Operation Of Ventilation Systems Research Articles

A novel high-dimensional sensor calibration framework integrating thermodynamic laws in complex HVAC systems

Accurate calibration of sensors is critical for ensuring energy efficient operation of Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings. Due to the high dimensionality of sensor data and the complexity of multiple-fault scenarios, calibrating sensors in large and complex HVAC systems presents significant challenges. To address this issue, this study introduces a novel sensor calibration framework that integrates thermodynamic laws for high-dimensional sensor calibration in complex HVAC systems. The traditional calibration method heavily relies on accurate data, making it difficult to apply in practical engineering projects. The innovative aspect of our method lies in its integration of thermodynamic laws, such as mass balance and energy conservation, with sensor calibration framework. This approach enables the framework to handle high-dimensional sensor measurements effectively without any training data. We compared five optimization algorithms and applied them to a central cooling system in Hong Kong. The results demonstrated that the simulated annealing (SA) is the most robust for solving the calibration problem, even in scenarios with up to 21 faulty sensors, with the calibrated sensor accuracy meeting the standards for conventional chiller plant operations. This novel framework provides a robust and reliable solution for high-dimensional sensor calibration in large and complex HVAC systems, addressing the growing need for precise sensor calibration as the number of installed sensors increases.

The Effects of an Adaptive Ventilation Control System on Indoor Air Quality and Energy Consumption

Indoor air quality (IAQ) and energy consumption (Q) are well-known building estimators, but they are used separately. Energy consumption is used during the design stage, while IAQ is used during operation. The novelty of our approach is that we propose using both estimators simultaneously during building operations. The purpose of this study was to find an adaptive ventilation strategy that maintained good indoor air quality with minimal energy consumption. The second novelty of our approach consists of IAQ estimation. While the operation of ventilation systems depends only on the indoor carbon dioxide (CO2) concentration at present, our novel approach uses a more global IAQ index that includes four different air pollutants. Physical models are used for the hourly prediction of the two indices: global IAQ and Q. This study presents a comparative analysis of several ventilation operations strategies: fixed versus adaptive. The main findings show that a decrease in the ventilation rate, na, from 3.5 h−1 to 2.0 h−1 leads to a diminishment in energy consumption of 42.9%, maintaining the global IAQ index under the limited health risk value (VRL). Moreover, an adaptive ventilation strategy of na, maintaining the global IAQ index value under VRL, achieves a further reduction in energy consumption of 72.9%, highlighting its efficiency.

Multi-point temperature or humidity prediction for office building indoor environment based on CGC-BiLSTM deep neural network

The aim of this study is to predict the temperature or humidity changes at multiple relevant points in a building using a deep neural network architecture with multi-task learning to provide more reference information for the design and optimal operation of heating and ventilation systems. For this purpose, traditional multi-task prediction algorithm architecture is combined with Customized Gate Control and other neural networks to build deep neural network architectures for indoor environments with multi-point temperature or humidity prediction tasks. To test the prediction effectiveness of the architecture, a task of predicting temperature or humidity 24 h in advance was designed on a real office building indoor environment dataset, and the prediction results were compared with other single-task and multi-task prediction models. Two experimental conditions were designed for this study, one using the complete training set and the other reducing the training set at a certain point. Through the final prediction results, it is found that the multi-task prediction architecture used in this paper shows better or nearly optimal results compared to other prediction models under both working conditions. This study provides some reference value for the application of multi-task prediction algorithms to the task of predicting indoor environments in buildings.

Indoor Air Quality Assessments in 10 Long-Term Care Facilities during the COVID-19 Pandemic, California, 2021–2023

