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.     

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.     

Protecting a whole building from critical indoor contamination with optimal sensor network design and source identification methods

To maintain a healthful and secure indoor environment, it is crucial to design an effective indoor air quality (IAQ) sensor network and interpret sensor outputs for prompt IAQ controls. This paper introduces how a probability concept based inverse modeling method – the adjoint probability method – can be used to assist in designing a high-performance IAQ sensor network and identifying potential contaminant source locations for an entire building based on limited sensor outputs. The study proposes a new IAQ sensor network design and optimization method for buildings with one or more compartments on the basis of the probability calculation. With responses from optimized sensors, a two-stage integrated inverse prediction algorithm is developed that is able to identify a potential IAQ source zone (or room) in a building as well as an exact location within the room. The paper demonstrates the design of sensor networks and the application of the source identification algorithms for a residential dwelling. The case study verifies the feasibility, effectiveness and accuracy of the proposed sensor design method and the two-stage algorithm for indoor contaminant control.     

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.     

Experimental verification of tracking algorithm for dynamically-releasing single indoor contaminant

Identifying contaminant sources in a precise and rapid manner is critical to indoor air quality (IAQ) management as disclosed source information can facilitate proper and effective IAQ controls in environments with airborne infection, fire smoke and chemical pollutant release etc. Probability-based inverse modeling method was shown feasible for locating single instantaneous source in IAQ events. To tackle more realistic sources of continuous release, this paper advances the method to identify continuously releasing single contaminant source. The study formulates a suite of inverse modeling algorithms that can promptly locate dynamic source with known release time for IAQ events. Two field experiments are employed to verify the prediction: one in a multi-room apartment and the other in a hospital ward which was involved in a SARS outbreak in Hong Kong in 2003. The developed algorithms promptly and accurately identify the source locations in both cases.     

辨识室内空气污染源的反问题建模

当发生室内空气污染事故时,获知污染源释放的位置与强度等信息至关重要.利用污染物传感器提供的信息来推断室内空气污染源的研究属于反问题建模.反问题属于病态问题,因而必须采取一些特定的策略才能让反问题获得求解.本文总结了国内外有关应用反问题建模来辨识室内空气污染源的研究进展,以及反问题建模在传热、水污染以及大气污染等领域内的研究概况.辨识室内空气污染源的研究方法可归纳为四类,即分析法、优化法、概率法以及直接求解法.直接求解法不需要使用过多的假设,而且能够较好平衡计算效率以及计算精度,因而比较适于室内环境中污染源的辨识.     

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.     

确定建筑材料有害物散发量的三种传质模型

室内空气污染源散发量的确定是建立室内空气质量(IAQ)模型的重要步骤之一。目前研究建筑装饰材料和建筑涂料散发污染物的模型主要有经验模型和理论模型两类。经验模型简洁，但应用受到测试条件的限制，不具有普适性。基于传质理论而提出的理论模型目前研究较多，本文对研究建筑材料散发有害物的三个传质模型进行了评述。VB模型是一个简单的溶剂基室内涂料散发的总挥发性有机物(TVOC)的传质模型，低估了污染物的长期散发量。对流传质模型是基于界面平衡所导出的稳态模型，适用于固体和液体等材料的散发过程。Yang等提出的四层传质模型通常需与数值求解结合，计算较麻烦，但能较好地确定建筑材料散发挥发性有机物的散发量。     

建筑热环境和室内空气质量综合模拟软件介绍

本文首先阐述了研究室内空气品质(IAQ)问题的重要性和必要性,介绍了目前已有的与室内空气品质相关的模拟软件。而后,基于"室内健康、环保和节能"的理念,提出了新的室内污染物浓度动态网络模拟分析与评估软件(DeST-IAQ)的理论框架,主要是将自然通风通路和机械通风网络集成,构成建筑通风和污染物传播的多区域网络模型,实现建筑通风和污染物浓度的联合动态预测,解决建筑通风模型、热模型和IAQ模型统一模拟的难题,满足建筑物能耗和室内空气品质预测的双重需求。该软件已在一些实际案例中得到了初步应用,其实用性具有较好的实验验证。     

