A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Estimating contaminant distribution from finite sensor data: Perron Frobenious operator and ensemble Kalman Filtering
Abstract Accurate and rapid monitoring of indoor air quality is critical to ensure occupant safety in the built environment. This is especially important in events where hazardous substances are released, and prompt estimation of contaminant distribution will facilitate quick evacuation and control response. The built environment is usually equipped with a finite set of sensors that measure local concentration of contaminants. The goal is to use this streaming dataset to estimate the contaminant concentration distribution in the complete domain. We accomplish this by integrating two powerful concepts. We utilize an operator theoretic approach – specifically the Perron-Frobenius (P-F) operator – to model the contaminant transport. Previous work has shown that the PF approach is a fast, effective, and accurate paradigm for sensor placement and contaminant transport prediction. The PF approach is integrated with an Ensemble Kalman Filter to rapidly estimate contaminant distribution under unknown release scenarios, given minimal sensor data. The framework is illustrated for two scenarios: a 2D problem involving an office space, and a 3D problem involving a furnished hotel room. Both examples show that the contaminant distribution is accurately predicted within a few sensor measurement cycles. The general applicability of the framework is illustrated by testing the framework for multiple, unknown release locations. This approach provides a unified, extendable framework for rapid contaminant estimation.
Graphical abstract Estimating contaminant distribution using Perron Frobenious operator in association with EnkF Estimator. Display Omitted
Highlights A methodology for estimating contaminant distribution using limited sensor data. A operator-theoretic framework using Perron-Frobenious operator approach for fast, robust and accurate contaminant transport analysis. Using the Ensemble Kalman Filtering integrated with the linear Perron-Frobenius (PF) operator approach for designing an estimator. The approach is illustrated for two and three dimensional problems. We also showcase the versatility of once constructed PF operator for designing the sensor monitoring network as well. Application and suitability of this work belongs to problems associated with indoor air quality, chemical and biological warfare and transmission of infectious diseases.
Estimating contaminant distribution from finite sensor data: Perron Frobenious operator and ensemble Kalman Filtering
Abstract Accurate and rapid monitoring of indoor air quality is critical to ensure occupant safety in the built environment. This is especially important in events where hazardous substances are released, and prompt estimation of contaminant distribution will facilitate quick evacuation and control response. The built environment is usually equipped with a finite set of sensors that measure local concentration of contaminants. The goal is to use this streaming dataset to estimate the contaminant concentration distribution in the complete domain. We accomplish this by integrating two powerful concepts. We utilize an operator theoretic approach – specifically the Perron-Frobenius (P-F) operator – to model the contaminant transport. Previous work has shown that the PF approach is a fast, effective, and accurate paradigm for sensor placement and contaminant transport prediction. The PF approach is integrated with an Ensemble Kalman Filter to rapidly estimate contaminant distribution under unknown release scenarios, given minimal sensor data. The framework is illustrated for two scenarios: a 2D problem involving an office space, and a 3D problem involving a furnished hotel room. Both examples show that the contaminant distribution is accurately predicted within a few sensor measurement cycles. The general applicability of the framework is illustrated by testing the framework for multiple, unknown release locations. This approach provides a unified, extendable framework for rapid contaminant estimation.
Graphical abstract Estimating contaminant distribution using Perron Frobenious operator in association with EnkF Estimator. Display Omitted
Highlights A methodology for estimating contaminant distribution using limited sensor data. A operator-theoretic framework using Perron-Frobenious operator approach for fast, robust and accurate contaminant transport analysis. Using the Ensemble Kalman Filtering integrated with the linear Perron-Frobenius (PF) operator approach for designing an estimator. The approach is illustrated for two and three dimensional problems. We also showcase the versatility of once constructed PF operator for designing the sensor monitoring network as well. Application and suitability of this work belongs to problems associated with indoor air quality, chemical and biological warfare and transmission of infectious diseases.
Estimating contaminant distribution from finite sensor data: Perron Frobenious operator and ensemble Kalman Filtering
Sharma, Himanshu (author) / Vaidya, Umesh (author) / Ganapathysubramanian, Baskar (author)
Building and Environment ; 159
2019-05-13
Article (Journal)
Electronic Resource
English
Estimating Ice-Affected Streamflow by Extended Kalman Filtering
| Online Contents | 1998
Estimating Ice-Affected Streamflow by Extended Kalman Filtering
| British Library Online Contents | 1998