Institute Of Mechanics,Chinese Academy of Sciences
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Thermodynamic-kinetic relationship in Pd-based metallic glasses
No full textEstablishing a direct correlation between thermodynamic and kinetic behaviors in metallic glasses is of paramount importance, yet it remains an unresolved challenge in the field. Here, we conduct a comprehensive investigation on Pd-based metallic glasses, integrating dynamic mechanical analysis and differential scanning calorimetry to probe the interplay between mechanical relaxation and thermodynamic properties. Our results demonstrate that the temperature-dependent evolution of excess entropy remarkably parallels the kinetic spectrum, providing compelling evidence for a strong thermodynamic-kinetic relationship. Notably, we quantitatively explore the relationship between stress relaxation kinetics and excess entropy. This work provides new insights into the intrinsic coupling between thermodynamic disorder and mechanical relaxation behaviors in metallic glasses, offering a novel framework for understanding glass transition dynamics
Creep strain and stress state-dependent creep asymmetry during early-stage room-temperature creep in a titanium alloy
No full textRoom-temperature (RT) creep may happen below yield stress in titanium alloys, while the creep asymmetry remains pending under various stress states. The creep behavior and plastic damage were investigated during the early-stage RT-creep up to 60 h in a titanium alloy. The investigated TC4 ELI Ti-alloy is a high-purity ("Extra-Low-Interstitial") version of Ti-6Al-4V with a near- alpha type microstructure after thermomechanical treatment. Three kinds of creep testing were conducted, including axial tension, compression, and torsion, respectively. The microstructure and especially, dislocation behaviors were analyzed in detail by using electron backscattered diffraction, transmission electron microscopy, and X-ray diffraction after an interrupted and terminated creep testing. The creep strain differs, which is 5 %, 1 %, and 0.5 % under tension, compression, and torsion, respectively. It undoubtedly indicates the presence of creep asymmetry. To clarify the creep mechanism, the slip system was then analyzed. It is shown that the prismatic slip is dominant during tensile creep, while the pyramidal slip appears most during compressive creep. The limited slip transmission and immobile dislocations result in lower creep strain. The reason behind the creep asymmetry is attributed to the stress state, producing the activation of various slip systems, along with the evolution of true stress. Finally, the mechanistic origins are also discussed as to the distinctive creep rate under various stress states. (c) 2025 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology
Development of a two-domain-approach-based multi-scale model for the two-phase flows in space accumulators in microgravity
No full textManagement of cryogenic fluid is critical for space accumulators in both loop heat pipe (LHP) and mechanically pumped two-phase loop (MPTL) system. To achieve proper fluid transport in the extreme environments in space, some complex structures are used in these apparatuses including porous meshes and porous vanes. The coexistence of free flow regions and porous medium regions results in a common cross-scale two-phase flow in the multi-scale structures. However, there is a lack of reliable mathematical methods to describe such flows, and thus the flow dynamics in space accumulators are hard to analyze. To solve this problem, we build a coupled multiscale two-phase flow mathematical model based on the two-domain approach: on Onsager's variational principle, minimizing the energy dissipation of the system to derive fluid dynamic equations and interface evolution equations. Navier-Stokes equations for the free flow region and Darcy equation for the porous medium region are derived separately. To account for capillary-driven flow in microgravity, the Darcy equation is modified by explicitly including the capillary force. The boundary conditions that couple fluid dynamic equations and the interface capture methods are incorporated into the model. After validation, the model is applied to analyze transient two-phase flow behavior inside two typical space accumulators in microgravity: one for LHP and the other for MPTL. The flow characteristics are demonstrated, and different porous structures are compared for geometric optimization purposes. The results show that the primary wick of the LHP accumulator with a small pore radius (rc1 = 20 mu m) generates substantial capillary pressure (-170 Pa) to maintain fluid circulation, and the secondary wick with a large pore radius (rc2 = 50 mu m) enables efficient liquid delivery. In the accumulator of MPTL system, the implementation of porous mesh with a large pore radius (rc = 80 mu m) significantly enhances the liquid replenishment rate (i.e., 0.017 m/s)
Data-driven enhanced rough contact mechanics: PINN estimation of gap distribution across length scales for partial contacts
No full textIn this study, we employ Green's function molecular dynamics (GFMD) to simulate non-adhesive elastic contact between a half-space and a randomly rough counterface in (1+1) dimensions, obtaining gap distributions across varying length scales and Hurst exponents. Using the GFMD-generated dataset and incorporating the convection-diffusion equation form (derived in prior and current work) as a physical constraint, we predict gap distributions via Physics-Informed Neural Network (PINN). Results demonstrate that under partial contact conditions-where analytical solutions are unavailable-PINN predictions assuming drift and diffusion coefficients scale with length exhibit high agreement with GFMD. Furthermore, PINN successfully predicts gap distributions and relative contact areas at larger scales using small-scale training data, closely matching GFMD benchmarks. This establishes PINN as an effective tool for rough surface contact problems, particularly when analytical solutions are absent or computational models are prohibitively expensive
