High Computational Accuracy Research Articles

Research on Two Refined Algorithms for Creep Calculation for Reinforced Concrete Components

Existing refined creep calculation methods primarily include the numerical recursive method. Previous algorithms merely provided a theoretical possibility for refined creep calculations. Their massive computational load, complex derivation processes, and limited applicability conditions made it difficult to apply them practically in engineering. Therefore, this study improves upon existing refined creep algorithms by proposing two new calculation methods: the trapezoidal method and the difference method. The study shows that when calculating creep under different conditions, the proposed methods only require adjustments to certain elements in the calculation model, without the need to re-derive the creep calculation formula; this thus provides a convenient solution for creep calculation under varying operating conditions. Compared to the traditional numerical recursive method, the proposed methods significantly reduce computational effort; compared to the midpoint integration method, the proposed methods offer broader applicability. Under different calculation conditions, both methods exhibit high computational accuracy.

Enhanced soft Monte Carlo simulation coupled with support vector regression for structural reliability analysis

In structural reliability analysis, it is a major challenge to develop a general method that can ensure high computational accuracy and low computational cost for low failure probability and high-dimensional problems. In this study, a novel enhanced simulation method termed enhanced soft Monte Carlo simulation coupled with support vector regression (EMCS-SVR) is proposed. First, a generalized enhanced simulation (ES) scaling formula is proposed as an improved scheme. Furthermore, the soft Monte Carlo simulation is combined with generalized ES scaling formula and SVR model for evaluating the failure probability. The efficiency and accuracy of the ESMCS-SVR are verified by comparing with an existing popular method in four numerical examples and three engineering examples.

Fast iterative position estimation method of permanent magnet synchronous linear motor

Abstract In this paper, a Fast Iterative Phase Estimation (FIPE) algorithm is proposed for robotic grinding force control devices using Permanent Magnet Synchronous Linear Motors (PMSLM), requiring high positional accuracy and response speed. FIPE obtains optimal phase estimates through iterative searching and exhibit excellent dynamic performance. The proposed method is optimized by iterative optimization algorithms, which significantly reduces the number of iterations and enhance the response speed of position estimation. In addition, the effects of harmonics, amplitude mismatch, noise and DC errors in the raw signal on the FIPE algorithm are discussed, and a shallow neural network is introduced to compensate the periodic errors. The experimental results show that the FIPE algorithm has higher calculation accuracy and dynamic performance than the traditional PLL such as SRF-PLL, ANF-PLL and LKF-PLL, and has faster iteration speed than the traditional position estimator such as FPS-PLL.

Practical Streamline Method for Quantitatively Characterizing Waterflooding in Narrow Channel Reservoirs.

In waterflooding development of narrow channel reservoirs, the water cut rises quickly, and the reservoir becomes nearly fully flooded, yet oil recovery remains low. The narrow strip sand body and long-term water injection create a complex oil and water distribution, making it difficult to evaluate the degree of reservoir utilization during waterflooding. This paper establishes a practical streamline method to quantitatively characterize the waterflooding mobilization degree of narrow channel reservoirs. The streamline method, based on the boundary element method, first characterizes the narrow channel reservoir with complex morphologies and calculates the pressure field. Then, the velocity field is determined, and the streamlines are tracked using the Euler method. The characteristic line method is adopted to solve the saturation equation along each streamline, resulting in the overall saturation distribution of the model. Compared to the stream-tube and numerical simulation methods, the practical streamline method has several advantages, including the automatic generation of flow paths, low calculation dimensions, fast calculation speed, and high calculation accuracy. The method accurately describes the complex boundaries of the channel, realizes the quantitative characterization of waterflooding mobilization degree partitioning, and clarifies the streamline patterns and oil-water flow processes. Finally, the method was applied to a typical narrow channel reservoir to quantitatively evaluate the waterflooding mobilization degree of a well group, resulting in reasonable development and adjustment suggestions. These findings provide guidance for development adjustments in similar types of narrow channel reservoirs.

