Operation Of Ventilation Systems Research Articles

Experimental possibilities for industrial ventilation

The activity of evaluation and verification of industrial ventilation installations has a strong preventive role in terms of explosion risk mainly due to the fact that of the three determining elements that can lead to an explosion phenomenon, ventilation installations can determine the presence of two elements namely the presence of fuel (gases, vapours, dusts, mists) and the source of initiation (hot surface, flame, sparks of mechanical origin, electrical sparks, static electricity, etc.). The development of experimental systems on„ which particular hazardous aspects that may occur during the operation of industrial ventilation systems can be studied are vital to avoid dangerous situations in the current operation of ventilation installations. Knowledge of the dynamics of formation of explosive / toxic / asphyxiating environments is achieved by analysing explosive mixtures in relation to specific explosive intervals and is one of the most important priorities in ensuring optimal health and safety at work in industrial activities. The information obtained by analysing the dynamics of the formation of explosive / toxic / asphyxiating atmospheres is extremely useful for personnel responsible for health and safety at work, because with their help relevant decisions can be made to ensure safety and health conditions at the level of industrial premises. The paper presents an experimental equipment usable for the study of industrial ventilation systems.

Research on the Prediction of Energy-saving Potential of Traffic Tunnel Based on BP Neural Network

Accurate grasp of underground building environmental temperature and humidity and cooling and heating loads is the basis for energy-saving analysis, ventilation and air conditioning system design and operation control. In order to come up with a method that can quickly and accurately predict the environmental temperature and humidity and energy-saving potential of underground power station traffic tunnel, this paper takes Xiangyou pumped storage power station traffic tunnel as the research object and designs a BP neural network model for traffic tunnel temperature and relative humidity prediction with respect to the main environmental influencing factors. Comparing with the test data, it is verified that the model has high prediction accuracy. At the same time, the prediction model was used to calculate the energy saving potential of traffic tunnel ventilation. The results showed that the relative error of predicting the energy saving potential of traffic tunnel in summer was less than 2% and in winter was less than 0.3%. This prediction method can obtain the traffic tunnel air temperature and humidity and ventilation energy saving potential simply and accurately, so that the design can be developed in the direction favorable to the energy saving of underground buildings.

Real-time monitoring of occupancy activities and window opening within buildings using an integrated deep learning-based approach for reducing energy demand

Occupancy behaviour in buildings can impact the energy performance and operation of heating, ventilation and air-conditioning (HVAC) systems. HVAC, which uses conventional control strategies or “fixed” setpoint schedules, could not adjust to the conditioned spaces' actual requirements, resulting in building spaces being over or under-conditioned. While the unintended opening of windows can lead to substantial heat loss and consequently raises energy consumption. To optimise building operations, it is necessary to employ solutions such as demand-driven controls, which can monitor the utilisation of indoor spaces and provide the actual thermal comfort requirements of occupants. This study presents a novel vision-based deep learning framework for occupancy activity detection and recognition including the manual window operations in buildings. A region-based Convolutional Neural Network (R-CNN) model was trained and deployed to a camera for real-time detection and recognition. Based on the field experiments conducted within a case study University building, overall accuracy of 85.63% was achieved for occupancy activity detection and 92.20% for window operation detection. Building energy simulation and various scenario-based cases were used to assess the impact of such an approach on the building energy demand and provide insights into how the proposed detection method can enable HVAC systems to respond to dynamic changes within indoor spaces. Results showed that the proposed approach could reduce the over-or under-estimation of occupancy heat gains compared with the use of “fixed” or static profiles. In addition, the approach can help alert building users or managers about windows left open unintentionally, which can reduce unnecessary ventilation heat losses. Furthermore, the approach can also predict the room CO2 concentration and advise occupants about a suitable natural ventilation strategy. The study highlighted the potential of the multi-purpose detection approach, but further development is necessary, including optimisation of the deep learning model, full integration with HVAC controls and further model training and field testing.

