수중 로봇 실험 보완과 대체 가능성을 위한 동적 메시 CFD 시뮬레이션 구축 및 검증
Development and Validation of a Dynamic Mesh CFD Simulation for Enhancing and Potentially Replacing Underwater Robot Experiments
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초록
This study focuses on the validation of a dynamic mesh-based Computational Fluid Dynamics (CFD) simulation for an underwater robot, using pressure sensor data for experimental comparison. The CFD simulation models the complex interactions between the underwater vehicle and the surrounding fluid under various velocity and posture conditions that are difficult to replicate in physical experiments. To validate the accuracy of the simulation, data was collected from multiple pressure sensors, a Doppler Velocity Log (DVL), and an Inertial Measurement Unit (IMU) mounted on a Remotely Operated Vehicle (ROV). The collected pressure data was compared against the CFD simulation results to assess the simulation’s reliability in replicating real-world fluid-vehicle interactions. This validation demonstrated that CFD simulations can serve as a reliable tool for augmenting or potentially replacing experimental data, particularly in scenarios where physical experimentation is constrained by cost or environmental limitations.
Abstract
This study focuses on the validation of a dynamic mesh-based Computational Fluid Dynamics (CFD) simulation for an underwater robot, using pressure sensor data for experimental comparison. The CFD simulation models the complex interactions between the underwater vehicle and the surrounding fluid under various velocity and posture conditions that are difficult to replicate in physical experiments. To validate the accuracy of the simulation, data was collected from multiple pressure sensors, a Doppler Velocity Log (DVL), and an Inertial Measurement Unit (IMU) mounted on a Remotely Operated Vehicle (ROV). The collected pressure data was compared against the CFD simulation results to assess the simulation’s reliability in replicating real-world fluid-vehicle interactions. This validation demonstrated that CFD simulations can serve as a reliable tool for augmenting or potentially replacing experimental data, particularly in scenarios where physical experimentation is constrained by cost or environmental limitations.