scholarly journals Physics-Guided Deep Learning for Drag Force Prediction in Dense Fluid-Particulate Systems

Big Data ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 431-449 ◽  
Author(s):  
Nikhil Muralidhar ◽  
Jie Bu ◽  
Ze Cao ◽  
Long He ◽  
Naren Ramakrishnan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drag Force ◽  
Force Prediction ◽  
Dense Fluid ◽  
Particulate Systems

Development of CFD Methodology for Drag Force Prediction on Passenger Car with Rear Mounted Spoiler

2012 ◽  
Author(s):  
Santosh Pachpund ◽  
Jaiganesh Madhavan ◽  
Ganesh Pandit ◽  
Thomas Chimner
Keyword(s):  
Drag Force ◽  
Passenger Car ◽  
Force Prediction

Prediction models establishment and comparison for guiding force of high-temperature superconducting maglev based on deep learning algorithms

2021 ◽  
Author(s):  
Zhihao Ke ◽  
Xiaoning Liu ◽  
Yining Chen ◽  
Hongfu Shi ◽  
Zigang Deng
Keyword(s):  
Deep Learning ◽  
High Temperature ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Learning Algorithms ◽  
Learning Rate ◽  
Force Prediction ◽  
Validation Method ◽  
Hts Maglev ◽  
Fold Cross Validation

Abstract By the merits of self-stability and low energy consumption, high temperature superconducting (HTS) maglev has the potential to become a novel type of transportation mode. As a key index to guarantee the lateral self-stability of HTS maglev, guiding force has strong non-linearity and is determined by multitudinous factors, and these complexities impede its further researches. Compared to traditional finite element and polynomial fitting method, the prosperity of deep learning algorithms could provide another guiding force prediction approach, but the verification of this approach is still blank. Therefore, this paper establishes 5 different neural network models (RBF, DNN, CNN, RNN, LSTM) to predict HTS maglev guiding force, and compares their prediction efficiency based on 3720 pieces of collected data. Meanwhile, two adaptively iterative algorithms for parameters matrix and learning rate adjustment are proposed, which could effectively reduce computing time and unnecessary iterations. And according to the results, it is revealed that, the DNN model shows the best fitting goodness, while the LSTM model displays the smoothest fitting curve on guiding force prediction. Based on this discovery, the effects of learning rate and iterations on prediction accuracy of the constructed DNN model are studied. And the learning rate and iterations at the highest guiding force prediction accuracy are 0.00025 and 90000, respectively. Moreover, the K-fold cross validation method is also applied to this DNN model, whose result manifests the generalization and robustness of this DNN model. The imperative of K-fold cross validation method to ensure universality of guiding force prediction model is likewise assessed. This paper firstly combines HTS maglev guiding force prediction with deep learning algorithms considering different field cooling height, real-time magnetic flux density, liquid nitrogen temperature and motion direction of bulk. Additionally, this paper gives a convenient and efficient method for HTS guiding force prediction and parameter optimization.

scholarly journals Deep Learning for Musculoskeletal Force Prediction

2018 ◽  
Vol 47 (3) ◽  
pp. 778-789 ◽  
Author(s):  
Lance Rane ◽  
Ziyun Ding ◽  
Alison H. McGregor ◽  
Anthony M. J. Bull
Keyword(s):  
Deep Learning ◽  
Force Prediction

scholarly journals PhyNet: Physics Guided Neural Networks for Particle Drag Force Prediction in Assembly

2020 ◽  
pp. 559-567
Author(s):  
Nikhil Muralidhar ◽  
Jie Bu ◽  
Ze Cao ◽  
Long He ◽  
Naren Ramakrishnan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Drag Force ◽  
Force Prediction

Modeling of the filtered drag force in gas–solid flows via a deep learning approach

