Search Results (126)

We present a supervised learning method to estimate two local descriptors of time-series dynamics, the mean-reversion rate $\theta$ and a heavy-tail estimate $\alpha$, from short windows of data. These parameters summarize recovery behavior and tail heaviness and are useful for interpreting stochastic signals in sensing applications. The method is trained on synthetic, dimensionless Ornstein–Uhlenbeck processes with $\alpha$-stable noise, ensuring robustness for non-Gaussian and heavy-tailed inputs. Gradient-boosted tree models (CatBoost) map window-level statistical features to discrete $\alpha$ and $\theta$ categories with high accuracy and predominantly adjacent-class confusion. Using the same trained models, we analyze daily financial returns, daily sunspot numbers, and NASA POWER climate fields for Austria. The method detects changes in local dynamics, including shifts in the financial tail structure after 2010, weaker and more irregular solar cycles after 2005, and a redistribution in clear-sky shortwave irradiance around 2000. Because it relies only on short windows and requires no domain-specific tuning, the framework provides a compact diagnostic tool for signal processing, supporting the characterization of local variability, detection of regime changes, and decision making in settings where long-term stationarity is not guaranteed. Full article

Impulsive overtopping generated by dam-break surges is a critical hazard for dikes and flood-protection embankments, especially in reservoirs and mountainous catchments. Unlike classical coastal wave overtopping, which is governed by long, irregular wave trains and usually characterized by mean overtopping discharge over many waves, these dam-break-type events are dominated by one or a few strongly nonlinear bores with highly transient overtopping heights. Accurately predicting the resulting overtopping levels under such impulsive flows is therefore important for flood-risk assessment and emergency planning. Conventional cluster-then-predict approaches, which have been proposed in recent years, often first partition data into subgroups and then train separate models for each cluster. However, these methods often suffer from rigid boundaries and ignore the uncertainty information contained in clustering results. To overcome these limitations, we propose a GMM+MoE framework that integrates Gaussian Mixture Model (GMM) soft clustering with a Mixture-of-Experts (MoE) predictor. GMM provides posterior probabilities of regime membership, which are used by the MoE gating mechanism to adaptively assign expert models. Using SPH-simulated overtopping data with physically interpretable dimensionless parameters, the framework is benchmarked against XGBoost, GMM+XGBoost, MoE, and Random Forest. Results show that GMM+MoE achieves the highest accuracy ($R^2=0.9638$ on the testing dataset) and the most centralized residual distribution, confirming its robustness. Furthermore, SHAP-based feature attribution reveals that relative propagation distance and wave height are the dominant drivers of overtopping, providing physically consistent explanations. This demonstrates that combining soft clustering with adaptive expert allocation not only improves accuracy but also enhances interpretability, offering a practical tool for dike safety assessment and flood-risk management in reservoirs and mountain river valleys. Full article

Available models for mass transport in microfluidic electrochemical sensors fall short in capturing critical features of millimeter-scale devices 3D-printed using fused deposition modeling, including inherent porosity and non-flat electrode geometries, thereby reducing their predictive power and transferability. Meanwhile, growing interest in low-cost and accessible fabrication methodologies has driven the quantitative use of these devices without first understanding the effects of such structural features on current responses. Here, the quantitative electrochemical performance of millimeter-scale 3D-printed devices with channel band electrodes is studied through computational and theoretical methods aimed at understanding their fundamental behavior. Simulations and dimensionless analysis reveal the influence of electrode shape and porosity on current responses under laminar flow. An adjusted Levich model is proposed to incorporate non-flat electrode geometries, while two new analytical models—general and transition-specific—predict currents through all mass transport regimes (convection, diffusion, and transition) that can simultaneously emerge due to porosity effects. Moreover, we introduce a low-cost “print–pause–print” fabrication strategy for such systems, employing a desktop 3D printer and 3D pen, which allows electrode integration and activation through polishing and “in-channel” electrochemical treatment. These advances facilitate developing next-generation 3D-printed milli fluidic electrochemical platforms with improved performance and scalability. Full article

