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.     

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.     

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.     

基桩反射波法三维干扰成因与最佳采样方法研究

研究了基桩反射波法三维干扰信号的成因和消除三维干扰的最佳采样方法。通过模态叠加法,发现三维干扰主要来源于使桩顶表面产生非均匀轴向运动的轴对称径向振动模态,桩顶表面的模态位移零点即为三维干扰最小点。通过参数分析,提出了三维干扰最小点位置的函数表达式,从而修正了现有基桩检测规范推荐的采样位置。基于上述模态在桩顶面位移零点两侧位移反向的特点,提出了双速度平均法,该方法利用双通道仪的两个探头在干扰最小点两侧采样并平均以达到消除三维干扰的目的。与干扰最小点采样法相比,该方法测点选择范围更大,信号质量更优。工程实测数据和数值模拟结果证明了上述结论的正确性。     

Identification of contaminant sources in enclosed spaces by a single sensor

To protect occupants from infectious diseases or possible chemical/biological agents released by a terrorist in an enclosed space, such as an airliner cabin, it is critical to identify gaseous contaminant source locations and strengths. This paper identified the source locations and strengths by solving inverse contaminant transport with the quasi-reversibility (QR) and pseudo-reversibility (PR) methods. The QR method replaces the second-order diffusion term in the contaminant transport equation with a fourth-order stabilization term. By using the airflow pattern calculated by computational fluid dynamics (CFD) and the time when the peak contaminant concentration was measured by a sensor in downstream, the QR method solves the backward probability density function (PDF) of contaminant source location. The PR method reverses the airflow calculated by CFD and solves the PDF in the same manner as the QR method. The position with the highest PDF is the location of the contaminant source. The source strength can be further determined by scaling the nominal contaminant concentration computed by CFD with the concentration measured by the sensor. By using a two-dimensional and a three-dimensional aircraft cabin as examples of enclosed spaces, the two methods can identify contaminant source locations and strengths in the cabins if the sensors are placed in the downstream location of the sources. The QR method performed slightly better than the PR method but with a longer computing time. PRACTICAL IMPLICATIONS: The paper presents a method that can be used to find a gaseous contaminant source location and determine its strength in enclosed spaces with the data of contaminant concentration measured by one sensor. The method can be a very useful tool to find where, what, and how the contamination has happened. The method is also useful for optimally placing sensors in enclosed spaces. The results can be applied to develop appropriate measures to protect occupants in enclosed environments from infectious diseases or chemical/biological warfare agents released by a terrorist.     

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.     

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.     

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.     

Characterizing transportation of indoor gaseous contaminant using the state space method

This paper employs the state space method to characterize transportation of indoor gaseous pollutant in steady airflow field. From the differential equations governing contaminant transportation in space, the state space equation for transportation is proposed and the analytical solution is obtained. In the method, the matrix covering hologram of the transportation is derived. The state space equation is validated with the analytic solution for the case of the simultaneous transportation of the pollution for piston flow. Similarly, the concentration from the proposed method for a 2-D case also agrees well with the result from CFD method based on the experimentally validated flow field. Based upon the analytic solution of the state equation, it is easily known that the influence of the initial concentration distribution and the pollution source on the concentration at the specific point. In addition, assisted by Chen’s zero equation turbulence model [1], the concentration field for a 3-D case is simulated by the presented method. It is found that there exists a regular stage at which the relative effect of the initial concentration distribution and the source on the concentration field will not change with time.     

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.     

基于简化分区模型的室内污染源位置辨识方法

本文对在已知速度场中反求污染源位置的方法进行了初步探索:将室内空间划分为有限个区域,通过简化室内污染物输运方程,依据已知的速度场和测得的定点污染物浓度,通过一次简单运算,反求污染源位置;然后基于数值模拟方法给出了在实际房间中的应用实例.结果表明该方法能很快判断污染源位置,且具有较为满意的准确度,有望实现在线、实时探测污染源;最后对该方法的不足和可能的改进措施进行了讨论.     

斜拉索索力识别精度的提高方法

针对基于环境激励的斜拉索实测信号由于环境干扰而呈现出非平稳性和非平滑性的问题,提出了利用五点三次平滑法对快速傅里叶变换(FFT)得到的频域数据进行平滑处理,即考虑多因素影响来提高斜拉索索力识别精度。以实际斜拉桥工程中的拉索实测信号为研究对象,应用五点三次平滑法对拉索信号进行平滑消噪处理,考虑采样时间和采样点数对拉索频率识别精度的影响,利用求极值方法确定拉索各阶频率值及其位置,比较了基频法、频差法和峰值法确定拉索索力的计算精度,进行了索力测试的误差分析。结果表明:五点三次平滑法结合极值处理可以很好地识别拉索各阶频率,各因素对拉索频率的识别精度影响也有所不同,为频率法更加准确识别斜拉索索力提供了方便;同时也为开发高精度的索力测试仪器提供了有效依据。     

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.     

