Search Results (4,579)

Reclaimed asphalt pavement (RAP) reduces energy consumption and enhances economic benefits by recycling road materials, making it an effective approach for the sustainable use of solid waste resources. The performance of reclaimed asphalt pavement is significantly affected not only by the degradation of asphalt binders due to aging but also by the dosage of the rejuvenator used. The master curve of the complex shear modulus is widely recognized as a valuable tool for characterizing the rheological properties of asphalt binders. First, a virgin asphalt binder with a grade of SK70 was subjected to varying degrees of aging, followed by the rejuvenation of the aged asphalt using different dosages of the rejuvenator. Second, frequency sweeps were conducted on the aged and rejuvenated asphalt binders at various temperatures. Complex modulus master curves were constructed, and the CAM model was applied to fit these curves. The viscoelastic properties of asphalt at different aging levels and rejuvenator dosages were then analyzed based on the CAM parameters. Next, by applying a curve-shifting technique based on the least squares method to a reference state, both the time–temperature–aging (TTA) and time–temperature–regenerator (TTR) master curves of the complex modulus were constructed. The relationships between aging shift factors and aging times, as well as between regenerator shift factors and dosages, were established to predict the complex moduli of both aged and rejuvenated asphalt. Finally, the shear stress–strain relationships and material integrity of aged and rejuvenated asphalt were evaluated to assess their fatigue performance. The results indicated that aging significantly increases the complex modulus of asphalt, with TFOT (Thin Film Oven Test) aging having a more pronounced impact than PAV (Pressurized Aging Vessel) aging, resulting in reduced viscous deformation and an increased risk of cracking. Rejuvenator dosage reduces the complex modulus, with a 6% dosage effectively restoring mechanical properties and enhancing low-temperature performance. The TTA master curve demonstrates a strong linear correlation between aging shift factors and time, allowing for accurate predictions of the complex modulus of aged asphalt. Similarly, the TTR master curve reveals a linear relationship between regenerator dosage and shift factor, offering high predictive accuracy for optimizing regenerator dosages in engineering applications. The study further explores how varying levels of aging and rejuvenator dosage affect fatigue life under different strain conditions, uncovering complex behaviors influenced by these aging and regeneration processes. Full article

Given the non-stationarity, nonlinearity, and high complexity of polysilicon prices in the photovoltaic (PV) industry chain, this paper introduces upstream and downstream material prices of the PV industry chain and macroeconomic indicators as influencing factors. The VMD–SSA–LSTM combination model is constructed to predict polysilicon prices, which is based on Variational Mode Decomposition (VMD) and utilizes the Sparrow Search Algorithm (SSA) to optimize the Long Short-Term Memory (LSTM) network. The research shows that decomposing the original polysilicon time series using the VMD algorithm effectively extracts the main features of polysilicon price data, reducing data instability. By optimizing the learning rate, hidden layer nodes, and regularization coefficients of the LSTM model using the Sparrow Search Algorithm, the model achieves higher convergence accuracy. Compared to the traditional LSTM model and VMD–LSTM model, the VMD–SSA–LSTM model exhibits the smallest error and the highest goodness-of-fit on the polysilicon dataset, demonstrating higher predictive accuracy for polysilicon prices, which provides more accurate reference data for market analysis and pricing decisions of the polysilicon industry. Full article

Accurate estimation of the potential increase in the carbon (C) sink function of forests is required for climate mitigation and C neutrality assessments. Also, accurate forest carbon density estimates are critical for understanding national- and global-level carbon cycling and storage and can inform climate change mitigation. This study established a stand C density growth model to further analyze the C sink potential of planted Mongolian pine (Pinus sylvestris var. mongolica) forests. Samples (390) from fixed plots of Mongolian pine were collected in Heilongjiang Province, Northeast China. The site index (SCI) and stand density index (SDI) were introduced to a constructed stand C density growth model, with an optimal model selected through model fitting. The effect of SDI on stand C density in different SCI grouping intervals was assessed. Total C sequestration of Mongolian pine was calculated using the established C density model. Sample plots with higher C density in each forest age stand were selected to establish a model of potential C sequestration for Mongolian pine, and the difference between this rate and the average was compared to obtain the potential increase in C sink capacity of the forest stand. Slightly different fitting accuracies among the different C density growth models were observed, with the Richards model showing the best performance, which improved through the introduction of the SCI and SDI. Stand C density was associated with an increasing trend in SCI, which within each SCI subgroup was related to the increasing SDI trend. The potential C sequestration rate of the stand was close to the average between years 5 and 13. The average C sequestration rate peaked at 3.86 Mg·ha⁻¹·year⁻¹ at year 13, whereas the potential C sequestration rate peaked at 4.42 Mg·ha⁻¹·year⁻¹ in year 15. A gap between the potential and average C sequestration rate existed between ages 13 and 45, indicating the possibility for an increased C sink function in this forest age range. The Richards growth model incorporating SCI and SDI provided a better reflection of the C density of the Mongolian pine plantation, and the established stand C sequestration rate model showed that the optimal increment in the plantation C sink function can be obtained between years 13 and 45. The results of this study can guide C sink management in the Mongolian pine plantation. Full article

