Traditional two-dimensional enhanced surfaces in cryogenic channels often face bottlenecks due to prohibitive pressure drop penalties under high-velocity conditions. To address this fundamental trade-off, a Triply Periodic Minimal Surfaces (TPMS)-based heat exchanger was investigated to achieve a more efficient and compact cryogenic system. Hybrid TPMS structures with varying mixing ratios were designed by extracting the optimal flow and heat transfer characteristics from pure Diamond, Gyroid, and Primitive structures. Accordingly, Computational Fluid Dynamics (CFD) simulation was conducted to evaluate the thermal-hydraulic performance of hybrid TPMS structures with cryogenic conditions. Numerical results reveal that a [Formula: see text] mixing ratio maximizes heat transfer enhancement, while it incurs a severe pressure drop penalty. Based on the integration of the volumetric heat transfer capacity [Formula: see text] and the Performance Evaluation Criterion (PEC), the Diamond-Gyroid type HX with a [Formula: see text] mixing ratio was identified as the optimal balance. This structure achieves a peak PEC of 1.733 and outperforms the pure Gyroid HX. Furthermore, the heat transfer enhancement analysis with the Field Synergy Principle (FSP) reveals that the hybrid topological transitions induce potent secondary flows and disrupt thermal boundary layers, effectively optimizing the synergy between the velocity and temperature gradient fields. This study substantiates the immense potential of hybrid TPMS structures, offering critical guidance for the design and optimization of heat exchangers operating under cryogenic conditions.HighlightsCryogenic convective heat transfer in hybrid TPMS is numerically uncovered.Proposed hybrid Diamond-Gyroid type HXs enhance comprehensive performance.The [Formula: see text] hybrid structure cuts pressure drop by [Formula: see text] with a peak PEC of 1.733.Topological transitions induce secondary flows to disrupt thermal boundary layers.
Based on rainfall data and high-precision topographic data, a hydrological-hydrodynamic model was employed to conduct numerical reconstruction of the flash flood disaster process that occurred in Zanhuang County, Hebei Province, on July 29, 2023. The main river channel in the study area is approximately 30 km long, with a total drop of 1,000 m, slope changing from 12% to 1%, and 21 tributaries. This configuration fully captures the complete variation process in mountainous rivers: from steep to gentle slopes, from small to large peak discharges, from shallow to deep water depths, and from high to low Froude numbers. Combined with post-disaster field investigations, the rapid transition patterns of riverbed scour and deposition in the river reaches were revealed, and the disaster characteristics of flash floods in each reach were summarized. Based on the research findings, the following recommendations are proposed: (1) To improve the reliability of critical rainfall and water level thresholds for flash flood early warning, water level monitoring points in flash flood gullies should be installed near structures and locations with minimal scour-deposition changes, such as check dams, low weirs, and bedrock outcrops in erosional reaches. (2) Due to the difficulty of hydrodynamic modelling in simulating the rapid transition of riverbed scour and deposition, regular sediment removal is recommended for aggradation reaches; meanwhile, scour protection should be strengthened for embankments, bridges, and roadbeds in high-velocity reaches. (3) Under supercritical flow conditions, the backwater effect of downstream reaches on upstream flood routeing is relatively minor; therefore, for flood control planning purposes, high-precision topographic surveys need only focus on areas near disaster-bearing bodies.
The blade metal angles in multi-stage axial fans are key factors determining blade additional work and efficiency. Conventional blade matching designs typically rely on one-dimensional and two-dimensional methods, which are highly dependent on empirical models. Therefore, it is of considerable importance to conduct multi-blade-row metal angle matching optimization based on three-dimensional simulations. This paper adopts a surrogate-assisted multi-objective optimization framework, taking the last three blade rows of a three-stage axial fan as the optimization target. A three-dimensional blade parameterization tool is coupled with a multi-population differential evolution algorithm (MPDE) to reconstruct the original geometry with high fidelity by minimizing the Chamfer distance, and the inlet and outlet metal angles at three critical spanwise sections are selected as design variables. High-fidelity Reynolds-averaged Navier-Stokes (RANS) simulations are performed at 100% and 80% of the design rotational speed to generate the training dataset. An artificial neural network surrogate model is trained using five-fold cross-validation for hyperparameter tuning, and the non-dominated sorting genetic algorithm II (NSGA-II) is subsequently employed to search for the Pareto-optimal front. The optimization results show that the overall isentropic efficiency of the three-stage fan is improved by 0.61% at the design rotational speed and 0.81% at 80% design rotational speed, respectively. These results validate the feasibility and effectiveness of multi-objective aerodynamic optimization based on three-dimensional simulations of multistage fan blades.
