Development of Artificial Neural Network Based Metamodels for Inactivation of Anthrax Spores in Ventilated Spaces Using Computational Fluid Dynamics

ABSTRACT

Linear, quadratic, and artificial neural network (ANN)-based metamodels were developed for predicting the extent of anthrax spore inactivation by chlorine dioxide in a ventilated three-dimensional space over time from computational fluid dynamics model (CFD) simulation data. Dimensionless groups were developed to define the design space of the problem scenario. The Hammersley sequence sampling (HSS) method was used to determine the sampling points for the numerical experiments within the design space. A CFD model, comprised of multiple submodels, was applied to conduct the numerical experiments. Large eddy simulation (LES) with the Smagorinsky subgrid-scale model was applied to compute the airflow. Anthrax spores were modeled as a dispersed solid phase using the Lagrangian treatment. The disinfectant transport was calculated by solving a mass transport equation. Kinetic decay constants were included for spontaneous decay of the disinfectant and for the reaction of the disinfectant with the surfaces of the three-dimensional space. To enhance the mixing of the disinfectant with the room air, a momentum source was included in the simulation. An inactivation rate equation accounted for the reaction between the spores and the disinfectant. The ANN-based metamodels were most successful in predicting the number of viable bioaerosols remaining in an arbitrary enclosed space. Sensitivity analysis showed that the mass fraction of the disinfectant, inactivation rate constant, and contact time had the most influence on the inactivation of the spores.

IMPLICATIONS This investigation presents a framework for the development of user-friendly models; metamodels for the prediction of the number of viable spores remaining in an indoor room during disinfection from accurate but time-consuming CFD studies. During any decontamination event, to know when to stop pumping in the disinfectant and to know what level of log reduction of the spores have been achieved before even starting decontamination would provide valuable guidance. The neural network based metamodels can be applied to obtain quick and relatively accurate answers. This would be necessary when immediate information is required during emergencies.     

Development of artificial neural network based metamodels for inactivation of anthrax spores in ventilated spaces using computational fluid dynamics

Linear, quadratic, and artificial neural network (ANN)-based metamodels were developed for predicting the extent of anthrax spore inactivation by chlorine dioxide in a ventilated three-dimensional space over time from computational fluid dynamics model (CFD) simulation data. Dimensionless groups were developed to define the design space of the problem scenario. The Hammersley sequence sampling (HSS) method was used to determine the sampling points for the numerical experiments within the design space. A CFD model, comprised of multiple submodels, was applied to conduct the numerical experiments. Large eddy simulation (LES) with the Smagorinsky subgridscale model was applied to compute the airflow. Anthrax spores were modeled as a dispersed solid phase using the Lagrangian treatment. The disinfectant transport was calculated by solving a mass transport equation. Kinetic decay constants were included for spontaneous decay of the disinfectant and for the reaction of the disinfectant with the surfaces of the three-dimensional space. To enhance the mixing of the disinfectant with the room air, a momentum source was included in the simulation. An inactivation rate equation accounted for the reaction between the spores and the disinfectant. The ANN-based metamodels were most successful in predicting the number of viable bioaerosols remaining in an arbitrary enclosed space. Sensitivity analysis showed that the mass fraction of the disinfectant, inactivation rate constant, and contact time had the most influence on the inactivation of the spores.     

The distribution of a generalized diversity index due to Good

Good (1953, 1982) proposed a generalized diversity index which includes as special cases both Shannon's and Simpson's indices. This index can be further generalized as described in Baczkowski et al. (1997, 1998). In this paper the first four moments of this generalized index are derived for both a general species abundance distribution and the case with all species abundances equal, the equiprobable case. This allows the skewness and kurtosis of the index to be determined and thus gives information about the distribution of the index.