This study presents a novel Enhanced Oil Recovery (EOR) method using Smart Black Nanocards (SLNs) to mitigate the environmental impact of conventional thermal recovery, especially under global warming. Unlike prior studies focusing on wettability alteration via adsorption, this research innovatively models ‘oil film detachment’ in a reservoir simulator to achieve wettability alteration. Using the CMG-STARS (2020) simulator, this study highlights SLNs’ superior performance over traditional chemical EOR and spherical nanoparticles by reducing residual oil saturation and shifting wettability toward water-wet conditions. The structural disjoining pressure (SDP) of SLNs reaches 20.99 × 10³ Pa, 16.5 times higher than spherical particles with an 18.5 nm diameter. Supported by the Percus–Yevick (PY) theory, the numerical model achieves high accuracy in production history matching, with oil recovery and water cut fitting within precision error ranges of 0.02 and 0.05, respectively. This research advances chemical EOR technologies and offers an environmentally sustainable, efficient recovery strategy for low-permeability and heavy oil reservoirs, serving as a promising alternative to thermal methods. Full article

The Length of Day (LOD) and the Universal Time (UT1) play crucial roles in satellite positioning, deep space exploration, and related fields. The primary method for predicting LOD and UT1 is least squares fitting combined with autoregressive (AR) models. Polynomial Curve Fitting (PCF) has greater accuracy in capturing long-term trends compared to standard least squares fitting. In this study, PCF combined with Weighted Least Squares (WLS) is employed to fit and extrapolate the periodic and trend components of the LOD series after removing tidal influences. Additionally, considering the time-varying characteristics of the LOD series, a Long Short-Term Memory (LSTM) network is utilized to predict the residuals derived from the fitting process. The 14 C04 LOD series released by the International Earth Rotation and Reference System Service (IERS) is used as the base series, with 70 LOD and UT1-UTC prediction experiments conducted during the period from 1 September 2021–31 December 2022. The results indicate that the PCF+WLS+LSTM method is well-suited for medium- and long-term (90–360 days) prediction of the LOD and UT1-UTC. Significant improvements in prediction accuracy were obtained for periods ranging from 90–360 days, particularly beyond 150 days, where the average accuracy improved by over 20% compared to IERS Bulletin A. Specifically, the largest prediction accuracy increase for LOD and UT1-UTC was 49.5% and 59.2%, respectively. Full article

Anomalous Sound Detection (ASD) systems are pivotal in the Industrial Internet of Things (IIoT). Through the early detection of machines’ anomalies, these systems facilitate proactive maintenance, thereby mitigating potential losses. Although prior studies have improved system accuracy using various advanced machine learning technologies, they frequently neglect the associated substantial computing and storage demands, which are crucial in resource-constrained IIoT environments. In this paper, we propose an ASD system that is efficiently optimized for both software and hardware considerations regarding edge intelligence. For the software aspect, we identify signal variation as a critical issue for ASD. Hence, we introduce a suite of lightweight yet robust processing techniques that enhance accuracy while minimizing resource consumption. As for the hardware aspect, we find that memory constraints may be a significant challenge for deploying ASD systems on microcontrollers (MCUs). Therefore, we propose a memory-aware pruning algorithm specialized for ASD to fit into MCUs’ constraints. Finally, we evaluate our method on the DCASE dataset, and the results show that our system achieves favorable outcomes in both accuracy and resource efficiency, marking our contribution to ASD system practice. Full article