Comparison of Data from an IAQ Test House with Predictions of an IAQ Computer Model

One of the objectives of EPA's indoor air quality (IAQ) program is to provide guidance on the impact of indoor sources on IAQ. A computer model, EXPOSURE, was developed to assist in this effort. EXPOSURE calculates pollutant concentration as a function of time for each room of the building. EXPOSURE includes effects of sources, sinks, room-to-room air movement, and air exchange with the outdoors. Several experiments designed to evaluate the impact of indoor sources on IAQ are described. Measured concentrations are compared with concentrations predicted by an IAQ model. The measured concentrations are in excellent agreement with the predictions. The model predictions and the experimental data demonstrate the importance of sinks in determining long-term IAQ.     

Inverse identification of multiple outdoor pollutant sources with a mobile sensor

Air pollution is becoming more and more severe in large cities. Accurate and rapid identification of outdoor pollutant sources can facilitate proper and effective air quality management in urban environments. Traditional “trial–error” process is time consuming and is incapacity in distinguishing multiple potential sources, which is common in urban pollution. Inverse prediction methods such as probability based adjoint modelling method have shown viability for locating indoor contaminant sources. This paper advances the adjoint probability method to track outdoor pollutant sources of constant release. The study develops an inverse modelling algorithm that can promptly locate multiple outdoor pollutant sources with limited pollution information detected by a movable sensor. Two numerical field experiments are conducted to illustrate and verify the predictions: one in an open space and the other in an urban environment. The developed algorithm promptly and accurately identifies the source locations in both cases. The requirement of an accurate urban building model is the primary prerequisite of the developed algorithm for urban application.     

空调系统新风和回风对室内空气品质的影响

分析了目前空调系统新风和回风对室内空气品质的影响，认为室内污染的不断增加是室内品质变坏的主要原因；新风的质量直接影响室内空气品质；回风是室内空气的二次污染源，导出了回风对室内空气污染的理论计算公式，文中认为新风率是20%能明显提高室内空气品质。     

室内颗粒污染的源辨识与源解析

辨识室内颗粒物来源与分析室内颗粒物元素特征称为源辨识与源解析,是进行室内空气污染控制与净化的理论依据与前提条件。本文通过对室内空气品质（IAQ）模型进行理论分析,阐明了室内外污染源与室内颗粒物浓度之间的关系。指出室内颗粒污染物研究应根据污染源已知与未知两种情况进行讨论,并针对不同的情况分别采用源辨识与源解析技术。     

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.     

The effect of source motion on contaminant distribution in the cleanrooms

In the recent decades, cleanrooms have found growing applications in broad range of industries such as pharmacy and microelectronics. Concerns about negative effects of the contaminant exposure on the human health and product quality motivate many researchers towards understanding of the airflow and contaminant distribution though these environments. With an improvement in computational capacity of the computers, computational fluid dynamics (CFD) technique has become a powerful tool to study the engineering problems including indoor air quality (IAQ). In this research, indoor airflow in a full-scale cleanroom is investigated numerically using Eulerian-Eulerian approach. To evaluate the ventilation system effectiveness, a new index, called final efficiency, is introduced which takes all aspects of the problem into account. The results show that the contaminant source motion and its path have a great influence on the contaminant dispersion through the room. Based on the results, the contaminant distribution indexes, e.g. final efficiency and spreading radius, are improved when the source motion path is in the dominant direction of the ventilation airflow. Consequently, the efficiency of an air distribution system which provides a directional airflow pattern shows the least source path dependency. This study and its results may be useful to gain better understanding of the source motion effects on the indoor air quality (IAQ) and to design more effective ventilation systems.     

