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Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.6K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
4.6K
Theorems of Pappus and Guldinus: Problem Solving01:12

Theorems of Pappus and Guldinus: Problem Solving

879
Pappus and Guldinus's theorems are powerful mathematical principles that are used for finding the surface area and volume of composite shapes. For example, consider a cylindrical storage tank with a conical top. Finding the surface area or volume can be challenging for such complex shapes. These theorems are particularly useful in calculating the volume and surface area of such systems. Here, the cylindrical storage tank with a conical top can be broken down into two simple shapes: a...
879
Unsymmetric Loading of Thin-Walled Members: Problem Solving01:07

Unsymmetric Loading of Thin-Walled Members: Problem Solving

256
The shear center of a channel section with uniform thickness, height, and width, is determined by computing the shear force in the member and calculating the moments of inertia of the sections.
To compute the shear forces, find the shear flow at a specific distance from the endpoint using the vertical shear and the moment of inertia values. The total shear force on the flange is calculated by integrating the shear flow from one end of the flange to the other.
Next, calculate the moments of...
256
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

150
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
150
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

5.9K
It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
5.9K
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

182
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
182

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Updated: Nov 3, 2025

Convergent Polishing: A Simple, Rapid, Full Aperture Polishing Process of High Quality Optical Flats & Spheres
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Penetration Depth Between Two Convex Polyhedra: An Efficient Stochastic Global Optimization Approach.

Mark A Abramson, Griffin D Kent, Gavin W Smith

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    Summary
    This summary is machine-generated.

    This study introduces an efficient method for calculating interference depth between polyhedra in aerospace design. The approach accurately identifies and quantifies false positive interferences, improving design consistency checks.

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    Area of Science:

    • Computational Geometry
    • Aerospace Engineering
    • Computer Graphics

    Background:

    • Ensuring spatial separation of objects is critical in aerospace design.
    • Discretization of geometry models can introduce false positive interferences.
    • Accurate interference detection is necessary to focus on genuine design issues.

    Purpose of the Study:

    • To develop an efficient method for computing the depth of interference between two polyhedra.
    • To enable the removal of false positive interferences in aerospace design.
    • To improve the accuracy and efficiency of geometric consistency checks.

    Main Methods:

    • Formulation of the penetration depth problem as a constrained five-variable global optimization problem.
    • Derivation of an equivalent unconstrained, two-variable nonsmooth problem.
    • Application of a stochastic multistart optimization algorithm to solve the derived problem.

    Main Results:

    • The proposed algorithm effectively computes the penetration depth between polyhedra.
    • Numerical results demonstrate the effectiveness and efficiency of the method on test cases.
    • The approach successfully quantifies interferences, distinguishing true from false positives.

    Conclusions:

    • The novel optimization approach provides an effective solution for computing interference depth.
    • This method enhances the reliability of geometric consistency checks in aerospace design.
    • Accurate interference depth calculation allows engineers to focus on critical design elements.