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 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

A numerical set-up for the simulation of infection probability from SARS- CoV-2 in public transport vehicles

In this work, a numerical framework aimed at simulating the transport of contaminants and infectious agents within a closed domain is presented. The method employs mature CFD algorithms to calculate air fields with reasonable computational costs. The main objective is to give fast response to stakeholders about air quality indicators in the design phase of HVAC systems. A discussion regarding the size and characteristics of different contaminants is proposed, highlighting the most appropriate methods and coefficients needed to simulate their transport. Next, the methodology employed to evaluate the risk of infection is presented. The numerical set-up, based on the buoyantBoussinesqPimpleFoam solver in OpenFOAM, was tuned by simulating the well-known case of the heated floor cavity, providing accurate results. Hence, the case study of a transport vehicle of generic shape is presented, in order to show possible results in terms of air-age distribution, PM2.5 distribution, and global infection risk matrix.This work has been developed in the context of the Rolen Purifica Bus R&D project, partially financed by INNOTEC (ACCIO - Agència per la Competitivitat de l’Empresa, Generalitat de ´ Catalunya). J. Vera has been financially supported by the Ministerio de Educación y Ciencia (MEC), Spain, (FPI grant PRE2018-084017). N. Morozova is supported by the by the Ministerio de Economía y Competitividad, Spain [FPU16/06333 predoctoral contract].Peer ReviewedPostprint (published version

Investigation of attitudes toward gambling with implicit association test and self-reported measures

Las actitud hacia los juegos de azar se han medido principalmente con métodos explícitos, aunque se ha afirmado que las medidas de actitud explícitas podrían tener limitaciones significativas debido a la conveniencia social. El objetivo de la investigación es identificar la valencia de las actitudes hacia los juegos de azar utilizando mediciones implícitas y explícitas en los jugadores sociales sin problemas, que visitan regularmente los establecimientos de juego y las personas que no van a los establecimientos de juego. Métodos: IAT de una sola categoría; Breen and Zuckerman's Gambling Attitudes and Beliefs Scale (GABS). Muestra 50 participantes. Edad 18-45 (Me = 31,5). Grupos: "Jugadores" - visitantes de casinos al menos una vez a la semana, jugadores sociales sin problemas - 25 y "No jugadores" - 25. Resultados: Las medidas de actitudes de GABS exponen que las actitudes "al nivel de juego en el grupo "Jugadores" excede las actitudes "al nivel de apuestas en el grupo "No jugadores". Las actitudes positivas y negativas hacia el juego se han identificado y medido con la ayuda de SC-IAT, en ambos grupos. Se ha demostrado que la severidad de las actitudes positivas y negativas implícitas en ambos grupos es la misma. El porcentaje máximo de las coincidencias de las actitudes hacia el juego ganado con GABS e IAT es del 52%. Conclusión: es posible que la presencia de actitudes negativas hacia los juegos de azar proporcione una posibilidad a los jugadores sociales no problemáticos para superar la adicción al juego.Attitudes towards gambling have been mostly measured with explicit methods, although it has been stated that explicit attitude measures could have significant limitations due to the social desirability. The aim of research is to identify the valence of attitudes to gambling using implicit and explicit measurements in none-problem social gamblers, who regularly visit gaming establishments and people who do not go to the gaming establishments. Methods: Single-Category IAT; Breen and Zuckerman’s Gambling Attitudes and Beliefs Scale (GABS). Sample 50 participants. Age 18-45 (Me=31,5). Groups: “Gamblers” - casino visitors at least once a week, non-problem social gamblers - 25 and “Non-Gamblers” - 25. Results: GABS measures of attitudes expose that the attitudes’ to gambling level in the group “Gamblers” exceeds the attitudes’ to gambling level in the group "Non-Gamblers”. The positive and negative attitudes toward gambling have been identified, measured with the help of SC-IAT, in both groups. It has been shown that the severity of implicit positive and negative attitudes in both groups is the same. The maximum percentage of the coincidences of the attitudes toward gambling gained with GABS and IAT are 52%. Conclusion: It is possible that the presence of negative attitudes toward gambling can provide a possibility to non-problematic social gamblers to overcome gambling addiction.peerReviewe

Investigating the capabilities of CFD-based data-driven models for indoor environmental design and control

In this work, we study the accuracy of CFD-based data-driven models, which predict comfort-related flow parameters in a ventilated cavity with a heated floor. We compare the computational cost and accuracy of three different models, namely artificial neural network, support vector regression, and gradient boosting regression. The tested scenarios include short and long cavities with different inlet velocities. Among the studied frameworks, the artificial neural network provides the most accurate predictions for most of the tested flow configurations. However, test configurations with jet separation and a secondary vortex are more difficult to predict correctly; thus more high-fidelity data is required in order to construct a more robust and reliable model.This work is supported by the Ministerio de Economía y Competitividad, Spain [ENE2017-88697-R]. N. Morozova is supported by the by the Ministerio de Economía y Competitividad, Spain [FPU16/06333 predoctoral contract]. Part of the calculations was performed on the MareNostrum 4 supercomputer at the Barcelona Supercomputing Center [RES project IM-2021-1-0015]. The authors thankfully acknowledge these institutions.Postprint (published version

CFD-based multi-fidelity surrogate model for prediction of flow parameters in a ventilated room

