Computational analysis of wind interactions for comparing different buildings sites in terms of natural ventilation

Nowadays building designers have to face up to new strategies to achieve the best sustainable building designs. Well planned natural ventilation strategies in building design may contribute to a significant reduction on building’s energy consumption. Natural ventilation strategies are conditioned to the particular location of each building. To improve natural ventilation performance of a building, the analysis of the influence of the location and the surrounding buildings on wind flow paths around the design building is a must. New computational tools such as Computational Fluid Dynamics (CFD) are particularly suited for modelling outdoor wind conditions and the influence on indoor air conditions prior to building construction. Hence, reliable methodologies are necessary to support building design decisions related to naturally ventilated buildings prior to construction.This paper presents a case study for the selection of the best future building location attending to natural ventilation behaviour inside the building, conditioned by different evolving environment. A validated CFD model is used to represent outdoor and indoor spaces. The methodology explains how to qualitatively and quantitatively analyze wind paths around and through a building to quantify the natural ventilation performance. The best location, from two real possible solutions, is then selected.     

Prediction of indoor temperature and relative humidity using neural network models: model comparison

The use of neural networks grows great popularity in various building applications such as prediction of indoor temperature, heating load and ventilation rate. But few papers detail indoor relative humidity prediction which is an important indicator of indoor air quality, service life and energy efficiency of buildings. In this paper, the design of indoor temperature and relative humidity predictive neural networks in our test house was developed. The test house presented complicated physical features which are difficult to simulate with physical models. The work presented in this paper aimed to show the suitability of neural networks to perform predictions. Nonlinear AutoRegressive with eXternal input (NNARX) model and genetic algorithm were employed to construct networks and were detailed. The comparison between the two methods was also made. Applicability of some important mathematical validation criteria to practical reality was examined. Satisfactory results with correlation coefficients 0.998 and 0.997 for indoor temperature and relative humidity were obtained in the testing stage.     

General modeling for model-based FDD on building HVAC system

Fault detection and diagnosis (FDD) can be realized with models. It can be used to find the cause of the degradation on energy efficiency and indoor climate quality in building heating, ventilation and air-conditioning (HVAC) systems. Real buildings are diverse. It requires a general modeling method. General modeling concept comprises three steps: hierarchical modeling procedure, parameterization and tuning procedure. In the procedure of hierarchical modeling, the process is split into levels ranging from global to micro level. The objective is to detect faults on the various levels. Consequently, different models for each level should be built. It is important to increase the accuracy of the models and let the models applicable for FDD on building HVAC systems. Parameterization and tuning procedure are necessary. An example model for a real air-conditioned room shows the results of general models and the results after tuning.     

Coupled urban wind flow and indoor natural ventilation modelling on a high-resolution grid: A case study for the Amsterdam ArenA stadium

Wind flow in urban environments is an important factor governing the dispersion of heat and pollutants from streets, squares and buildings. This paper presents a coupled CFD modelling approach for urban wind flow and indoor natural ventilation. A specific procedure is used to efficiently and simultaneously generate the geometry and the high-resolution body-fitted grid for both the outdoor and indoor environment. This procedure allows modelling complex geometries with full control over grid quality and grid resolution, contrary to standard semi-automatic unstructured grid generation procedures. It also provides a way to easily implement various changes in the model geometry and grid for parametric studies. As a case study, a parametric analysis of natural ventilation is performed for the geometrically complex Amsterdam ArenA stadium in the Netherlands. The turbulent wind flow and temperature distribution around and inside the stadium are solved with the 3D steady Reynolds-averaged Navier–Stokes equations. Special attention is given to CFD solution verification and validation. It is shown that small geometrical modifications can increase the ventilation rate by up to 43%. The coupled modelling approach and grid generation procedure presented in this paper can be used similarly for future studies of wind flow and related processes in complex urban environments.     

Evaluating interface designs for user-system interaction media in buildings

Occupant control actions in a building (i.e., user interactions with environmental systems for heating, cooling, ventilation, lighting, etc.) can significantly affect both indoor climate in and the environmental performance of buildings. Nonetheless, relatively few systematic (long-term and high-resolution) efforts have been made to observe and analyze the means and patterns of such user-system interactions with building systems. Specifically, the necessary requirements for the design and testing of hardware and software systems for user-system interfaces have not been formulated in a rigorous and reliable manner. This paper includes an exploration of the requirements and functionalities of user interfaces for building systems in sentient buildings. We first compare a number of commercial user-interface products for building control systems. Thereby, we consider three dimensions (information types, control options, and hardware) and seven criteria (functional coverage, environmental information feedback, intuitiveness, mobility, network, input, and output). We then present the results of an experiment, in which 40 participants examined and evaluated a selected number of user interfaces for buildings’ control systems, mainly in view of their first impressions, user-interface layout design, and as well as ease of learning. The outcome of these studies are expected to serve as the starting point for developing a new generation of user-interface models to promote higher levels of connectivity between occupants and sentient environments.     

