Search Results (193)

In this study, an uncertainty quantification (UQ) and sensitivity analysis (SA) workflow was developed for the input parameters of the CANDLE module, which is currently being tested and verified for calculating the downward relocation and solidification of molten core material. The workflow consists of three steps: (i) Morris screening to reduce the input set, (ii) Sobol variance decomposition on the screened subset to compute Sobol sensitivity indices, and (iii) uncertainty propagation using a 2 × 2 design that combines two sampling schemes (MC and LHS) with two dependence settings (independent and correlated inputs). The four cases considered were independent MC, correlated MC, independent LHS, and correlated LHS–Iman–Conover (LHS-IC). We considered 16 input parameters and three output figures of merit (FOMs) and compared the four cases in terms of propagated uncertainty and Shapley-based importance rankings, thereby distinguishing the effects of the sampling scheme, the imposed input dependence, and their interaction. The results show that the molten mass of the current material in the source node is the dominant factor governing the drained melt mass and the remaining melt mass in the receiving node, whereas the cold-wall surface temperature has a significant effect on the mass of molten material that solidifies in the receiving node. The mass of molten material that remains available in the receiving node is mainly governed by the coupled effects of the molten mass of the current material at the source node, the length of the receiving node, and the velocity limit. Under the non-uniform input-parameter distributions adopted in this study, LHS broadened the range of the outputs. After input correlations were introduced, the output distributions changed slightly. This study improves the understanding of input parameter sensitivities and uncertainty propagation in the CANDLE module. It also demonstrates the practical use of LHS-IC for module-level UQ/SA with correlated inputs, providing guidance for subsequent model improvements and parameter tuning. Full article

Shaped charge is widely used in petroleum drilling, yet the inherent parametric uncertainty of oil shale introduces significant uncertainties that affect perforation outcomes. The complex coupling of oil shale constitutive parameters under extreme strains poses challenges for uncertainty quantification. A coupled algorithm integrating an improved material point method (MPM) and polynomial chaos expansion (PCE) is presented, and polynomial chaos expansion (PCE) is used to systematically analyze the uncertainty and sensitivity of shaped charge jet penetration depth. Mechanical parameters from oil shale samples at Checun Coal Mine well No. 1 were tested to define key parameter ranges and establish a reliable uncertainty space. A benchmark simulation of a single isolated shaped charge jet validated the algorithm, and Sobol’ global sensitivity analysis identified internal friction angle, density, and Poisson’s ratio as strongly sensitive parameters, while tensile strength, Young’s modulus, and cohesion showed weak sensitivity, supporting surrogate model dimensionality reduction. Composite detonation models of three and five charges further revealed the effects of multi-projectile blast wave coupling on jet dynamics, providing new theoretical insights into cluster effects under high-energy, high-pressure, and extreme-strain conditions. Sensitivity and uncertainty analyses based on surrogate models emphasized the critical influence of internal friction angle alongside Poisson’s ratio and density. A reliable numerical framework is established for multi-physics coupled simulations of geomechanical responses under complex multi-source explosive loading. Full article

Understanding the influence of structural parameters on the seismic response of wooden houses is essential for improving structural performance and model reliability. However, conducting extensive parametric studies using nonlinear time-history analysis is computationally expensive. To address this issue, this study proposes a machine learning (ML) surrogate framework for efficiently evaluating the seismic response of a wooden house and interpreting the importance of structural parameters. A dataset consisting of 289 nonlinear structural simulations was used to train the surrogate model, enabling efficient evaluation of parameter importance through multiple sensitivity analysis methods. A Gradient Boosting regression model was developed to approximate the results of nonlinear structural analyses. The surrogate model predicted the maximum inter-story drift with high accuracy, achieving a coefficient of determination of R² = 0.90. Using the trained surrogate model, six sensitivity analysis methods were applied: SHAP, Structural Perturbation, Drop-column Importance, Permutation Importance, Sobol sensitivity analysis, and the Morris method. The results showed that most sensitivity analysis methods consistently identified wall-related parameters, particularly W1, W3, and W4, as the dominant factors influencing structural response. This tendency was observed in both elastic and nonlinear response ranges, although the influence of these parameters became more pronounced under nonlinear conditions. While the Morris method produced slightly different sensitivity magnitudes due to its screening-based formulation, it still identified the same dominant parameters as the other approaches. The results demonstrate that the proposed ML surrogate framework, combined with explainable AI techniques, can effectively identify key structural parameters governing the seismic response of wooden structures. This approach provides a computationally efficient tool for structural sensitivity analysis and may support improved structural modeling and seismic performance evaluation. Full article

