Autor: (ata). - Katalog OPAC zbiorów

Tytuł:: Experimental and numerical parametric study of resistance spot welding process of AISI 1008 steel sheets
Autorzy:: Khabaz-Aghdam, Ata
Rahmani, Azhdar
Fadaei, Abbas
Powiązania:: https://bibliotekanauki.pl/articles/279896.pdf
Data publikacji:: 2019
Wydawca:: Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Tematy:: resistance spot welding
nugget geometry
residual stresses
simulation
experimentation
Opis:: In the present research, a parametric study in Resistance Spot Welding (RSW) of thin AISI 1008 steel sheets is investigated via FEM. All the time steps through the RSW process, including squeeze time, welding time, holding time and cooling time are taken into account. First, the effects of various parameters such as electrical current, welding time and electrode tip diameter are investigated in the nugget geometry. Then, a time history stress diagram and residual stresses are obtained in RSW weldment. FEM results are validated very well by some experiments which were performed in two parts of nugget geometry and residual stresses.
Źródło:: Journal of Theoretical and Applied Mechanics; 2019, 57, 4; 807-820
1429-2955
Pojawia się w:: Journal of Theoretical and Applied Mechanics
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 8.

Tytuł:: Mechanical guidelines on the properties of human healthy arteries in the design and fabrication of vascular grafts: experimental tests and quasi-linear viscoelastic model
Autorzy:: Faturechi, Rahim
Hashemi, Ata
Abolfathi, Nabiollah
Solouk, Atefeh
Powiązania:: https://bibliotekanauki.pl/articles/307007.pdf
Data publikacji:: 2019
Wydawca:: Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:: elastyczność
model konstytutywny
tętnica
elasticity
constitutive model
human artery
artificial artery
quasi-linear viscoelasticity
Opis:: Knowledge of mechanical behavior of healthy human arteries as the guidelines to target properties of vascular grafts deserves special attention. There is a lack of mathematical model to characterize mechanical behavior of biomaterial while many mathematical models to reflect mechanics of human arteries have been proposed. The objective of this paper was set to measure mechanical properties of healthy human arteries including Common Carotid Artery (CCA), Abdominal Aorta Artery (AAA), Subclavian Artery (SA), Common Iliac Artery (CIA) and Right and Left Iliac Artery (RIA and LIA) and compare them to those of commercial ePTFE and Dacron®. Methods: Series of stress relaxation and strain to failure tests vere performed on all samples. The experimental data was utilized to develop quasi-linear viscoelastic (QLV) model of both natural and artificial arteries. Results: ePTFE is the stiffest sample, while the CCA is the most compliant one among all. RIA and CIA are more viscous than the other natural arteries, while AA and CCA are less viscous. The proposed model demonstrated an accurate fit to the experimental results, a proof of its ability to model both nonlinear elasticity and viscoelasticity of the human arteries and commercial ones. Conclusions: ePTFE and Dacron® are much stiffer than human arteries that may lead to the disruption of blood hemodynamic and may not be biomechanically feasible as a replacement.
Źródło:: Acta of Bioengineering and Biomechanics; 2019, 21, 3; 13-21
1509-409X
2450-6303
Pojawia się w:: Acta of Bioengineering and Biomechanics
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 9.

Tytuł:: Air Quality Assessment and Forecasting Using Neural Network Model
Autorzy:: Hamdan, Mohammad A.
Ata, Mohammad F. Bani
Sakhrieh, Ahmad H.
Powiązania:: https://bibliotekanauki.pl/articles/1838288.pdf
Data publikacji:: 2021
Wydawca:: Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:: air pollutant
ANN
MATLAB
forecasting
Opis:: Air pollution is a major obstacle faced by all countries which impacts the environment, public health, socioeconomics, and agriculture. In this study, the air pollutants in the city of Amman were presented and analyzed. Nonlinear Autoregressive Exogenous (NARX) model was used to forecast the daily average levels of pollutants in Amman, Jordan. The model was built using the MATLAB software. The model utilized a Marquardt-Levenberg learning algorithm. Its performance was presented using different indices, R2 (Coefficient of Determination), R (Coefficient of Correlation), NMSE (Normalized Mean Square Error), and Plots representing network predictions vs original data. Historical measurements of air pollutants were obtained from 4 of the Ministry of Environment (MoEnv) air quality monitoring stations in Amman. The meteorological data representing three years (2015, 2016, and 2017) were used as predictors to train the Artificial Neural Network (ANN) while the data of the year 2018 were used to test it. The results showed good performance when forecasting SO2, O3, CO, and NO2, and acceptable performance when forecasting Particulate Matter (PM10) at the given 4 locations.
Źródło:: Journal of Ecological Engineering; 2021, 22, 6; 1-11
2299-8993
Pojawia się w:: Journal of Ecological Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 10.

Tytuł:: Air Quality Assessment and Forecasting Using Neural Network Model
Autorzy:: Hamdan, Mohammad A.
Ata, Mohammad F. Bani
Sakhrieh, Ahmad H.
Powiązania:: https://bibliotekanauki.pl/articles/1838392.pdf
Data publikacji:: 2021
Wydawca:: Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:: air pollutant
ANN
MATLAB
forecasting
Opis:: Air pollution is a major obstacle faced by all countries which impacts the environment, public health, socioeconomics, and agriculture. In this study, the air pollutants in the city of Amman were presented and analyzed. Nonlinear Autoregressive Exogenous (NARX) model was used to forecast the daily average levels of pollutants in Amman, Jordan. The model was built using the MATLAB software. The model utilized a Marquardt-Levenberg learning algorithm. Its performance was presented using different indices, R2 (Coefficient of Determination), R (Coefficient of Correlation), NMSE (Normalized Mean Square Error), and Plots representing network predictions vs original data. Historical measurements of air pollutants were obtained from 4 of the Ministry of Environment (MoEnv) air quality monitoring stations in Amman. The meteorological data representing three years (2015, 2016, and 2017) were used as predictors to train the Artificial Neural Network (ANN) while the data of the year 2018 were used to test it. The results showed good performance when forecasting SO2, O3, CO, and NO2, and acceptable performance when forecasting Particulate Matter (PM10) at the given 4 locations.
Źródło:: Journal of Ecological Engineering; 2021, 22, 6; 1-11
2299-8993
Pojawia się w:: Journal of Ecological Engineering
Dostawca treści:: Biblioteka Nauki

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