Gelled fuel combines the storage stability of solid-like materials and the flow adaptability of liquid-like substances, emerging as a crucial development in fuel technology for propulsion systems. However, the coupled thixotropic and shear-thinning properties of gelled fuel render its flow behavior inside nozzles highly complex, with the nozzle configuration directly affecting flow uniformity, pressure loss, and subsequent atomization efficiency. To reveal the regulatory aspects of typical nozzle configurations on the flow characteristics of gelled fuel, this study suggests a thixotropic shear-thinning constitutive model based on the "Carreau-Yasuda model + second-order kinetic equation". We carry out numerical simulations on the flow of 5% organic kerosene gel in three typical nozzle configurations (straight, converging, and tapered injectors). We investigate the effects of configuration parameters on the flow and the results show that the straight pipe nozzle only exhibits obvious structural breakdown in the wall region, while the gel in the core region maintains high structural integrity with insufficient viscosity attenuation;the converging nozzle can significantly enhance the shearing effect, accelerate the spatial homogenization of the gel's microstructural breakdown, and result in a more uniform viscosity distribution at the outlet section; the tapered injector acts as a localized deformation intensifier. Increasing the cone angle enhances shear localization, strengthens near-wall structural breakdown and viscosity reduction, but also shortens the residence time, leading to a competitive effect between shear intensity and residence time in the rheological evolution.

Proton exchange membrane fuel cells (PEMFCs) are highly susceptible to performance limitations at high current densities due to competing gas and liquid transport mechanisms. While microporous layers (MPLs) reduce contact resistance, they often exacerbate the risk of flooding. This study leverages the topological advantages of highly interconnected cage-like MPLs. By integrating X-ray computed tomography (X-CT) with digital techniques (reconstructed geometry and numerical simulation), it reveals the mass transfer mechanisms within cross-scale composite microstructures. Considering in-situ compression and surface wettability, the research delves into the influence of MPL thickness and crack morphology on multiphase flow dynamics. The results indicate that despite the significant non-steady-state velocity fluctuations and reduced liquid permeability caused by cascading Haines jumps due to increased MPL thickness, its unique high-porosity cage-like network structure still maintains robust oxygen diffusion performance. In-situ compaction further weakens the macropore transport capacity of the gas diffusion layer (GDL), leading to a significant reduction in permeability. In contrast, fractures effectively break through transport bottlenecks. Vertical through-holes maximize oxygen diffusion efficiency through the shortest path effect, while 45 degrees inclined fractures optimize gas-liquid convective permeability while enabling directed high-speed drainage of liquid water. This study elucidates the regulation mechanism of microstructural parameters across scales, providing theoretical guidance for designing next-generation gas diffusion layers that combine high gas-phase diffusion with efficient drainage capabilities.

The gas diffusion layer (GDL) of proton exchange membrane fuel cells (PEMFCs) is a critical component for the transport of reactants. The efficiency of reactant gas transport remains a major technical challenge in the field today. The anisotropic structure of the GDL gives rise to substantial variations of gas diffusion as well as permeability in different directions. The study employs X-CT technology to obtain the actual GDL's geometry, aiming to investigate a spatial structure at the microscale and its gas transport characteristics. The computational fluid dynamics (CFD) method is used to simulate and study the gas diffusivity and gas permeability of GDL with four different thicknesses. The numerical simulation results show that the diffusivity and permeability in the through-plane (TP) direction are lower than those in the in-plane (IP) direction. Moreover, the effective diffusion coefficient (EDC) decreases with increasing thickness, but is also dependent on the solid fibre structure of GDL. Horizontal alignment of the carbon fibers and the disc-shaped adhesive contributes to the anisotropy between the TP and IP directions, resulting in anisotropic gas transport. The purpose of the study is to supply critical references for manufacturing techniques and optimization of gas transport in GDLs.

