scholarly journals Experiment based calibration of k − ε turbulence model in OpenFOAM package for mountain slope flows using machine learning techniques

2021 ◽  
Vol 33 (4) ◽  
pp. 227-240
Author(s):  
Daria Igorevna Romanova
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Turbulence Model ◽  
Free Surface Flows ◽  
Machine Learning Techniques ◽  
Mountain Slope ◽  
Surface Flows ◽  
Learning Techniques ◽  
Central Caucasus ◽  
Flow Velocity Profile

We calibrate the k − ε turbulence model for free surface flows in the channel or on the slope. To calibrate the turbulence model, an experiment is carried out in an inclined rectangular research tray. In the experiment, the pressure values in the flow are measured at different distances from the bottom using a Pitot tube; after transforming data, the flow velocity profile is obtained. The k − ε turbulence model is calibrated based on experimental data using the Nelder-Mead optimization algorithm. The calibrated turbulence model is then used to calculate the outburst of a lake near the glacier Maliy Azau on the Elbrus (Central Caucasus).

scholarly journals Pattern Discovery in White Etching Crack Experimental Data Using Machine Learning Techniques

2019 ◽  
Vol 9 (24) ◽  
pp. 5502 ◽  
Author(s):  
Baher Azzam ◽  
Freia Harzendorf ◽  
Ralf Schelenz ◽  
Walter Holweger ◽  
Georg Jacobs
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Feature Selection Method ◽  
Chemical Compounds ◽  
Machine Learning Techniques ◽  
Oil Composition ◽  
The Past ◽  
Learning Techniques ◽  
Physical Tests ◽  
The Relationship

White etching crack (WEC) failure is a failure mode that affects bearings in many applications, including wind turbine gearboxes, where it results in high, unplanned maintenance costs. WEC failure is unpredictable as of now, and its root causes are not yet fully understood. While WECs were produced under controlled conditions in several investigations in the past, converging the findings from the different combinations of factors that led to WECs in different experiments remains a challenge. This challenge is tackled in this paper using machine learning (ML) models that are capable of capturing patterns in high-dimensional data belonging to several experiments in order to identify influential variables to the risk of WECs. Three different ML models were designed and applied to a dataset containing roughly 700 high- and low-risk oil compositions to identify the constituting chemical compounds that make a given oil composition high-risk with respect to WECs. This includes the first application of a purpose-built neural network-based feature selection method. Out of 21 compounds, eight were identified as influential by models based on random forest and artificial neural networks. Association rules were also mined from the data to investigate the relationship between compound combinations and WEC risk, leading to results supporting those of previous analyses. In addition, the identified compound with the highest influence was proved in a separate investigation involving physical tests to be of high WEC risk. The presented methods can be applied to other experimental data where a high number of measured variables potentially influence a certain outcome and where there is a need to identify variables with the highest influence.

Machine Learning Techniques for Fault Diagnosis of Rotating Machines Using Spectrum Image of Vibration Orbits

2020 ◽  
Author(s):  
Clayton Eduardo Rodrigues ◽  
Cairo Lúcio Nascimento Júnior ◽  
Domingos Alves Rade
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Hydrodynamic Instability ◽  
Computational Cost ◽  
Simulated Data ◽  
Machine Learning Techniques ◽  
Disk System ◽  
Rotating Machine ◽  
Learning Techniques ◽  
Run Up

A comparative analysis of machine learning techniques for rotating machine faults diagnosis based on vibration spectra images is presented. The feature extraction of dierent types of faults, such as unbalance, misalignment, shaft crack, rotor-stator rub, and hydrodynamic instability, is performed by processing the spectral image of vibration orbits acquired during the rotating machine run-up. The classiers are trained with simulation data and tested with both simulation and experimental data. The experimental data are obtained from measurements performed on an rotor-disk system test rig supported on hydrodynamic bearings. To generate the simulated data, a numerical model of the rotating system is developed using the Finite Element Method (FEM). Deep learning, ensemble and traditional classication methods are evaluated. The ability of the methods to generalize the image classication is evaluated based on their performance in classifying experimental test patterns that were not used during training. The obtained results suggest that despite considerable computational cost, the method based on Convolutional Neural Network (CNN) presents the best performance for classication of faults based on spectral images.

scholarly journals Monte Carlo, fitting and Machine Learning for Tau leptons

2019 ◽  
Author(s):  
Vladimir Cherepanov ◽  
Elzbieta Richter-Was ◽  
Zbigniew Andrzej Was
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Monte Carlo ◽  
Lepton Pair ◽  
Machine Learning Techniques ◽  
Tau Leptons ◽  
Monte Carlo Generator ◽  
Great Care ◽  
Learning Techniques ◽  
Lepton Decay

Status of \tauτ lepton decay Monte Carlo generator TAUOLA, and its main recent applications are reviewed. It is underlined, that in recent efforts on development of new hadronic currents, the multi-dimensional nature of distributions of the experimental data must be taken with a great care. Studies for H \to \tau\tau ; \tau \to hadronsH→ττ;τ→hadrons indeed demonstrate that multi-dimensional nature of distributions is important and available for evaluation of observables where \tauτ leptons are used to constrain experimental data. For that part of the presentation, use of the TAUOLA program for phenomenology of HH and ZZ decays at LHC is discussed, in particular in the context of the Higgs boson parity measurements with the use of Machine Learning techniques. Some additions, relevant for QED lepton pair emission and electroweak corrections are mentioned as well.

scholarly journals Constraining Fission Yields Using Machine Learning

2019 ◽  
Vol 211 ◽  
pp. 04006 ◽  
Author(s):  
Amy Lovell ◽  
Arvind Mohan ◽  
Patrick Talou ◽  
Michael Chertkov
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Monte Carlo ◽  
Kinetic Energy ◽  
Spontaneous Fission ◽  
Machine Learning Techniques ◽  
Yield Model ◽  
Model Free ◽  
Learning Techniques ◽  
Fission Yields

Having accurate measurements of fission observables is important for a variety of applications, ranging from energy to non-proliferation, defense to astrophysics. Because not all of these data can be measured, it is necessary to be able to accurately calculate these observables as well. In this work, we exploit Monte Carlo and machine learning techniques to reproduce mass and kinetic energy yields, for phenomenological models and in a model-free way. We begin with the spontaneous fission of 252Cf, where there is abundant experimental data, to validate our approach, with the ultimate goal of creating a global yield model in order to predict quantities where data are not currently available.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques
Sign in / Sign up

Export Citation Format

Share Document