Machine learning analysis for condensation flow heat transfer in mini/ micro-channels
No full textMiniature condensers have emerged as an efficient solution for thermal management of compact high-power devices due to their exceptional heat dissipation capability. However, accurate prediction of heat transfer coefficient(HTC) remains challenging due to complex flow and thermal behaviors in two-phase heat transfer. This study employed explainable machine learning to develop condensation HTC prediction models in mini/micro channels. A multidimensional feature database containing 4003 experimental data points across 19 fluids in hydraulic diameter 0.1mm <= D <= 4.8 mm was constructed. Four machine learning models, including Artificial Neural Network (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR), were developed utilizing the database to explore their potential in predicting condensation HTC. The models were validated through internal and external datasets, with comparison against six traditional correlations. The SHapley Additive exPlanations (SHAP) method was subsequently applied to explain the XGBoost prediction mechanism. Results demonstrate all machine learning models achieved satisfactory performance compared to traditional correlations, with XGBoost exhibiting optimal accuracy and generalization. It attained a coefficient of determination (R2) of 0.993 and a mean absolute relative deviation (MARD) of 3.6 % across the database, with strong generalization even for new fluid datas. SHAP explanation revealed Froude number and dimensionless vapor velocity were critical features, while the influence of features such as thermal conductivity and mass flux on the model's prediction aligned with the trend of physical laws and experimental results, effectively enhancing predictive rationality of "black-box" models. This work shows machine learning's significant potential for two-phase heat transfer prediction, providing an efficient predictive tool for mini/microchannel condenser design
Emulating scaled drag-free control dynamics on a ground simulator testbed
No full textAs a key technique for gravitational wave detection, drag-free control presents significant challenges for ground experimental validation. This work addresses the problem of experimentally emulating the scaled drag-free control on a ground simulator composed of an air-bearing testbed and two inverted pendulums. In this paper, the dynamics models are deduced and simplified. The dynamics similarity conditions are determined through the use of the Pi theorem. And the concepts of the equivalent stiffness and equivalent mass of the inverted pendulum are proposed to establish the similar dynamics equations. Subsequently, scaling laws are derived to design the simulator testbed. Basic scaling laws are introduced to evaluate the scaled control index. Besides, an underactuated closed-loop control strategy employing redundant adjustable thrusters is devised for the ground experiment. Finally, the precise tracking control of the air-bearing testbed relative to two pendulums is realized on the ground drag-free simulator, emulating the scaled displacement control mode with two test masses. And the scaled control indexes corresponding to space mission are validated in the resulting drag-free simulation system. The effectiveness of the proposed approach is confirmed by the scaling equivalence experiments of dragfree control with two test masses
Flow boiling heat transfer in enhanced tubes with herringbone and dimple structure
No full textThis paper experimentally investigates the flow boiling heat transfer characteristics of smooth tubes (ST) and advanced new enhanced tubes (HT and EHT) made of aluminum, using R32 refrigerant. The effects of the tubes on heat transfer coefficients (HTC) and frictional pressure drop during flow boiling were explored. The study examined the flow boiling heat transfer performance of the enhanced tubes and ST tubes under varying conditions of mass flux (100 similar to 350 kg/m(2)s), saturation temperatures (279 similar to 288 K), and refrigerant inlet vapor quality (0.1 similar to 0.9), and analyzed the impact of the enhancement structures on flow boiling heat transfer. The results are that the HTC increases with rising saturation temperature, mass flux, and average vapor quality, with the HT tube exhibiting the best HTC, followed by the EHT tube. The result is that the frictional pressure drop increases with mass flux and average vapor quality and decreases with saturation temperature. Among them, the EHT tube exhibits the highest frictional pressure drop, while the pressure drop of the HT tube is closer to that of the ST tube. Additionally, four flow boiling heat transfer correlations were predicted the HTC of the ST tube, and the correlation with the best prediction results was selected to predict the HTC for the HT and EHT tubes. The revised correlation can verify over 95 % of the data within a +/- 15 % range, establishing a new HTC correlation for enhanced tubes
Data-driven modeling of wind farm wake flow based on multi-scale feature recognition
