Contaminant Source Location Research Articles

A COMPUTATIONAL AND EXPERIMENTAL INVESTIGATION OF AIR FLOW AND CONTAMINANTS CONCENTRATION IN A LABORATORY WITH MIXING VENTILATION SYSTEM

In this research an experimental and computational analysis under Iraqi climate of temperature distribution, velocity and contaminants concentration in the air-conditioned laboratory have been studied for two case studies. Case-I chosen internal combustion engine laboratory of dimensions (10.3 x 8.7 x 3.75) m with a different number of engines used in the experiments and Case-II chosen laboratory of chemical and petroleum products dimensions (3.7x3.6x3.3)m with different locations of the supply air terminal diffuser compared to the existing design situation. ANSYS FLUENT 14 used to simulate the model's laboratories for compared between the present practical work and numerical work results which are acquired by using RNG K-ε and SST k-ω models and found that the SST k-ω model more accurate. The SST (k-w) turbulence model were employed to solve the governing equations numerically with Reynold number 28,933 and validated by comparing the numerical results with experimental data, and this comparison gives a good agreement. The numerical results for case-I compared with experimental data, while for case-II, the numerical results compared with the standard value due to Iraqi cooling code and ASHRAE standards. The results showed that the mixing ventilation system is able to remove various types of pollutants effectively up to 90% in addition to providing human thermal comfort conditions with the effectively of heat removal up to 85% for the state of the internal combustion laboratory. The second case at laboratory of chemical and petroleum products when the supply air terminal diffuser in the same side, the results showed an increase in comfort conditions by up to 40%.Lastly, from the research, it was found that the pollutant transportation and distribution depend in general upon several factors such as type and location of contaminant source, building geometry, the arrangement of air terminal diffuser opening, and thermal/fluid boundary conditions for example flow rate. Numerical simulation of the velocity and diffusion fields in a conventional flow in laboratories is very useful in comprehending flow and diffusion patterns within different changes of the flow conditions.

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 of contaminant emission source in atmosphere based on optimal correlated matching of concentration distribution

Abstract Source location is crucial to manage contaminant emissions in atmosphere, In order to determine the source location without dependence on the absolute measurement data, a method based on optimal correlated matching of concentration distribution (OCMCD) was proposed. First, the estimation efficiency, accuracy and dependence on source strength of OCMCD were compared with the common method which estimates multiple parameters of the source term simultaneously. The results show that the method of OCMCD performs better than the common multiple parameters estimation method based on the mean errors between prediction and measurement in both estimation accuracy and efficiency. The test results with different sets of source strength manifest that OCMCD relies minimally on the source strength Then, a wind direction correction parameter and a weighted term of normalization concentration error were introduced into the model to compensate some missed information and improve the location results. The influence of data noises on the estimation accuracy of OCMCD method was also verified by adding extra manual noises on the measurement data. Then, the dependence of estimation performance with OCMCD method on atmosphere conditions were investigated statistically with experiment release cases. The results showed that source location was identified well in most of cases. Finally, OCMCD method was extended to determine the source location during the source trace process with a mobile sensor. The test results with a simulation scenario based on Zigzag search strategy demonstrate that the source location determined by OCMCD source criterion is much closer to the real source position than that determined by the criterion of the maximum concentration. Therefore, the results have proven the feasibility and superiority of OCMCD proposed in this paper to estimate source location in cases of both static sensor distribution and mobile sensors. OCMCD will be a potentially useful method to identify emission source location in atmosphere.

Identification of a Contaminant Source Location in a River System Using Random Forest Models

We consider the problem of identifying the source location of a contaminant via analyzing changes in concentration levels observed by a sensor network in a river system. To address this problem, we propose a framework including two main steps: (i) pre-processing data; and (ii) training and testing a classification model. Specifically, we first obtain a data set presenting concentration levels of a contaminant from a simulation model, and extract numerical characteristics from the data set. Then, random forest models are generated and assessed to identify the source location of a contaminant. By using the numerical characteristics from the prior step as their inputs, the models provide outputs representing the possibility, i.e., a value between 0 and 1, of a spill event at each candidate location. The performance of the framework is tested on a part of the Altamaha river system in the state of Georgia, United States of America.