ObjectivesThis study aimed to assess indoor air quality (IAQ) in long-term care facilities (LTCFs) in California during the COVID-19 pandemic and evaluate their implementation of IAQ best practices described by public health authorities to control respiratory pathogen transmission via inhalation. DesignThis observational study conducted IAQ assessments in a convenience sample of LTCFs to gather qualitative data on the implementation of IAQ best practices. The design included 5 pilot visits to develop a standardized method of data collection and then systematic data collection at 10 facilities. Setting and ParticipantsThe study focused on 10 LTCFs across California, chosen from facilities that responded to flyers advertising free IAQ assessments. Some of the facilities had previously experienced COVID-19 outbreaks affecting residents and staff. MethodsState health department industrial hygienists performed site visits to collect data on each facility's heating, ventilation, and air-conditioning (HVAC) system operation, outdoor air introduction, recirculated air filtration, use of portable air cleaners, and directional airflow in isolation areas to evaluate implementation of IAQ best practices in each of these areas. Qualitative data were obtained through visual inspections and interviews with maintenance personnel. ResultsFindings indicated suboptimal implementation of IAQ best practices across the assessed facilities: no facility operated HVAC systems continuously, 40% had all outdoor air dampers open, 20% used MERV-13 or higher rated filters, 20% used portable air cleaners, and 20% performed directional airflow assessment and management for isolating COVID-19 cases. Conclusions and ImplicationsMost LTCFs assessed were not adhering to IAQ best practices, highlighting a significant opportunity for improvement. IAQ best practices described in this study are achievable with existing systems and are critical for reducing virus transmission through the air in LTCFs. The findings underscore the need for more systematic assessments and improvements in IAQ within LTCFs to protect staff and residents.

Climate-adaptive fire smoke ventilation strategies for atrium-type metro stations: A NSGA-II multi-objective optimisation study

This study explores the impact of climate variability on the operation of sustainable ventilation systems in urban infrastructure, with a focus on smoke movement in metro stations under varied external climatic conditions. Smoke characteristics and ventilation strategies in an atrium-type metro station are investigated using numerical simulations. The simulations reveal the influence of external climate on smoke movement through the atrium and roof windows, with particular attention to outdoor temperature and wind velocity. The study designs different ventilation strategies for smoke control that incorporate meteorological data, evaluating smoke visibility, CO distribution, and smoke extraction efficiency. The findings indicate that under severe external climates of low temperatures and high wind velocities, smoke extraction from roof window outlets is challenging. The adverse effects of external climate decrease the natural smoke extraction efficiency, increase the CO concentration, and reduce the safe visibility area. However, improved ventilation measures, such as the platform make-up air system and roof mechanical extraction system, can significantly enhance smoke control performance and facilitate safe evacuation. The study also employs the NSGA-II multi-objective optimisation method to obtain optimal ventilation strategies for different climates, balancing three optimisation objectives of operating energy consumption, safe visibility area, and smoke extraction efficiency. The outcomes show that the recommended values of Vp are 20%–30 % (0 °C ≤ To≤40 °C) and 40 % (−40 °C ≤ To<0 °C), vr are 0.3 (10 °C ≤ To≤40 °C) and 0.4 (−40 °C ≤ To < −10 °C), and Vc are all lower than 4 %. As wind velocity increases, the recommended values need to be enhanced. This research contributes to sustainable urban development by offering a framework for managing public safety and energy efficiency in the context of climate variability.

Application of energy combined thermal comfort in intelligent building management in complex environments

The efficient operation of heating ventilation and air conditioning systems relies on advanced control strategies. However, current control methods are often limited by issues such as uncertain system parameter information and spatial coupling constraints related to the supply rate of the air supply fan. To this end, an energy joint thermal comfort management method for complex environments in multiple regions is proposed. The long-term total cost minimization of the system is established, and then the Lyapunov optimization technology is used to design the distributed control algorithm. Simulation validation shows that the proposed method reduces the energy cost by an average of 11.24% compared to other methods with a thermal discomfort cost coefficient of 0. The average temperature deviation in the area is improved by 0.15 °C and 0.68 °C, respectively. The method saves more than 10% of the total energy cost under different thermal perturbations with an average total temperature deviation of 0.04 °C. The results indicate that the proposed energy joint thermal comfort management method can flexibly balance energy costs and user thermal comfort without knowing any prior information of system parameters, which can also greatly protect user privacy information. This method has application value in the control of heating ventilation and air conditioning systems in complex environments such as commercial buildings.

46 Principles of industrial ventilation system design and operation

46 Principles of industrial ventilation system design and operation

Analysis of distribution method of designed air quantity in coal mine ventilation—a case study

In a coal mine, air leakage exists in some roadways through doors and other ventilation structures inevitably. Based on this opinion, there are different views on whether these roadways must be assigned airflow in coal mine ventilation design. This paper analyses some relevant regulations and criteria on the designed air quantity of coal mines. Then, based on the ventilation design of the Guizhou Yizhong Coal Mine, through the study of the calculation of needed air quantity of every working place and its distribution method in coal mine ventilation design, this paper puts forward that explosion-proof door, safety exit, and other short distance roadways with ventilation structures need not assign airflow in coal mine ventilation design, while some long-distance roadways need. Additionally, it presents the main reason to support this opinion, gives the distribution method of inner air leakage quantity, which comes with the calculation of the designed mine total air quantity, puts forward the remedy method for the air leakage through ventilation structures in a coal mine ventilation system, then offers the mine operator with the basic opinions for the day-to-day planning and effective operation of a coal mine ventilation system.

