Hybrid models for jets and plumes in a flowing ambient fluid

1998 ◽  
Author(s):  
Kwok Leung Pun
Keyword(s):  
Hybrid Models ◽  
Ambient Fluid ◽  
Flowing Ambient Fluid

Research on Jet Current Field Characteristic under Waves by Numerical Modelling in Flowing Ambient Fluid

2014 ◽  
Vol 989-994 ◽  
pp. 3460-3463
Author(s):  
Ming Chang Li ◽  
Qi Si ◽  
Ying Wang
Keyword(s):  
Present Model ◽  
Sewage Treatment ◽  
Variable Density ◽  
Ambient Fluid ◽  
Buoyant Jet ◽  
Current Field ◽  
Efficient Tool ◽  
Simulation Results ◽  
Construction Characteristic ◽  
Flowing Ambient Fluid

A variable density anisotropic turbulent buoyant jet model is applied for simulating the current field under waves. The numerical simulation results show the present model is efficient to describe the construction characteristic of current field. This model could be an efficient tool for sewage treatment by deep sea discharge.

Drop formation in a co-flowing ambient fluid

2004 ◽  
Vol 59 (15) ◽  
pp. 3045-3058 ◽  
Author(s):  
Carsten Cramer ◽  
Peter Fischer ◽  
Erich J. Windhab
Keyword(s):  
Drop Formation ◽  
Ambient Fluid ◽  
Flowing Ambient Fluid

Numerical Simulation of Sewage Treatment by Deep Sea Discharge in Flowing Ambient Fluid

2013 ◽  
Vol 409-410 ◽  
pp. 310-313 ◽  
Author(s):  
Ming Chang Li ◽  
Guang Yu Zhang ◽  
Qi Si ◽  
Shu Xiu Liang ◽  
Zhao Chen Sun
Keyword(s):  
Numerical Simulation ◽  
Deep Sea ◽  
Recirculation Zone ◽  
Sewage Treatment ◽  
Ambient Fluid ◽  
Buoyant Jet ◽  
Current Field ◽  
Flowing Ambient Fluid

Sewage deep sea discharge is used in nearshore district, which has a characteristic of jet. In this paper, anisotropic turbulent buoyant jet model is applied for simulating the current field of jet and the distribution of pollutant dilution. The scale of recirculation zone is very important parameter for sewage dilution, which is simulated in this paper.

Combination of End-to-End and Hybrid Models for Speech Recognition

2020 ◽  
Author(s):  
Jeremy H.M. Wong ◽  
Yashesh Gaur ◽  
Rui Zhao ◽  
Liang Lu ◽  
Eric Sun ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Hybrid Models ◽  
End To End

Intercomparison of downscaling methods for daily precipitation with emphasis on wavelet-based hybrid models

2021 ◽  
pp. 126373
Author(s):  
Yeditha Pavan Kumar ◽  
Rathinasamy Maheswaran ◽  
Ankit Agarwal ◽  
Bellie Sivakumar
Keyword(s):  
Daily Precipitation ◽  
Hybrid Models

Spatial Prediction of Landslides Along National Highway-6, Hoa Binh Province, Vietnam Using Novel Hybrid Models

2021 ◽  
pp. 1-22
Author(s):  
Ha Thi Hang ◽  
Hoang Tung ◽  
Pham Duy Hoa ◽  
Nguyen Viet Phuong ◽  
Tran Van Phong ◽  
...  
Keyword(s):  
Spatial Prediction ◽  
Hybrid Models ◽  
National Highway ◽  
Hoa Binh

scholarly journals Multi-Transformer: A New Neural Network-Based Architecture for Forecasting S&P Volatility

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1794
Author(s):  
Eduardo Ramos-Pérez ◽  
Pablo J. Alonso-González ◽  
José Javier Núñez-Velázquez
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Short Term Memory ◽  
Risk Measures ◽  
Hybrid Models ◽  
Stock Volatility ◽  
Management Actions ◽  
Equity Risk ◽  
Hedging Strategies ◽  
Volatility Models

Events such as the Financial Crisis of 2007–2008 or the COVID-19 pandemic caused significant losses to banks and insurance entities. They also demonstrated the importance of using accurate equity risk models and having a risk management function able to implement effective hedging strategies. Stock volatility forecasts play a key role in the estimation of equity risk and, thus, in the management actions carried out by financial institutions. Therefore, this paper has the aim of proposing more accurate stock volatility models based on novel machine and deep learning techniques. This paper introduces a neural network-based architecture, called Multi-Transformer. Multi-Transformer is a variant of Transformer models, which have already been successfully applied in the field of natural language processing. Indeed, this paper also adapts traditional Transformer layers in order to be used in volatility forecasting models. The empirical results obtained in this paper suggest that the hybrid models based on Multi-Transformer and Transformer layers are more accurate and, hence, they lead to more appropriate risk measures than other autoregressive algorithms or hybrid models based on feed forward layers or long short term memory cells.

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