Methods: Twelve patients with epilepsy who underwent radiofrequency thermocoagulation were retrospectively reviewed in this study. A coupled model based on model subsets of the neural mass model was constructed by calculating partial directed coherence as the coupling matrix from stereoelectroencephalography (SEEG) signals. Multi-channel time-varying model parameters of excitation and inhibitions were identified by fitting the real SEEG signals with the coupled model. Further incorporating these model parameters, the coupled model virtually removed contacts destroyed in radiofrequency thermocoagulation or selected randomly. Subsequently, the coupled model after virtual surgery was simulated. Results: The identified excitatory and inhibitory parameters showed significant difference before and after seizure onset (p < 0.05), and the trends of parameter changes aligned with the seizure process. Additionally, excitatory parameters of epileptogenic contacts were higher than that of non-epileptogenic contacts, and opposite findings were noticed for inhibitory parameters. The simulated signals of postoperative models to predict surgical outcomes yielded an area under the curve (AUC) of 83.33% and an accuracy of 91.67%. Conclusion: The multi-channel coupled model proposed in this study with physiological characteristics showed a desirable performance for preoperatively predicting patients' prognoses. [ABSTRACT FROM AUTHOR]

Spectroscopy is a technique that analyzes the interaction between matter and light as a function of wavelength. It is the most convenient method for obtaining qualitative and quantitative information about an unknown sample with reasonable accuracy. However, traditional spectroscopy is reliant on bulky and expensive spectrometers, while emerging applications of portable, low-cost and lightweight sensing and imaging necessitate the development of miniaturized spectrometers. In this study, we have developed a computational spectroscopy method that can provide single-shot operation, sub-nanometer spectral resolution, and direct materials characterization. This method is enabled by a metasurface integrated computational spectrometer and deep learning algorithms. The identification of critical parameters of optical cavities and chemical solutions is demonstrated through the application of the method, with an average spectral reconstruction accuracy of 0.4 nm and an actual measurement error of 0.32 nm. The mean square errors for the characterization of cavity length and solution concentration are 0.53 % and 1.21 %, respectively. Consequently, computational spectroscopy can achieve the same level of spectral accuracy as traditional spectroscopy while providing convenient, rapid material characterization in a variety of scenarios. [ABSTRACT FROM AUTHOR]

The role of load modeling in power systems is crucial for both operational and regulatory considerations. It is essential to develop an effective and reliable method for optimizing load modeling parameter identification. In this paper, the dung beetle algorithm is improved by using the good point set, and a load model parameter identification strategy based on the good point set dung beetle optimization algorithm (GDBO) within the framework of the measurement-based load modeling method. The proposed parameter identification strategy involves utilizing PMU voltage data as input, selecting a comprehensive load model, and refining the initialization process based on the good point set to mitigate the influence of local maxima. Through iterative optimization of the objective function using the Dung Beetle Optimizer (DBO) algorithm, the optimal parameters for the comprehensive load model are determined, enhancing the model's ability to accurately capture the power curve. Analysis of examples pertaining to PMU-measured modeling parameter identification reveals that the proposed GDBO algorithm, which incorporates a good point set, outperforms alternative methods such as the improved differential evolution algorithm (IDE), particle swarm optimization algorithm (PSO), grey wolf optimization algorithm (GWO), and conventional DBO algorithm. This demonstrates the superior performance of the introduced approach in the context of load model parameter identification. [ABSTRACT FROM AUTHOR]

This article discusses an identification and modeling approach of a reluctance synchronous motor (RSM) based on the running rotor technique. The applied flux linkage approximation functions reflect the self-saturation and cross-saturation effects, and the applied mathematical model is continuous and differentiable. The proper design of the experiment is discussed, and relevant recommendations are made to ensure the mitigation of procedural mistakes in the experiments. A detailed analysis of the impact of configuration faults on the obtained experimental data is provided, considering distortions in the obtained flux linkage and inductance surfaces. Considering the achieved model accuracy, a novel model evaluation considering the achieved model accuracy technique based on transient current response is proposed. [ABSTRACT FROM AUTHOR]

This paper proposed a multi-innovation Nesterov accelerated gradient (MNAG) parameter identification method for the autoregressive exogenous (ARX) model. First, a momentum acceleration term is added stochastic gradient descent (SGD) algorithm to increase the convergence rate of the SGD. Second, the parameter updating process is expanded from a single batch of current information iteration to the multiple batches of both previous and current information iteration, which extended the algorithm from single-innovation Nesterov accelerated gradient (NAG) to multi-innovation NAG parameter identification method. That enhances the algorithm's anti-noise and anti-abnormal data abilities, and its data utilization rate. Then, the convergence of the MNAG parameter identification method is proven. The effectiveness of the MNAG parameter identification method is verified by numerical simulation and the rotational speed system of a ring-pendulum double-sided polisher. [ABSTRACT FROM AUTHOR]

This paper presents the results of spectroscopic measurements of plasma in a linear simulator of a plasma-beam installation (PBI) for conducting materials science research. For the first time, non-contact optical diagnostic methods were employed to measure the plasma parameters in the PBI. These measurements were allowed for the clarification of the PBI plasma parameters and the identification of transition zones in different operational modes. Analysis of the plasma emission spectrum enabled the identification of impurity spectral lines and the estimation of key plasma parameters under varying experimental conditions. The spectra were recorded using two optical spectrometers covering a wavelength range of 200–800 nm. The experimental conditions varied in terms of electron beam accelerating voltage (1–5 kV), working gas pressure (1–6 mTorr), and target bias voltage (from −500 to −100 V). The Boltzmann plot method and Stark broadening of the Balmer lines were used to estimate electron temperature and density. Based on the obtained spectroscopic data, the electron density and the electron temperature were determined. The methods described in this study are applicable to linear plasma devices. [ABSTRACT FROM AUTHOR]

