Browsing by Author "Garrecht, Harald (Prof.)"

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Open Access
Performance-oriented design and assessment of naturally ventilated buildings
(2021) Sakiyama, Nayara R. M.; Garrecht, Harald (Prof.)
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.
Open Access
Real-size structural health monitoring of a pre-stressed concrete bridge based on long-gauge fiber Bragg grating sensors
(2021) Sakiyama, Felipe Isamu H.; Garrecht, Harald (Prof.)
The ability to track the structural condition of existing structures is one of engineers, governments, and estate managers’ main con-cerns. In bridge maintenance programs, for example, visual in-spection predominates nowadays as the primary source of infor-mation. Nonetheless, visual inspections alone are insufficient to satisfy the current needs for structural safety assessment. The in-creasing demand for civil infrastructures, the aging of existing assets, and the strengthening of safety and liability laws have led to the inclusion of structural health monitoring (SHM) techniques into the structural management process. With the latest develop-ments in the sensors field and computational power, real-scale SHM deployment has become logistically and economically feasi-ble. However, it is still challenging to perform a quantitative evalua-tion of the structural condition based on measured data. Although the current approaches of SHM systems using traditional single-point sensors - such as electric strain sensors, accelerometers, and GPS-based sensors - have appropriate measurement precision for SHM purposes, they present challenges when deployed in real-scale applications, given the limited number of possible points to assess the structural behavior and the harsh environmental condi-tions during operation. When it comes to prestressed and rein-forced concrete structures, structural monitoring and damage identification present further challenges. They are affected by vari-ous chemical, physical and mechanical degradation processes and have a heterogeneous composition and non-linear behavior. On the other hand, fiber optic (FO) technology can provide integrated sensing in extensive measurement lengths with high sensitivity, durability, and stability, making them ideal for SHM of concrete structures. From this perspective, extensive research on structural health monitoring has been developed in the last decades. How-ever, the transfer rate from laboratory experiments to real-case applications is still unsatisfactory. This research addressed the main limitations that slow the deployment and the acceptance of real-size structural health monitoring systems in bridge mainte-nance programs. It proposed a long-term SHM concept to moni-tor prestressed concrete bridges, enabling the real-time detection of inherent damaging processes such as prestressing tendon break and crack opening and providing meaningful structural in-formation to support decision-making within bridge maintenance programs. An SHM system based on long-gauge fiber Bragg grat-ing (LGFBF) sensors was designed and deployed in a real-size prestressed concrete bridge. Autonomous and intelligent meas-urement tasks with data management and post-processing tools were implemented to operate the SHM system and delivery the expected results. A novel runtime algorithm for real-time analysis based on random variables correlation for condition monitoring was implemented to automatically detect unexpected events, such as local structural failure, within many random dynamic loads. Additionally, an integrated methodology for data interpretation and model updating built on data feature extraction using the principal component analysis (PCA), finite element (FE) modeling, and Monte Carlo simulations was proposed to identify existing damages and optimize the FE model updating process. The re-sults showed that the deployed SHM system successfully translates the massive raw data into meaningful information to access struc-tural response, predict damage formation, and calibrate a FE model of the monitored structure. Finally, the proposed real-time analysis algorithm delivers a reliable notification system that allows bridge managers to track unexpected events as a basis for deci-sion-making.