Conventional signal-to-image conversion methods often overlook the physical correspondence of vibration signals, limiting diagnostic interpretability. To address this, we propose a physics-guided image construction strategy that incorporates dimensionless indicators to adaptively weight grayscale regions, enhancing the physical consistency and the discriminability among different fault types. Furthermore, a novel Cheap Channel Obfuscation module is introduced to suppress noise, decouple feature channels, and preserve the critical information within lightweight models. Integrated with ShuffleNetV2, our method achieves high diagnostic accuracy. Experimental validation for CWRU and SEU bearing datasets yields accuracies of 100% and 99.91%, respectively, demonstrating superior performance with minimal parameters. This approach offers a technically robust and computationally efficient fault diagnosis solution, with promising potential for deployment in resource-limited industrial environments. Full article

Although numerous studies have used ML to predict gear transmission error, few have provided a normalized, interpretable risk metric for early tolerance assessment. This work fills that gap by proposing the Tonal Risk Index (TRI). Kinematic Transmission Error (KTE) is a well-established primary excitation source of tonal gear noise in electric vehicle drivetrains. This study introduces the TRI, a novel, dimensionless indicator that quantifies relative tonal noise risk directly from predicted KTE values. We employ a large-scale dataset of 39,984 Monte Carlo simulations comprising 15 manufacturing tolerance and process-shift variables, with KTE values as the target. Baseline linear regression failed to capture the strongly non-linear relationships between tolerances and KTE (R² ≈ 0), whereas non-linear models—Random Forest and XGBoost—achieved high predictive accuracy (R² ≈ 0.82). Feature importance analysis revealed that pitch error, radial run-out, and misalignment are consistently the most influential parameters, with notable interaction effects such as pitch error × run-out and misalignment × form-defect shift. The TRI normalises predicted KTE values to a 0–1 scale, enabling rapid comparison of tolerance configurations in terms of tonal excitation risk. This approach supports early-stage design decision-making, reduces reliance on high-fidelity simulations and physical prototypes, and aligns with sustainability objectives by lowering material usage and energy consumption. The results demonstrate that data-driven surrogate models, combined with the TRI metric, can effectively bridge the gap between manufacturing tolerances and NVH performance assessment. Full article

Learning from demonstration with multiple executions must contend with time warping, sensor noise, and alternating quasi-stationary and transition phases. We propose a label-free pipeline that couples unsupervised segmentation, duration-explicit alignment, and probabilistic encoding. A dimensionless multi-feature saliency (velocity, acceleration, curvature, direction-change rate) yields scale-robust keyframes via persistent peak–valley pairs and non-maximum suppression. A hidden semi-Markov model (HSMM) with explicit duration distributions is jointly trained across demonstrations to align trajectories on a shared semantic time base. Segment-level probabilistic motion models (GMM/GMR or ProMP, optionally combined with DMP) produce mean trajectories with calibrated covariances, directly interfacing with constrained planners. Feature weights are tuned without labels by minimizing cross-demonstration structural dispersion on the simplex via CMA-ES. Across UAV flight, autonomous driving, and robotic manipulation, the method reduces phase-boundary dispersion by 31% on UAV-Sim and by 30–36% under monotone time warps, noise, and missing data (vs. HMM); improves the sparsity–fidelity trade-off (higher time compression at comparable reconstruction error) with lower jerk; and attains nominal 2σ coverage (94–96%), indicating well-calibrated uncertainty. Ablations attribute the gains to persistence plus NMS, weight self-calibration, and duration-explicit alignment. The framework is scale-aware and computationally practical, and its uncertainty outputs feed directly into MPC/OMPL for risk-aware execution. Full article

