Phased Array Radar Back Frame Lightweight Reliability Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing reliability optimization methods for phased array radar antennas face challenges in obtaining accurate probability models due to limited experimental data and complex multi-layer nesting algorithms, leading to inefficiencies in designing a lightweight back frame that meets flatness requirements.
Innovation Solution
The method employs an interval-probability uncertainty measurement model to establish a lightweight reliability model for the back frame, using a minimum total weight as a target function, and performs Lagrangian transformations to calculate optimal reliability parameters, reducing the reliance on extensive data samples and simplifying the optimization process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional reliability optimization methods using probability models are employed, then measurement precision of uncertainty parameters is improved, but device complexity and calculation burden increase significantly
Solution Approach 1:
The patent replaces complex probability models with simpler interval models that require minimal data. Instead of constructing elaborate probability distributions requiring extensive experimental data, the method uses straightforward interval bounds that can be determined with limited measurements, effectively using a simpler, more disposable modeling approach.
Solution Approach 2:
The patent transforms the modeling approach by changing from probability distribution parameters to interval parameters. This parameter transformation simplifies the uncertainty representation while maintaining measurement precision, avoiding the complexity of probability model construction while still capturing uncertainty effects.
2Reliability
If accurate probability models are constructed with extensive experimental data, then reliability measurement is improved, but loss of time and productivity decrease due to data collection requirements
Solution Approach 1:
The patent applies partial action by using only the essential minimum data required for interval model construction rather than exhaustive data collection for probability models. This partial approach achieves sufficient reliability measurement without the excessive time investment in comprehensive experimental data gathering.
Solution Approach 2:
The patent performs preliminary action by establishing interval models with minimal initial data, allowing design optimization to proceed without waiting for extensive experimental data collection. This preliminary modeling enables early design iterations while maintaining reliability considerations.
3Measurement precision
If multi-layer nesting algorithms are used for reliability optimization, then measurement precision of uncertainty is improved, but calculation amount increases hugely
Solution Approach 1:
The patent extracts the essential uncertainty measurement function from the complex multi-layer nesting algorithm framework. By isolating and simplifying the core uncertainty characterization to interval-based measurements, the method removes unnecessary algorithmic layers while preserving measurement precision.
Solution Approach 2:
The patent inverts the traditional approach by moving from complex algorithmic processing of uncertainty to simple interval-based uncertainty characterization. This inversion simplifies the calculation burden while maintaining measurement precision by reversing the conventional wisdom that more complex algorithms are needed for better uncertainty measurement.
4Weight of moving object
If lightweight back frame design is pursued with weight as target function, then weight of back frame is reduced, but reliability of structure may deteriorate
Solution Approach 1:
The patent changes the reliability assessment parameters from complex probability-based metrics to simple interval-based constraints. This parameter change enables efficient integration of reliability requirements into the lightweight design optimization, allowing weight reduction while maintaining structural reliability through simplified constraint formulation.
Data Source
AI summary
The present application discloses a lightweight method for a back frame of phased array radar antenna. The method includes: step 1: establishing a back frame lightweight reliability model; step 2: calculating a displacement constraint condition and reliability indicating that displacement amount maximum value of the back frame of antenna does not exceed a displacement threshold; and step 3: calculating a maximum probability failure point of a displacement constraint condition after a Lagrangian transformation under a preset condition, and calculating an optimal solution of the back frame lightweight reliability model, to determine a phased array radar antenna lightweight reliability parameter. Through technical solutions in the present application, the problem that it is difficult to obtain an accurate probability model of a phased array radar antenna back frame is resolved, thereby a calculation amount in a reliability optimization process is greatly reduced.