Development of a Modular Virtual Calibration Platform for Power Machinery

The hardware-in-the-loop (HIL)-based virtual calibration technology for power machinery significantly enhances the efficiency and flexibility of electronic controller calibration, reducing dependency on physical experiments and lowering development costs. This paper presents an independently developed modular virtual calibration platform, designed based on the ISO/IEC 42010 architecture approach and the V-Model development process. The platform adopts a modular layered architecture comprising the physical layer, communication layer, model layer, software layer, and application layer. Each layer is independently developed and seamlessly integrated, ensuring excellent scalability and maintainability. Through performance validation conducted under steady-state and transient operating conditions of the diesel/natural gas dual-fuel engine electronic control unit (ECU), experimental results demonstrated that the platform possesses high computational accuracy, rapid response capability, and stable operation across different scenarios. The experiments validated that the platform can effectively replicate the real behavior of the controlled units in a virtual environment, confirming its adaptability and reliability. The platform offers an efficient and reliable solution for power machinery controller calibration.

Modified Green-Ampt model for infiltration analysis in loess area and its parameter determination and verification

Water infiltration into soil is important in geotechnical engineering. The classical Green-Ampt (GA) infiltration model is widely used in soil infiltration due to its physical significance, but it ignores the actual unsaturated layer in the infiltration process and has some deficiencies. Thus, the present study established a modified GA infiltration model (MLGA model) using Darcy’s infiltration law and continuity equation to fully consider the variation characteristics of the soil water profile in the infiltration process. The Philip model and the GA model have the same physical basis. By combining the internal relationship between the infiltration rate and cumulative infiltration of the two models, the expression of matrix suction of the MLGA model was obtained. The existing measured data was used to verify the correctness and applicability of the MLGA model. Finally, the sensitivity analysis of the critical parameters of the model was performed. The results showed that the MLGA model is reliable and can be used for water infiltration analysis in loess sites. When the MLGA model is used to calculate the infiltration depth, the maximum relative error between the calculated value and the measured data is less than 10%, and the average relative error is 5.54%, which indicates that the model has a high calculation accuracy. The average error of the MLGA model is 24.47% of the Kostiakov model and 80.42% of the existing modified GA model (LGAM model). Sensitivity analysis of the saturated permeability coefficient, initial water content, saturated water content, and matrix suction was performed using the single-factor disturbance method. The influence of each parameter on infiltration depth was summarized, and the sensitivity of each parameter was quantitatively evaluated, which revealed that saturated water content is a highly sensitive parameter. When using the MLGA model to calculate infiltration depth, the accuracy of saturated water content should be ensured first. Finally, the difference between the MLGA model and the LGAM model in describing the whole process of water infiltration is discussed, and the influence mechanism of critical parameters on the water infiltration process is deeply analyzed. The established MLGA model provides theoretical support for further study of the loess water infiltration mechanism.

A complete framework for cosmological emulation and inference with CosmoPower

Abstract We present a coherent, re-usable python framework building on the CosmoPower emulator code for high-accuracy calculations of cosmological observables with Einstein-Boltzmann codes. For detailed statistical analyses, such codes require high computing power, making parameter space exploration costly, especially for beyond-ΛCDM analyses. Machine learning-enabled emulators of Einstein-Boltzmann codes are becoming an increasingly popular solution to this problem. To enable generation, sharing and use of emulators for inference, we define standards for robustly describing, packaging and distributing them. We present software for easily performing these tasks in an automated and replicable manner and provide examples and guidelines for generating emulators and wrappers for using them in popular cosmological inference codes. We demonstrate our framework with a suite of high-accuracy emulators for the CAMB code’s calculations of CMB Cℓ, P(k), background evolution, and derived parameter quantities. We show these emulators are accurate enough for analysing both ΛCDM and a set of extension models (Neff, ∑mν, w0wa) with stage-IV observatories, recovering the original high-accuracy spectra to tolerances well within the cosmic variance uncertainties. We show our emulators also recover cosmological parameters in a simulated cosmic-variance limited experiment, finding results well within 0.1σ of the input cosmology, while requiring ≲ 1/50 of the evaluation time.

Optimizing Accuracy and Computational intensity of Web Usage Mining for E-commerce Recommendations Using LLMs

