Indoor CBR Contamination Prediction Using CFD and Pressure Models
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Solution Overview
Problem
Conventional CBR contaminant spread prediction technologies are limited in predicting indoor contamination spread, especially in complex building environments, and struggle with accurately securing source information during CBR incidents due to restricted air current and weather influences.
Innovation Solution
A system and method utilizing both low-fidelity and high-fidelity CBR spread modeling techniques, including computational fluid dynamics (CFD) and large eddy simulation (LES), to predict contamination concentration in indoor spaces by acquiring input information on indoor spaces, CBR sources, and environmental settings, and generating models based on mass conservation and immersed boundary methods for detailed contamination analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional CBR contamination spread prediction systems are used, then outdoor contamination spread can be predicted, but indoor contamination spread prediction is limited due to restricted air current and weather influences
Solution Approach 1:
The system changes the modeling parameters from outdoor weather-based diffusion models to indoor pressure difference-based flow models. It uses indoor-specific parameters such as pressure differences between zones, indoor air flow patterns, and building envelope characteristics to replace outdoor parameters like wind speed and atmospheric stability, enabling reliable indoor contamination prediction
Solution Approach 2:
The indoor space is segmented into multiple zones with distinct pressure characteristics. The system divides the building into pressure zones and contamination zones, allowing separate analysis of air flow paths and contamination spread in each zone, thereby improving prediction accuracy for complex indoor environments
2Productivity
If low-fidelity modeling is used for quick computation, then computation speed is improved, but detailed contamination analysis is limited
Solution Approach 1:
The system implements a dynamic modeling approach that adapts the level of fidelity based on computational requirements. Low-fidelity models provide quick initial assessments, while high-fidelity CFD models are activated when detailed analysis is needed. The system dynamically adjusts computational resources and model complexity according to the specific prediction scenario
Solution Approach 2:
The system employs nested modeling where low-fidelity zone-based models are embedded within high-fidelity CFD simulations. The low-fidelity model provides boundary conditions and initial estimates for the high-fidelity model, allowing efficient computation for general trends while enabling detailed local analysis when required
3Measurement precision
If high-fidelity CFD modeling is used for detailed analysis, then contamination concentration precision is improved, but computation time increases
Solution Approach 1:
The system applies high-fidelity CFD modeling selectively to only those zones and time periods where detailed analysis is most critical. Rather than running full high-fidelity simulations for entire buildings and extended periods, it focuses computational effort on high-priority areas, achieving necessary precision while minimizing computation time
4Speed
If source information is not secured early during CBR incidents, then response time is improved, but prediction accuracy deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-loading building geometry, zone configurations, and pressure relationship data before incidents occur. It pre-establishes computational models and boundary conditions so that when contamination source information becomes available, the system can immediately integrate this data into ongoing simulations without delays for data preparation or model setup
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate and detailed prediction of CBR contamination spread in indoor environments, providing quick computation for contamination spread zones and detailed analysis through high-fidelity modeling, effectively addressing limitations of outdoor-focused prediction systems.
Implementation Method 1
calculating an averaged contamination concentration in a designed indoor zone by using an indoor spread model for a concentration change rate of the contamination source according to a flow analysis by a pressure difference
Implementation Method 2
low-fidelity CBR spread modeling technique based the low of mass conservation
Implementation Method 3
calculating a contamination concentration for each lattice previously partitioned in a target indoor zone by using a computational fluid dynamics technique
Implementation Method 4
CFD based high-fidelity CBR spread modeling technique
Implementation Method 5
predicting transfer and spread of chemical, biological, and radioactive contaminants in an indoor space
Implementation Method 6
a process of transferring and diffusing target pollutants into a calculation area
Data Source
AI summary
The system for predicting the indoor contamination spread for CBR according to the present invention includes an input information acquisition unit acquiring model input information including at least any one information of information on an indoor space to be modeled, CBR contamination source information, and environmental setting information, a low-fidelity indoor spread modeling unit calculating an averaged contamination concentration in a designed indoor zone by using an indoor spread model for a concentration change rate of the contamination source according to a flow analysis by a pressure difference based on the acquired indoor space information, and a high-fidelity indoor spread modeling unit calculating a contamination concentration for each lattice previously partitioned in a target indoor zone by using a computational fluid dynamics technique from the acquired indoor space information.