empirical coefficient

简明释义

经验系数

英英释义

例句

1.In this study, the empirical coefficient 经验系数 was derived from extensive field measurements.

在这项研究中，经验系数 empirical coefficient 是通过广泛的现场测量得出的。

2.Using the empirical coefficient 经验系数, engineers can design safer structures.

利用经验系数 empirical coefficient，工程师可以设计出更安全的结构。

3.The formula includes an empirical coefficient 经验系数 that varies based on the experimental conditions.

该公式包含一个根据实验条件变化的经验系数 empirical coefficient。

4.The empirical coefficient 经验系数 helps in predicting the behavior of materials under stress.

该经验系数 empirical coefficient 有助于预测材料在应力下的行为。

5.The researchers calculated the empirical coefficient 经验系数 to better understand the relationship between temperature and pressure.

研究人员计算了经验系数 empirical coefficient，以更好地理解温度与压力之间的关系。

作文

In the realm of scientific research and engineering, the term empirical coefficient refers to a numerical value derived from experimental data rather than theoretical calculations. This coefficient plays a critical role in various fields, including physics, chemistry, and environmental science, where precise measurements and real-world observations are essential for accurate modeling and predictions. For instance, when studying fluid dynamics, researchers often rely on empirical coefficients to characterize the behavior of fluids under different conditions, such as temperature, pressure, and flow rate.The significance of empirical coefficients lies in their ability to bridge the gap between theory and practice. While theoretical models provide a framework for understanding complex phenomena, they may not always account for the myriad variables present in real-world situations. By utilizing empirical coefficients, scientists can enhance the accuracy of their models and ensure that they reflect actual observed behaviors.One notable example of an empirical coefficient is the drag coefficient in aerodynamics. This coefficient quantifies the drag force experienced by an object moving through a fluid, such as air or water. It is determined through experiments and varies depending on factors like the shape of the object, the Reynolds number, and the surface roughness. Engineers and designers use this empirical coefficient to optimize the performance of vehicles, aircraft, and even buildings, ensuring they can withstand environmental forces while minimizing energy consumption.Moreover, empirical coefficients are not limited to physical sciences; they also find applications in social sciences and economics. For instance, in econometrics, researchers often employ empirical coefficients to analyze relationships between economic variables based on historical data. These coefficients help economists make informed predictions about future trends and inform policy decisions that can impact society at large.The process of determining empirical coefficients typically involves extensive experimentation and data collection. Researchers conduct controlled experiments to isolate specific variables and measure their effects. Statistical methods are then employed to analyze the data and derive the coefficients that best fit the observed results. This rigorous approach ensures that the empirical coefficients obtained are reliable and can be used confidently in further research or practical applications.In conclusion, the concept of empirical coefficients is fundamental to the advancement of scientific knowledge and technological innovation. By relying on data-driven values derived from real-world observations, researchers can create more accurate models and predictions across various disciplines. The ongoing refinement of these coefficients, as new data becomes available, continues to enhance our understanding of the world around us and enables us to tackle complex challenges effectively. As we move forward, the importance of empirical coefficients will only grow, underscoring the need for a robust empirical approach in both scientific inquiry and practical problem-solving.

在科学研究和工程的领域中，术语经验系数指的是一个从实验数据中得出的数值，而不是通过理论计算得出的。这个系数在物理、化学和环境科学等多个领域中发挥着关键作用，在这些领域中，精确的测量和现实世界的观察对于准确建模和预测至关重要。例如，在研究流体动力学时，研究人员通常依赖于经验系数来描述流体在不同条件下（如温度、压力和流速）行为。经验系数的重要性在于它们能够弥合理论与实践之间的差距。虽然理论模型为理解复杂现象提供了框架，但它们可能并不总是考虑到现实世界中存在的众多变量。通过利用经验系数，科学家可以提高模型的准确性，确保它们反映实际观察到的行为。一个著名的经验系数例子是空气动力学中的阻力系数。这个系数量化了物体在流体（如空气或水）中移动时所经历的阻力。它是通过实验确定的，并且取决于物体的形状、雷诺数和表面粗糙度等因素。工程师和设计师利用这个经验系数来优化车辆、飞机甚至建筑物的性能，确保它们能够抵御环境力量，同时最小化能耗。此外，经验系数不仅限于自然科学；它们还在社会科学和经济学中找到应用。例如，在计量经济学中，研究人员经常使用经验系数来分析基于历史数据的经济变量之间的关系。这些系数帮助经济学家对未来趋势做出明智的预测，并为可能影响社会的政策决策提供信息。确定经验系数的过程通常涉及广泛的实验和数据收集。研究人员进行控制实验，以孤立特定变量并测量其影响。然后采用统计方法分析数据，并推导出最佳拟合观察结果的系数。这种严格的方法确保获得的经验系数是可靠的，可以在进一步研究或实际应用中自信地使用。总之，经验系数的概念是科学知识和技术创新进步的基础。通过依赖于从现实世界观察中得出的数据驱动值，研究人员可以在各个学科中创建更准确的模型和预测。随着新数据的出现，这些系数的持续完善不断增强我们对周围世界的理解，使我们能够有效应对复杂挑战。随着我们不断前进，经验系数的重要性只会增加，突显出在科学探究和实际问题解决中采用稳健经验方法的必要性。