Web usage mining has become critical in enhancing e-commerce platforms by understanding user behavior and providing personalized recommendations. However, traditional algorithms often suffer from high computational intensity and limited accuracy when processing large-scale web log data. This research proposes a novel approach leveraging Large Language Models (LLMs) to analyse and optimize web log data for e-commerce recommendation systems. To take into account the rich contextual understanding of LLMs and their powerful feature extraction capability, the presented approach is set to enhance precision while reducing computation overhead. By utilizing the E-commerce Website Logs dataset, this research preprocesses raw log data into structured features such as session durations, clickstream patterns, and page dwell times. This approach is then tested against five other existing algorithms: Random Forest, Support Vector Machine (SVM), k-Nearest Neighbours (k-NN), Gradient Boosting, and Deep Neural Networks in terms of accuracy, F1-score, precision, recall, sensitivity and root mean square error (RMSE). Apart from prediction quality, the computational efficiency of each algorithm is also analysed in terms of training time, inference time, and resource usage. Results show that the LLM-based model consistently outperforms traditional algorithms across all the evaluation metrics, such as achieving higher accuracy, precision, and recall along with substantially lower RMSE. In addition, the proposed method has faster processing times and lower computational intensity, which makes it more suitable for large-scale, real-time recommendation tasks. This paper contributes to the field by showing the potential of LLMs in web usage mining and recommendation systems, offering a scalable solution that bridges the gap between accuracy and efficiency. These findings emphasize the integration of advanced models of machine learning, such as LLMs, into e-commerce platforms for the ever-growing demands of today's modern platforms and to ensure computational feasibility.

Reconstructing higher-order constitutive equations based on deep learning

To overcome the problems of high computational complexity, low efficiency, and limited accuracy in the iterative solution of traditional high-order constitutive equations, this paper proposes a deep learning-based high-order constitutive equation reconstruction (DLHCER) method. The method embeds the nonlinear terms into the source terms of the Navier–Stokes (NS) equations, and the DLHCER control equations are obtained by physical reconstruction. In the training stage, the error between the experimental data and computational fluid dynamics simulation data is utilized to generate the training data and establish the mapping relationship between the free incoming flow variables and the characteristic nonlinear variables. Ultimately, the complex nonlinear terms in DLHCER are fitted and predicted by an agent model to capture the complex relationship between the source term parameters and the incoming flow conditions, thus replacing the traditional iterative solution process. Experimental results show that the average relative error between the theoretical and predicted values of the DLHCER model is 0.302%. The agent model is further analyzed for Shapley additive explanations interpretability to clarify the relationship between network inputs and outputs. Through the comparative validation of nonlinear coupled constitutive relations, Navier–Stokes–Fourier and DLHCER on one-dimensional positive excitation wave arithmetic cases, the results show that DLHCER significantly simplifies the computational process by directly correcting the free parameters of the source term, avoids the cumbersome iterative process, and realizes the double enhancement of computational efficiency and accuracy. Therefore, DLHCER is expected to provide strong support for higher-order constitutive equations in engineering applications in the field of dilute gases.

Synthetic CT generation using Zero TE MR for head-and-neck radiotherapy.

MRI-based synthetic CTs (synCTs) show promise to replace planning CT scans in various anatomical regions. However, the head-and-neck region remains challenging because of patient-specific air, bone and soft tissues interfaces and oropharynx cavities. Zero-Echo-Time (ZTE) MRI can be fast and silent, accurately discriminate bone and air, and could potentially lead to high dose calculation accuracy, but is relatively unexplored for the head-and-neck region. Here, we prospectively evaluated the dosimetric accuracy of a novel, fast ZTE sequence for synCT generation. The method was developed based on 127 patients and validated in an independent test (n = 17). synCTs were generated using a multi-task 2D U-net from ZTE MRIs (scanning time: 2:33 min (normal scan) or 56 s (accelerated scan)). Clinical treatment plans were recalculated on the synCT. The Hounsfield Units (HU) and dose-volume-histogram parameters were compared between the synCT and CT. Subsequently, synthetic treatment plans were generated to systematically assess dosimetry accuracy in different anatomical regions using dose-volume-histogram parameters. The mean absolute error between the synCT and CT was 94 ± 11 HU inside the patient contour. For the clinical plans, 98.8 % of PTV metrics deviated less than 2 % between synCT and CT and all OAR metrics deviated less than 1 Gy. The synthetic plans showed larger dose differences depending on the location of the PTV. Excellent dose agreement was found based on clinical plans between the CT and a ZTE-MR-based synCT in the head-and-neck region. Synthetic plans are an important addition to clinical plans to evaluate the dosimetric accuracy of synCT scans.