Analysis and development of criteria for the operational efficiency of urban electric buses

Problem. Currently, manufacturers and research specialists primarily utilize the amount of autonomous mileage and/or the specific electricity consumption of autonomous DEEs per unit of mileage to describe the general characteristics of urban electric buses. However, the structural mass and the key operational parameter, the nominal passenger capacity, are often disregarded. Therefore, comparing electric buses of the same type with identical dimensional parameters based solely on these criteria is illogical and incorrect. Goal. This study aims to establish criteria for the operational efficiency of urban low-floor electric buses of various types, considering autonomous DEE and their charging or replacement systems. These criteria should account for structural mass and nominal passenger capacity, facilitating a proper comparative assessment and the selection of the most optimal options for use in urban passenger transportation systems. Methodology. Criteria for the operational efficiency of city electric buses of various types are proposed based on an analysis of electricity consumption required for both movement and the operation of heating, ventilation, and air conditioning systems. The criteria consider the mass in the equipped state and the main parameter - nominal passenger capacity. Results. Several operational efficiency criteria for urban electric buses, incorporating mass parameters and passenger capacity, are suggested. The feasibility of their application is demonstrated through a comparison of five sketch projects of electric buses of various types with modern models. Originality. The proposed criteria for the operational efficiency of urban electric buses offer a method for adequately comparing any selected models, regardless of their types and technical specifications. Practical value. The proposed criteria for the operational efficiency of urban electric buses of various types can be beneficial for transport company specialists in selecting suitable rolling stock for specific routes. Additionally, design bureau and scientific and technical center specialists can utilize these criteria during the design process of new urban electric transport models.

Advancements in smoke control strategies for metro tunnel cross-passage: A theoretical and numerical study on critical velocity and driving force

Cross-passage is a commonly encountered structure within metro tunnels, providing a swift route for evacuating personnel from the accident tunnel to the safe tunnel opposite. Ventilation in the cross-passage of metro tunnels is established through the collaborative operation of ventilation systems on both sides of the tunnel. Concurrently, smoke movement within the metro tunnel is impacted by factors such as train blockage and the accumulation of heat within the train carriages. The former correlations need further refinement to predict the critical velocity and driving force required to prevent smoke from spreading into a metro tunnel cross-passage. One-dimensional theoretical analysis and full-scale cold smoke experiments were performed to investigate the relationship between the air supply parameters of tunnel fans on both sides and the ventilation velocity in the cross-passage. A calculation model of fan type selection in the opposite side safe tunnel for smoke control in the tunnel cross-passage is proposed. The influence of train location, fire heat release rate, and main tunnel ventilation velocity on critical velocity in the cross-passage was quantified by numerical simulations. The results show that the critical velocity in the cross-passage under unobstructed conditions surpasses that under blocked conditions. Meanwhile, the critical velocity exhibits relative stability under both unobstructed and blocked conditions. On the basis of the dimensionless analysis, a piecewise function was proposed to predict the critical velocity in tunnel cross-passage. The outcomes of this study provide valuable guidance for the implementation of fire prevention and smoke control measures in tunnels with similar structures.

Full-scale experimental, numerical, and theoretical research on the spatio-temporal evolution of the flow structure in longitudinal and semi-transversal ventilated tunnels

This paper presents full-scale experimental, numerical, and theoretical research on the spatial and temporal structure of airflow for different modes of ventilation system operation in a road tunnel. The tunnel under investigation is equipped with a semi-transverse ventilation system. There are 19 pairs of axial jet fans in each tunnel tube and 164 air supply vents on both sides of a tube located just above the road surface. A set of seven stand poles with mounted vane anemometers were arranged along the tunnel tube axis. Different configurations of axial jet fans and air supply vents were employed, and changes in airflow velocity were recorded. Since the main objective of the study is to achieve a deeper understanding of the examined phenomena, a numerical model of the flows in the tunnel was built, and the acquired data was used to validate it. Some theoretical considerations are provided and also validated. All of this together reveals a very uneven distribution of longitudinal air velocity along the tunnel axis. Then, time constants when switching the fans on and off are determined. It is shown that the time needed to achieve a stable flow after the fans are turned on is in the order of five minutes, and the decay time after the fans are turned off is much longer; it takes about 15 min to calm down the flow. Next, it is determined that the transverse air supply significantly changes the airflow structure: the average longitudinal air velocity at the inlet portal was up to 25% lower than without it, while values at the outlet portal were similar. Finally, it is indicated that axial fans that operate in the suction mode are similarly efficient to those in the pumping mode.