Gas–liquid multiphase pumps are essential for deep-sea oil and gas production; however, their performance is severely limited under high gas volume fraction (GVF > 30%) conditions due to inefficient energy transfer and flow instability. In this study, a hybrid sensitivity analysis framework combining the Morris screening method and Sobol global sensitivity analysis is developed to quantitatively investigate the effects of impeller geometric parameters on pump performance at a GVF of 80%. Euler–Euler two-phase CFD simulations coupled with Python-based automated sampling are employed. The results show that the impeller outer diameter, axial length, and blade wrap angle are the three most influential parameters. The impeller outer diameter contributes 35.7% to the pressure rise, while an axial length exceeding 44 mm induces axial backflow and reduces efficiency by 8.2%. A critical wrap angle of 114° is identified for gas–liquid energy distribution, beyond which large-scale gas vortices intensify flow instability. Based on these findings, a hierarchical optimization strategy is proposed, resulting in a 6.8% improvement in efficiency and a 12.3% increase in pressure rise. Full article

This study investigates the spatiotemporal variability of the near-surface air temperature lapse rate (NSATLR) in Calabria, a region representative of typical Mediterranean environmental and climatic conditions. Through the integration of observational datasets and model simulations, a global sensitivity analysis using the Sobol method, and Bayesian linear regression modelling across annual, seasonal, and monthly scales, the primary drivers of near-surface air temperature (NSAT) variability were identified. Results demonstrate that altitude is the dominant factor influencing temperature distribution, with minimal contributions from other geographical parameters such as latitude, longitude, and proximity to the sea. The Bayesian models yielded robust performance for mean and maximum temperatures, while minimum temperature proved more challenging to predict. Lapse rate analyses confirmed a consistent inverse relationship between temperature and elevation, with the steepest gradients observed for Tmin. In particular, a significant long-term decline in lapse rates over the past 70 years, especially during winter and autumn, points to accelerated warming at higher elevations, primarily driven by rising Tmin values. This trend suggests a gradual homogenization of temperature across altitudes, with important implications for ecosystem dynamics, snowpack stability, and climate-sensitive sectors such as agriculture and urban planning. Full article

Friction dampers are widely used as passive energy dissipation devices in seismic protection systems; however, their response depends on numerous interacting parameters, complicating design. This study focuses on the CAR1 friction damper, investigating parameter influence and enabling efficient inverse identification of those that meet prescribed performance objectives. A finite element (FE) model reproduces the nonlinear damper behavior under seismic loading validated by previous experimental results. Based on the finite element (FE) dataset, an artificial neural network (ANN) is developed as a surrogate model to approximate the system response. This approach aims to overcome the excessive computational cost of the finite element method when performing optimization tasks involving numerous model evaluations. Global sensitivity analysis using the FAST and Sobol indices quantifies the influence of the parameters, revealing that a subset governs most of the variability for the target control axial force and dissipated energy. Building on these results, an inverse ANN has been contacted to optimize the parameters of the device based on (i) target control axial force, and (ii) maximum dissipated energy to a target displacement, providing a practical, physically informed tool for tailoring CAR1 friction dampers to specific seismic objectives. Full article

