A Mixed Reality application for studying the improvement of HVAC systems in learning factories

Heating, ventilation and air conditioning (HVAC) systems in factories provide controlled conditions for workers and production equipment. At the same time, these systems are responsible for a significant share of industrial energy consumption. Commonly, HVAC systems are treated separately from produ...

Full description

Saved in:
Bibliographic Details
Published inProcedia manufacturing Vol. 45; pp. 373 - 378
Main Authors Czarski, Marvin, Ng, Yen Ting, Vogt, Marcus, Juraschek, Max, Thiede, Bastian, Tan, Puay Siew, Thiede, Sebastian, Herrmann, Christoph
Format Journal Article
LanguageEnglish
Published Elsevier B.V 2020
Subjects
Online AccessGet full text

Cover

Loading…
More Information
Summary:Heating, ventilation and air conditioning (HVAC) systems in factories provide controlled conditions for workers and production equipment. At the same time, these systems are responsible for a significant share of industrial energy consumption. Commonly, HVAC systems are treated separately from production systems. However, numerous interactions and cross-influences occur affecting the overall energy efficiency and air quality. With analyzing and understanding these indoor air conditions the goal is to enable future engineers and experts to design and set them up in a way that improves human comfort, while reducing energy consumption. To achieve this, a cyber-physical system approach in a learning factory is presented. Based on data provided by the learning factory infrastructure, a building performance simulation with an integrated computational fluid dynamics simulation is composed. With the implementation in the learning factory, different ventilation and operation scenarios can be examined in learning scenarios and trainings to convey competencies about cyber-physical production systems in general and influences on the connection to HVAC systems. A mixed reality application provides three-dimensional visualization of the cyber-model and computed results for the learners.
ISSN:2351-9789
2351-9789
DOI:10.1016/j.promfg.2020.04.039