Using artificial neural networks to predict indoor particulate matter and TVOC concentration in an office building: Model selection and method development

Indoor particulate matter and TVOC pose a significant threat to the health of staff working in office buildings. The accurate prediction of indoor particulate matter and TVOC concentrations is crucial for the automatic operation of ventilation and filtration system to control these indoor pollutants. Artificial neural networks have the advantages of high accuracy, real-time monitoring, and multi-source data integrations. Their performances in predicting particulate matter and TVOC concentrations in office buildings need to be further studied. In this study, on-site sampling was conducted in a typical office building at northern China. A standardized database of indoor parameters for four different offices was created with a total of 6072 datasets for each parameter. Three artificial neural network models were used in this study: the back propagation neural network (BP-ANN), the multi-layer neural network (MLNN), as well as the long-term and short-term memory neural network (LSTM). Among these three models, the performance of the MLNN model was the best in predicting PM2.5 and PM10 concentrations. It achieved a FB ranging from -0.02 to -0.01, an NMSE ranging from 0.46 to 0.49 μg/m3, and an R2 between 0.78 and 0.81. The MLNN model and the random forest (RF) classification method were further used to predict indoor TVOC concentrations. The RF model achieved a relatively better performance with a prediction accuracy of 89.2 %. In addition, the models’ generalization abilities were further evaluated by using some smaller datasets. For the MLNN model, when predicting indoor PM2.5 concentration, as the amount of training data decreased from 80 % to 20 %, its FB decreased from 0.41 to 0.03, its NMSE changed from 1.53 μg/m3 to 0.53 μg/m3, and its R2 decreased from 0.69 to 0.07. The results in this study can contribute to the use of artificial intelligence algorithm in office buildings aiming at indoor pollutants control.

Ventilation regulations and occupant practices: undetectable pollution and invisible extraction

This sociotechnical investigation examines the use of ventilation systems in homes in London, UK. These homes were built with ventilation systems as described by guidance in the UK Building Regulations Approved Document F. These systems are assumed to provide adequate ventilation rates. However, previous measurements in these homes show that ventilation rates are inadequate. Using social practice theory as a framework to analyse the qualitative data, the intended use of the ventilation systems is compared to participants’ actual practices of manipulating the indoor air, revealing discrepancies between the two. Occupants had limited knowledge of indoor pollutants but were highly motivated to control and interact with the smells and air in their homes. They primarily used technologies that were not part of the planned system, because the latter’s functioning was opaque to occupants and not well connected to their other practices. The highlighted discrepancies in four case study homes between planned and actual ventilation system operation help to identify how future systems could be improved to ensure adequate ventilation rates and good indoor air quality in airtight homes. Policy relevance This research investigates the extent to which the intended operation of domestic ventilation systems set out in Approved Document F to the UK Building Regulations is accomplished in practice. The findings show that ventilation equipment is not used as intended. The research suggests a need for future guidance to more actively consider routes by which occupants can learn how to use and maintain their ventilation systems, and how to identify and remove indoor air pollution. There is a risk of continuing underventilation in homes unless efforts are made to ensure the systems are easily interpretable and robust to a reasonable range of internal conditions and social contexts.

A Comprehensive Index for Evaluating the Effectiveness of Ventilation-Related Infection Prevention Measures with Energy Considerations: Development and Application Perspectives

In the wake of the COVID-19 pandemic, prioritizing indoor air quality has emerged as a crucial measure for preventing infections. Effective ventilation is vital in mitigating airborne pathogen transmission and maintaining a healthy indoor environment by diluting and removing infectious particles from enclosed spaces. However, increasing the supply of pathogen-free air to enhance infection control can lead to a rise in energy consumption. Nevertheless, evaluating the overall efficacy of ventilation-based infection prevention strategies while considering their energy requirements has posed challenges. This scientific paper introduces the ICEE (Infection Control’s Energy Efficiency) index, a newly developed simple integrated index to assess the effectiveness of ventilation strategies in reducing infection risks while accounting for associated energy demands. The paper reviews the current understanding of ventilation strategies, their impact on infection prevention, and their corresponding energy consumption. By employing a straightforward analytical approach, this metric offers a comprehensive framework to optimize ventilation systems for both infection prevention and energy efficiency. To quantify infection risk, a simplified equation model is utilized, incorporating factors such as ventilation effectiveness and filter efficiency, in case of recirculation. Energy demand is determined using approximations and relevant values from existing literature. Reference cases are defined, distinguishing between natural and mechanically ventilated scenarios, as these reference situations influence the energy-related effects of any implemented measures. The paper outlines the methodology employed to develop the index and illustrates its applicability through exemplary measures. The proposed index yields valuable insights for the design, operation, and retrofitting of ventilation systems, enabling informed decision-making towards fostering a healthier and more sustainable built environment.