Non-linear models of temperature sensor dynamics

To describe temperature sensor dynamics, linear models have been widely used in the literature of the subject. These models are derived on the basis of transmittance and they express the sensor response to a unit temperature step. However, such linear models can only be applied for a small temperature increase, ranging from 20 to 30 °C, because the temperature sensor can then be regarded as a linear element. This paper presents non-linear models which adequately describe temperature sensor dynamics within the temperature increase range (of random values) where the sensor can be considered as a linear or a non-linear element. These models are derived on the basis of the Stone-Weierstrass theorem describing uniform approximations of continuous functions. The use of non-linear models in the dynamic method of measuring high temperatures beyond the permissible sensor range is given as an example.     

The effects of solar radiation and black body re-radiation on thermal comfort

When the sun shines on people in enclosed spaces, such as in buildings or vehicles, it directly affects thermal comfort. There is also an indirect effect as surrounding surfaces are heated exposing a person to re-radiation. This laboratory study investigated the effects of long wave re-radiation on thermal comfort, individually and when combined with direct solar radiation. Nine male participants (26.0 +/- 4.7 years) took part in three experimental sessions where they were exposed to radiation from a hot black panel heated to 100 degrees C; direct simulated solar radiation of 600 Wm(-2) and the combined simulated solar radiation and black panel radiation. Exposures were for 30 min, during which subjective responses and mean skin temperatures were recorded. The results showed that, at a surface temperature of 100 degrees C (close to maximum in practice), radiation from the flat black panel provided thermal discomfort but that this was relatively small when compared with the effects of direct solar radiation. It was concluded that re-radiation, from a dashboard in a vehicle, for example, will not have a major direct influence on thermal comfort and that existing models of thermal comfort do not require a specific modification. These results showed that, for the conditions investigated, the addition of re-radiation from internal components has an effect on thermal sensation when combined with direct solar radiation. However, it is not considered that it will be a major factor in a real world situation. This is because, in practice, dashboards are unlikely to maintain very high surface temperatures in vehicles without an unacceptably high air temperature. This study quantifies the contribution of short- and long-wave radiation to thermal comfort. The results will aid vehicle designers to have a better understanding of the complex radiation environment. These include direct radiation from the sun as well as re-radiation from the dashboard and other internal surfaces.     

On the effect of wind direction and urban surroundings on natural ventilation of a large semi-enclosed stadium

Natural ventilation of buildings refers to the replacement of indoor air with outdoor air due to pressure differences caused by wind and/or buoyancy. It is often expressed in terms of the air change rate per hour (ACH). The pressure differences created by the wind depend - among others - on the wind speed, the wind direction, the configuration of surrounding buildings and the surrounding topography. Computational Fluid Dynamics (CFD) has been used extensively in natural ventilation research. However, most CFD studies were performed for only a limited number of wind directions and/or without considering the urban surroundings. This paper presents isothermal CFD simulations of coupled urban wind flow and indoor natural ventilation to assess the influence of wind direction and urban surroundings on the ACH of a large semi-enclosed stadium. Simulations are performed for eight wind directions and for a computational model with and without the surrounding buildings. CFD solution verification is conducted by performing a grid-sensitivity analysis. CFD validation is performed with on-site wind velocity measurements. The simulated differences in ACH between wind directions can go up to 75% (without surrounding buildings) and 152% (with surrounding buildings). Furthermore, comparing the simulations with and without surrounding buildings showed that neglecting the surroundings can lead to overestimations of the ACH with up to 96%.     

Behavioral,data-driven,agent-based evacuation simulation for building safety design using machine learning and discrete choice models

To improve occupant safety during building emergencies, evacuation simulations have been widely used for building safety design. Since occupant behavior is a determining factor for the outcome of building emergencies, accurately capturing how occupants make decisions and integrating occupants’ decision-making processes in evacuation simulations is important. In this study, based on the results of fire evacuation experiments in a virtual metro station, how different social (crowd flow) and environmental (visual access and vertical movement) factors would affect individuals’ wayfinding behavior was predicted using machine learning and discrete choice models. The trained models were further employed in agent-based evacuation simulations to examine crowd evacuation performance under different building design scenarios. Both the machine learning and discrete choice models could accurately predict individuals’ directional choices during emergency evacuations. Different building attributes could collectively influence occupant behavior, leading to distinct exit choices and evacuation times. While both the trained machine learning and discrete choice models generated similar results, the discrete choice model had better interpretability. Moreover, by comparing the trained models in this study with a model developed in a prior study, it was found that agents had significantly distinct responses to different building designs. Critical factors (e.g., type and size of buildings, occupants’ familiarity with the building) for the applicability of evacuation models were identified. Furthermore, recommendations were provided for future research that aims at employing evacuation simulations for building design evaluation and optimization.     