This study performs a sensitivity analysis of CO₂ emissions from clinker and cement production using life cycle assessment (LCA). Both local and global sensitivity analyses (LSA and GSA) are conducted. LSA uses outputs from the GCCA EPD tool—developed by the Global Cement and Concrete Association to facilitate Environmental Product Declarations—and examines correlations between perturbed input variables and the resulting output changes. For GSA, we present an analytical derivation of Sobol’ indices. We derive quantitative relationships between alternative materials and fuels and key technical indices, while preserving clinker and cement quality throughout the sensitivity analysis. Increasing the share of the alternative fuels (AFs) categories and of recycled concrete produces a negative percentage change in CO₂ emitted from the clinker (CO₂/CL). The largest CO₂/CL reductions arise from high-biomass fuels, followed by alternative solid fuels and refuse-derived fuels, shredded tires, and, lastly, recycled concrete. The clinker-to-cement ratio (CL/CEM) dominates the CO₂ emitted in cement production (1% change → 0.926–0.956% change), while clinker-level CO₂ reductions transmit to cement with only minor variation, confirmed by Sobol’ indices. Aside from reducing CO₂/CL by increasing alternative materials and fuels, the two principal approaches to lowering CO₂/CEM are: (i) minimizing clinker content in cement where permitted by applicable standards while maintaining the same performance, and (ii) designing new cement types that deliver equivalent performance with lower clinker content. Full article

This work investigates the geometric design and optimisation of a dynamic inductive power transfer coupler for two-wheeled electric vehicles under misalignment and magnetic-field exposure constraints. A computational three-dimensional finite-element model of a shielded rectangular coupler is developed to characterise coupling coefficients and magnetic flux density levels on control planes along the longitudinal travel range and under lateral and angular misalignments. Two simulation datasets are generated: one varying only geometric parameters at a nominal position for surrogate construction and global sensitivity analysis, and a second jointly sampling geometry, the travel range and misalignments for optimisation. Sparse Polynomial Chaos Expansions and Canonical Low-Rank Approximation surrogates are built to quantify Sobol’ indices, revealing that a small subset of primary-side geometric variables dominates both coupling efficiency and magnetic field levels. Random forest regressors are then trained on the extended dataset and embedded in the Non-dominated Sorting Genetic Algorithm II to solve a multi-objective optimisation problem that maximises worst-case coupling, improves robustness to misalignment, and enforces magnetic-field leakage limits. Optimal designs were obtained, and a subset was selected for re-evaluation using the finite-element method. The results confirm that the proposed surrogate-assisted framework yields coupler geometries with enhanced coupling and reduced magnetic field leakage while respecting the mechanical constraints for the electric motorcycle system. Full article

Large-scale backward-curved centrifugal fans without volutes are extensively employed in enclosed air-cooled electric motors owing to their exceptional heat dissipation performance. This category of fans features substantial blade dimensions and a multitude of optimization parameters, which introduce challenges such as diminished predictive accuracy in high-dimensional optimization spaces. To address these issues, this paper proposes a blade optimization design methodology based on a GA-Kriging surrogate model. Sobol’s global sensitivity analysis is first employed to reduce model dimensionality. Subsequently, a high-fidelity aerodynamic performance prediction model is constructed through the integration of a Genetic Algorithm (GA) and a Kriging model. A constrained optimization is then conducted with volumetric flow rate and static pressure as the design objectives, and shaft power along with geometric point coordinates as the constraints. Experimental test results demonstrate that the fan optimized via the surrogate model, while maintaining low prediction error, achieves a 14% increase in volumetric flow rate and a 20% improvement in static pressure. This outcome indicates a significant enhancement in the overall aerodynamic performance. Full article