A time-domain semi-empirical simulation model based on the wake oscillator approach is developed to investigate the coupled in-line (IL) and cross-flow (CF) vortex-induced vibration (VIV) of a flexible riser in uniform oscillatory flow. A novel nondimensionalization method is introduced by utilizing the dimensionless parameter $StKC$, which effectively replicates the fundamental lift frequency caused by the complex vortex motion around the riser. The structural responses of the riser are described using the Euler–Bernoulli beam theory, and the van der Pol equations are used to calculate the fluid forces acting on the riser, which can replicate the nonlinear vortex dynamics. The coupled equations are discretized in both time and space with a finite difference method (FDM), enabling iterative computations of the VIV responses of the riser. A total of six cases are examined with four different Keulegan–Carpenter ($KC$) numbers (i.e., $KC=31$, 56, 121, and 178) to investigate the VIV characteristics of small-scale and large-scale risers in uniform oscillatory flow. Key features such as intermittent VIV, amplitude modulation, and hysteresis, as well as the VIV development process, are analyzed in detail. The simulation results show good agreement with the experimental data, indicating that the proposed numerical model is able to reliably reproduce the riser VIV in uniform oscillatory flow. Overall, the VIV characteristics of the large-scale riser resemble those of the small-scale riser but exhibit higher vibration modes, stronger traveling wave features, and more complex energy transfer mechanisms. Full article

Disc-shaped Autonomous Underwater Helicopters (AUHs) offer superior maneuverability but suffer from high hydrodynamic drag, which limits their operational endurance. To address this challenge, this study proposes a robust optimization framework for a novel sea-turtle-inspired AUH. A parametric hull, governed by two dimensionless shape factors based on modified Myring equations, was established to facilitate systematic exploration. To reduce the high computational cost of direct CFD evaluations, a high-precision Gaussian Process Regression (GPR) surrogate model was constructed from a small dataset of 24 samples. The core methodological innovation is T-NSGA-II, an algorithm featuring hyperparameters that are systematically optimized by the Optuna framework. In comparative evaluations, the T-NSGA-II-generated Pareto front demonstrated clear superiority over the standard NSGA-II, identifying designs with significantly lower drag for an equivalent vertical force. A key scientific contribution of this research is the identification of a distinct performance gap on the Pareto front. This phenomenon is interpreted not as an algorithmic artifact but as a ‘natural gap’, reflecting a deep physical trade-off, with potential underlying causes including a critical transition in flow physics or a topological shift in the optimal hull geometries. This work not only delivers a suite of optimized, practical AUH designs but also presents a powerful, intelligent optimization methodology that is capable of revealing fundamental physical trade-offs in complex engineering problems. Full article

This study introduces an analytical framework that integrates the state-space method with generalized thermoelasticity theory to obtain exact solutions for the static and dynamic behaviors of laminated plates featuring imperfect interfaces and resting on a Winkler foundation. The model comprehensively accounts for the foundation-structure interaction, interfacial imperfection, and the coupling between the thermal and mechanical fields. A parametric analysis explores the impact of the dimensionless foundation coefficient, interface flexibility coefficient, and thermal conductivity on the static and dynamic behaviors of the laminated plates. The results indicate that a lower foundation stiffness results in higher sensitivity of structural deformation with respect to the foundation parameter. Furthermore, an increase in interfacial flexibility significantly reduces the global stiffness and induces discontinuities in the distribution of stress and temperature. Additionally, thermal conductivity governs the continuity of interfacial heat flux, while thermo-mechanical coupling amplifies the variations in specific field variables. The findings offer valuable insights into the design and reliability evaluation of composite structures operating in thermally coupled environments. Full article

Eskebornite (CuFeSe₂), a member of the I–III–VI₂ ternary semiconductor family, was explored in this study as a potential thermoelectric material, offering new insights into its synthesis, structural characteristics, and transport behavior. Structurally analogous to chalcopyrite (CuFeS₂)—an extensively studied antiferromagnetic semiconductor—eskebornite remains relatively underexplored, particularly regarding its solid-state synthesis and thermoelectric performance. To address this gap, pure eskebornite was synthesized via mechanical alloying followed by hot pressing, a method that enables the fine control of its phase composition and microstructural features. The synthesized undoped CuFeSe₂ exhibited p-type nondegenerate semiconducting behavior, with electrical conductivity increasing monotonically over the temperature range of 323–623 K, indicative of thermally activated carrier transport. Simultaneously, a decreasing trend in thermal conductivity with temperature was observed, likely resulting from intensified phonon scattering, which serves to suppress heat transport and enhance the thermoelectric efficiency by maintaining a thermal gradient across the material. A peak in the Seebeck coefficient occurred between 473 and 523 K, suggesting the onset of intrinsic carrier excitation and a transition in dominant carrier transport mechanisms. The material exhibited a maximum power factor of 1.55 μWm⁻¹K⁻², while the dimensionless thermoelectric figure of merit (ZT) reached a peak value of 0.37 × 10⁻³ at 523 K. Although the ZT remains low, these results underscore the potential of eskebornite as a thermoelectric candidate, with substantial room for optimization through chemical doping, microstructural engineering, or nanostructuring approaches to enhance the carrier mobility and reduce the lattice thermal conductivity. Full article