In this work, we present a multi-fidelity machine learning surrogate model, which predicts comfort-related flow parameters in a ventilated room with a heated floor. The model uses coarse- and fine-grid CFD data obtained using LES turbulence models. The dataset is created by changing the width aspect ratio of the rooms, inlet flow velocity, and temperature of the hot floor. The surrogate model takes the values of temperature and velocity magnitude at four different cavity locations as inputs. These probes are located such that they could be replaced by actual sensor readings in a practical case. The model’s output is a set of comfort-related flow parameters. We test two multi-fidelity approaches based on Gaussian process regression (GPR), among them GPR with linear correction (LC GPR), and multi-fidelity GPR (MF GPR) or cokriging. The computational cost and accuracy of these approaches are compared with GPRs based on single-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-kriging approach demonstrates the best trade-off between computational cost and accuracy.This work has been financially supported by the project RETOtwin [PDC2021-120970-I00] funded by MCIN/AEI/10.13039/501100011033 and European Union Next Generation EU/PRTR. N. Morozova is supported by the by the Ministerio de Economía y Competitividad, Spain [FPU16/06333 predoctoral contract]. E. Burnaev is supported by RFBR grant 21-51-12005 NNIO a. C. Oliet, is suppoted by the Catalan Government as a Serra Húnter lecturer. The calculations were performed on the MareNostrum 4 supercomputer at the Barcelona Supercomputing Center [RES project IM-2021-1-0015]. The authors thankfully acknowledge these institutions.Postprint (published version

On the feasibility of affordable high-fidelity CFD simulations for indoor environment design and control

Computational fluid dynamics (CFD) is a reliable tool for indoor environmental applications. However, accurate CFD simulations require large computational resources, whereas significant cost reduction can lead to unreliable results. The high cost prevents CFD from becoming the primary tool for indoor environmental simulations. Nonetheless, the growth in computational power and advances in numerical algorithms provide an opportunity to use accurate and yet affordable CFD. The objective of this study is to analyze the feasibility of fast, affordable, and high-fidelity CFD simulations for indoor environment design and control using ordinary office computers. We analyze two representative test cases, which imitate common indoor airflow configurations, on a wide range of different turbulence models and discretizations methods, to meet the requirements for the computational cost, run-time, and accuracy. We consider statistically steady-state simulations for indoor environment design and transient simulations for control. Among studied turbulence models, the no-model and large-eddy simulation with staggered discretizations 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 decadesThis work is supported by the Ministerio de Economía y Competitividad, Spain [ENE2017-88697-R]. N. Morozova is supported by the by the Ministerio de Economía y Competitividad, Spain [FPU16/06333 predoctoral contract]. Part of the calculations was performed on the MareNostrum 4 supercomputer at the Barcelona Supercomputing Center [RES project I-2019-2-0021]. The authors thankfully acknowledge these institutions. The authors would also like to thank our colleague MSc Xavier Álvarez Farré for the productive discussions.Peer ReviewedPostprint (author's final draft

A CFD-based multi-fidelity surrogate model for prediction of flow parameters in a ventilated room

In this work, we present a multi-fidelity machine learning surrogate model, which predicts comfort-related flow parameters in a ventilated room with a heated floor. The model uses coarseand fine-grid CFD data obtained using LES turbulence models. The dataset is created by changing the width aspect ratio of the rooms, inlet flow velocity, and temperature of the hot floor. The surrogate model takes the values of temperature and velocity magnitude at four different cavity locations as inputs. These probes are located such that they could be replaced by actual sensor readings in a practical case. The model's output is a set of comfort-related flow parameters. We test two multi-fidelity approaches based on Gaussian process regression (GPR), among them GPR with linear correction (LC GPR), and multi-fidelity GPR (MF GPR) or cokriging. The computational cost and accuracy of these approaches are compared with GPRs based on single-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-kriging approach demonstrates the best trade-off between computational cost and accuracy

Co-Aggregation of S100A9 with DOPA and Cyclen-Based Compounds Manifested in Amyloid Fibril Thickening without Altering Rates of Self-Assembly.

The amyloid cascade is central for the neurodegeneration disease pathology, including Alzheimer's and Parkinson's, and remains the focus of much current research. S100A9 protein drives the amyloid-neuroinflammatory cascade in these diseases. DOPA and cyclen-based compounds were used as amyloid modifiers and inhibitors previously, and DOPA is also used as a precursor of dopamine in Parkinson's treatment. Here, by using fluorescence titration experiments we showed that five selected ligands: DOPA-D-H-DOPA, DOPA-H-H-DOPA, DOPA-D-H, DOPA-cyclen, and H-E-cyclen, bind to S100A9 with apparent Kd in the sub-micromolar range. Ligand docking and molecular dynamic simulation showed that all compounds bind to S100A9 in more than one binding site and with different ligand mobility and H-bonds involved in each site, which all together is consistent with the apparent binding determined in fluorescence experiments. By using amyloid kinetic analysis, monitored by thioflavin-T fluorescence, and AFM imaging, we found that S100A9 co-aggregation with these compounds does not hinder amyloid formation but leads to morphological changes in the amyloid fibrils, manifested in fibril thickening. Thicker fibrils were not observed upon fibrillation of S100A9 alone and may influence the amyloid tissue propagation and modulate S100A9 amyloid assembly as part of the amyloid-neuroinflammatory cascade in neurodegenerative diseases