Simulation of ventilated facades in hot and humid climates

The Hong Kong climate is sub-tropical with hot and humid weather from May to September and temperate climate for the remaining 7 months period. A mechanical ventilation and air-conditioning (MVAC) system is usually operated to avoid the high temperatures resulting in high peak cooling loads. The facade design has a significant influence on the energy performance of office buildings. This work evaluates different ventilated facade designs in respect to energy savings.Thermal building simulations (TRNSYS) were linked to nodal airflow network simulations (COMIS) for detailed ventilated double-skin facade performance. In order to validate the model, simulations were carried out for an office building in Lisboa; the results were compared with measured data from the same building. The simulation results of surface and air temperatures show good agreement with the measurements. The results of the study can be used to reduce surface temperatures by using different materials for the roller blind that is positioned in the cavity of the double-skin facade. The results can further be used to reduce the high peak cooling loads during the summer period. This may result in significant energy savings and a reduction in the system's cooling capacity. It proved that a careful facade design can play an important role in highly glazed buildings and provides potential for energy efficiency.     

A data-centric bottom-up model for generation of stochastic internal load profiles based on space-use type

There is currently no established methodology for the generation of synthetic stochastic internal load profiles for input into building energy simulation. In this paper, a Functional Data Analysis approach is used to propose a new data-centric bottom-up model of plug loads based on hourly data monitored at a high spatial resolution and by space-use type for a case-study building. The model comprises a set of fundamental Principal Components (PCs) that describe the structure of all data samples in terms of amplitude and phase. Scores (or weightings) for each daily demand profile express the contribution of each PC to the demand. Together the principal components and the scores constitute a structure-based model potentially applicable beyond the building considered. The results show good agreement between samples generated using the model and monitored data for key parameters of interest including the timing of the daily peak demand.     

A new energy analysis tool for ground source heat pump systems

A new tool, suitable for energy analysis of vertical ground source heat pump systems, is presented. The tool is based on analytical equations describing the heat exchanged with the ground, developed in Matlab^® environment. The time step of the simulation can be freely chosen by the user (e.g. 1, 2 h etc.) and the calculation time required is very short. The heating and cooling loads of the building, at the afore mentioned time step, are needed as input, along with the thermophysical properties of the soil and of the ground heat exchanger, the operation characteristic curves of the system's heat pumps and the basic ground source heat exchanger dimensions. The results include the electricity consumption of the system and the heat absorbed from or rejected to the ground. The efficiency of the tool is verified through comparison with actual electricity consumption data collected from an existing large scale ground coupled heat pump installation over a three-year period.     

Substructure Identification and Health Monitoring Using Noisy Response Measurements Only

Abstract:   A probabilistic substructure identification and health monitoring methodology for linear systems is presented using measured response time histories only. A very large number of uncertain parameters have to be identified if one considers the updating of the entire structure. For identifiability, one then would require a very large number of sensors. Furthermore, even when such a large number of sensors are available, processing of vast amount of the corresponding data raises computational difficulties. In this article a substructuring approach is proposed, which allows for the identification and monitoring of some critical substructures only. The proposed method does not require any interface measurements and/or excitation measurements. No information regarding the stochastic model of the input is required. Specifically, the method does not require the response to be stationary and does not assume any knowledge of the parametric form of the spectral density of the input. Therefore, the method has very wide applicability. The proposed approach allows one to obtain not only the most probable values of the updated model parameters but also their associated uncertainties using only one set of response data. The probability of damage can be computed directly using data from the undamaged and possibly damaged structure. A hundred-story building model is used to illustrate the proposed method.     

Evaluation of methods for determining demand-limiting setpoint trajectories in buildings using short-term measurements

This paper presents the evaluation of three different methods for determining zone temperature setpoint variations that limit peak electrical demand in buildings. The methods were developed in a companion paper [Lee K-H, Braun JE. Development of methods for determining demand-limiting setpoint trajectories in buildings using short-term measurements. Building and Environment 2007, in press, doi:10.1016/j.buildenv.2007.11.004] and are evaluated in the current paper through simulation for a small, medium, and large commercial building. Inverse models were employed for the simulation where the parameters were estimated with nonlinear regression techniques using hourly data. Two of the demand-limiting methods are based on the use of simple building models that capture dynamics of the building cooling loads in response to setpoint variations over a short time scale. The third method is data driven and only relies on load data to directly determine setpoint variations that minimize peak cooling demand. All three demand-limiting methods work well in terms of peak demand reduction for individual buildings. However, the data-driven method has slightly better performance than the other methods, is easier to implement, and is directly applicable for peak load reduction of aggregated buildings.     

