CFD results calibration from sparse sensor observations with a case study for indoor thermal map

TL;DR

This paper introduces a novel method to calibrate CFD simulation results using sparse sensor data, improving accuracy while maintaining the overall profile, demonstrated through a thermal map case study.

Contribution

It presents a new calibration approach for CFD results inspired by image editing, addressing a gap in existing CFD calibration research.

Findings

Effective calibration of CFD results with sparse sensors.

Preserves the overall original profile of the simulation.

Validated on a thermal map case study with positive results.

Abstract

Current CFD calibration work has mainly focused on the CFD model calibration. However no known work has considered the calibration of the CFD results. In this paper, we take inspiration from the image editing problem to develop a methodology to calibrate CFD simulation results based on sparse sensor observations. We formulate the calibration of CFD results as an optimization problem. The cost function consists of two terms. One term guarantees a good local adjustment of the simulation results based on the sparse sensor observations. The other term transmits the adjustment from local regions around sensing locations to the global domain. The proposed method can enhance the CFD simulation results while preserving the overall original profile. An experiment in an air-conditioned room was implemented to verify the effectiveness of the proposed method. In the experiment, four sensor observations were used to calibrate a simulated thermal map with 167x365 data points. The experimental results show that the proposed method is effective and practical.