Performance-oriented design and assessment of naturally ventilated buildings

Abstract

A high-performance building must fulfill comfort and energy efficiency requirements. Possible solutions include passive strategies, such as improving the building envelope and taking advantage of natural light and ventilation. Natural ventilation (NV), for instance, can provide both thermal comfort and energy savings. However, its performance relies on building design and interaction with the local environmental characteristics. In this study, Natural Ventilation Potential (NVP) was analyzed under two approaches: a general evaluation using meteorological data and a specific investigation through building simulation, using an experimental house as a reference case located in a temperate climate with warm summer. Although there are many parameters and metrics applied in assessing NVP, predicting building air change rates (ACH) and airflows is a challenge for designers seeking to deal with this passive strategy. Among the methods available for this task, Computational Fluid Dynamics (CFD) appears as the most compelling, in ascending use. However, CFD simulations have high computational costs, besides requiring a range of settings and skills that inhibit its wide application. Therefore, a pragmatic CFD framework to promote wind-driven assessments through 3D parametric modeling platforms was proposed as an attractive alternative to enable the tool application. The approach addresses all simulation steps: geometry and weather definition, model set-up, control, results edition, and visualization. Besides, it explores alternatives to display and compute ACH and parametrically generates horizontal planes across the spaces to calculate surface average air velocities. Usually, network models throughout Building Energy Simulation (BES) are the most employed NV investigations approach, especially in annual analysis. Nevertheless, as the wind is a significant driving force for ventilation, wind pressure coefficients (Cp) represent a critical boundary condition when assessing building airflows, influencing BES models’ results. The Cp values come from either a primary source that includes CFD simulations or a secondary one where the primary is considered the most reliable. In this sense, a performance metric was proposed, namely the Natural Ventilation Effectiveness (NVE). It verifies when outdoor airflows can maintain indoor temperatures within a comfortable range. The metric uses BES results, and within this context, the impact of five different Cp sources on its outputs was investigated. Three secondary sources and surface-averaged Cp values calculated with CFD for both the whole façade and windows were considered. The differences between the CFD Cp values are minor when wind direction is normal to the surface, with more significant discrepancies for the openings close to roof eaves. Although there was considerable variance among the Cp sources, its effect on the NVE was relatively small. Additionally, when designing high-performance buildings for cold climates, efficient insulating systems are encouraged since they help reduce heat losses through the building envelope, thus promoting building energy savings. Still, climate exposure deteriorates material properties, compromising a building’s energy performance over its lifetime. Therefore, this aging impact on the hygrothermal performance of an aerogel-based insulating system was investigated through a large-scale test, U-Value measurements, and heat and moisture transfer (HMT) models, calibrated with the experimental data. A low thermal conductivity degradation was measured after the tests, showing that its effectiveness is not harshly compromised throughout its life-cycle. Finally, this research performed parametric modeling and optimization to minimize annual building energy demand and maximize NVE. The workflow was divided into i) model setting, ii) sensitivity analyses (SA), and iii) multi-objective optimization (MOO), with a straightforward process implemented through a parametric platform. Input variables dimension was firstly reduced with SA, and the last step ran with a model-based optimization algorithm (RBFOpt). MOO results showed a remarkable potential for NV and heating energy savings. The design solutions could be employed in similar typologies and climates, and the adopted framework configures a practical and replicable approach for design approaches aiming to develop high-performance buildings through MOO.