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

Development of numerical and data models for the support of digital twins in offshore wind engineering

Error on title page. Date of award is 2022.As offshore wind farms grow there is a continued demand for reduced costs. Maintenance costs and downtime can be reduced through greater information on the asset in relation to its operational loads and structural resistance to damage and so there is an increasing interest in digital twin technologies. Through digital twins, an operational asset can be replicated computationally, thus providing more information. Modelling these aspects requires a wide variety of models in different fields. To advance the feasibility of digital twin technology this thesis aims to develop the multi-disciplinary set of modelling domains which help form the basis of future digital twins. Throughout this work, results have been validated against operational data recorded from sensors on offshore structures. This has provided value and confidence to the results as it shows how well the mix of state-of-the art models compare to real world engineering systems. This research presents a portfolio of five research areas which have been published in a mix of peer-reviewed journal articles and conference papers. These areas are: 1) A computational fluid dynamics (CFD) model of an offshore wind farm conducted using a modified solver in the opensource software. This work implements actuator disk turbine models and uses Reynolds averaged Naiver Stokes approaches to represent the turbulence. This investigates the impact of modelling choices and demonstrates the impact of varied model parameters. The results are compared to operational site data and the modelling errors are quantified. There is good agreement between the models and site data. 2) An expansion on traditional CFD approaches through incorporating machine learning (ML). These ML models are used to approximate the results of the CFD and thereby allow for further analysis which retains the fidelity of CFD at comparatively negligible computational cost. The results are compared to operational site data and the errors at each step are quantified for validation. 3) A time-series forecasting of weather variables based on past measured data. A novel approach for forecasting time-series is developed and compared to two existing methods: Markov-Chains and Gradient Boosting. While this new method is more complex and requires more time to train, it has the desirable feature of incorporating seasonality at multiple timescales and thus providing a more representative time-series. 4) An investigation of the change in modal parameters in an offshore wind jacket structure from damages or from changing operational conditions. In this work the detailed design model of the structure from Ramboll is used. This section relates the measurable modal parameters to the operational condition through a modelling approach. 5) A study conducted using accelerometer data from an Offshore Substation located in a wind farm site. Operational data from 12 accelerometers is used to investigate the efficacy of several potential sensor layouts and therefore to quantify the consequence of placement decisions. The results of these developments are an overall improvement in the modelling approaches necessary towards the realisation of digital twins as well as useful development in each of the component areas. Both areas related to wind loading as well as structural dynamics have been related to operational data. The validation of this link between the measured and the modelled domains facilitates operators and those in maintenance in gaining more information and greater insights into the conditions of their assets.As offshore wind farms grow there is a continued demand for reduced costs. Maintenance costs and downtime can be reduced through greater information on the asset in relation to its operational loads and structural resistance to damage and so there is an increasing interest in digital twin technologies. Through digital twins, an operational asset can be replicated computationally, thus providing more information. Modelling these aspects requires a wide variety of models in different fields. To advance the feasibility of digital twin technology this thesis aims to develop the multi-disciplinary set of modelling domains which help form the basis of future digital twins. Throughout this work, results have been validated against operational data recorded from sensors on offshore structures. This has provided value and confidence to the results as it shows how well the mix of state-of-the art models compare to real world engineering systems. This research presents a portfolio of five research areas which have been published in a mix of peer-reviewed journal articles and conference papers. These areas are: 1) A computational fluid dynamics (CFD) model of an offshore wind farm conducted using a modified solver in the opensource software. This work implements actuator disk turbine models and uses Reynolds averaged Naiver Stokes approaches to represent the turbulence. This investigates the impact of modelling choices and demonstrates the impact of varied model parameters. The results are compared to operational site data and the modelling errors are quantified. There is good agreement between the models and site data. 2) An expansion on traditional CFD approaches through incorporating machine learning (ML). These ML models are used to approximate the results of the CFD and thereby allow for further analysis which retains the fidelity of CFD at comparatively negligible computational cost. The results are compared to operational site data and the errors at each step are quantified for validation. 3) A time-series forecasting of weather variables based on past measured data. A novel approach for forecasting time-series is developed and compared to two existing methods: Markov-Chains and Gradient Boosting. While this new method is more complex and requires more time to train, it has the desirable feature of incorporating seasonality at multiple timescales and thus providing a more representative time-series. 4) An investigation of the change in modal parameters in an offshore wind jacket structure from damages or from changing operational conditions. In this work the detailed design model of the structure from Ramboll is used. This section relates the measurable modal parameters to the operational condition through a modelling approach. 5) A study conducted using accelerometer data from an Offshore Substation located in a wind farm site. Operational data from 12 accelerometers is used to investigate the efficacy of several potential sensor layouts and therefore to quantify the consequence of placement decisions. The results of these developments are an overall improvement in the modelling approaches necessary towards the realisation of digital twins as well as useful development in each of the component areas. Both areas related to wind loading as well as structural dynamics have been related to operational data. The validation of this link between the measured and the modelled domains facilitates operators and those in maintenance in gaining more information and greater insights into the conditions of their assets

