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Robust Design of Natural Ventilation Systems

Project Team

Catherine GorléMartin FischerChen Chen

Overview

Natural ventilation has the potential to significantly reduce energy consumption in buildings, but designing natural ventilation systems is a challenging task because of the many uncertainties in the building's design and operating parameters. Our project goal is tsupport the design of more robust systems by developing efficient multi-fidelity modeling frameworks to predict the performance of natural ventilation systems with quantified confidence intervals.  To validate our models, we focus on the natural ventilation system in the Yang and Yamazaki Environment and Energy Building (Y2E2) building, which houses the Civil and Environmental Engineering Department. The building is equipped with an extensive measurement system: more than 5 years of energy consumption and building  conditions data from 2,400 sensors are available at a 1-minute interval .

Project Background

Research Motivation

40% of the total US energy consumption is in residential and commercial buildings. Efficient natural ventilation strategies could save 10% to 30% of that energy consumption, but design procedures for optimal and robust natural ventilation systems are not well established. The primary challenge is that natural ventilation flow and heat transfer phenomena are strongly influenced by the building’s highly variable and uncertain operating conditions, which translates into a risk of failing to meet thermal comfort and air quality criteria. This risk can be mitigated by accounting for the effect of these uncertainties during the design process, thereby making natural ventilation an efficient and attractive alternative to mechanical ventilation.

Industry Example

The Y2E2 building was designed to use 56 percent less energy than a comparable structure built in a traditional fashion. It has a variety of sustainable features to achieve this goal, including the use of a passive natural ventilation system to keep the building cool and ventilated in hotter months. The hallways, open areas, and lounges connected to 4 atria are cooled using a buoyancy-driven night flush. Cool air enters through mechanically operated windows on the 1st through 3rd floors and drives out warmer air through mechanically operated louvers at the top of each atrium. The resulting overnight cooling of the building’s thermal mass balances out the subsequent heating during the day. 

The Y2E2 building provides a unique test case to evaluate the the true predictive capabilities of a natural ventilation modeling framework in an operational building.  The measurement system records air temperature measurements on each floor in all four atria, together with the outdoor temperature, wind speed, and wind direction.  Through validation with real building data we can ensure that all dominant uncertainties present in reality are accounted for.

Research Objectives

We will focus on 3 specific objectives that will significantly advance the model performance evaluation:

  1. Perform additional measurements to characterize uncertainty in parameters that were previously shown to affect the model predictions:
    1. the initial thermal mass and office wall surface temperatures, and
    2. the window flow rates calculated from the models.
  2. Implement a dynamic thermal simulation CFD model to represent the effect of convective cooling on the natural ventilation flow rates and resulting temperature.
  3. Use inference methods to characterize uncertainty in the internal load and infiltration, which are inherently variable over a building's life span.

These additional measurements and improved models will be integrated into the modeling framework of the Y2E2 natural ventilation system to enable detailed evaluation of the predictive capabilities.

Expected Results

The research will result in a validated multi-fidelity computational framework for designing natural ventilation systems in buildings. By combining models with different levels of fidelity with advanced uncertainty quantification algorithms, we will support important decisions throughout the entire design process. Simple but robust models with very fast turnaround times will support initial design choices, such as the sizing and location of windows and atria. More detailed and expensive simulations can verify the simple model’s assumptions and fine-tune the design in the detailed design stages. Throughout the design process, the model will be able to account for the inherent variability in the building’s operating conditions. 

The resulting modeling framework will support the design of robust natural ventilation systems that can compensate for variability in operating conditions over the building’s lifespan. This will effectively mitigate the risk currently associated with naturally ventilated buildings, thereby promoting their more widespread implementation, and significantly reducing building energy consumption.

Project Updates

Over the past year, we have successfully performed field measurements and inverse analysis to improve model validation. Field measurements focussed on the characterization of two dominant uncertain parameters, and of spatial variability in the temperature field: thermal mass temperatures were measured using thermocouples, flow rates were measured using hot wires, and spatial variability in the temperature field was recorded using thermocouples in different locations.  These measurements, in combination with additional CFD simulations, indicate that spatial variability in the temperature field is the most likely reason for the discrepancies observed in the previous study. The results were presented at the 2017 APS Division of Fluid Dynamics conference and at the 2018 International Symposium on Computational Wind Engineering. In future work, we will implement a more extensive sensor network to support further validation and improvement of the modeling framework. In addition, we will use a more advanced inference method to quantify uncertainty in the time-dependent internal load.

Original Research Proposal

cife2017_gorle_naturalventilation.pdf

Funding Year: 
2018
Stakeholder Categories: 
Operators/Facility Managers
Designers