Tilt Pad Bearing Distributed Pad Inlet Temperature with Machine Learning -Part I: Static and Dynamic Characteristics

2021 ◽  
pp. 1-45
Author(s):  
Jongin Yang ◽  
Alan Palazzolo
Keyword(s):  
Machine Learning ◽  
Dynamic Characteristics ◽  
Leading Edge ◽  
Inlet Temperature ◽  
Static And Dynamic Characteristics ◽  
Axial Temperature ◽  
Computational Speed ◽  
Morton Effect ◽  
Comparable Accuracy ◽  
Volume Method

Abstract Uncertainty in mixing coefficients MC for estimating pad leading edge film temperature in tilt pad journal bearings, reduces the reliability of predicted characteristics. A 3D Hybrid Between Pad (HBP) model, utilizing CFD and machine learning ML, is developed to provide the radial and axial temperature distributions at the leading-edge. This provides a ML derived, 2D film temperature distribution in place of a single uniform temperature. This has a significant influence on predicted journal temperature, dynamic coefficients, and Morton Effect response. An innovative Finite-Volume-Method (FVM) solver significantly increases computational speed, while maintaining comparable accuracy with CFD. Part I provides methodology and simulation results for static and dynamic characteristics, while Part II applies this to Morton Effect response.

Tilt Pad Bearing Distributed Pad Inlet Temperature with Machine Learning -Part II: Morton Effect

2021 ◽  
pp. 1-45
Author(s):  
Jongin Yang ◽  
Alan Palazzolo
Keyword(s):  
Machine Learning ◽  
Cross Section ◽  
Leading Edge ◽  
Thermal Boundary Condition ◽  
Inlet Temperature ◽  
Axial Distribution ◽  
Morton Effect ◽  
Improving Accuracy ◽  
Volume Method ◽  
Asymmetric Heating

Abstract The Morton Effect (ME) occurs when a bearing journal experiences asymmetric heating due to synchronous vibration, resulting in thermal bowing of the shaft and increasing vibration. An accurate prediction of the journal's asymmetric temperature distribution is critical for reliable ME simulation. This distribution is strongly influenced by the film thermal boundary condition at the pad inlets. Part I utilizes machine learning ML to obtain a 2D radial and axial distribution of temperatures over the leading edge film cross section. The hybrid finite volume method FVM – bulk flow method of Part I eliminated film temperature discontinuities, and is utilized in Part II for improving accuracy and efficiency of ME simulation.

Computational Fluid Dynamics Based Mixing Prediction for Tilt Pad Journal Bearing TEHD Modeling—Part II: Implementation With Machine Learning

2020 ◽  
Vol 143 (1) ◽  
Author(s):  
Jongin Yang ◽  
Alan Palazzolo
Keyword(s):  
Machine Learning ◽  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Three Dimensional ◽  
Leading Edge ◽  
Cfd Modeling ◽  
Inlet Temperature ◽  
Training Set ◽  
Simplified Approach ◽  
Flow Mixing

Abstract Reynolds based thermo-elasto-hydrodynamic (TEHD) simulations of tilting pad journal bearings (TPJBs) generally provide accurate results; however, the uncertainty of the pad’s leading edge thermal boundary conditions causes uncertainty of the results. The highly complex thermal-flow mixing action between pads (BPs) results from the oil supply nozzle jets and geometric features. The conventional Reynolds approach employs mixing coefficients (MCs), estimated from experience, to approximate a uniform inlet temperature for each pad. Part I utilized complex computational fluid dynamics (CFD) flow modeling to illustrate that temperature distributions at the pad inlets may deviate strongly from being uniform. The present work retains the uniform MC model but obtains the MC from detailed three-dimensional CFD modeling and machine learning, which could be extended to the radially and axially varying MC case. The steps for implementing an artificial neural network (ANN) approach for MC regression are provided as follows: (1) utilize a design of experiment step for obtaining an adaptable training set, (2) conduct CFD simulations on the BP to obtain the outputs of the training set, (3) apply an ANN learning process by Levenverg–Mardquart backpropagation with the Bayesian regularization, and (4) couple the ANN MC results with conventional TEHD Reynolds models. An approximate log fitting method provides a simplified approach for MC regression. The effectiveness of the Reynolds TEHD TPJB model with ANN regression-based MC distributions is confirmed by comparison with CFD based TEHD TPJB model results. The method obtains an accuracy nearly the same as the complete CFD model, but with the computational economy of a Reynolds approach.

scholarly journals On Characteristics of Symbolic Execution in the Problem of Assessing the Quality of Obfuscating Transformations

2021 ◽  
Vol 28 (1) ◽  
pp. 38-51
Author(s):  
Petr D. Borisov ◽  
Yury V. Kosolapov
Keyword(s):  
Machine Learning ◽  
Dynamic Characteristics ◽  
Symbolic Execution ◽  
Specific Class ◽  
Static Characteristics ◽  
Learning Methods ◽  
Static And Dynamic Characteristics ◽  
Machine Learning Methods ◽  
Relevant Task

