Neural Network Fluid Flow Simulation for Fast Aerodynamic Prediction
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Solution Overview
Problem
Current methods for designing aerodynamic components, such as those in gas turbine engines, are inefficient due to the complexity of computational fluid dynamics (CFD) simulations and the need for extensive prototyping, which is time-consuming and resource-intensive.
Innovation Solution
The use of neural networks, specifically trained with pre-computed CFD outputs, to simulate fluid flow and predict aerodynamic performance of technical objects, allowing for faster optimization of design parameters and reduced need for physical prototypes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If CFD simulations are used to model aerodynamic behaviour, then design optimisation can be achieved before prototyping, but the simulation process takes days or weeks to complete
Solution Approach 1:
The neural network is trained in advance on a large dataset of CFD simulation results, storing the learned relationships between geometry parameters and flow characteristics. During actual design optimisation, the pre-trained network instantly predicts aerodynamic performance without running new CFD simulations, reducing design cycle time from weeks to minutes while maintaining high accuracy through the preliminary learning phase
2Manufacturing precision
If multiple CFD runs are performed to optimise multiple design parameters, then comprehensive design optimisation is achieved, but computational resource consumption increases significantly
Solution Approach 1:
Instead of running multiple computationally expensive CFD simulations to evaluate different design parameters, the invention creates a neural network copy that has learned the complex flow physics from a single comprehensive CFD dataset. This neural network copy can then rapidly evaluate numerous design parameter combinations with minimal computational resources, maintaining design optimisation accuracy while dramatically reducing energy consumption
Data Source
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AI summary
A method of using a computer implemented neural network for a simulation of aerodynamic performance of a technical object having a geometry, the method comprising: Training the neural network using a plurality of sets of encodings of pre-computed computational fluid dynamics, CFD, outputs, wherein the training is generated using inputs comprising: a geometry of at least one training technical object; spatial locations of input nodes of the neural network as node attributes; a relationship between the geometry of the at least one training technical object and the neural network input node locations; associated boundary conditions; operating conditions; and computed outputs comprising flow fields and aerodynamic performance parameters; the training using a loss function that evaluates an error between a neural network output and the pre-computed CFD outputs to produce a trained neural network; using the trained neural network with new inputs to generate as output a predicted aerodynamic performance of the technical object, wherein the relationship between the geometry of the at least one training technical object and the neural network input node locations comprises a vector with at least two parameters.