Mechanic of Advanced and Smart Materials

Abstract Ultrasonic levitation, as an advanced non-contact particle manipulation technology, has gained prominence in modern research due to its independence from material physical properties and extensive applications in fields such as pharmaceuticals, microelectronics, and sonochemistry. However, optimal utilization of this technology requires a deep insight into parameters affecting particle stability and dynamics. In this study, the impact of initial particle release position—a less-explored key factor—on the dynamic behavior of particles levitated in an ultrasonic levitation system was investigated. Acoustic pressure at 20 kHz was modeled using Multiphysics simulation in COMSOL software, and the behavior of 20 polypropylene particles (diameter: 3 mm, density: 910 kg/m³) at various initial positions (ranging from 0.23 to 8.01) was analyzed. Drag forces, acoustic pressure forces, and gravitational forces were considered effective forces. Results revealed that particles released near pressure nodes exhibited the lowest oscillation amplitude and shortest stabilization time. As the initial release distance from pressure nodes increased, both oscillation amplitude and stabilization time increased. This factor’s influence on stabilization time was more pronounced near the reflector than near the transducer, indicating that particles released close to the reflector achieve a more stable levitated state compared to those released close to the transducer. Experimental validation showed significant agreement with simulation results.

Abstract This paper presents the dynamic modeling of a robotic manipulator capable of fluid conveyance using the Gibbs-Appell formulation. The primary objective is to derive efficient recursive equations for systems with non-material control volumes, where constant-velocity fluid flow induces complex inertial and Coriolis forces. First, the kinematic relations are derived using a material approach and the Denavit-Hartenberg convention. Subsequently, the equations of motion are formulated by calculating the Gibbs function (acceleration energy) for the rigid links, the conveyed fluid, and the concentrated masses of the actuators. This approach significantly reduces computational complexity, providing an ideal framework for real-time control. To validate the model, a 5-DOF manipulator was simulated under a Computed Torque Control scheme in two scenarios: "No-Fluid" and "Fluid-Conveying." The results demonstrate that fluid flow increases joint torques by up to 18% and alters transient responses. The proposed model successfully maintained the tracking error within 15 mrad despite hydrodynamic disturbances, proving its high fidelity in analyzing fluid-structure interaction.

Abstract Nanoparticles can enhance the thermophysical properties of base fluids, leading to increased efficiency, especially in heat transfer applications. Therefore, achieving optimized thermophysical properties of nanofluids is of particular importance. In this study, two multilayer feedforward artificial neural networks (ANN) were designed and trained to predict the relative viscosity and thermal conductivity ratio of a water-based hybrid nanofluid MWCNT-Y2O3 (with a nanoparticle weight ratio of 80:20). The nanofluid samples studied contained varying volume concentrations of MWCNT-Y2O3 nanoparticles (from 0.01 to 0.2 percent) in the base fluid. Experimental data for relative viscosity and thermal conductivity ratio at different temperatures (from 25°C to 60°C) were available. For each ANN designed to estimate either the relative viscosity or the thermal conductivity ratio outputs, regression plots corresponding to the training, validation, and testing data sets demonstrated the networks' excellent performance. The mean and maximum relative percentage errors obtained for the testing data were as follows: for relative viscosity output, 0.5120% mean error and 2.5450% maximum error; for thermal conductivity ratio output, 0.1733% mean error and 0.2874% maximum error. Moreover, based on the developed model, a multi-objective optimization problem was formulated to simultaneously determine the minimum relative viscosity and maximum thermal conductivity ratio of the nanofluid. This problem was solved using the multi-objective particle swarm optimization (MOPSO) metaheuristic method. Consequently, the optimal objective function values and input parameters were obtained, and the Pareto optimal points were graphically illustrated.

Abstract The challenge of designing and implementing optimal data fusion methods that are both robust to uncertainties and simple enough for practical deployment has become a significant topic of interest in a wide range of navigation and positioning systems. In this study, inspired by the principles of Proportional-Integral-Derivative (PID) control theory and integrating them with the conventional structure of the standard Kalman filter, we propose a novel data fusion approach. This method is specifically designed to improve robustness against measurement uncertainties from the Doppler Velocity Log (DVL) sensor in an integrated marine navigation system based on INS/DVL. The proposed approach aims to enhance the system’s resilience without introducing excessive computational complexity. Simulation results demonstrate that the integrated navigation system using the proposed algorithm outperforms traditional Kalman filter-based systems in terms of accuracy and response time, particularly under conditions involving sensor errors or uncertainty. These findings highlight the potential of the method for real-world applications in marine navigation scenarios.