Coloured low-emissivity films for building envelopes for year-round energy savings

Buildings are responsible for over 40% of total US energy use, of which about 40% is directly related to the operation of heating, ventilation and air-conditioning (HVAC) systems. Saving energy to heat and cool buildings would contribute substantially to sustainability. Here we propose a category of coloured low-emissivity films for building walls that constitute the main component of the building thermal envelope. We demonstrate high reflectance (~90%) in infrared wavelength range and selective reflectance in the visible light wavelength range for desired colours. These films can help minimize radiative heat exchange between the indoor and outdoor environments, thus saving energy for all-year cooling and heating while satisfying the required aesthetical effect. Simulations show that these films can help reduce heat gain and loss by up to 257.6 MJ per installation wall area annually. In the case of a typical midrise apartment building, the HVAC system can save up to 124.46 GJ (equal to 9.87% of the building’s HVAC energy consumption). By rough estimation, a global CO2 emission reduction of 1.14 billion metric tons annually could be achieved. Our work provides insights for innovative energy-saving building envelope materials that can help achieve global carbon neutrality and sustainability.

Predictive modelling and optimization of HVAC systems using neural network and particle swarm optimization algorithm

The concept of maintaining indoor environmental quality comprising building indoor temperature, relative humidity, CO2, and volatile organic compound (VOC) level poses new challenges to the optimal operation of heating, ventilation and air-conditioning (HVAC) systems. While existing case studies demonstrate the energy-saving potentials for efficient HVAC operation, there is a lack of studies quantifying energy savings whilst considering indoor environmental conditions. This study proposes a state-of-the-art modelling and optimization approach to minimize the energy consumption of the HVAC systems without compromising indoor environmental quality. While the primary objective of ensuring optimal operation of HVAC systems is to minimize energy consumption, controlling indoor environmental parameters to remain within the acceptable range imposes excess energy use. These two conflicting objectives constitute a multi-variable constrained optimization problem that has been solved using a particle swarm optimization (PSO) algorithm. Real-time predictive models are developed for the individual indoor environmental parameters and HVAC energy consumption using a Nonlinear Autoregressive Exogenous (NARX) neural network (NN). During model development, models' performance is optimized in terms of complexity, predictive accuracy, and ease of application to a real system. The proposed predictive models are then optimized to provide an optimal control setting for the HVAC systems considering seasonal variations. The results indicate that it is possible to reduce 7.8% of total energy, without negotiating indoor environmental conditions, e.g., air temperature 19.60–28.20°C and relative humidity 30–65% as per ASHRAE Standard 55, and CO2 ≤ 800 ppm and VOC ≤1000 ppm as per AS 1668.2.

A coupled deep learning-based internal heat gains detection and prediction method for energy-efficient office building operation

Occupants' behaviour and the use of electrical equipment can significantly impact the building energy demand. Accurate occupancy and equipment usage information are key to improving the performance of demand-driven control, which can automatically adjust the heating, cooling and ventilation system operation. Employing static schedules is commonly used for the operation of heating, ventilation and air-conditioning systems, while it cannot satisfy the actual requirements due to the dynamic variations within the conditioned spaces. This study introduces a coupled real-time occupancy and equipment usage detection and recognition approach using deep learning and computer vision techniques for efficient building energy controls. The experimental results presented an overall equipment detection and occupancy activity detection accuracy of 78.39% and 93.60%. To investigate the influence of the implementation of the approach on building energy demand, a case study office building was selected to conduct experimental tests and modeled using a building energy simulation tool. Four scenarios with different occupancy and equipment profiles were defined and evaluated. The simulation results showed that heat gains, when employing static profiles were larger than the heat gains predicted when using the deep learning influenced profiles. Up to 53.95% lower heat gains were estimated when using both occupancy and equipment detection approaches than static schedules solely. The results highlighted the importance of monitoring real-time occupancy and electrical equipment usage and the advantages of using deep learning detection techniques to provide data for demand-driven controls, optimising building energy efficiency while maintaining a comfortable indoor environment.