Indoor contaminant source identification by inverse zonal method: Levenberg–Marquardt and conjugate gradient methods

ABSTRACTIndoor contaminant characteristics have been identified traditionally by inverse computational fluid dynamics methods. However, these methods are too time-consuming and costly. In this study, an inverse zonal method was developed as a simple model to provide an alternative and inexpensive approach for identifying indoor contaminant source characteristics. In the direct problem, experimental data and computational fluid dynamics results were employed to validate the reliability of the direct zonal method. This comparison showed the capability of the direct zonal method to simulate the contaminant airflow distribution in indoor environments. Finally, the inverse zonal method was used to predict the location and strength of an unknown indoor contaminant source with prior knowledge of the source release time. The inverse techniques for solving the identification problem were the Levenberg−Marquardt and conjugate gradient methods. The results showed that these inverse methods could identify the characteristics of a gaseous emission source.Abbreviations: CFD: computational fluid dynamics; ICFD: inverse computational fluid dynamics; IZM: inverse zonal model; POMA: pressurized zonal model with the air diffuser; QR: quasi-reversibility

Inverse modeling of indoor instantaneous airborne contaminant source location with adjoint probability-based method under dynamic airflow field

Accurate and prompt identification of airborne contaminant source location in indoor environment is of vital importance for building operation safety. Successful inverse tracking algorithm to identify airborne contaminant location usually requires limited sensor readings and indoor airflow information as input. Such mathematical algorithm under steady-state indoor airflow scenario has been intensively investigated. However, in many built environment scenarios, air velocity direction and magnitude keeps changing with time, making the transportation process of airborne contaminant significantly different from it under steady-state airflow. This paper mainly focuses on the airborne contaminant source location identification under dynamic airflow, by employing an adjoint probability-based inverse tracking method. The mathematical model and process of indoor instantaneous contaminants source location identification under dynamic air flow field is investigated and presented. Case studies using Computational Fluid Dynamics (CFD) tool by unsteady RANS simulation are conducted on a subway station model as well as an aircraft cabin, in which case experimental validation is also conducted. The capability of the new method is verified for air contaminant source location identification under dynamic indoor airflow.Practical implications: Successful identification of airborne contaminant source location under dynamic airflow field implies the capability of the method to locate an airborne contaminant source by limited sensor information and time-dependent airflow field in real-time. Practically the time-dependent airflow field data can be obtained through CFD simulation with unsteady RANS model with monitored time-dependent boundary condition.

Inverse Modeling of Indoor Instantaneous Airborne Contaminant Source Location and Releasing Time in Unsteady Airflow

Identification of airborne pollutant source location in indoor environment is significant for the indoor environment and occupants safety. Previous studies focus on unsteady air flow condition were proved valid to identify the source location with the information of boundary condition and known pollutant releasing time based on the adjoint probability algorithm. However, since pollutant releasing time is possibly unknown, it is necessary to investigate the source location identification with unknown releasing time under unsteady airflow condition. This paper mainly focuses on the airborne pollutant source location identification under unsteady airflow with unknown pollutant releasing time, by employing adjoint probability-based inverse tracking method. Two cases studies using CFD tools with RANS model under unsteady airflow were conducted. It is proved that the adjoint probability-based inverse tracking method is cable of identifying the air pollutant source location under unsteady airflow with unknown releasing time.

