Improving the Measurement of Volume Fraction of Multiphase Fluids Based on Attenuation of Gamma Rays Without the Use of Artificial Intelligence

MAPAN ◽  
2021 ◽  
Author(s):  
Ali Rabani Nejad ◽  
Dariush Naderi ◽  
Saeed Setayeshi
Keyword(s):  
Artificial Intelligence ◽  
Gamma Rays ◽  
Volume Fraction ◽  
Multiphase Fluids

Prediction of Electrical Conductivity of Cu-15Ni-8Sn-XSi Alloys Using the Physical Model and Artificial Intelligence Model

2013 ◽  
Vol 652-654 ◽  
pp. 1138-1142 ◽  
Author(s):  
Shan Feng Fang ◽  
Yuan Shan Han ◽  
Ming Pu Wang
Keyword(s):  
Artificial Intelligence ◽  
Electrical Conductivity ◽  
Phase Transformation ◽  
Physical Model ◽  
Volume Fraction ◽  
Least Square ◽  
Support Vector ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Experimental Values

The kinetics of phase transformation in Cu-15Ni-8Sn-XSi alloys alloy was studied through the measurement of the relationship between electric conductivity and volume friction of the new phase. The phase transformation kinetics equation was deduced from the Avrami empirical formula based on the linear relationship between the electrical conductivity and the volume fraction of the phase transformation. The electrical conductivity calculated by the physical model was also obtained. As comparisons, a new model based on least square support vector machines (LSSVM) and capable of forecasting electrical property of Cu-15Ni-8Sn-XSi alloys has been proposed and tested on the same data. The present calculated results of both the physical and artificial intelligence models are in very good agreement with the experimental values. Two models are feasible and efficient to forecast the electrical conductivity of Cu-15Ni-8Sn-XSi alloys.

A Class of Conservative Phase Field Models for Multiphase Fluid Flows

2013 ◽  
Vol 81 (2) ◽  
Author(s):  
Jun Li ◽  
Qi Wang
Keyword(s):  
Incompressible Fluid ◽  
Phase Field ◽  
Volume Fraction ◽  
Fluid Phase ◽  
Mass Conservation ◽  
Potential Growth ◽  
Fluid Mixture ◽  
Volume Fractions ◽  
Phase Field Models ◽  
Multiphase Fluids

Hydrodynamic phase field models for multiphase fluids formulated using volume fractions of incompressible fluid components do not normally conserve mass. In this paper, we formulate phase field theories for mixtures of multiple incompressible fluids, using volume fractions, to ensure conservation of mass and momentum for the fluid mixture as well as the total volume for each fluid phase. In this formulation, the mass-average velocity is nonsolenoidal when the densities of incompressible fluid components in the mixture are not equal, making it a bona fide compressible model subject to an internal constraint. Derivation of mass conservation and energy dissipation in phase field models based on both Allen–Cahn dynamics and Cahn–Hilliard dynamics are presented. One salient feature of the phase field models is that the hydrostatic pressure is coupled with the transport of the volume fractions making the momentum transport and the volume fraction transport fully coupled in light of the mass conservation. Near equilibrium dynamics are explored using a linear analysis. In the case of binary fluid mixtures, one potential growth mode is identified in all the models for a class of free energy, which has been adopted for multiphase fluids. The growth is either absent for all waves or of a longwave feature.

scholarly journals Performance and application analysis of ANFIS artificial intelligence for pressure prediction of nanofluid convective flow in a heated pipe

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Iman Behroyan ◽  
Ali Taghvaie Nakhjiri ◽  
Azam Marjani ◽  
Saeed Shirazian
Keyword(s):  
Heat Transfer ◽  
Artificial Intelligence ◽  
Fluid Flow ◽  
Turbulent Flow ◽  
Cfd Simulation ◽  
Fuzzy Inference ◽  
Volume Fraction ◽  
Physical Phenomenon ◽  
Cfd Modeling ◽  
Inference System