空调设备排热对传热、新风冷负荷的影响

建立了室内外热环境耦合计算模型。通过实地测量,验证了模型模拟结果的准确性。采用该模型,以某典型办公建筑群为研究对象,探讨了空调设备排热对传热冷负荷及新风冷负荷的影响。空调设备排热加大了传热冷负荷及新风冷负荷,对新风冷负荷的影响更为显著。     

Rational selection of near-extreme coincident weather data with solar irradiation for risk-based air-conditioning design

The selection of design solar irradiance in the current ASHRAE and CIBSE design handbooks is independent on design dry-bulb and wet-bulb temperatures. The probability that the load would not exceed the system capacity determined on this basis may not match the reliability level that the design weather data were meant to safeguard. Hence, a statistic method was developed for the rational selection of coincident solar irradiance, dry-bulb and wet-bulb temperatures. The new method combines, based on the principle of heat transfer, the three weather parameters and building characteristics into a single impact factor, which may be called as equivalent temperature. This ensures that the joint probability of occurrence of the three coincident weather parameters can match the capacity reliability level of air-conditioning systems, which may be required by building owners. The method was applied to historic weather records of 25 years in Hong Kong to generate coincident design weather data. These new design weather data were compared with those design weather data produced by the traditional method. Results show that traditional design solar irradiance, dry-bulb and wet-bulb temperatures may be significantly overestimated in many conditions. Moreover, the new method allows HVAC engineers to determine the peak cooling load directly without the need for calculating 24 h cooling loads on one design day for every month of the year. This greatly simplifies the design cooling load calculation. Although the new design weather data are only for buildings whose thermal lag is less than 1 h, the method provides a basis for further generating design weather data for buildings with a large thermal lag.     

Model-based demand-limiting control of building thermal mass

This paper describes the development and evaluation of a model-based approach for minimizing peak cooling demand using energy storage inherent in building structures. On any day where the strategy is invoked, the building is precooled with zone temperature setpoints at the low end of comfort prior to a demand-limiting (DL) period. The zone temperatures are then adjusted upwards during the demand-limiting period following a trajectory that keeps the peak cooling load below a specified target. The cooling demand target and setpoint trajectory are determined using a building model that is trained using field data. The overall approach was demonstrated for a building representative of a small commercial facility. The first step involved training the inverse model using a few weeks of hourly data. The model was then used to study the potential for peak load reduction and to determine setpoint trajectories that were implemented at the site. The demand-limiting strategy resulted in approximately 30% reductions in peak cooling loads compared to night setup control for a 5-h on-peak period of 1 PM to 6 PM.     

The effect of urban evaporation on building energy demand in an arid environment

An open-air scaled urban surface (OASUS) was used to physically model the influence of urban structure on microclimatic conditions that affect the cooling requirements of buildings. The OASUS scale-model consists of an extensive urban-like building/street array constructed at an open site in the arid Negev region of southern Israel. Building rows are comprised of hollow concrete masonry blocks and have thermal and optical properties analogous to common local construction materials. Previous experiments with the scale-model were limited to “dry” conditions, with only negligible exchanges of latent heat. Considering that one of the main advantages of using the scale-model facility is to be able to control factors affecting microclimate, this study analyses the impact of adding moisture to the scaled “streets” between “building” rows, and gauges the impact of outdoor evaporative cooling on the energy demand of adjacent buildings. Measurements carried out during the summer month of August 2006 at the scale-model facility were used to obtain street canyon air temperatures, which in turn provided input for a dynamic energy simulation of indoor cooling loads in an actual building. The simulation model was calibrated with simultaneously measured data from a nearby residential building. Results suggest that the cooling factor in a street canyon is a direct function of the relative availability of moisture, with respect not only to horizontal area but also to the “complete” three-dimensional urban surface. In addition, simulation results of building energy demand show the importance of accounting for urban density when planning the disposition of vegetated surfaces for cooling purposes.     

Dynamic modeling of fire spread in building

Modeling fire spread in a building is a key factor of a fire risk analysis used for fire safety designs of large buildings. In this paper, a dynamic model of fire spread considering fire spread in both horizontal and vertical directions is described. The algorithms for simulating the fire spread process in buildings and calculating dynamic probability of fire spread for each compartment at each time step of simulation are proposed. The formulae used in calculating the input data for the dynamic fire spread model are derived. The dynamic fire spread model can easily be applied for any building including high-rise buildings. A detailed example of calculation of fire spread in a two-storey office building is described.     

