A DFN-based method for fast prediction of ships' added resistance in heading waves

2022 ◽  
Vol 245 ◽  
pp. 110484
Author(s):  
Wenyang Duan ◽  
Ke Yang ◽  
Limin Huang ◽  
Yu Jing ◽  
Shan Ma
Keyword(s):  
Added Resistance ◽  
Fast Prediction

Effects of openings on the seismic behavior and performance level of concrete shear walls

2019 ◽  
Author(s):  
Hossein Alimohammadi ◽  
Mostafa Dalvi Esfahani ◽  
Mohammadali Lotfollahi Yaghin
Keyword(s):  
Static Analysis ◽  
Cross Section ◽  
Seismic Behavior ◽  
Shear Walls ◽  
Shear Wall ◽  
Constant Cross Section ◽  
Added Resistance ◽  
Different Shapes ◽  
And Performance ◽  
Abaqus Software

In this study, the seismic behavior of the concrete shear wall considering the opening with different shapes and constant cross-section has been studied, and for this purpose, several shear walls are placed under the increasingly non-linear static analysis (Pushover). These case studies modeled in 3D Abaqus Software, and the results of the ductility coefficient, hardness, energy absorption, added resistance, the final shape, and the final resistance are compared to shear walls without opening.

Added Resistance and 2DOF Motion Analysis of KVLCC2 in Regular Head Waves using Dynamic Overset Scheme

2018 ◽  
Vol 55 (5) ◽  
pp. 385-393 ◽  
Author(s):  
Yoo-Chul Kim ◽  
Yoonsik Kim ◽  
Jin Kim ◽  
Kwang-Soo Kim
Keyword(s):  
Motion Analysis ◽  
Added Resistance ◽  
Head Waves ◽  
Regular Head Waves

Identification, Prediction and Data Analysis of Noncoding RNAs: A Review

2019 ◽  
Vol 15 (3) ◽  
pp. 216-230 ◽  
Author(s):  
Abbasali Emamjomeh ◽  
Javad Zahiri ◽  
Mehrdad Asadian ◽  
Mehrdad Behmanesh ◽  
Barat A. Fakheri ◽  
...  
Keyword(s):  
Computational Methods ◽  
State Of The Art ◽  
Noncoding Rnas ◽  
Research Field ◽  
Design Strategies ◽  
Structural Prediction ◽  
Computational Costs ◽  
Cellular Processes ◽  
Laboratory Techniques ◽  
Fast Prediction

Background:Noncoding RNAs (ncRNAs) which play an important role in various cellular processes are important in medicine as well as in drug design strategies. Different studies have shown that ncRNAs are dis-regulated in cancer cells and play an important role in human tumorigenesis. Therefore, it is important to identify and predict such molecules by experimental and computational methods, respectively. However, to avoid expensive experimental methods, computational algorithms have been developed for accurately and fast prediction of ncRNAs.Objective:The aim of this review was to introduce the experimental and computational methods to identify and predict ncRNAs structure. Also, we explained the ncRNA’s roles in cellular processes and drugs design, briefly.Method:In this survey, we will introduce ncRNAs and their roles in biological and medicinal processes. Then, some important laboratory techniques will be studied to identify ncRNAs. Finally, the state-of-the-art models and algorithms will be introduced along with important tools and databases.Results:The results showed that the integration of experimental and computational approaches improves to identify ncRNAs. Moreover, the high accurate databases, algorithms and tools were compared to predict the ncRNAs.Conclusion:ncRNAs prediction is an exciting research field, but there are different difficulties. It requires accurate and reliable algorithms and tools. Also, it should be mentioned that computational costs of such algorithm including running time and usage memory are very important. Finally, some suggestions were presented to improve computational methods of ncRNAs gene and structural prediction.

scholarly journals Machine learning and simulation-based surrogate modeling for improved process chain operation

2021 ◽  
Author(s):  
André Hürkamp ◽  
Sebastian Gellrich ◽  
Antal Dér ◽  
Christoph Herrmann ◽  
Klaus Dröder ◽  
...  
Keyword(s):  
Surrogate Modeling ◽  
Temperature History ◽  
Injection Molding Process ◽  
Process Chain ◽  
Molding Process ◽  
Whole Process ◽  
Virtual Production ◽  
Sensor Model ◽  
Fast Prediction ◽  
Operation Phase

AbstractIn this contribution, a concept is presented that combines different simulation paradigms during the engineering phase. These methods are transferred into the operation phase by the use of data-based surrogates. As an virtual production scenario, the process combination of thermoforming continuous fiber-reinforced thermoplastic sheets and injection overmolding of thermoplastic polymers is investigated. Since this process is very sensitive regarding the temperature, the volatile transfer time is considered in a dynamic process chain control. Based on numerical analyses of the injection molding process, a surrogate model is developed. It enables a fast prediction of the product quality based on the temperature history. The physical model is transferred to an agent-based process chain simulation identifying lead time, bottle necks and quality rates taking into account the whole process chain. In the second step of surrogate modeling, a feasible soft sensor model is derived for quality control over the process chain during the operation stage. For this specific uses case, the production rejection can be reduced by 12% compared to conventional static approaches.

scholarly journals A Simple Model for Portable and Fast Prediction of Execution Time and Power Consumption of GPU Kernels

2021 ◽  
Vol 18 (1) ◽  
pp. 1-25
Author(s):  
Lorenz Braun ◽  
Sotirios Nikas ◽  
Chen Song ◽  
Vincent Heuveline ◽  
Holger Fröning
Keyword(s):  
Simple Model ◽  
Power Consumption ◽  
Execution Time ◽  
Fast Prediction

scholarly journals Correction: Fast prediction of oxygen reduction reaction activity on carbon nanotubes with a localized geometric descriptor

2021 ◽  
Vol 23 (7) ◽  
pp. 4454-4454
Author(s):  
Kunran Yang ◽  
Jeremie Zaffran ◽  
Bo Yang
Keyword(s):  
Carbon Nanotubes ◽  
Oxygen Reduction Reaction ◽  
Oxygen Reduction ◽  
Chem Phys ◽  
Reduction Reaction ◽  
Oxygen Reduction Reaction Activity ◽  
Fast Prediction ◽  
Phys Chem Chem Phys

Correction for ‘Fast prediction of oxygen reduction reaction activity on carbon nanotubes with a localized geometric descriptor’ by Kunran Yang et al., Phys. Chem. Chem. Phys., 2020, 22, 890–895, DOI: 10.1039/C9CP04885E.

scholarly journals A big data approach to the ultra-fast prediction of DFT-calculated bond energies

2013 ◽  
Vol 5 (1) ◽  
Author(s):  
Xiaohui Qu ◽  
Diogo ARS Latino ◽  
Joao Aires-de-Sousa
Keyword(s):  
Big Data ◽  
Bond Energies ◽  
Fast Prediction
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