OPTIMAL SENSOR PLACEMENT FOR PREDICTION OF WIND ENVIRONMENT AROUND BUILDINGS

Ph.DDOCTOR OF PHILOSOPH

Studies of Sensor Data Interpretation for Asset Management of the Built Environment

Sensing in the built environment has the potential to reduce asset management expenditure and contribute to extending useful service life. In the built environment, measurements are usually performed indirectly; effects are measured remote from their causes. Modelling approximations from many sources, such as boundary conditions, geometrical simplifications and numerical assumptions result in important systematic uncertainties that modify correlation values between measurement points. In addition, conservative behavior models that were employed - justifiably during the design stage, prior to construction - are generally inadequate when explaining measurements of real behavior. This paper summarizes the special context of sensor data interpretation for asset management in the built environment. Nearly twenty years of research results from several doctoral thesis and fourteen full-scale case studies in four countries are summarized. Originally inspired from research into model based diagnosis, work on multiple model identification evolved into a methodology for probabilistic model falsification. Throughout the research, parallel studies developed strategies for measurement system design. Recent comparisons with Bayesian model updating have shown that while traditional applications Bayesian methods are precise and accurate when all is known, they are not robust in the presence of approximate models. Finally, details of the full-scale case studies that have been used to develop model falsification are briefly described. The model-falsification strategy for data interpretation provides engineers with an easy-to-understand tool that is compatible with the context of the built environment

Energy use in residential buildings: Impact of building automation control systems on energy performance and flexibility

This work shows the results of a research activity aimed at characterizing the energy habits of Italian residential users. In detail, by the energy simulation of a buildings sample, the opportunity to implement a demand/response program (DR) has been investigated. Italian residential utilities are poorly electrified and flexible loads are low. The presence of an automation system is an essential requirement for participating in a DR program and, in addition, it can allow important reductions in energy consumption. In this work the characteristics of three control systems have been defined, based on the services incidence on energy consumptions along with a sensitivity analysis on some energy drivers. Using the procedure established by the European Standard EN 15232, the achievable energy and economic savings have been evaluated. Finally, a financial analysis of the investments has been carried out, considering also the incentives provided by the Italian regulations. The payback time is generally not very long: depending on the control system features it varies from 7 to 10 years; moreover, the automation system installation within dwellings is a relatively simple activity, which is characterized by a limited execution times and by an initial expenditure ranging in 1000 € to 4000 €, related to the three sample systems

Development of CFD-based multi-fidelity surrogate models for indoor environmental applications

