Publications
2012
Chen, Y. Lisa; Wen, Jin
Inverse estimation of indoor airflow patterns using singular value decomposition Journal Article
In: Applied Mathematical Modelling, vol. 36, no. 6, pp. 2627-2641, 2012, ISSN: 0307-904X.
Abstract | Links | BibTeX | Tags: Indoor airflow patterns, Inverse models, Sensor system design, Singular value decomposition
@article{CHEN20122627,
title = {Inverse estimation of indoor airflow patterns using singular value decomposition},
author = {Y. Lisa Chen and Jin Wen},
url = {https://www.sciencedirect.com/science/article/pii/S0307904X11006019},
doi = {https://doi.org/10.1016/j.apm.2011.09.045},
issn = {0307-904X},
year = {2012},
date = {2012-01-01},
journal = {Applied Mathematical Modelling},
volume = {36},
number = {6},
pages = {2627-2641},
abstract = {The fast pace in the development of indoor sensors and communication technologies is allowing a great amount of sensor data to be utilized in various areas of indoor air applications, such as estimating indoor airflow patterns. The development of such an inverse model and the design of a sensor system to collect appropriate data are discussed in this study. Algebraic approaches, including singular value decomposition (SVD), are evaluated as methods to inversely estimate airflow patterns given limited sensor measurements. In lieu of actual sensor data, computational fluid dynamics data are used to evaluate the accuracy of the airflow patterns estimated by the inverse models developed in this study. It was found that the airflow patterns estimated by the linear inverse SVD model were as accurate as those estimated by the nonlinear inverse-multizone model. For the zones tested, sensor measurements along on the walls and near the inlet and outlet provided the greatest improvement in the accuracy of the estimated airflow patterns when compared with the results using measurements from other locations.},
keywords = {Indoor airflow patterns, Inverse models, Sensor system design, Singular value decomposition},
pubstate = {published},
tppubtype = {article}
}
The fast pace in the development of indoor sensors and communication technologies is allowing a great amount of sensor data to be utilized in various areas of indoor air applications, such as estimating indoor airflow patterns. The development of such an inverse model and the design of a sensor system to collect appropriate data are discussed in this study. Algebraic approaches, including singular value decomposition (SVD), are evaluated as methods to inversely estimate airflow patterns given limited sensor measurements. In lieu of actual sensor data, computational fluid dynamics data are used to evaluate the accuracy of the airflow patterns estimated by the inverse models developed in this study. It was found that the airflow patterns estimated by the linear inverse SVD model were as accurate as those estimated by the nonlinear inverse-multizone model. For the zones tested, sensor measurements along on the walls and near the inlet and outlet provided the greatest improvement in the accuracy of the estimated airflow patterns when compared with the results using measurements from other locations.
Chen, Y. Lisa; Wen, Jin
The selection of the most appropriate airflow model for designing indoor air sensor systems Journal Article
In: Building and Environment, vol. 50, pp. 34-43, 2012, ISSN: 0360-1323.
Abstract | Links | BibTeX | Tags: Airflow model selection, Computational fluid dynamics, Multizone model, Sensor system design, Zonal model
@article{CHEN201234,
title = {The selection of the most appropriate airflow model for designing indoor air sensor systems},
author = {Y. Lisa Chen and Jin Wen},
url = {https://www.sciencedirect.com/science/article/pii/S0360132311003568},
doi = {https://doi.org/10.1016/j.buildenv.2011.10.007},
issn = {0360-1323},
year = {2012},
date = {2012-01-01},
journal = {Building and Environment},
volume = {50},
pages = {34-43},
abstract = {Current indoor air sensor system design is mostly by intuition or experience rather than by design. Sensor systems that are intended to ensure the safety and well-being of building occupants should be systematically designed and their performance evaluated. The literature shows that selection of an airflow model for sensor system design has been either out of convenience (thus choosing a simpler multizone model) or for proven accuracy (thus choosing a more complex computational fluid dynamics (CFD) model). In this study, the most appropriate airflow model for use in designing indoor air sensor systems was selected without compromising design objectives for 12 test zones. The test zones differed in the diffuser location, furniture presence and location, and airtightness level. Simulated contaminant concentrations from multizone, zonal, and CFD models were used to design sensor systems that either minimized detection time or occupant exposure. In lieu of experimental data, the performance of the sensor systems designed using multizone and zonal model data were benchmarked using CFD data. The sensor systems designed using CFD data were considered the “best-performing” in lieu of actual performance data. It was found that multiple-sensor systems designed using multizone model (the simplest of the three airflow models tested) data performed just as well as the best-performing sensor systems. For 1-sensor systems, common engineering practices resulted in performance comparable to the best-performing ones. These results applied even though the test zones were not considered well-mixed, and furniture and infiltration was modeled.},
keywords = {Airflow model selection, Computational fluid dynamics, Multizone model, Sensor system design, Zonal model},
pubstate = {published},
tppubtype = {article}
}
Current indoor air sensor system design is mostly by intuition or experience rather than by design. Sensor systems that are intended to ensure the safety and well-being of building occupants should be systematically designed and their performance evaluated. The literature shows that selection of an airflow model for sensor system design has been either out of convenience (thus choosing a simpler multizone model) or for proven accuracy (thus choosing a more complex computational fluid dynamics (CFD) model). In this study, the most appropriate airflow model for use in designing indoor air sensor systems was selected without compromising design objectives for 12 test zones. The test zones differed in the diffuser location, furniture presence and location, and airtightness level. Simulated contaminant concentrations from multizone, zonal, and CFD models were used to design sensor systems that either minimized detection time or occupant exposure. In lieu of experimental data, the performance of the sensor systems designed using multizone and zonal model data were benchmarked using CFD data. The sensor systems designed using CFD data were considered the “best-performing” in lieu of actual performance data. It was found that multiple-sensor systems designed using multizone model (the simplest of the three airflow models tested) data performed just as well as the best-performing sensor systems. For 1-sensor systems, common engineering practices resulted in performance comparable to the best-performing ones. These results applied even though the test zones were not considered well-mixed, and furniture and infiltration was modeled.