Fractured-porous carbonate reservoirs, mainly composed of dolomites and crystalline rocks with various rock types and extremely poor initial porosity and permeability, are dominated by tectonic fractures and exhibit extreme heterogeneity. The fracture system plays a predominant role in hydrocarbon fluid transport. Compared with conventional sandstone reservoirs, fracture geometry and topological structure parameters are key factors for the accuracy and computational efficiency of numerical simulation history matching in fractured reservoirs. To address the matching issue, this paper introduces an artificial intelligence history matching method combining the Monte Carlo experimental planning method with an artificial neural network and a particle swarm optimization algorithm. Taking reservoir geological parameters and phase infiltration properties as the objective function, this method performs reservoir production history matching to correct the geological model. Through case studies, it is verified that this method can accurately correct the geological model of fractured-porous reservoirs and match the observed production data. This research represents a collaborative effort among multiple disciplines, integrating advanced algorithms and geological knowledge with the expertise of computer scientists, geologists, and engineers. Currently the world’s major oilfields history fitting is mainly based on reservoir engineers’ experience to fit; the method is applicable to major oilfields, but the fitting accuracy and fitting efficiency is severely limited, the fitting accuracy is less than 75%, while the artificial intelligence history fitting method shows a stronger applicability; intelligent history fitting is mainly based on the integrity of the field data, and as far as the theory is concerned, the accuracy of the intelligent history fitting can be up to 100%. Therefore, AI history fitting can provide a significant foundation for mine field research. Future research could further explore interdisciplinary collaboration to address other challenges in reservoir characterization and management. Full article

In this paper, nonlinear system identification using Bayesian network has been implemented to discover open-loop lateral-directional aerodynamic model parameters of an agile aircraft using a grey box modelling structure. Our novel technique has been demonstrated on simulated flight data from an F-16 nonlinear simulation of its Flight Dynamic Model (FDM). A mathematical model has been obtained using time series analysis of a Box–Jenkins (BJ) model structure, and parameter refinement has been performed using Bayesian mechanics. The aircraft nonlinear Flight Dynamic Model is adequately excited with doublet inputs, as per the dictates of its natural frequency, in accordance with non-parametric modelling (Finite Impulse Response) estimates. Time histories of optimized doublet inputs in the form of aileron and rudder deflections, and outputs in the form of roll and yaw rates are recorded. Dataset is pre-processed by implementing de-trending, smoothing, and filtering techniques. Blend of System Identification time-domain grey box modelling structures to include Output Error (OE) and Box–Jenkins (BJ) Models are stage-wise implemented in multiple flight conditions under varied stochastic models. Furthermore, a reduced order parsimonious model is obtained using Akaike information Criteria (AIC). Parameter error minimization activity is conducted using the Levenberg–Marquardt (L-M) Algorithm, and parameter refinement is performed using the Bayesian Algorithm due to its natural connection with grey box modelling. Comparative analysis of different nonlinear estimators is performed to obtain best estimates for the lateral–directional aerodynamic model of supersonic aircraft. Model Quality Assessment is conducted through statistical techniques namely: Residual Analysis, Best Fit Percentage, Fit Percentage Error, Mean Squared Error, and Model order. Results have shown promising one-step model predictions with an accuracy of 96.25%. Being a sequel to our previous research work for postulating longitudinal aerodynamic model of supersonic aircraft, this work completes the overall aerodynamic model, further leading towards insight to its flight control laws and subsequent simulator design. Full article

Fractional calculus plays a pivotal role in modern scientific and engineering disciplines, providing more accurate solutions for complex fluid dynamics phenomena due to its non-locality and inherent memory characteristics. In this study, Caputo’s time fractional derivative operator approach is employed for heat and mass transfer modeling in unsteady Maxwell fluid within a cylinder. Governing equations within a cylinder involve a system of coupled, nonlinear fractional partial differential equations (PDEs). A machine learning technique based on the Levenberg–Marquardt scheme with a backpropagation neural network (LMS-BPNN) is employed to evaluate the predicted solution of governing flow equations up to the required level of accuracy. The numerical data sheet is obtained using series solution approach Homotopy perturbation methods. The data sheet is divided into three portions i.e., $80\%$ is used for training, $10\%$ for validation, and $10\%$ for testing. The mean-squared error (MSE), error histograms, correlation coefficient (R), and function fitting are computed to examine the effectiveness and consistency of the proposed machine learning technique i.e., LMS-BPNN. Moreover, additional error metrics, such as R-squared, residual plots, and confidence intervals, are incorporated to provide a more comprehensive evaluation of model accuracy. The comparison of predicted solutions with LMS-BPNN and an approximate series solution are compared and the goodness of fit is found. The momentum boundary layer became higher and higher as there was an enhancement in the value of Caputo, fractional order α = 0.5 to α = 0.9. Higher thermal boundary layer (TBL) profiles were observed with the rising value of the heat source. Full article

