Effects of recirculation and air change per hour on COVID-19 transmission in indoor settings: A CFD study with varying HVAC parameters

Heliyon, Journal Year: 2024, Volume and Issue: 10(15), P. e35092 - e35092

Published: July 24, 2024

COVID-19 has already claimed over 7 million lives and infected 775 people globally [1]. SARS-CoV-2, the virus that causes Covid-19, spreads primarily through droplets from people's airways, rendering Heating, Ventilation, Air Conditioning (HVAC) systems critical in controlling infection risk levels indoor environment. To understand dynamics of exhaled aerosols percentage particles are inhaled, escaped, recirculated, or trapped on different surfaces for a variety environmental settings, we have presented our findings Computational Fluid Dynamics (CFD) modeling to investigate impact changing HVAC parameters this paper. When combined with spatial temporal distribution droplets, method can be used assess potential strengthen resilience. This finding demonstrates viability usefulness CFD confined environments. Our study raising Change per Hour (ACH) 2 8 reduces particle inhalation by nearly 70 %. Additionally, limiting amount air recirculation increasing fresh helps reduce number airborne an space. respiratory droplet-related transmission provide relevant recommendations appropriate authority, same computational approach could applied wide range ventilated environments such as public buses, restaurants, exhibitions, theaters.

Language: Английский

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Open Tool for Automated Development of Renewable Energy Communities: Artificial Intelligence and Machine Learning Techniques for Methodological Approach

Energies, Journal Year: 2024, Volume and Issue: 17(22), P. 5726 - 5726

Published: Nov. 15, 2024

The architecture, engineering, construction, and operations (AECO) sector exerts a considerable influence on energy consumption CO2 emissions released into the atmosphere, making notable contribution to climate change. It is therefore imperative that efficiency in buildings prioritized order reduce environmental impacts meet targets set out European 2030 Agenda. In this context, renewable communities (RECs) have potential play an important role, promoting use of at local level, optimizing management, reducing by sharing resources advanced technologies. This paper introduces open tool (OT) designed for configuration systems dedicated RECs. OT considers several inputs, including thermal electrical loads, consumption, type building, surface area, population size. employs artificial intelligence (AI) algorithms machine learning (ML) techniques generate forecast optimized scenarios sizing photovoltaic systems, thermal, storage, estimation emission reductions. features user-friendly interface, enabling even non-experts obtain comprehensive configurations RECs, aiming accelerate transition toward sustainable efficient district driving positive impact fostering greener future cities.

Language: Английский

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SS-CWGAN: A novel fault diagnosis model for building HVAC systems under limited labeled data

Energy and Buildings, Journal Year: 2024, Volume and Issue: 319, P. 114540 - 114540

Published: July 10, 2024

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Advance and prospect of machine learning based fault detection and diagnosis in air conditioning systems

Renewable and Sustainable Energy Reviews, Journal Year: 2024, Volume and Issue: 205, P. 114853 - 114853

Published: Aug. 26, 2024

Language: Английский

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A novel load allocation strategy based on the adaptive chiller model with data augmentation

Energy, Journal Year: 2024, Volume and Issue: 309, P. 133064 - 133064

Published: Sept. 1, 2024

Language: Английский

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Automated mold defects classification in paintings: A comparison of machine learning and rule-based techniques

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(1), P. e0316996 - e0316996

Published: Jan. 24, 2025

Mold defects pose a significant risk to the preservation of valuable fine art paintings, typically arising from fungal growth in humid environments. This paper presents novel approach for detecting and categorizing mold paintings. The technique leverages feature extraction method called Derivative Level Thresholding pinpoint suspicious regions within an image. Subsequently, these are classified as using either morphological filtering or machine learning models such Classification Regression Trees (CART) Linear Discriminant Analysis (LDA). efficacy methods was evaluated Features Dataset (MFD) separate set test images. Results indicate that both improve accuracy precision defect detection compared no classifier. However, CART algorithm exhibits superior performance, increasing by 32% 53% while maintaining high (96%) even with imbalanced dataset. innovative has potential transform managing paintings offering more precise efficient means identification. By enabling early defects, this can play crucial role safeguarding invaluable artworks future generations.

Language: Английский

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Development of a domain adversarial fault diagnosis model for information poor variable refrigerant flow systems based on transfer learning

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 111917 - 111917

Published: Jan. 1, 2025

Language: Английский

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Fault Diagnosis of Wheel Tread Based on Deep Transfer Convolution Neural Network

Journal of Failure Analysis and Prevention, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 17, 2025

Language: Английский

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Effects of sensor measurement error on fault detection and diagnosis model for data center composite cooling system

International Journal of Refrigeration, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

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Diagnostic Bayesian network in building energy systems: Current insights, practical challenges, and future trends

Energy and Buildings, Journal Year: 2025, Volume and Issue: unknown, P. 115845 - 115845

Published: May 1, 2025

Language: Английский

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