Identification of contaminant sources in enclosed environments by inverse CFD modeling

In case contaminants are found in enclosed environments such as aircraft cabins or buildings, it is useful to find the contaminant sources. One method to locate contaminant sources is by inverse computational fluid dynamics (CFD) modeling. As the inverse CFD modeling is ill posed, this paper has proposed to solve a quasi-reversibility (QR) equation for contaminant transport. The equation improves the numerical stability by replacing the second-order diffusion term with a fourth-order stabilization term in the governing equation of contaminant transport. In addition, a numerical scheme for solving the QR equation in unstructured meshes has been developed. This paper demonstrates how to use the inverse CFD model with the QR equation and numerical scheme to identify gaseous contaminant sources in a two-dimensional aircraft cabin and in a three-dimensional office. The inverse CFD model could identify the contaminant source locations but not very accurate contaminant source strength because of the dispersive property of the QR equation. The results also show that this method works better for convection dominant flows than the flows that convection is not so important. PRACTICAL IMPLICATIONS: This paper presents a methodology that can be used to find contaminant source locations and strengths in enclosed environments with the data of airflow and contaminants measured by sensors. The method can be a very useful tool to find where, what, and how contamination has happened. The results can be used to develop appropriate measures to protect occupants in the enclosed environments from infectious diseases or terrorist releases of chemical/biological warfare agents as well as to decontaminate the environments.     

Principles and applications of probability-based inverse modeling method for finding indoor airborne contaminant sources

Building indoor air quality (IAQ) has received growing attentions lately because of the extended time people spend indoors and the increasing reports of health problems related to poor indoor environments. Recent alarms to potential terrorist attacks with airborne chemical and biological agents (CBA) have further highlighted the research needs on building vulnerability and protection. To maintain a healthful and safe indoor environment, it is crucial to identify contaminant source locations, strengths, and release histories. Accurate and prompt identification of contaminant sources can ensure that the contaminant sources can be quickly removed and contaminated spaces can be effectively isolated and cleaned. This paper introduces a probability concept based prediction method—the adjoint probability method-that can track potential indoor airborne contaminant sources with limited sensor outputs. The paper describes the principles of the method and presents the general modeling algorithm and procedure that can be implemented with current computational fluid dynamics (CFD) or multi-zone airflow models. The study demonstrates the application of the method for identifying airborne pollutant source locations in two realistic indoor environments with few sensor measurement outputs. The numerical simulations verify the feasibility and accuracy of the method for indoor pollutant tracking applications, which forms a good foundation for developing an intelligent and integrated indoor environment management system that can promptly respond to indoor pollution episodes with effective detection, analysis, and control.     

Predicting transient particle transport in enclosed environments with the combined computational fluid dynamics and Markov chain method

To quickly obtain information about airborne infectious disease transmission in enclosed environments is critical in reducing the infection risk to the occupants. This study developed a combined computational fluid dynamics (CFD) and Markov chain method for quickly predicting transient particle transport in enclosed environments. The method first calculated a transition probability matrix using CFD simulations. Next, the Markov chain technique was applied to calculate the transient particle concentration distributions. This investigation used three cases, particle transport in an isothermal clean room, an office with an underfloor air distribution system, and the first‐class cabin of an MD‐82 airliner, to validate the combined CFD and Markov chain method. The general trends of the particle concentrations vs. time predicted by the Markov chain method agreed with the CFD simulations for these cases. The proposed Markov chain method can provide faster‐than‐real‐time information about particle transport in enclosed environments. Furthermore, for a fixed airflow field, when the source location is changed, the Markov chain method can be used to avoid recalculation of the particle transport equation and thus reduce computing costs.     

Identifying decaying contaminant source location in building HVAC system using the adjoint probability method

Building heating, ventilation and air-conditioning (HVAC) system can be potential contaminant emission source. Released contaminants from the mechanical system are transported through the HVAC system and thus impact indoor air quality (IAQ). Effective control and improvement measures require accurate identification and prompt removal of contaminant sources from the HVAC system so as to eliminate the unfavourable influence on the IAQ. This paper studies the application of the adjoint probability method for identifying a dynamic (decaying) contaminant source in building HVAC system. A limited number of contaminant sensors are used to detect contaminant concentration variations at certain locations of the HVAC ductwork. Using the sensor inputs, the research is able to trace back and find the source location. A multi-zone airflow model, CONTAM, is employed to obtain a steady state airflow field for the studied building with detailed duct network, upon which the adjoint probability based inverse tracking method is applied. The study reveals that the adjoint probability method can effectively identify the decaying contaminant source location in building HVAC system with few properly located contaminant concentration sensors.     