In robotic-assisted laminectomy decompression, stable and precise vertebral plate cutting remains challenging due to manual dependency and the absence of adaptive skill-learning mechanisms. This paper presents an advanced robotic vertebral plate-cutting system that leverages patient-specific anatomical variations and replicates the surgeon’s cutting technique through a trajectory parameter prediction model. A spatial mapping relationship between artificial and patient vertebrae is first established, enabling the robot to mimic surgeon-defined trajectories with high accuracy. The robotic system’s trajectory planning begins with acquiring point cloud data of the vertebral plate, which undergoes preprocessing, Non-Uniform Rational B-Splines (NURBS) fitting, and parametric discretization. Using the processed data, a spatial mapping method translates the surgeon’s cutting path to the robotic coordinate system, with simulation validating the trajectory’s adherence to surgical requirements. To further enhance the accuracy and stability of trajectory planning, a Backpropagation(BP) neural network is implemented, providing predictive modeling for trajectory parameters. The analysis and training of the neural network confirm its effectiveness in capturing complex cutting trajectories. Finally, experimental validation, involving an artificial vertebral body model and cutting trials on patient vertebrae, demonstrates the proposed method’s capability to deliver enhanced cutting precision and stability. This skill-learning-based, personalized trajectory planning approach offers significant potential for improving the safety and quality of orthopedic robotic surgeries. Full article

The accurate estimation of reference crop evapotranspiration (ET₀) is essential for crop water consumption modeling and agricultural water resource management. In the present study, three bionic algorithms (aquila optimizer (AO), tuna swarm optimization (TSO), and sparrow search algorithm (SSA)) were combined with an extreme learning machine (ELM) model to form three mixed models (AO-ELM, TSO-ELM, and SSA-ELM). The accuracy of the ET₀ estimates for five climate regions in China from 1970 to 2019 was evaluated using the FAO-56 Penman–Monteith (P-M) equation. The results showed that the predicted values of the three mixed models and the ELM model fitted the P-M calculated values well. R² and RMSE were 0.7654–0.9864 and 0.1271–0.7842 mm·d⁻¹, respectively, for which the prediction accuracy of the AO-ELM model was the highest. The performance of the AO-ELM combination5 (maximum temperature (T_max), minimum temperature (T_min), total solar radiation (R_s), sunshine duration (n)) was most significantly improved on the basis of the ELM model. The prediction accuracy for the stations in the plateau mountain climate (PMC) region was the best, while the prediction accuracy for the stations in the tropical monsoon climate region (TPMC) was the worst. In addition to the wind speed (U₂) in the temperate continental climate region (TCC)—which was the largest variable affecting ET₀—n, R_a, and total solar radiation (R_s) in the other climate regions were more important than relative humidity (RH) and wind speed (U₂) in predicting ET₀. Therefore, AO-ELM4 was selected for the TCC region (with T_max, T_min, R_s, and U₂ as inputs) and AO-ELM5 (with T_max, T_min, R_s, and n as inputs) was selected for the TMC, PMC, SMC, and TPMC regions when determining the best model for each climate region with limited meteorological data. Full article

Path planning and collision avoidance issues are key to the autonomous navigation of unmanned surface vehicles (USVs). This study proposes an adaptive differential evolution algorithm model integrated with the analytic hierarchy process (AHP-ADE). The traditional differential evolution algorithm is enhanced by introducing an elite archive strategy and adaptively adjusting the scale factor F and the crossover factor $CR$ to balance global and local search capabilities, preventing premature convergence and improving the search accuracy. Additionally, the collision risk index (CRI) model is optimized and combined with the quaternion ship domain, enhancing the precision of CRI calculations and USV autonomous collision avoidance capabilities. The improved CRI model, the International Regulations for Preventing Collisions at Sea, and the optimal collision avoidance distance were incorporated as evaluation factors in a fitness function assessment, with weights determined through the AHP to enhance the rationality and accuracy of the fitness function. The proposed AHP-ADE algorithm was compared with the improved particle swarm algorithm, and the performance of the algorithm was comprehensively evaluated using safety, economy, and operational efficiency. Simulation experiments on the MATLAB platform demonstrated that the proposed AHP-ADE algorithm exhibited better performance in scenarios involving multiple ship encounters, thus proving its effectiveness. Full article