Dynamic aggregated building electricity load modeling and simulation

In order to support the growing interest in demand response modeling and analysis, there is a need for physical-based building load models. This work presents a new approach for simulating electrical power flow in buildings. The new approach handles the power flow capacity shortage in existing building simulation programs, which have been used for the past few decades by building energy communities. The suggested approach represents the building as a group of electrical networks, organized in hierarchical levels. On the top level, the user defines key parameters such as rated power and power factor of existing loads. The power cables are modeled by their equivalent PI model. Accurate simulation models are developed for solving the building network equations where building loads are integrated into building network. Smart meters are implemented at different locations for power quality and energy auditing. Two case studies of residential and commercial buildings are investigated to prove the capability of the introduced approach. A comparison with EnergyPlus, as verified building energy software, is introduced to prove the ability of the proposed Matlab-based model to evaluate the annual energy consumption of the building. All results show the accuracy and ability of the proposed approach for simulating the electrical power flow of the building and can be integrated with renewable and storage energy.     

Recursive Approximation of Complex Behaviours With IoT-Data Imperfections

This paper presents an approach to recursively estimate the simplest linear model that approximates the time-varying local behaviors from imperfect(noisy and incomplete) measurements in the internet of things(IoT) based distributed decision-making problems. We first show that the problem of finding the lowest order model for a multi-input single-output system is a cardinality(l0) optimization problem, known to be NP-hard.To solve the problem a simpler approach is proposed which uses the recently developed atomic norm concept and the modified Frank-Wolfe(mFW) algorithm is introduced. Further, the paper computes the minimum data-rate required for computing the models with imperfect measurements. The proposed approach is illustrated on a building heating, ventilation, and air-conditioning(HVAC) control system that aims at optimizing energy consumption in commercial buildings using IoT devices in a distributed manner. The HVAC control application requires recursive thermal dynamical model updates due to frequently changing conditions and non-linear dynamics. We show that the method proposed in this paper can approximate such complex dynamics on single-board computers interfaced to sensors using unreliable communication channels. Real-time experiments on HVAC systems and simulation studies are used to illustrate the proposed method.     

Optimum decoding-based smoothing algorithm for dynamic systems

A new smoothing algorithm for discrete models is presented. For the disturbance noise and the observation noise, only independency is assumed. Moreover the models’ functions are not limited to continuous functions, i.e. they can be non-continuous. This algorithm estimates the states by first quantizing them and then using the Viterbi decoding algorithm. Simulation results have shown that for some non-linear models the new algorithm performs better than the extended Kalman filter algorithm, while it performs almost as well as the Kalman filter algorithm for linear models with gaussian noise.     

A Quasi-ARMAX approach to modelling of non-linear systems

This paper proposes a class of quasi-ARMAX models for non-linear systems. Similar to ordinary non-linear ARMAX models, the quasi-ARMAX models are flexible black-box models, but they have various linearity properties similar to those of linear ARMAX models. A modelling scheme is introduced to construct models consisting of two parts: a macro-part and a kernel-part. By using Taylor expansion and other mathematical transformation techniques, it is first constructed as a class of quasi-ARMAX interfaces (macro-parts) that have various linearity properties but contain some complicated coefficients. MIMO neurofuzzy models (kernel-parts) are then introduced to represent the complicated coefficients. It is shown that the proposed quasi-ARMAX models have both good approximation ability and some easy-to-use properties. The proposed models have been successfully applied to prediction, fault detection and adaptive control of non-linear systems.     

Constructing NARMAX models using ARMAX models

This paper outlines how it is possible to decompose a complex non-linear modelling problem into a set of simpler linear modelling problems. Local ARMAX models valid within certain operating regimes are interpolated to construct a global NARMAX (non-linear NARMAX) model. Knowledge of the system behaviour in terms of operating regimes is the primary basis for building such models, hence it should not be considered as a pure black-box approach, but as an approach that utilizes a limited amount of a priori system knowledge. It is shown that a large class of non-linear systems can be modelled in this way, and indicated how to decompose the systems range of operation into operating regimes. Standard system identification algorithms can be used to identify the NARMAX model, and several aspects of the system identification problem are discussed and illustrated by a simulation example.     

虚拟游戏场景中的模型研究

游戏游戏建筑模型是虚拟虚拟游戏模型中的基本对象,已有的对游戏建筑的表示局限于某一层次级别,比如块模型、带屋顶的模型、结构模型和室内模型.引入了连续层次级别这一概念来对地理信息系统中的建筑进行表示和建模,而连续层次级别方法统一了虚拟三维游戏模型中不同类型的建筑的表示.     

