Search Results (5)

Accurate prediction of hydrodynamic forces on confined oscillating structures is essential in applications related to nuclear engineering, energy systems, offshore devices, and mechanical components subjected to flow-induced vibrations. In this work, two computational fluid dynamics (CFD) methodologies implemented in ANSYS CFX are compared to determine the added-mass coefficients for a square cross-section cylinder confined within a square container: a deforming-mesh method (DMM) and an immersed-boundary method (IBM). Unlike previous studies restricted either to concentric square cylinders or to eccentric configurations treated with potential flow, the present study addresses eccentric confined configurations by solving the incompressible Navier–Stokes equations and focuses primarily on the prediction of added mass under strong confinement. Horizontal, vertical, and combined eccentric displacements are analyzed in detail. Mesh-independence, domain-size sensitivity, and temporal-convergence analyses are performed. Results show that both methods provide closely matching added-mass predictions over a wide range of eccentricities, with relative differences typically below 1% for moderate eccentricities, although discrepancies increase under extreme confinement. Relative to the concentric configuration, the added-mass coefficient increases by about 44% for the most eccentric vertical case and by about 87% for the most eccentric corner-approach case. Force decomposition and pressure-field analysis show that this increase is governed primarily by pressure-induced inertial effects, whereas viscous shear plays a secondary role under the conditions considered. From a practical standpoint, the immersed-boundary method reduced the computational time by approximately 92% in the most demanding case. Full article

The hot deformation behavior of 4Cr16MoCu martensitic stainless steel alloyed with 1% Cu was investigated through hot compression tests at temperatures ranging from 900 to 1150 °C and strain rates of 0.001 to 1 s⁻¹. The addition of Cu is strategically employed to synergistically enhance precipitation hardening and corrosion resistance, yet its complex interplay with hot deformation mechanisms remains poorly understood, demanding systematic investigation. The results revealed a narrow forging temperature range and significant strain rate sensitivity, with deformation resistance increasing markedly at higher strain rates. An Arrhenius constitutive model incorporating a seventh-degree polynomial for strain compensation was developed to describe the flow stress dependence on deformation temperature and strain rate. The model demonstrated high accuracy, with a correlation coefficient (R²) of 0.9917 and an average absolute relative error (AARE) of 3.8%, providing a reliable theoretical foundation for practical production applications. Furthermore, a hot processing map was constructed based on the dynamic material model (DMM), and the optimal hot working parameters were determined through microstructural analysis: an initial forging temperature of 1125 °C, a final forging temperature of 980 °C, and a strain rate of 0.1 s⁻¹. These conditions resulted in a fine and uniform grain structure, while strain rates above 0.18 s⁻¹ were identified as unfavorable due to the risk of uneven deformation. Full article

High-temperature compression tests on Mg-11.5Li-2.5Zn-0.35Al-0.3Y (in wt.%) were carried out on a Gleeble-3500 thermal simulator. Flow stress and microstructural evolution were analyzed at different temperatures (T = 473 K, 523 K, 573 K, and 623 K) and strain rates ($\dot{\epsilon }$ = 1 s⁻¹, 0.1 s⁻¹, 0.01 s⁻¹, and 0.001 s⁻¹). On this basis, the constitutive model of the alloy was established using the Arrhenius-type constitutive model, and the thermal processing map of the alloy was drawn based on the DMM (dynamic material modeling) theory. The experimental results show that the flow stress of the Mg-11.5Li-2.5Zn-0.35Al-0.3Y alloy decreases with an increase in temperature and a decrease in strain rate. The grain size increases uniformly with the increase in temperature, while a sudden increase occurs with the decrease in strain rate. The predicted value of the model is compared with the experimental value to verify the correctness of the model, and the correlation coefficient, R = 0.9690, was calculated, which further proves the applicability of the model to the Mg-11.5Li-2.5Zn-0.35Al-0.3Y alloy. This alloy can be safely plastic-deformed 473 K~623 K and 0.001 s⁻¹~1 s⁻¹. Full article

AlCoCrFeNiTi_0.5 high-entropy alloy (HEA) shows excellent properties in hardness and corrosion resistance. AlCoCrFeNiTi_0.5 HEA was prepared using a non-consumable vacuum arc furnace. Hot-deformation behavior of AlCoCrFeNiTi_0.5 HEA was explored under 1073–1373 K with a strain rate between 0.001 and 1 s⁻¹ using a Gleeble-3800 thermomechanical simulator. The constitutive equation was established using the Arrhenius model, and the deformation activation energy and material constant were obtained. The processing map of HEA within 0.3–0.6 deformation was drawn according to dynamic material model (DMM). The results show that the hot-deformation process of HEA is dominated by work hardening combined with dynamic recovery, and dynamic recrystallization. The flow stress of HEA is significantly affected by deformation temperature and strain rate. The constitutive equation was constructed and verified, and the correlation coefficient of R² = 0.9873 indicated that the constitutive equation can be used to accurately predict the flow stress of HEA. The processing map of HEA shows that the optimal hot-working process parameters are in the range of temperature 1150–1300 K and strain rate 0.002–0.05 s⁻¹. This work will provide theoretical guidance for the hot-processing of HEA, which effectively promotes the application of the HEA in industry. Full article

Facial Action Units (AUs) correspond to the deformation/contraction of individual facial muscles or their combinations. As such, each AU affects just a small portion of the face, with deformations that are asymmetric in many cases. Generating and analyzing AUs in 3D is particularly relevant for the potential applications it can enable. In this paper, we propose a solution for 3D AU detection and synthesis by developing on a newly defined 3D Morphable Model (3DMM) of the face. Differently from most of the 3DMMs existing in the literature, which mainly model global variations of the face and show limitations in adapting to local and asymmetric deformations, the proposed solution is specifically devised to cope with such difficult morphings. During a training phase, the deformation coefficients are learned that enable the 3DMM to deform to 3D target scans showing neutral and facial expression of the same individual, thus decoupling expression from identity deformations. Then, such deformation coefficients are used, on the one hand, to train an AU classifier, on the other, they can be applied to a 3D neutral scan to generate AU deformations in a subject-independent manner. The proposed approach for AU detection is validated on the Bosphorus dataset, reporting competitive results with respect to the state-of-the-art, even in a challenging cross-dataset setting. We further show the learned coefficients are general enough to synthesize realistic 3D face instances with AUs activation. Full article