Constraint Programming for Fire Suppression Pipe Layout Optimization
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Solution Overview
Problem
Traditional fire suppression system design is time-consuming, costly, and relies heavily on expert knowledge, making it inefficient for optimizing pipe layouts in terms of cost, weight, and time.
Innovation Solution
A method utilizing Constraint Programming (CP) to encode the problem domain, explore solutions, and validate them using a hydraulic calculator, which includes visualizing valid solutions in 3D format, to optimize pipe layouts by filtering out unfeasible designs and determining agent flow rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual design methods are used, then expert knowledge can ensure design quality, but design time and cost increase significantly
Solution Approach 1:
The patent replaces manual mechanical design processes with automated computational systems. Constraint programming models and solvers automatically generate and evaluate pipe layouts, substituting human expert manual work with algorithmic optimization that maintains design quality while dramatically reducing design time.
Solution Approach 2:
The system enables self-service design automation where the constraint programming solver independently explores the design space, evaluates configurations against hydraulic constraints, and generates optimized layouts without requiring continuous human intervention or expert knowledge input.
2Adaptability or versatility
If traditional iterative design processes are used, then design flexibility is maintained, but productivity decreases
Solution Approach 1:
The patent implements dynamic design optimization where the constraint programming solver adaptively explores the design space based on specified constraints. The system dynamically adjusts the search strategy and evaluates configurations iteratively, maintaining design flexibility while achieving rapid convergence to optimized solutions.
Solution Approach 2:
The system allows flexible modification of design parameters such as pipe diameters, lengths, and configurations through the constraint programming model. Users can adjust constraints and objectives, and the solver automatically re-optimizes the design, providing both flexibility and high productivity.
3Reliability
If comprehensive design validation is performed, then solution reliability is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary validation by encoding hydraulic constraints and design requirements directly into the constraint programming model before optimization. This pre-validation framework ensures that only feasible solutions are explored, reducing the need for extensive post-processing validation while maintaining solution reliability.
Solution Approach 2:
The system implements feedback mechanisms where the constraint solver continuously evaluates configurations against hydraulic constraints and design requirements. Invalid configurations are rejected and the search space is pruned accordingly, providing continuous feedback that guides the optimization toward valid solutions without requiring complex external validation systems.
Data Source
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AI summary
A method (100) for optimizing a layout for a distribution pipe system is described herein, said method comprising: specifying a problem domain of the system and encoding the problem domain into a Constraint Programming "CP" model (110), producing a constraint program solver (120) and using said solver to explore the space in which the system is to be positioned and identify a problem or problems in said space, finding a solution to said problem or problems (130), and checking (135) if said solution is valid and wherein if said solution is valid. In some examples described herein, the method further comprises the step of visualising said valid solution by converting the solution into a 3D visual format (140) and outputting (150) the valid solution in said 3D format.