The long-term behavior of reinforced concrete (RC) structures under sustained loading is strongly affected by creep and cracking, particularly under service conditions where tension stiffening and curvature changes are significant. This study investigates the flexural response of cracked RC beams through combined numerical and experimental analyses. A new 1D finite element model is proposed, integrating nonlinear material behavior, damage mechanics, and time-dependent effects, including creep in both compression and tension. The model relies on a layered fiber section approach and uses a Newton–Raphson iterative procedure to solve equilibrium, allowing accurate prediction of strain, curvature, and internal force evolution over time. The model shows excellent agreement with experimental observations and ABAQUS simulations, accurately capturing deflection trends and crack development. Its performance is further validated using a database of 55 RC beams, including specimens with recycled aggregates and fiber reinforcement. Across this dataset, 84.5% of predicted deflections fall within ±1 mm of measured values, with an R² of 0.960, demonstrating strong reliability. A Sobol-based sensitivity analysis identifies load ratio as the most influential parameter on long-term deflection, followed by concrete strength and humidity. Overall, the model offers an efficient and robust tool for long-term deflection prediction, bridging simplified design rules and complex 3D simulations. Full article

Spiral-groove dry gas seals are widely used in turbomachinery. However, high-fidelity numerical simulations remain challenging because the gas film is micron-scale and features high shear and pronounced boundary-layer effects, while experimental studies are often expensive due to the large design space and tight machining tolerances. To address these issues, this study integrates a Kriging surrogate model with surrogate-based optimization (SBO) to systematically identify the key structural and operating parameters governing seal performance. The results quantify the individual effects of key geometric parameters, providing practical guidance for spiral-groove seal design and optimization. The Kriging model captures the nonlinear relationships between performance and design variables and shows good generalization, with a maximum residual standard deviation of 2.78 and all others below 1.0. Sobol analysis reveals that structural parameters dominate performance: groove depth and width exhibit total-effect indices of approximately 0.74 and 0.56, respectively, while rotational speed is the most influential operating parameter (≈0.75). Among eight structural variables, groove depth is the most critical, increasing leakage by more than 200% as it rises from 5 to 8 μm, followed by spiral angle and groove number; all remaining parameters each contribute less than 10%. Full article

In the single-winding consequent-pole bearingless permanent magnet synchronous motor (SW-CP-BPMSM), the torque and suspension systems utilize a shared winding configuration. This structure significantly intensifies inter-system coupling. Furthermore, the presence of non-linear and strongly coupled relationships among structural parameters, combined with inherent coupling and conflicts between optimization objectives, makes the unified optimization of key performance indicators for both the torque and suspension systems a substantial challenge. To address these issues, this paper proposes a dual-system interactive optimization strategy based on the classification of sensitive variables. First, the strategy employs the Sobol method to conduct a global sensitivity analysis. By defining dual-system coupled sensitive parameters and single-system sensitive parameters, the method achieves dimensionality reduction through parameter classification. Subsequently, Response Surface Methodology (RSM) and Back Propagation (BP) neural network surrogate models are constructed for the suspension and torque systems, respectively. A progressive optimization process—comprising single-system optimization followed by dual-system interactive optimization—is then performed on the single-system and dual-system sensitive variables to determine the final optimal parameters. Finally, a comparative simulation analysis of the key performance indicators for both the torque and suspension systems before and after optimization is conducted. The results validate the feasibility and effectiveness of the proposed optimization strategy. Full article