Conventional tunnel disaster prevention systems are constrained by several critical limitations, such as inadequate accuracy in geological detection, oversimplified modeling, delayed simulation responses, subjective risk assessment, and coarse decision-making. These constraints hinder comprehensive situational awareness and scientific disaster control in complex tunnel environments. Artificial intelligence (AI) offers essential support for the digital-intelligent upgrading of tunnel disaster prevention by enabling automated interpretation of predictive data, multi-source information modeling, rapid disaster scenario simulation, and scientifically-grounded risk evaluation. This study systematically investigates digital-intelligent methods for tunnel disaster prevention and safety protection following the technical chain of “geological prediction–modeling–simulation–assessment and decision-making.” For geological information prediction, research focuses on improving the accuracy and efficiency of data analysis through algorithmic optimization to achieve rapid and reliable perception under complex conditions. In modeling, machine learning–based intelligent modeling approaches and digital twin (DT)–driven cyber–physical integration are discussed. Regarding disaster simulation, deep-learning-based surrogate models are summarized for their applications in disaster forecasting, effectively overcoming the high computational cost and response delay inherent in conventional simulation methods. In risk assessment and decision-making, the applications of machine learning (ML) and emerging large language models (LLMs) are examined, highlighting advances in risk identification, prediction, and reasoning. Despite these advancements, challenges persist, including strong reliance on limited data sources, lack of physical constraints and interpretability, insufficient generalization across scenarios, and restricted real-time capability. Future developments are expected to pursue deeper integration of data-driven and physics-informed approaches, improve the utilization of multi-source heterogeneous data and scenario generalization, enhance model robustness and interpretability, and incorporate lightweight architectures with dynamic updating mechanisms, thereby enabling real-time perception, full-chain simulation, and intelligent risk management throughout the entire tunnel engineering processes.

The performance of solid oxide electrolysis cells (SOECs) is closely related to its flow channel structure, stoichiometric ratio, and operating temperature. In this study, multiphysics numerical models were developed, incorporating coupled heat and mass transport alongside electrochemical reaction processes. The model validity was confirmed by comparing simulated results with experimentally measured I-V curves. The impacts of the fuel-to-air stoichiometric ratio (F:A from 2:1 to 2:4), channel aspect ratio (L:W from 1:1 to 4:1), temperature (from 873.15 K to 1073.15 K), and flow arrangements (co-current vs. counter-current) on the performance of a single-channel electrolyzer were systematically investigated, complemented by an analysis of multi-channel behavior under cross-flow conditions. The findings reveal that among the investigated parameters, temperature exerts the most significant influence on cell performance. Increasing the temperature facilitates the substitution of electrical energy with thermal energy, reducing the cell voltage from 1.5364 V to 1.1142 V at 1.2 A/cm2. Furthermore, varying the stoichiometric ratio effectively improves the oxygen partial pressure in the catalyst layer, thereby reducing concentration polarization. At the same current density, the required cell voltage decreased from 1.5364 V to 1.5153 V. Increasing the channel aspect ratio improves mass transport, reducing the required cell voltage from 1.5444 V to 1.5053 V. In contrast, flow arrangements were found to have a negligible impact on overall performance, though the counter-flow arrangement demonstrated marginal superiority over the co-flow arrangement.

Periodic lattice structures demonstrate considerable potential for applications in heat exchange and thermal management systems. To further improve the thermal performance of the previously developed X-lattice periodic cellular structure with good heat transfer performance, this study proposes three variants with one, two, and three cell layers, denoted as CXL, DXL and TXL based on variations in pore density. The influence of pore density on turbulent flow and heat transfer characteristics was systematically investigated. Numerical simulations were carried out following model validation. The results indicate that under constant Reynolds number, pressure drop, and pumping power conditions, the overall heat transfer performance improves with increasing pore density. Specifically, the maximum enhancement in the overall Nusselt number reaches 163 %, 91 %, and 112 %, respectively. Increased pore density substantially augments the bulk turbulence intensity, which is efficiently conveyed to the endwalls via unique spiral flow structures. Consequently, local heat transfer is markedly improved on both the endwalls and the ligaments, with maximum enhancement rates of up to 48 % and 74 %, respectively. Furthermore, the underlying heat transfer enhancement mechanisms are elucidated. Quantitative analysis reveals that the improved thermal performance is primarily attributed to the significantly enhanced local heat transfer over the lattice core and the increased core surface area. By systematically evaluating thermal performance and clarifying the mechanisms through which pore density enhances heat transfer, this study provides valuable guidance for future engineering applications.