This study presents, for the first time, a detailed linear stability analysis (LSA) of bedform evolution under low-flow conditions using a one-dimensional vertically averaged and moment (1D-VAM) approach. The analysis focuses exclusively on bedload transport. The classical Saint-Venant shallow water equations are extended to incorporate non-hydrostatic pressure terms and a modified moment-based Chézy resistance formulation is adopted that links bed shear stress to both the depth-averaged velocity and its first moment (near-bed velocity). Applying a small-amplitude perturbation analysis to an initially flat bed, while neglecting suspended load and bed slope effects, reveals two distinct modes of morphological instability under low-Froude-number conditions. The first mode, associated with ripple formation, features short wavelengths independent of flow depth, following the relation F² = 1/(kh), and varies systematically with both the Froude and Shields numbers. The second mode corresponds to dune formation, emerging within a dimensionless wavenumber range of 0.17 to 0.9 as roughness increases and the dimensionless Chézy coefficient $C^{\ast }$ decreases from 20 to 10. The resulting predictions of the dominant wavenumbers agree well with recent experimental observations. Critically, the model naturally produces a phase lag between sediment transport and bedform geometry without empirical lag terms. The 1D-VAM framework with Exner equation offers a physically consistent and computationally efficient tool for predicting bedform instabilities in erodible channels. This study advances the capability of conventional depth-averaged models to simulate complex bedform evolution processes. Full article

The development of shale gas relies on hydraulic fracturing technology and requires the injection of a large amount of fracturing fluid. The well shut-off period after fracturing can promote water infiltration and suction. Optimizing the well shut-off time is crucial for enhancing the recovery rate. Among existing methods, the dimensionless time model is widely used, but it has limitations because it does not represent the length of on-site scale features. In this study, we focused on the shut-in time for a deep shale gas well (Lu-A) in Luzhou and a medium-deep shale gas well (Wei-B) in Weiyuan. By integrating the spontaneous seepage and aspiration experiments in the laboratory and the post-pressure backflow data (including mineralization degree, liquid volume recovery rate, etc.), a multi-scale well shutdown time prediction model considering the characteristic length was established. The experimental results show that the spontaneous resorption characteristic times of Lu-A and Wei-B are 3 h and 22 h, respectively. Based on the inversion of crack monitoring data, the key parameters such as the weighted average crack width (1.73/1.30 mm) and crack spacing (0.20/0.32 m) of Lu-A and Wei-B were obtained. Through the scale upgrade calculation of the feature length (0.10/0.16 m), the system determined that the optimal well shutdown times for the two wells were 14.5 days and 16.7 days, respectively. The optimization method based on a multi-parameter analysis of backflow fluid proposed in this study not only solves the limitations of the traditional dimensionless time model in characterizing the feature length but also provides a theoretical basis for the formulation of the well shutdown system and nozzle control strategy of shale gas wells. Full article