Indoor Air Quality Optimization by Thermal Displacement Air-Conditioning Methods

Background/Objectives: We have intended to investigate air velocity, CO 2 concentration and temperature distribution by computational fluid dynamics software for comfort issues. Methods/Statistical Analysis: For designing purpose, the experimental data on temperature, humidity and CO 2 concentration were collected for an internet room. The same geometry of the room is simulated with same existing method of mixing ventilation. The profile of the experiment and the simulation are found as same for CO 2 concentration and Temperature. Findings: Air supply is a critical factor to evaluate the heat distribution, air velocity and solid contaminants inside the room. The air supply diffuser location, contaminant source location and air supply method are tested numerically by comparing mixing ventilation and displacement ventilation methods. ANSYS CFX is used to obtain the temperature, velocity, and CO 2 concentration. In this work the displacement ventilation method shows better distribution of air and the ventilation efficiency with less consumption of electrical energy for cooling load compared with the existing mixing ventilation method. Applications/Improvements: In the present system the temperature of inlet air is 15 o C but if the system is modified to displacement ventilation efficiency the temperature of inlet air is 18 o C and this itself provide better comfort condition. Hence the saving of electrical energy is also possible.

Joint identification of contaminant source location, initial release time, and initial solute concentration in an aquifer via ensemble Kalman filtering

AbstractWhen a contaminant is detected in a drinking well, source location, initial contaminant release time, and initial contaminant concentration are, in many cases, unknown; the responsible party may have disappeared and the identification of when and where the contamination happened may become difficult. Although contaminant source identification has been studied extensively in the last decades, we propose—to our knowledge, for the first time—the use of the ensemble Kalman filter (EnKF), which has proven to be a powerful algorithm for inverse modeling. The EnKF is tested in a two‐dimensional synthetic deterministic aquifer, identifying, satisfactorily, the source location, the release time, and the release concentration, together with an assessment of the uncertainty associated with this identification.

Identification of indoor contaminant source location by a single concentration sensor.

Methods of maximum correlation coefficient (MCC) and the minimum discrete degree (MDD) are developed to identify the location of indoor contaminant source. These two methods are simple, effective, and economic due to the need of only one sensor. The methods are validated by a three-dimensional case study. The effects of the sampling time, the sampling interval, and the sensor response time and measurement error on the location identification of the contaminant source are analyzed. The results indicate that the identification performance of the MDD method is better than that of the MCC method; however, the MDD requires a fast response and high-accuracy sensor. MCC method not only has smaller effects of response time and measurement error compared with the MDD method but it also does not require high-performance (accuracy) sensor and it is not suitable for fast identification in a short time. For source location identification, the two methods need to properly choose sampling time, sampling interval, and response time.

Numerical study of the double diffusive convection phenomena in a closed cavity with internal CO2 point sources

Heat and mass transfer by natural convection in a square cavity with turbulent flow has been analyzed, covering a wide range of Rayleigh number of 104⩽Ra⩽1010 with a constant value of CH=3000ppm. A contaminant point source (CO2) is considered in five different positions inside the cavity. Governing equations of mass, energy and concentration were solved by the finite volume method, and a k–ε turbulence model was used for the treatment of high Rayleigh numbers. From the results, it was observed that contaminant point sources located near bottom wall decrease indoor temperatures, whereas point sources situated near upper wall decrease contaminant concentration levels. In general, temperatures and contaminant concentrations decrease as the Rayleigh number increases. Additionally, it was observed how the Nusselt number is directly affected by the contaminant source location.

Applying Hybrid Heuristic Approach to Identify Contaminant Source Information in Transient Groundwater Flow Systems

Simultaneous identification of the source location and release history in aquifers is complicated and time-consuming if the release of groundwater contaminant source varies in time. This paper presents an approach called SATSO-GWT to solve complicated source release problems which contain the unknowns of three location coordinates and several irregular release periods and concentrations. The SATSO-GWT combines with ordinal optimization algorithm (OOA), roulette wheel approach, and a source identification algorithm called SATS-GWT. The SATS-GWT was developed based on simulated annealing, tabu search, and three-dimensional groundwater flow and solute transport model MD2K-GWT. The OOA and roulette wheel method are utilized mainly to reduce the size of feasible solution domain and accelerate the identification of the source information. A hypothetic site with one contaminant source location and two release periods is designed to assess the applicability of the present approach. The results indicate that the performance of SATSO-GWT is superior to that of SATS-GWT. In addition, the present approach works very effectively in dealing with the cases which have different initial guesses of source location and measurement errors in the monitoring points as well as problems with large suspicious areas and several source release periods and concentrations.