Location identification for indoor instantaneous point contaminant source by probability-based inverse Computational Fluid Dynamics modeling

Indoor pollutions jeopardize human health and welfare and may even cause serious morbidity and mortality under extreme conditions. To effectively control and improve indoor environment quality requires immediate interpretation of pollutant sensor readings and accurate identification of indoor pollution history and source characteristics (e.g. source location and release time). This procedure is complicated by non-uniform and dynamic contaminant indoor dispersion behaviors as well as diverse sensor network distributions. This paper introduces a probability concept based inverse modeling method that is able to identify the source location for an instantaneous point source placed in an enclosed environment with known source release time. The study presents the mathematical models that address three different sensing scenarios: sensors without concentration readings, sensors with spatial concentration readings, and sensors with temporal concentration readings. The paper demonstrates the inverse modeling method and algorithm with two case studies: air pollution in an office space and in an aircraft cabin. The predictions were successfully verified against the forward simulation settings, indicating good capability of the method in finding indoor pollutant sources. The research lays a solid ground for further study of the method for more complicated indoor contamination problems. PRACTICAL IMPLICATIONS: The method developed can help track indoor contaminant source location with limited sensor outputs. This will ensure an effective and prompt execution of building control strategies and thus achieve a healthy and safe indoor environment. The method can also assist the design of optimal sensor networks.     

Inverse modeling methods for indoor airborne pollutant tracking: literature review and fundamentals

Reduction in indoor environment quality calls for effective control and improvement measures. Accurate and prompt identification of contaminant sources ensures that they can be quickly removed and contaminated spaces isolated and cleaned. This paper discusses the use of inverse modeling to identify potential indoor pollutant sources with limited pollutant sensor data. The study reviews various inverse modeling methods for advection-dispersion problems and summarizes the methods into three major categories: forward, backward, and probability inverse modeling methods. The adjoint probability inverse modeling method is indicated as an appropriate model for indoor air pollutant tracking because it can quickly find source location, strength and release time without prior information. The paper introduces the principles of the adjoint probability method and establishes the corresponding adjoint equations for both multi-zone airflow models and computational fluid dynamics (CFD) models. The study proposes a two-stage inverse modeling approach integrating both multi-zone and CFD models, which can provide a rapid estimate of indoor pollution status and history for a whole building. Preliminary case study results indicate that the adjoint probability method is feasible for indoor pollutant inverse modeling. PRACTICAL IMPLICATIONS: The proposed method can help identify contaminant source characteristics (location and release time) with limited sensor outputs. This will ensure an effective and prompt execution of building management strategies and thus achieve a healthy and safe indoor environment. The method can also help design optimal sensor networks.     

Comparison of different decontaminant delivery methods for sterilizing unoccupied commercial airliner cabins

Effective decontamination is crucial if an airliner cabin is contaminated by biological contaminants, such as infectious disease viruses or intentionally released biological agents. This study used computational fluid dynamics (CFD) method as a tool and vaporized hydrogen peroxide (VHP) as an exemplary decontaminant and Geobacillus stearothermophilus spores as a simulant contaminant to investigate three VHP delivery methods for sterilizing two different airliner cabins. The CFD first determined the airflow and the transient distributions of the contaminant and decontaminant in cabins. Auxiliary equations were implemented into the CFD model for evaluating efficacy of the sterilization process. The improved CFD model was validated by the measured airflow and simulated contaminant distributions obtained from a cabin mockup and the measured efficacy data from the literature. The three decontaminant delivery methods were (1) to supply the mixed VHP and air through the environmental control system of a cabin, (2) to send mixed VHP and air through a front door and to extract them from a back door of a cabin, and (3) to send directly VHP to a cabin and enhance the mixing with air in the cabin by fans. The two air cabins studied were a single-aisle and a twin-aisle airliner one. The results show that the second decontaminant delivery method (displacement method) was the best because the VHP distributions in the cabins were most uniform, the sterilization time was moderate, and the corrosion risk was low. The method displaced the existing air by the air/disinfectant solution, rather than dispersive mixing as the other two methods.     