A Review of Laboratory and Numerical Techniques to Simulate Turbulent Flows

Turbulence is still an unsolved issue with enormous implications in several fields, from the turbulent wakes on moving objects to the accumulation of heat in the built environment or the optimization of the performances of heat exchangers or mixers. This review deals with the techniques and trends in turbulent flow simulations, which can be achieved through both laboratory and numerical modeling. As a matter of fact, even if the term “experiment” is commonly employed for laboratory techniques and the term “simulation” for numerical techniques, both the laboratory and numerical techniques try to simulate the real-world turbulent flows performing experiments under controlled conditions. The main target of this paper is to provide an overview of laboratory and numerical techniques to investigate turbulent flows, useful for the research and technical community also involved in the energy field (often non-specialist of turbulent flow investigations), highlighting the advantages and disadvantages of the main techniques, as well as their main fields of application, and also to highlight the trends of the above mentioned methodologies via bibliometric analysis. In this way, the reader can select the proper technique for the specific case of interest and use the quoted bibliography as a more detailed guide. As a consequence of this target, a limitation of this review is that the deepening of the single techniques is not provided. Moreover, even though the experimental and numerical techniques presented in this review are virtually applicable to any type of turbulent flow, given their variety in the very broad field of energy research, the examples presented and discussed in this work will be limited to single-phase subsonic flows of Newtonian fluids. The main result from the bibliometric analysis shows that, as of 2021, a 3:1 ratio of numerical simulations over laboratory experiments emerges from the analysis, which clearly shows a projected dominant trend of the former technique in the field of turbulence. Nonetheless, the main result from the discussion of advantages and disadvantages of both the techniques confirms that each of them has peculiar strengths and weaknesses and that both approaches are still indispensable, with different but complementary purposes

Analysis and Reduced-Order Modeling of Urban Airflow and Pollutant Dispersion under Thermal Stratification Conditions

