Thermal field reconstruction and compressive sensing using proper orthogonal decomposition

Matulis, John; Bindra, Hitesh

doi:10.3389/fenrg.2024.1336540

Title: Thermal field reconstruction and compressive sensing using proper orthogonal decomposition

Journal Article · 16 February 2024 · Frontiers in Energy Research

DOI: https://doi.org/10.3389/fenrg.2024.1336540 · OSTI ID:2305787

Matulis, John; Bindra, Hitesh

Model order reduction allows critical information about sensor placement and experiment design to be distilled from raw fluid mechanics simulation data. In many cases, sensed information in conjunction with reduced order models can also be used to regenerate full field variables. In this paper, a proper orthogonal decomposition (POD) inferencing method is extended to the modeling and compressive sensing of temperature, a scalar field variable. The method is applied to a simulated, critically stable, incompressible flow over a heated cylinder (Re = 1000) with Prandtl number varying between 0.001 and 50. The model is trained on pressure and temperature data from simulations. Field reconstructions are then generated using data from selected sensors and the POD model. Finally, the reconstruction error is evaluated across all Prandtl numbers for different numbers of retained modes and sensors. The predicted trend of increasing reconstruction accuracy with decreasing Prandtl number is confirmed and a Prandtl number/sensor count error matrix is presented.

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Sponsoring Organization:: USDOE

OSTI ID:: 2305787

Journal Information:: Frontiers in Energy Research, Journal Name: Frontiers in Energy Research Vol. 12; ISSN 2296-598X

Publisher:: Frontiers Media SACopyright Statement

Country of Publication:: Switzerland

Language:: English

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