Enhancing Smart Grid Security Using BLS Privacy Blockchain With Siamese Bi‐LSTM for Electricity Theft Detection

ABSTRACTEnergy management inside a blockchain framework developed for smart grids is primarily concerned with improving intrusion detection to protect data privacy. The emphasis is on real‐time detection of cyberattacks and preemptive forecasting of possible risks, especially in the realm of electricity theft within smart grid systems. Existing Electricity Theft Detection techniques for smart grids have obstacles such as class imbalance, which leads to poor generalization, increased complexity due to large EC data aspects, and a high false positive rate in supervised models, resulting in incorrect classification of regular customers as abnormal. To provide security in the smart grid, a novel BLS Privacy Blockchain with Siamese Bi‐LSTM is proposed. Initially, the privacy‐preserving Boneh‐Lynn‐Shacham blockchain technique is built on BLS Short signature and hash algorithms, which mitigate misclassification rates and false positives in the detection of smart grid attacks. Then, a hybrid framework employs an intrusion detection algorithm based on Siamese Bidirectional Long Short‐Term Memory to semantically distinguish between harmful and authentic behaviors, thereby improving data quality and predictive capabilities. Furthermore, a Recurrent Neural Network‐Generative Adversarial Network is presented for detecting electricity fraud, which addresses the issue of class imbalance. This uses both supervised and unsupervised loss functions to produce synthetic theft samples that closely resemble actual theft incidents. From the experiment, it is showing that the proposed models perform with high accuracy and low error rates. The proposed model from the outcomes when compared to other existing models achieves high accuracy, detection rate, recall, and low computation time.

Algorithms for Solving the Equilibrium Composition Model of Arc Plasma.

In the present study, the Homotopy Levenberg-Marquardt Algorithm (HLMA) and the Parameter Variation Levenberg-Marquardt Algorithm (PV-LMA), both developed in the context of high-temperature composition, are proposed to address the equilibrium composition model of plasma under the condition of local thermodynamic and chemical equilibrium. This model is essentially a nonlinear system of weakly singular Jacobian matrices. The model was formulated on the basis of the Saha and Guldberg-Waage equations, integrated with Dalton's law of partial pressures, stoichiometric equilibrium, and the law of conservation of charge, resulting in a nonlinear system of equations with a weakly singular Jacobian matrix. This weak singularity primarily arises due to significant discrepancies in the coefficients between the Saha equation and the Guldberg-Waage equation, attributed to differing chemical reaction energies. By contrast, the coefficients in the equations derived from the other three principles within the equilibrium composition model are predominantly single-digit constants, further contributing to the system's weak singularity. The key to finding the numerical solution to nonlinear equations is to set reasonable initial values for the iterative solution process. Subsequently, the principle and process of the HLMA and PV-LMA algorithms are analyzed, alongside an analysis of the unique characteristics of plasma equilibrium composition at high temperatures. Finally, a solving method for an arc plasma equilibrium composition model based on high temperature composition is obtained. The results show that both HLMA and PV-LMA can solve the plasma equilibrium composition model. The fundamental principle underlying the homotopy calculation of the (n-1) -th iteration, which provides a reliable initial value for the n-th LM iteration, is particularly well suited for the solution of nonlinear equations. A comparison of the computational efficiency of HLMA and PV-LMA reveals that the latter exhibits superior performance. Both HLMA and PV-LMA demonstrate high computational accuracy, as evidenced by the fact that the variance of the system of equations ||F|| < 1 × 10-15. This finding serves to substantiate the accuracy and feasibility of the method proposed in this paper.

Bar Axial Force Correction Method for the Elastic Performance of Truss Materials With Periodic Units

ABSTRACTA bar axial force correction method is proposed on the basis of the representative volume element (RVE) method in this paper, which can predict the elastic performance of truss materials with periodic units. Based on the bar axial force correction method, the problem that the RVE method that node reaction forces on the common boundary may be unbalanced can be solved. For truss materials with periodic units, the effective elastic parameters and nodal displacement responses based on the proposed method are completely consistent those for the asymptotic homogenization (AH) method. Meanwhile, the proposed method is simpler than the AH method. Furthermore, compared with the axial compression experiment, the elastic modulus error of the proposed method is 3.81%. Thus, the correctness of this method is proved. Finally, the comparisons of different homogenization methods are analyzed in the numerical examples. The numerical examples show that the calculation results of the proposed method are the same as those of the AH method. Compared with the AH method, the maximum error of effective elastic parameters and nodal displacements for the RVE method are 22.51% and 13.65%, respectively. Compared with the AH method, the maximum error of effective elastic parameters and nodal displacements for the two‐node (TN) method are 14.43% and 10.60%, respectively. Therefore, the bar axial force correction method has a wider range of applications and higher computational accuracy.

