scholarly journals Viscoelastic viscoplastic damage model development, parameter estimation, and comparison to PBX9501 experimental data

2013 ◽  
Author(s):  
Miles A. Buechler
Keyword(s):  
Experimental Data ◽  
Parameter Estimation ◽  
Model Development ◽  
Damage Model

scholarly journals Application of Artificial Intelligence (AI) for Sustainable Highway and Road System

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 60
Author(s):  
Md Arifuzzaman ◽  
Muhammad Aniq Gul ◽  
Kaffayatullah Khan ◽  
S. M. Zakir Hossain
Keyword(s):  
Experimental Data ◽  
High Performance ◽  
Adhesion Force ◽  
Model Development ◽  
Real Life ◽  
Bayesian Optimization ◽  
Support Vector ◽  
Adhesive Properties ◽  
Bayesian Optimization Algorithm ◽  
The Mean

There are several environmental factors such as temperature differential, moisture, oxidation, etc. that affect the extended life of the modified asphalt influencing its desired adhesive properties. Knowledge of the properties of asphalt adhesives can help to provide a more resilient and durable asphalt surface. In this study, a hybrid of Bayesian optimization algorithm and support vector regression approach is recommended to predict the adhesion force of asphalt. The effects of three important variables viz., conditions (fresh, wet and aged), binder types (base, 4% SB, 5% SB, 4% SBS and 5% SBS), and Carbon Nano Tube doses (0.5%, 1.0% and 1.5%) on adhesive force are taken into consideration. Real-life experimental data (405 specimens) are considered for model development. Using atomic force microscopy, the adhesive strength of nanoscales of test specimens is determined according to functional groups on the asphalt. It is found that the model predictions overlap with the experimental data with a high R2 of 90.5% and relative deviation are scattered around zero line. Besides, the mean, median and standard deviations of experimental and the predicted values are very close. In addition, the mean absolute Error, root mean square error and fractional bias values were found to be low, indicating the high performance of the developed model.

scholarly journals Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies

2018 ◽  
Author(s):  
Jukka Intosalmi ◽  
Adrian C. Scott ◽  
Michelle Hays ◽  
Nicholas Flann ◽  
Olli Yli-Harja ◽  
...  
Keyword(s):  
Experimental Data ◽  
Time Course ◽  
Model Development ◽  
Growth Dynamics ◽  
Colony Growth ◽  
Data Driven ◽  
Coarse Grained ◽  
Intercellular Signaling ◽  
Growth Data ◽  
Metabolic Coupling

AbstractMotivationMulticellular entities, such as mammalian tissues or microbial biofilms, typically exhibit complex spatial arrangements that are adapted to their specific functions or environments. These structures result from intercellular signaling as well as from the interaction with the environment that allow cells of the same genotype to differentiate into well-organized communities of diversified cells. Despite its importance, our understanding on how cell–cell and metabolic coupling produce functionally optimized structures is still limited.ResultsHere, we present a data-driven spatial framework to computationally investigate the development of one multicellular structure, yeast colonies. Using experimental growth data from homogeneous liquid media conditions, we develop and parameterize a dynamic cell state and growth model. We then use the resulting model in a coarse-grained spatial model, which we calibrate using experimental time-course data of colony growth. Throughout the model development process, we use state-of-the-art statistical techniques to handle the uncertainty of model structure and parameterization. Further, we validate the model predictions against independent experimental data and illustrate how metabolic coupling plays a central role in colony formation.AvailabilityExperimental data and a computational implementation to reproduce the results are available athttp://research.cs.aalto.fi/csb/software/multiscale/[email protected],[email protected]

TCAD simulations of non-irradiated and irradiated low-gain avalanche diodes and comparison with measurements

2022 ◽  
Vol 17 (01) ◽  
pp. C01022
Author(s):  
T. Croci ◽  
A. Morozzi ◽  
F. Moscatelli ◽  
V. Sola ◽  
G. Borghi ◽  
...  
Keyword(s):  
Experimental Data ◽  
High Energy Physics ◽  
Signal To Noise Ratio ◽  
Damage Model ◽  
Surface Damage ◽  
High Energy ◽  
Silicon Detectors ◽  
Avalanche Diodes ◽  
Silicon Sensors ◽  
Energy Physics

