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Residual stress is rarely considered as an initial condition for Finite Element Analysis (FEA), or used in the component design. A comprehensive numerical and experimental investigation into prestressed steel sheets is conducted. Residual stresses at specific locations were introduced into the steel sheets by dimple formation. FEA is used to simulate dynamic dimple forming of a mild steel disk due to a tool impact and detect the existence of prestress after dimple formation. A physical drop-hammer was used to form a range of dimple sizes in disks to experimentally validate the FEA predictions associated with dimple depth. Dimples were then created using a screw press and the prestress was measured by strain gauges beyond the zone of permanent out-of-plane deformation associated with a dimple. The experimental strain gauge measurements during dimple formation were found to be consistent with FEA predictions. The results demonstrated that the predicted and measured radial and tangentially projected stress components remain in steel sheets after forming a dimple. Furthermore, the predicted prestress was greater near to the dimple formation and occurred at higher magnitudes in the tangential direction than the radial direction. The highest prestress remaining in the disk was at the magnitude of the material's ultimate tensile stress, predicted in the tangential direction at the edge of the dimple. The numerical and experimental results showed the three stages of prestress formation: the development of stresses during dimple formation, the elastic recovery after the dimple forming, and the final prestressed equilibrium state of the rimmed disk. [ABSTRACT FROM AUTHOR]

To understand the connection among the brain and facial muscles, we decoded the correlation of facial muscles and brain activities. We applied information theory (Shannon entropy) to EEG and EMG signals recorded from 12 subjects (7 M, 5 F) during rest and stimulation using three odors (pineapple, banana, vanilla). The results indicated that a larger variation in the molecular complexity of odors causes a larger variation in the information of EMG and EEG signals. Therefore, facial muscles' activity is strongly coupled (r = −0.8399) to the brain's activity. Our analysis method could be further employed to examine the relationship between the brain and other organs by comparing other physiological signals with the brain signals. [ABSTRACT FROM AUTHOR]

Our skin reacts to various stimuli that we receive. Since all parts of the human body are controlled by the brain, a relationship should exist among brain and skin activities. This study evaluates the relation among skin and brain activities. As such, we benefited from the information-based analysis. We collected GSR and EEG signals of eight participants in various olfactory stimulations. Accordingly, we ran Shannon entropy-based analysis to evaluate the correlation between the information contents of these two signals. The results showed that the alterations in the complexity of stimulus and the information of GSR and EEG signals are strongly correlated. We also verified the results of the analysis of Shannon entropy of signals by calculating their Hurst exponent to quantify their memory. According to the results, the alterations of the memory of EEG and GSR signals are similar to the alterations of the information of these signals. [ABSTRACT FROM AUTHOR]

This paper carries out experimental and numerical investigations on the axial performance of circular Reinforced Concrete (RC) precast slender columns with an intermediate connection. A series of tests are carried out to study the effects of longitudinal reinforcement ratio (ρ), and embedded length of steel bars (Lb) of precast columns on their axial performance under concentric loading. Test results show that increasing the reinforcement ratio and the embedded length of the precast columns significantly improves the axial performance of the precast slender columns. Nonlinear finite element models (FEM) are developed to simulate the performance of the columns experimentally observed. The accuracy of the FEM is validated against the test results and found to reasonably predict the axial performance of tested columns. [ABSTRACT FROM AUTHOR]

This paper reports on an investigation into the relationship between stiffness and applied force of an advanced biological composite structure using four techniques: experimental observation; finite element analysis (FEA); regression learner analysis; and, artificial neural networks (ANNs). The entire hydrated third metacarpal bones (MC3) from 16 thoroughbred horses were loaded in compression in an MTS machine. The stiffness was then determined from the applied force, MC3 displacement, and load exposure time. A variety of mathematical functions were fitted to the sample data points using MATLAB to demonstrate force-dependent stiffness. Two functions were found that exhibited a strong correlation between force and stiffness (R2 = 0.75). Additionally, a power function was found that demonstrated a stronger correlation between the stiffness and force (R2 = 0.81) if the exposure time was also incorporated. FEA considered the calculated force-dependent stiffness when assigning material properties. FEA results were compared with experimental data (for verification and validation), and a good agreement was found for the displacement (RMSE = 0.032 mm) and strain (RMSE = 61.85 με). Machine learning regression models were also employed to predict the stiffness of this complex structure. Applied force, exposure time, MC3 geometry (length and area of cross sections), and age were defined as the independent variables. The regression learner offered excellent reliability (R2 = 0.98) for the prediction of stiffness. Also, feedforward back-propagation artificial neural networks were employed to improve and generalize the stiffness prediction ability to a wider population. ANN regression analysis showed R = 0.992 for training, R = 0.99 for testing, and R = 0.991 for the all datasets. To confirm its accuracy, the ANN was used to predict stiffness of specific samples that were not used in its training. This offered excellent reliability in predicting real-world data that ANNs have not seen before (R = 0.976). [ABSTRACT FROM AUTHOR]

In this study, a series of experiments is conducted in which a plenum experiences forced oscillation. The oscillating system is driven by a periodic force arising from regular volumetric fluctuations analogous to fixed-height water waves entering a surface-effect ship (SES) plenum below a heave deck. An analytical study is then conducted in which a MATLAB Simulink model is created to incorporate the same parameters taken into account during the experiments. The results of both the experiments and the simulation demonstrated that as the frequency associated with the plenum volume's forced oscillations approaches the natural frequency of the mass/plenum system, the vibration magnitude of the heave deck increases as predicted. Resonance is observed in vibrations of the heave deck. The mean hover height of the heave deck increases predictably and remains high at higher frequencies when the forced oscillations' frequency approaches the natural frequency of the mass/plenum system. Higher frequency oscillations lead to air accumulation in the plenum. Since there was a significant difference between the experimentally-measured (and simulated) natural frequency and the theoretical natural frequency of a similar, but rigid-walled plenum, it appeared that the natural frequency of the heave deck above the plenum is reduced by the flexibility of the seals and water surface under a SES. • Identifying relevant parameters of a SES air plenum to predict dynamic instabilities. • A more complete analytical model of the air cushion of a SES is developed. • The combined effect of pressure and air density is considered. • The rates of change of air pressure and air density are also considered. • Flexibility of plenum boundaries affects the natural frequency of an air cushion. [ABSTRACT FROM AUTHOR]