Characterization of tornado like flows for improving vortex ventilation performance

The innovative local ventilation systems using vortex flows resembling a tornado have been proposed to improve ventilation performance. The effect of two primary operating conditions, the side jet velocity and exhaust flow rate which are essential for the formation of vortex structures and significantly affect the resulting vortex characteristics, on the vortex characteristics are investigated by employing a particle image velocimetry (PIV) technique. The vortex core is widened and the vortex strength increased when the side jet velocity is increased. When the induced swirling force is sufficient, a higher exhaust flow rate can form a stronger and more stable vortex. Vortex characteristics revealed that the optimal side jet velocity is 1.73 m/s corresponding Res of 2332.4 with a normalized exhaust flow rate of 0.155. In order to quantify the effectiveness of the vortex ventilation system, the concentration of ultra-fine particulate matters was monitored. The ventilation performance has a similar trend with vortex size, circulation and swirl ratio over core region. We herein propose a new-type ventilation system and a useful analysis method based on PIV to characterize tornado-like flows in vortex ventilation systems. In addition, these flow and ventilation results can be used as basic data in the design and operation of vortex ventilation systems.

Mechanisms and ways of transmission of SARS-CoV-2 coronavirus infection

The relevance of the problem. A new coronavirus infection caused by the SARS-CoV-2 virus has created an emergency situation of international importance. The incidence of it is characterized by a rapid spread across all countries, high morbidity rates, severe complications and mortality. Despite the fact that there are currently vaccines, it is not yet possible to stop the infection. Of particular importance as a preventive measure is the impact on the second link of the epidemic process. In this connection, obtaining objective data on the mechanisms and ways of spreading the SARS-CoV-2 virus will allow for more rational preventive measures and significantly reduce the risk of transmission of infection. The aim is to analyze current data on the mechanisms and ways of spreading the COVID-19 pathogen from available literature sources and to consider possible measures to influence the second link of the epidemiological chain in order to reduce the risks of infection. Materials and methods. The selection of peer-reviewed literature published from 2019 to 2021 was carried out. in the PubMed database and e-Library.ru, as well as links in articles. To obtain statistical data, the Internet resources of the World Health Organization (WHO), open data of Rospotrebnadzor, the Ministry of Health of the Russian Federation, Rosstat were used. Results. Analysis of scientific sources containing information on the mechanisms and ways of transmission of the new coronavirus infection COVID-19 indicates that the authors in their publications mention almost all the mechanisms and ways of transmission of this disease, but to varying degrees of their significance. The main mechanism of the spread of the SARS-CoV-2 virus is aspiration with airborne and airborne dust transmission routes. At the same time, the effect of contact, fecal-oral mechanisms is not excluded, the probability of the implementation of the vertical mechanism of transmission of the pathogen from the mother to the child, as well as the genital tract, is being studied. An important measure for the prevention of a new infection is the impact on the second link of the epidemic process, especially ensuring the highly efficient operation of ventilation and air conditioning systems in medical institutions and public places with a massive crowd of people, as well as compliance with personal preventive measures and disinfection measures.

Design and implementation of an occupant-centered self-learning controller for decentralized residential ventilation systems

