Study of the low cycle fatigue of 12Cr18Ni10Ti steel based on fractal analysis and artificial intelligence approaches

2021 ◽  
Vol 87 (9) ◽  
pp. 59-67
Author(s):  
A. A. Khlybov ◽  
Yu. G. Kabaldin ◽  
M. S. Anosov ◽  
D. A. Ryabov ◽  
D. A. Shatagin
Keyword(s):  
Neural Network ◽  
Growth Rate ◽  
Fractal Dimension ◽  
Low Temperatures ◽  
Main Crack ◽  
Fatigue Loading ◽  
Wide Range ◽  
Lower Temperature ◽  
Number Of Cycles ◽  
Dimension Index

The evolution of the structure and assessment of the age limit of steel 12Cr18Ni10Ti upon fatigue loading is considered using neural network modeling and approaches of fractal analysis of the microstructure. An algorithm for processing images of the microstructures has been developed to improve their quality. An indicator of the fractal dimension of the image is used as a quantitative indicator for assessing the evolution of the microstructure of the surface metal layer. A quantitative assessment of the structures at different stress amplitudes is carried out in a wide range of low temperatures using the fractal dimension index. Correlation of the fractal dimension index with the run of the sample material is shown. The appearance of the main crack was observed in the range of 0.7 - 0.8 from the number of cycles to failure, after which the crack growth rate increased. At a lower temperature, the main crack is formed later, but further loading results in a higher crack growth rate. Formation of the secondary phases in austenitic steel at a lower temperature occurred at earlier stages than that at a temperature of t = +20°C, which led to hardening of the material. An artificial neural network (ANN) has been developed and trained for assessing structural changes in metal proceeding from the fractal dimensionality of the microstructure images at different stages of fatigue loading. The developed neural network made it possible to estimate with a sufficiently high accuracy the number of cycles before damage of the sample and the residual life of the material. Thus, the developed ANN can be used to assess the current state of the material in a wide range of low temperatures.

scholarly journals Temperature, by Controlling Growth Rate, Regulates CRISPR-Cas Activity inPseudomonas aeruginosa

mBio ◽  
2018 ◽  
Vol 9 (6) ◽  
Author(s):  
Nina Molin Høyland-Kroghsbo ◽  
Katrina Arcelia Muñoz ◽  
Bonnie L. Bassler
Keyword(s):  
Growth Rate ◽  
Low Temperatures ◽  
Defense Mechanism ◽  
Slow Growth ◽  
Adaptive Immune System ◽  
Additional Time ◽  
Content Type ◽  
Bacteriophage Infection ◽  
Life Threatening ◽  
Lower Temperature

ABSTRACTClustered regularly interspaced short palindromic repeat (CRISPR)-associated (CRISPR-Cas) systems are adaptive defense systems that protect bacteria and archaea from invading genetic elements. InPseudomonas aeruginosa, quorum sensing (QS) induces the CRISPR-Cas defense system at high cell density when the risk of bacteriophage infection is high. Here, we show that another cue, temperature, modulatesP. aeruginosaCRISPR-Cas. Increased CRISPR adaptation occurs at environmental (i.e., low) temperatures compared to that at body (i.e., high) temperature. This increase is a consequence of the accumulation of CRISPR-Cas complexes, coupled with reducedP. aeruginosagrowth rate at the lower temperature, the latter of which provides additional time prior to cell division for CRISPR-Cas to patrol the cell and successfully eliminate and/or acquire immunity to foreign DNA. Analyses of a QS mutant and synthetic QS compounds show that the QS and temperature cues act synergistically. The diversity and level of phage encountered byP. aeruginosain the environment exceed that in the human body, presumably warranting increased reliance on CRISPR-Cas at environmental temperatures.IMPORTANCEP. aeruginosais a soil dwelling bacterium and a plant pathogen, and it also causes life-threatening infections in humans. Thus,P. aeruginosathrives in diverse environments and over a broad range of temperatures. SomeP. aeruginosastrains rely on the CRISPR-Cas adaptive immune system as a phage defense mechanism. Our discovery that low temperatures increase CRISPR adaptation suggests that the rarely occurring but crucial naive adaptation events may take place predominantly under conditions of slow growth, e.g., during the bacterium’s soil dwelling existence and during slow growth in biofilms.