Spatio-temporal measurements of turbulent free surface flows remain challenging with in situ point methods. This study explores the application of an inexpensive depth-sensing RGB-D camera, the Intel^® RealSense™ D455, to capture detailed water surface measurements of a highly turbulent, self-aerated flow in the case of a stepped spillway. Ambient lighting conditions and various sensor settings, including configurations and parameters affecting data capture and quality, were assessed. A free surface profile was extracted from the 3D measurements and compared against phase detection conductivity probe (PDCP) and ultrasonic sensor (USS) measurements. Measurements in the non-aerated region were influenced by water transparency and a lack of detectable surface features, with flow depths consistently smaller than USS measurements (up to 32.5% less). Measurements in the clear water region also resulted in a “no data” region with holes in the depth map due to shiny reflections. In the aerated flow region, the camera effectively detected the dynamic water surface, with mean surface profiles close to characteristic depths measured with PDCP and within one standard deviation of the mean USS flow depths. The flow depths were within 10% of the USS depths and corresponded to depths with 80–90% air concentration levels obtained with the PDCP. Additionally, the depth camera successfully captured temporal fluctuations, allowing for the calculation of time-averaged entrapped air concentration profiles and dimensionless interface frequency distributions. This facilitated a direct comparison with PDCP and USS sensors, demonstrating that this camera sensor is a practical and cost-effective option for detecting free surfaces of high velocity, aerated, and dynamic flows in a stepped chute. Full article

This paper presents a comprehensive vehicle model featuring an active suspension system integrated with semi-active seat and engine mounting controls. The time-domain stochastic excitation of the four tires was modeled using the filtered white noise method, and the required road excitation was simulated using MATLAB software R2022b. Four comprehensive performance indices, including engine dynamic displacement, vehicle body acceleration, suspension dynamic deflection, and tire dynamic displacement, were selected and made dimensionless by the performance indices of a passive suspension under the same working conditions to construct the fitness function. A fractional-order PI^λD^μ (FOPID) controller was proposed, and its structural parameters were optimized using a gray wolf optimization algorithm. Furthermore, the optimized FOPID controller was evaluated under five road conditions, and its performance was compared with integer-order PID control and passive suspensions. The results demonstrate that the FOPID controller effectively improves the smoothness of the vehicle by reducing engine mounting deflection, vehicle body acceleration, suspension deflection, and tire displacement. Moreover, the simulation results indicate that, compared to the passive suspension, the FOPID-controlled suspension achieves an average optimization of over 42% in the root mean square (RMS) of body acceleration under random road conditions, with an average optimization of more than 38% for suspension deflection, 4.3% for engine mounting deflection, and 2.5% for tire displacement. In comparison to the integer-order PID-controlled suspension, the FOPID-controlled suspension demonstrates an average improvement of 28% in the RMS of acceleration and a 2.1% improvement in suspension deflection under random road conditions. However, the engine mounting deflection and tire displacement are reduced by 0.05% and 0.3%, respectively. FOPID control has better performance in vehicle acceleration control but shows asymmetrical effects on tire dynamic deflection. Full article

The interaction between surface water and groundwater has been extensively studied due to its water management implications and the potential environmental impacts arising from it. Experimental studies and numerical modeling have supported analytical solutions; these solutions have been proposed for specific cases in which the aim has been to understand discharge/recharge and aquifer characterization. In this study, new graphical solutions or type curves are provided to estimate the subsurface flow and thermal–mechanical parameters in anisotropic porous media. Using the non-dimensionalization technique of the governing equations, new dimensionless groups (lumped parameters) that govern the solution of both the mechanical problem (uncoupled) and the thermal problem are obtained. From these groups, and by applying the pi theorem and examining numerical simulations of numerous cases, user-friendly type curves are obtained. The recharge flow and hydraulic conductivity are calculated when the thermal properties, geometrical parameters, and temperature variables are known. To evaluate the reliability of the type curves, two real case studies are presented: the interaction between the Guadalfeo River and the Motril-Salobreña coastal aquifer, and the artificial recharge program in the coastal aquifer of Agua Amarga in southern Spain. For verification, the groundwater flow obtained from the type curves is compared with the recharge data. In the case of the river–aquifer interaction, the recharge flow obtained is 13% less than that estimated in previous studies. Regarding the artificial recharge of the coastal aquifer, the flow obtained is 21% less than the volume irrigated over the salt marsh. The uncertainties related to hydrogeological features are considered to have the greatest influence on the error. Full article