Search Results (334)

Accurate rate of penetration (ROP) prediction in heterogeneous formations remains a key challenge for drilling optimization, as existing empirical models rely on fixed-structure coefficients unable to adapt to rapid lithological transitions. This study presents a novel exponential-form ROP model integrating surface drilling parameters (weight on bit (W), rotary speed (N), torque (T), and standpipe pressure (SPP)), log-derived geomechanical properties (dynamic combined compressibility modulus for carbonates; total porosity for sandstones), and three physically motivated energy parameters: rotational mechanical power per unit bit area (Prot), axial crushing energy (AE/AEs), and hydraulic cleaning efficiency (Hce). Bit wear is quantified through a modified Hareland and Hoberock wear function requiring no laboratory measurements. Parameter selection used combined Pearson and Spearman correlation analysis across 16 candidate variables from a raw dataset of 9375 depth readings for Well A and 4443 for Well B (at 0.25 m intervals). The model was developed using nonlinear least squares regression (Levenberg–Marquardt algorithm) in MATLAB. Validated on two vertical wells penetrating mixed carbonate and clastic sequences in a Middle Eastern offshore field and benchmarked against four classical formulations, the model achieves R² = 0.6568–0.6766 across full heterogeneous sections, improving on the best benchmark by margins of 0.36–0.46. Under lithology-specific calibration, R² advances to 0.8239–0.9139, with MAPE reducing to 5.71%. The model is limited to two vertical wells in a single field; further field validation is recommended before broader deployment. Full article

Accurate and fast fault detection is essential to ensure the stability and reliability of High Voltage AC (HVAC) transmission systems. Conventional protection methods, including impedance-based and traveling wave techniques, may exhibit reduced performance under noisy operating conditions, system uncertainties, and complex fault scenarios while often requiring separate approaches for fault classification, location detection, and stability assessment. This paper proposes a unified Artificial Neural Network (ANN) based framework for simultaneous fault classification, location detection, and stability assessment using Critical Clearing Time (CCT) within a single HVAC transmission line model. A detailed MATLAB Simulink model is developed to generate a structured dataset comprising twelve fault scenarios, including single-line, double-line, three-phase, and ground faults at different locations along the transmission line. Three-phase voltages and currents, along with zero-sequence components, are used as input features. The ANN model is trained using the Levenberg–Marquardt (LM) optimization algorithm, which was comparatively evaluated against Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) and demonstrated faster convergence, lower prediction error, and higher regression accuracy. To further evaluate the robustness of the proposed framework under high-impedance fault conditions, supplementary simulations were performed using fault resistance values of 10 Ω and 50 Ω in addition to the baseline 0.01 Ω case. The resulting datasets were combined to form an expanded training and evaluation dataset, enabling comprehensive validation of the proposed LM-trained ANN under varying fault resistance conditions. Using the baseline dataset, the proposed framework achieved a high regression coefficient (R = 0.9882) and low mean squared error (MSE = 0.1386), demonstrating accurate fault classification and precise per-kilometer fault location estimation. Furthermore, the integration of fault inception time and duration enables direct computation of CCT, allowing the model to distinguish between stability-critical and non-critical fault conditions. The results confirm that the proposed framework provides a comprehensive and efficient solution for real-time fault analysis by combining classification, localization, temporal analysis, and stability-aware decision support within a single model. Full article

To address the low computational efficiency of conventional forward modeling that hinders real-time inversion of borehole resistivity logging data in deviated/horizontal wells through anisotropic formations, this paper presents an adaptive inversion method based on a deep neural network forward surrogate model. A resistivity response database covering deviation angle, mud invasion, and anisotropy is constructed using three-dimensional finite-element forward modeling. The deep neural network architecture is systematically optimized by varying the number of hidden layers and neurons per layer, comparing five activation functions, evaluating four training algorithms, and testing five batch size ratios. The resulting deep learning-based forward model achieves a speedup of over two orders of magnitude compared with 3D finite-element modeling while maintaining high accuracy (maximum relative error < 1%). By integrating this fast forward model with an adaptively modified Levenberg–Marquardt algorithm, rapid inversion in anisotropic formations is realized. Numerical simulations and field data processing demonstrate that the proposed method accurately extracts uninvaded resistivity, invasion depth, and anisotropy coefficient, with an efficiency gain of approximately 98% over traditional approaches. Reconstructed logs show excellent agreement with measured data, providing robust support for real-time evaluation of deviated and horizontal wells. Full article