The Growth of Fish

1928 ◽  
Vol 6 (2) ◽  
pp. 125-130
Author(s):  
J. GRAY
Keyword(s):  
Growth Rate ◽  
High Temperature ◽  
Temperature Coefficient ◽  
Low Temperatures ◽  
Morphological Development ◽  
Full Time ◽  
Larval Life ◽  
Egg Shell ◽  
Lower Temperature ◽  
The Moment

1. Normal trout larvae can be raised from eggs incubated at any temperature between 2.8° C. and 13° C. without high mortality. Above 15° C. the mortality is high. 2. The morphological development of the embryo at the moment of hatching depends on the temperature of incubation. When eggs are incubated at low temperatures the embryos, at the moment of hatching, are significantly larger than those hatching from eggs which have been incubated at higher temperatures. Precocious hatching at high temperatures is probably associated with a high temperature coefficient of the hatching enzyme which softens the egg-shell. 3. Increasing the temperature of incubation increases the growth-rate of the embryo, but, at the end of larval life, the full-time embryo is smaller than after slower development at a lower temperature. At higher temperatures a larger proportion of the available yolk is required for the maintenance of the embryo, leaving a smaller proportion available for the formation of new tissue.

Research of the cold resistance of metals by indentation with registration of an acoustic emission signal

2020 ◽  
pp. 61-64
Author(s):  
Yu.G. Kabaldin ◽  
A.A. Khlybov ◽  
M.S. Anosov ◽  
D.A. Shatagin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Low Temperatures ◽  
Cold Resistance ◽  
Emission Signal ◽  
Impact Bending ◽  
Artificial Neural

The study of metals in impact bending and indentation is considered. A bench is developed for assessing the character of failure on the example of 45 steel at low temperatures using the classification of acoustic emission signal pulses and a trained artificial neural network. The results of fractographic studies of samples on impact bending correlate well with the results of pulse recognition in the acoustic emission signal. Keywords acoustic emission, classification, artificial neural network, low temperature, character of failure, hardness. [email protected]

scholarly journals Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Charles Gbenga Williams ◽  
Oluwapelumi O. Ojuri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydraulic Conductivity ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Coarse Grained ◽  
Soil Types ◽  
Wide Range ◽  
Artificial Neural ◽  
Input Variables

AbstractAs a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better.

scholarly journals Theoretical and Experimental Fatigue Strength Calculations of Lips Compensating Circumferential Backlash in Gear Pumps

Energies ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 251
Author(s):  
Piotr Osiński ◽  
Grzegorz Chruścielski ◽  
Leszek Korusiewicz
Keyword(s):  
Maximum Stress ◽  
Theoretical Calculations ◽  
Fatigue Loading ◽  
Minimum Thickness ◽  
Bending Tests ◽  
External Gear Pumps ◽  
Gear Pumps ◽  
In Series ◽  
Number Of Cycles ◽  
Technical Solutions

This article presents theoretical and experimental calculations of the minimum thickness of a compensation lip used in external gear pumps. Pumps of this type are innovative technical solutions in which circumferential backlash (clearance) compensation is used to improve their volumetric and overall efficiency. However, constructing a prototype of such a pump requires long-lasting research, and the compensation lip is its key object, due to the fact that it is an element influenced by a notch and that it operates in unfavorable conditions of strong fatigue stresses. The theoretical calculations presented in this article are based on identifying maximum stress values in a fatigue cycle and on implementing the stress failure condition and the conditions related to the required value of the fatigue safety factor. The experimental research focuses on static bending tests of the lips as well as on the fatigue loading of the lips in series of tests at increasing stress values until lip failure due to fatigue. The tests allowed the minimum lip thickness to be found for the assumed number of fatigue cycles, which is 2.5 times the number of cycles used in wear margin tests.

LATTICE DYNAMICS OF THE BINARY APERIODIC CHAINS OF ATOMS I: FRACTAL DIMENSION OF PHONON SPECTRA

1995 ◽  
Vol 09 (12) ◽  
pp. 1429-1451 ◽  
Author(s):  
WŁODZIMIERZ SALEJDA
Keyword(s):  
Fractal Dimension ◽  
Lattice Dynamics ◽  
Nearest Neighbor ◽  
Average Distance ◽  
Local Maximum ◽  
Fractal Dimensions ◽  
Dimension Formula ◽  
Phonon Spectra ◽  
Wide Range ◽  
Silver Mean

The microscopic harmonic model of lattice dynamics of the binary chains of atoms is formulated and studied numerically. The dependence of spring constants of the nearest-neighbor (NN) interactions on the average distance between atoms are taken into account. The covering fractal dimensions [Formula: see text] of the Cantor-set-like phonon spec-tra (PS) of generalized Fibonacci and non-Fibonaccian aperiodic chains containing of 16384≤N≤33461 atoms are determined numerically. The dependence of [Formula: see text] on the strength Q of NN interactions and on R=mH/mL, where mH and mL denotes the mass of heavy and light atoms, respectively, are calculated for a wide range of Q and R. In particular we found: (1) The fractal dimension [Formula: see text] of the PS for the so-called goldenmean, silver-mean, bronze-mean, dodecagonal and Severin chain shows a local maximum at increasing magnitude of Q and R>1; (2) At sufficiently large Q we observe power-like diminishing of [Formula: see text] i.e. [Formula: see text], where α=−0.14±0.02 and α=−0.10±0.02 for the above specified chains and so-called octagonal, copper-mean, nickel-mean, Thue-Morse, Rudin-Shapiro chain, respectively.