Sediment bags as an integrator of fecal contamination in aquatic systems

The identification of sources of fecal pollution in urban streams or storm sewers is often difficult since fecal contamination events frequently are episodic and/or have ceased before a survey can be undertaken. As an alternative to sampling the water column and/or natural sediments, sediment bags suspended in the water column can act to integrate water quality data with respect to fecal coliform concentrations. Furthermore, sediment bags retain coliform bacteria after their initial sorption to the sand substrate. As a result, they can identify contaminant sources even though, as is often the case, the sampling survey is carried out after the pollution event.     

Evaluation on sampling point densities for assessing indoor air quality

In Hong Kong, the Environmental Protection Department (HKEPD) has launched an indoor air quality (IAQ) certification scheme to promote an acceptable IAQ in workplaces. However, the associated uncertainties and measurement efforts have not been detailed for practical measurement in indoor spaces. In this study, indoor carbon dioxide (CO₂) concentration is selected as an indicator of the IAQ to investigate the probable errors and measurement efforts in different sampling schemes regarding the sampling point density. In particular, a one-year measurement for sample-spatial average indoor CO₂ concentration at 17 sampling locations in a typical large office (floor area=1200 m²) has been used to evaluate the probable errors of the sample-spatial average concentrations using different sampling point densities. The result shows that the measured concentration at a single sampling point could not be representative for the space and more than one sampling locations would be required in order to increase the measurement accuracy. Mathematical expressions for the sample-spatial average concentration at a confidence level at certain sampling point densities are proposed. When doubled the required measurement points, it was found that the probability of obtaining a measured CO₂ concentration at the confidence level of 95% could be increased from 70% to 90%, as compared with the current sampling practice. It is recommended to specify the measurement uncertainties in future codes so that effort for IAQ measurements in indoor spaces could be determined for practical strategies.     

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.     

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.     

三维激光扫描灰度信息在结构变形场计算中的应用

非接触测量技术是结构测量领域的最新进展之一，这类测量方法无需在被测对象上安装传感器，因而在结构试验以及土木工程现场实测中具有一定的优势。三维激光扫描作为一种非接触测量新技术，可以直接获取目标物表面点密集的三维坐标和灰度值，受测试环境干扰小，因而在土木工程领域的应用日益受到关注。在激光扫描技术的基础上，提出一种同名点的匹配方法，以提高变形场计算的精度。这种方法利用激光扫描的灰度信息，对扫描得到的点云坐标和灰度数据进行像素化处理，然后用相关系数法确定同名点并计算位移场。通过一钢梁试验对该方法进行验证，将结果与导杆位移计以及有限元分析的结果作了比对，结果显示该方法可以提高同名点识别的精度，进而提高结构变形计算的准确性，且可以同时获得构件包括平面内和平面外的变形。     

一起静电引发的重大火灾事故调查及思考

静电火花属微弱火源,其产生、积聚和放电过程较复杂,调查取证比较困难,一般需要在排除其他火源可能性以后再作认定。就此问题通过一起看似简单实则复杂的重大火灾事故原因认定,通过调查走访和现场实验,进行定性分析和定量计算,还原了事实真相。阐述如何准确认定静电火灾,总结了常见各类静电火灾的发生条件、特点、能量和认定要点。     

多热源多环供热管网水力建模与实验研究

为了进一步研究多热源多环集中供热管网水力特性,基于流体网络分析法,针对公共管道上是否存在用户,以及存在用户时是否存在水力交汇用户的不同情况,提出公共管道断开省略、两端点设置虚拟用户或虚拟热源等相应的建模方法。通过已搭建的多热源多环供热管网实验台,分别对3种不同工况单热源双环、双热源双环变工况、三热源三环理想工况进行建模仿真和实验研究,并在考虑仪表测量误差的基础上进行误差分析。结果发现,在3种工况下,仿真数据与实验数据的相对误差绝对值的最大值分别为1.96%、3.60%和1.00%,验证了该仿真模型的有效性和准确性,进而证明了建模方法的合理性和适用性。