Estimating uncertainty in terrestrial critical loads and their exceedances at four sites in the UK

Critical loads are the basis for policies controlling emissions of acidic substances in Europe and elsewhere. They are assessed by several elaborate and ingenious models, each of which requires many parameters, and have to be applied on a spatially-distributed basis. Often the values of the input parameters are poorly known, calling into question the validity of the calculated critical loads. This paper attempts to quantify the uncertainty in the critical loads due to this "parameter uncertainty", using examples from the UK. Models used for calculating critical loads for deposition of acidity and nitrogen in forest and heathland ecosystems were tested at four contrasting sites. Uncertainty was assessed by Monte Carlo methods. Each input parameter or variable was assigned a value, range and distribution in an objective a fashion as possible. Each model was run 5000 times at each site using parameters sampled from these input distributions. Output distributions of various critical load parameters were calculated. The results were surprising. Confidence limits of the calculated critical loads were typically considerably narrower than those of most of the input parameters. This may be due to a "compensation of errors" mechanism. The range of possible critical load values at a given site is however rather wide, and the tails of the distributions are typically long. The deposition reductions required for a high level of confidence that the critical load is not exceeded are thus likely to be large. The implication for pollutant regulation is that requiring a high probability of non-exceedance is likely to carry high costs. The relative contribution of the input variables to critical load uncertainty varied from site to site: any input variable could be important, and thus it was not possible to identify variables as likely targets for research into narrowing uncertainties. Sites where a number of good measurements of input parameters were available had lower uncertainties, so use of in situ measurement could be a valuable way of reducing critical load uncertainty at particularly valuable or disputed sites. From a restricted number of samples, uncertainties in heathland critical loads appear comparable to those of coniferous forest, and nutrient nitrogen critical loads to those of acidity. It was important to include correlations between input variables in the Monte Carlo analysis, but choice of statistical distribution type was of lesser importance. Overall, the analysis provided objective support for the continued use of critical loads in policy development.     

Simulation of maintenance costs in UK local authority sport centres

The costs of building maintenance, particularly in high-demand public sector facilities, can account for a significant proportion of the whole life cycle costs. The ability to accurately predict these maintenance costs can provide an essential management tool during the operational stages. A thorough investigation of existing techniques and metrics to predict these costs provides the basis of this research, which proposes an innovative simulation-based approach to maintenance costs modelling in UK local authority sports buildings. The results obtained show that gross floor area, swimming pool size, and the number of users are the critical factors influencing maintenance costs in sport centre buildings. Probabilistic representations of these variables are used in a stochastic cost-forecasting model, which reveals that the Weibull distribution is suitable for representing the maintenance cost function. The results are validated through the application of the Kolmogrov-Smirnov test, the importance of this test, and the data pre-analysis used in the construction and validation of the input probability distributions, are also discussed.     

On the stochastic nature of compact debris flight

The stochastic nature of debris flight is investigated through a series of Monte Carlo simulations based on the debris flight equations for compact debris presented by Holmes (2004). Any given debris flight situation presents a number of uncertainties such as the size of the piece of debris and the time-varying turbulent wind flow. Current debris flight models are largely deterministic and do not account for such uncertainty in input parameters. The simulations presented model the flight of a single spherical particle whose diameter is given by a probability distribution function, driven by a turbulent wind with velocity fluctuations appropriate to the atmospheric boundary layer. The model predicts the mean and standard deviation of the particle flight distance and impact kinetic energy. Results show that introducing uncertainty in particle diameter, horizontal turbulence intensity, or vertical turbulence intensity leads to larger mean values for flight distance and impact kinetic energy, compared to the condition where there is no variability in input parameters. Introducing input parameter variability also leads to variability in flight distance and impact kinetic energy that is quantified in this study. While the simulations presented do not realistically characterize the complex flow within an urban canopy, the results provide significant physical insight into the influence of particle size variability and turbulence on the mean and standard deviation of the flight distance and impact kinetic energy.     

Inter-related effects of cooling strategies and building features on energy performance of office buildings

The paper compares effects on thermal performance and energy use of various pre-cooling and ventilation strategies, which might be used for reducing peak power demands in typical office buildings located in moderately warm climatic regions. Simulations were performed for different features of the building envelope, and for two levels of internal heat load.Results indicate: significant reductions of required daytime peak power loads may be obtained by cooling strategies that contribute to lowering internal mass temperatures. For buildings with large internal heat loads, intensive night pre-cooling is the most effective strategy for smoothing required power loads. However, for non-loaded buildings, it largely increases total energy loads, and night-time peak power loads. Intensive night ventilation reduces required peak power loads as well as total cooling energy loads for both building types. For non-loaded buildings, it is an extremely efficient strategy, whereas the efficacy of other pre-cooling strategies is highly questionable. Further research should include secondary effects (on required peak power loads, total energy loads, and electricity consumption) as they may decrease the efficiency differences between the two strategies.     

