A Transfer Operator Methodology for Optimal Sensor Placement Accounting for Uncertainty

Sensors in buildings are used for a wide variety of applications such as monitoring air quality, contaminants, indoor temperature, and relative humidity. These are used for accessing and ensuring indoor air quality, and also for ensuring safety in the event of chemical and biological attacks. It follows that optimal placement of sensors become important to accurately monitor contaminant levels in the indoor environment. However, contaminant transport inside the indoor environment is governed by the indoor flow conditions which are affected by various uncertainties associated with the building systems including occupancy and boundary fluxes. Therefore, it is important to account for all associated uncertainties while designing the sensor layout. The transfer operator based framework provides an effective way to identify optimal placement of sensors. Previous work has been limited to sensor placements under deterministic scenarios. In this work we extend the transfer operator based approach for optimal sensor placement while accounting for building systems uncertainties. The methodology provides a probabilistic metric to gauge coverage under uncertain conditions. We illustrate the capabilities of the framework with examples exhibiting boundary flux uncertainty

A framework for indoor air quality sensor placement accounting for uncertainties and performing risk assessments

Monitoring and maintaining air quality in a built environment is essential for occupants health and safety. An indoor environment is subjected to various particulate, gaseous matter etc. Exposure to these contaminants can result in various health problems such as asthma, skin diseases and in some case cancer. Therefore indoor air quality monitoring sensors are important for early detection of these contaminants. An indoor contaminant is transported via the airflow. Various building uncertainties affect the airflow. Therefore it is important to account these uncertainties for designing optimal sensor network. Further, in case of an accidental or intentional release of hazardous contaminants, the network should also assist for risk assessments such as after release contaminant source distribution and identifying source location. The purpose of this research is to develop a unified framework for designing an optimal contaminant monitoring sensor network accounting building uncertainties and develop a methodology for carrying risk assessment under hazardous contaminant release. The framework uses the discrete form of Perron-Frobenius (P-F) transfer operator to carry fast, accurate contaminant transport analysis. The work develops a methodology for accounting occupancy and weather uncertainties to designing the sensor network. Once constructed the P-F operator is also used with an Ensemble Kalman Filter (EnKF) estimator to estimate contaminant distribution using sensor measurement. Further, for identifying the release location a Bayesian inference method is developed using the constructed P-F operator. The developed framework can be used in developing strategies for people evacuation during toxic contaminant release containment of airborne infectious disease. It can also be integrated with it with the buildings to make smart HVAC systems

Contaminant transport at large Courant numbers using Markov matrices

Volatile organic compounds, particulate matter, airborne infectious disease, and harmful chemical or biological agents are examples of gaseous and particulate contaminants affecting human health in indoor environments. Fast and accurate methods are needed for detection, predictive transport, and contaminant source identification. Markov matrices have shown promise for these applications. However, current (Lagrangian and flux based) Markov methods are limited to small time steps and steady-flow fields. We extend the application of Markov matrices by developing a methodology based on Eulerian approaches. This allows construction of Markov matrices with time steps corresponding to very large Courant numbers. We generalize this framework for steady and transient flow fields with constant and time varying contaminant sources. We illustrate this methodology using three published flow fields. The Markov methods show excellent agreement with conventional PDE methods and are up to 100 times faster than the PDE methods. These methods show promise for developing real-time evacuation and containment strategies, demand response control and estimation of contaminant fields of potential harmful particulate or gaseous contaminants in the indoor environment

Modeling, Monitoring and Fault Diagnosis of Spacecraft Air Contaminants

Control of air contaminants is a crucial factor in the safety considerations of crewed space flight. Indoor air quality needs to be closely monitored during long range missions such as a Mars mission, and also on large complex space structures such as the International Space Station. This work mainly pertains to the detection and simulation of air contaminants in the space station, though much of the work is easily extended to buildings, and issues of ventilation systems. Here we propose a method with which to track the presence of contaminants using an accurate physical model, and also develop a robust procedure that would raise alarms when certain tolerance levels are exceeded. A part of this research concerns the modeling of air flow inside a spacecraft, and the consequent dispersal pattern of contaminants. Our objective is to also monitor the contaminants on-line, so we develop a state estimation procedure that makes use of the measurements from a sensor system and determines an optimal estimate of the contamination in the system as a function of time and space. The real-time optimal estimates in turn are used to detect faults in the system and also offer diagnoses as to their sources. This work is concerned with the monitoring of air contaminants aboard future generation spacecraft and seeks to satisfy NASA's requirements as outlined in their Strategic Plan document (Technology Development Requirements, 1996)