This thesis presents a methodology for CFD-based multi-fidelity surrogate models for indoor environmental applications. The main idea of this work is to develop a model that has accuracy comparable to CFD simulations but at a considerably lower computational cost. It can perform real-time or faster than real-time simulations of indoor environments using ordinary office computers. This work can be divided into three main parts. In the first part, a rigorous analysis of the feasibility of affordable high-fidelity CFD simulations for indoor environment design and control is carried out. In this chapter, we analyze two representative test cases, which imitate common indoor airflow configurations, on a wide range of different turbulence models and discretization methods to meet the requirements for the computational cost, run-time, and accuracy. We apply the knowledge on the growth in computational power and advances in numerical algorithms in order to analyze the possibility of performing accurate yet affordable CFD simulations on ordinary office computers. The no-model and LES with staggered discretizations studied turbulence models show the best performance. We conclude that high-fidelity CFD simulations on office computers are too slow to be used as a primary tool for indoor environment design and control. Taking into account different laws of computer growth prediction, we estimate the feasibility of high-fidelity CFD on office computers for these applications for the next decades. The second part of this thesis is dedicated to developing a surrogate data-driven model to predict comfort-related flow parameters in a ventilated room. This chapter uses a previously tested ventilated cavity with a heated floor case. The developed surrogate model predicts a set of comfort-related flow parameters, such as the average Nusselt number on the hot wall, jet separation point, average kinetic energy, average enstrophy, and average temperature, which were also comprehensively studied in the previous part of the thesis. The developed surrogate model is based on the gradient boosting regression, chosen due to its accurate performance among four tested machine learning methods. The model inputs are the temperature and velocity values in different locations, which act as a surrogate of the sensor readings. The locations and the number of these sensors were determined by minimizing the prediction error. This model does not require the repetition of CFD simulations to be applied since the structure of the input data imitates sensor readings. Furthermore, the low computational cost of model execution and good accuracy makes it an effective alternative to CFD for applications where rapid predictions of complex flow configurations are required, such as model predictive control. The third part of the thesis is an extension of the surrogate model developed in the second part. In this chapter, we implement a multi-fidelity approach to reduce the computational cost of the training dataset generation. The developed surrogate model is based on Gaussian process regression (GPR), a machine learning approach capable of handling multi-fidelity data. The variable fidelity dataset is constructed using coarse- and fine-grid CFD data with the LES turbulence model. We test three multi-fidelity approaches: GPR trained on both high- and low-fidelity data without distinction, GPR with linear correction, and multi-fidelity GPR or co-cringing. The computational cost and accuracy of these approaches are compared with GPRs based only on high- or low-fidelity data. All of the tested multi-fidelity approaches successfully reduce the computational cost of dataset generation compared to high-fidelity GPR while maintaining the required level of accuracy. The co-cringing approach demonstrates the best trade-off between computational cost and accuracy.Esta tesis presenta una metodología para modelos sustitutos de fidelidad múltiple basados en CFD para aplicaciones de ambiente interior. La idea principal de este trabajo es desarrollar un modelo que tenga una precisión comparable a las simulaciones CFD pero a un costo computacional considerablemente inferior. La metodologia permite realizar simulaciones en tiempo real o más rápido que en tiempo real utilizando ordinadores de oficina ordinarios. Este trabajo se puede dividir en tres partes principales. En la primera parte, se lleva a cabo un análisis de la viabilidad de simulaciones CFD asequibles de alta fidelidad para el diseño y control de ambientes interiores. En este capítulo, analizamos dos casos, que imitan configuraciones comunes de flujo de aire interior, en una amplia gama de diferentes modelos de turbulencia y métodos de discretización. Aplicamos el conocimiento sobre el crecimiento de la potencia computacional para analizar la posibilidad de realizar simulaciones CFD precisas pero asequibles en ordinadores de oficina ordinarios. Los modelos de turbulencia LES y sin modelo con discretizaciones escalonadas muestran el mejor rendimiento. Concluimos que las simulaciones CFD de alta fidelidad son demasiado lentas para ser utilizadas como herramienta principal para el diseño y control de ambientes interiores. Teniendo en cuenta las diferentes leyes de predicción del crecimiento de la potencia computacional, estimamos la viabilidad de CFD de alta fidelidad en ordinadores de oficina para estas aplicaciones durante las próximas décadas. La segunda parte de esta tesis está dedicada al desarrollo de un modelo sustituto basado en datos para predecir los parámetros de flujo en una habitación ventilada. El modelo sustituto desarrollado predice un conjunto de parámetros de flujo, como el número de Nusselt promedio en la pared caliente, el punto de separación del chorro, la energía cinética promedia, la entrofia promedia y la temperatura promedia. El modelo sustituto desarrollado se basa en la regresión de aumento de gradiente, elegida debido a su rendimiento preciso entre cuatro métodos de aprendizaje automático probados. Las entradas del modelo son los valores de temperatura y velocidad en diferentes ubicaciones, que actúan como un sustituto de las lecturas del sensor. Las ubicaciones y el número de estos sensores se determinaron minimizando el error de predicción. Este modelo no requiere la aplicación de la repetición de simulaciones CFD ya que la estructura de los datos de entrada imita las lecturas del sensor. Además, el bajo costo computacional de la ejecución del modelo y la buena precisión lo convierten en una alternativa eficaz a la CFD para aplicaciones en las que se requieren predicciones rápidas de configuraciones de flujo complejas, como el control predictivo del modelo. La tercera parte de la tesis es una extensión del modelo sustituto desarrollado en la segunda parte. En este capítulo, implementamos un enfoque de fidelidad múltiple para reducir el costo computacional de la generación del conjunto de datos de entrenamiento. El modelo sustituto desarrollado se basa en la regresión de procesos gaussianos (GPR), un enfoque de aprendizaje automático capaz de manejar datos de fidelidad múltiple. El conjunto de datos de fidelidad variable se construye utilizando datos CFD. Probamos tres enfoques de fidelidad múltiple: GPR entrenado en datos de alta y baja fidelidad sin distinción, GPR con corrección lineal y GPR de fidelidad múltiple o co-krigeaje. El costo computacional y la precisión de estos enfoques se comparan con los GPR basados solo en datos de alta o baja fidelidad. Todos los enfoques de fidelidad múltiple probados reducen con éxito el costo computacional de la generación de conjuntos de datos en comparación con GPR de alta fidelidad mientras mantienen el nivel requerido de precisión. El enfoque de co-krigeaje demuestra la mejor compensación entre el costo computacional y la precisión.Postprint (published version

Development of CFD-based multi-fidelity surrogate models for indoor environmental applications

