The parallel multi-channel structure of the Casing Once-through Steam Generator (COTSG) is prone to inducing flow instability under boiling two-phase flow conditions, posing a threat to equipment safety. In this study, the subchannel analysis method is applied to investigate flow instability in the COTSG. A multi-region subchannel model of the COTSG is developed using the subchannel code CTF, and its reliability is validated through comparison with experimental data and the RELAP5 code. The effects of primary-side flow distribution, transverse mixing, tube bundle scale, and local blockage on the simulation results are systematically examined. The results indicate that more reliable outcomes are achieved by allocating primary-side flow according to the number of heat transfer casings while accounting for transverse mixing. A simplified subchannel model with 19 casing tubes provides a favorable balance between computational accuracy and efficiency. System instability is initially triggered in the edge channels with the highest thermal load, where out-of-phase oscillations first appear between edge channels and subsequently induce flow oscillations in the inner channels. In the multi-channel model, a blockage in a single secondary-side channel enhances system stability, whereas a primary-side blockage reduces system stability. The extent of this effect is related to the location and axial position of the blockage. This study provides a new approach for refined thermal–hydraulic design and safety analysis of COTSGs.
To address the lack of interpretability in Convolutional Neural Networks (CNNs) for ship-radiated noise classification, this paper proposes a visual analysis framework integrating Guided Backpropagation (GBP) and Gaussian Mixture Models (GMMs). Firstly, a convolutional kernel visualization process based on GBP is constructed, combined with a multi-frame feature alignment algorithm to eliminate time-frequency interference from harmonic line components, enabling the precise reconstruction of deep convolutional kernel response features. Secondly, a GMM-based pattern decomposition method is proposed, which models activation locations to decompose a single convolutional kernel into multiple sub-patterns with distinct frequency distributions. Experiments were conducted on a CNN model with CQT spectrogram inputs, validated on the DeepShip dataset. The visualization results reveal that deep convolutional kernels contain two primary patterns: line spectra and background, detecting harmonic line components and continuous spectrum features in LOFAR spectrograms, respectively. After GMM decomposition, the response regions of the sub-patterns become more concentrated, feature expression becomes more consistent, and an association mechanism linking “convolutional kernel - sub-pattern - ship class” is established. This research provides a quantitative basis for understanding CNN decision logic, filling a gap in interpretability studies for ship-radiated noise classification models.
Enhancing convective heat transfer in circular tubes is crucial for compact heat exchangers, but conventional passive structures often suffer from weak heat transfer in downstream recirculation zones. To address this limitation, a bionic fish-scale composite sphere enhanced heat transfer tube (CBS) is proposed by introducing hemispherical disturbance elements onto a bionic fish-scale substrate to control the wake recirculation zone. The effects of sphere diameter, concave/convex form, and arrangement pattern are systematically investigated. Using the SST k-ω turbulence model, flow and heat transfer of eight CBS configurations in a circular tube are numerically simulated for Re = 11225~33675. The enhancement mechanism is analyzed via field synergy, the energy penalty is evaluated by entropy generation, and the overall thermohydraulic performance is assessed using the performance evaluation criterion (PEC). Compared with the bionic fish-scale tube (CB), CBS simultaneously enhances heat transfer and reduces flow resistance over the entire Re range. The optimal configuration increases Nu by 14.03% and decreases f by 23.30% at Re = 33675. Sphere diameter dominates thermohydraulic performance, followed by concave/convex form, while arrangement pattern has the least effect. CBS_2 achieves the highest field synergy number Fc and a maximum PEC of 1.28 at Re = 11225, with the lowest total entropy generation ratio S/S0 = 0.55. Mechanism-driven correlations for Nu and f are established, with prediction errors within ±5% for Nu/NuCB and ±10% for f/fCB. The composite structure shows strong potential for compact tubular heat exchangers.
Solid-state electrolytes (SSEs) are key materials for next-generation high-energy batteries because of their enhanced chemical and mechanical stabilities. Poly(ethylene oxide) (PEO)-based solid polymer electrolytes (SPEs) exhibit great physical contact with electrodes, electrochemical compatibility with lithium (Li) metal anodes, as well as easy processibility and high economic efficiency, having become the pioneer and one frontrunner for developing all-solid-state high-energy batteries. However, PEO-based SPEs also suffer from a trade-off between ionic conductivity and mechanical strength, an insufficient cationic transference number, and a weak high-voltage stability, limiting their practical achievement in desirable power and energy density. Herein, we present a comprehensive overview on the intramolecular design strategies of PEO, which has the potential to fundamentally tackle above challenges compared to the intermolecular plasticizer or ceramic blending approaches. Topological and chemical designs for target mechano-electro-chemical performance are classified and summarized in detail. On this basis, a perspective on the unconquered issues and future directions is proposed, providing guidance for the design and application of high-performance SSEs for next-generation high-energy batteries, with special emphasis on the rational integration of intramolecular and intermolecular methods and the development of advanced manufacture techniques for flexible yet robust thin films.
Output current capability can be expanded by high-power converters through the paralleling of IGBTs. However, parameter mismatch and layout asymmetry often lead to imbalanced current distribution. To address this problem, this paper presents a dynamic current trajectory feedback-based gate drive strategy (DCTF-GDS). Based on double-pulse tests under asymmetric layout conditions and within the allowable range of turn-off voltage overshoot and EMI suppression, a larger reference di/dt value is employed to realize current balancing and optimize switching losses. The proposed strategy employs di/dt feedback to reduce control delays and incorporates a mode-switching scheme to accommodate different switching stages, ensuring reliable operation of both the IGBT and the gate driver. In addition, a segmented bidirectional voltage regulation circuit is utilized to eliminate short-circuit risks. Experimental validation was carried out on an asymmetric parallel IGBTs platform. The results show that the dynamic current imbalance is limited to within 5