2010
Chen, Y. Lisa; Wen, Jin
Comparison of sensor systems designed using multizone, zonal, and CFD data for protection of indoor environments Journal Article
In: Building and Environment, vol. 45, no. 4, pp. 1061-1071, 2010, ISSN: 0360-1323.
Abstract | Links | BibTeX | Tags: Computational fluid dynamics (CFD), Indoor airflow modeling, Multizone model, Sensor system design, Zonal model
@article{CHEN20101061,
title = {Comparison of sensor systems designed using multizone, zonal, and CFD data for protection of indoor environments},
author = {Y. Lisa Chen and Jin Wen},
url = {https://www.sciencedirect.com/science/article/pii/S0360132309003096},
doi = {https://doi.org/10.1016/j.buildenv.2009.10.015},
issn = {0360-1323},
year = {2010},
date = {2010-01-01},
journal = {Building and Environment},
volume = {45},
number = {4},
pages = {1061-1071},
abstract = {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.},
keywords = {Computational fluid dynamics (CFD), Indoor airflow modeling, Multizone model, Sensor system design, Zonal model},
pubstate = {published},
tppubtype = {article}
}
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.
2008
Chen, Y. Lisa; Wen, Jin
Sensor system design for building indoor air protection Journal Article
In: Building and Environment, vol. 43, no. 7, pp. 1278-1285, 2008, ISSN: 0360-1323.
Abstract | Links | BibTeX | Tags: Chemical and biological warfare (CBW) agent, Indoor air quality, Sensor system design
@article{CHEN20081278,
title = {Sensor system design for building indoor air protection},
author = {Y. Lisa Chen and Jin Wen},
url = {https://www.sciencedirect.com/science/article/pii/S036013230700114X},
doi = {https://doi.org/10.1016/j.buildenv.2007.03.011},
issn = {0360-1323},
year = {2008},
date = {2008-01-01},
journal = {Building and Environment},
volume = {43},
number = {7},
pages = {1278-1285},
abstract = {Many new biological and chemical sensors have been or are continuously being developed for infrastructure and environmental protection, e.g., for protecting the quality of water and indoor and outdoor air. However, there is a lack of fundamental system-level research leading to the development of sensor networks that both maximize protection and minimize the system cost for indoor air protection. Four key parameters are usually used to evaluate sensor performance: sensor sensitivity, probability of correct detection, false positive rate, and response time. The optimal design of a sensor system is affected by the above sensor performance parameters. This paper describes a preliminary study to: (1) identify simplified simulation and optimization strategies that can be used for sensor system design; (2) examine the relationships between sensor location, sensitivity, and quantity, and (3) use both detection time and total occupant exposure as optimization objective functions for sensor system design. Common building attack scenarios, using a typical chemical and biological warfare (CBW) agent, are simulated for a small commercial building. Genetic algorithm (GA) is then applied to optimize the sensor sensitivity, location, and quantity, thus achieving the best system behavior while also reducing the total system cost. Assuming that each attack scenario has the same probability for occurrence, optimal system designs that account for the simulated possible attack scenarios are obtained.},
keywords = {Chemical and biological warfare (CBW) agent, Indoor air quality, Sensor system design},
pubstate = {published},
tppubtype = {article}
}
Many new biological and chemical sensors have been or are continuously being developed for infrastructure and environmental protection, e.g., for protecting the quality of water and indoor and outdoor air. However, there is a lack of fundamental system-level research leading to the development of sensor networks that both maximize protection and minimize the system cost for indoor air protection. Four key parameters are usually used to evaluate sensor performance: sensor sensitivity, probability of correct detection, false positive rate, and response time. The optimal design of a sensor system is affected by the above sensor performance parameters. This paper describes a preliminary study to: (1) identify simplified simulation and optimization strategies that can be used for sensor system design; (2) examine the relationships between sensor location, sensitivity, and quantity, and (3) use both detection time and total occupant exposure as optimization objective functions for sensor system design. Common building attack scenarios, using a typical chemical and biological warfare (CBW) agent, are simulated for a small commercial building. Genetic algorithm (GA) is then applied to optimize the sensor sensitivity, location, and quantity, thus achieving the best system behavior while also reducing the total system cost. Assuming that each attack scenario has the same probability for occurrence, optimal system designs that account for the simulated possible attack scenarios are obtained.