Damage diagnosis of high-rise buildings under variable ambient conditions using subdomain approach

Hybrid ventilation is an effective approach to reduce cooling energy consumption by combining natural and mechanical ventilation. Previous studies of full-scale whole-building measurements of high-rise hybrid ventilation are quite limited due to the complexities of buildings and variable ambient conditions. As a result, validated and accurate whole-building simulations of hybrid ventilation often cannot be found in the literature. This paper reports a series of full-scale measurements of hybrid ventilation in a 17-storey high-rise building and associated whole-building simulations by 15-zone detailed and a 5-zone simplified multizone models. The paper is one of the first studies of using multizone models and real-world full-scale data and sharing key operational and performance experience and case studies of high-rise hybrid ventilation. Both the test data and the validated simulation models can be used for the comparison and validation of simulation models. The 5-zone simplified model developed from this study was able to model such a complex high-rise building by only a few zones, making possible the on-line model predictive control of a high-rise building. This was illustrated in this paper by an example of optimizing the uniformity of the hybrid ventilation on different floors by modifying inlet areas.

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Effects of Inflow Condition on RANS and LES Predictions of the Flow around a High-Rise Building

Fluids ◽

10.3390/fluids5040233 ◽

2020 ◽

Vol 5 (4) ◽

pp. 233

Author(s):

Giulio Vita ◽

Simone Salvadori ◽

Daniela Anna Misul ◽

Hassan Hemida

Keyword(s):

Navier Stokes ◽

Ambient Conditions ◽

Engineering Applications ◽

Building Model ◽

High Rise ◽

High Rise Building ◽

Eddy Simulation ◽

Turbulent Inflow ◽

Flow Features ◽

Inflow Condition

An increasing number of engineering applications require accurate predictions of the flow around buildings to guarantee performance and safety. This paper investigates the effects of variations in the turbulent inflow, as predicted in different numerical simulations, on the flow pattern prediction around buildings, compared to wind tunnel tests. Turbulence characteristics were assessed at several locations around a model square high-rise building, namely, above the roof region, at the pedestrian level, and in the wake. Both Reynolds-averaged Navier–Stokes (RANS, where turbulence is fully modelled) equations and large-eddy simulation (LES, where turbulence is partially resolved) were used to model an experimental setup providing validation for the roof region. The performances of both techniques were compared in ability to predict the flow features. It was found that RANS provides reliable results in regions of the flow heavily influenced by the building model, and it is unreliable where the flow is influenced by ambient conditions. In contrast, LES is generally reliable, provided that a suitable turbulent inflow is included in the simulation. RANS also benefits when a turbulent inflow is provided in simulations. In general, LES should be the methodology of choice if engineering applications are involved with the highly separated and turbulent flow features around the building, and RANS provides reliable information when regions of high wind speed and low turbulence are investigated.

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Novelty Detection Using Auto-Associative Neural Network

10.1115/imece2001/dsc-24571 ◽

2001 ◽

Author(s):

Hoon Sohn ◽

Keith Worden ◽

Charles R. Farrar

Keyword(s):

Novelty Detection ◽

Primary Objective ◽

Ambient Conditions ◽

Damage Diagnosis ◽

Operation Conditions ◽

Structural Deterioration ◽

System Changes ◽

Natural Variations ◽

True System ◽

Auto Associative Neural Network

Abstract The primary objective of novelty detection is to examine if a system significantly deviates from the initial baseline condition of the system. In reality, the system is often subject to changing environmental and operation conditions affecting its dynamic characteristics. Such variations include changes in loading, boundary conditions, temperature, and humidity. Most damage diagnosis techniques, however, generally neglect the effects of these changing ambient conditions. Here, a novelty detection technique is developed explicitly taking into account these natural variations of the system in order to minimize false positive indications of true system changes. Auto-associative neural networks are employed to discriminate system changes of interest such as structural deterioration and damage from the natural variations of the system.

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