Latent GAN: Using a Latent Space-Based GAN for Rapid Forecasting of CFD Models

2021 ◽  
pp. 360-372
Author(s):  
Jamal Afzali ◽  
César Quilodrán Casas ◽  
Rossella Arcucci
Keyword(s):  
Cfd Models ◽  
Latent Space

The Dark Tetrad

2015 ◽  
Vol 36 (4) ◽  
pp. 228-236 ◽  
Author(s):  
Janko Međedović ◽  
Boban Petrović
Keyword(s):  
Personality Traits ◽  
Antisocial Behavior ◽  
Second Order ◽  
The Other ◽  
Negative Pole ◽  
Latent Space ◽  
Order Factor

Abstract. Machiavellianism, narcissism, and psychopathy are personality traits understood to be dispositions toward amoral and antisocial behavior. Recent research has suggested that sadism should also be added to this set of traits. In the present study, we tested a hypothesis proposing that these four traits are expressions of one superordinate construct: The Dark Tetrad. Exploration of the latent space of four “dark” traits suggested that the singular second-order factor which represents the Dark Tetrad can be extracted. Analysis has shown that Dark Tetrad traits can be located in the space of basic personality traits, especially on the negative pole of the Honesty-Humility, Agreeableness, Conscientiousness, and Emotionality dimensions. We conclude that sadism behaves in a similar manner as the other dark traits, but it cannot be reduced to them. The results support the concept of “Dark Tetrad.”

Positional Estimation Within a Latent Space Model for Networks

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Susan Shortreed ◽  
Mark S. Handcock ◽  
Peter Hoff
Keyword(s):  
Random Effects ◽  
Network Data ◽  
Random Effects Models ◽  
Space Model ◽  
Network Analyses ◽  
Recent Advances ◽  
Latent Space ◽  
Latent Space Model ◽  
Modeling Data

Recent advances in latent space and related random effects models hold much promise for representing network data. The inherent dependency between ties in a network makes modeling data of this type difficult. In this article we consider a recently developed latent space model that is particularly appropriate for the visualization of networks. We suggest a new estimator of the latent positions and perform two network analyses, comparing four alternative estimators. We demonstrate a method of checking the validity of the positional estimates. These estimators are implemented via a package in the freeware statistical language R. The package allows researchers to efficiently fit the latent space model to data and to visualize the results.

Separating Homophily and Peer Influence with Latent Space

2013 ◽  
Author(s):  
Joseph P Davin ◽  
Sunil Gupta ◽  
Mikolaj Jan Piskorski
Keyword(s):  
Peer Influence ◽  
Latent Space

Integrating High Purity Oxygen, Process Modeling, and CFD Models for Doubling Treatment Capacity in a Full Scale Leachate Wastewater Treatment

2015 ◽  
Vol 2015 (6) ◽  
pp. 1647-1657
Author(s):  
Malcolm Fabiyi ◽  
Asun Larrea ◽  
Wladimir Sarmiento-Darkin ◽  
Tony Wang ◽  
Simon Ho ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Process Modeling ◽  
High Purity ◽  
Full Scale ◽  
Cfd Models ◽  
Oxygen Process

scholarly journals Uncertainties Analysis on the Prediction of Flow and Heat Transfer of Liquid Sodium with CFD Technology

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zhanwei Liu ◽  
Xinyu Li ◽  
Tenglong Cong ◽  
Rui Zhang ◽  
Lingyun Zheng ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Nusselt Number ◽  
Friction Coefficient ◽  
Liquid Sodium ◽  
Heat Transfer Characteristics ◽  
Backward Facing Step ◽  
Heating Wall ◽  
Transfer Characteristics ◽  
Cfd Models ◽  
Flow And Heat Transfer

The prediction of flow and heat transfer characteristics of liquid sodium with CFD technology is of significant importance for the design and safety analysis of sodium-cooled fast reactor. The accuracies and uncertainties of the CFD models should be evaluated to improve the confidence of the numerical results. In this work, the uncertainties from the turbulent model, boundary conditions, and physical properties for the flow and heat transfer of liquid sodium were evaluated against the experimental data. The results of uncertainty quantization show that the maximum uncertainties of the Nusselt number and friction coefficient occurred in the transition zone from the inlet to the fully developed region in the circular tube, while they occurred near the reattachment point in the backward-facing step. Furthermore, in backward-facing step flow, the maximum uncertainty of temperature migrated from the heating wall to the geometric center of the channel, while the maximum uncertainty of velocity occurred near the vortex zone. The results of sensitivity analysis illustrate that the Nusselt number was negatively correlated with the thermal conductivity and turbulent Prandtl number, while the friction coefficient was positively correlated with the density and Von Karman constant. This work can be a reference to evaluate the accuracy of the standard k-ε model in predicting the flow and heat transfer characteristics of liquid sodium.