The inverse kinematics (IK) problem of the UR3 collaborative manipulator is addressed through a singularity-aware optimization framework and a statistically grounded benchmarking methodology. The IK task is formulated as a full-pose optimization problem minimizing a physically scaled residual combining Cartesian position and orientation errors. Emphasizing consistency between error formulation and optimization paradigms, a matrix-based pose-error representation is adopted as a numerically stable residual for stochastic search. Simultaneously, a smooth Jacobian-conditioning penalty is incorporated to mitigate instability near ill-conditioned configurations. Five metaheuristic solvers (PSO, GWO, GA, JADE, ALO) are implemented under a unified, reproducible experimental protocol with common maximum search settings. The Levenberg–Marquardt (LM) numerical method is included as a deterministic baseline to compare gradient-based precision against derivative-free global exploration. Performance is evaluated across nominal, industrial, and near-singular poses using 1000 Monte Carlo runs per configuration. Final-solution accuracy, variability, and computational time are analyzed directly from the Monte Carlo outcome distributions, descriptive statistics, and nonparametric rank-based tests. Results indicate that LM achieves superior numerical precision and computational speed. Among the metaheuristics, GA provides the lowest mean objective values and the smallest objective dispersion across the three tested poses, whereas JADE is the fastest solver. GWO provides an intermediate solution profile, with competitive objective values and substantially shorter execution times than GA and ALO. The optimized solutions are first verified in a RoboDK virtual environment. Subsequently, representative GWO-based configurations are experimentally validated on a physical UR3 robot through both isolated static poses and a continuous multi-pose trajectory tracking task, confirming practical kinematic feasibility and sequential stability. The proposed framework establishes a reproducible benchmark for statistically robust evaluation of metaheuristic-based IK optimization in collaborative robotics. Full article

This study reports cellulase production by Bacillus paralicheniformis using Plumeria alba leaf powder under submerged fermentation with a focus on systematic bioprocess optimization. Physical parameters were first optimized using a one-factor-at-a-time (OFAT) approach, followed by optimization of yeast extract, MgSO₄ and (NH₄)₂SO₄ via a central composite design (CCD) and response surface methodology (RSM). An artificial neural network (ANN) with a 5:3:1 network trained by the Levenberg–Marquardt algorithm further improved prediction of carboxylmethylcellulase (CMCase) and filter paper cellulase (FPase) activities. This study is the first to exploit Plumeria alba leaf powder as an untapped, low-cost lignocellulosic substrate for cellulase production by B. paralicheniformis and uniquely benchmarks RSM against ANN-based modeling to identify superior predictive frameworks for bioprocess optimization. Under optimized conditions (24 h, 4% w/v substrate, 1% v/v inoculum), the maximum FPase and CMCase activities reached 60.53 IU/mL/min and 332.10 IU/mL/min respectively. Partial characterization showed optimum FPase and CMCase activities at 50 °C and 70 °C, respectively, at pH 7.5. Enzymes also showed activation by NaCl and some select solvents while tolerating a broad range of metal ions. The enzymatic hydrolysis of P. alba biomass released 59.42 mg/mL total reducing sugars after 8hr, confirming efficient saccharification from a low-cost feedstock. The ANN model (R² = 97.59% for CMCase; 85.95% for FPase) outperformed RSM (R² = 85.95% and 78.25%, respectively), while radial basis function optimization reached 99.99%. These findings highlight B. paralicheniforms cellulase as a promising biocatalyst for biorefinery applications and demonstrate the value of integrating RSM and ANN for process optimization. Full article

