The churning losses in a gearbox consume significant amounts of energy and reduce the effective life of mechanical equipment. The present work is aimed to investigate the splash lubrication of graphene-gear oil nanolubricants in a spur gearbox to meet the need of net zero emission and minimizing mechanical losses in automotive transmission. Graphene-gear oil nanolubricants were formulated and tested for thermophysical properties. A heavy-duty gearbox was selected to develop a computational fluid dynamics (CFD) model for evaluating churning power losses and investigating sloshing effect with graphene-gear oil nanolubricants. Fluent software was used to simulate a splash flow of lubricant confined in a 2D gearbox geometry using dynamic meshing and volume of fluid (VOF) method. The numerical simulation was performed for 0.3%, 0.6%, and 0.9% volume fraction of nanolubricants carried out at an operating temperature range of 20°C-40°C. The presence of graphene nanoparticles in gear oil reduced churning power losses, and the least churning power loss was observed for 0.3% volume fraction. The numerical solutions from the CFD model showed a good consistency with theoretical results. A novel semi-empirical model was also proposed to correlate churning losses with Reynolds number and rotational speed using the Buckingham π theorem.
In this research, the hydrothermal and irreversibility behaviors of forced convection flow of water-copper (H2O-Cu) nanofluid under the influence of an oriented magnetic field in a chamber with diagonal walls are investigated. To reach this aim, the streamlines and temperature contours and also distributions of friction coefficient, Nusslet number, and entropy production number (Ns) are assessed against the different magnitudes of Cu nanoparticle concentrations and the intensity and orientation angle of the magnetic field. It should be noted that the magnitudes of NS are computed according to the second law of thermodynamics. It could be found that the Hartmann number and orientation angle of the magnetic field change both the trend and the values of the hydrodynamic, thermal, and irreversibility attributes, while the concentration of Cu nanoparticles only affects the magnitudes of those. However, the results of this paper may be beneficial in the design of many engineering and thermal equipment.
Graphene can be a promising material for flexible body armor owing to its exceptional in-plane strength and out-of-plane flexibility. However, finite element (FE) simulations of impact-induced wave propagation often deviate from molecular dynamics (MD) predictions: conventional shell assumptions yield higher cone and transverse wave velocities and underestimate out-of-plane flexibility due to overestimated bending stiffness. To address this, membrane and bending stiffness were independently derived from MD-based tensile and bending tests and incorporated into the FE shell model. The improved model accurately reproduces MD results for wave propagation velocities (cone, transverse, and axial) and displacement responses (in-plane and out-of-plane) under hypervelocity projectile impact. Parametric analysis reveals that cone wave propagation is highly sensitive to projectile velocity and mass, while target size and temperature play a minor role. The initial stage of cone propagation is dominated by momentum transfer, whereas the late-stage behavior is influenced by dissipation and wave reflections. Overall, the study demonstrates that MD-derived stiffness values, particularly bending, substantially enhance the predictive capability of FE simulations. These insights can further support perforation and failure analysis under extreme impact conditions.
Faults in aero-engine combustors involve the coupling of multiphysics fields across multiple scales, highlighting the necessity of multiscale modeling and analysis. The traditional diagnosis method based on a physical model may fail to fully characterize the dynamic characteristics, resulting in a high false positive rate. This paper constructs a fault prediction and diagnosis system that cooperates with a digital twin and spatial-temporal graph convolutional network (ST-GCN) to achieve high-precision real-time diagnosis. A three-dimensional digital twin model integrating combustion flow field, temperature field, and pressure field is constructed, and dynamic multiscale feature extraction is performed in combination with an ST-GCN. The Kalman filter is used to realize the coordinated optimization of the physical model and the data-driven model. Experiments show that the system has a false negative rate (FNR) of 0.159 for diagnosing uneven airflow distribution faults; the diagnosis delay for six types of faults is 8.02-11.26 ms; the prediction accuracy is above 93.22% in a high-noise environment with a signal-to-noise ratio (SNR) of 10 dB. The system effectively solves the problem of precise diagnosis of multiphysics field coupling faults and provides a new technical architecture for aero-engine management.
The aim of this work is to solve the problems of untimely acquisition of local meteorological elements, insufficient adaptability of forecasting systems in carbon-related applications, and inefficient meteorological data transmission in the context of carbon neutrality. This paper constructs a meteorological guarantee technology path based on multiscale observation integration for carbon neutrality scenarios, covering three aspects: Firstly, a multiscale meteorological field reconstruction mechanism that integrates global satellite coverage with localized ground measurements through improved Kalman filtering and terrain-guided interpolation to improve the spatiotemporal resolution of meteorological elements. Secondly, a customized weather research and forecasting regional model is embedded with a greenhouse gas diffusion module and an integrated circular assimilation system to enhance the ability to predict CO2 emission behavior. Thirdly, a lightweight distributed transmission mechanism is designed based on the message queuing telemetry transport protocol to achieve quasi-real-time data aggregation from edge collection, compression, and push to the center. The experimental results in this paper show that the proposed high-precision meteorological support and data transmission technology can reduce the wind speed error to 1.9 m/s, the wind direction error to 15.6, and the boundary layer height error to 78.9 m in meteorological forecasting; the data transmission delay is 112.3 ms under a load of 15 & times; 10(2) messages/minute, which improves the accuracy and reliability of meteorological services in carbon neutral scenarios.
