Search results for: S.A. Dizaji

Authors: Z. Hormozi Moghaddam, M. Mokhtari-Dizaji, M. Movahedin, M. E. Ravari

Abstract:

Mechanical index (MI) is used for quantifying acoustic cavitation and the relationship between acoustic pressure and the frequency. In this study, modeling of the MI was applied to provide treatment protocol and to understand the effective physical processes on reproducibility of stem cells. The acoustic pressure and MI equations are modeled and solved to estimate optimal MI for 28, 40, 150 kHz and 1 MHz frequencies. Radial and axial acoustic pressure distribution was extracted. To validate the results of the modeling, the acoustic pressure in the water and near field depth was measured by a piston hydrophone. Results of modeling and experiments show that the model is consistent well to experimental results with 0.91 and 0.90 correlation of coefficient (p<0.05) for 1 MHz and 40 kHz. Low intensity ultrasound with 0.40 MI is more effective on the proliferation rate of the spermatogonial stem cells during the seven days of culture, in contrast, high MI has a harmful effect on the spermatogonial stem cells. This model provides proper treatment planning in vitro and in vivo by estimating the cavitation phenomenon.

Keywords: ultrasound, mechanical index, modeling, stem cell

Procedia PDF Downloads 323

Authors: B. Paygozar, S.A. Dizaji, A.C. Kandemir

Abstract:

In this study, the effects of the modified adhesive including different concentrations of Nano-silica are surveyed on the bonding strength of the adhesive scarf joints. The nanoparticles are added in two different concentrations, to an epoxy-based two-component structural adhesive, Araldite 2011, to survey the influences of the nanoparticle weight percentage on the failure load of the joints compared to that of the joints manufactured by the neat adhesive. The effects of being exposure to a moist ambience on the joint strength are also investigated for the joints produced of both neat and modified adhesives. For this purpose, an ageing process was carried out on the joints of both neat and improved kinds with variable immersion periods (20, 40 and 60 days). All the specimens were tested under a quasi-static tensile loading of 2 mm/min speed so as to find the quantities of the failure loads. Outcomes indicate that the failure loads of the joints with modified adhesives are measurably higher than that of the joint with neat adhesive, even while being put for a while under a moist condition. Another result points out that humidity lessens the bonding strength of all the joints of both types as the exposure time increases, which can be attributed to the change in the failure mode.

Keywords: bonding strength, humidity, nano-silica, scarf joint

Procedia PDF Downloads 166

Authors: B. Paygozar, S.A. Dizaji

Abstract:

Adhesive bonding is a highly valued type of fastening mechanical parts in complex structures, where joining some simple components is always needed. This method is of several merits, such as uniform stress distribution, appropriate bonding strength, and fatigue performance, and lightness, thereby outweighing other sorts of bonding methods. This study is to investigate the failure load of adhesive single-strap joints, including adherends of different sizes and materials. This kind of adhesive joint is very practical in different industries, especially when repairing the existing joints or attaching substrates of dissimilar materials. In this research, experimentally validated numerical analyses carried out in a commercial finite element package, ABAQUS, are utilized to extract the failure loads of the joints, based on the cohesive zone model. In addition, the stress analyses of the substrates are performed in order to acquire the effects of lowering the thickness of the substrates on the stress distribution inside them to avoid designs suffering from the necking or failure of the adherends. It was found out that this method of bonding is really feasible in joining dissimilar materials which can be utilized in a variety of applications. Moreover, the stress analyses indicated the minimum thickness for the adherends so as to avoid the failure of them.

Keywords: cohesive zone model, dissimilar materials, failure load, single strap joint

Procedia PDF Downloads 111

Authors: Mehrdad Shafiei Dizaji, Hoda Azari

Abstract:

The research explores the pioneering integration of Physics-Informed Neural Networks (PINNs) into the domain of Ground-Penetrating Radar (GPR) data prediction, akin to advancements in medical imaging for tracking tumor progression in the human body. This research presents a detailed development framework for a specialized PINN model proficient at interpreting and forecasting GPR data, much like how medical imaging models predict tumor behavior. By harnessing the synergy between deep learning algorithms and the physical laws governing subsurface structures—or, in medical terms, human tissues—the model effectively embeds the physics of electromagnetic wave propagation into its architecture. This ensures that predictions not only align with fundamental physical principles but also mirror the precision needed in medical diagnostics for detecting and monitoring tumors. The suggested deep learning structure comprises three components: a CNN, a spatial feature channel attention (SFCA) mechanism, and ConvLSTM, along with temporal feature frame attention (TFFA) modules. The attention mechanism computes channel attention and temporal attention weights using self-adaptation, thereby fine-tuning the visual and temporal feature responses to extract the most pertinent and significant visual and temporal features. By integrating physics directly into the neural network, our model has shown enhanced accuracy in forecasting GPR data. This improvement is vital for conducting effective assessments of bridge deck conditions and other evaluations related to civil infrastructure. The use of Physics-Informed Neural Networks (PINNs) has demonstrated the potential to transform the field of Non-Destructive Evaluation (NDE) by enhancing the precision of infrastructure deterioration predictions. Moreover, it offers a deeper insight into the fundamental mechanisms of deterioration, viewed through the prism of physics-based models.

Keywords: physics-informed neural networks, deep learning, ground-penetrating radar (GPR), NDE, ConvLSTM, physics, data driven

Procedia PDF Downloads 15