Bubble retention caused by the high viscosity of gelled propellants poses a significant challenge to combustion stability and performance in propulsion systems. To address this, the present study investigates the dynamic behavior of a single bubble in shear-thinning gelled propellants flowing through corrugated channels. Numerical simulations are conducted employing the Volume of Fluid (VOF) approach, with a modified Carreau-Yasuda model applied to represent the non-Newtonian viscosity characteristics. The effects of channel geometry, temperature, and inlet velocity on bubble dynamics and apparent viscosity are analyzed. The results indicate that bubble velocity is highest in trapezoidal channels, followed by sinusoidal and smooth channels. Increasing the corrugation amplitude enhances bubble speed, while higher temperatures reduce it. At low inlet velocities, the bubble maintains its shape; at moderate velocities, it deforms and recovers; and at high velocities, it splits. These findings provide valuable insights into bubble behavior in gelled propellants and contribute to the optimization of propulsion system design.

The lithium plating reaction in graphite electrodes acts as a root cause for the accelerated degradation and the internal short circuits in lithium-ion batteries. Here, an electrochemical model based on multi-scale microstructural images was established to identify lithium plating-stripping processes, thereby supporting the predictive outcomes of electrochemical monitoring techniques. Experiments revealed that the open-circuit voltage differential curve (dOCV/dt) led to ambiguous delineation of the safe state-of-charge (SOC) operating range. The established lithium plating-stripping model was used to compare with experimental results, revealing the dynamic evolution of electrode-scale kinetics and quantified the impact of lithium metal residue on electrode performance. Ex situ X-ray computed tomography (XCT) captured micrometer-resolution microstructural details of graphite electrodes and plated lithium, enabling further correlation of spatially heterogeneous lithium plating-stripping reactions with electrode microstructure. The sensitivity of lithium plating to electrode microstructure was examined at the particle scale, attributed to competition between electrode kinetic rates and active reaction areas. Theoretical mechanism analysis and experimental results from high-energy-density electrodes demonstrated that positioning small particles on the current collector side effectively mitigates solid-state diffusion polarization while confining side reactions to a limited area. The integration of experiments and multiscale modeling elucidates the relationship between lithium plating-stripping reactions and electrode structure, providing mechanistic insights for similar structural optimization designs.

The efficient and accurate prediction of structural responses in reinforced concrete components under blast loading plays a critical role in emergency repair decision, structural strengthening, and protective design. Existing rapid methods for calculating structural response, such as analytical models and lightweight data-driven approaches, are computationally efficient. However, they are limited in accurately resolving three-dimensional structural response fields. A Graph Neural Network (GNN)-based model for the rapid prediction of damage in reinforced concrete (RC) columns was proposed in this paper. By leveraging the neighborhood node aggregation mechanism of GNNs, the model efficiently transmits mechanical correlation information within the structure. This allows the model to establish an end-to-end mapping between blast load inputs and the 3D structural response of the component, enabling rapid prediction of the column’s damage state. Furthermore, a multi-scenario feature coupling training strategy is introduced to significantly enhance the model’s generalization capability. This strategy enables the GNN model to effectively adapt to variations in key design and loading parameters, including reinforcement ratios, explosive charge weights, and blast locations. The results demonstrate that the proposed model achieves a prediction time of merely 55 milliseconds per instance, representing a computational speed improvement of four orders of magnitude over conventional methods; meanwhile, the prediction error remains below 3.33%. Furthermore, it delivers high-precision damage predictions across various blast scenarios. The proposed study successfully highlights the significant potential of GNN-based approaches in predicting blast-induced damage and offers an innovative, data-driven solution for rapid structural assessment and protective design in the field of blast engineering.

Carbon corrosion induced by anode localized flooding severely compromises the durability of proton exchange membrane fuel cell (PEMFC). Limited by the computational stability and efficiency, existing simulations are always in 2D or single-channel scales, which overlooks the influence of the actual flow field structure in commercial PEMFC on carbon corrosion behavior. In this study, a performance-coupled 3D carbon corrosion model is established to investigate the carbon corrosion behavior and performance degradation in a 306 cm2 commercial-scale PEMFC under anode localized flooding conditions. The research demonstrates that the carbon corrosion zone exhibits a quasi-trapezoidal distribution influenced by hydrogen transport and in-plane proton conduction. Carbon loading undergoes rapid loss during the initial flooding phase, followed by a gradual leveling off. After 120 min of local flooding, the PEMFC exhibits an electrochemically active surface area (ECSA) loss of 23.97 % and an output power loss of 16.83 %. This model provides deeper insights into carbon corrosion behavior under localized flooding in large-scale PEMFC and offers a valuable reference for formulating carbon corrosion mitigation strategies.

