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相关概念视频

Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

6.5K
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...
6.5K
Elastic Collisions: Introduction01:00

Elastic Collisions: Introduction

14.9K
An elastic collision is one that conserves both internal kinetic energy and momentum. Internal kinetic energy is the sum of the kinetic energies of the objects in a system. Truly elastic collisions can only be achieved with subatomic particles, such as electrons striking nuclei. Macroscopic collisions can be very nearly, but not quite, elastic, as some kinetic energy is always converted into other forms of energy such as heat transfer due to friction and sound. An example of a nearly...
14.9K
Elastic Collisions: Case Study01:15

Elastic Collisions: Case Study

20.1K
Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
20.1K
Types of Collisions - II01:19

Types of Collisions - II

9.5K
When two or more objects collide with each other, they can stick together to form one single composite object (after collision). The total mass of the object after the collision is the sum of the masses of the original objects, and it moves with a velocity dictated by the conservation of momentum. Although the system's total momentum remains constant, the kinetic energy decreases, and thus such a collision is an inelastic collision. Most of the collisions between objects in daily life are...
9.5K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

2.8K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
2.8K
Lattice Centering and Coordination Number02:33

Lattice Centering and Coordination Number

11.3K
The structure of a crystalline solid, whether a metal or not, is best described by considering its simplest repeating unit, which is referred to as its unit cell. The unit cell consists of lattice points that represent the locations of atoms or ions. The entire structure then consists of this unit cell repeating in three dimensions. The three different types of unit cells present in the cubic lattice are illustrated in Figure 1.
Types of Unit Cells
Imagine taking a large number of identical...
11.3K

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相关实验视频

基于不变网络的格子博尔兹曼方法的机器学习增强碰撞运算符.

Mario Christopher Bedrunka1, Tobias Horstmann2, Ben Picard3

  • 1Bonn-Rhein-Sieg University of Applied Sciences, University of Siegen, Chair of Fluid Mechanics, Paul-Bonatz-Straße 9-11, 57076 Siegen-Weidenau, Germany and Institute of Technology, Resource and Energy-efficient Engineering (TREE), Grantham-Allee 20, 53757 Sankt Augustin, Germany.

Physical review. E
|December 23, 2025
PubMed
概括

机器学习通过优化格子博尔茨曼方法来增强计算流体动力学模拟.

相关实验视频

科学领域:

  • 计算流体动力学的流体动力学.
  • 机器学习 机器学习
  • 数字模拟的数字模拟.

背景情况:

  • 格子博尔茨曼法 (LBM) 是流体动力学的数值解法.
  • 将机器学习 (ML) 集成到LBM中可以提高准确性和稳定性.
  • 碰撞操作员是ML集成到LBM的关键组件.

研究的目的:

  • 开发一个新的神经碰撞操作员 (NCO) 用于LBM.
  • 使用ML提高LBM模拟的稳定性和准确性.
  • 优化非物理时刻的放松率,以提高稳定性.

主要方法:

  • 构建了一个不变的神经网络,在等价碰撞运算机上起作用.
  • 训练了NCO使用强制的同otropic流模拟与光谱强迫.
  • 通过定制的损失函数,最大限度地降低了能量频谱差异和量身定制的数值消散.

主要成果:

  • 与BGK和KBC运营商相比,NCO表现出更好的准确性和稳定性.
  • 精确预测极低分辨率的三维泰勒-格林 (TGV) 流动中的动态.
  • 在动荡的三维流模拟中强大的性能.

结论:

  • 该NCO提供了一种有前途的方法来增强LBM模拟.
  • 将ML集成到LBM碰撞操作员中可以获得更准确,更稳定的结果.
  • 替代训练程序使高雷诺兹数模拟能够在减少内存足迹的情况下进行.