Airborne Contaminant Dispersal in Critical Built Environments

The Indoor Air Quality (IAQ), being one of the most significant exposures to human beings, encompasses the concepts of comfort and safety from unwanted contaminants. Whereas the thermal comfort is controlled through proper conditioning and distribution of ventilated air, controlling the airborne contaminants requires careful investigation of the flow characteristics. IAQ translates to different requirements, depending on the intended use of the indoor environment. In critical indoor spaces such as Operating Rooms and Cleanrooms, the principal focus of IAQ is to remove/contain/divert contaminants flowing with the airstream to maintain the required sterility, as contamination can lead to adverse patient/product outcomes. The airborne contaminants, generally submicron-sized particles, are controlled by directional airflow through differential pressure, depending on whether the space needs to exfiltrate (e.g., Operating Room – positive pressure) or contain (e.g., Isolation Room – negative pressure) the airborne contaminants. The current design paradigm that determines such pressure differential assumes steady-state conditions. Theoretically, during the steady-state, the rate of flow velocity change is zero, resulting in a constant flow field in time, and the distribution of contaminants in the space can be modeled using ordinary differential equations. Therefore, the steady-state assumption must hold to explain the contamination dispersal. However, in practice, transient occupant interventions like a door opening and walking through the steady-state flow fields alter the flow characteristics. In response, this dissertation examines how occupant-introduced transient events affect the steady-state flow. This study aims to quantify and identify patterns of the changes in the flow characteristics for different scenarios of realistic door openings and human walks under a range of ventilation rates through controlled experiments and numerical simulations. Through specifically designed experiments, the impacts of door operation and occupant walking were characterized and quantified based on different levels of supply flow rates from the ventilation system. The results of the experiments suggested that special considerations were required to control for the transient phenomena and the pressure differential. The walking and door opening experiments also found distinguishable changes in the flow characteristics under each separate interaction between the indoor environment and the occupant. It was interesting to note that even though the magnitude of the effects was different for different levels of initial condition and intervention types, the changes in the flow properties exhibited identical patterns that were possible to model and make predictions. Thus, this dissertation considers the sporadic transient interventions from the occupants (e.g., - door opening and walking) as events and discusses an approximation method called ‘Event-Based Modeling’ (EBM) using the collected data through these experiments. Two-dimensional numerical models were developed to obtain additional data on the changes in airflow characteristics and were used to model and test the accuracy of EBM’s prediction capabilities. The results demonstrated that the predictions from EBM were accurate, and the computational efficiency is improved compared to the traditional numerical simulation approach. This method can eliminate parallel modeling of the same phenomena, providing alternatives to simulate complex and computationally intensive transient events repeatedly. As a potential application, the changes in flow velocities from human-environment interactions in a critical indoor environment like an operating room can be predicted using the EBM method. This way, the ventilation systems can be designed as occupant-centric and energy-efficient by considering the impacts of the transient events instead of only considering the steady-state events

New developments and applications in modelling occupational exposure to airborne contaminants