AbstractHeat transfer augmentation of the nanofluids is still an attractive concept for researchers due to rising demands for designing efficient heat transfer fluids. However, the pressure loss arisen from the suspension of nanoparticles in liquid is known as a drawback for developing such novel fluids. Therefore, prediction of the nanofluid pressure, especially in internal flows, has been focused on studies. Computational fluid dynamics (CFD) is a commonly used approach for such a prediction of fluid flow. The CFD tools are perfect and precise in prediction of the fluid flow parameters. But they might be time-consuming and expensive, especially for complex models such as 3-dimension modeling and turbulent flow. In addition, the CFD could just predict the pressure, and it is disabled for finding the relationship of such variables. This study is intended to show the performance of the artificial intelligence (AI) algorithm as an auxiliary method for cooperation with the CFD. The turbulent flow of Cu/water nanofluid warming up in a pipe is considered as a sample of a physical phenomenon. The AI algorithm learns the CFD results. Then, the relation between the CFD results is discovered by the AI algorithm. For this purpose, the adaptive network-based fuzzy inference system (ANFIS) is adopted as AI tool. The intelligence condition of the ANFIS is checked by benchmarking the CFD results. The paper outcomes indicated that the ANFIS intelligence is met by employing gauss2mf in the model as the membership function and x, y, and z coordinates, the nanoparticle volume fraction, and the temperature as the inputs. The pressure predicted by the ANFIS at this condition is the same as that predicted by the CFD. The artificial intelligence of ANFIS could find the relation of the nanofluid pressure to the nanoparticle fraction and the temperature. The CFD simulation took much more time (90–110 min) than the total time of the learning and the prediction of the ANFIS (369 s). The CFD modeling was done on a workstation computer, while the ANFIS method was run on a normal desktop.

On the possible gamma-ray source in Cygnus

1967 ◽  
Vol 31 ◽  
pp. 469-471
Author(s):  
J. G. Duthie ◽  
M. P. Savedoff ◽  
R. Cobb
Keyword(s):  
Gamma Rays ◽  
Gamma Ray ◽  
Right Ascension

A source of gamma rays has been found at right ascension 20h15m, declination +35°, with an uncertainty of 6° in each coordinate. Its flux is (1·5 ± 0·8) x 10-4photons cm-2sec-1at 100 MeV. Possible identifications are reviewed, but no conclusion is reached. The mechanism producing the radiation is also uncertain.

Solar Flare Energetic Neutral Emission Measurements in the Project Coronas-I

1994 ◽  
Vol 144 ◽  
pp. 635-639
Author(s):  
J. Baláž ◽  
A. V. Dmitriev ◽  
M. A. Kovalevskaya ◽  
K. Kudela ◽  
S. N. Kuznetsov ◽  
...  
Keyword(s):  
Solar Flare ◽  
Energy Range ◽  
Gamma Rays ◽  
Pulse Shape ◽  
Gamma Ray ◽  
Pulse Shape Discrimination ◽  
Shape Discrimination ◽  
Solar Neutron ◽  
Low Altitude

AbstractThe experiment SONG (SOlar Neutron and Gamma rays) for the low altitude satellite CORONAS-I is described. The instrument is capable to provide gamma-ray line and continuum detection in the energy range 0.1 – 100 MeV as well as detection of neutrons with energies above 30 MeV. As a by-product, the electrons in the range 11 – 108 MeV will be measured too. The pulse shape discrimination technique (PSD) is used.

Chemical partitioning of elements in Ni-base MC-carbide reinforced eutetic superalloys

1983 ◽  
Vol 41 ◽  
pp. 250-251
Author(s):  
E. F. Koch ◽  
E. L. Hall ◽  
S. W. Yang
Keyword(s):  
Alloy Composition ◽  
Volume Fraction ◽  
Lattice Mismatch ◽  
Turbine Blades ◽  
Eutectic Alloys ◽  
Alloy Matrix ◽  
X Ray ◽  
Gas Turbine Blades ◽  
Analytical Electron ◽  
Analytical Electron Microscope

The plane-front solidified eutectic alloys consisting of aligned tantalum monocarbide fibers in a nickel alloy matrix are currently under consideration for future aircraft and gas turbine blades. The MC fibers provide exceptional strength at high temperatures. In these alloys, the Ni matrix is strengthened by the precipitation of the coherent γ' phase (ordered L12 structure, nominally Ni3Al). The mechanical strength of these materials can be sensitively affected by overall alloy composition, and these strength variations can be due to several factors, including changes in solid solution strength of the γ matrix, changes in they γ' size or morphology, changes in the γ-γ' lattice mismatch or interfacial energy, or changes in the MC morphology, volume fraction, thermal stability, and stoichiometry. In order to differentiate between these various mechanisms, it is necessary to determine the partitioning of elemental additions between the γ,γ', and MC phases. This paper describes the results of such a study using energy dispersive X-ray spectroscopy in the analytical electron microscope.

