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Full-Text Articles in Biomedical Engineering and Bioengineering
Stochastic Reconstruction Of Multiple Source Atmospheric Contaminant Dispersion Events, Derek Wade, Inanc Senocak
Stochastic Reconstruction Of Multiple Source Atmospheric Contaminant Dispersion Events, Derek Wade, Inanc Senocak
Mechanical and Biomedical Engineering Faculty Publications and Presentations
Reconstruction of intentional or accidental release of contaminants into the atmosphere using concentration measurements from a sensor network constitutes an inverse problem. An added complexity arises when the contaminant is released from multiple sources. Determining the correct number of sources is critical because an incorrect estimation could mislead and delay response efforts. We present a Bayesian inference method coupled with a composite ranking system to reconstruct multiple source contaminant release events. Our approach uses a multi-source data-driven Gaussian plume model as the forward model to predict the concentrations at sensor locations. Bayesian inference with Markov chain Monte Carlo (MCMC) sampling …
Stochastic Reconstruction Of Multiple Source Atmospheric Contaminant Dispersion Events, Derek Wade, Inanc Senocak
Stochastic Reconstruction Of Multiple Source Atmospheric Contaminant Dispersion Events, Derek Wade, Inanc Senocak
Inanc Senocak
Reconstruction of intentional or accidental release of contaminants into the atmosphere using concentration measurements from a sensor network constitutes an inverse problem. An added complexity arises when the contaminant is released from multiple sources. Determining the correct number of sources is critical because an incorrect estimation could mislead and delay response efforts. We present a Bayesian inference method coupled with a composite ranking system to reconstruct multiple source contaminant release events. Our approach uses a multi-source data-driven Gaussian plume model as the forward model to predict the concentrations at sensor locations. Bayesian inference with Markov chain Monte Carlo (MCMC) sampling …