ANALYTICAL DESCRIPTION OF THE POTENTIAL OF ELECTROSTATIC MULTIPOLE SYSTEMS BASED ON A CONDUCTING CIRCULAR CYLINDER

An urgent task of corpuscular optics and scientific instrumentation is the creation of new methods for calculating the physical and instrumental parameters of mass spectrometers. Leveraging the increased capabilities of computational technology, these methods provide a solid basis for the design and calculation of instruments with improved analytical capabilities. In this work, a method was developed for calculating the electrostatic field of multipole systems based on a conducting circular cylinder. This method uses the broad analytical capabilities of the theory of functions of a complex variable (TFCV). Analytical expressions are found for the electrostatic field potential of a quadrupole system for the case of infinitely narrow gaps between the electrodes. Analytical expressions for the derivatives of the potential are also obtained. A study was carried out of the influence of the finite size of the gaps between the electrodes on the field configuration. For this purpose, numerical simulations of planar electric fields satisfying the Laplace equation were carried out. The calculation of the electrostatic field potential was carried out using the boundary element method in two stages. First, the distribution of electric charge at the boundary was calculated according to the known boundary potential distribution, that is, the “inverse” problem was solved. Then, using the found values of the charge distribution and the found potential values, the “direct” problem was solved. To solve this problem, a method was developed for solving integral equations with singular and quasi-singular kernels, which provides high accuracy in field calculations for electron-optical systems (EOS) with rectilinear boundaries. The inaccuracies in the calculations resulted solely from rounding mistakes. In the case of an Equation of State (EOS) characterized by curved boundaries, the precision is dependent exclusively on how accurately the boundaries are represented using linear segments. For the purpose of segmenting the curved borders of the second-order EOS, these boundaries were broken down into arcs with an angular measurement not exceeding one degree.

Analysis of the dynamic response of aerostatic bearings based on the dynamic equilibrium position method

To fully consider the influence of time-varying pressure and film thickness, the transient Reynolds equation is typically employed for numerical computations in the analysis of gas bearing-rotor systems. Solving the transient Reynolds equation is complex and time-intensive. To enhance computational efficiency, this paper presents an optimized algorithm, termed as the equilibrium position (EP) method, which leverages the solution of the steady-state Reynolds equation to analyze the axis shift induced by rotor imbalance. By neglecting certain transient terms while retaining the essential physics, the proposed method achieves high computational accuracy and significantly reduces computational time. The computational errors are evaluated, and comparisons with orbit method simulations demonstrate the method's efficiency and accuracy. In applications such as predicting rotor vibration amplitude or estimating unbalanced forces during ultra-precision machining and rotor design, the EP method offers a streamlined computation process without compromising on result fidelity.

Design optimization of vibration amplitude reduction based on virtual prototype and machine learning

The traditional design optimization of vibration amplitude reduction mainly has the disadvantages of low modeling and prediction accuracy as well as low optimization efficiency. Therefore, this paper presents a design optimization method for vibration amplitude reduction based on virtual prototyping and machine learning, which combines the high accuracy of numerical calculations with the efficiency of machine learning, overcoming the shortcomings of traditional methods. Firstly, sample points are collected through the design of experiments and virtual prototype simulation. Then, based on the sampled data, a prediction model for the relationship between the design parameters and the amplitude of the product is established using Genetic Algorithm-Support Vector Regression (GA-SVR). On the basis of the GA-SVR prediction model, a multi-objective optimization model of product is established, and Multiple Objectives Particle Swarm Optimization -entropy weight- Technique for Order Preference by Similarity to Ideal Solution (MOPSO-entropy weight-TOPSIS) is used to solve for the optimal design parameters. Finally, the washing machine suspension system is used as an example to verify the effectiveness of the model. The results show that, compared with the original design scheme, the design scheme obtained by the model can reduce the amplitude of the washing machine suspension system by 12.68%, and reduce the total weight of the counterweight by 7.35%. This method is conducive to the intelligent and efficient design optimization of vibration amplitude reduction, and is of great significance to product life cycle design.

