Lecture

Mechanical Motion of Cones

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In course

PHYS-100: Advanced physics I (mechanics)

La Physique Générale I (avancée) couvre la mécanique du point et du solide indéformable. Apprendre la mécanique, c'est apprendre à mettre sous forme mathématique un phénomène physique, en modélisant la situation et appliquant les lois de la physique. ...

Description

This lecture covers the advanced physics concepts related to the motion of cones, focusing on the rotation and rolling without slipping of cone bases. It also delves into the decomposition of vectors to describe the angular velocity and trajectory of points on the cone.

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Instructor

Paolo Ricci

Paolo Ricci earned his masters degree in nuclear engineering at the Politecnico di Torino, Turin (Italy) in 2000. His doctoral studies were conducted at the Los Alamos National Laboratory, with focus on kinetic simulation of magnetic reconnection in the Earth's magnetotail. He spent two-and-a-half years as a postdoctoral researcher at Dartmouth College's Department of Physics and Astronomy, where he worked on gyrokinetic simulations of the Z pinch. He joined the EPFL's Swiss Plasma Center (SPC), as a EURATOM fellow in 2006, was named Tenure Track Assistant Professor in June 2010, and Associate Professor in August 2016. He is at the head of the SPC theory group. Paolo Ricci is the recipient of the 2016 Section de Physique Teaching Prize and of the 2021 Craie d'Or award from the EPFL physics bachelor students.

Official source

Ontological neighbourhood

Physics

Mechanics: Classical mechanics

Mathematics

Algebra: Linear algebra

Related concepts (31)

Rotation

Rotation or rotational motion is the circular movement of an object around a central line, known as axis of rotation. A plane figure can rotate in either a clockwise or counterclockwise sense around a perpendicular axis intersecting anywhere inside or outside the figure at a center of rotation. A solid figure has an infinite number of possible axes and angles of rotation, including chaotic rotation (between arbitrary orientations), in contrast to rotation around a axis.

QR decomposition

In linear algebra, a QR decomposition, also known as a QR factorization or QU factorization, is a decomposition of a matrix A into a product A = QR of an orthonormal matrix Q and an upper triangular matrix R. QR decomposition is often used to solve the linear least squares problem and is the basis for a particular eigenvalue algorithm, the QR algorithm. Any real square matrix A may be decomposed as where Q is an orthogonal matrix (its columns are orthogonal unit vectors meaning ) and R is an upper triangular matrix (also called right triangular matrix).

Angular momentum

In physics, angular momentum (sometimes called moment of momentum or rotational momentum) is the rotational analog of linear momentum. It is an important physical quantity because it is a conserved quantity – the total angular momentum of a closed system remains constant. Angular momentum has both a direction and a magnitude, and both are conserved. Bicycles and motorcycles, flying discs, rifled bullets, and gyroscopes owe their useful properties to conservation of angular momentum.

Velocity

Velocity is the speed and the direction of motion of an object. Velocity is a fundamental concept in kinematics, the branch of classical mechanics that describes the motion of bodies. Velocity is a physical vector quantity: both magnitude and direction are needed to define it. The scalar absolute value (magnitude) of velocity is called , being a coherent derived unit whose quantity is measured in the SI (metric system) as metres per second (m/s or m⋅s−1). For example, "5 metres per second" is a scalar, whereas "5 metres per second east" is a vector.

Cholesky decomposition

In linear algebra, the Cholesky decomposition or Cholesky factorization (pronounced ʃəˈlɛski ) is a decomposition of a Hermitian, positive-definite matrix into the product of a lower triangular matrix and its conjugate transpose, which is useful for efficient numerical solutions, e.g., Monte Carlo simulations. It was discovered by André-Louis Cholesky for real matrices, and posthumously published in 1924. When it is applicable, the Cholesky decomposition is roughly twice as efficient as the LU decomposition for solving systems of linear equations.

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