Dynamic Testing Sequence Optimization for Rocket Engines
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Solution Overview
Problem
Existing methods for testing complex machines like liquid-propellant rocket-engines are costly and inefficient, as they do not account for real-time changes in resource consumption or priority/risk during testing, leading to incomplete tests and resource wastage.
Innovation Solution
A method that dynamically adjusts the sequence of operating points by modifying distance coefficients and recalculating the optimum sequence using the traveling salesman problem algorithm, allowing for real-time consideration of resource consumption and priority/risk changes during testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a predetermined sequence of operating points is used for testing, then the test procedure is simple to implement, but the test cannot adapt to real-time changes in resource consumption or priority/risk, leading to incomplete tests and resource wastage
Solution Approach 1:
The patent applies dynamics by transforming the static predetermined sequence into a dynamic adaptive sequence. The algorithm continuously recalculates the optimal sequence based on real-time resource consumption and priority/risk changes, making the test procedure flexible and responsive to current conditions while maintaining systematic control through automated recalculation.
Solution Approach 2:
The patent implements feedback by monitoring actual resource consumption and priority/risk levels during testing, then using this information to adjust the remaining sequence of operating points. The system feeds back the current state to the optimization algorithm, which recalculates the optimal path, creating a closed-loop adaptive control system.
2Loss of energy
If the test is interrupted before reaching all operating points, then resources are saved in the short term, but new expensive tests must be performed to complete the evaluation
Solution Approach 1:
The patent applies preliminary action by pre-calculating multiple optimal sequences and using dynamic programming to determine the best path forward based on current progress and resource status. This allows the system to make optimal decisions about which operating points to prioritize next, ensuring that even if interrupted, the test progresses efficiently toward complete coverage without wasting resources on suboptimal paths.
3Productivity
If all operating points are tested in a fixed sequence, then complete coverage is achieved, but the number and importance of operating points reached may be maximized only if the sequence is optimized
Solution Approach 1:
The patent replaces manual or static mechanical sequencing with an automated computational optimization system. The algorithm uses mathematical optimization (dynamic programming and traveling salesman problem variants) to automatically determine the optimal sequence, substituting complex computational processes for simple fixed scheduling and achieving maximum productivity through intelligent routing.
Data Source
AI summary
The invention relates to the field of technical testing, and more particularly to a method of testing a machine, the method comprising: at least one step (S101) of determining a plurality of operating points for said machine, each operating point being defined by a duration and a specific value of at least one operating parameter of the machine; a step of calculating a set of distances between pairs of operating points; a step (S106) of selecting an optimum sequence of operating points by applying an algorithm for solving the traveling salesman problem to said set of distances between pairs of operating points; and a step (S107) of controlling the operation of said machine according to said optimum sequence of operating points.