Gelled fuels have gained attention for their enhanced safety and higher energy density in aerospace propulsion, yet their complex rheological behavior poses challenges for atomization modeling and performance prediction. This study develops a constitutive model for thixotropic organic kerosene gel and integrates it with a VOF-LESbased numerical framework to investigate the primary atomization characteristics under varying inlet conditions and Reynolds numbers. The simulations explore breakup mechanisms, jet penetration, fuel-air mixing, and atomization efficiency, with conventional kerosene as a baseline for comparison. Under uniform inlet conditions, atomization is predominantly driven by the frontal impact between the jet tip and the quiescent ambient gas, leading to lateral dispersion and front-end ligament breakup with relatively stable liquid core structures. In contrast, turbulent inflows introduce strong perturbations and turbulent kinetic energy, intensifying surface instabilities, promoting earlier and more complete breakup, and producing more numerous and smaller droplets. Increasing Reynolds number enhances inertial forces and weakens the suppressive effects of gel rheology, though efficiency gains diminish at high values due to energy dissipation and droplet coalescence. Compared to conventional kerosene, gel fuels demonstrate lower atomization efficiency and mixing due to higher viscosity and internal structural resistance. These findings offer valuable insights into the atomization dynamics of gelled propellants, supporting improved injector design and combustion performance optimization.

The gas diffusion layer (GDL) is a critical component for gas transport in proton exchange membrane fuel cells (PEMFCs). Gas transport efficiency significantly impacts PEMFC performance. This study employs integrated micro-computed tomography (Micro-CT) for 3D reconstruction of the GDL and utilizes computational fluid dynamics (CFD) to investigate gas diffusion and convection in GDLs with varying perforation/thickness ratios. Results indicate that perforations enhance the effective diffusion coefficient (EDC) of GDLs, while GDL thickness has minimal impact on EDC. EDC variations are primarily influenced by internal geometric structures. The anisotropy arising from fiber orientation during carbon paper manufacturing results in higher diffusion rates in the in-plane (IP) direction than in the through-plane (TP) direction. Perforations reduce inlet pressure and improve gas flow, thereby increasing GDL permeability; conversely, permeability decreases with increasing GDL thickness. High-velocity regions in the GDL correspond to large pore areas, indicating that pore distribution influences gas transport. This study aims to elucidate the fundamental mechanisms of gas transport in GDLs as functions of thickness and perforation, thereby providing crucial theoretical guidance for GDL design.

Temperature variations significantly affect the performance and safety of lithium-ion batteries (LIBs), particularly under extreme conditions and high charge/discharge rates. Uneven heat generation, limited heat dissipation, and residual energy accumulation exacerbate thermal effects, leading to capacity degradation, reduced efficiency, and safety hazards. Addressing these challenges requires a multiscale understanding of the thermal behavior of LIBs. This review provides an integrated, multiscale perspective on battery thermal safety, spanning material-level design, cell-level modeling and state estimation, and system-level thermal management. We first examine the fundamental thermal mechanisms and modeling approaches governing heat generation, transport, and accumulation across varying operating conditions at the cell level. We then explore material-level strategies to mitigate low-temperature degradation and enhance high-temperature stability, enabling reliable all-climate operation. Building on these insights, we assess system-level thermal management approaches, including internal and external preheating, as well as active and passive cooling methods. Particular attention is given to immersion cooling, which offers superior heat dissipation, improved temperature uniformity, and enhanced safety. We comprehensively review immersion cooling technologies, including system configurations, fluid selection, operational parameters, and recent advances in both single-phase and two-phase cooling. By bridging physical mechanisms, modeling frameworks, and engineering solutions across scales, this review highlights key challenges, identifies critical research gaps, and outlines future directions for achieving adaptive and robust thermal safety in LIBs. The insights provided aim to support the development of safer, more reliable, and thermally resilient LIB systems for transportation and large-scale energy storage applications.

