Fluid Flow Simulation Using Donor Property Transfer
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Solution Overview
Problem
Current computational modeling techniques face challenges in efficiently simulating complex fluid flow scenarios, particularly in unsteady flows involving powered systems and control devices, due to high computational resource demands.
Innovation Solution
The proposed solution involves identifying donors and recipients within a computer-based model to simulate fluid flow for multiple time steps. This includes extracting donor properties from extraction surfaces and assigning them to boundary surfaces of recipients, allowing for the generation of an output flow field for subsequent time steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computational modeling techniques are used to simulate complex fluid flow scenarios, then simulation accuracy is maintained, but computational resource requirements become prohibitively high
Solution Approach 1:
The computational domain is segmented into multiple sub-domains with different levels of detail. Regions of interest are simulated with high fidelity while less critical regions use coarser modeling, allowing accurate simulation of complex flow scenarios while reducing overall computational resource requirements.
Solution Approach 2:
Flow field data from previously computed time steps or similar flow scenarios is copied and reused as initial conditions or boundary conditions for new simulations. This avoids redundant computations while maintaining simulation accuracy for unsteady flow scenarios involving powered systems and control devices.
2Measurement precision
If high-fidelity simulations of unsteady flow scenarios are performed, then flow field accuracy is improved, but simulation time increases significantly
Solution Approach 1:
Pre-computation of flow field characteristics, boundary conditions, and initial states is performed for standard operating scenarios. These pre-computed data are stored and rapidly retrieved during actual simulations, significantly reducing simulation time while maintaining flow field accuracy for unsteady scenarios.
Solution Approach 2:
The simulation dynamically adjusts computational parameters such as grid resolution, time step size, and solver complexity based on flow conditions. During steady-state regions, coarser parameters reduce computation time, while during transient events, finer parameters maintain flow field accuracy.
3Reliability
If detailed modeling of powered systems and control devices is implemented, then system fidelity is improved, but computational complexity becomes unmanageable
Solution Approach 1:
The system model is segmented into independent subsystems (powered systems, control devices, fluid domains) that can be simulated separately and coupled through interface conditions. This reduces computational complexity by avoiding full-system simultaneous computation while maintaining overall system fidelity.
Solution Approach 2:
Interface boundary conditions act as intermediaries between different subsystem models. These intermediaries allow detailed modeling of critical components while using simplified representations for less critical parts, managing computational complexity without sacrificing essential system fidelity.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed to improve fluid flow simulations. An example apparatus includes a property identifier to, prior to execution of the computer-based model, identify donors and recipients representative of one or more model regions of the computer-based model to simulate for a plurality of time steps including a first time step and a second time step, the donors having donor properties, in response to computing a flow field for the first time step of the computer-based model, a property extractor to extract the donor properties from extraction surfaces of the donors, and a property assignor to assign the donor properties to boundary surfaces of respective ones of the recipients, and a flow field generator to generate an output flow field for the second time step based on the recipients having the assigned donor properties.