大疆无人机气流仿真：基于ANSYS Fluent模块的深度剖析

ANSYS FluentBased airflow simulation of DJI drone  A Professional Technical Guide

Introduction:

ANSYS Fluent, a widely adopted commercial Computational Fluid Dynamics (CFD) software globally, is rooted in its extensive capability in modeling fluid dynamics, thermal transfer, and catalytic reaction phenomena. With a robust market share of 60% in the American industry, its applications are as diverse as they are critical, spanning aerospace engineering, automotive design, oil and gas, and turbine manufacturing. The flexibility of ANSYS Fluent lies in its provision of a multitude of meshing strategies and solution equations, which allow for meticulous simulations tailored to specific needs. This guide walks through the intricate steps of performing an airflow simulation on a DJI drone model using ANSYS Fluent, encompassing model preparation, grid generation, setting up the simulation, and interpreting results.

Model Simplification:

Objective: Achieving a CAD model that facilitates the simulation process by reducing geometrical complexities to ensure the model's functionality and airworthiness are accurately captured. This involves streamlining the original drone design to simplify essential features while minimizing the impact on aerodynamic properties.

Procedure: The complex origami drone model is simplified by focusing on the drone's key dimensions—the fuselage's length, width, and height, as well as its streamline shape, and arm dimensions. Irrelevant or minuscule components are abstracted to maintain the integrity of the airflow analysis.

Workbench Utilization:

Preparation: Opening ANSYS Workbench, a suite that integrates geometry, mesh, and Fluent modules, is the initial step in setting up a simulation workflow. By dividing the process into distinct modules (geometry, mesh, Fluent), users benefit from a more streamlined approach that minimizes errors and streamlines large tasks.

Configuration: Models are loaded into Workbench, and the computational domain is meticulously segmented into flow and rotation zones, with entities like the drone and rotor blades, respectively, delineated accurately.

Grid Generation:




Objective: Creating a refined computational mesh that reflects the realworld scenario, vital for achieving numerical accuracy and compliance with ANSYS Fluent's prescriptions.

Technique: The meshing operation employs Fluent's meshing capabilities, enabling users to generate highquality meshes that adapt to the complex geometry of the drone model. The mesh is structured to closely match the airframe's physical dimensions and sharp edges, with a specific focus on areas experiencing dynamic pressure and flow separation.

Boundary Conditions Setup:

Parameter Selection: The simulation is configured to mimic the operational conditions of the drone, particularly highlighting the airflow dynamics through the choice of boundary conditions. Inlet velocity is set to a constant 1 m/s, aligning with typical usage scenarios. Outlets are adjusted to match atmospheric pressure, ringbalancing the pressure forces within the model.

Computational Strategies:

Simulation Type: The simulation is set for a transient regime, selected to track the dynamic interaction between the drone's rotor and stator over time, rather than averaging these interactions temporally.

Meshing Parameters: Taking a strategic approach, a grid quality guarantee is set with a minimum of negative volume elements. This step ensures that the computational mesh is robust enough to withstand the stresses of a transient analysis. Additionally, the selection of a sliding mesh and its parameters, specifically a growth rate of 1.2 and a rolling rate of 0.6, is pivotal for capturing the complex motion of the drone's rotating components accurately.

Comprehensive Results:

The simulation outcomes serve as a bridge between input parameters and expected airflow behaviors. Through NHlib visualization, ANSYS Fluent presents detailed results, including cloud maps that illuminate the distribution of air velocities across the drone's surfaces and vector plots that underline the direction of airflow, providing a comprehensive understanding of aerodynamic performance under the specified conditions.

Conclusion:

The article serves as a thorough, practical guide, detailing the systematic approach toairflow simulation on a DJI drone model using ANSYS Fluent. It emphasizes the strategic selection of model simplification techniques, grid generation, boundary condition configurations, and the deployment of computational strategies, all of which are essential steps in achieving accurate results in CFD simulations. Understanding and executing these steps with precision enables engineers and researchers to optimize drone designs for enhanced performance, reliability, and efficiency in realworld operational scenarios.

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