Different thermal stratification conditions, namely, stable, isothermal (or neutral), and unstable, can locally occur in urban areas. Alteration in the thermal condition of an urban area may significantly change the airflow pattern and pollutant dispersion process by affecting both the mean and fluctuating components of the variables. The unstable effects can increase the vertical flow movement, while the stable ones can suppress it. Furthermore, unstable conditions increase turbulence kinetic energy (TKE), which increases the fluctuations in concentration. On the other hand, stable conditions lead to buoyant destruction. Due to frequent changes in the boundary conditions, a model is required for monitoring these situations, which can be used as a fast-response (near real-time) model. This thesis aims to propose a systematic approach for analysis and reduced-order modeling of the airflow and concentration fields under non-isothermal conditions. The present study uses a high-fidelity computational fluid dynamics approach, i.e., embedded large eddy simulation (ELES), to simulate the impact of the aforementioned thermal conditions on the airflow and concentration fields. The model considers the pros of both the Reynolds-averaged Navier-Stokes, RANS, (i.e., high speed), and large eddy simulation, LES, (i.e., high accuracy) approaches. After thoroughly analyzing the results, the proper orthogonal decomposition (POD) and frequency analyses are performed to investigate the impact of thermal conditions on the turbulence structure of the flow field. Considering the most energetic POD modes can lead to a good approximation of the whole airflow field, which is an important finding in developing a reduced-order model (ROM). Due to the limitations arising from the linear nature of POD, convolutional autoencoder (CAE)-based methods are used for model order reduction, using the unstable dataset generated by ELES. In addition to the conventional CAE, multiscale CAE (MS-CAE) and self-attention CAE (SA-CAE) are developed to capture multiscale and long-range dependencies among the datapoints, respectively. Afterwards, a parallel long short-term memory (LSTM) network is used to compute the temporal dynamics of the low-dimensional subspaces. ROMs maintain prediction accuracy at an acceptable level compared to ELES, while reducing the data reconstruction time to the order of seconds

Euromech Colloquium 509: Vehicle Aerodynamics. External Aerodynamics of Railway Vehicles, Trucks, Buses and Cars - Proceedings

During the 509th Colloquium of the Euromech society, held from March 24th & 25th at TU Berlin, fifty leading researchers from all over europe discussed various topics affecting both road vehicle as well as railway vehicle aerodynamics, especially drag reduction (with road vehicles), cross wind stability (with trains) and wake analysis (with both). With the increasing service speed of modern high-speed railway traffic, aerodynamic aspects are gaining importance. The aerodynamic research topics comprise both pure performance improvements, such as the continuous lowering of aerodynamic drag for energy efficiency, as well as safety relevant topics, such as cross-wind stability. The latter topic was most recently brought to attention when a swiss narrow-gauge train overturned during the severe storm Kyrill in january 2007. The shape of the train head usually has largest influence on cross wind stability. Slipstream effects of passing trains cause aerodynamic loads on objects and passengers waiting at platforms. The strength of the slipstream is determined by both the boundary layer development along the length of the train and the wake developing behind the tail of the train. Since high-speed trains can be considered to be as smooth as technically possible, attention is drawn to the wake region. The wake of the train again is also one important factor for the total drag of a train. Due to the fact that trains are bidirectional, optimisation of the leading car of a train with respect to drag and cross wind performance while simultaneously minimising the wake of the train for drag and slipstream performance is a great challenge. Modern optimisation tools are used to aid this multi-parameter multi-constraint design optimisation in conjunction with both CFD and wind tunnel investigations. Since many of the aerodynamic effects in the railway sector are of similar importance to road vehicles, the aim of the colloquium is to bridge the application of shape optimisation principles between rail- and road vehicles. Particular topics to be addressed in the colloquium are: Drag, Energy consumption and emissions: Due to increase in energy cost, drag reduction has gained focus in the past years and attention will grow in the future. Pressure induced drag is of common importance for both rail- and road vehicles. The optimisation of head- and tail shape for road vehicles as well as for bi-directional vehicles (trains) is in the focus. Interference drag between adjacent components shall also be treated. Slipstream Effects: Are a safety issue for high-train operation (Prams sucked into track due to train-induced draught flows) when trains passing platforms at high speeds. For Road vehicles, the ride stability of overtaking cars is influenced by the wake of the leading trucks and busses. Common interest is the minimisation of wake effects for both rail and road vehicles. Cross-Wind Safety, Ride stability under strong winds: Both are safety issues for rail- and road vehicles. Aerodynamic forces shall be minimised (roll moment for trains and also yaw moment for road vehicles). Strategies for Vehicle shape optimisation (head, tail and roof shape) in order to minimise aerodynamic moments. Possibilities of Flow control. Optimisation strategies: Parametrisation, analyses (CFD), Optimisation tools and methods, Application to Drag, Cross-Wind, Ride stability and Snow issue