scholarly journals Uncertainty Assessment of Entropy-Based Circular Channel Shear Stress Prediction Models Using a Novel Method

Geosciences ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 308
Author(s):  
Amin Kazemian-Kale-Kale ◽  
Azadeh Gholami ◽  
Mohammad Rezaie-Balf ◽  
Amir Mosavi ◽  
Ahmed A. Sattar ◽  
...  
Keyword(s):  
Shear Stress ◽  
Error Estimation ◽  
Prediction Models ◽  
Uncertainty Assessment ◽  
Previous Method ◽  
New Method ◽  
Confidence Bound ◽  
Stress Prediction ◽  
Novel Method ◽  
Open Question

Entropy models have been recently adopted in many studies to evaluate the shear stress distribution in open-channel flows. Although the uncertainty of Shannon and Tsallis entropy models were analyzed separately in previous studies, the uncertainty of other entropy models and comparisons of their reliability remain an open question. In this study, a new method is presented to evaluate the uncertainty of four entropy models, Shannon, Shannon-Power Law (PL), Tsallis, and Renyi, in shear stress prediction of the circular channels. In the previous method, the model with the largest value of the percentage of observed data within the confidence bound (Nin) and the smallest value of Forecasting Range of Error Estimation (FREE) is the most reliable. Based on the new method, using the effect of Optimized Forecasting Range of Error Estimation (FREEopt) and Optimized Confidence Bound (OCB), a new statistic index called FREEopt-based OCB (FOCB) is introduced. The lower the value of FOCB, the more certain the model. Shannon and Shannon PL entropies had close values of the FOCB equal to 8.781 and 9.808, respectively, and had the highest certainty, followed by ρgRs and Tsallis models with close values of 14.491 and 14.895, respectively. However, Renyi entropy, with the value of FOCB equal to 57.726, had less certainty.

scholarly journals A Bayesian Monte-Carlo Uncertainty Model for Assessment of Shear Stress Entropy

2020 ◽  
Author(s):  
Amin Kazemian-Kale-Kale ◽  
Azadeh Gholami ◽  
Mohammad Rezaie-Balf ◽  
Amir Mosavi ◽  
Ahmed A. Sattar ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Shear Stress ◽  
Flow Shear ◽  
Uncertainty Model ◽  
Bayesian Monte Carlo ◽  
Novel Method ◽  
Stress Estimation ◽  
Flow Shear Stress ◽  
Open Question ◽  
Uncertainty Method

The entropy models have been recently adopted in many studies to evaluate the distribution of the shear stress in circular channels. However, the uncertainty in their predictions and their reliability remains an open question. We present a novel method to evaluate the uncertainty of four popular entropy models, including Shannon, Shannon-Power Low (PL), Tsallis, and Renyi, in shear stress estimation in circular channels. The Bayesian Monte-Carlo (BMC) uncertainty method is simplified considering a 95% Confidence Bound (CB). We developed a new statistic index called as FREEopt-based OCB (FOCB) using the statistical indices Forecasting Range of Error Estimation (FREE) and the percentage of observed data in the CB (Nin), which integrates their combined effect. The Shannon and Shannon PL entropies had close values of the FOCB equal to 8.781 and 9.808, respectively, had the highest certainty in the calculation of shear stress values in circular channels followed by traditional uniform flow shear stress and Tsallis models with close values of 14.491 and 14.895, respectively. However, Renyi entropy with much higher values of FOCB equal to 57.726 has less certainty in the estimation of shear stress than other models. Using the presented results in this study, the amount of confidence in entropy methods in the calculation of shear stress to design and implement different types of open channels and their stability is determined.

scholarly journals Prediction Models of Shear Parameters and Dynamic Creep Instability for Asphalt Mixture under Different High Temperatures

Polymers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2542
Author(s):  
Junxiu Lv ◽  
Xiaoyuan Zhang
Keyword(s):  
Shear Stress ◽  
Shear Strength ◽  
Stress Level ◽  
Prediction Models ◽  
Asphalt Mixture ◽  
Confining Pressure ◽  
Shear Strength Parameters ◽  
Dynamic Creep ◽  
Shear Stress Level ◽  
Creep Instability

This study mainly investigates the prediction models of shear parameters and dynamic creep instability for asphalt mixture under different high temperatures to reveal the instability mechanism of the rutting for asphalt pavement. Cohesive force c and internal friction angle φ in the shear strength parameters for asphalt mixture were obtained by the triaxial compressive strength test. Then, through analyzing the influence of different temperatures on parameters c and φ, the prediction models of shear strength parameters related to temperature were developed. Meanwhile, the corresponding forecast model related to confining pressure and shear strength parameters was obtained by simplifying the calculation method of shear stress level on the failure surface under cyclic loading. Thus, the relationship of shear stress level with temperature was established. Furthermore, the cyclic time FN of dynamic creep instability at 60 °C was obtained by the triaxial dynamic creep test, and the effects of confining pressure and shear stress level were considered. Results showed that FN decreases exponentially with the increase in stress levels under the same confining pressure and increases with the increase in confining pressure. The ratio between shear stress level and corresponding shear strength under the same confining pressure was introduced; thus, the relationship curve of FN with shear stress level can eliminate the effect of different confining pressures. The instability prediction model of FN for asphalt mixture was established using exponential model fitting analysis, and the rationality of the model was verified. Finally, the change rule of the parameters in the instability prediction model was investigated by further changing the temperature, and the instability forecast model in the range of high temperature for the same gradation mixture was established by the interpolation calculation.