Mechanical ventilation systems acquired relevance in the past decades as a solution to guarantee air exchange in residential energy retrofit. The individualization of the control strategies to the user preferences could enhance the acceptance of innovative technologies and meet the energy efficiency targets for buildings. In this publication, a novel occupant-centered self-learning control strategy for decentralized ventilation systems is introduced. The controller has a standard-based default control profile, which learns the occupants’ preferences using a classification algorithm. The proposed controller is first simulated and compared to other available mechanical ventilation control strategies. A stochastic model for the manual operation of ventilation systems based on artificial comfort profiles is implemented in the simulation model. Results show that the proposed controller improves the indoor environmental conditions (measured with the relative humidity and CO2 concentration) without disregarding energy efficiency. The self-learning controller can successfully adapt itself to these profiles. The proposed solution was implemented in a real apartment. Measurements were carried out for three months, where two occupants operated the ventilation devices. The installed ventilation concept succeeded in maintaining an acceptable indoor environment, while the distinctive user profiles were learned in each room. The bedrooms were shifted towards lower air exchange rates, while in the rest of the rooms higher air exchange rates were preferred. This implementation confirms the individualization potential of this controller. Finally, the novel self-learning strategy provides an adaptive solution for residential decentralized ventilation controllers, targeting the occupant preferences regarding comfort, health and energy efficiency.

Concentration profile and water level effect on the evaporation in the spent nuclear fuel storage pool using a diffusion approach

In the spent nuclear fuel storage pool (SFSP) of GA Siwabessy Multipurpose Reactor (GAS-MPR), there are three status of pool water level with unknown effect of water level on evaporation rate. This study aims to determine the effect of pool water level on the evaporation rate in the SFSP, so that it can be a consideration in minimizing the evaporation rate. A prediction of water evaporation rate can be explained as a diffusion model through a stagnant gas film. A numerical method, 4th order Runge-Kutta was used to describe the concentration profile of water and air over a SFSP column to the top of the pool. The effect of the water level can be explained then. The various distance of the water to the pool top at constant water temperature and constant air temperature shows a result that the evaporation rate is decreasing while the distance of the water to the pool top is increasing. The investigation results conclude that the closer of the liquid-water surface to the gas-air flow, increases the evaporation rate. This approach can be applied in a SFSP that has an abundant amount of water, the water temperature remained constant by operation of the water-cooling system, air temperature, pressure, and relative humidity remained constant as well by the operation of ventilation and air conditioning (VAC) system. Those constant conditions were stated as normal operation in SFSP of GAS-MPR. In the case of abnormal conditions such as the VAC not operating or the water-cooling system not operating, there will be dynamics of pool water and air temperatures which require further modeling research.

Ventilation System Operation to Minimize the COVID-19 Airborne Transmission in Schools

Minimizing the SARS-CoV-2 virus transmission is essential to face the COVID-19 pandemic. This is even more important for highly crowded indoor environments, e.g. schools, where the mitigation solutions based on social distancing and hand washing seem to be not effective to reduce the virus airborne transmission mode, which is the main route of transmission. To minimize the airborne virus transmission a proper ventilation is necessary. In the study, a simplified mass balance equation (box-model) was applied to school scenarios in order to determine the required conditions to maintain the infection risk below an acceptable level. In particular, the required air exchange rates for mechanically-ventilated classrooms and the adequate airing procedures for naturally ventilated classrooms were determined. Moreover, for naturally ventilated classrooms, a control strategy based on the measurement of CO2 indoor concentration was also developed.

Thermal load prediction and operation optimization of office building with a zone-level artificial neural network and rule-based control

Precise and quick thermal load prediction for buildings is imperative in realizing the flexibility of building energy systems. Operation optimization based on the prediction results can effectively mitigate the energy consumption and operation cost. Here, an artificial neural network (ANN) model is built to predict the load demand and energy consumption of an office building. Thermal zones division is considered to improve the prediction accuracy. Measured data are collected to establish and validate the ANN model. The total number of samples for training, validation and testing are 13674, 3978 and 3977, respectively. A rule-based control optimization model is introduced and combined with ANN model to optimize the operation of heating, ventilation and air-conditioning system. Building energy flexibility is activated by optimization model in a time-of-use electricity price scenario. The results indicate that ANN model has high precision and the coefficient of variation of root mean squared errors corresponding to the load demand prediction and energy consumption prediction are 10.76% and 15.59%, respectively. Furthermore, the optimization results can reduce the operation cost by 39.22% and 44.41% in the heating and cooling season, respectively.