Résumé: L'évaluation de l'exposition aux nuisances professionnelles représente une étape importante dans l'analyse de poste de travail. Les mesures directes sont rarement utilisées sur les lieux même du travail et l'exposition est souvent estimée sur base de jugements d'experts. Il y a donc un besoin important de développer des outils simples et transparents, qui puissent aider les spécialistes en hygiène industrielle dans leur prise de décision quant aux niveaux d'exposition. L'objectif de cette recherche est de développer et d'améliorer les outils de modélisation destinés à prévoir l'exposition. Dans un premier temps, une enquête a été entreprise en Suisse parmi les hygiénistes du travail afin d'identifier les besoins (types des résultats, de modèles et de paramètres observables potentiels). Il a été constaté que les modèles d'exposition ne sont guère employés dans la pratique en Suisse, l'exposition étant principalement estimée sur la base de l'expérience de l'expert. De plus, l'émissions de polluants ainsi que leur dispersion autour de la source ont été considérés comme des paramètres fondamentaux. Pour tester la flexibilité et la précision des modèles d'exposition classiques, des expériences de modélisations ont été effectuées dans des situations concrètes. En particulier, des modèles prédictifs ont été utilisés pour évaluer l'exposition professionnelle au monoxyde de carbone et la comparer aux niveaux d'exposition répertoriés dans la littérature pour des situations similaires. De même, l'exposition aux sprays imperméabilisants a été appréciée dans le contexte d'une étude épidémiologique sur une cohorte suisse. Dans ce cas, certains expériences ont été entreprises pour caractériser le taux de d'émission des sprays imperméabilisants. Ensuite un modèle classique à deux-zone a été employé pour évaluer la dispersion d'aérosol dans le champ proche et lointain pendant l'activité de sprayage. D'autres expériences ont également été effectuées pour acquérir une meilleure compréhension des processus d'émission et de dispersion d'un traceur, en se concentrant sur la caractérisation de l'exposition du champ proche. Un design expérimental a été développé pour effectuer des mesures simultanées dans plusieurs points d'une cabine d'exposition, par des instruments à lecture directe. Il a été constaté que d'un point de vue statistique, la théorie basée sur les compartiments est sensée, bien que l'attribution à un compartiment donné ne pourrait pas se faire sur la base des simples considérations géométriques. Dans une étape suivante, des données expérimentales ont été collectées sur la base des observations faites dans environ 100 lieux de travail différents: des informations sur les déterminants observés ont été associées aux mesures d'exposition des informations sur les déterminants observés ont été associé. Ces différentes données ont été employées pour améliorer le modèle d'exposition à deux zones. Un outil a donc été développé pour inclure des déterminants spécifiques dans le choix du compartiment, renforçant ainsi la fiabilité des prévisions. Toutes ces investigations ont servi à améliorer notre compréhension des outils des modélisations ainsi que leurs limitations. L'intégration de déterminants mieux adaptés aux besoins des experts devrait les inciter à employer cet outil dans leur pratique. D'ailleurs, en augmentant la qualité des outils des modélisations, cette recherche permettra non seulement d'encourager leur utilisation systématique, mais elle pourra également améliorer l'évaluation de l'exposition basée sur les jugements d'experts et, par conséquent, la protection de la santé des travailleurs. Abstract Occupational exposure assessment is an important stage in the management of chemical exposures. Few direct measurements are carried out in workplaces, and exposures are often estimated based on expert judgements. There is therefore a major requirement for simple transparent tools to help occupational health specialists to define exposure levels. The aim of the present research is to develop and improve modelling tools in order to predict exposure levels. In a first step a survey was made among professionals to define their expectations about modelling tools (what types of results, models and potential observable parameters). It was found that models are rarely used in Switzerland and that exposures are mainly estimated from past experiences of the expert. Moreover chemical emissions and their dispersion near the source have also been considered as key parameters. Experimental and modelling studies were also performed in some specific cases in order to test the flexibility and drawbacks of existing tools. In particular, models were applied to assess professional exposure to CO for different situations and compared with the exposure levels found in the literature for similar situations. Further, exposure to waterproofing sprays was studied as part of an epidemiological study on a Swiss cohort. In this case, some laboratory investigation have been undertaken to characterize the waterproofing overspray emission rate. A classical two-zone model was used to assess the aerosol dispersion in the near and far field during spraying. Experiments were also carried out to better understand the processes of emission and dispersion for tracer compounds, focusing on the characterization of near field exposure. An experimental set-up has been developed to perform simultaneous measurements through direct reading instruments in several points. It was mainly found that from a statistical point of view, the compartmental theory makes sense but the attribution to a given compartment could ñó~be done by simple geometric consideration. In a further step the experimental data were completed by observations made in about 100 different workplaces, including exposure measurements and observation of predefined determinants. The various data obtained have been used to improve an existing twocompartment exposure model. A tool was developed to include specific determinants in the choice of the compartment, thus largely improving the reliability of the predictions. All these investigations helped improving our understanding of modelling tools and identify their limitations. The integration of more accessible determinants, which are in accordance with experts needs, may indeed enhance model application for field practice. Moreover, while increasing the quality of modelling tool, this research will not only encourage their systematic use, but might also improve the conditions in which the expert judgments take place, and therefore the workers `health protection