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.     

Coupling the multizone airflow and contaminant transport software CONTAM with EnergyPlus using co-simulation

Building modelers need simulation tools capable of simultaneously considering building energy use, airflow and indoor air quality (IAQ) to design and evaluate the ability of buildings and their systems to meet today’s demanding energy efficiency and IAQ performance requirements. CONTAM is a widely-used multizone building airflow and contaminant transport simulation tool that requires indoor temperatures as input values. EnergyPlus is a prominent whole-building energy simulation program capable of performing heat transfer calculations that require interzone and infiltration airflows as input values. On their own, each tool is limited in its ability to account for thermal processes upon which building airflow may be significantly dependent and vice versa. This paper describes the initial phase of coupling of CONTAM with EnergyPlus to capture the interdependencies between airflow and heat transfer using co-simulation that allows for sharing of data between independently executing simulation tools. The coupling is accomplished based on the Functional Mock-up Interface (FMI) for Co-simulation specification that provides for integration between independently developed tools. A three-zone combined heat transfer/airflow analytical BESTEST case was simulated to verify the co-simulation is functioning as expected, and an investigation of a two-zone, natural ventilation case designed to challenge the coupled thermal/airflow solution methods was performed.     

An integrated modeling tool for simultaneous analysis of thermal performance and indoor air quality in buildings

To analyze the thermal performance and indoor air quality (IAQ) in building simultaneously and quickly, we have developed an integrated modeling tool to simulate the dynamic indoor multi-parameters distributions and concentrations. The tool can take the parameters including indoor temperature, indoor humidity, and pollutant concentrations (e.g., volatile organic compounds (VOC) CO₂, particulate matter (PM)), as well as the heating/cooling load of heating, ventilating, and air-conditioning (HVAC) system into account. It couples a new zonal approach based on room air age. This paper presents the basic concept and flow chart in developing the modeling tool, and demonstrates the tool's application in a hypothetical health care building. The tool could be used for design of HVAC system with IAQ control devices and for the simultaneous analysis of thermal performance and IAQ in buildings.     

Modeling volatile organic compound (VOC) concentrations due to material emissions in a real residential unit. Part I: Methodology and a preliminary case study

High volatile organic compound (VOC) concentrations following building decoration have been observed frequently. In reality, however, residents do not know the indoor VOC concentration levels until the buildings are tested, which seldom provides a preventive measure. While several indoor air quality (IAQ) simulation programs have been developed to predict indoor contaminant levels, case studies in the literature are scarce regarding the predictability of indoor VOC concentrations as well as how such predictions could be performed in real buildings. In this paper, we intended to conduct a proof-of-concept study whether simulations can help to reveal some of the key features of VOC concentrations during indoor decoration process. We conducted a case study, simulated and measured the VOC concentrations of a residential unit during the room decoration process. Results show that while certain agreement was achieved between the measurement and simulation, application of IAQ models to real buildings is challenging under the best of circumstances—single zone spaces with very few emission materials inside.     

Locating time-varying contaminant sources in 3D indoor environments with three typical ventilation systems using a multi-robot active olfaction method

For a sudden contaminant release in an indoor environment, source localization can provide critical information for preventing and mitigating indoor air pollution and its related health and security problems. Considerable research has focused on locating indoor contaminant sources with instantaneous or constant release rates; however, few studies on locating indoor sources with time-varying release rates have been reported. This study proposed a multi-robot active olfactory method for promptly locating time-varying sources in 3D indoor environments. The method extends our previously proposed method for 2D indoor environments by redefining and reprogramming it in a 3D coordinate system and proposing a 3D source declaration algorithm. Via more than 200 numerical experiments in 3D indoor environments with mixing, displacement, and piston ventilation systems, the method was fully demonstrated and validated. The results show the applicability and reliability of the method and reveal the effects of space style, ventilation mode, source release rate, source location, and obstacle layout on source localization.