This thesis presents a methodology for CFD-based multi-fidelity surrogate models for indoor environmental applications. The main idea of this work is to develop a model that has accuracy comparable to CFD simulations but at a considerably lower computational cost. It can perform real-time or faster than real-time simulations of indoor environments using ordinary office computers. This work can be divided into three main parts. In the first part, a rigorous analysis of the feasibility of affordable high-fidelity CFD simulations for indoor environment design and control is carried out. In this chapter, we analyze two representative test cases, which imitate common indoor airflow configurations, on a wide range of different turbulence models and discretization methods to meet the requirements for the computational cost, run-time, and accuracy. We apply the knowledge on the growth in computational power and advances in numerical algorithms in order to analyze the possibility of performing accurate yet affordable CFD simulations on ordinary office computers. The no-model and LES with staggered discretizations studied turbulence models show the best performance. We conclude that high-fidelity CFD simulations on office computers are too slow to be used as a primary tool for indoor environment design and control. Taking into account different laws of computer growth prediction, we estimate the feasibility of high-fidelity CFD on office computers for these applications for the next decades. The second part of this thesis is dedicated to developing a surrogate data-driven model to predict comfort-related flow parameters in a ventilated room. This chapter uses a previously tested ventilated cavity with a heated floor case. The developed surrogate model predicts a set of comfort-related flow parameters, such as the average Nusselt number on the hot wall, jet separation point, average kinetic energy, average enstrophy, and average temperature, which were also comprehensively studied in the previous part of the thesis. The developed surrogate model is based on the gradient boosting regression, chosen due to its accurate performance among four tested machine learning methods. The model inputs are the temperature and velocity values in different locations, which act as a surrogate of the sensor readings. The locations and the number of these sensors were determined by minimizing the prediction error. This model does not require the repetition of CFD simulations to be applied since the structure of the input data imitates sensor readings. Furthermore, the low computational cost of model execution and good accuracy makes it an effective alternative to CFD for applications where rapid predictions of complex flow configurations are required, such as model predictive control. The third part of the thesis is an extension of the surrogate model developed in the second part. In this chapter, we implement a multi-fidelity approach to reduce the computational cost of the training dataset generation. The developed surrogate model is based on Gaussian process regression (GPR), a machine learning approach capable of handling multi-fidelity data. The variable fidelity dataset is constructed using coarse- and fine-grid CFD data with the LES turbulence model. We test three multi-fidelity approaches: GPR trained on both high- and low-fidelity data without distinction, GPR with linear correction, and multi-fidelity GPR or co-cringing. The computational cost and accuracy of these approaches are compared with GPRs based only on high- or low-fidelity data. All of the tested multi-fidelity approaches successfully reduce the computational cost of dataset generation compared to high-fidelity GPR while maintaining the required level of accuracy. The co-cringing approach demonstrates the best trade-off between computational cost and accuracy.Esta tesis presenta una metodología para modelos sustitutos de fidelidad múltiple basados en CFD para aplicaciones de ambiente interior. La idea principal de este trabajo es desarrollar un modelo que tenga una precisión comparable a las simulaciones CFD pero a un costo computacional considerablemente inferior. La metodologia permite realizar simulaciones en tiempo real o más rápido que en tiempo real utilizando ordinadores de oficina ordinarios. Este trabajo se puede dividir en tres partes principales. En la primera parte, se lleva a cabo un análisis de la viabilidad de simulaciones CFD asequibles de alta fidelidad para el diseño y control de ambientes interiores. En este capítulo, analizamos dos casos, que imitan configuraciones comunes de flujo de aire interior, en una amplia gama de diferentes modelos de turbulencia y métodos de discretización. Aplicamos el conocimiento sobre el crecimiento de la potencia computacional para analizar la posibilidad de realizar simulaciones CFD precisas pero asequibles en ordinadores de oficina ordinarios. Los modelos de turbulencia LES y sin modelo con discretizaciones escalonadas muestran el mejor rendimiento. Concluimos que las simulaciones CFD de alta fidelidad son demasiado lentas para ser utilizadas como herramienta principal para el diseño y control de ambientes interiores. Teniendo en cuenta las diferentes leyes de predicción del crecimiento de la potencia computacional, estimamos la viabilidad de CFD de alta fidelidad en ordinadores de oficina para estas aplicaciones durante las próximas décadas. La segunda parte de esta tesis está dedicada al desarrollo de un modelo sustituto basado en datos para predecir los parámetros de flujo en una habitación ventilada. El modelo sustituto desarrollado predice un conjunto de parámetros de flujo, como el número de Nusselt promedio en la pared caliente, el punto de separación del chorro, la energía cinética promedia, la entrofia promedia y la temperatura promedia. El modelo sustituto desarrollado se basa en la regresión de aumento de gradiente, elegida debido a su rendimiento preciso entre cuatro métodos de aprendizaje automático probados. Las entradas del modelo son los valores de temperatura y velocidad en diferentes ubicaciones, que actúan como un sustituto de las lecturas del sensor. Las ubicaciones y el número de estos sensores se determinaron minimizando el error de predicción. Este modelo no requiere la aplicación de la repetición de simulaciones CFD ya que la estructura de los datos de entrada imita las lecturas del sensor. Además, el bajo costo computacional de la ejecución del modelo y la buena precisión lo convierten en una alternativa eficaz a la CFD para aplicaciones en las que se requieren predicciones rápidas de configuraciones de flujo complejas, como el control predictivo del modelo. La tercera parte de la tesis es una extensión del modelo sustituto desarrollado en la segunda parte. En este capítulo, implementamos un enfoque de fidelidad múltiple para reducir el costo computacional de la generación del conjunto de datos de entrenamiento. El modelo sustituto desarrollado se basa en la regresión de procesos gaussianos (GPR), un enfoque de aprendizaje automático capaz de manejar datos de fidelidad múltiple. El conjunto de datos de fidelidad variable se construye utilizando datos CFD. Probamos tres enfoques de fidelidad múltiple: GPR entrenado en datos de alta y baja fidelidad sin distinción, GPR con corrección lineal y GPR de fidelidad múltiple o co-krigeaje. El costo computacional y la precisión de estos enfoques se comparan con los GPR basados solo en datos de alta o baja fidelidad. Todos los enfoques de fidelidad múltiple probados reducen con éxito el costo computacional de la generación de conjuntos de datos en comparación con GPR de alta fidelidad mientras mantienen el nivel requerido de precisión. El enfoque de co-krigeaje demuestra la mejor compensación entre el costo computacional y la precisión.Enginyeria tèrmic