Optimal real-time dispatching of chillers and thermal storage tank in a university campus central plant

Recent studies have indicated a great potential for applying predictive methodologies to the operation of Heating, Ventilation and Air Conditioning (HVAC) systems. Particularly for Centralized Chiller Plants with Thermal Energy Storage (TES) used for District Cooling, there is a substantial opportunity for cost savings when responding to variations in electricity price and ambient temperature. The present work addresses the problem of closed-loop scheduling of a large-scale chiller plant with TES tank under a Day-Ahead (DA) electricity price program. The main contributions include: (i) formulating the problem for a real large-scale complex system; (ii) comparing different dispatching policies with varying degrees of optimality, constraint satisfaction and operational complexity; and (iii) designing an optimization-based scheduling tool and describing its implementation and results. The proposed Mixed-Integer Linear Programming (MILP) formulation extends existing models by explicitly accounting for the effect of chilled water return temperature on the energy balances, considering real plant features, and performing pre-calculation of bounds on equipment and storage capacity. The study is performed with real process data from the central chiller plant located on the campus of the University of California, Davis. The comparison between different dispatching policies highlights the benefits of adopting an optimization-based operational strategy, indicating a cost reduction of up to 32% compared to other suboptimal policies. The study also demonstrates how additional constraints that reduce operational complexity affect the closed-loop performance of the optimization-based method, serving as a guideline for designing the optimization tool.

A Strategy to Maximally Utilize Outdoor Air for Indoor Thermal Environment

In order to reduce the energy consumption of HVAC systems in buildings, the use of energy-saving solutions is necessary. One of these solutions is ventilation, which is usually used for maintaining acceptable indoor air quality and thermal comfort. As the change in outdoor environment is unpredictable and the occupant control is spontaneous, it is critical to control the windows and HVAC systems to achieve a maximum use of outdoor air for indoor ventilation. A new rule-based control strategy that could change the opening factor of windows is proposed in this study and its effectiveness was tested in five representative climates, ranging from a subtropical region to a severely cold region. A building model was set up and the indoor air temperature and energy consumption were predicted using EnergyPlus. The results show that the proposed control strategy can utilize ventilation to maintain a comfortable indoor environment with an annual uncomfortable percentage in an occupied period lower than 5%, thus leading to an energy-saving rate of 13.5–55.6%. The simulation results indicate that there are periods of ventilation available during the summer in climate zones with hot summers and warm winters, whereas the control strategy has a better energy-saving performance in temperate areas. This study conducted a preliminary exploration for practical applications of the combined operation of controllable natural ventilation and HVAC systems in buildings.

Vision-based human activity recognition for reducing building energy demand

Occupancy behaviour in buildings can impact the energy performance and the operation of heating, ventilation and air-conditioning systems. To ensure building operations become optimised, it is vital to develop solutions that can monitor the utilisation of indoor spaces and provide occupants’ actual thermal comfort requirements. This study presents the analysis of the application of a vision-based deep learning approach for human activity detection and recognition in buildings. A convolutional neural network was employed to enable the detection and classification of occupancy activities. The model was deployed to a camera that enabled real-time detections, giving an average detection accuracy of 98.65%. Data on the number of occupants performing each of the selected activities were collected, and deep learning–influenced profile was generated. Building energy simulation and various scenario-based cases were used to assess the impact of such an approach on the building energy demand and provide insights into how the proposed detection method can enable heating, ventilation and air-conditioning systems to respond to occupancy’s dynamic changes. Results indicated that the deep learning approach could reduce the over- or under-estimation of occupancy heat gains. It is envisioned that the approach can be coupled with heating, ventilation and air-conditioning controls to adjust the setpoint based on the building space’s actual requirements, which could provide more comfortable environments and minimise unnecessary building energy loads. Practical application Occupancy behaviour has been identified as an important issue impacting the energy demand of building and heating, ventilation and air-conditioning systems. This study proposes a vision-based deep learning approach to capture, detect and recognise in real-time the occupancy patterns and activities within an office space environment. Initial building energy simulation analysis of the application of such an approach within buildings was performed. The proposed approach is envisioned to enable heating, ventilation and air-conditioning systems to adapt and make a timely response based on occupancy’s dynamic changes. The results presented here show the practicality of such an approach that could be integrated with heating, ventilation and air-conditioning systems for various building spaces and environments.