Abstract In this research, the primary resonance of a fractional viscoelastic microtube reinforced with graphene nanoplatelets conveying fluid and resting on a nonlinear viscoelastic foundation has been investigated. The main objective of this study is to analyze the effects of the graphene nanoplatelets weight fraction, the coefficients of the nonlinear fractional viscoelastic model, and damping on the nonlinear vibration behavior of the cantilever fluid-conveying microtube system.

Using micromechanics theory, the modified couple stress theory, and mathematical modeling of the mechanical properties of graphene nanoplatelets and nonlinear fractional viscoelasticity within the framework of Euler-Bernoulli beam theory, the governing equations of motion for the fluid-conveying microtube were derived. The equations were discretized using the Galerkin method and subsequently solved by the multiple scales method. The results showed that an increase in the graphene nanoplatelets weight fraction parameter leads to a decrease in the nonlinear stiffness of the system. Furthermore, the response amplitude of the microtube undergoes a very significant reduction with an increase in the fractional viscoelastic derivative parameter. The findings of this investigation can be applied in the design and development of fluid force microscopes, medical instruments, and drug delivery systems.

Abstract Measurement of cell stiffness, which results from changes in the cytoskeletal structure of cancer cells, can be used as a promising approach for the early detection of cancer. One of the methods for investigating the mechanical properties of the cell surface is the use of atomic force microscopy (AFM), which can be used to determine the stiffness of the cell membrane by indenting the cell. From a biomechanical point of view, cancer can be investigated by determining the Young’s modulus of cancer tissue. The determination and measurement of Young’s modulus in cancer cells can be performed either experimentally or using mathematical modeling approaches. In this study, Young’s modulus of cells is determined by analyzing force-indentation curves from experimental studies. For this purpose, force- indentation data measured with the AFM are used for two cell types: healthy and cancerous cervical and prostate cells. The mechanical contact models of Hertz, JKR and Tatara are then applied to the data to calculate the Young’s modulus. The results of these three models show that the Young’s modulus values obtained with the Hertz and Tatara models are very close to each other, while the value obtained with the JKR model is significantly lower. This discrepancy is attributed to the inclusion of adhesion forces in the JKR model. Based on the results of this study, the difference in Young’s modulus between cancerous and healthy cells could serve as a potential indicator for early cancer detection.

Abstract The aim of this project is to present an approach for developing a real-time hand gesture recognition which uses only a webcam and Computer Vision technology, such as image processing that can recognize several gestures for using in computer interface interaction. The most important goal of this project is to simulate playing a virtual piano using hand gesture recognition and recognizing specific gestures for each piano note. Implementing this virtual piano is done using a Personal Computer in MATLAB and also in Visual Studio C++ by OpenCV library environments and some comparisons is reported. The results show that implementing using OpenCV library is more fast and has higher performance than using MATLAB. Hand gesture distinguish accuracy is about 86.45% in MATLAB environment and about 92.7% using OpenCV library. Comparing the results based on consumed time to correctly distinguish a hand gesture is about 1.39 seconds in MATLAB environment and about 1.19 seconds using OpenCV library.

Abstract Hydraulic oils in mobile machinery face intense mechanical and thermal stresses, which accelerate their degradation. This study provides a side by side field comparison of how VG46 and VG68 hydraulic oils age in sugarcane harvesters working in the hot, dusty climate of Khuzestan, Iran. A continuous 1,000 hour field test was conducted, with oil samples collected every 100 hours in accordance with ISO standards. Key properties-including rheological characteristics, chemical stability, and particle contamination levels-were analyzed (following ISO and ASTM methods) to assess degradation under real operating conditions. The results show that kinematic viscosity at 40°C decreased by 23.2% for VG46 but only 12.1% for VG68, indicating that mechanical shear degradation dominated over thermal oxidation. The viscosity index dropped by 16% for VG46 versus 10% for VG68, confirming mechanical scission of polymer chains in the viscosity index improvers. Chemically, VG46 exhibited a higher oxidation rate, with a sharp increase in total acid number between 300 and 400 hours—a clear sign of premature antioxidant depletion. Regarding contamination, VG46 reached a final cleanliness code of 24/24/23 (NAS Class >12), whereas VG68 maintained a superior code of 23/22/21 (NAS Class 12). These findings suggest that VG68 offers better rheological and tribological stability under severe duty cycles. Therefore, using VG68 hydraulic oil can significantly reduce the risk of sudden component failures and extend the service life of hydraulic power transmission systems operating in harsh environments.