Abstract In this work, the results of Technology-CAD (TCAD) device-level simulations of non-irradiated and irradiated Low-Gain Avalanche Diode (LGAD) detectors and their validation against experimental data will be presented. Thanks to the intrinsic multiplication of the charge within these silicon sensors, it is possible to improve the signal to noise ratio thus limiting its drastic reduction with fluence, as it happens instead for standard silicon detectors. Therefore, special attention has been devoted to the choice of the avalanche model, which allows the simulation findings to better fit with experimental data. Moreover, a radiation damage model (called “New University of Perugia TCAD model”) has been fully implemented within the simulation environment, to have a predictive insight into the electrical behavior and the charge collection properties of the LGAD detectors, up to the highest particle fluences expected in the future High Energy Physics (HEP) experiments. This numerical model allows to consider the comprehensive bulk and surface damage effects induced by radiation on silicon sensors. By coupling the “New University of Perugia TCAD model” with an analytical model that describes the mechanism of acceptor removal in the multiplication layer, it has been possible to reproduce experimental data with high accuracy, demonstrating the reliability of the simulation framework.

Creep Failure Simulations for 316H at 550°C

2012 ◽  
Author(s):  
Nak Hyun Kim ◽  
Yun Jae Kim ◽  
Catrin M. Davies ◽  
Kamran M. Nikbin ◽  
David W. Dean
Keyword(s):  
Experimental Data ◽  
Damage Model ◽  
Creep Damage ◽  
Inelastic Strain ◽  
Compact Tension ◽  
Creep Failure ◽  
Creep Ductility ◽  
Summation Rule ◽  
Creep Damage Model ◽  
Damage Summation

In this work a method to simulate failure due to creep is proposed using finite element damage analysis. The creep damage model is based on the creep ductility exhaustion concept. Incremental damage is defined by the ratio of incremental inelastic (plastic & creep) strain and multi-axial ductility. A simple linear damage summation rule is applied. When accumulated damage becomes unity, element stresses are reduced to almost zero to simulate progressive crack growth. The model is validated through comparison with experimental data on various sized compact tension, C(T), specimens of 316H stainless steel at 550 °C. The influence of the inelastic strain rate on the uniaxial ductility is considered. Good agreement is found between the simulated results and the experimental data.

Model Verification of Mixed Dynamic Systems

1978 ◽  
Vol 100 (2) ◽  
pp. 266-273 ◽  
Author(s):  
J. D. Chrostowski ◽  
D. A. Evensen ◽  
T. K. Hasselman
Keyword(s):  
Experimental Data ◽  
Parameter Estimation ◽  
Dynamic Systems ◽  
Statistical Parameter ◽  
Model Verification ◽  
Passive Networks ◽  
Parameter Values ◽  
Basic Configuration ◽  
General Method

A general method is presented for using experimental data to verify math models of “mixed” dynamic systems. The term “mixed” is used to suggest applicability to combined systems which may include interactive mechanical, hydraulic, electrical, and conceivably other types of components. Automatic matrix generating procedures are employed to facilitate the modeling of passive networks (e.g., hydraulic, electrical). These procedures are augmented by direct matrix input which can be used to complement the network model. The problem of model verification is treated in two parts; verification of the basic configuration of the model and determination of the parameter values associated with that configuration are addressed sequentially. Statistical parameter estimation is employed to identify selected parameter values, recognizing varying degrees of uncertainty with regard to both experimental data and analytical results. An example problem, involving a coupled hydraulic-mechanical system, is included to demonstrate application of the method.

scholarly journals Chemo-Mechanical Model for the Expansion of Concrete Due to Alkali Silica Reaction

2020 ◽  
Vol 10 (11) ◽  
pp. 3807 ◽  
Author(s):  
Lianfang Sun ◽  
Xingji Zhu ◽  
Xiaoying Zhuang ◽  
Goangseup Zi
Keyword(s):  
Experimental Data ◽  
Mechanical Model ◽  
Pore Solution ◽  
Damage Model ◽  
Alkali Silica Reaction ◽  
Swelling Capacity ◽  
Simulation Results

A chemo-damage model is proposed to predict the expansion caused by the alkali silica reaction (ASR). The model covers the formation of the pre-expansion gel driven by alkali and the swelling of the gel driven by water. The swelling capacity of the ASR gel is quantified by the sodium to calcium ratio in the pore solution. The bound alkali in the gel recycled by calcium is also considered in this model. Both external alkali supply and internal alkali released from aggregates are included. Several sets of experimental data are compared with the simulation results for the verification of the model.

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