The requirements for accurate models of renewable energy power plants are urgent for power system operation analysis. Most existing model research in this area is for wind turbine and photovoltaic (PV) power generation units; a rare renewable power plant model validation mainly adopts the single-machine infinite-bus system. The single equivalent machine method is always used, and the interactions between the power plant and the grid are ignored. The voltage at the interface bus is treated as constant, although this is not consistent with its actual characteristics. The phase shifter method of hybrid dynamic simulation has been applied in the model validation of wind farms. However, this method is heavily dependent on phasor measurement units (PMU) data, resulting in a limited application scope, and it is difficult to realize the model error location step by step. In this paper, the fast-responding generator method is used for renewable power plant model validation. The complete scheme comprising model validation, error localization, parameter sensitivity analysis, and parameter correction is proposed. Model validation is conducted based on measured records from a large-scale PV power plant in northwest China. The comparison of simulated and measured data verifies the feasibility and accuracy of the proposed scheme. Compared to the conventional model validation method, the maximum deviation of the active power simulation values obtained by the method proposed in this paper is only 38.8% of that of the conventional method, and the overall simulation curve fits the actual measured values significantly better. Full article

The effective planning of UAV trajectories in a 3D environment presents a complex global optimization challenge that must account for numerous constraints, including urban settings, mountainous terrain, obstacles, no-fly zones, flight boundaries, travel distances, and trajectory change rates. This paper addresses the limitations of the whale optimization algorithm in 3D trajectory planning—specifically its slow convergence, low accuracy, and susceptibility to local optimum—by proposing an improved whale optimization algorithm. This enhancement incorporates an inverse learning mechanism to increase the diversity of the initial population and integrates a nonlinear convergence factor with a random number generation mechanism to optimize the balance between global and local search capabilities. Our findings indicate that for both the standard and improved whale optimization algorithms, each individual in the population represents a feasible solution, corresponding one-to-one with distributed trajectories in the search space. Given that route planning typically occurs in three dimensions, there is spatial symmetry among the multiple potential trajectories from the starting point to the endpoint. The optimization algorithm identifies the optimal solution by exploring these symmetric trajectory paths, ultimately selecting the most favorable one based on additional constraints (e.g., no-fly zones and fuel consumption). Moreover, the convergence of the whale optimization algorithm depends on the diversity of individuals in the population and the thorough exploration of the search space. This symmetry facilitates a more uniform exploration of various trajectories by the population. In some instances, the optimization algorithm has achieved a 7.00% improvement in fitness value, a 10.05% reduction in optimal distance, and a 28.73% decrease in standard deviation. The increase in optimal values and the decrease in worst-case values underscore the effectiveness of the optimization algorithm, while the reduction in standard deviation reflects the stability of the algorithm’s output data. These results further confirm the advantages of the optimized algorithm. Full article

In order to ensure the safe operation of adjacent buried pipelines under blast vibration, it is of great practical engineering significance to accurately predict the peak vibration velocity ofburied pipelines under blasting loads. Relying on the test results of the buried steel pipe blast model test, a sensitivity analysis of relevant influencing factors was carried out by using the gray correlation analysis method. A least squares support vector machine (LS-SVM) model was established to predict the peak vibration velocity of the pipeline and determine the best parameter combination in the LS-SVM model through a local particle swarm optimization (PSO), and the results of the PSO-LSSVM model were predicted. These were compared with BP neural network model and Sa’s empirical formula. The results show that the fitting correlation coefficient (R2), root mean square error (RMSE), average relative error (MRE), and Nash coefficient (NSE) of the PSO-LSSVM model for the prediction of pipeline peak vibration velocity are 91.51%, 2.95%, 8.69%, and 99.03%, showing that the PSO-LSSVM model has a higher prediction accuracy and better generalization ability, which provides a new idea for the vibration velocity prediction of buried pipelines under complex blasting conditions. Full article