A Review of the Current Status and Prospects of Improving Indoor Environment for Lightweight Buildings in High-Altitude Cold Regions

Lightweight structures, characterized by rapid assembly, are vital for creating habitats in outdoor environments, but their implementation in high-plateau cold regions encounters significant challenges in heating and ventilation. This paper systematically introduces the environmental characteristics and reviews the demands and primary influencing factors of indoor environments in these regions. The advantages and limitations of underground lightweight construction are also discussed. Current research indicates that evaluation methods for air quality in high-altitude cold regions require further development. Reducing building heat loss and minimizing cold air infiltration can enhance indoor environments and lower energy consumption. However, it is essential to establish effective ventilation strategies to prevent the accumulation of air pollutants. Then, potential passive ventilation improvement measures suitable for the environmental characteristics of high-cold plateaus are outlined. The application potential and possible limitations of these measures are summarized, providing references for future research. Finally, the main research methods for ventilation and heating within building interiors are organized and discussed. Findings indicate that computational fluid dynamics models are predominantly used, but they demonstrate low efficiency and high resource consumption for medium- to large-scale applications. Integrating these models with network models can achieve a balance of high computational accuracy and efficiency.

Biomimetic Active Stereo Camera System with Variable FOV.

Inspired by the biological eye movements of fish such as pipefish and sandlances, this paper presents a novel dynamic calibration method specifically for active stereo vision systems to address the challenges of active cameras with varying fields of view (FOVs). By integrating static calibration based on camera rotation angles with dynamic updates of extrinsic parameters, the method leverages relative pose adjustments between the rotation axis and cameras to update extrinsic parameters continuously in real-time. It facilitates epipolar rectification as the FOV changes, and enables precise disparity computation and accurate depth information acquisition. Based on the dynamic calibration method, we develop a two-DOF bionic active camera system including two cameras driven by motors to mimic the movement of biological eyes; this compact system has a large range of visual data. Experimental results show that the calibration method is effective, and achieves high accuracy in extrinsic parameter calculations during FOV adjustments.

Fine structure of the doublet P levels of boron

We report high-accuracy calculations of the ground and the lowest eight excited Po2 states of the two stable isotopes of the boron atom, B10 and B11, as well as of the boron atom with an infinite nuclear mass ∞B. The nonrelativistic wave function of each of the states is generated in an independent variational calculation by expanding it in terms of a large number, 12000–17000, of all-electron explicitly correlated Gaussian (ECG) functions whose nonlinear parameters are extensively optimized with a procedure that employs analytic energy gradient determined with respect to these parameters. These highly accurate wave functions are used to compute the fine-structure splittings using the first order of the perturbation theory (∼α2), where α is the fine-structure constant, which are then corrected for the electron magnetic moment anomaly (∼α3). As the nonrelativistic Hamiltonian explicitly depends on the mass of the nucleus, the recoil corrections up to the order of α2 are automatically accounted for in the fine-structure calculations. Furthermore, the off-diagonal corrections to the fine structure (∼α4) are estimated using the multireference methods based on one-electron Gaussian orbitals. The results obtained in this paper are considerably more accurate than those available in the literature. Moreover, we report accurate splittings for a number of excited Po2 states, for which there have been no reliable experimental or theoretical data at all. The calculated values presented in this paper may serve as a valuable guide for future experimental measurements of the fine structure of the boron atom. As the fine structure of an atom provides a spectral signature that can facilitate atom's detection, our data can also aid the search for trace amounts of boron in the interstellar medium. Published by the American Physical Society 2024

An analytical model for estimating aerodynamic damping of wind turbines in the shutdown state

As wind farms are constantly being constructed, the risk of tower failure for wind turbines increases significantly under strong winds. Compared with the extensively concerned wind-induced behaviors during the operating state, those ones during the shutdown state attract little attention but may lead to serious problems of damages or even collapse. To clearly grasp the aero-structure interaction in the shutdown state, this paper develops an analytical model for estimating aerodynamic damping of wind turbines. In this method, an analytical expression of aerodynamic damping coupling matrix is derived via the combination of multibody dynamics and first-order Taylor expansion. This matrix is further quantified as the ratio of modal aerodynamic damping with the aid of state-space equation and complex eigenvalue analysis. This treatment can facilitate the straightforward application of efficient calculation methods, such as frequency domain analysis and uncoupled analysis. More importantly, the developed model is able to simultaneously consider multiple realistic factors, such as blade flexibility, tower top rotation, yaw error, wind shear, and pitch angle. This model may have the high calculation efficiency and accuracy, as well as strong applicability for estimating the aerodynamic damping. Numerical examples based on a typical 5 MW wind turbine are employed to validate the effectiveness of the developed model. Experimental analyses demonstrate that this model outperforms the existing formula and presents a high consistency with OpenFAST in the estimation of aerodynamic damping. Meanwhile, the influence of multiple realistic factors is quantitatively analyzed, which even makes the estimation error exceed 70%.