The cold V-bending of Grade 4 titanium bone plates at room temperature is a critical forming operation that must be optimized to control strain localization and springback and to reduce the risk of surface cracking. This study proposes a combined experimental, numerical, and artificial intelligence-based approach for the analysis and optimization of this process. Tensile tests were first performed to characterize the mechanical behavior of the material and to calibrate the constitutive law used in the finite element model. The numerical model was then validated through comparison with experimental V-die bending results. A design of experiments was subsequently applied to investigate the effects of sheet thickness, die shoulder distance, punch radius, and punch displacement on two key responses: equivalent plastic strain (PEEQ) and spring back. The results show that sheet thickness and die shoulder distance are the most influential parameters. In addition, artificial neural network models were developed to predict process responses, and Bayesian regularization showed the best overall predictive performance among the tested ANN training algorithms, namely Levenberg–Marquardt, Bayesian regularization, and scaled conjugate gradient. The proposed framework provides a basis for optimizing the forming of titanium orthopedic implants. Full article

Reliable mathematical models are essential for high-performance analysis and optimization of complex power and energy systems. However, inherent nonlinearities pose significant challenges to accurate model identification. The Hammerstein model, a typical block oriented nonlinear system, consists of a static nonlinear block followed by a linear dynamic block. This paper investigates the data-driven modeling method for the Hammerstein model and proposes a hierarchical identification strategy that integrates the correlation analysis with the Levenberg–Marquardt algorithm. Unlike traditional methods, this hierarchical algorithm strategy decouples the linear and nonlinear modules to avoid parameter coupling and reduces computational complexity. Simulations on a solid oxide fuel cell system and a real-world wind power system confirm the effectiveness and feasibility of the proposed method. The results demonstrate that the hierarchical identification strategy achieves accurate parameter estimation with satisfactory convergence performance. Full article

Acoustic emission (AE) source localization provides important spatial information for damage characterization and fracture evolution analysis in construction materials, while its accuracy and applicability are strongly dependent on sensor network design. This study proposes an optimization framework for selecting an effective six-sensor network for velocity-free AE source localization in construction materials. The source coordinates are determined by solving a nonlinear inverse problem using the Levenberg–Marquardt algorithm, and candidate sensor subsets are evaluated by combining location error metrics with the number of effective localization results to quantify the effective monitoring range for damage characterization. The framework is investigated through numerical simulations and pencil-lead break tests on a 600 mm × 600 mm ceramic tile. Among different six-sensor configurations, the best-performing layouts place four sensors at the outer corners and two sensors at the horizontal or vertical inner corners. A benchmark comparison with the Fisher-information-based optimized sensor network and sensitivity analyses further show that the optimized sensor network maintains higher effective monitoring ranges under arrival-time noise, velocity uncertainty, and sensor coordinate perturbations. The proposed approach provides a useful reference for robust and cost-effective AE sensor network design in damage monitoring, fracture characterization, and nondestructive evaluation of construction materials. Full article

Fractional-order dynamic systems, due to their long memory and nonlocality, have significant advantages in describing the dynamic behavior of complex engineering systems. However, existing identification methods often struggle to balance modeling accuracy and model structural complexity under conditions of strong nonlinearity, strong coupling, and multiple operating conditions. To address the challenge of modeling complex fractional-order systems with strong nonlinearity, strong coupling, and multiple operating conditions, this paper proposes a fuzzy multi-model modeling and identification method based on the decomposition-synthesis approach. First, a fractional-order fuzzy multi-model structure is constructed to characterize the dynamic characteristics of such complex systems. Second, an improved SKFCM hybrid clustering algorithm is proposed, combining K-means clustering and satisfactory fuzzy C-means clustering. This optimizes the cluster center selection strategy and overcomes the shortcomings of traditional satisfactory FCM algorithms, such as random initial membership and unreasonable cluster center selection, thus achieving a reasonable determination of the number of local models. Finally, the least-squares and Levenberg–Marquardt algorithms are integrated to interactively identify local model parameters, system fractional-order, and fuzzy scheduling function parameters, solving key difficulties such as unknown fractional-order s in antecedent variables, numerous parameter couplings, and difficulty in determining the antecedent space. Through academic examples and simulations of a robotic arm system, the proposed method effectively achieves high-precision modeling of complex fractional-order systems, demonstrating strong feasibility and superiority. Full article