CFD boundary conditions for contaminant dispersion,heat transfer and airflow simulations around human occupants in indoor environments

Indoor computational fluid dynamics (CFD) simulations can predict contaminant dispersion around human occupants and provide valuable information in resolving indoor air quality or homeland security problems. The accuracy of CFD simulations strongly depends on the appropriate setting of boundary conditions and numerical simulation parameters. The present study explores influence of the following three key boundary condition settings on the simulation accuracy: (1) contaminant source area size, (2) convective/radiative heat fluxes, and (3) shape/size of human simulators. For each of the boundary conditions, numerical simulations were validated with experimental data obtained in two different environmental chambers. In CFD simulations, a small release area of a contaminant point source causes locally high concentration gradients that require a very fine local grid system. This fine grid system can slow down the simulations substantially. The convergence speed of calculation is greatly increased by the source area enlargement. This method will not influence the simulation accuracy of passive point source within well-predicted airflow field. However, for active point source located within complicated airflow filed, such an enlargement should be carried out cautiously because simulation inaccuracy might be introduced. For setting thermal boundary conditions, convection to radiation heat flux ratio is critical for accurate CFD computations of temperature profiles around human simulators. The recommended convection to radiation (C:R) ratio is 30:70 for human simulators. Finally, simplified human simulators can provide accurate temperature profiles within the whole domain of interest. However, velocity and contaminant concentration simulations require further work in establishing the influence of simplifications on the simulation accuracy in the vicinity of the human simulator.     

Removal of contaminants released from room surfaces by displacement and mixing ventilation: modeling and validation

This paper presents the experimental and numerical modeling of contaminant dispersion in a full-scale environmental chamber with different room air distribution systems. For the experimental modeling, an area source with uniform emissions of a hypothetical contaminant (SF6) from the entire floor surface is designed and constructed. Two different types of ventilation are studied: displacement and mixing ventilation. A computer model for predicting the contaminant dispersion in indoor spaces was validated with experimental data. The validated model is used to study the effects of airflow and the area-source location on contaminant dispersion. Results show that the global airflow pattern has a strong impact on the distribution of the contaminants. In general, the personal exposure could be estimated by analyzing the relative source positions in the airflow pattern. Accordingly, the location of an exhaust diffuser may not greatly affect the airflow pattern, but can significantly affect the exposure level in the room. PRACTICAL IMPLICATIONS: When designing ventilation in addition to bringing fresh air to occupants, it is important to consider the removal of contaminants released in the off-gassing of building materials. Typical indoor off-gassing examples are emissions of volatile organic compounds from building enclosure surfaces such as flooring and painted walls. In this study, we conducted experimental and numerical modeling of different area sources in a mock-up office setup, with displacement or mixing ventilation. Displacement ventilation was as successful as mixing ventilation in removing the contaminant source from the floor area. Actually, the most important consideration in the removal of these contaminants is the relative position of the area source to the main airflow pattern and the occupied zone.     

Mitigation of cross-contamination in an aircraft cabin via localized exhaust

Most aircraft cabin ventilation designs currently use a 50% mix of fresh and recirculated, filtered air and supply approximately 8–10 l/s per person. In order to make the most efficient use of the air supply at hand, the 50% of cabin air that is exhausted from the aircraft should remove with it as much contaminant from within the cabin as possible. This will thereby reduce cross-contamination among passengers and improve overall air quality. This study examines the use of localized suction orifices near and around the source occupant to unobtrusively ingest the individual’s thermal plume and exhaust it from the aircraft cabin before contaminants entrained in the plume can significantly mix with the bulk airflow. Through the use of Computational Fluid Dynamics (CFD), various suction seat designs have been tested for their contaminant removal effectiveness and subsequent cross-contamination reduction. CFD results indicate significant improvements over conventional mixing air ventilation systems with a 40–50% decrease in passenger exposure predicted in a conventional coach-class seating arrangement.     

Contaminant dispersion with personal displacement ventilation,Part I: Base case study

Personal displacement ventilation (PDV) is a new ventilation concept that combines the positive features of displacement ventilation with those of task conditioning or personalized ventilation. PDV is expected to create a micro-environment around an occupant to control the environment individually. In this study, a base PDV case with a contaminant source at different locations was modeled for contaminant dispersion in a full-scale chamber. Computational fluid dynamics (CFD) was used to simulate the indoor airflow and pollutant transport, and the simulation results were validated against the experimental data. The contaminant concentration field for three different contaminant source locations was analyzed. Based on our results, it seems that this kind of PDV system cannot create the expected “micro-environment” to avoid the disturbance of the outside airflow. Further studies on how to improve the PDV performance are given in the companion paper.     