A Gaussian process emulator approach for rapid contaminant characterization with an integrated multizone-CFD model

This paper explores a Gaussian process emulator based approach for rapid Bayesian inference of contaminant source location and characteristics in an indoor environment. In the pre-event detection stage, the proposed approach represents transient contaminant fate and transport as a random function with multivariate Gaussian process prior. Hyper-parameters of the Gaussian process prior are inferred using a set of contaminant fate and transport simulation runs obtained at predefined source locations and characteristics. This paper uses an integrated multizone-CFD model to simulate contaminant fate and transport. Mean of the Gaussian process, conditional on the inferred hyper-parameters, is used as a computationally efficient statistical emulator of the multizone-CFD simulator. In the post event-detection stage, the Bayesian framework is used to infer the source location and characteristics using the contaminant concentration data obtained through a sensor network. The Gaussian process emulator of the contaminant fate and transport is used for Markov Chain Monte Carlo sampling to efficiently explore the posterior distribution of source location and characteristics. Efficacy of the proposed method is demonstrated for a hypothetical contaminant release through multiple sources in a single storey seven room building. The method is found to infer location and characteristics of the multiple sources accurately. The posterior distribution obtained using the proposed method is found to agree closely with the posterior distribution obtained by directly coupling the multizone-CFD simulator with the Markov Chain Monte Carlo sampling.

Localization of Contamination Sources in Drinking Water Distribution Systems: A Method Based on Successive Positive Readings of Sensors

This paper addresses the problem of the localization of contamination sources after deliberate contaminations in drinking water distribution systems (DWDS). The proposed methodology is based on the information given by successive positive readings of sensors. Thus, it is possible to estimate the localization of the contamination sources based on only the first sensor that detected a contamination, and then update the results when more information is available. From the tests performed on a real drinking water distribution system, it was possible to observe that as new sensors detect changes in contaminant concentration, other possible contaminations may be detected and the location of contamination sources may be more restricted. The results achieved for the two set of sensors considered in the study contained the correct locations and the instants of contaminations previously simulated. Two case studies were also analysed to study the effect of the occurrence of false positives. It was concluded that it is not always possible to verify the occurrence of those anomalies and when it is verified, it is not possible to distinguish between a false positive and a false negative. The occurrence of false positives did not affect also the results related with the real detections.

Applying neural networks to solve the inverse problem of indoor environment

The computational fluid dynamics (CFD) analysis and back propagation neural network (BPNN) were employed to solve the inverse problem of indoor environment (IPIE). The approaches to identify the contaminant source locations and inlet boundary conditions by the measurement data at specified observation locations were put forward separately. The inverse design of the indoor boundary conditions by the desired distribution of predicted mean vote (PMV) in the control domain was also presented. The result demonstrated the utility of our proposed method in solving the IPIE.

Bayesian Approach for Real-Time Probabilistic Contamination Source Identification

Drinking water distribution system models have been increasingly utilized in the development and implementation of contaminant warning systems. This study proposes a Bayesian approach for probabilistic contamination source identification using a beta-binomial conjugate pair framework to identify contaminant source locations and times and compares the performance of this algorithm to previous work based on a Bayes’ rule approach. The proposed algorithm is capable of directly assigning a probability to a potential source location and updating the probability through the use of a backtracking algorithm and Bayesian statistics. The evaluation of the performance associated with the two algorithms was conducted by a simple comparison, as well as a simulation study in terms of a conservative chemical intrusion event through both a small skeletonized network and a large all-pipe distribution system network. Results from the simple comparison showed that the beta-binomial approach was more responsive to changes in sensor signals. In terms of intrusion events, the beta-binomial approach was more selective than the Bayes’ rule approach in identifying potential source node–time pairs and provided the flexibility to account for multiple possible injection locations.