A New “Bounce-Normal” Boundary in DPD Calculations for the Reduction of Density Fluctuations

2008 ◽  
Author(s):  
Emadaldin Moeendarbary ◽  
K. Y. Lam ◽  
T. Y. Ng
Keyword(s):  
Shear Stress ◽  
Protein Separation ◽  
Dissipative Particle Dynamics ◽  
Computational Cost ◽  
Colloidal Suspensions ◽  
Density Fluctuations ◽  
Polymer Chains ◽  
New Method ◽  
Mems Devices ◽  
Macroscopic Properties

Dissipative Particle Dynamics (DPD) is a mesoscopic fluid modeling method, which facilitates the simulation of the statics and dynamics of complex fluid systems at physically interesting length and time scales. Currently, there are various applications of DPD, such as colloidal suspensions, multi-phase flow, rheology of polymer chains, DNA macromolecular suspension, etc., which employ this technique for their numerical simulation. The DPD technique is capable of modeling macroscopic properties of the bulk flow very well, but difficulties arise if the flows are confined through wall-bounded regions, or when different boundaries simultaneously exist in the simulation domain. These boundaries cause negative effects on the macroscopic temperature, density and velocity profiles, as well as the shear stress and pressure distributions. In particular, the interaction of DPD particles with solid boundaries causes large density fluctuations at the near wall regions. This density distortion leads to pronounced fluctuations in the pressure and shear stress, which are not actually present. To overcome these serious deficiencies, we introduce a new method in this work, which uses a combination of randomly distributed wall particles and a novel reflection adaptation at the wall. This new methodology is simple to implement and incurs no additional computational cost. More importantly, it does not cause any distortion in the macroscopic properties. This novel reflection adaptation is a novel version of the bounce back reflection, which we shall term the bounce-normal reflection. The most important characteristic of this method is that it reduces density fluctuations near the boundaries without affecting the velocity and temperature profiles. This new method is easily applicable to any wall-bounded problem with stationary boundaries and it has a very good consistency with macroscopic features. The eventual objective of this numerical development work is to investigate suspension flow through micro/nano channels of fluidic NEMS/MEMS devices, with applications to DNA and protein separation. These micro/nano channel devices, consisting of many entropic traps, are designed and fabricated for the separation of proteins and long DNA molecules.

scholarly journals Strategic Team AI Path Plans: Probabilistic Pathfinding

2008 ◽  
Vol 2008 ◽  
pp. 1-6 ◽  
Author(s):  
Tng C. H. John ◽  
Edmond C. Prakash ◽  
Narendra S. Chaudhari
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Computer Games ◽  
Fitness Function ◽  
New Method ◽  
High Quality ◽  
Plan Generation ◽  
Novel Method ◽  
Games And Simulations

This paper proposes a novel method to generate strategic team AI pathfinding plans for computer games and simulations using probabilistic pathfinding. This method is inspired by genetic algorithms (Russell and Norvig, 2002), in that, a fitness function is used to test the quality of the path plans. The method generates high-quality path plans by eliminating the low-quality ones. The path plans are generated by probabilistic pathfinding, and the elimination is done by a fitness test of the path plans. This path plan generation method has the ability to generate variation or different high-quality paths, which is desired for games to increase replay values. This work is an extension of our earlier work on team AI: probabilistic pathfinding (John et al., 2006). We explore ways to combine probabilistic pathfinding and genetic algorithm to create a new method to generate strategic team AI pathfinding plans.

scholarly journals SENTENCE ORDERING USING CLUSTER CORRELATION AND PROBABILITY IN MULTI-DOCUMENTS SUMMARIZATION

2017 ◽  
Vol 10 (2) ◽  
pp. 77
Author(s):  
I Gusti Agung Socrates Adi Guna ◽  
Suci Nur Fauziah ◽  
Wanvy Arifha Saputra
Keyword(s):  
Previous Method ◽  
New Method ◽  
User Evaluation ◽  
Document Summarization ◽  
Chronological Sequence ◽  
Effect Relation ◽  
Cluster Correlation

