Instantaneous 3D flame imaging by background-oriented schlieren tomography

Grauer, Samuel J.; Unterberger, Andreas; Rittler, Andreas; Daun, Kyle J.; Kempf, Andreas; Mohri, Khadijeh

doi:10.1016/j.combustflame.2018.06.022

Cited by 107 publications

(56 citation statements)

References 54 publications

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“…A standard premixed natural gas/air laboratory Bunsen flame was considered here, which on average has a conical shape. The Bunsen simulation was previously generated for testing our background-oriented schlieren tomography (BOST) code [5]. The HRR was cropped and downsampled to a size of NX = 50 by NY = 60 by NZ = 50 voxels by cubic interpolation, resulting in a voxel resolution of 2 mm after binning.…”

Section: The Bunsen Flame Phantommentioning

confidence: 99%

“…The LES ran on a grid containing 30 million cells, with a premixed generated manifold combustion model, in combination with an artificial flame thickening method [50]. Mohri et al [16] used the simulation as a phantom for the first time in a high resolution CTC study, and Grauer et al [5] used it as well for validation purposes. We cropped and downsampled the HRR field to a size of NX = 66 by NY = 43 by NZ = 66 voxels by cubic interpolation.…”

Section: The Swirl Flame Phantommentioning

confidence: 99%

“…In the last decades 3D computed tomography (CT) methods have seen widespread use in numerous areas of engineering and physical sciences research. The methods have combined CT with various different measurement techniques such as laser absorption spectroscopy [1][2][3], schlieren [4,5], chemiluminescence [6][7][8][9][10][11][12][13][14][15][16][17] and particle image velocimetry [18,19]. Generally, projection measurements that are obtained at different angles around the probed volume are used for reconstructions.…”

Section: Introductionmentioning

confidence: 99%

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3D Evolutionary Reconstruction of Scalar Fields in the Gas-Phase

2019

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An evolutionary reconstruction technique (ERT) was developed for three-dimensional (3D) reconstruction of luminescent objects, in particular turbulent flames for the first time. The computed tomography (CT) algorithm is comprised of a genetic algorithm (GA) and a ray-tracing software. To guide the reconstruction process, a mask is introduced. It uses a Metropolis algorithm (MA) to sample locations where specific genetic operators can be applied. Based on an extensive parameter study, performed on several types of phantoms, the ability of our algorithm for 3D reconstructions of fields with varying complexities is demonstrated. Furthermore, it was applied to three experiments, to reconstruct the instantaneous chemiluminescence field of a bunsen flame, a highly turbulent swirl flame and the turbulent Cambridge-Sandia stratified flame. Additionally, we show direct and quantitative comparison to an advanced computed tomography of chemiluminescence (CTC) method that is based on an algebraic reconstruction technique (ART). The results showed good agreement between CTC and ERT using both phantom data from flame simulations, and experimental data.

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Section: The Bunsen Flame Phantommentioning

confidence: 99%

Section: The Swirl Flame Phantommentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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3D Evolutionary Reconstruction of Scalar Fields in the Gas-Phase

2019

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“…Schlieren imagery in fire plume applications has been usually deployed to a controlled burner flame rather than vegetative fuel beds. Recently Grauer et al (2018) applied background-oriented optical tomography and reconstructed the 3D instantaneous refractive index field of a turbulent flame. Typically, schlieren images have not been processed to obtain secondary data such as velocity fields and important parameters related to the flow structure.…”

Section: Introductionmentioning

confidence: 99%

Using Background-Oriented Schlieren to Visualize Convection in a Propagating Wildland Fire