Accurate prediction of the aerodynamic loads on high-speed trains (HSTs) under crosswind conditions is essential for reliable safety assessment. However, the empirical closure coefficients embedded in Reynolds-averaged Navier-Stokes (RANS) turbulence models introduce parametric uncertainty that may propagate substantially into aerodynamic predictions. This study presents a controlled comparative analysis of closure-coefficient uncertainty in the Standard k-ϵ and Shear Stress Transport (SST) k-ω models under an identical numerical framework. Latin hypercube sampling and non-intrusive polynomial chaos are employed for uncertainty propagation, together with total-order Sobol indices and correlation analysis to characterise global and spatial parameter sensitivities. The results show that the lateral force coefficient (Cy) is particularly sensitive to closure-coefficient variations, with UQ2 values of 43.95% for the Standard k-ϵ model and 50.78% for the SST k-ω model. Although the SST k-ω model exhibits more localised pressure uncertainty in several separated-flow regions, it does not consistently yield lower uncertainty in the integrated aerodynamic loads. The two models also display fundamentally different sensitivity patterns. The Standard k-ϵ model is strongly dominated by [Formula: see text], which contributes a total-order Sobol index of 0.9835 to Cy, whereas the SST k-ω model exhibits distributed multi-parameter sensitivity, with [Formula: see text] providing the largest contribution of 0.5100. These parameters are also strongly correlated with Cy, with Pearson correlation coefficients of −0.7768 and −0.7089, respectively. The results demonstrate that turbulence-model calibration for HST crosswind aerodynamics should account for model-dependent parameter sensitivity and provide a quantitative basis for uncertainty-aware model selection and calibration.
Transient wave-based techniques for leak detection have garnered significant attention due to their high efficiency and accuracy. However, they are yet to be effectively applied to practical pipe systems, primarily due to their inability to adequately handle the various complexities and uncertainties encountered in real-world applications. Among these challenges is the presence of noise within the pipe system and measurement devices, where recent studies indicate significant portion of this noise largely follows a [Formula: see text]-stable distribution model. To address this practical challenge, the study introduces a robust Bayesian framework for transient-based leak detection that overcomes a critical limitation arising from the reliance on Gaussian noise assumptions; while traditional methods often fail when faced with impulsive, heavy-tailed noise characteristics, this research explicitly incorporates an [Formula: see text]-stable distribution to model non-Gaussian ambient and measurement noise in the pipe system, and simultaneously quantify leak parameters, including key water-pipe uncertainties, such as wave speed and friction factor. Using the transient wave-based method as a forward approach, an inverse Bayesian Markov Chain Monte Carlo approach is formulated, specifically with the Metropolis-Hastings algorithm, to process observed signals contaminated by [Formula: see text]-stable noise, and estimate the posterior probability densities of the unknown parameters; while eliminating the need for signal pre-processing or filtering. Despite the challenges posed by noise, the proposed algorithm successfully estimates (1) leak location and magnitude for small leaks under different[Formula: see text] values, even for signal-to-noise-ratios as low as [Formula: see text]; and (2) uncertainties in both water-pipe wave speed and friction factor. Numerical tests are performed in scenarios involving single and multiple leaks; finally, the proposed approach is validated by recent laboratory experiments, where unfiltered signals are processed within symmetric and asymmetric [Formula: see text]-stable likelihood frameworks, through which unknown leaks and pipe parameters are successfully estimated, leading to conclusions based on the key findings and their implications.