Predicting performance of EPB TBMs by using a stochastic model implemented into a deterministic model

The current study is an attempt to address the stochastic nature of the rock excavation process by suggesting a stochastic performance prediction model implemented into a deterministic model developed for hard rock TBMs. Full-scale linear cutting experiments using constant cross-section and V-type of disc cutters are performed on two different limestone samples to provide the basic input required for the deterministic model used for estimation of instantaneous penetration rate, daily advance rate, thrust and torque requirements of TBMs. Stochastic estimation is performed by using a Monte Carlo simulation program by applying iterations to implement the probabilistic distribution of each model parameter and provide knowledge of a confidence level. Results of the suggested model are verified by measuring the field performance of two earth pressure balance (EPB) TBMs excavating competent rocks in semi-closed mode. The results indicate that the suggested model works well for prediction of instantaneous cutting/penetration rate for both TBMs and both types of disc cutters. However, an improvement on the model is required for estimation of cutterhead torque and thrust of EPB TBMs. The stochastic model implemented into the deterministic model results in almost similar predictions with the deterministic model in 50% (best guess) probability. However, the stochastic modeling provides a tool for exploring the full implications of linear cutting experiments and allows assessing the probability of occurrence and predicting variations of the TBM performance parameters, covering the uncertainties/risks.     

水源热泵机组的节能效果

建立了水源热泵机组的仿真模型,通过实验对该模型进行了验证。应用该模型,以能耗最小为原则,优化了满足建筑物冷负荷需求时水源热泵机组的组合方式。结果表明,水源热泵机组相对于普通冷水机组,在供冷季最多可以获得13.1%的节能效果。     

Effect of structural uncertainty on seismic response of steel moment-resisting frames equipped with tuned mass dampers

Tuned mass dampers (TMDs) can be used to absorb the input energy of the applied loads, and reduce the response of building frames. However, inherited uncertainties in structural characteristics of building frames can significantly affect their response and counteract the effectiveness of vibration absorbing devices such as TMDs. In this study, by calculating cumulative damage indices for stories of the structure, failure probability of two steel moment-resisting frames equipped with TMDs has been studied in presence of uncertainty in characteristics of the structure. Cumulative inelastic deformation of structural elements in each story has been used to calculate the damage representative of that story, based on weighted average approach. Even though the cumulative response of the deterministic model of the structures is reduced by installing TMDs, the results of the numerical simulations on the probabilistic response of the sample structures indicate that for the records that cause excessive damage in the lower stories of the structures, the effect of TMDs on failure probability of the structure can be detrimental.     

低矮房屋风致结构响应极值不确定性分析

风致结构响应极值估算在结构抗风的可靠度设计中十分重要。在整个极值估算过程中,由于许多不定或随机的因素存在(如：极值自身、估算方法、样本采集、极值概率模型等),得到的极值通常存在不确定性。在各种影响因素中,该文将考虑结构响应极值变量本身的随机特性,对任意分位点处响应极值的不确定性进行分析。首先,利用有限元软件对低矮房屋模型进行框架结构设计并优化,加载风压荷载得到结构响应时程数据。然后,基于Hermite多项式模型(HPM)转换过程方法,估算得到响应的极值Ⅰ型分布(Gumbel);基于该极值估算方法,提出时程样本偏度、峰度、零超越次数与Gumbel分布两个参数之间的经验公式。接着,考虑前四阶矩的不确定性,利用经验公式以及多步概率分析,对任意分位点处响应极值的不确定性进行估计。最后,给出相关结论。     

An enhanced linear regression-based building energy model (LRBEM+) for early design

The design community lacks simple, data-driven energy assessment tools to explore energy-efficient alternatives during the early stages of building design. A promising option is to utilize a whole building energy simulation engine (e.g. EnergyPlus) within a Monte Carlo simulation framework to develop a linear regression-based building energy model (LRBEM) that can predict idealized heating and cooling loads based on parameters relevant to early design. Previous work was limited to medium-sized US commercial office buildings with rectangular geometries. A key limitation is addressed in this paper by considering complex geometries. A reformulated model, LRBEM+, is developed and tested with a suite of building geometries that represent limiting cases. The resultant relative error between LRBEM+ and EnergyPlus is generally less than 10%. Furthermore, LRBEM+ correctly predicts the direction and magnitude of changes in heating and cooling loads in response to changes in the most influential early design parameters.     

Variability between domestic buildings: the impact on energy use

Variations in operational use (in the time domain) and in design and use (between buildings) are critical for district systems. The effects on energy use of behavioural (stochastic profiles of occupancy and end uses) and physical variations (size, orientation, insulation and air tightness) amongst many buildings is examined. Rather than investigating just the variability of these factors, the aim is to identify subsequent impacts on building energy use. To achieve this, dynamic building energy simulations in EnergyPlus are performed. Results include total demands and their distributions, and temporal and probabilistic profiles. Very large variations in total heating demand are noted. Temporal profiles show changes in peak loads, load durations and periods of zero load. Probabilistic profiles and cumulative distributions show that a few buildings are responsible for the majority of total loads. Full detailed simulations are identified as critical when assessing temporal effects such as peak loads and storage sizing.