2020 ◽  
Vol 225 ◽  
pp. 115835 ◽  
Author(s):  
Yu Zhang ◽  
Ming Jiang ◽  
Xiao Chen ◽  
Yaxiong Yu ◽  
Qiang Zhou
Keyword(s):  
Deep Learning ◽  
Drag Force ◽  
Learning Approach

scholarly journals Distal-end force prediction of tendon-sheath mechanisms for flexible endoscopic surgical robots using deep learning

2019 ◽  
Vol 134 ◽  
pp. 323-337 ◽  
Author(s):  
Xiaoguo Li ◽  
Lin Cao ◽  
Anthony Meng Huat Tiong ◽  
Phuoc Thien Phan ◽  
Soo Jay Phee
Keyword(s):  
Deep Learning ◽  
Tendon Sheath ◽  
Surgical Robots ◽  
Force Prediction

scholarly journals Lashing Force Prediction Model with Multimodal Deep Learning and AutoML for Stowage Planning Automation in Containerships

Logistics ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 1
Author(s):  
Chaemin Lee ◽  
Mun Keong Lee ◽  
Jae Young Shin
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Explicit Calculation ◽  
Force Prediction ◽  
Machine Learning Model ◽  
Stowage Planning ◽  
Force Calculation ◽  
Very High ◽  
Stowage Plan ◽  
Classification Society

The calculation of lashing forces on containerships is one of the most important aspects in terms of cargo safety, as well as slot utilization, especially for large containerships such as more than 10,000 TEU (Twenty-foot Equivalent Unit). It is a challenge for stowage planners when large containerships are in the last port of region because mostly the ship is full and the stacks on deck are very high. However, the lashing force calculation is highly dependent on the Classification society (Class) where the ship is certified; its formula is not published and it is different per each Class (e.g., Lloyd, DNVGL, ABS, BV, and so on). Therefore, the lashing result calculation can only be verified by the Class certified by the Onboard Stability Program (OSP). To ensure that the lashing result is compiled in the stowage plan submitted, stowage planners in office must rely on the same copy of OSP. This study introduces the model to extract the features and to predict the lashing forces with machine learning without explicit calculation of lashing force. The multimodal deep learning with the ANN, CNN and RNN, and AutoML approach is proposed for the machine learning model. The trained model is able to predict the lashing force result and its result is close to the result from its Class.

Modeling Non-Spherical Particulate Systems in the DEM Framework

2020 ◽  
Author(s):  
Vivek Srinivasan ◽  
Danesh Tafti
Keyword(s):  
Computational Cost ◽  
Spherical Particles ◽  
Flow Solver ◽  
Factors Affecting ◽  
Practical Applications ◽  
Dense Fluid ◽  
Conservation Of Momentum ◽  
Major Factors ◽  
Dem Model ◽  
Particulate Systems

Abstract Particulate systems in practical applications have mostly been represented using spherical shapes, even though the majority of particles in archetypal fluid-solid systems are non-spherical. Modeling dense fluid-particulate systems using non-spherical particles involves increased complexity, with computational cost manifesting as the biggest bottleneck. In this research, a novel Discrete Element Method (DEM) model that incorporates geometry definition, collision detection, and post-collision kinematics has been developed to accurately simulate non-spherical particulate systems. Superellipsoids, which account for 80% of particles commonly found in nature, are used to represent non-spherical shapes. Collisions between these particles are processed using a hierarchical detection method. An event-driven model and a time-driven model have been employed in the current framework to resolve collisions. The collision model’s influence on non-spherical particle dynamics is verified by observing the conservation of momentum and total kinetic energy. Furthermore, the non-spherical DEM model is coupled with an in-house fluid flow solver (GenIDLEST). The combined CFDDEM model results are validated by comparing to experimental measurements in a fluidized bed. The parallel scalability of the non-spherical DEM model is evaluated in terms of its efficiency and speedup. Major factors affecting wall clock time of simulations are analyzed and an estimate of the model’s dependency on these factors is given.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning
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