Morphology, Distribution and Identification of Micro-Constituents Along Grain Boundaries in Nickel-Base Alloys

1973 ◽  
Vol 31 ◽  
pp. 176-177
Author(s):  
D. E. Fornwalt ◽  
A. R. Geary ◽  
B. H. Kear
Keyword(s):  
Electron Microscope ◽  
Grain Boundaries ◽  
Volume Fraction ◽  
Rupture Life ◽  
Stress Rupture ◽  
High Volume ◽  
Precipitate Morphology ◽  
Stress Rupture Life ◽  
Nickel Base ◽  
Scanning Electron

A systematic study has been made of the effects of various heat treatments on the microstructures of several experimental high volume fraction γ’ precipitation hardened nickel-base alloys, after doping with ∼2 w/o Hf so as to improve the stress rupture life and ductility. The most significant microstructural chan§e brought about by prolonged aging at temperatures in the range 1600°-1900°F was the decoration of grain boundaries with precipitate particles.Precipitation along the grain boundaries was first detected by optical microscopy, but it was necessary to use the scanning electron microscope to reveal the details of the precipitate morphology. Figure 1(a) shows the grain boundary precipitates in relief, after partial dissolution of the surrounding γ + γ’ matrix.

Ovarian Dysfunction in Fetally Irradiated Beagles

1977 ◽  
Vol 35 ◽  
pp. 658-659
Author(s):  
T. M. Seed ◽  
M. H. Sanderson ◽  
D. L. Gutzeit ◽  
T. E. Fritz ◽  
D. V. Tolle ◽  
...  
Keyword(s):  
Gamma Rays ◽  
Low Dose ◽  
Long Term Effects ◽  
Ovarian Dysfunction ◽  
Dose Rates ◽  
Highly Sensitive ◽  
Microscopic Evaluation ◽  
Ovarian Pathology ◽  
Normal Offspring

The developing mammalian fetus is thought to be highly sensitive to ionizing radiation. However, dose, dose-rate relationships are not well established, especially the long term effects of protracted, low-dose exposure. A previous report (1) has indicated that bred beagle bitches exposed to daily doses of 5 to 35 R 60Co gamma rays throughout gestation can produce viable, seemingly normal offspring. Puppies irradiated in utero are distinguishable from controls only by their smaller size, dental abnormalities, and, in adulthood, by their inability to bear young.We report here our preliminary microscopic evaluation of ovarian pathology in young pups continuously irradiated throughout gestation at daily (22 h/day) dose rates of either 0.4, 1.0, 2.5, or 5.0 R/day of gamma rays from an attenuated 60Co source. Pups from non-irradiated bitches served as controls. Experimental animals were evaluated clinically and hematologically (control + 5.0 R/day pups) at regular intervals.

Instrumentation For Quantitative Microscopy

1977 ◽  
Vol 35 ◽  
pp. 206-209
Author(s):  
B. Ralph ◽  
A.R. Jones
Keyword(s):  
Volume Fraction ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Automatic Data ◽  
Phase Volume Fraction ◽  
Statistical Confidence ◽  
Resultant Data ◽  
Automatic Data Collection ◽  
Third Dimension ◽  
Evolution Of Microstructure

In all fields of microscopy there is an increasing interest in the quantification of microstructure. This interest may stem from a desire to establish quality control parameters or may have a more fundamental requirement involving the derivation of parameters which partially or completely define the three dimensional nature of the microstructure. This latter categorey of study may arise from an interest in the evolution of microstructure or from a desire to generate detailed property/microstructure relationships. In the more fundamental studies some convolution of two-dimensional data into the third dimension (stereological analysis) will be necessary.In some cases the two-dimensional data may be acquired relatively easily without recourse to automatic data collection and further, it may prove possible to perform the data reduction and analysis relatively easily. In such cases the only recourse to machines may well be in establishing the statistical confidence of the resultant data. Such relatively straightforward studies tend to result from acquiring data on the whole assemblage of features making up the microstructure. In this field data mode, when parameters such as phase volume fraction, mean size etc. are sought, the main case for resorting to automation is in order to perform repetitive analyses since each analysis is relatively easily performed.

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