distributed parameter network

简明释义

分布参数网络

英英释义

例句

1.Engineers often use distributed parameter networks 分布参数网络 to analyze the behavior of electrical circuits.

工程师常常使用分布参数网络 distributed parameter networks 来分析电路的行为。

2.In control systems, a distributed parameter network 分布参数网络 can be used to model the dynamics of temperature distribution in a furnace.

在控制系统中，分布参数网络 distributed parameter network 可用于模拟炉内温度分布的动态变化。

3.Researchers are exploring the applications of distributed parameter networks 分布参数网络 in fluid dynamics.

研究人员正在探索分布参数网络 distributed parameter networks 在流体动力学中的应用。

4.The simulation of a distributed parameter network 分布参数网络 helps predict how systems respond to various inputs.

对分布参数网络 distributed parameter network 的模拟有助于预测系统如何响应各种输入。

5.The design of a distributed parameter network 分布参数网络 requires advanced mathematical modeling techniques.

设计一个分布参数网络 distributed parameter network 需要先进的数学建模技术。

作文

In the field of engineering and applied mathematics, the concept of a distributed parameter network is essential for understanding systems that are governed by partial differential equations. These networks are characterized by parameters that are distributed over a spatial domain rather than being concentrated at discrete points. This characteristic allows for a more accurate representation of physical systems, such as heat conduction in a solid or fluid flow in a pipe. The analysis of distributed parameter networks is crucial for designing efficient control systems in various applications, including robotics, telecommunications, and environmental monitoring.One significant advantage of modeling systems as distributed parameter networks is the ability to capture the dynamics of the system over time and space. For instance, in thermal systems, temperature may vary continuously across a material, necessitating a model that accounts for this distribution. Traditional lumped parameter models, which assume uniform properties, can lead to inaccuracies in predictions and control strategies. Therefore, engineers and scientists often prefer distributed parameter networks when dealing with complex systems where spatial variations are significant.The mathematical foundation of distributed parameter networks lies in the use of partial differential equations (PDEs). These equations describe how physical quantities change over both time and space. Solving PDEs can be challenging, but advancements in numerical methods, such as finite element analysis (FEA) and computational fluid dynamics (CFD), have made it feasible to analyze these networks effectively. By discretizing the spatial domain, engineers can simulate the behavior of distributed parameter networks under various conditions, leading to better design and optimization of real-world systems.Moreover, distributed parameter networks are particularly relevant in control theory, where the goal is to develop controllers that can manage systems with spatially distributed characteristics. For example, in a temperature control system for a large building, the heating and cooling elements must work together to maintain a comfortable environment throughout the entire space. A controller designed for a distributed parameter network can take into account the varying temperature gradients across different rooms, leading to more efficient energy usage and improved occupant comfort.In addition to their applications in engineering, distributed parameter networks also find relevance in fields such as biology and economics. In biology, the spread of diseases can be modeled using distributed parameter networks to understand how infections propagate through populations. In economics, spatial models can help analyze how resources are allocated across different regions, taking into account the distribution of factors such as labor and capital.In conclusion, the study of distributed parameter networks provides valuable insights into the behavior of complex systems in various fields. Their ability to model spatially distributed phenomena makes them indispensable tools for engineers, scientists, and researchers. As technology continues to advance, the importance of distributed parameter networks will only grow, paving the way for innovative solutions to some of the most pressing challenges in our world today.

在工程和应用数学领域，分布参数网络的概念对于理解由偏微分方程支配的系统至关重要。这些网络的特征是参数在空间域上分布，而不是集中在离散点上。这一特性使得能够更准确地表示物理系统，例如固体中的热传导或管道中的流体流动。对分布参数网络的分析对于设计各种应用中高效的控制系统至关重要，包括机器人技术、电信和环境监测。将系统建模为分布参数网络的一个显著优势是能够捕捉系统随时间和空间变化的动态。例如，在热系统中，温度可能在材料中连续变化，因此需要一个考虑这种分布的模型。传统的集总参数模型假设属性均匀，可能导致预测和控制策略的不准确。因此，工程师和科学家在处理空间变化显著的复杂系统时，往往更喜欢分布参数网络。分布参数网络的数学基础在于使用偏微分方程（PDE）。这些方程描述了物理量如何随时间和空间变化。解决PDE可能具有挑战性，但数值方法的进步，如有限元分析（FEA）和计算流体动力学（CFD），使得有效分析这些网络成为可能。通过离散化空间域，工程师可以模拟分布参数网络在各种条件下的行为，从而更好地设计和优化现实世界的系统。此外，分布参数网络在控制理论中尤为相关，其目标是开发能够管理具有空间分布特征的系统的控制器。例如，在大型建筑的温度控制系统中，供暖和制冷元件必须协同工作，以保持整个空间的舒适环境。为分布参数网络设计的控制器可以考虑不同房间之间的温度梯度变化，从而实现更高效的能源使用和改善的居住者舒适度。除了在工程中的应用，分布参数网络还在生物学和经济学等领域找到相关性。在生物学中，可以使用分布参数网络来建模疾病传播，以了解感染如何在人群中传播。在经济学中，空间模型可以帮助分析资源如何在不同地区分配，同时考虑劳动力和资本等因素的分布。总之，研究分布参数网络为各个领域复杂系统的行为提供了宝贵的见解。它们能够建模空间分布现象，使其成为工程师、科学家和研究人员不可或缺的工具。随着技术的不断进步，分布参数网络的重要性只会增加，为我们今天面临的一些最紧迫的挑战铺平道路。