Motivated by the demographic effects, we propose and analyze a novel opinion dynamics model with density-dependent interactions and a nonlinear communication function reminiscent of the celebrated Hegselmann-Krause (H-K) model. The model also incorporates heterogeneity through intrinsic opinion tendencies, leading to richer dynamics. In the homogeneous case, where all agents share the same intrinsic tendency, the relative dissimilarities between opinions converge to zero and a single cluster forms. In contrast, under heterogeneous tendencies, we prove that multiple clusters emerge together with velocity alignment within each cluster, provided suitable structural conditions on the system parameters. These theoretical findings are further supported by numerical simulations, which offer qualitative insights beyond the analytical results.
Abstract Pigging is widely employed to remove deposits from water distribution pipelines, yet technical guidelines for determining the optimal number of passes remain insufficiently established. This study investigates the mechanistic reason why repeated pigging provides superior cleaning performance compared with a single pass by integrating visualization experiments and computational fluid dynamics (CFD) analysis. A serrated scraper pig, consisting of two rows of circumferential serrated rubber pads with a Shore hardness of 60D, was tested in a laboratory-scale recirculating system using a 100 mm diameter transparent acrylic test pipe and artificial sediments designed to simulate strongly adhered pipeline deposits. Direct visualization showed that the second pigging pass achieved a sediment removal efficiency of 37.55%, which was approximately five times higher than that of the first pass (7.46%) and substantially greater than that of conventional flushing (0.55%). Transient CFD simulations (pimpleFoam solver with the γ – Re θ transition model coupled to the shear stress transport (SST) k – ω turbulence model) clarified the physical basis for this enhancement. During the first pass, sediment accumulation within the serrated pad gaps suppresses bypass flow development. After these gaps are partially cleared, accelerated annular flow develops more effectively during the subsequent pass, increasing the local wall shear stress and skin-friction coefficient in the noncontact regions. This combined action of mechanical scraping and hydrodynamic shear explains why the major cleaning effect appears primarily after the first pass. The main practical implication is that at least two pigging passes are recommended for the effective removal of strongly adhered deposits. These findings provide a mechanistic basis for optimizing pigging protocols in water distribution pipeline maintenance.
Ultrasonic testing offers a promising non-destructive approach for state-of-charge (SoC) estimation of lithium-ion batteries (LiBs). However, its practical deployment is hindered by domain shifts arising from variations in measurement conditions, particularly ambient temperature and sensor coupling conditions. Variations in these measurement conditions significantly alter macroscopic acoustic wave propagation—such as signal attenuation and baseline time-of-flight—producing distinct signal distributions at identical SoC levels. Existing methods mitigate this issue through multi-domain training or target-domain access, both of which are costly or infeasible in field deployment scenarios. In this work, we formulate single-source domain generalization as a practical problem setting for ultrasonic SoC estimation and propose SoC-Aware Contrastive Representation Learning (SCARL), a two-stage framework that learns domain-invariant representations from a single measurement condition. In the first stage, a frequency-domain preprocessing based on fast Fourier transform log-magnitude extraction suppresses condition-dependent phase variations, and a supervised contrastive learning objective with stochastic noise augmentation trains an encoder to cluster samples by SoC level while remaining invariant to measurement-condition-specific signal variations. In the second stage, the frozen encoder is coupled with a regressor to map the learned representations to continuous SoC values. Experimental results demonstrate that SCARL, trained exclusively on a single-source domain, achieves competitive SoC estimation accuracy on unseen domains, outperforming conventional approaches that assume access to multi-domain data. To the best of our knowledge, this is the first study to address single-source domain generalization in ultrasonic SoC estimation, enabling reliable cross-domain prediction without target-domain data collection or model recalibration.