Amid increasingly severe global climate change, corporate social responsibility (CSR) is crucial for enabling low-carbon decision-making within supply chains and achieving dual-carbon goals. Existing research on CSR often overlooks scenarios where retailers act as the primary agents of carbon emission reduction. Furthermore, there is a paucity of studies integrating evolutionary game theory into pricing models to investigate CSR issues. To address these gaps, this paper constructs a game-theoretic model combining Stackelberg and evolutionary games to explore the dynamic decision-making problem of supply chain enterprises undertaking CSR under retailer-led emission reduction. The findings reveal that: (1) When both manufacturers and retailers undertake CSR, retail and wholesale prices reach their lowest levels, while carbon emission reduction efforts, total market demand, manufacturer utility, retailer utility, and total supply chain utility are maximized. (2) When a firm undertakes CSR, the level of carbon emission reduction, total market demand, and the profits of the manufacturer, retailer, and entire supply chain all increase with the firm's CSR commitment level and the ratio of the potential market size of low-carbon consumers to that of ordinary consumers. (3) The evolutionary stable strategy (ESS) for both manufacturers and retailers is to undertake CSR. Additionally, the initial proportion of firms undertaking CSR, the low-carbon preference intensity of low-carbon consumers, and an increase in the ratio of low-carbon consumers' potential scale to that of ordinary consumers accelerate the adoption of CSR by supply chain members. This research provides valuable insights for supply chain enterprises formulating CSR-related strategic decisions and accelerating carbon emission reduction.
The importance of efficient control of the delivery chain substances is that it faces numerous challenges that at once affect expenses, the extent of satisfaction skilled via clients, and the general performance of the enterprise. Therefore, premiere supply chain fabric management is hard to attain in modern-day complex commercial enterprise contexts due to unpredictable price calls, useful resource optimization, and ever-changing marketplace conditions. A suggested intelligent optimization management system (IOMS) primarily based on a cloud computing (CC) (IOMS-CC) platform allows decision-making models to optimize cost calls for useful resource optimization and ever-changing marketplace situations. The IOMS-CC system manages supply chain materials successfully by decreasing lead instances, optimizing aid allocation, and minimizing inventory-keeping charges through selection-making models. Therefore, supply chain control agencies can quickly reply to changing markets and client desires for the usage of IOMS-CC to suit the requirements of numerous delivery chain conditions. According to the effects of the simulations, IOMS-CC improves the company's performance while concurrently optimizing price evaluation and improving patron pleasure. The advised system is proven through massive simulations and overall performance assessments, proving its superiority over traditional methods. IOMS-CC facilitates firms to stay bendy, resilient, and aggressive in trendy ever-converting markets.
Kidney stones are calcified mineral deposits in the kidneys that can produce acute pain, blood in urine, and longterm kidney impairment if not detected early. Detecting these stones accurately from ultrasound images is often challenging, as conventional methods struggle with noise, overlapping structures, and variations in stone appearance. To address these challenges, a ShuffleV2 Residual Network (ShV2R-Net) is explored in this work for automated kidney stone detection from ultrasound images. Here, an ultrasound kidney image is composed and passed to image denoising for eliminating unwanted noise using anisotropic diffusion. Then, kidney region segmentation using Multi-Convolutional Channel Residual Spatial Attention U-Network with focal loss and trained based on a Giant Trevally Optimizer (MCRSAU-Net_Fl-GTO) isolates the kidney from surrounding structures in the image. Besides, features, like gray-level co-occurrence matrix (GLCM) texture features, discrete wavelet transform, and shape features are extracted. Finally, kidney stones are detected using ShV2R-Net, which is developed by fusing Shuffle Network Version 2 (ShuffleNet V2), deep residual network, and harmonic analysis. Moreover, ShV2R-Net acquired a maximum true positive rate of 95.654%, accuracy of 93.765%, and true negative rate of 91.876% with K-fold 8. The proposed approach offers a practical and efficient tool for clinicians, enabling early diagnosis, reducing diagnostic errors, and supporting timely treatment planning for patients with kidney stones.
The present work extends recently proposed physics-based refinement criteria to dynamic crack propagation problems and discusses the implementation of the same in an open-source finite element package, FEniCS in both two and three dimensions. The efficacy and the robustness (in terms of the degrees offreedom) of the implementation is demonstrated against uniform refinement and other adaptive methods proposed in the literature. In addition to the standard benchmark problems, we also discuss the effect of Poisson's ratio and Young's modulus mismatch on the crack path in a glass composite. From this study, it can been seen that the proposed approach requires a fewer number of elements when compared to uniform refinement to yield comparable results. The current implementation provides a starting point to an efficient framework to fracture problems for practical engineering with less experience with coding skills.