Development of Virtual Sensor Based on LSTM-Autoencoder to Detect Faults in Supply Chilled Water Temperature Sensor

Supply chilled water temperature (SCWT) is an important variable for the efficient and stable operation of heating, ventilation, and air conditioning (HVAC) systems. A precisely measured value ensured by the continuous reliability of the temperature sensor is essential for optimal control of an HVAC system because temperature sensor faults can affect the chiller operation and waste energy. Therefore, temperature sensor fault-detection strategies are imperative for maintaining a comfortable indoor thermal environment and ensuring the efficient and stable operation of HVAC systems. This study proposes a fault-detection method for an SCWT sensor using a virtual sensor based on a long short-term memory-autoencoder. The fault-detection performance is evaluated considering a case study under various sensor fault scenarios to evaluate changes in indoor thermal comfort and energy consumption after correcting sensor faults detected by the virtual sensor. The results verify excellent fault-detection performance in various fault scenarios (F-1 scores ranging from 0.9350 to 1.000). After correcting the SCWT fault, indoor thermal comfort is steadily maintained without additional energy consumption (indoor set-point temperature unmet hour reduced by a maximum of 105.7 hours, and energy consumption decreased by up to 1.8%).

DeepVision based detection for energy-efficiency and indoor air quality enhancement in highly polluted spaces

Cooking can generate substantial heat from cooking equipment, potentially resulting in reduced thermal comfort levels if this excess heat is not adequately dissipated. Additionally, it can significantly affect indoor air quality (IAQ), not only in kitchen spaces but also in adjacent areas lacking sufficient ventilation. To ensure a healthy and comfortable indoor environment while avoiding unnecessary energy use, this research proposes an approach for detecting equipment usage. Utilizing deep learning and computer vision techniques (referred to as DeepVision), this method aids the operation of demand-driven ventilation systems in highly polluted spaces. A Faster RCNN model was employed and trained to detect kitchen equipment usage in real-time. The ventilation rate for the kitchen space was adjusted based on this real-time detection. Experimental tests were carried out in a case study kitchen and results showed that the detection model achieved an overall F1 score of 0.9142. Overall, the model achieved good performance in real-time detection, accurately identifying appliances in use, such as stoves, ovens, and toasters. Field experimental results showed the advantages of combining mechanical ventilation methods, such as extractor fans and cooker hoods, to mitigate IAQ issues while also achieving energy savings. Moreover, the energy simulations demonstrate its potential to reduce energy consumption by dynamically adjusting ventilation rates in response to real-time equipment usage. When the fan speed was varied according to the real-time detection method, the PM2.5 concentration in the cooking period was 16.5 % lower, with only a 3.7 % rise in fan power and a 0.8 % rise in daily heating demand, compared to using a constant fan speed.

Energy optimization for HVAC systems in multi-VAV open offices: A deep reinforcement learning approach

