Modeling the Ambient Conditions of a Manufacturing Environment Using Computational Fluid Dynamics (CFD)
MS in Mechanical Engineering (M.S.M.E.)
Degree Granting Department
Nancy Diaz-Elsayed, Ph.D.
Susana Lai-Yuen, Ph.D.
Wenbin Mao, Ph.D.
CFD Simulation, Digital Twin, Green Manufacturing, Smart Manufacturing
As manufacturing equipment evolve to higher speed and require high precision operations, the impact of environmental changes on machine accuracy becomes critical. Due to thermal expansion, the structure of the machine can change when ambient temperature varies. When the airflow in the laboratory changes, this also alters the operator's thermal comfort. Either the change in machine structure or operator comfort can ultimately affect machine accuracy. The manufacturing industry is currently using heating, ventilation, and air-conditioning (HVAC) systems to regulate the temperature of the working environment. However, since conventional HVAC systems determine whether to activate the HVAC system by collecting the temperature difference around the thermostats, this situation delays air activation time and misses the optimal time to change the factory workshop's internal temperature. This not only fails to ensure the accuracy of high-precision equipment, but also causes energy waste, so an accurate method of monitoring the indoor environment is beneficial to the development of green manufacturing and smart manufacturing.
This research uses computational fluid dynamics (CFD) techniques to simulate thermal and airflow distribution in three controlled experiments in the S3 laboratory at the University of South Florida. The experiments were conducted to investigate the changes in temperature and airflow throughout the laboratory influenced by the access of machine operators, computer numerical control (CNC) machine start-up and shutdown, seasonal changes, and the opening and closing of the laboratory door. This experiment is conducted to verify the accuracy of the simulation results and investigate the effects of temperature changes and airflow changes on the thermal deformation of the CNC machine.
The average percentage error of the temperature for the three control experiments varied from 0.08% to 0.15%. The error range of the airflow velocity varied from 0 m/s to 0.65 m/s. The maximum structural change in the height of the CNC machine was found to be 0.085 mm. Even with seasonal changes, such errors show the advantages of CFD technology over conventional HVAC systems. In conclusion, CFD simulation technology can address structural changes in high-precision equipment due to temperature changes, changes in operator comfort due to changes in airflow, and has the potential to reduce energy waste with more informed control of the HVAC system.
Scholar Commons Citation
Liu, Yang, "Modeling the Ambient Conditions of a Manufacturing Environment Using Computational Fluid Dynamics (CFD)" (2021). USF Tampa Graduate Theses and Dissertations.