Fire Suppressant System Design Optimization via CFD Simulation
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Solution Overview
Problem
The design of fire suppressant systems is time-intensive and inefficient due to the large number of possible components, agents, and variations, requiring manual expertise and extensive computational resources, making optimal design impractical for meeting safety and regulatory standards in confined spaces like aircraft.
Innovation Solution
A method and system that utilize a database search for components, three-dimensional computational fluid dynamic modeling, and constraint programming to optimize fire suppressant system design, including hydraulic flow evaluation and iterative parameter selection, to generate and evaluate system layouts efficiently, ensuring optimal agent coverage and flow rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual design methods with expert knowledge are used, then design expertise can be applied, but the design process becomes time-intensive and inefficient
Solution Approach 1:
The patent replaces manual mechanical design processes with automated computer-based systems. The optimization engine automatically generates, evaluates, and compares design configurations using algorithms rather than manual expert iteration, significantly reducing design time while maintaining or improving design quality through systematic evaluation of multiple parameters simultaneously.
Solution Approach 2:
The patent creates virtual copies of physical fire suppressant systems through detailed 3D computational models. These digital replicas allow for rapid simulation and evaluation of design performance without requiring physical prototypes or manual testing, enabling quick iteration and optimization of design parameters.
2Manufacturing precision
If extensive computational modeling is performed to evaluate design variations, then optimal design can be achieved, but computational resources and time requirements become impractical
Solution Approach 1:
The patent segments the design evaluation process into distinct computational stages: initial feasibility screening using simplified criteria, followed by detailed CFD modeling only for promising candidates. This hierarchical approach divides the large computational task into manageable segments, reducing overall computational requirements while maintaining design optimization quality.
Solution Approach 2:
The patent applies partial computational action by performing simplified preliminary evaluations on all design variations before applying full computational CFD modeling only to selected candidates. This selective approach avoids excessive computational effort on all possibilities while ensuring optimal designs are identified through detailed analysis of the most promising configurations.
3Reliability
If the number of possible components and variations is increased to meet safety standards, then system performance can be optimized, but the complexity of the design process increases
Solution Approach 1:
The patent employs dynamic optimization algorithms that automatically adapt the design search process based on performance feedback. The system dynamically adjusts evaluation criteria and component selections to meet safety standards without requiring manual configuration of complex parameters, reducing design complexity while ensuring safety compliance through automated constraint satisfaction.
Solution Approach 2:
The optimization engine performs self-service by automatically evaluating design configurations against safety and performance criteria without requiring manual expert intervention. The system autonomously navigates the complex design space, selecting components and configurations that satisfy regulatory requirements while optimizing system performance, thereby reducing the perceived complexity for the designer.
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
This approach significantly reduces designer effort and time, enabling the rapid generation and analysis of multiple design scenarios, resulting in a cost-effective and efficient optimal design that meets all system requirements, including safety and regulatory standards.
Implementation Method 1
performing three dimensional computational fluid dynamic, CFD, modelling on the 3D model to evaluate performance of the system layout
Data Source
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AI summary
A method and system for optimizing design of a fire suppressant system for a space, the method comprising: receiving (100) system requirements defining constraints of the system; defining (200) system parameters including required coverage of a fire suppressing agent and required flow rate of the agent based on the system requirements; searching a database of system components and selecting components able to satisfy the system requirements and provide the required coverage and flow rate; optimally combining (300) the selected components into a system layout; representing the system layout as a three dimensional, 3D, model; performing (500) three dimensional computational fluid dynamic, CFD, modelling on the 3D model to evaluate performance of the system layout; and determining (600), based on the CFD modelling, whether the system layout is an optimal design for the space.