Experimental Investigation and Modelling of the Angular Velocity as a Function of the Radius in Molten Ga75In25 Alloy and Mercury Stirred by Rotation Magnetic Field | Chapter 3 | Current Research Progress in Physical Science Vol. 9

The simulation of the solidification of alloys (like the steels or aluminium alloys) which calculates the melt flow induced by RMF, needs a correct angular velocity vs radius function of the melt. Because it is impossible to obtain information about the melt flow from industrial casting directly, this information can only be obtained from well-monitored experiments using low melting point metals or alloys (e.g., Hg, Ga, GaIn). In this work, the measuring methods suitable for determining this function and analysed for their advantages and disadvantages were first summarised. The angular velocity as a function of the radius was measured by two different methods.

The first one is the Pressure Compensation Method (PCM). In this case, closed TEFLON crucibles with 60 mm length and 12.5 mm radius and Ga75wt%In25wt% alloy were used. The angular velocity (ω) was calculated from the compensation pressure measured at r = 5, 7.5, 10, and 12.5 mm in the 0 – 90 mT range of magnetic induction.

In the case of the second one, a series of data from the literature was processed. The experiments were carried out with the Turbine Wheel Method (TWM), Hg flowing in a glass tube with a diameter of 60 mm, which can be considered practically infinitely long. The value of ω was determined as a function of the radius by an anemometer immersed in Hg. The maximum value of B was 25 mT. A Two-poles inductor with 50 Hz was used in both cases.

After processing the data, it was found that (i) the angular velocity varies significantly along the radius, growing from the wall to the centre of the crucible, contrary to the assumption often used in simulations that it is constant and (ii) at the wall of the crucible, the angular velocity is not zero, contrary to the generally accepted fact that it is definitely zero.

A physical model and two types of mathematical descriptions were developed based on the two ω (B, r) datasets. The two types of ω (B, r) functions could be suitable for the validation of the simulation of melt flow during the solidification of alloys produced by RMF.

 

Author (s) Details

Andras Roosz
Institute of Physical Metallurgy, Metalforming and Nanotechnology, University of Miskolc, 3515 Miskolc, Hungary and HUN-REN-ME Materials Science Research Group, 3515 Miskolc, Hungary.

 

Arnold Ronafoldi
Institute of Physical Metallurgy, Metalforming and Nanotechnology, University of Miskolc, 3515 Miskolc, Hungary and HUN-REN-ME Materials Science Research Group, 3515 Miskolc, Hungary.

 

Maria Sveda
Institute of Physical Metallurgy, Metalforming and Nanotechnology, University of Miskolc, 3515 Miskolc, Hungary and HUN-REN-ME Materials Science Research Group, 3515 Miskolc, Hungary.

 

Zsolt Veres
Institute of Physical Metallurgy, Metalforming and Nanotechnology, University of Miskolc, 3515 Miskolc, Hungary and HUN-REN-ME Materials Science Research Group, 3515 Miskolc, Hungary.

 

Please see the book here:- https://doi.org/10.9734/bpi/crpps/v9/4705

Extraction of Agricultural Workers’ Physical Timeline Data for Acceleration and Angular Velocity through Deep Learning Based System | Chapter 7 | Science and Technology - Recent Updates and Future Prospects Vol. 6

 The present study uses timeline matrix-formed datasets with a variety of common Japanese-styled agri-tools. We base our deep learning approach on achievements in the acoustic deep learning fields, where timeline physical data is transformed into WAV formatted sound file data. Recent improvements in analyzing agri-work have considered up-to-date technologies with deep learning approaches to understand how solutions reflect the experience of traditional agri-workers.  To comprehend acceleration and angular velocity, physical timeline data can be used to derive certain physical features of workers. There is still need for improvement even if different strategies have been put into place internationally for both indoor and outdoor agricultural (agri-) working areas. We apply a deep learning-based method and qualitatively demonstrate the classification of physical timeline datasets. To create our dataset, our subjects were six experienced agri-manual workers and six completely inexperienced men. The targeted task was cultivating the semi-crunching position using a simple, Japanese-style hoe. We captured the subjects’ acceleration and angular velocity data from an integrated multi-sensor module mounted on a wood lilt 15 cm from the gripping position of the dominant hand. We used Python code and recent distributed libraries for computation. For data classification, we successively executed a Recurrent Neural Network (RNN), which we evaluated using wavelet analyses such as the Fast Fourier Transform (FFT). These methods of analyzing digital data could be of practical use for providing key suggestions to improve daily tasks. Future users could automatically or semi-automatically apply our approaches to classify a wide variety of digital matrix-formed data. In the long term, we aim to check and improve the system durability, long-term performance, and other methodological mixing patterns.

 

Author (s) Details

 

Shinji Kawakura
Kobe University, Kobe-shi, Hyogo, 657-0066, Japan.

 

Ryosuke Shibasaki
The University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan and Reitaku University, Kashiwa-shi, Chiba, 277-8686, Japan.

 

Please see the book here:- https://doi.org/10.9734/bpi/strufp/v6/820

Approximate Inertial Coordinate System Selections for Rotation Problems: The Gravitational Field of the Celestial Body Higher than the Object being Rotated: An Observational Study | Chapter 5 | New Insights into Physical Science Vol. 5

 

Selection of the coordinate system is essential for rotation problems. Otherwise, mistakes may occur
due to inaccurate measurement of angular speed. Approximate inertial coordinate system selections
for rotation problems should be the gravitational field of the celestial body higher than the object being
rotated: (1) the Earth fixed Cartesian coordinate system for normal rotation problem; (2) heliocentric -
a geocentric Cartesian coordinate system for satellites orbiting the Earth; (3) the Galaxy Heart -
heliocentric Cartesian coordinates for Earth's rotation around the Sun. In astrophysics, mass
calculation error and angular velocity measurement error lead to a black hole conjecture.

Author(s) Details

Zifeng Li
Petroleum Engineering Department, Yanshan University, Qinhuangdao 066004, Hebei, China.

View Book :- https://bp.bookpi.org/index.php/bpi/catalog/book/245