ON THE PROBLEM OF EMERGENCY SITUATION CONTROL IN TRANSPORT TUNNELS

The problem of control of emergency situation arisen as a result of fire in motor transport tunnels is considered. The dynamics of air flow variation caused by fire and resulting possible changes in ventilation system operation character are analyzed. It is stated that fire temperatures and intensities indicated in Code are significantly lower compared to their actual values; at the initial stage of fire ventilators are more effective for smoke removal than for heat removal; 30 MV intensity' is sufficient for ventilation system collapse in the very first minutes of dire; the only way to avoid collapse is immediate dissection of tunnel into short isolated sections.

COVID-19 Impact on Operation and Energy Consumption of Heating, Ventilation and Air-Conditioning (HVAC) Systems

Heating, ventilation and air-conditioning (HVAC) system is favourable for regulating indoor temperature, relative humidity, airflow pattern and air quality. However, HVAC systems may turn out to be the culprit of microbial contamination in enclosed spaces and deteriorate the environment due to inappropriate design and operation. In the context of COVID-19, significant transformations and new requirements are occurring in HVAC systems. Recently, several updated operational guidelines for HVAC systems have been issued by various institutions to control the airborne transmission and mitigate infection risks in enclosed environments. Challenges and innovations emerge in response to operational variations of HVAC systems. To efficiently prevent the spread of the pandemic and reduce infection risks, it is essential to have an overall understanding of impacts caused by COVID-19 on HVAC systems. Therefore, the objectives of this article are to: (a) provide a comprehensive review of the airborne transmission characteristics of SARS-CoV-2 in enclosed spaces and a theoretical basis for HVAC operation guideline revision; (b) investigate HVAC-related guidelines to clarify the operational variations of HVAC systems during the pandemic; (c) analyse how operational variations of HVAC systems affect energy consumption; and (d) identify the innovations and research trends concerning future HVAC systems. Furthermore, this paper compares the energy consumption of HVAC system operation during the normal times versus pandemic period, based on a case study in China, providing a reference for other countries around the world. Results of this paper offer comprehensive insights into how to keep indoor environments safe while maintaining energy-efficient operation of HVAC systems.

Mathematical Model for Automated Heat Flow Control of an Energy­Efficient Ventilation System

Purpose of research.In modern ventilation and air conditioning systems (VAC), one of the main components is an automatic control system (ACS) which performs various functions and also provides highly efficient operation in the range from shutdown functions to centralized regulation and control of climate parameters (temperature, humidity, monitoring concentrations of harmful substances, air speed). The goal is to study a mathematical model of heat flow control of a supply and exhaust ventilation system with a builtin integrated recuperative heat exchanger for the purpose of utilization of low-temperature heat of ventilation gases and emissions with the associated production of thermoelectricity.Methods.To achieve these goals, we used methods of mathematical simulation and computational model development. The automatic control of VAC is based on the principle of feedback – regulation of processes by obtaining information from external sensors based on mathematical simulation of physical processes occurring in the building or structure serviced.Results.An experimental supply and exhaust system with a plate heat exchanger-recuperator operates in a quasi-steady heat transfer mode. Exhaust air removed from the room is used as a heating medium. At the same time, the system is controlled using an independent scheme of connection to the heat supply system. The air heated in the room is considered as an incompressible gas, the heat exchange between the heating and heated heat transfer media is a steady­state process, the turbulence of the heating and heated flow of heat transfer media is isotropic. The result of the study is a mathematical model of heat flow control in the supply and exhaust ventilation system with a builtin integrated heat exchanger-recuperator. The optimal values of the heat energy consumed and the parameters of the ventilation system operation are obtained.Conclusion.A mathematical model of heat flow control in a supply and exhaust ventilation system with a builtin integrated heat exchanger-recuperator is proposed and investigated. The optimal values of the consumed heat energy and the parameters of the ventilation system operation are obtained.