Water in Proton Exchange Membrane Fuel Cells (PEMFCs) holds significance and complexity. The study of water is crucial for enhancing the efficiency and extending the lifespan of the batteries. This study used micro-CT technology to obtain tomographic images of the gas diffusion layer (GDL) in PEMFCs. Subsequently, the samples were reconstructed in three dimensions using Avizo, and the internal fluid flow in the GDL was simulated using the Volume of Fluid (VOF) method. The local and average porosities of all sample types were calculated, providing insight into the distribution of the internal pore structure of the GDL. Analyzed the impact of the pressure difference at the inlet and outlet(Delta P), contact angle, and the thickness of the model on the flow of liquid. The research results indicate that for the TGP-H-60 model of GDL, the Delta P must be at least 6 kPa to allow liquid water to flow from one end to the other. The contact angle within the GDL significantly impacts the removal of liquid water. In practical applications, the selection of GDL thickness must ensure mechanical strength while also considering fluid transport efficiency to enhance battery performance. An overly thick can make the flow of water more difficult, resulting in flooding phenomena.

Thermal runaway (TR) is a severe challenge to the widespread commercial adoption of high energy-density lithium-ion batteries (LIBs). Nonetheless, the current strategies lack responsiveness for both extreme heat dissipation and explosion suppression. Here, a thermal safety protection strategy based on liquid immersion cooling (LIC) is proposed. The peak temperature of overcharge-induced TR is decreased below 300 degrees C through boiling heat exchange of FS49, rapidly (3 min) stabilizing the LIB temperature around 49 degrees C. Simultaneously, critical radicals are captured by FS49 in the combustion chain reaction, reducing emissions of combustible toxic gases by approximately 62.65%. This effectively prevents LIB explosions and secondary re-ignition disasters. Surprisingly, when applied as 1 mm interlayers between cells for a pack with four LIBs, the FS49 not only suppresses the TR propagation but also maintains the adjacent LIB temperature at 53.89 degrees C. Additionally, it is further demonstrated that the thermal safety of a large-scale 36-cell LIB pack through finite volume method simulations. This strategy can represent a critical step forward in enhancing the safety performance of electric vehicles and grid-scale energy storage systems.

To meet the increasing demands for high energy density and safety in aerospace propulsion, gel propellants have drawn significant attention due to their dual solid-liquid characteristics. However, their high viscosity and complex rheology suppress interfacial instabilities and hinder jet breakup, posing critical challenges to efficient atomization. This study develops a high-fidelity numerical framework coupling the VOF method, large eddy simulation (LES), and adaptive mesh refinement (AMR), incorporating a thixotropic shear-thinning model based on experimental data for 5 % organic kerosene gel. Considering the strong mechanical vibrations in rocket engines, the primary atomization behavior under periodic perturbations with varying amplitude and frequency is systematically investigated, with a focus on jet evolution, breakup mechanisms, droplet characteristics, and mixing efficiency. Under high-speed injection, periodic forcing intensifies upstream core instability and promotes downstream fragmentation, generating numerous fine droplets. Increased perturbation amplitude enhances radial spreading and multiscale breakup, while frequency primarily adjusts droplet size uniformity but contributes little to penetration, with a saturation effect observed. Despite large number of small, slow droplets are generated near the jet core and upstream, the jet remains largely dominated by an unbroken liquid core and ligaments. Compared to kerosene, the gel exhibits significantly poorer atomization performance, producing larger, slower, and more localized droplets due to its rheological resistance to instability growth and spatial dispersion. This work provides quantitative insights into the atomization dynamics of gel fuels and establishes a theoretical foundation for rheological control strategies in propulsion applications.

Hydrodynamic ram (HRAM) from fragmentation-driven impacts poses a significant threat to liquid-filled structures under high-velocity loading. Two major challenges complicate its analysis: off-center impacts generate angle-and distance-dependent pressure fields that hinder consistent comparisons, and the phenomenon itself involves highly complex processes including debris-cloud formation, cavity dynamics, and intense fluid--structure interaction (FSI). In this study, HRAM induced by the penetration of a Zr55Cu30Al10Ni5 fragment was investigated through coordinated experiments and numerical simulations, with the fluid modeled using Arbitrary Lagrangian-Eulerian (ALE) and Smoothed Particle Hydrodynamics (SPH). To address the bias introduced by off-center impacts, a pressure-offset normalization model was formulated to transform pressure data from arbitrary impact locations into an equivalent center-impact condition, thereby enabling consistent comparisons between tests and simulations while reducing the need for multiple off-axis cases. On this basis, the performance of ALE and SPH was evaluated for key HRAM features, and the potential mechanisms underlying their error sources were analyzed to provide insight into their numerical behaviors. Results show that ALE more faithfully reproduces near-field peak shocks and global fluid-structure interaction leading to target deformation, whereas SPH better resolves debris-driven cavity formation and multi-angle far-field pressure propagation. These findings establish a unified, experimentally anchored framework for interpreting HRAM induced by fragmented projectiles and support the development of improved predictive and numerical models for liquid-filled systems under high-velocity impact threats.

