Modeling and Full-Scale Validation of Buoyancy-Driven Ventilation in Y2E2
by Chen Chen and Lup Wai Chew
Project timeline: 2017-2022
Motivation and Objective
Natural ventilation has the potential to save 10% to 30% of building energy consumption. Robust design of natural ventilation systems can be challenging, since the flow is strongly influenced by variability in the building's design and operating conditions. The goal of this project is to propose and validate an efficient multi-fidelity modeling framework that can predict buoyancy-driven natural ventilation with quantified confidence intervals. Fast, robust models will support initial design choices; more expensive, detailed simulations will support fine-tuning the design. To evaluate the predictive capabilities in an operational building, we focus on modeling and measurements of the natural ventilation system in the Yang and Yamazaki Environment and Energy Building (Y2E2) building.
Methods and Results
CFD-based design of full-scale experiment
Initial modeling results indicated that the building's sensors might be in locations that are not representative of the volume-averaged temperature. Hence, we used CFD simulations with uncertainty quantification (UQ) to inform a robust experimental design that optimizes temperature sensor placement. The use of UQ enables identifying optimal locations of temperature sensors under uncertain boundary and initial conditions. Based on the CFD we defined and evaluated three metrics, Jvol,mean, Jmax, and Jmin, to identify locations that will provide measurements that are representative of the volume-average temperature, and of hot and cold regions, respectively.
Full-scale validation of natural ventilation models
The full-scale experiment was conducted during 12 nights. 20 thermistors were used to measure the indoor air temperature; 34 thermistors were used to measure floor/sidewall/ceiling temperature with a sampling rate of 1s. For validation, we selected a night with low wind speed, such that the ventilation is primarily buoyancy-driven. Comparison between CFD and experiments demonstrates that CFD can well predict buoyancy-driven natural ventilation, given accurate thermal boundary conditions.
Using LES to obtain building-specific correlations for flow and heat transfer rates
The validated CFD model can then be used to derive correlations for the convective heat transfer and the natural ventilation flow rate. The results suggest that standard correlations for these processes can differ significantly from the building-specific values, and the CFD-based correlations can thus be used in a dynamic thermal model to improve its accuracy. The resulting model accurately predicts indoor air temperature, surface temperature and heat transfer, while the model employing the standard correlations (without inputs from CFD) only predicts the indoor air temperature accurately. The fast yet accurate CFD-based dynamic thermal model can be used to support uncertainty quantification, quantifying the effect of variability in the operating conditions and supporting real-time prediction of the natural ventilation process.
- CFD-based experimental design:
C. Chen and C. Gorlé, “Optimal temperature sensor placement in buildings with buoyancy-driven natural ventilation using computational fluid dynamics and uncertainty quantification,” Building and Environment, 207, 108496, 2022.
- Full-scale measurements and thermal model validation:
C. Chen and C. Gorlé , “Characterizing spatial variability in the temperature field to support thermal model validation in a naturally ventilated building,” submitted to Building Simulation.
- Full-scale validation of CFD:
C. Chen and C. Gorlé , “Full-scale validation of CFD simulations of buoyancy-driven natural ventilation in a three story office building,” submitted to Building and Environment.
- Building-specific correlations for flow and heat transfer:
L.W. Chew, C. Chen, and C. Gorlé, “A multi-fidelity simulation framework for predicting buoyancy-driven natural ventilation”, submitted to Building and Environment.
Conference presentations & publications
- Chen, C. & Gorlé, C. "Full-scale validation of natural ventilation models using uncertainty quantification," APS DFD (2020): [Link]
- Chen, C. & Gorlé, C. "Temperature measurements in Stanford’s Y2E2 building for validation of natural ventilation models," The 15th International Conference on Wind Engineering, Beijing, China (2019)
- Chen, C. & Gorlé, C. "Uncertainty quantification in CFD simulations of natural ventilation to support designing experiments for model validation," APS DFD (2019)
- Chen, C. & Gorlé, C. "Assimilation of CFD to design experiments for validation of natural ventilation models in Stanford’s Y2E2 building," Engineering Mechanics Institute Conference (2019)
Lamberti, G.& Gorlé, C. "Uncertainty Quantification for modeling night-time ventilation in Stanford's Y2E2 building." Energy and Buildings, 168. (2018)
This research was supported by a Seed Research Grant from the Center for Integrated Facility Engineering at Stanford University. Lup Wai Chew's contribution was supported by a postdoctoral fellowship from the National University of Signapore.