The discrete element method (DEM) is especially suitable for discrete materials such as soil or rock because of its excellent accuracy, and the finite difference method (FDM) is often applied to the analysis of geotechnical engineering because of its high computational efficiency. The discrete-continuous coupled method integrates the advantages of FDM and DEM and has been applied in the stability analysis of tunnel surrounding rock. However, the calibration of macroscopic and microscopic parameters of tunnel surrounding rock in this new numerical method has not been systematically studied. Meanwhile, the method for determining the discrete domain range in the coupled model of tunnel has not been fully addressed. Both issues require rigorous examination to establish a standardized method for the discrete-continuous coupled numerical model of tunnels. Therefore, the calibration method and size effect of rock mechanics parameters were first studied through direct shear tests and uniaxial compression tests using DEM. Subsequently, a discrete-continuous coupled model of tunnel was established, and the accuracy of the model was verified through comparative validation. Finally, the method for determining the optimal range of the discrete domain in the coupled model for tunnel was discussed. The results indicate that there is no direct correspondence between the macro and micro parameters of the surrounding rock, and they need to be calibrated through mechanical tests such as direct shear and compression tests that consider size effects. The criteria for stress continuity and displacement continuity were proposed, which can be used to prove the accuracy of the coupled model for tunnel. As the discrete domain expands, the displacement gradually decreases due to the reduction of boundary effects and converges to the correct value. However, the number of particles and relative computation time gradually increase, resulting in a decrement in computational efficiency. Considering the accuracy and efficiency of the calculation, it is recommended that the optimal discrete domain be 4R x 4R.
A factor is introduced into the Poiseuille flow term in Zhang's multiscale flow equation to account for the Stokes surface roughness effect in multiscale hydrodynamic lubrication with very low film thicknesses. For the mean hydrodynamic film thickness far greater than the surface roughness or for the wavelength of the rough surface far greater than the mean hydrodynamic film thickness, this factor approaches unity and the lubrication equation becomes the Reynoldstype equation. Otherwise, the value of this factor is less than unity but positive, and the lubrication equation becomes the equivalent Reynolds-type equation. The interpretation is that the liquid lubricant vortex flow caused by the Stokes surface roughness reduces the magnitude of the Poiseuille flow rate of the lubricant through the lubricated contact. The modifying factor is regressed out as a function of the magnitude and the wavelength of the surface roughness. Its value is reduced (below unity) with the increase of the surface roughness or with the reduction of the wavelength of the rough surface. The results show that the carried load of the hydrodynamic lubricated thrust bearing with very low clearance calculated by this modified lubrication equation is greater than that calculated by considering the Reynolds surface roughness. It qualitatively agrees with the classic calculation results of the Stokes surface roughness effect in continuum lubrication. The study may form the framework of the efficient simulation of the Stokes surface roughness effect in multiscale hydrodynamic lubrication or mixed lubrication with low film thicknesses where both the effects of the adsorbed layer and the surface roughness are involved.
In a time-harmonic setting, coupling of a high-dimensional [HighD-two-dimensional (2D) or three-dimensional (3D)] sub-model and a low-dimensional (LowD, 1D) sub-model (reduced from the former when circumstances allow such a reduction) is considered, forming a hybrid mixed-dimensional model. In particular, the coupling of 2D or 3D elasticity with a Bernoulli-Euler beam is under investigation for time-harmonic bending problems. A finite element scheme is applied, where the HighD part is modeled via standard isoparametric elements, and the LowD part is provided with C1 continuity by using Hermite-cubic elements. The paper proposes special procedures performed at the discrete level to couple the two sub-domains. Continuity of the displacements and bending rotations is partially enforced strongly and partially weakly. The performance of the method is illustrated by several numerical examples.
This paper introduces an advanced Computational Analytical Micromechanics (CAM) framework for linear thermoelastic composites (CMs) with periodic microstructures. The approach is based on an exact new Additive General Integral Equation (AGIE), formulated for compactly supported loading conditions, such as body forces and localized thermal effects (for example laser heating). In addition, new general integral equations (GIEs) are established for arbitrary mechanical and thermal loading. A unified iterative scheme is developed for solving the static AGIEs, where the compact support of loading serves as a new fundamental training parameter. At the core of the methodology lies a generalized Representative Volume Element (RVE) concept that extends Hill classical definition of the RVE. Unlike conventional RVEs, this generalized RVE is not fixed geometrically but emerges naturally from the characteristic scale of localized loading, thereby reducing the analysis of an infinite periodic medium to a finite, data-driven domain. This formulation automatically filters out nonrepresentative subsets of effective parameters while eliminating boundary effects, edge artifacts, and finite-size sample dependencies. Furthermore, the AGIE-based CAM framework integrates seamlessly with machine learning (ML) and neural network (NN) architectures, supporting the development of accurate, physics-informed surrogate nonlocal operators.