The effects of ventilation and filtration on indoor PM2.5 in office buildings in four countries

Fine particulate matter (PM2.5) is an airborne pollutant associated with negative acute and chronic human health outcomes. Although the majority of PM2.5 research has focused on outdoor exposures, people spend the majority of their time indoors, where PM2.5 of outdoor origin can penetrate. In this work, we measured indoor PM2.5 continuously for one year in 37 urban commercial offices with mechanical or mixed-mode ventilation in China, India, the United Kingdom, and the United States. We found that indoor PM2.5 concentrations were generally higher when and where outdoor PM2.5 was elevated. In India and China, mean workday indoor PM2.5 levels exceeded the World Health Organization's 24-hour exposure guideline of 25 μg/m3 about 17% and 27% of the time, respectively. Our statistical models found evidence that the operation of mechanical ventilation systems could mitigate the intrusion of outdoor PM2.5: during standard work hours, a 10 μg/m3 increase in outdoor PM2.5 was associated with 19.9% increase in the expected concentration of indoor PM2.5 (p < 0.0001), compared to a larger 23.4% increase during non-work hours (p < 0.0001). Finally, our models found that using filters with ratings of MERV 13–14 or MERV 15+ was associated with a 30.9% (95% CI: −55.0%, +6.2%) or 39.4% (95% CI: −62.0%, −3.4%) reduction of indoor PM2.5, respectively, compared to filters with lower MERV 7–12 ratings. Our results demonstrate the potential efficacy of mechanical ventilation with efficient filtration as a public health strategy to protect workers from PM2.5 exposure, particularly where outdoor levels of PM2.5 are elevated.

Expanding the FDS Simulation Capabilities to Fire Tunnel Scenarios Through a Novel Multi-scale Model

Computational Fluid Dynamics (CFD) is widely used to simulate tunnels and partially substitute on-site tests. As technology advances, new application opportunities appear; some examples are the optimal operation of ventilation and emergency systems, risk assessment of tunnels and training of the operators. Even when the computational capacity of computers has grown, CFD is still constrained by the large amount of computational resources needed in long tunnels. This introduces a need for methods able to reduce the amount of time required for simulations. To face this need, a novel 1D–3D multiscale model is presented in this paper. The model incorporates the code Whitesmoke into FDS (Fire Dynamics Simulator) through a direct coupling. Whitesmoke manages the fluid dynamics, temperature and concentration of species in the 1D portion, while FDS calculates these fields in the portion where fire occurs. Using this multiscale model, the computation time for long tunnels is reduced, proportionally to the 1D length in the domain. Also, additional simulation capabilities particularly useful for tunnel analysis are obtained. Some new characteristics are pressure boundary conditions can be easily imposed at the tunnel portals or at the ventilation shafts; the characteristic curves of the fans/jet-fans can be included, also considering the degradation effects due to smoke propagation; the piston effect can be properly considered. Our research verifies most of its capabilities, also clarifying its limitations and the criteria used to set the domain for the analysis. As a final step, the model is tested in a tunnel with a cross section of 4.8 m and 600 m of length with a 2 MW fire, comparing its performance with a full 3D FDS simulation. The difference in temperature and velocity is minimal for most of the domain, making It a good way to optimize resource usage in large simulations. Furthermore, the multiscale manages to reduce the computational time of more than a 50%.