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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)

Ceiling fans can reduce the cooling demand in an indoor area, improve thermal comfort, and save energy. Despite the numerous potential advantages of applying ceiling fans, the literature shows that the uptake is still limited, due in part to the lack of concise information on the ceiling fan's impacts and methods of evaluating its performance. A critical literature review on ceiling fans synthesises what we know regarding its implications and what is required in the future. Hence, this study aims to critically review the effects of ceiling fans on the indoor environment (i.e., airflow, thermal comfort, and Indoor Air Quality (IAQ)) and energy usage. The results show that thermal comfort and airflow are the most investigated parameters among different ceiling fan implications for indoor areas. In contrast, limited attention is given to IAQ. The findings also highlight a knowledge gap in hybrid ventilation, a combination of natural ventilation and ceiling fans, and its impacts on IAQ, vital during pandemics. The necessity for more studies on the effects of ceiling fans on energy savings for cooling purposes, which is a crucial issue due to the global energy crisis, is also argued. Additionally, this review identifies and outlines the applied methods to evaluate ceiling fan impacts on indoor areas and lays out the advantages and limitations concerning different scenarios. This paper contributes to the literature by providing a summary of available knowledge and future research directions. It also assists building designers with decision making on using ceiling fans individually and/or as a cooling assistant strategy.

We present a probabilistic approach to designing an indoor sampler network for detecting an accidental or intentional chemical or biological release, and demonstrate it for a real building. In an earlier paper, Sohn and Lorenzetti(1) developed a proof of concept algorithm that assumed samplers could return measurements only slowly (on the order of hours). This led to optimal detect to treat architectures, which maximize the probability of detecting a release. This paper develops a more general approach, and applies it to samplers that can return measurements relatively quickly (in minutes). This leads to optimal detect to warn architectures, which minimize the expected time to detection. Using a model of a real, large, commercial building, we demonstrate the approach byoptimizing networks against uncertain release locations, source terms, and sampler characteristics. Finally, we speculate on rules of thumb for general sampler placement.

We present a probabilistic approach to designing an indoor sampler network for detecting an accidental or intentional chemical or biological release, and demonstrate it for a real building. In an earlier paper, Sohn and Lorenzetti(1) developed a proof of concept algorithm that assumed samplers could return measurements only slowly (on the order of hours). This led to optimal detect to treat architectures, which maximize the probability of detecting a release. This paper develops a more general approach, and applies it to samplers that can return measurements relatively quickly (in minutes). This leads to optimal detect to warn architectures, which minimize the expected time to detection. Using a model of a real, large, commercial building, we demonstrate the approach byoptimizing networks against uncertain release locations, source terms, and sampler characteristics. Finally, we speculate on rules of thumb for general sampler placement.