Obfuscation is used to protect programs from analysis and reverse engineering. There are theoretically effective and resistant obfuscation methods, but most of them are not implemented in practice yet. The main reasons are large overhead for the execution of obfuscated code and the limitation of application only to a specific class of programs. On the other hand, a large number of obfuscation methods have been developed that are applied in practice. The existing approaches to the assessment of such obfuscation methods are based mainly on the static characteristics of programs. Therefore, the comprehensive (taking into account the dynamic characteristics of programs) justification of their effectiveness and resistance is a relevant task. It seems that such a justification can be made using machine learning methods, based on feature vectors that describe both static and dynamic characteristics of programs. In this paper, it is proposed to build such a vector on the basis of characteristics of two compared programs: the original and obfuscated, original and deobfuscated, obfuscated and deobfuscated. In order to obtain the dynamic characteristics of the program, a scheme based on a symbolic execution is constructed and presented in this paper. The choice of the symbolic execution is justified by the fact that such characteristics can describe the difficulty of comprehension of the program in dynamic analysis. The paper proposes two implementations of the scheme: extended and simplified. The extended scheme is closer to the process of analyzing a program by an analyst, since it includes the steps of disassembly and translation into intermediate code, while in the simplified scheme these steps are excluded. In order to identify the characteristics of symbolic execution that are suitable for assessing the effectiveness and resistance of obfuscation based on machine learning methods, experiments with the developed schemes were carried out. Based on the obtained results, a set of suitable characteristics is determined.

High-Voltage Ultra-Fast Pulse Diode Stack Based on 4H-SiC

2016 ◽  
Vol 858 ◽  
pp. 786-789 ◽  
Author(s):  
Vladimir A. Ilyin ◽  
Alexey V. Afanasyev ◽  
Boris V. Ivanov ◽  
Alexey F. Kardo-Sysoev ◽  
Victor V. Luchinin ◽  
...  
Keyword(s):  
High Voltage ◽  
Dynamic Characteristics ◽  
Leading Edge ◽  
Edge Length ◽  
Static And Dynamic Characteristics ◽  
Switching Performance ◽  
Bandgap Narrowing ◽  
Heavily Doped ◽  
Simulation Results ◽  
Fast Pulse

The paper reports on the results of the studies of static and dynamic characteristics of 4H-SiC drift step recovery diodes (DSRDs) assembled in diode stacks. Switching performance of single dies has been simulated and experimentally confirmed. It was established that the switching process is determined primarily by the incomplete ionization of acceptors in 4H-SiC and by the bandgap narrowing in heavily doped emitters. Based on the simulation results the optimized die size has been selected. For DSRD stacks of 4 and 8 dies I-V and C-V measurements are reported. The stacks were dynamically tested in a special oscillator circuit. Repetitive voltage pulses of 10.5 kV with the leading edge length of 900 ps were demonstrated.

30 kV Pulse Diode Stack Based on 4H-SiC

2018 ◽  
Vol 924 ◽  
pp. 841-844 ◽  
Author(s):  
Vladimir A. Ilyin ◽  
Alexey V. Afanasyev ◽  
Yuri S. Demin ◽  
Boris V. Ivanov ◽  
Alexey F. Kardo-Sysoev ◽  
...  
Keyword(s):  
Dynamic Characteristics ◽  
Leading Edge ◽  
Optimal Performance ◽  
Switching Speed ◽  
Molding Compound ◽  
Static And Dynamic Characteristics ◽  
Oscillator Circuit ◽  
Blocking Voltage ◽  
In Series ◽  
Voltage Pulses

The paper reports on the studies of static and dynamic characteristics of 30 kV diode stacks based on 4H-SiC drift step recovery diodes (DSRDs). It was found that the optimal performance in terms of blocking voltage and switching speed can be achieved with 2 kV DSRD dies. Fifteen 2 kV DSRD dies were connected in series and sealed with molding compound. The stacks were dynamically tested in a special oscillator circuit. Repetitive voltage pulses of 30.5 kV with the leading edge of 1.6 ns were demonstrated.

Invited PaperTemperature dependence of SiGe HBT static and dynamic characteristics

1998 ◽  
Vol 08 (PR3) ◽  
pp. Pr3-81-Pr3-86
Author(s):  
F. Aniel ◽  
N. Zerounian ◽  
A. Gruhle ◽  
C. Mähner ◽  
G. Vernet ◽  
...  
Keyword(s):  
Dynamic Characteristics ◽  
Static And Dynamic Characteristics ◽  
Sige Hbt

Unsteady simulations of migration and deposition of fly-ash particles in the first-stage turbine of an aero-engine

2021 ◽  
pp. 1-21
Author(s):  
Z. Hao ◽  
X. Yang ◽  
Z. Feng
Keyword(s):  
Fly Ash ◽  
Film Cooling ◽  
Particle Deposition ◽  
Flow Characteristics ◽  
Leading Edge ◽  
Velocity Model ◽  
Operating Conditions ◽  
Inlet Temperature ◽  
Internal Cooling ◽  
Aero Engine

Abstract Particulate deposits in aero-engine turbines change the profile of blades, increase the blade surface roughness and block internal cooling channels and film cooling holes, which generally leads to the degradation of aerodynamic and cooling performance. To reveal particle deposition effects in the turbine, unsteady simulations were performed by investigating the migration patterns and deposition characteristics of the particle contaminant in a one-stage, high-pressure turbine of an aero-engine. Two typical operating conditions of the aero-engine, i.e. high-temperature take-off and economic cruise, were discussed, and the effects of particle size on the migration and deposition of fly-ash particles were demonstrated. A critical velocity model was applied to predict particle deposition. Comparisons between the stator and rotor were made by presenting the concentration and trajectory of the particles and the resulting deposition patterns on the aerofoil surfaces. Results show that the migration and deposition of the particles in the stator passage is dominated by the flow characteristics of fluid and the property of particles. In the subsequential rotor passage, in addition to these factors, particles are also affected by the stator–rotor interaction and the interference between rotors. With higher inlet temperature and larger diameter of the particle, the quantity of deposits increases and the deposition is distributed mainly on the Pressure Side (PS) and the Leading Edge (LE) of the aerofoil.

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