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相关概念视频

Thermal expansion and Thermal stress: Problem Solving01:27

Thermal expansion and Thermal stress: Problem Solving

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San Francisco's Golden Gate Bridge is exposed to temperatures ranging from -15 °C to 40 °C. At its coldest, the main span of the bridge is 1275 m long. Assuming that the bridge is made entirely of steel, what is the change in its length between these temperatures?
To solve the problem, first, identify the known and unknown quantities. The initial length (L) of the bridge is 1275 m, the coefficient of linear expansion (α) for steel is 12 x 10-6/°C, and the change in...
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Thermal Strain01:19

Thermal Strain

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Thermal strain is a concept that arises when we consider how temperature changes affect structures. Unlike the conventional assumption that structures remain constant under load, real-world scenarios often involve temperature fluctuations that can significantly impact these structures. Consider a homogeneous rod with a uniform cross-section resting freely on a flat horizontal surface. If the rod's temperature increases, the rod elongates. This elongation is proportional to the temperature...
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Thermal Expansion01:22

Thermal Expansion

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The expansion of alcohol in a thermometer is one of many commonly encountered examples of thermal expansion, which is the change in size or volume of a given system as its temperature changes. The most visible example is the expansion of hot air. When air is heated, it expands and becomes less dense than the surrounding air, which then exerts an upward force on the hot air to, for example, make steam and smoke rise, and hot air balloons float. The same behavior happens in all liquids and gases,...
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Thermal Stress01:09

Thermal Stress

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If the temperature of an object is changed while it is prevented from expanding or contracting, the object is subjected to stress. The stress is compressive if the object expands in the absence of constraint and tensile if it contracts. This stress resulting from temperature change is known as thermal stress. It can be quite large and can cause damage. To avoid this stress, engineers may design components so they can expand and contract freely. For instance, on highways, gaps are deliberately...
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Expansion and Contraction in Masonry Walls01:19

Expansion and Contraction in Masonry Walls

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Masonry walls are subject to slight expansion and contraction due to variations in temperature and moisture. Thermal movement in masonry is relatively straightforward to measure and plan for. On the other hand, moisture movement poses more of a challenge. New clay masonry units typically absorb water and expand over time under normal environmental conditions. Conversely, new concrete masonry units tend to shrink as they lose the excess moisture acquired during their production process.
To...
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Temperature Dependent Deformation01:12

Temperature Dependent Deformation

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In a nonhomogeneous rod made up of steel and brass, restrained at both ends and subjected to a temperature change, several steps are involved in calculating the stress and compressive load. Due to the problem's static indeterminacy, one end support is disconnected, allowing the rod to experience the temperature change freely. Next, an unknown force is applied at the free end, triggering deformations in the rod's steel and brass portions. These deformations are then calculated and added...
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Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation
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通过多步式机器学习探索负热膨胀材料与散装框架结构及其相关扩展关系.

Yu Cai1,2, Chunyan Wang1,2,3, Huanli Yuan3

  • 1Key Laboratory for Special Functional Materials of Ministry of Education, and School of Materials and Engineering, Henan University, Kaifeng 475001, China. jiayu@henu.edu.cn.

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概括

机器学习有效地从数据库中识别出超过1000个潜在的负热膨胀 (NTE) 材料. 这种方法预测了系数和温度范围,有助于设计新型NTE材料.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算材料科学科学 计算材料科学
  • 机器学习在材料发现中

背景情况:

  • 对负热膨胀 (NTE) 材料的实验性发现具有挑战性.
  • 机器学习 (ML) 提供了一种数据驱动的方法来加速材料发现.
  • 可以利用现有的材料数据库来识别新的NTE候选人.

研究的目的:

  • 采用多步骤机器学习方法来识别潜在的NTE材料.
  • 预测负热膨胀系数 (CNTE) 和操作温度范围.
  • 为设计指导建立材料属性和NTE行为之间的关系.

主要方法:

  • 使用多步骤机器学习方法与数据增强和交叉验证.
  • 来自无机晶体结构数据库 (ICSD) 和其他来源的选材料.
  • 为了验证,采用了准和近似 (QHA) 的第一原则计算.

主要成果:

  • 确定了大约1000种具有散装框架结构的候选材料 (氧化物,化物,化物).
  • 预测的CNTE值和温度范围,用于确定材料.
  • 大约有57种材料显示了NTE的100%预测概率.
  • 机器学习的预测与第一原则计算有很好的一致性.
  • 根据平均电子负性,孔隙性和温度范围,为CNTE建立了三个通用关系.

结论:

  • 在发现和预测NTE材料方面,ML方法是有效的.
  • 确定关键值和关系可以指导新NTE材料的设计.
  • 这项工作扩大了已知和潜在的NTE材料库.