Experimental and CFD study of unsteady airborne pollutant transport within an aircraft cabin mock-up

It has been documented that diseases can spread within an aircraft cabin from the sneezing, coughing or breathing of a sick passenger. To understand the spreading mechanism it is very important to quantify the airflow and droplet transmission around a sneezing/coughing incident. In this project, tracer gas experiments were carried out in a full-scale Boeing 767-300 mock-up to study the global transport process of contaminated air within the cabin. Computational fluid dynamics (CFD) simulation was also used to provide additional information for understanding the principle. A steady airflow field was simulated first and then it was compared with the experimental data. The global airflow patterns were similar to those observed experimentally. This velocity field was adopted as the initial condition for further unsteady pollutant transport simulation. Experimental and simulated results were compared and discussed to develop a relationship between concentration and airflow pattern, source location, transport direction, and ventilation rate. Finally, the overall picture of concentration evolution by both experimental and simulated approaches was discussed.     

Comparison of sensor systems designed using multizone,zonal, and CFD data for protection of indoor environments

Sensors that detect chemical and biological warfare agents can offer early warning of dangerous contaminants. However, current sensor system design is mostly by intuition and experience rather than by systematic design. To develop a sensor system design methodology, the proper selection of an indoor airflow model is needed. Various indoor airflow models exist in the literature, from complex computational fluid dynamics (CFD) to simpler approaches such as multizone and zonal models. Airflow models provide the contaminant concentration data, to which an optimization method can be applied to design sensor systems. The authors utilized a subzonal modeling approach when using a multizone model and were the first to utilize a zonal model for systematic sensor system design. The objective of the study was to examine whether or not data from a simpler airflow model could be used to design sensor systems capable of performing just as well as those designed using data from more complex CFD models. Three test environments, a small office, a large hall, and an office suite were examined. Results showed that when a unique sensor system design was not needed, sensor systems designed using data from simpler airflow models could perform just as well as those designed using CFD data. Further, only for the small office did the common engineering sensor system design practice of placing a sensor at the exhaust result in sensor system performance that was equivalent to one designed using CFD data.     

Predicting airflow distribution and contaminant transport in aircraft cabins with a simplified gasper model

This study investigated the air distribution and contaminant transport in aircraft cabins with gaspers by using computational fluid dynamics (CFD). If the detailed gasper geometry were used in the CFD simulations, the grid number would be unacceptably high. To reduce the grid number, this investigation proposed a method for simplifying the gasper geometry. The method was then validated by two sets of experimental data obtained from a cabin mockup and a real aircraft cabin. It was found that for the cabin mockup, the CFD simulation with the simplified gasper model reduced the grid number from 1.58 to 0.3 million and the computing cost from 2 days to 1 hour without compromising the accuracy. In the five-row economy-class cabin of the MD-82 airplane, the CFD simulation with the simplified gasper model was acceptable in predicting the distribution of air velocity, air temperature, and contaminant concentration.     

Experimental studies of thermal environment and contaminant transport in a commercial aircraft cabin with gaspers on

Gaspers installed in commercial airliner cabins are used to improve passengers' thermal comfort. To understand the impact of gasper airflow on the air quality in a cabin, this investigation measured the distributions of air velocity, air temperature, and gaseous contaminant concentration in five rows of the economy‐class section of an MD‐82 commercial aircraft. The gaseous contaminant was simulated using SF₆ as a tracer gas with the source located at the mouth of a seated manikin close to the aisle. Two‐fifths of the gaspers next to the aisle were turned on in the cabin, and each of them supplied air at a flow rate of 0.66 l/s. The airflow rate in the economy‐class cabin was controlled at 10 l/s per passenger. Data obtained in a previous study of the cabin with all gaspers turned off were used for comparison. The results show that the jets from the gaspers had a substantial impact on the air velocity and contaminant transport in the cabin. The air velocity in the cabin was higher, and the air temperature slightly more uniform, when the gaspers were on than when they were off, but turning on the gaspers may not have improved the air quality.     

Experimental and numerical investigation of airflow and contaminant transport in an airliner cabin mockup

The study of airflow and contaminant transport in airliner cabins is very important for creating a comfortable and healthy environment. This paper shows the results of such a study by conducting experimental measurements and numerical simulations of airflow and contaminant transport in a section of half occupied, twin-aisle cabin mockup. The air velocity and air temperature were measured by ultrasonic and omni-directional anemometers. A gaseous contaminant was simulated by a tracer gas, sulfur hexafluoride or SF₆, and measured by a photo-acoustic multi-gas analyzer. A particulate contaminant was simulated by 0.7 μm di-ethyl-hexyl-sebacat (DEHS) particles and measured by an optical particle sizer. The numerical simulations used the Reynolds averaged Navier–Stokes equations based on the RNG k–ε model to solve the air velocity, air temperature, and gas contaminant concentration; and employed a Lagrangian method to model the particle transport. The numerical results quantitatively agreed with the experimental data while some remarkable differences exist in airflow distributions. Both the experimental measurements and computer simulations were not free from errors. A complete and accurate validation for a complicated cabin environment is challenging and difficult.     