Urban street canyons exert a critical influence on local microclimates; however, the dynamics of mixed convective airflow under unsteady wind and thermal forcing remain poorly quantified. This study systematically investigates the spatiotemporal characteristics of airflow within symmetric and asymmetric street canyons through integrated long-term field measurements and complementary CFD simulations. Field data collected over 120 monitoring days at the Weishui Campus of Chang’an University were analyzed using the Levenberg–Marquardt nonlinear curve-fitting algorithm. The analysis demonstrates that sine functions accurately represent diurnal surface temperature variations during consecutive clear sky periods, whereas polynomial functions of varying orders are required to characterize meteorologically complex episodes, including cold-wave cooling and seasonal transitions. Ambient wind patterns outside the canyon were further classified into two characteristic variation modes: stepwise and gradual. Complementary unsteady RANS simulations, with wall boundary conditions derived directly from the fitted field data, reveal that canyon geometry and meteorological forcing jointly govern the evolution of airflow structures and thermal distributions across seasons. In the symmetric canyon, the flow transitions from complex multi-vortex activity in spring and summer to a more stable regime in autumn, with two well-defined counter-rotating vortices emerging during winter cold-wave events. In the asymmetric canyon, strong summer solar heating sustains a dominant leeward vortex with a strengthening secondary structure, whereas winter cold wave intrusion generates a hierarchically nested vortex system in which secondary and tertiary vortices progressively develop and detach. By coupling empirical surface temperature functions with CFD boundary conditions, this study advances the precision of predictive microclimate models and provides an evidence-based framework for optimizing street canyon geometry to enhance ventilation performance, energy efficiency, and outdoor thermal comfort. Full article

Structural health monitoring (SHM) is a critical research topic in civil engineering for assessing the integrity of constructed facilities, yet its widespread deployment is often hindered by the high cost of commercial equipment. This study introduces an accessible, vibration-based SHM system consisting of a slave unit for data acquisition via an MPU6050 sensor and a master unit for long-range wireless transmission using the LoRa protocol. To overcome the inherent noise levels of inexpensive MEMS sensors, we propose a robust modal identification framework that utilizes the Levenberg–Marquardt optimization method combined with a sliding window strategy to accurately estimate damped natural frequencies. Experimental validation conducted on a steel beam demonstrates the technical viability of this event-triggered IoT architecture. The designed system achieved a relative error of only 6.38% in natural frequency identification compared to a high-precision commercial reference system. Ultimately, this framework provides a technically sound, resource-efficient solution for structural assessment. Full article

Continuous streamflow monitoring is fundamental for water management in high-mountain Andean basins. Traditionally, this process relies on empirical regressions, although artificial intelligence (AI) has recently emerged as a robust alternative. However, extreme geomorphological dynamics compromise classical hydraulic methods, while AI models frequently lack physical validation. In this context, this study compares the performance of Artificial Neural Networks against traditional methods to reduce uncertainty in stage–discharge rating curves. The methodology, applied to a nested basin scheme in Loja, Ecuador, contrasted traditional exponential fits with a Multilayer Perceptron optimized using the Levenberg–Marquardt algorithm. The analysis included the evaluation of uncertainty bands and a sub-hourly spatial validation based on the principle of mass conservation. Results evidence that AI refines statistical accuracy (NSE > 0.95) and effectively adapts to bed non-linearity; nevertheless, cross-validation revealed a high susceptibility to algorithmic overfitting. It is concluded that while AI offers superior analytical flexibility for interpolating non-linear dynamics, traditional methods remain more robust for extreme flood extrapolation. Furthermore, while AI reduces computational complexity, it entails a higher “data cost” requiring denser field gauging campaigns. Operational viability requires rigorous dynamic uncertainty controls and spatial water balance validation. Full article