This study used vibration testing signals of soil samples under external loading to identify modal parameters (including natural frequencies and damping ratios) with different compaction degrees. Based on these parameters, a novel approach was proposed for reliable roadbed vibration compaction control and compaction process optimization. The experimental section utilized six soil samples with varying compaction degrees as experimental subjects, using the hammering method as the excitation mode. Subsequently, the frequency response function and modal parameters of the sample system were obtained through the acquisition, analysis, and parameter identification of samples' acceleration signals. Firstly, samples with compaction degrees ranging from 88 % to 97 % primarily exhibited three modes, with the second modal frequency response displaying the weakest amplitude, and the fundamental mode being the dominant one. Additionally, parameter identification results revealed that the fundamental modal frequency exhibited a significant negative exponential growth with increasing compaction degree, while the second and third modal frequencies showed significant linear growth. Furthermore, the average damping ratio also demonstrated a tendency toward linear change with increasing compaction degree. Finally, the feasibility of modal parameters being actively used in practical engineering is discussed. Consequently, this study aimed to propose an indicator system for accurately assessing the bearing level of compacted soils from a modal dynamics perspective and to integrate modal dynamic indicators with density-class indicators into further optimization design work on road compaction processes. [ABSTRACT FROM AUTHOR]

Accurate estimation of State-of-Charge (SoC) is essential for ensuring the safe and efficient operation of electric vehicles (EVs). Currently, second-order RC equivalent circuit models do not account for the influence of battery charging and discharging states on battery parameters. Additionally, offline parameter identification becomes inaccurate as the battery ages. Online identification requires real-time parameter updates during the SoC estimation process, which increases the computational complexity and reduces the computational efficiency of real vehicle Battery Management System (BMS) chips. To address these issues, this paper proposes a SoC estimation method that combines online and offline identification based on an optimized second-order RC equivalent circuit model, which distinguishes it from existing methods in the field. On the basis of the traditional second-order RC model, the Ohmic resistance (R0), polarization resistance (R1), polarization capacitance (C1), diffusion resistance (R2), and diffusion capacitance (C2) during the charging and discharging processes are discussed separately. R0, which does not change frequently, is identified offline, while R1, R2, C1, and C2, which dynamically change with time and current, are identified online. To thoroughly verify the feasibility of the proposed method, we construct an SoC estimation test bench, which allows us to adjust the battery's surface temperature in real time using a temperature control chamber. Experimental validation under Federal Urban Driving Schedule (FUDS) (−10 °C to 45 °C, 80% battery capacity) and Dynamic Stress Test (DST) (−10 °C to 45 °C, 8% battery capacity) conditions demonstrate that our method improves SoC estimation accuracy by 16.28% under FUDS and 28.2% under DST compared to the improved GRU-based transfer learning method, while maintaining system SoC estimation efficiency. [ABSTRACT FROM AUTHOR]

With the large-scale integration of wind power, it is essential to establish an electromagnetic transient (EMT) model of a wind turbine system. Focusing on the problem of the difficulty in obtaining the parameters of the direct-driven permanent magnet synchronous generator (PMSG) model, this manuscript proposes a method based on trajectory sensitivity analysis and improved gray wolf optimization (IGWO) for identifying the parameters of the PMSG EMT model. First, a model of a PMSG wind turbine is established on an EMT simulation platform. Then, the key parameters of the model are determined based on the sensitivity analysis. Five control parameters are selected as the key parameters for their higher sensitivity indexes. Finally, the key parameters are accurately identified, using the proposed IGWO algorithm. The final case study demonstrates that the proposed IGWO algorithm has better optimization performance compared with the GWO algorithm and particle swarm optimization (PSO) algorithm. In addition, the simulation waveforms show that the identified parameters are accurate and applicable to other operating conditions. [ABSTRACT FROM AUTHOR]

Wire arc additive manufacturing enables the production of components with high deposition rates and the incorporation of multiple materials. However, the manufactured components possess a wavy surface, which is a major difficulty when it comes to simulating the mechanical behavior of wire arc additively manufactured components and evaluation of experimental full-field measurements. In this work, the wavy surface of a thick-walled tube is measured with a portable 3D scanning technique first. Then, the surface contour is considered numerically using the finite cell method. There, hierarchic shape functions based on integrated Legendre polynomials are combined with a fictitious domain approach to simplify the discretization process. This enables a hierarchic p-refinement process to study the convergence of the reaction quantities and the surface strains under tension–torsion load. Throughout all considerations, uncertainties arising from multiple sources are assessed. This includes the material parameter identification, the geometry measurement, and the experimental analysis. When comparing experiment and numerical simulation, the in-plane surface strains are computed based on displacement data using radial basis functions as ansatz for global surface interpolation. It turns out that the finite cell method is a suitable numerical technique to consider the wavy surface encountered for additively manufactured components. The numerical results of the mechanical response of thick-walled tubes subjected to tension–torsion load demonstrate good agreement with real experimental data, particularly when employing higher-order polynomials. This agreement persists even under the consideration of the inherent uncertainties stemming from multiple sources, which are determined by Gaussian error propagation. [ABSTRACT FROM AUTHOR]