Longitudinal tunnel ventilation control. Part 1: Modelling and dynamic feedforward control

Road tunnels exceeding a certain minimum length are equipped with a ventilation system. In case of a fire it is used to achieve a predefined air flow velocity in the tunnel by adequately controlling the installed jet fans in order to ensure sufficient visibility for persons to safely follow the escape routes. As the dynamics of the air flow in road tunnels strongly depend on the tunnel length, short tunnels with longitudinal ventilation systems pose a challenging control task. In this paper, non-linear dynamic feedforward control is proposed for longitudinal ventilation control in case of an emergency. For this purpose, an analytical non-linear zero-dimensional model of the air flow is feedback linearised. Due to its special properties, which are presented and analysed, two different versions of feedforward control are proposed: One is focused on performance, the other on robustness. Finally, the beneficial behaviour of the presented two-degrees-of-freedom control approach is demonstrated by its application to an Austrian motorway tunnel.     

On unconditional conservation of kinetic energy by finite-difference discretizations of the linear and non-linear convection equation

In the past the development of kinetic energy conserving finite-difference methods mostly focused on second-order accurate central methods defined on uniform grids. Nowadays the need for high-order accurate discretizations, to perform for instance accurate numerical simulations of turbulent flow, calls for the development of novel kinetic energy conserving discretization schemes. Instead of choosing a fixed basis discretization up front, in this paper a different, more general, approach is applied. For a Cartesian mesh, sets of conditions are presented such that all discretizations of the linear or non-linear convection equation which obey these conditions, unconditionally conserve kinetic energy.For the linear convection equation it is shown that on a uniform grid it is necessary and sufficient for a discretization to be central in order to be fully conservative, that is: such discretizations not only unconditionally conserve kinetic energy but also unconditionally conserve momentum. On non-uniform grids an algorithm is introduced that can be used to generate fully conservative discretizations that are at least first-order accurate.The derivation of the discretization conditions for the non-linear convection equation is performed in the two-dimensional (2D) linear case. Some examples on uniform grids and on non-uniform grids are presented. It is shown that on uniform grids no upper limit exists with respect to the accuracy of the kinetic energy conserving method. For the higher-dimensional linear and non-linear convection equation the same set of conditions, which ensure the unconditional conservation of kinetic energy, are found as in the 2D linear case. Other results too are found to be straightforward generalizations of the corresponding 2D linear results.It is shown that the fourth-order unconditionally kinetic energy conserving discretization on a staggered mesh introduced in this paper is well suited to simulate the initial development of an inviscid shear layer instability in a divergence-free flow.     

A mobile robot based system for fully automated thermal 3D mapping

It is hard to imagine living in a building without electricity and a heating or cooling system these days. Factories and data centers are equally dependent on a continuous functioning of these systems. As beneficial as this development is for our daily life, the consequences of a failure are critical. Malfunctioning power supplies or temperature regulation systems can cause the close-down of an entire factory or data center. Heat and air conditioning losses in buildings lead to a large waste of the limited energy resources and pollute the environment unnecessarily. To detect these flaws as quickly as possible and to prevent the negative consequences constant monitoring of power lines and heat sources is necessary. To this end, we propose a fully automatic system that creates 3D thermal models of indoor environments. The proposed system consists of a mobile platform that is equipped with a 3D laser scanner, an RGB camera and a thermal camera. A novel 3D exploration algorithm ensures efficient data collection that covers the entire scene. The data from all sensors collected at different positions is joined into one common reference frame using calibration and scan matching. In the post-processing step a model is built and points of interest are automatically detected. A viewer is presented that aids experts in analyzing the heat flow and localizing and identifying heat leaks. Results are shown that demonstrate the functionality of the system.     

Neural generators of sparse local linear models for achieving both accuracy and interpretability

For reliability, it is important for the predictions made by machine learning methods to be interpretable by humans. In general, deep neural networks (DNNs) can provide accurate predictions, although it is difficult to interpret why such predictions are obtained by the DNNs. On the other hand, interpretation of linear models is easy, although their predictive performance is low because real-world data are often intrinsically non-linear. To combine both the benefits of the high predictive performance of DNNs and the high interpretability of linear models into a single model, we propose neural generators of sparse local linear models (NGSLL). Sparse local linear models have high flexibility because they can approximate non-linear functions. NGSLL generates sparse linear weights for each sample using DNNs that take the original representations of each sample (e.g., word sequence) and their simplified representations (e.g., bag-of-words) as input. By extracting features from the original representations, the weights can contain rich information and achieve a high predictive performance. In addition, the prediction is interpretable because it is obtained through the inner product between the simplified representations and the sparse weights, where only a small number of weights are selected by our gate module in NGSLL. In experiments on image, text and tabular datasets, we demonstrate the effectiveness of NGSLL quantitatively and qualitatively by evaluating the prediction performance and visualizing generated weights.