Marine concrete is often exposed to multiple aggressive ions, so mechanical deterioration cannot be reliably interpreted using single-ion durability concepts. This study investigates ocean-oriented concretes incorporating high contents of mineral admixtures under coupled sulfate/chloride/carbonate/magnesium actions and develops a pore-structure-based D–P dual-parameter framework linking microstructural descriptors to macroscopic peak stress and peak strain. Three binder systems were designed: ordinary Portland cement concrete (OPC), cement–silica fume concrete (CSC, 20% silica fume), and cement–silica fume–fly ash concrete (CSFC, 20% silica fume + 50% fly ash). Specimens were immersed for 12 and 24 months in four representative binary-salt solutions. Porosity evolution and pore-size-class distributions were quantified by low-field NMR, while pore complexity was characterized using multi-scale fractal dimensions. The results show that mineral admixtures generally refine the pore system and improve the integrity of fine pores; CSFC exhibits the most robust microstructural stability across the tested environments, whereas CSC shows a pronounced degradation of fine-pore structure under CE4. A second-order response surface model built on Z-score normalized fractal dimension (D) and porosity (P) achieves reliable predictability for peak strain (R² = 0.85) and peak stress (R² = 0.79). Global Sobol sensitivity analysis reveals distinct controlling mechanisms: peak strain is predominantly governed by porosity (S_P = 85.9%), whereas peak stress is controlled by the combined effects of porosity, pore complexity, and their interaction (S_P = 42.4%, S_D = 19.8%, S_{D × P} = 37.8%). Local sensitivity mapping further identifies high-sensitivity regimes at extreme pore states, providing mechanistic guidance for mixture optimization. Overall, the proposed D–P framework quantitatively bridges pore volume/geometry evolution and mechanical degradation, offering a practical predictive tool for durability-oriented design of marine concretes under multi-ionic attack. Full article

Fault detection and diagnosis (FDD) are critical for maintaining efficiency and operational stability of comminution systems. However, conventional methods struggle to capture their complex dynamic behaviour, while data-driven approaches are constrained by limited labelled fault data and the need for interpretable diagnostic models. Progress is further hindered by the scarcity of publicly available industrial datasets. This study presents an explainable FDD framework that integrates unsupervised autoencoder (AE)-based anomaly detection with variance-based global sensitivity analysis (GSA) for quantitative fault diagnosis. A simulated comminution control system was developed to enable controlled validation under realistic operating variability. Multiple AE architectures were trained with hyperparameters optimised using chaotic particle swarm optimisation and evaluated using statistical and reconstruction-based metrics combined with multi-criteria decision analysis. The sparse AE achieved the best performance, with an MSE of 5.6 × 10⁻⁵, F1-score of 0.9930, and accuracy of 0.986 in detecting faults in $P_{80}$ and $P_{20}$. To diagnose detected faults, Sobol’s variance-based GSA was applied to quantify both the main and interaction effects of operational variables on particle size distribution. The results identify circuit feed rate, ball mill critical speed, and the pulp solids fraction supplied to the hydrocyclones as dominant drivers of faults associated with product coarsening, whereas circuit feed rate and ball mill critical speed primarily govern ultrafine particle generation. By integrating deep learning with explainable sensitivity analysis, this study advances transparent and quantitative diagnosis of complex mineral processing systems. Full article

Addressing climate change necessitates coordinated efforts across multiple sectors, with agriculture representing a significant source of greenhouse gas (GHG) emissions. This requires sophisticated mitigation strategies at the farm level. Digital decision support tools (DSTs) tailored for this purpose play a crucial role in accelerating farm-level decarbonization. Ensuring the reliability and accuracy of these DSTs mandates thorough model robustness validation. This study validates a farm-level GHG accounting and decarbonization DST using Sobol and Morris global sensitivity analyses to evaluate output robustness and to identify key input parameters critical for reliable mitigation planning. Both sensitivity analysis methods provide a comprehensive assessment of the tool’s robustness and highlight parameters most influencing farm-level GHG emission outcomes. Results show consistent outcomes across sensitivity approaches, reinforcing confidence in the tool’s application for emission reduction planning. The sensitivity analysis results indicate that the tool delivers reliable outcomes across various sensitivity analysis methods, thereby enhancing confidence in its suitability for decarbonization planning. Furthermore, the findings of this study provide a methodological foundation for future advancements and expanded use within the agriculture sector. This supports the DST’s effectiveness in prioritizing mitigation strategies and planning emission reduction pathways at the farm scale, while providing a transparent template to guide future tool improvements and broader agricultural applications. Full article