Novel air distribution systems for commercial aircraft cabins

Air distribution systems in commercial aircraft cabins are important for providing a healthy and comfortable environment for passengers and crew. The mixing air distribution systems used in existing aircraft cabins create a uniform air temperature distribution and dilute contaminants in the cabins. The mixing air distribution systems could spread infectious airborne diseases. To improve the air distribution system design for aircraft cabins, this investigation proposed an under-floor displacement air distribution system and a personalized air distribution system. This study first validated a computational fluid dynamics (CFD) program with the experimental data of airflow, air temperature, and tracer-gas concentration from an environmental chamber. Then the validated CFD program was used to calculate the distributions of the air velocity, air temperature, and CO₂ concentration in a section of Boeing 767 aircraft cabin with the mixing, under-floor displacement, and personalized air distribution systems, respectively. By comparing the air and contaminant distributions in the cabin, this study concluded that the personalized air distribution system provided the best air quality without draft risk.     

A Simple Method Using Tracer Gas to Identify the Main Airflow and Contaminant Paths within a Room

The main airflow and contaminant paths or the spatial distribution of the age of air (or contaminant) in a room are of great interest in estimating venrilation efficiency. A simple meusurement method is presented which consists of injecting one or more tracer gases at locations of interest and analysing the concentration at several other locations, carefully chosen for best accuracy. Response functions can be fitted to these measurements, which are the age of the tracers or of the air or the concentration of the tracers as a function of the location. The salient paths, such as the dead zones, are also determined from these functions. The paper presents the method, its application and validation in a well controlled test room.     

Role of air changes per hour (ACH) in possible transmission of airborne infections

The cost of nosocomial infections in the United States is estimated to be 4 billion to4 billion to 5 billion annually. Applying a scientifically based analysis to disease transmission and performing a site specific risk analysis to determine the design of the ventilation system can provide real and long term cost savings. Using a scientific approach and convincing data, this paper hypothetically illustrates how a ventilation system design can be optimized to potentially reduce infection risk to occupants in an isolation room based on a thorough risk assessment without necessarily increasing ventilation airflow rate. A computational fluid dynamics (CFD) analysis was performed to examine the transport mechanism, particle path and a suggested control strategy for reducing airborne infectious disease agents. Most studies on the transmission of infectious disease particles have concentrated primarily on air changes per hour (ACH) and how ACH provides a dilution factor for possible infectious agents. Although increasing ventilation airflow rate does dilute concentrations better when the contaminant source is constant, it does not increase ventilation effectiveness. Furthermore, an extensive literature review indicates that not every exposure to an infectious agent will necessarily cause a recipient infection. The results of this study suggest a hypothesis that in an enclosed and mechanically ventilated room (e.g., an isolation room), the dominant factor that affects the transmission and control of contaminants is the path between the contaminant source and exhaust. Contaminants are better controlled when this path is uninterrupted by an air stream. This study illustrates that the ventilation system design, i.e., when it conforms with the hypothesized path principle, may be a more important factor than flow rate (i.e., ACH). A secondary factor includes the distance from the contaminant source. This study provides evidence and supports previous studies that moving away from the patient generally reduces the infection risk in a transient (coughing) situation, although the effect is more pronounced under higher flow rate. It is noted that future research is needed to determine the exact mode of transmission for most recently identified organisms.     

Prompt tracking of indoor airborne contaminant source location with probability-based inverse multi-zone modeling

Indoor air quality (IAQ) has a significant influence on occupants' comfort, health, productivity, and safety. Existing studies show that the primary causes of many IAQ problems are various airborne contaminants that either are generated indoors or penetrate into indoor environments with passive or active airflows. Accurate and prompt identification of contaminant sources can help determinate appropriate IAQ control solutions, such as, eliminating contaminant sources, isolating and cleaning contaminated spaces. This study develops a fast and effective inverse modeling method for identifying indoor contaminant source characteristics. The paper describes the principles of the probability-based adjoint inverse modeling method and formulates a multi-zone model based inverse prediction algorithm that can rapidly track contaminant source location with known source release time in a building with many compartments. The paper details the inverse modeling procedure with modification of an existing multi-zone airflow and contaminant transport simulation program. The application of the method has been demonstrated with two case studies: contaminant releases in a multi-compartment residential house and in a complex institutional building. The numerical experiments tested the source identification capability of the program for various contaminant sensing scenarios. The investigation verifies the effectiveness and accuracy of the developed method for indoor contaminant source tracking, which will be further explored to identify more complicated indoor contamination episodes.