scholarly journals Learning the Incremental Warp for 3D Vehicle Tracking in LiDAR Point Clouds

2021 ◽  
Vol 13 (14) ◽  
pp. 2770
Author(s):  
Shengjing Tian ◽  
Xiuping Liu ◽  
Meng Liu ◽  
Yuhao Bian ◽  
Junbin Gao ◽  
...  
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Vehicle Tracking ◽  
Complex Nature ◽  
Global Feature ◽  
Tracking Accuracy ◽  
State Estimator ◽  
Latent Space ◽  
3D Shapes ◽  
Cloud Tracking

Object tracking from LiDAR point clouds, which are always incomplete, sparse, and unstructured, plays a crucial role in urban navigation. Some existing methods utilize a learned similarity network for locating the target, immensely limiting the advancements in tracking accuracy. In this study, we leveraged a powerful target discriminator and an accurate state estimator to robustly track target objects in challenging point cloud scenarios. Considering the complex nature of estimating the state, we extended the traditional Lucas and Kanade (LK) algorithm to 3D point cloud tracking. Specifically, we propose a state estimation subnetwork that aims to learn the incremental warp for updating the coarse target state. Moreover, to obtain a coarse state, we present a simple yet efficient discrimination subnetwork. It can project 3D shapes into a more discriminatory latent space by integrating the global feature into each point-wise feature. Experiments on KITTI and PandaSet datasets showed that compared with the most advanced of other methods, our proposed method can achieve significant improvements—in particular, up to 13.68% on KITTI.

scholarly journals Performance Prediction of a Hard-Chine Planing Hull by Employing Different CFD Models

2021 ◽  
Vol 9 (5) ◽  
pp. 481
Author(s):  
Azim Hosseini ◽  
Sasan Tavakoli ◽  
Abbas Dashtimanesh ◽  
Prasanta K. Sahoo ◽  
Mihkel Kõrgesaar
Keyword(s):  
Water Flow ◽  
Performance Prediction ◽  
Cfd Modeling ◽  
Computational Time ◽  
Resistance Force ◽  
Stagnation Line ◽  
Mesh Structure ◽  
Cfd Models ◽  
High Speeds ◽  
Des Model

This paper presents CFD (Computational Fluid Dynamics) simulations of the performance of a planing hull in a calm-water condition, aiming to evaluate similarities and differences between results of different CFD models. The key differences between these models are the ways they use to compute the turbulent flow and simulate the motion of the vessel. The planing motion of a vessel on water leads to a strong turbulent fluid flow motion, and the movement of the vessel from its initial position can be relatively significant, which makes the simulation of the problem challenging. Two different frameworks including k-ε and DES (Detached Eddy Simulation) methods are employed to model the turbulence behavior of the fluid motion of the air–water flow around the boat. Vertical motions of the rigid solid body in the fluid domain, which eventually converge to steady linear and angular displacements, are numerically modeled by using two approaches, including morphing and overset techniques. All simulations are performed with a similar mesh structure which allows us to evaluate the differences between results of the applied mesh motions in terms of computation of turbulent air–water flow around the vessel. Through quantitative comparisons, the morphing technique has been seen to result in smaller errors in the prediction of the running trim angle at high speeds. Numerical observations suggest that a DES model can modify the accuracy of the morphing mesh simulations in the prediction of the trim angle, especially at high-speeds. The DES model has been seen to increase the accuracy of the model in the computation of the resistance of the vessel in a high-speed operation, as well. This better level of accuracy in the prediction of resistance is a result of the calculation of the turbulent eddies emerging in the water flow in the downstream zone, which are not captured when a k-ε framework is employed. The morphing approach itself can also increase the accuracy of the resistance prediction. The overset method, however, overpredicts the resistance force. This overprediction is caused by the larger vorticity, computed in the direction of the waves, generated under the bow of the vessel. Furthermore, the overset technique is observed to result in larger hydrodynamic pressure on the stagnation line, which is linked to the greater trim angle, predicted by this approach. The DES model is seen to result in extra-damping of the second and third crests of transom waves as it calculates the stronger eddies in the wake of the boat. Overall, a combination of the morphing and DES models is recommended to be used for CFD modeling of a planing hull at high-speeds. This combined CFD model might be relatively slower in terms of computational time, but it provides a greater level of accuracy in the performance prediction, and can predict the energy damping, developed in the surrounding water. Finally, the results of the present paper demonstrate that a better level of accuracy in the performance prediction of the vessel might also be achieved when an overset mesh motion is used. This can be attained in future by modifying the mesh structure in such a way that vorticity is not overpredicted and the generated eddies, emerging when a DES model is employed, are captured properly.

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