Magnetic Quantum Oscillations from Surface States of Bi Nanowires

2011 ◽  
Vol 1350 ◽  
Author(s):  
L. A. Konopko ◽  
T. E. Huber ◽  
A. A. Nikolaeva
Keyword(s):  
Single Crystal ◽  
Low Temperatures ◽  
Surface States ◽  
Charge Carriers ◽  
Quantum Oscillations ◽  
Wide Range ◽  
Spin Orbit Interactions ◽  
Conducting Tube ◽  
Magnetic Quantum Oscillations ◽  
Strong Spin

ABSTRACTIn this work, we report the results of studies of the transverse magnetoresistance (MR) of single-crystal Bi nanowires with diameter d<80 nm. The single-crystal nanowire samples were prepared by the Taylor-Ulitovsky technique. Due to the semimetal-to-semiconductor transformation and high density of surface states with strong spin-orbit interactions, the charge carriers are confined to the conducting tube made of surface states. The non monotonic changes of transverse MR that are equidistant in a direct magnetic field were observed at low temperatures in a wide range of magnetic fields up to 14 T. The period of oscillations depends on the wire diameter d as for the case of longitudinal MR. An interpretation of transverse MR oscillations is presented.

Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Sandip K Lahiri ◽  
Kartik Chandra Ghanta
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Misclassification Error ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Pipeline Design ◽  
Hybrid Artificial Neural Network

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.

Robust sideslip angle observer of commercial vehicles based on cornering stiffness estimation using neural network

2022 ◽  
pp. 095440702110724
Author(s):  
Chenyu Zhou ◽  
Liangyao Yu ◽  
Yong Li ◽  
Jian Song
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Stability Control ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Commercial Vehicles ◽  
Sideslip Angle ◽  
The Neural Network ◽  
Wide Range ◽  
The Mathematical Model

Accurate estimation of sideslip angle is essential for vehicle stability control. For commercial vehicles, the estimation of sideslip angle is challenging due to severe load transfer and tire nonlinearity. This paper presents a robust sideslip angle observer of commercial vehicles based on identification of tire cornering stiffness. Since tire cornering stiffness of commercial vehicles is greatly affected by tire force and road adhesion coefficient, it cannot be treated as a constant. To estimate the cornering stiffness in real time, the neural network model constructed by Levenberg-Marquardt backpropagation (LMBP) algorithm is employed. LMBP is a fast convergent supervised learning algorithm, which combines the steepest descent method and gauss-newton method, and is widely used in system parameter estimation. LMBP does not rely on the mathematical model of the actual system when building the neural network. Therefore, when the mathematical model is difficult to establish, LMBP can play a very good role. Considering the complexity of tire modeling, this study adopted LMBP algorithm to estimate tire cornering stiffness, which have simplified the tire model and improved the estimation accuracy. Combined with neural network, A time-varying Kalman filter (TVKF) is designed to observe the sideslip angle of commercial vehicles. To validate the feasibility of the proposed estimation algorithm, multiple driving maneuvers under different road surface friction have been carried out. The test results show that the proposed method has better accuracy than the existing algorithm, and it’s robust over a wide range of driving conditions.

Artificial Neural Network and the Taguchi Method Application for Robust Wheatstone Bridge Design

1994 ◽  
Author(s):  
Jung-eui Hong ◽  
Cihan H. Dagli ◽  
Kenneth M. Ragsdell
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wheatstone Bridge ◽  
Parameter Design ◽  
System Response ◽  
Measurement Circuit ◽  
Wide Range ◽  
Reduce Measurement Error ◽  
On Line ◽  
Artificial Neural

Abstract The primary function of the Wheatstone bridge is to measure an unknown resistance. The elements of this well-known measurement circuit will take on different values depending upon the range and accuracy required for a particular application. The Taguchi approach to parameter design is used to select values for the measurement circuit elements so as to reduce measurement error. Next we introduce the use of an artificial neural network to extrapolate limited experimental results to predict system response over a wide range of applications. This approach can be employed for on-line quality control of the manufacture of such device.

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