Harvesting 3D multiphysics modeling techniques for smart and sustainable university campus

• Main Authors: Zhang, Xiaoqin, Thomas, Rithika, Jadhav, Nilesh, Lee, Jimmy, Conaghan, Catherine, Rawte, Rohan, Mehta, Priyanka
• Other Authors: Energy Research Institute @NTU
• Format: Article
• Language: English
• Published: 2018
• Subjects: Energy Modeling; DRNTU::Engineering::Mechanical engineering::Energy conservation; Sustainable Campus
• Online Access: https://hdl.handle.net/10356/80725; http://hdl.handle.net/10220/46587
• Institution: Nanyang Technological University

The EcoCampus Initiative is a novel flagship RD&D programme built on applied research and test-bedding of innovative technologies. The initiative covers the whole of Nanyang Technological University’s (NTU) 200-hectare campus and adjoining 50 hectares JTC Corporation’s CleanTech Park. The initiative was launched with a mission to achieve an impactful target of a 35% reduction in energy, water and waste intensity by 2020 (baseline, 2011), while leading the development and adoption of innovative technologies. The initiative has successfully collaborated with more than 20 industries and provided them the campus as testbed. The challenge ahead is to scrutinize the tested technologies, determine the best performing ones and find out the most optimum scale and location for their deployment. To aid such decision making, EcoCampus has collaborated with IES (Integrated Environmental Solutions) to exploit their IESVE™ technology. The project aims at developing a Multiphysics 3D Virtual Model of the campus that can virtually simulate the technologies through advanced Multiphysics modelling techniques. In addition to the technology performance analysis, IES’ innovative process solution, Ci2 (Collect, Investigate, Compare, Invest), is deployed to optimize the building performance in NTU Campus. The process focusses on Collecting historical data and identifying data gaps. This is followed by Investigating data using advanced IESVE™ analytics. This stage provides insights that may be hidden within the existing data. Once the story behind the data is known, the data is Compared against a calibrated model. Benchmarking analysis, decisions on refurbishing etc. are made during this stage. ROI, payback periods, NPV etc., prediction is also made in ‘Compare’ stage. Lastly, the Invest stage helps the decision makers to determine and prioritise the most attractive investment solutions for a sustainable and energy efficient operational building. This paper focuses on requirements for setting up a design model and modifying it to understand building behaviour.