Most of the document summary are arranged extractive by taking important sentences from the document. Extractive based summarization often not consider the connection sentence.  A good sentence ordering should aware about rhetorical relations such as cause-effect relation, topical relevancy and chronological sequence which exist between the sentences.  Based on this problem, we propose a new method for sentence ordering in multi document summarization using cluster correlation and probability for English documents. Sentences of multi-documents are grouped based on similarity into clusters. Sentence extracted from each cluster to be a summary that will be listed based on cluster correlation and probability. User evaluation showed that the summary result of proposed method easier to understanding than the previous method. The result of ROUGE method also shows increase on sentence arrangement compared to previous method.

scholarly journals Disjoint combinations profiling (DCP): a new method for the prediction of antibody CDR conformation from sequence

2014 ◽  
Author(s):  
Dimitris Nikoloudis ◽  
Jim E. Pitts ◽  
José W. Saldanha
Keyword(s):  
Protein Engineering ◽  
Prediction Accuracy ◽  
Variable Region ◽  
New Method ◽  
Engineering Applications ◽  
Variable Regions ◽  
Blind Prediction ◽  
Novel Method ◽  
Complementarity Determining Regions ◽  
Sequence Rules

The accurate prediction of the conformation of Complementarity-Determining Regions (CDRs) is important in modelling antibodies for protein engineering applications. Specifically, the Canonical paradigm has proved successful in predicting the CDR conformation in antibody variable regions. It relies on canonical templates which detail allowed residues at key positions in the variable region framework or in the CDR itself for 5 of the 6 CDRs. While no templates have as yet been defined for the hypervariable CDR-H3, instead, reliable sequence rules have been devised for predicting the base of the CDR-H3 loop. Here a new method termed Disjoint Combinations Profiling (DCP) is presented, which contributes a considerable advance in the prediction of CDR conformations. This novel method is explained and compared with canonical templates and sequence rules in a 3-way blind prediction. DCP achieved 93% accuracy over 951 blind predictions and showed an improvement in cumulative accuracy compared to predictions with canonical templates or sequence-rules. In addition to its overall improvement in prediction accuracy, it is suggested that DCP is open to better implementations in the future and that it can improve as more antibody structures are deposited in the databank. In contrast, it is argued that canonical templates and sequence rules may have reached their peak.

3D Model of Shot Dynamics for Ultrasonic Shot Peening

2013 ◽  
Vol 768-769 ◽  
pp. 503-509 ◽  
Author(s):  
Jawad Badreddine ◽  
Emmanuelle Rouhaud ◽  
Matthieu Micoulaut ◽  
Sebastien Remy ◽  
Vincent Desfontaine ◽  
...  
Keyword(s):  
Residual Stress ◽  
Process Parameters ◽  
Spatial Data ◽  
Shot Peening ◽  
3D Model ◽  
Prediction Models ◽  
Dissipated Energy ◽  
Displacement Fields ◽  
Ultrasonic Shot Peening ◽  
Stress Prediction

This paper presents a 3D model that simulates an ultrasonic shot peening (USP) operation, using realistic process parameters and peening setups (part and chamber geometries). By simulating the shot dynamics (shot trajectories and impacts), statistical and spatial data are obtained for the peened component, i.e. surface coverage and coverage rate, impact speeds and angles, dissipated energy... This data can then be used for i) optimizing the design of peening chambers and process parameters and ii) predicting the residual stress and displacement fields induced by USP in the peened component. In fact, data from the 3D model can be used as initial data in existing residual stress prediction models. A chaining methodology was developed for this purpose and allows linking the choice of process parameters and USP setup to the induced residual stress displacement fields.

Biomechanical Analysis of the Sensitivity of Brain Tissue Responses to FE Head Models in the Study of Impact-Induced TBI

2020 ◽  
Author(s):  
Hesam S. Moghaddam ◽  
Asghar Rezaei ◽  
Mariusz Ziejewski ◽  
Ghodrat Karami
Keyword(s):  
Shear Stress ◽  
Mesh Size ◽  
Human Head ◽  
Biomechanical Analysis ◽  
Head Model ◽  
Tissue Responses ◽  
Stress Prediction ◽  
Brain Material ◽  
The Brain

Abstract A numerical investigation is conducted on the injury-related biomechanical parameters of the human head under blunt impacts. The objective of this research is twofold; first to understand the role of the employed finite element (FE) head model — with its specific components, shape, size, material properties, and mesh size — in predicting tissue responses of the brain, and second to investigate the fidelity of pressure response in validating FE head models. Accordingly, two independently established and validated FE head models are impacted in two directions under two impact severities and their predicted responses in terms of intracranial pressure (ICP) and shear stress are compared. The coup-counter ICP peak values are less sensitive to head model, mesh size, and the brain material. In all cases, maximum ICPs occur on the outer surface, vanishing linearly toward the center of the brain. Hence, it is concluded that different head models may simply reproduce the results of ICP variations due to impact. Shear stress prediction, however, is mainly affected by the head model, direction and severity of impact, and the brain material.

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