analytical model

简明释义

1. 分析模型; 2. 解析模型;

英英释义

例句

1.By utilizing an analytical model, the company was able to streamline its supply chain processes.

通过利用分析模型，公司能够简化其供应链流程。

2.The research team developed an analytical model to predict consumer behavior based on past purchasing data.

研究团队开发了一种分析模型，以根据过去的购买数据预测消费者行为。

3.Using an analytical model for traffic flow can help city planners optimize road usage.

使用分析模型来分析交通流量可以帮助城市规划者优化道路使用。

4.An analytical model was employed in the study to assess the impact of climate change on agricultural yields.

在这项研究中采用了分析模型来评估气候变化对农业产量的影响。

5.The financial analyst created an analytical model to evaluate the risk of different investment portfolios.

金融分析师创建了一个分析模型来评估不同投资组合的风险。

作文

In the realm of science and engineering, the term analytical model refers to a mathematical representation that captures the essential features of a physical system. These models are crucial for understanding complex phenomena and making predictions about how systems behave under various conditions. An analytical model is typically derived from fundamental principles, such as laws of physics or empirical observations, and it allows researchers to simulate real-world scenarios without the need for extensive experimentation. One of the primary advantages of using an analytical model is its ability to simplify complex systems into manageable equations. For example, in fluid dynamics, engineers often employ analytical models to predict how fluids will flow through different geometries. By applying the principles of conservation of mass, momentum, and energy, they can derive equations that describe the behavior of the fluid. This not only saves time and resources but also provides insights that can guide the design of more efficient systems.Moreover, analytical models are instrumental in optimization problems. In many industries, optimizing processes can lead to significant cost savings and improved performance. By utilizing an analytical model, engineers can identify the best parameters for a given system. For instance, in manufacturing, an analytical model can help determine the optimal speed and feed rate for machining processes to maximize productivity while minimizing wear on tools.However, it is essential to recognize the limitations of analytical models. While they provide valuable insights, they often rely on assumptions that may not hold true in all scenarios. For instance, an analytical model might assume that a material behaves linearly under stress, but in reality, materials can exhibit nonlinear behavior when subjected to extreme conditions. Therefore, it is crucial for researchers and engineers to validate their analytical models against experimental data to ensure their accuracy and reliability.Another aspect to consider is the difference between analytical models and numerical models. While analytical models provide exact solutions to equations, numerical models use computational methods to approximate solutions. Numerical models are particularly useful for complex systems where analytical models may be difficult or impossible to derive. For instance, in climate modeling, scientists often rely on numerical simulations to predict weather patterns and climate changes, as the underlying processes are too intricate for simple analytical models alone.In conclusion, analytical models are invaluable tools in the fields of science and engineering. They enable researchers to simplify complex systems, optimize processes, and make informed predictions. However, it is crucial to understand their limitations and the contexts in which they are applicable. As technology continues to advance, the integration of analytical models with numerical methods will likely enhance our ability to tackle increasingly complex challenges across various disciplines. By leveraging both approaches, we can gain a deeper understanding of the world around us and drive innovation in numerous fields.

在科学和工程领域，术语分析模型指的是一种数学表示，它捕捉了物理系统的基本特征。这些模型对于理解复杂现象和预测系统在各种条件下的行为至关重要。分析模型通常是基于基本原理（如物理定律或经验观察）推导而来的，它允许研究人员在不需要大量实验的情况下模拟现实场景。使用分析模型的主要优点之一是能够将复杂系统简化为可管理的方程式。例如，在流体动力学中，工程师们经常利用分析模型来预测流体如何通过不同几何形状流动。通过应用质量、动量和能量守恒原理，他们可以推导出描述流体行为的方程。这不仅节省了时间和资源，还提供了可以指导更高效系统设计的洞见。此外，分析模型在优化问题中也发挥着重要作用。在许多行业中，优化过程可以带来显著的成本节约和性能提升。通过利用分析模型，工程师可以识别给定系统的最佳参数。例如，在制造业中，分析模型可以帮助确定加工过程的最佳速度和进给率，以最大化生产率，同时最小化工具磨损。然而，必须认识到分析模型的局限性。虽然它们提供了有价值的见解，但它们通常依赖于在所有情况下可能不成立的假设。例如，分析模型可能假设材料在应力下表现线性，但实际上，材料在极端条件下可能表现出非线性行为。因此，研究人员和工程师必须根据实验数据验证他们的分析模型，以确保其准确性和可靠性。另一个需要考虑的方面是分析模型与数值模型之间的区别。虽然分析模型提供方程的精确解，但数值模型使用计算方法来近似解。数值模型在复杂系统中尤其有用，因为在这些情况下，可能难以或不可能推导出分析模型。例如，在气候建模中，科学家们通常依赖数值模拟来预测天气模式和气候变化，因为潜在过程对于简单的分析模型来说过于复杂。总之，分析模型是科学和工程领域中不可或缺的工具。它们使研究人员能够简化复杂系统、优化过程并做出明智的预测。然而，理解它们的局限性以及适用的背景至关重要。随着技术的不断进步，将分析模型与数值方法结合起来，可能会增强我们应对各个学科日益复杂挑战的能力。通过利用这两种方法，我们可以更深入地理解周围的世界，并在多个领域推动创新。