To address the catalyst layer carbon corrosion and performance degradation in large-scale proton exchange membrane fuel cells (PEMFCs) subjected to localized anode flooding, this study develops a three-dimensional multiphase model in combination with experimental approaches. The model couples multicomponent transport, carbon oxidation kinetics, and catalyst layer structural degradation to investigate the evolution of carbon corrosion behavior, cell performance degradation, the underlying mechanisms of operating condition impacts, and the formulation of effective mitigation strategies in commercial-size PEMFCs. The results indicate that the carbon corrosion region exhibits a “narrow-top, wide-bottom” spatial distribution along the hydrogen flow direction, while the carbon corrosion current rapidly decays to 49.9% of its initial value over the duration of the flooding. After 30 minutes of flooding, the cumulative carbon loss in the cathode catalyst layer reaches 16.85%, resulting in an 8.02% decrease in power output. Operating condition analysis reveals that the most severe overall performance loss (10.4%) occurs at an operating voltage of 0.7 V, and increasing the inlet relative humidity from 60% to 100% exacerbates the performance loss by 74.6%. Further evaluation of two mitigation strategies demonstrates that reducing the anodic oxygen reduction reaction (ORR) activity and decreasing the membrane oxygen permeability result in more advantages at low and high voltages, respectively. Specifically, reducing the anodic ORR activity by 50% at 0.5 V curtails the power loss by 38.4%, whereas decreasing the membrane oxygen permeability by 50% at 0.8 V mitigates the power loss by 39.6%.

Mass transport polarization induced by water blockage within the gas diffusion layer (GDL) of proton exchange membrane fuel cells (PEMFCs) constitutes a critical bottleneck limiting high-current-density performance. This study establishes a model of water invasion under compression and thickness changes, facilitated by in-situ X-ray computed tomography (X-CT) imaging and finite element modeling based on realistic geometric structures. Subsequently, it investigates the gas transport under liquid saturation. Extraction of the pore network model (PNM) reveals that both compression and water flooding significantly reduce the mean pore diameter, while exerting minimal impact on the coordination number. The results indicate that liquid water transport pathways exhibit scale-dependent characteristics. Furthermore, the effective diffusion coefficient (EDC) and permeability (K) vary linearly with thickness. Notably, 40 % compression causes an 80 % reduction in permeability, while significantly mitigating the pressure drop phenomenon. This work provides multiscale insights into mass transport limitations across various porous media.

Reconstructing flow fields from sparse observations constitutes a fundamental challenge in aerodynamic analysis, since existing methods often fail to reconcile accuracy with computational efficiency under severe data constraints. We introduce a Graph Transformer Reconstruction Network (GTREN), a novel model designed to infer latent data patterns from extremely sparse measurements and to reconstruct high-fidelity, full-field flow solutions. The approach represents discrete grid points as nodes within a graph data structure, enabling a principled encoding of spatial relationships. We integrate a Transformer-style attention mechanism into the graph network's message-passing operations, allowing the model to selectively emphasize salient neighbor interactions while attenuating irrelevant signals; this facilitates efficient and stable information propagation. After training, GTREN uncovers implicit correlations among sparse sensor measurements and successfully generalizes these relationships to reconstruct the entire flow field. Results indicate that, with only ten measurement points, the model accurately reconstructs both pressure and velocity distributions across the field. When compared to Computational Fluid Dynamics simulations, the mean squared error (MSE) of the reconstruction is as low as 0.05%. By contrast, a conventional graph neural network yields an MSE of 18.31%. We also provide a systematic analysis of how both the number and spatial arrangement of measurement points affect reconstruction accuracy. Our analysis reveals that, rather than merely increasing sensor count, strategically locating sensors within the core vortex-shedding region of the wake substantially improves reconstruction accuracy-offering practical guidance for flexible sensor deployment. Overall, GTREN achieves high-precision full-field reconstruction with very few pressure sensors, enabling real-time sparse-sensing flow-field monitoring.