Advances in Computational Methods for Large-Scale Structural Optimization

2009 ◽  
pp. 431-449 ◽  
Author(s):  
M. Papadrakakis ◽  
N.D. Lagaros
Keyword(s):  
Structural Optimization ◽  
Computational Methods ◽  
Large Scale

Large scale structural optimization: Computational methods and optimization algorithms

2001 ◽  
Vol 8 (3) ◽  
pp. 239-301 ◽  
Author(s):  
M. Papadrakakis ◽  
N. D. Lagaros ◽  
Y. Tsompanakis ◽  
V. Plevris
Keyword(s):  
Structural Optimization ◽  
Computational Methods ◽  
Large Scale ◽  
Optimization Algorithms

scholarly journals A study of computational methods for wake structure and base pressure prediction of a generic SUV model with fixed and rotating wheels

2017 ◽  
Vol 231 (9) ◽  
pp. 1222-1238 ◽  
Author(s):  
David Forbes ◽  
Gary Page ◽  
Martin Passmore ◽  
Adrian Gaylard
Keyword(s):  
Experimental Data ◽  
Computational Methods ◽  
Large Scale ◽  
Ground Plane ◽  
Computational Cost ◽  
Rear Wheel ◽  
Base Pressure ◽  
Detached Eddy Simulation ◽  
Wake Structure ◽  
Wake Interactions

This study is an evaluation of the computational methods in reproducing experimental data for a generic sports utility vehicle (SUV) geometry and an assessment on the influence of fixed and rotating wheels for this geometry. Initially, comparisons are made in the wake structure and base pressures between several CFD codes and experimental data. It was shown that steady-state RANS methods are unsuitable for this geometry due to a large scale unsteadiness in the wake caused by separation at the sharp trailing edge and rear wheel wake interactions. unsteady RANS (URANS) offered no improvements in wake prediction despite a significant increase in computational cost. The detached-eddy simulation (DES) and Lattice–Boltzmann methods showed the best agreement with the experimental results in both the wake structure and base pressure, with LBM running in approximately a fifth of the time for DES. The study then continues by analysing the influence of rotating wheels and a moving ground plane over a fixed wheel and ground plane arrangement. The introduction of wheel rotation and a moving ground was shown to increase the base pressure and reduce the drag acting on the vehicle when compared to the fixed case. However, when compared to the experimental standoff case, variations in drag and lift coefficients were minimal but misleading, as significant variations to the surface pressures were present.

Large scale structural optimization of trimetallic Cu–Au–Pt clusters up to 147 atoms

2017 ◽  
Vol 686 ◽  
pp. 103-110 ◽  
Author(s):  
Genhua Wu ◽  
Yan Sun ◽  
Xia Wu ◽  
Run Chen ◽  
Yan Wang
Keyword(s):  
Structural Optimization ◽  
Large Scale

scholarly journals How public perceptions of social distancing evolved over a critical time period: communication lessons learnt from the American state of Wisconsin

2020 ◽  
Vol 19 (05) ◽  
pp. A11
Author(s):  
Kaiping Chen ◽  
Luye Bao ◽  
Anqi Shao ◽  
Pauline Ho ◽  
Shiyu Yang ◽  
...  
Keyword(s):  
Public Health ◽  
Computational Methods ◽  
Large Scale ◽  
Critical Time ◽  
Effective Communication ◽  
Public Perceptions ◽  
Social Distancing ◽  
Time Period ◽  
Large Scale Survey ◽  
Main Barrier

Understanding how individuals perceive the barriers and benefits of precautionary actions is key for effective communication about public health crises, such as the COVID-19 outbreak. This study used innovative computational methods to analyze 30,000 open-ended responses from a large-scale survey to track how Wisconsin (U.S.A.) residents' perceptions of the benefits of and barriers to performing social distancing evolved over a critical time period (March 19th to April 1st, 2020). Initially, the main barrier was practical related, however, individuals later perceived more multifaceted barriers to social distancing. Communication about COVID-19 should be dynamic and evolve to address people's experiences and needs overtime.

Innovative Computational Methods for Structural Optimization

2009 ◽  
pp. 195-222 ◽  
Author(s):  
M. Papadrakakis ◽  
Y. Tsompanakis ◽  
N.D. Lagaros ◽  
E. Hinton ◽  
J. Sienz ◽  
...  
Keyword(s):  
Structural Optimization ◽  
Computational Methods

scholarly journals Prediction of drug-target interactions from multi-molecular network based on LINE network representation method

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Bo-Ya Ji ◽  
Zhu-Hong You ◽  
Han-Jing Jiang ◽  
Zhen-Hao Guo ◽  
Kai Zheng
Keyword(s):  
Computational Methods ◽  
Information Integration ◽  
Drug Target ◽  
Large Scale ◽  
New Drugs ◽  
Single Type ◽  
Information Network ◽  
Potential Drug Target ◽  
Potential Drug ◽  
Network Embedding

Abstract Background The prediction of potential drug-target interactions (DTIs) not only provides a better comprehension of biological processes but also is critical for identifying new drugs. However, due to the disadvantages of expensive and high time-consuming traditional experiments, only a small section of interactions between drugs and targets in the database were verified experimentally. Therefore, it is meaningful and important to develop new computational methods with good performance for DTIs prediction. At present, many existing computational methods only utilize the single type of interactions between drugs and proteins without paying attention to the associations and influences with other types of molecules. Methods In this work, we developed a novel network embedding-based heterogeneous information integration model to predict potential drug-target interactions. Firstly, a heterogeneous multi-molecuar information network is built by combining the known associations among protein, drug, lncRNA, disease, and miRNA. Secondly, the Large-scale Information Network Embedding (LINE) model is used to learn behavior information (associations with other nodes) of drugs and proteins in the network. Hence, the known drug-protein interaction pairs can be represented as a combination of attribute information (e.g. protein sequences information and drug molecular fingerprints) and behavior information of themselves. Thirdly, the Random Forest classifier is used for training and prediction. Results In the results, under the five-fold cross validation, our method obtained 85.83% prediction accuracy with 80.47% sensitivity at the AUC of 92.33%. Moreover, in the case studies of three common drugs, the top 10 candidate targets have 8 (Caffeine), 7 (Clozapine) and 6 (Pioglitazone) are respectively verified to be associated with corresponding drugs. Conclusions In short, these results indicate that our method can be a powerful tool for predicting potential drug-target interactions and finding unknown targets for certain drugs or unknown drugs for certain targets.

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