This study presents an AI-assisted thermodynamic and computational fluid dynamics (CFD) evaluation of a β-type Stirling engine to improve its thermal efficiency and indicated power output. The engine performance was investigated using Restricted Dimensions Thermodynamics (RDT), the Schmidt thermodynamic model, and three-dimensional CFD simulations under various operating and geometric conditions. Key parameters including rotational speed, phase angle, piston diameter, displacer stroke, porosity, and charged pressure were systematically analyzed to determine their influence on engine behavior. A feed-forward artificial neural network (ANN) trained using the Levenberg–Marquardt optimization algorithm was integrated with CFD-generated datasets to predict engine performance and accelerate the optimization process. The AI-assisted optimization was coupled with the Variable Step-size Simplified Conjugate Gradient Method (VSCGM) to identify near-optimal operating conditions while reducing computational cost. Simulation results demonstrated that the optimization process improved the indicated power from 180.33 W to 185.44 W and increased thermal efficiency from 10.32% to 11.54%. The results also showed close agreement between predicted and experimental pressure–temperature profiles, confirming the reliability of the proposed methodology. Furthermore, CFD analyses revealed that increasing piston diameter and optimizing porosity enhanced heat transfer and pressure distribution within the engine chambers, resulting in improved thermodynamic performance. The proposed AI-driven framework provides a reliable and computationally efficient approach for the design and optimization of advanced β-type Stirling engines operating under realistic thermal conditions. Full article

In this study, a fractional-order epidemic compartmental model is formulated using the Caputo derivative to account for the memory effects of the chikungunya virus. Based on Banach contractions, fixed-point theorems are used to prove existence and uniqueness, and fundamental properties such as positivity and boundedness are established. Normalized forward sensitivity indices are employed to evaluate the relative impact of model parameters on the transmission dynamics and control of the disease. To reduce the spreading of infection, an optimal control problem is formulated by introducing time-dependent control measures with four control strategies that include public health prevention, treatment enhancement, and vector-control measures. Necessary conditions for optimality are derived using Pontryagin’s Maximum Principle. The predictor–corrector Adams–Bashforth–Moulton scheme is applied across different fractional orders and effectively reduces infection levels. The influence of the fractional order $\xi$ on the epidemic dynamics is investigated, showing that lower values of $\xi$ slow disease progression through a memory effect inherent in the Caputo operator. Moreover, an artificial neural network (ANN) trained via the Levenberg–Marquardt algorithm independently validates the numerical solutions. Full article

To address the turn-off overvoltage challenge arising from the rapid interruption of Low Voltage DC Solid-State Circuit Breakers (SSCBs), this paper proposes a high-precision behavioral modeling method for domestic SiC MOSFETs. The model is constructed based on the physical structure of the device, integrating a modified EKV-based static current model and a voltage-dependent nonlinear parasitic capacitance model described by piecewise functions. Model parameters are efficiently extracted from datasheets and measurement data using a composite optimization strategy combining the Genetic Algorithm and the Levenberg–Marquardt algorithm. The model is implemented in LTspice, and its accuracy in both static and dynamic characteristics is validated by comparing the simulation waveforms with experimental results. Based on the validated model, the turn-off process is subdivided into four distinct stages, with an equivalent circuit established for each. A systematic analysis reveals the intrinsic physical mechanism of the voltage spike and oscillation, which results from interaction among the drive circuit parameters, system parameters, and the nonlinear capacitances of the device. The research outcomes provide effective theoretical guidance and a design tool for simulation modeling, turn-off stress assessment, and snubber circuit optimization for SSCBs utilizing SiC MOSFETs. Full article