FluidNeRF: A Scalar-Field Reconstruction Technique for Flow Diagnostics using Neural Radiance Fields

Kelly, Dustin; Thurow, Brian S.

doi:10.2514/6.2023-0412

Cited by 3 publications

(2 citation statements)

References 38 publications

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“…With the recent introduction of NeRF as a viable emission tomography method [23,24], this work provides deeper insight into the effects of network hyperparameters and external parameters including camera layout and image noise on reconstruction accuracy. This work found that network size can have an impact on the reconstruction quality, but with an associated impact on the computational time.…”

Section: Discussionmentioning

confidence: 99%

“…This work proposes and studies a NeRF-based 3D flow field tomography technique, referred to here as FluidNeRF. A preliminary proof-of-concept investigation of FluidNeRF was presented by Kelly and Thurow [23]. In addition, Zhang et al [24] concurrently demonstrated a similar NeRF based tomography method that they referred to as Neural Volume Reconstruction Technique, and Chu et al [25] presented a physics-based NeRF method for 3D smoke visualization.…”

Section: Introductionmentioning

confidence: 99%

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Investigation of a neural implicit representation tomography method for flow diagnostics

Kelly,

Thurow

2024

Meas. Sci. Technol.

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In this work, a new gridless approach to tomographic reconstruction of 3D flow fields is introduced and investigated. The approach, termed here as FluidNeRF, is based on the concept of volume representation through Neural Radiance Fields (NeRF). NeRF represents a 3D volume as a continuous function using a deep neural network. In FluidNeRF, the neural network is a function of 3D spatial coordinates in the volume and produces an intensity of light per unit volume at that position. The network is trained using the loss between measured and rendered 2D projections similar to other multi-camera tomography techniques. Projections are rendered using an emission-based integrated line-of-sight method where light rays are traced through the volume; the network is used to determine intensity values along the ray. This paper investigates the influence of the NeRF hyperparameters, camera layout and spacing, and image noise on the reconstruction quality as well as the computational cost. A DNS-generated synthetic turbulent jet is used as a ground-truth representative flow field. Results obtained with FluidNeRF are compared to an adaptive simultaneous algebraic reconstruction technique (ASART), which is representative of a conventional reconstruction technique. Results show that FluidNeRF matches or outperforms ASART in reconstruction quality, is more robust to noise, and offers several advantages that make it more flexible and thus suitable for extension to other flow measurement techniques and scaling to larger-scale problems.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigation of a neural implicit representation tomography method for flow diagnostics

Kelly,

Thurow

2024

Meas. Sci. Technol.

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Forward and Inverse Modeling of Depth-of-Field Effects in Background-Oriented Schlieren

Molnar,

LaLonde,

Combs

et al. 2024

AIAA Journal

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This paper reports a novel cone-ray model of background-oriented schlieren (BOS) imaging that accounts for depth-of-field effects. Reconstructions of the density field performed with this model are far more robust to the blur associated with a finite aperture than conventional reconstructions, which presume a thin-ray pinhole camera. Our model is characterized and validated using forward evaluations of simulated buoyancy-driven flow and both simulated and experimental BOS measurements of hypersonic flow over a sphere. Moreover, the model is embedded in a neural reconstruction algorithm, which is demonstrated with a total variation penalty and the compressible Euler equations. Our cone-ray technique dramatically improves the accuracy of BOS reconstructions: the shock interface is well-resolved in all our tests, irrespective of the camera’s aperture setting, which spans f-numbers from 22 down to 4.

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Investigation of a time-resolved tomographic reconstruction technique using Neural Radiance Fields

Kelly,

Thurow

2024

AIAA SCITECH 2024 Forum

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FluidNeRF: A Scalar-Field Reconstruction Technique for Flow Diagnostics using Neural Radiance Fields

Cited by 3 publications

References 38 publications

Investigation of a neural implicit representation tomography method for flow diagnostics

Investigation of a neural implicit representation tomography method for flow diagnostics

Forward and Inverse Modeling of Depth-of-Field Effects in Background-Oriented Schlieren

Investigation of a time-resolved tomographic reconstruction technique using Neural Radiance Fields

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