arXiv subjects

Senlin Zhu

Publications and source records attributed to Senlin Zhu.

3 recordsLinked to original sources

arXiv2026

Electric modification of mode competition in viscous films with insoluble surfactants on vertical fibers

This study investigates the coupled effects of an insoluble surfactant and a radial electric field on the stability of a viscous liquid film flowing down a vertical fiber. Starting from the governing equations in two dimensions, a reduced model in one dimension is derived using the long wave approximation to describe the coupled evolution of the interface and surfactant transport. Linear stability analysis identifies two distinct unstable modes: the Rayleigh-Plateau mode, which dominates at lower values of the Marangoni number $Ma$, and the Marangoni mode, which becomes dominant at higher values of $Ma$. The influence of the radial electric field is determined by the position of the outer electrode $\beta$. When $\beta<\mathrm{e}$, the electric field enhances both instabilities and narrows the stable interval in $Ma$ between the two modes. When $\beta>\mathrm{e}$, the electric field suppresses both modes and can completely eliminate the unstable region associated with the Marangoni mode even at a relatively small electric Weber number $E_b$. Continuation of the traveling wave solutions further shows that, when $\beta<\mathrm{e}$, the magnitude of the relative interfacial motion $I_{RP}$, generally increases with $E_b$. By contrast, the intensity of Marangoni convection $I_{M}$ varies only weakly at smaller values of $E_b$ and increases appreciably only when the electric field becomes sufficiently strong. Analysis of the stream function and the relative interfacial velocity reveals that the electric stress primarily intensifies the recirculation beneath the wave crest and reshapes the spatial distribution of the relative interfacial velocity.

physics.flu-dyn↗

arXiv2026

Marangoni modulation of coupled Rayleigh-Taylor and Faraday instabilities in vertically oscillated liquid films

We investigate the Marangoni modulation of coupled Rayleigh-Taylor and Faraday instabilities in a vertically oscillated Newtonian liquid film carrying insoluble surfactants. Linear stability analysis using Floquet theory reveals that an increasing Marangoni number (Ma) selectively suppresses subharmonic modes, driving the system into a harmonic-dominated regime. The interfacial response is found to be highly frequency-dependent. At low forcing frequencies, increasing Ma causes adjacent harmonic tongues to merge into a novel surfactant mode that migrates towards long wavelengths, ultimately coalescing with the RTI branch and fragmenting the dynamically stable window. Conversely, at high frequencies, surfactants monotonically elevate the harmonic instability threshold, significantly widening the stable parameter space. To uncover the underlying mechanisms, a long-wave asymptotic analysis is performed, demonstrating that the critical forcing amplitude factorizes into a static capillary-gravity margin and a dynamic elasto-inertial modulation, yielding a scaling law for the critical mode balance. Finally, nonlinear simulations based on a rigorous weighted-residual reduced model are utilized to dissect the spatial work performed by individual forces, which shows that surfactants modulate stability through phase-controlled Marangoni transport. In the RTI regime, increasing Ma reverses the transport direction and drives fluid into the peaks, inducing a transition from stabilization to destabilization. In the Faraday instability (FI) regime, the response exhibits a strong frequency dependence, governed by Marangoni transport that redistributes fluid away from interfacial peaks at high frequencies but toward them at low frequencies, thereby suppressing or enhancing the instability accordingly.

physics.flu-dyn↗

arXiv2025

Every Step Evolves: Scaling Reinforcement Learning for Trillion-Scale Thinking Model

We present Ring-1T, the first open-source, state-of-the-art thinking model with a trillion-scale parameter. It features 1 trillion total parameters and activates approximately 50 billion per token. Training such models at a trillion-parameter scale introduces unprecedented challenges, including train-inference misalignment, inefficiencies in rollout processing, and bottlenecks in the RL system. To address these, we pioneer three interconnected innovations: (1) IcePop stabilizes RL training via token-level discrepancy masking and clipping, resolving instability from training-inference mismatches; (2) C3PO++ improves resource utilization for long rollouts under a token budget by dynamically partitioning them, thereby obtaining high time efficiency; and (3) ASystem, a high-performance RL framework designed to overcome the systemic bottlenecks that impede trillion-parameter model training. Ring-1T delivers breakthrough results across critical benchmarks: 93.4 on AIME-2025, 86.72 on HMMT-2025, 2088 on CodeForces, and 55.94 on ARC-AGI-1. Notably, it attains a silver medal-level result on the IMO-2025, underscoring its exceptional reasoning capabilities. By releasing the complete 1T parameter MoE model to the community, we provide the research community with direct access to cutting-edge reasoning capabilities. This contribution marks a significant milestone in democratizing large-scale reasoning intelligence and establishes a new baseline for open-source model performance.

cs.CL↗