Blood Flow Simulation Using Response Surface Reduced Order Modeling
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Solution Overview
Problem
Current methods for simulating blood flow are inefficient for real-time applications, as high-fidelity models are computationally expensive and unsuitable for fast computation, limiting their ability to provide accurate and timely predictions in clinical settings.
Innovation Solution
A computer-implemented method using a response surface methodology to map parameters of a high-fidelity model to a reduced order model, allowing for real-time simulation of blood flow by parameterizing the reduced order model with values determined from high-fidelity simulations, enabling fast and accurate prediction of blood flow characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-fidelity models are used for blood flow simulation, then accuracy is improved, but computation time increases significantly
Solution Approach 1:
The patent performs preliminary high-fidelity simulations to generate training data before real-time prediction is needed. A response surface model is pre-computed from these high-fidelity results, enabling fast predictions without repeating expensive simulations. This separates the accuracy-critical offline training phase from the speed-critical online prediction phase.
Solution Approach 2:
The patent creates a simplified copy (response surface model) of the complex high-fidelity simulation model. This copy captures the essential input-output relationships of the high-fidelity model but can be evaluated much faster. The response surface serves as a surrogate that replicates high-fidelity accuracy at reduced computational cost.
2Productivity
If real-time simulation is implemented, then computation speed is improved, but model fidelity must be reduced
Solution Approach 1:
The patent introduces a response surface model as an intermediary between the high-fidelity simulation model and real-time prediction requirements. This intermediary layer translates complex simulation inputs into accurate predictions using pre-computed relationships, achieving both speed and accuracy that neither the original high-fidelity model nor simple reduced-order models can achieve alone.
Solution Approach 2:
The patent transforms the simulation problem by changing parameters from direct physical quantities to response surface coordinates. By pre-computing the response surface over a range of parameter values and using interpolation for new queries, the system achieves fast evaluation while maintaining accuracy through the mathematical relationships captured in the response surface.
Data Source
AI summary
Systems and methods are disclosed for blood flow simulation. For example, a method may include performing a plurality of blood flow simulations using a first model of vascular blood flow, each of the plurality of blood flow simulations simulating blood flow in a vasculature of a patient or a geometry based on the vasculature of the patient; based on results of the plurality of blood flow simulations, generating a response surface mapping one or more first parameters of the first model to one or more second parameters of a reduced order model of vascular blood; determining values for the one or more parameters of the reduced order model mapped, by the response surface, from parameter values representing a modified state of the vasculature; and performing simulation using the reduced order model parameterized by the determined values, to determine a blood flow characteristic of the modified state of the vasculature.