With global warming intensifying and resource conflicts escalating, the world is undergoing a transformative shift toward sustainable practices and energy-efficient solutions. With more than 32% of the global energy used by commercial and residential buildings, there is an urgent need to revisit traditional approaches to Building Energy Management (BEM). Within a BEMSplatform, regulating the operation of Heating, Ventilation, and Air Conditioning (HVAC) systems is more important, noting that HVAC systems account for about 40% of the total energy cost in the commercial sector.This paper offers a Deep Reinforcement Learning (DRL) algorithm as a data-driven approach to controlling HVAC operation to enhance the energy efficiency of commercial buildings with open offices while ensuring thermal comfort for occupants in different zones. Compared to alternative methods such as rule-based models and model-predictive control, data-driven models have shown promising results in optimizing building energy consumption without the need for building-specific thresholds, prior knowledge about the underlying physics of heat distribution, and digital mapping of the airflow. Despite the astonishing performance of modern DRL methods in controlling energy management, a particular energy-saving solution for open-plan offices with multiple Variable Air Volume (VAV) systems, where different zones cannot be treated independently, is still missing. Also, some of the existing methods suffer from key issues such as long training time and lack of generalizability for using over-complicated models, incorporating external factors that are hard to model and characterize, and including factors that are not typically accessible. To solve these issues, we propose a low-complexity DRL-based model with multi-input multi-output architecture for the HVAC energy optimization of open-plan offices, which uses only a handful of controllable and accessible factors. The efficacy of our solution is evaluated through extensive analysis of the overall energy consumption and thermal comfort levels compared to a baseline system based on the existing HVAC schedule from a real case. This comparison shows that our method achieves 37% savings in energy consumption with minimum temperature violation (<1%) of the desired temperature range during work hours. It takes only a total of 40 min for 5 epochs (about 7.75 min per epoch) to train a network with superior performance and covering diverse conditions for its low-complexity architecture; therefore, it easily adapts to changes in the building setups, weather conditions, occupancy rate, etc. Moreover, by enforcing smoothness on the control strategy, we suppress the frequent and unpleasant on/off transitions on HVAC units to avoid occupant discomfort and potential damage to the system. The generalizability of our model is verified by applying it to different building models and under various weather conditions.

Strategies for Effective Management of Indoor Air Quality in a Kindergarten: CO2 and Fine Particulate Matter Concentrations.

The educational and play-related activities of children proceed mainly indoors in a kindergarten. High concentrations of indoor PM2.5 and CO2 have been linked to various harmful effects on children, considerably impacting their educational outcomes in kindergarten. In this study, we explore different scenarios involving the operation of mechanical ventilation systems and air purifiers in kindergartens. Using numerical models to analyze indoor CO2 and PM2.5 concentration, we aim to optimize strategies that effectively reduce these harmful pollutants. We found that the amount of ventilation required to maintain good air quality, per child, was approximately 20.4 m3/h. However, we also found that as the amount of ventilation increased, so did the concentration of indoor PM2.5; we found that this issue can be resolved using a high-grade filter (i.e., a MERV 13 grade filter with a collection efficiency of 75%). This study provides a scientific basis for reducing PM2.5 concentrations in kindergartens, while keeping CO2 levels low.

Evaluation of the impact of ventilation system daily operation on air quality, comfort and well-being in primary schools

The Scol’air-FR project is issued from the need to establish an inventory of indoor air quality conditions in primary schools of the canton of Fribourg, Switzerland. In Fribourg, the official trend is to promote mechanical ventilation systems in schools to ensure a good air quality. A properly designed, built, commissioned, and operated mechanical ventilation system should be able to deliver the anticipated flow rate, regardless of who uses it - the only person involved being the person responsible for ventilation system operation. In some cases, however, the expected objectives are not achieved. This similar observation applies to natural ventilation. A good ventilation concept, properly implemented and operated, should also achieve the same objective if the outdoor air quality is good. The aim of this article is to identify differences, in terms of indoor air quality and well-being, between naturally and mechanically ventilated schools. Moreover, we expect to identify if trends are attributable to the operation of ventilation systems or to occupant behaviours. We observed lower carbonic gas levels in building equipped with mechanical ventilation systems. This finding can be extended to VOCs and relative humidity. We also identified different and recurrent issues among the different mechanical ventilation systems. Our results highlight issues measured in buildings equipped by mechanical ventilation systems are mostly related to the balance of the system, while in natural ventilation systems, IAQ related issues are mainly due to the present context and its influences on occupants’ behaviour.

Coupling BIM and detailed modelica simulations of HVAC systems in a common data environment

In current building design practices, the operation and performance of heating, ventilation and air conditioning (HVAC) systems are rarely documented in detail. This is mainly caused by the manual burden of generating detailed HVAC simulation models. To lower the barrier for detailed HVAC simulations, this paper presents an automated toolchain that generates and simulates models in Modelica language utilizing building information structured in a web-based common data environment. This approach differs from previous approaches by its focus on HVAC systems and integration with a common data environment over file-based BIM, which increases interoperability and allows users to run simulation studies in the cloud. The tool successfully generated and simulated a model of the HVAC systems in a small building under ideal and faulty operation of the heating system and thus demonstrated a fully interoperable data exchange between a common data environment and a simulation environment while showcasing the potential of analyzing HVAC systems with Modelica.