Controlling scaling metrics for improved characterization of well-head protection regions

Summary We addressed the value of hydrogeological information on the assessment of the risk that an operating pumping well is polluted. The work considered a heterogeneous aquifer and focused on the statistical characterization of the contaminant mass fraction from a diffused source recovered at the well and the solute arrival times. We explored the role of the key length scales that characterize and control the well capture region and its probabilistic delineation with respect to the contaminant source location and size. The impact of augmenting the data-base of hydraulic information on the reduction of uncertainty associated with the environmental scenario analyzed was then investigated. It was found that obtaining a robust characterization of the target Environmental Performance Metrics (EPMs) depends on the length scale considered. For the sampling scheme considered, the importance of conditioning on the probability distributions of solute mass fraction and travel times is strongly affected by the location of the contaminant source within the probabilistic well catchment. With reference to the characterization of the travel time distribution associated with the recovery of a given mass fraction, the worth of augmenting the hydraulic parameter data-sets tends to decrease with decreasing solute residence time within the well catchment.

Evaluation of RANS turbulence models for simulating wind-induced mean pressures and dispersions around a complex-shaped high-rise building

Re-ingestion of the contaminated exhaust air from the same building is a concern in high-rise residential buildings, and can be serious depending on wind conditions and contaminant source locations. In this paper, we aim to assess the prediction accuracy of three k-ɛ turbulence models, in numerically simulating the wind-induced pressure and indoor-originated air pollutant dispersion around a complex-shaped high-rise building, by comparing with our earlier wind tunnel test results. The building modeled is a typical, 33-story tower-like building consisting of 8-household units on each floor, and 4 semi-open, vertical re-entrant spaces are formed, with opposite household units facing each other in very close proximity. It was found that the predicted surface pressure distributions by the two revised k-ɛ models, namely the renormalized and realizable k-ɛ models agree reasonably with experimental data. However, with regard to the vertical pollutant concentration distribution in the windward re-entrance space, obvious differences were found between the three turbulence models, and the simulation result using the realizable k-ɛ model agreed the best with the experiment. On the other hand, with regard to the vertical pollutant concentration distribution in the re-entrant space oblique to the wind, all the three models gave acceptable predictions at the concentration level above the source location, but severely underestimated the downward dispersion. The effects of modifying the value of the turbulent Schmidt number in the realizable k-ɛ model were also examined for oblique-wind case. It was confirmed that the numerical results, especially the downward dispersion, are quite sensitive to the value of turbulent Schmidt number.

A Model to Determine the Location of Heavy Metal Contaminant Source

We conducted a tracing model which was based on the process of dopamine diffusion in human brains, taking the diffusion coefficients and absorption coefficients into consideration. By combining with the spatial distributions of heavy metals plotted by ArcGIS, we can determine the contaminant sources and the results have high accuracy. Besides, we made an improvement in consideration of different transmission modes of heavy metal contaminants and the corresponding dilution factors.

Improving Efficiency of the Bayesian Approach to Water Distribution Contaminant Source Characterization with Support Vector Regression

There are multiple sources of uncertainties in urban water-distribution systems, e.g., nodal water demand and sensor measurement error. All of these uncertainties increase the complexity of contaminant source identification in a sparse sensor network. The large number of attributes (e.g., contaminant source location, magnitude, injection starting time, and duration) of a contaminant event profile cannot be identified given limited sensor data. Instead, the uncertainties in the contaminant event profile need to be characterized. Markov chain Monte Carlo (MCMC) methods for Bayesian analyses allow for the characterization of the uncertainty in the contamination event profile. To account for stochastic water demands, which has been shown in some circumstances to be necessary if the contaminant event is to be properly characterized, the evaluation of the likelihood function is the most computationally expensive part of the MCMC implementation. Previous work applied Monte Carlo methods for error propagation (MCEP). The research reported in this paper investigates the application of support vector regression (SVR) to speed the likelihood evaluation. This coupled MCMC-SVR approach enables probabilistic inference of the contaminant event. An SVR model, which maps from the contaminant event space to the likelihood space, is built for each node in the network to evaluate the likelihood function during the MCMC chain evolution. For the case study investigated, MCMC-SVR is computationally feasible and robust in inferring the contaminant event. A comparison between MCMC-SVR and MCMC-MCEP reveals that there is no substantial difference between the inferences provided by the two models, whereas the former is more computationally efficient.