Errors in the power loss measured in clockwise and anticlockwise rotational magnetisation. Part 2: Physical phenomena

2006 ◽  
Vol 153 (4) ◽  
pp. 152-157 ◽  
Author(s):  
S. Zurek ◽  
T. Meydan
Keyword(s):  
Power Loss ◽  
Physical Phenomena

The Interpretation of Line Profiles

1977 ◽  
Vol 36 ◽  
pp. 191-215
Author(s):  
G.B. Rybicki
Keyword(s):  
Magnetic Fields ◽  
Atomic Physics ◽  
Velocity Fields ◽  
Spectral Lines ◽  
Inhomogeneous Structure ◽  
The Sun ◽  
Line Profiles ◽  
Physical Phenomena

Observations of the shapes and intensities of spectral lines provide a bounty of information about the outer layers of the sun. In order to utilize this information, however, one is faced with a seemingly monumental task. The sun’s chromosphere and corona are extremely complex, and the underlying physical phenomena are far from being understood. Velocity fields, magnetic fields, Inhomogeneous structure, hydromagnetic phenomena – these are some of the complications that must be faced. Other uncertainties involve the atomic physics upon which all of the deductions depend.

TEM visualization of surface replicated acid and base catalyzed sol-gel glasses

1986 ◽  
Vol 44 ◽  
pp. 448-449
Author(s):  
George C. Ruben ◽  
Merrill W. Shafer
Keyword(s):  
Elevated Temperatures ◽  
Sol Gel ◽  
Basic Medium ◽  
Glass Transition Point ◽  
Structural Space ◽  
Acid And Base ◽  
Organometallic Precursors ◽  
New Processing ◽  
Gel Glasses ◽  
Physical Phenomena

Traditionally ceramics have been shaped from powders and densified at temperatures close to their liquid point. New processing methods using various types of sols, gels, and organometallic precursors at low temperature which enable densificatlon at elevated temperatures well below their liquidus, hold the promise of producing ceramics and glasses of controlled and reproducible properties that are highly reliable for electronic, structural, space or medical applications. Ultrastructure processing of silicon alkoxides in acid medium and mixtures of Ludox HS-40 (120Å spheres from DuPont) and Kasil (38% K2O &62% SiO2) in basic medium have been aimed at producing materials with a range of well defined pore sizes (∼20-400Å) to study physical phenomena and materials behavior in well characterized confined geometries. We have studied Pt/C surface replicas of some of these porous sol-gels prepared at temperatures below their glass transition point.

New physical phenomena caused by magnetoelectric and antiferroelectric interactions in magnets

2005 ◽  
Vol 175 (5) ◽  
pp. 457 ◽  
Author(s):  
Evgenii A. Turov ◽  
Vladimir V. Nikolaev
Keyword(s):  
Physical Phenomena

System-in-Packages with Low Power Loss for Future Voltage Regulators

2010 ◽  
Vol 130 (9) ◽  
pp. 1630-1635
Author(s):  
Takayuki Hashimoto ◽  
Tetsuya Kawashima ◽  
Masaki Shiraishi ◽  
Noboru Akiyama ◽  
Tomoaki Uno ◽  
...  
Keyword(s):  
Low Power ◽  
Power Loss ◽  
Voltage Regulators

A Novel Power Loss Calculation Method for Power Magnetic Components in Power Converters

2021 ◽  
Vol 141 (1) ◽  
pp. 69-75
Author(s):  
Yuki Ishikura ◽  
Tatsuya Hosotani ◽  
Masayoshi Yamamoto
Keyword(s):  
Calculation Method ◽  
Power Loss ◽  
Power Converters ◽  
Loss Calculation ◽  
Magnetic Components

Operation of Voltage Reactive Power Control Devices based on Cooperation between Two-Voltage Class Transmission Network with PV Systems to Reduce Power Loss