Aminfar

Cobian-Iñiguez

Ghasemian

et al. 2019

Combustion Science and Technology

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Heat and mass transfer are important processes associated with wildland fire. Both radiant and convective heat transfer are important processes with convection often being the dominant mechanism. Unlike radiation, there is no direct method of measuring convection. Since convective heat transfer is governed by the fluid flow, understanding the fluid flow provides good understanding on the convective heat transfer. In fluid mechanics, flow visualization is a common methodology used to understand flow characteristics. Schlieren imagery is a common flow visualization technique which captures changes in fluid density such as the ones occur around a fire. Background-Oriented Schlieren (BOS) is a flow visualization technique that uses a background image with various patterns to visualize the density gradient caused by density fluctuations in a fluid. We applied BOS to measure the flow associated with laboratory-scale line fires. The reproducible fires were spreading in pine needle fuel beds in a wind tunnel with and without imposed wind. This initial application of BOS in a fire environment successfully visualized the flow around the flame. The visualized flow underwent a secondary process to produce the velocity field of the flow. Results indicate that even in conditions where the fire is known to be dominated by radiation, wind carried the thermal plume ahead of the flame front and expanded the thermal plume. In contrast, in the no wind condition, the thermal plume remained vertical above the fire. Using the BOS imagery, a new model for estimation of convective heat transfer was introduced. In addition to estimation of the convective heat transfer ahead of the fire, this new model enables visualization of convective motion.

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“…A widely used method for 3-D measurements (reconstructions) is computed tomography (CT) [28]. By simultaneously measuring the target at different angles and directions through low-dimensional sensors, high-dimensional flow fields can be reconstructed through inversion algorithms [32]. Such CT method requires multiple detectors installed at different angles and positions.…”

mentioning

confidence: 99%

Machine learning applied to retrieval of temperature and concentration distributions from infrared emission measurements

et al. 2019

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Inversion of temperature and species concentration distributions from radiometric measurements involves solving nonlinear, ill-posed and high-dimensional problems. Machine Learning approaches allow solving such highly nonlinear problems, offering an alternative way to deal with complex and dynamic systems with good flexibility. In this study, we present a machine learning approach for retrieving temperatures and species concentrations from spectral infrared emission measurements in combustion systems. The training spectra for the machine learning model were synthesized through calculations from HITEMP 2010 for gas mixtures of CO 2 , H 2 O, and CO. The method was tested for different line-of-sight temperature and concentration distributions, different gas path lengths and different spectral intervals. Experimental validation was carried out by measuring spectral emission from a Hencken flat flame burner with a Fourier-transform infrared spectrometer with different spectral resolutions. The temperature fields above the burner for combustion with equivalence ratios of φ =1, φ = 0.8, and φ = 1.4 were retrieved and were in excellent agreement with temperatures deduced from Rayleigh scattering thermometry. performed to address the inverse radiation problems using gradient-based [14] optimization methods. Griffith et al. [15,16] were the first to recognize that measurements of the transmissivity or emissivity of rotational spectral lines of a gas can reveal its temperature. In order to extract temperature, a nonlinear least-square method was used to fit the integrated transmissivity minima. Best et al. [17,18] combined tomography and Fourier transform infrared (FTIR) spectrometer transmission and emission spectra to extract temperature, concentration and soot volume fraction fields. By measuring spectral intensity of the CO 2 4.3 µm band, temperature profiles were retrieved in a number of ways [19,20]. At their time these results were not accurate enough due to lack of an accurate radiation prediction model and robust inverse algorithms. Song et al. [21,22] developed a spectral remote sensing technique to reconstruct CO 2 temperature profiles based on radiative intensity measurements. An accurate narrow band radiation model was employed and several Newton-type regression methods were tested. Due to the nonlinearity and ill-posedness of the problem, regularization of the inverse problems was applied to enforce some degree of smoothness to the solution. It is always difficult to select an appropriate regularization parameter and empirical values for the regularization parameter were employed. Ren and Modest [23] applied the Levenberg-Marquardt optimization method with Tikhonov regularization to reconstruct CO 2 temperature profiles and average concentrations from synthetic line-of-sight spectral intensity data. Two types of temperature profiles were tested for different gas path lengths and different CO 2 spectral bands. A new regularization selection method based on the combination of the L-curve criterion and the discre...

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Instantaneous 3D flame imaging by background-oriented schlieren tomography

Cited by 107 publications

References 54 publications

3D Evolutionary Reconstruction of Scalar Fields in the Gas-Phase

3D Evolutionary Reconstruction of Scalar Fields in the Gas-Phase

Using Background-Oriented Schlieren to Visualize Convection in a Propagating Wildland Fire

Machine learning applied to retrieval of temperature and concentration distributions from infrared emission measurements

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