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Lecture

Dimensional Analysis of Explosions

In course

ME-201: Continuum mechanics

Continuum conservation laws (e.g. mass, momentum and energy) will be introduced. Mathematical tools, including basic algebra and calculus of vectors and Cartesian tensors will be taught. Stress and deformation tensors will be applied to examples drawn from ...

Description

This lecture covers the dimensional analysis of large explosions using images from Life magazine, exploring the relationship between time and radius, making assumptions about energy release and shock wave characteristics, and analyzing tabulated values to predict explosive behavior.

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Instructor

John Martin Kolinski

Dr. Kolinski studied Applied Mathematics (Sc.M.) and Applied Physics (Ph.D.) at Harvard University, completing a PhD under the supervision of L. Mahadevan and Shmuel Rubinstein on the role of air in droplet impact. John did his post-doc at the Hebrew University of Jerusalem in Israel supported by the Fulbright post-doctoral fellowship. At HUJI, he worked on interfacial instabilities in soft matter in the labs of Eran Sharon and Jay Fineberg. John continues his research into interfacial mechanics at EPFL in EMSI, his newly founded laboratory for the study of Engineering Mechanics of Soft Interfaces.

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Related concepts (32)

Dimension

In physics and mathematics, the dimension of a mathematical space (or object) is informally defined as the minimum number of coordinates needed to specify any point within it. Thus, a line has a dimension of one (1D) because only one coordinate is needed to specify a point on it - for example, the point at 5 on a number line. A surface, such as the boundary of a cylinder or sphere, has a dimension of two (2D) because two coordinates are needed to specify a point on it - for example, both a latitude and longitude are required to locate a point on the surface of a sphere.

Predictive modelling

Predictive modelling uses statistics to predict outcomes. Most often the event one wants to predict is in the future, but predictive modelling can be applied to any type of unknown event, regardless of when it occurred. For example, predictive models are often used to detect crimes and identify suspects, after the crime has taken place. In many cases, the model is chosen on the basis of detection theory to try to guess the probability of an outcome given a set amount of input data, for example given an email determining how likely that it is spam.

Predictive analytics

Predictive analytics is a form of business analytics applying machine learning to generate a predictive model for certain business applications. As such, it encompasses a variety of statistical techniques from predictive modeling and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events. It represents a major subset of machine learning applications; in some contexts, it is synonymous with machine learning.

Energy

In physics, energy () is the quantitative property that is transferred to a body or to a physical system, recognizable in the performance of work and in the form of heat and light. Energy is a conserved quantity—the law of conservation of energy states that energy can be converted in form, but not created or destroyed. The unit of measurement for energy in the International System of Units (SI) is the joule (J).

Dimensionality reduction

Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension. Working in high-dimensional spaces can be undesirable for many reasons; raw data are often sparse as a consequence of the curse of dimensionality, and analyzing the data is usually computationally intractable (hard to control or deal with).

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