No full textAccurate and efficient predictions of wind flow developments with wake effects accounted are crucial for wind farm layouts and power forecasting. Existing methods can be broadly classified as physical measurement, numerical simulations, physics-based modeling, and data-driven modeling. The first two is of high cost in terms of time and resources, the third suffers from low accuracy due to limited physics modeled, while the last one takes advantage of the large amount of high-quality data available and has become increasingly popular. This study proposes a rapid data-driven modeling method for wind farm wake flow, inspired by video frame interpolation and based on the principle of similarity, which utilizes a multi-scale feature recognition technique. The method transforms wind farm field data into images and predicts wake flow by identifying, matching, and interpolating features from a limited set of wake flow images using the Scale-Invariant Feature Transform (SIFT) and Dynamic Time Warping (DTW) approaches. To demonstrate the effectiveness of the proposed method, six representative cases were evaluated, encompassing mini wind farms with varying turbine spacings, different turbine sizes, combinations of spacing and size variations, different numbers of turbines, and various degrees of wind direction misalignment. A Mean Absolute Percentage Error (MAPE) ranging from 0.68% to 2.28% is achieved. Due to its ability to flexibly compute both 2D and 3D wake flow fields, the proposed method offers unique computational efficiency advantages over Large Eddy Simulation (LES) and Meteodyn WT in scenarios where two-dimensional wake flow fields are sufficient to meet industrial requirements. Therefore, this method can be employed for the extension of the wake flow database serving wind farm design, power prediction, etc., as an alternative to measurements, numerical simulation, and physics-based modeling, balancing efficiency and accuracy
Exceptional tensile properties induced by interlayer-compatible deformation in a gradient ultra-nanograined Cu
No full textIn this study, a gradient ultra-nanograined (GUNG) Cu was prepared by surface rolling and shearing processing at liquid nitrogen temperature. Microstructural analysis reveals a significant presence of ultrananograins (similar to 5-20 nm) within the topmost surface layer (SL), transitioning to coarser grains beneath, culminating in a gradient structure over 600 mu m deep. The GUNG Cu exhibits an exceptional strength-ductility synergy, achieving yield strengths of 250-330 MPa and uniform elongations of 17 %-30 %. The deformation mechanisms of GUNG Cu are elucidated through in-situ electron backscatter diffraction and microscopic digital image correlation, highlighting the interlayer-compatible deformation of GUNG Cu under tensile loading. It is noteworthy that the topmost ultra-nanograined SL (within depths of 0-2 mu m) in GUNG Cu maintains high mechanical stability with minimal change in grain size during tensile plastic deformation, whereas the subsurface layer (at a depth of similar to 15 mu m) displays a deformation-driven grain coarsening behavior, facilitating deformation compatibility across individual layers. The enhanced strength-ductility synergy exhibited in GUNG Cu can be attributed to the interplay between interlayer compatible deformation and hetero-deformation induced (HDI) hardening, in which softer and harder layers interact with each other, thus promoting the strain hardening throughout the GUNG structure. The present findings provide a more profound understanding of deformation compatibility and HDI hardening mechanisms in gradient structures, demonstrating how tailored microstructural heterogeneity can potentially circumvent the traditional strength-ductility trade-off in nanostructured materials. (c) 2025 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology
An MPM-FDM coupled method for landslide analysis considering surface-subsurface conjugated water flow
No full textThe material point method (MPM) can effectively simulate large deformation problems involving hydromechanical coupling, such as rainfall-induced landslides. Current MPM formulations simulate rainfall boundaries by applying either pore water pressure or velocity boundaries directly. This method does not incorporate the effects of surface water ponding and runoff during heavy rainfall. To address this problem, this study proposes a coupled method that integrates the MPM with the finite difference method (FDM) for hydro-mechanical analysis. Underground water flow is modelled using a two-phase, two-point MPM with the Richards equation, while surface water flow is computed by FDM based on shallow water equations. The two models are coupled: the FDM provides the surface water flow velocity and pore water pressure for subsurface flow simulation in the MPM, while the MPM supplies the surface infiltration rate for surface water flow simulation in the FDM. The new method was validated against existing numerical simulations and centrifuge tests. It was found that the new method can effectively capture the interactions between surface and subsurface flows, as well as the shallow landslide involving surface erosion or washout, which existing MPM codes cannot simulate. Parametric studies further reveal that neglecting the coupling effects of surface-subsurface flow predicts deeper sliding surfaces and longer rainfall durations to failure due to the ignorance of surface ponding and positive pore water pressure at the ground surface. Considering surface water flow tends to shift the failure mode from "slide-to-flow" to "flowlike", especially when slope angle is larger and soil permeability is lower