Thermal-fluid Optimization Model of Small-scale Hydraulic Conduits
DOI:
https://doi.org/10.13052/ijfp1439-9776.2526Keywords:
Continuous adjoint method, hydraulic, multi-objective optimization, OpenFOAM, topology optimizationAbstract
Multiphysics topology optimization has applications in computer-aided design of products, including small-scale fluid power systems where flow efficiency, thermal management, and weight management matter. While algorithms exist that can optimize a single objective, there are no solutions that can simultaneously address all three of these factors. This study developed a multiphysics topology optimization process that uses a thermal-fluid-structure model to generate high-pressure hydraulic designs where passive cooling is built into the flow channels. Python was used with Open-Source Field Operation and Manipulation (OpenFOAM) for geometry creation, meshing, and finite volume and sensitivity analysis to implement the multi-objective optimization for small-scale fluid power systems. The process was performed iteratively to inform the next iteration’s geometry until an optimized design was reached.
The results show that pressure drop, fluid density, fluid velocity, and inlet diameter are positively correlated with capillary branching and that design space and viscosity are negatively correlated with capillary branching. Enhanced heat transfer came at the cost of pressure drop, where increasing the allowable pressure drop by 195% led to an increased temperature drop of 17%. Expanding the design space had the most significant impact on heat transfer, where extending the design space width by three times led to a 365% increase in temperature drop. Incorporating a curved exterior wall in the design space while holding the area and mesh node count constant led to a 3% increase in temperature drop while decreasing computational time by 68%. Lower viscosity of the working fluid leads to increased capillary branching with minimal impact on temperature drop (0.3%), while incorporating a temperature-dependent viscosity model led to a more prominent temperature drop (15%). Future work will expand the topology optimization method to incorporate structural optimization to handle load-bearing.
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