Open Data and Models for Energy and Environment

This Special Issue aims at providing recent advancements on open data and models. Energy and environment are the fields of application.For all the aforementioned reasons, we encourage researchers and professionals to share their original works. Topics of primary interest include, but are not limited to:Open data and models for energy sustainability;Open data science and environment applications;Open science and open governance for Sustainable Development Goals;Key performance indicators of data-aware energy modelling, planning and policy;Energy, water and sustainability database for building, district and regional systems; andBest practices and case studies

Numerical and experimental investigation of air pollutant dispersion in urban areas

Air pollution is predominantly an urban problem affecting residents living in or around cities. According to the 2014 report of the World Health Organization (WHO), air pollution is now the world’s largest single environmental health risk (WHO 2016). This problem is exacerbated by rapid global population growth (Wania, Bruse et al. 2012), and densely populated urban areas are hotspots of this high risk due to outdoor air pollutant exposure, which also affects indoor air quality. Despite the advancements in urban policies necessary for curtailing air pollutant emissions, it is vital to adopt appropriate strategies in urban planning to manage and reduce outdoor air pollution to minimise the negative impact on public health (Li, Shi et al. 2020). Natural ventilation in the built environment is associated with enhancing outdoor and indoor air quality due to its air pollutant mitigation capacity (Li, Ming et al. 2021). Therefore, natural ventilation capacity deserves special attention from a fundamental perspective, resulting in novel solutions for combating this global problem. This research project focuses on the underlying wind-structure interaction mechanisms involved in the air pollutant dispersion process around buildings. The effect of building cross-section shape and air pollutant density are investigated, and a new fundamental concept of air pollutant emission regions is introduced. The effect of building cross-section shape is further investigated in an idealised generic building cluster based on the fundamental flow structure. Additionally, mean and transient features of air pollutant dispersion based on both continuous air pollutant emission and stagnant air pollutants around a generic isolated building are explored in detail. Finally, two new indices based on air pollutant exposure time in a scaled model are proposed to capture full-scale air pollutant time integrated with air pollutant concentration

Examining trade-offs between social, psychological, and energy potential of urban form

Urban planners are often challenged with the task of developing design solutions which must meet multiple, and often contradictory, criteria. In this paper, we investigated the trade-offs between social, psychological, and energy potential of the fundamental elements of urban form: the street network and the building massing. Since formal methods to evaluate urban form from the psychological and social point of view are not readily available, we developed a methodological framework to quantify these criteria as the first contribution in this paper. To evaluate the psychological potential, we conducted a three-tiered empirical study starting from real world environments and then abstracting them to virtual environments. In each context, the implicit (physiological) response and explicit (subjective) response of pedestrians were measured. To quantify the social potential, we developed a street network centrality-based measure of social accessibility. For the energy potential, we created an energy model to analyze the impact of pure geometric form on the energy demand of the building stock. The second contribution of this work is a method to identify distinct clusters of urban form and, for each, explore the trade-offs between the select design criteria. We applied this method to two case studies identifying nine types of urban form and their respective potential trade-offs, which are directly applicable for the assessment of strategic decisions regarding urban form during the early planning stages