2020 ◽  
Vol 140 (6) ◽  
pp. 484-494
Author(s):  
Akihisa Kaneko ◽  
Shinya Yoshizawa ◽  
Yasuhiro Hayashi ◽  
Shuhei Sugimura ◽  
Yoshinobu Ueda ◽  
...  
Keyword(s):  
Power Control ◽  
Power Loss ◽  
Reactive Power ◽  
Transmission Network ◽  
Reactive Power Control ◽  
Pv Systems ◽  
Control Devices

Study on Power Fluctuation Dispatch and Capacity Design of Short Period Power Fluctuation Compensation System in Consideration of Power Loss

2014 ◽  
Vol 134 (1) ◽  
pp. 57-63
Author(s):  
Akihiro Teguri ◽  
Shunsuke Kawachi ◽  
Jumpei Baba ◽  
Eisuke Shimoda ◽  
Takayuki Sugimoto
Keyword(s):  
Power Loss ◽  
Compensation System ◽  
Capacity Design ◽  
Power Fluctuation ◽  
Short Period

The mathematical modelling of corrosion in hot dip galvanized steel reinforced concrete

2011 ◽  
Vol 2 (1) ◽  
pp. 1-12
Author(s):  
A. Hegyi ◽  
H. Vermeşan ◽  
V. Rus
Keyword(s):  
Mathematical Model ◽  
Reinforced Concrete ◽  
Numerical Model ◽  
Equation System ◽  
Galvanized Steel ◽  
Steel Reinforced Concrete ◽  
Numeric Model ◽  
Physical And Mathematical Models ◽  
Physical Phenomena ◽  
The Mathematical Model

Abstract In this paper we wish to present the numerical model elaborated in order to simulate some physical phenomena that influence the general deterioration of steel, whether hot dip galvanized or not, in reinforced concrete. We describe the physical and mathematical models, establishing the corresponding equation system, the initial and boundary conditions. We have also presented the numeric model associated to the mathematical model and the numeric methods of discretization and solution of the differential equations system that describes the mathematical model.

Correction: Cryogenic Characterization and Modeling of Silicon Superjunction MOSFET for Power Loss Estimation

2020 ◽  
Author(s):  
Md Maksudul Hossain ◽  
Arman Ur Rashid ◽  
Rosten Sweeting ◽  
Yuqi Wei ◽  
Haider Mhiesan ◽  
...  
Keyword(s):  
Power Loss ◽  
Loss Estimation

Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens

2020 ◽  
Author(s):  
Christian Devereux ◽  
Justin Smith ◽  
Kate Davis ◽  
Kipton Barros ◽  
Roman Zubatyuk ◽  
...  
Keyword(s):  
Drug Development ◽  
Autonomous Vehicles ◽  
Potential Energy Surfaces ◽  
Organic Molecules ◽  
Chemical Elements ◽  
Molecular Potential ◽  
Wide Range ◽  
Energy Surfaces ◽  
New Applications ◽  
Physical Phenomena

<p>Machine learning (ML) methods have become powerful, predictive tools in a wide range of applications, such as facial recognition and autonomous vehicles. In the sciences, computational chemists and physicists have been using ML for the prediction of physical phenomena, such as atomistic potential energy surfaces and reaction pathways. Transferable ML potentials, such as ANI-1x, have been developed with the goal of accurately simulating organic molecules containing the chemical elements H, C, N, and O. Here we provide an extension of the ANI-1x model. The new model, dubbed ANI-2x, is trained to three additional chemical elements: S, F, and Cl. Additionally, ANI-2x underwent torsional refinement training to better predict molecular torsion profiles. These new features open a wide range of new applications within organic chemistry and drug development. These seven elements (H, C, N, O, F, Cl, S) make up ~90% of drug like molecules. To show that these additions do not sacrifice accuracy, we have tested this model across a range of organic molecules and applications, including the COMP6 benchmark, dihedral rotations, conformer scoring, and non-bonded interactions. ANI-2x is shown to accurately predict molecular energies compared to DFT with a ~10<sup>6</sup> factor speedup and a negligible slowdown compared to ANI-1x. The resulting model is a valuable tool for drug development that can potentially replace both quantum calculations and classical force fields for myriad applications.</p>

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