Tomographic laser absorption spectroscopy using Tikhonov regularization

The application of tunable diode laser absorption spectroscopy (TDLAS) to flames with non-homogeneous temperature and concentration fields is an area where only few studies exist. Experimental work explores the performance of tomographic reconstructions of concentration and temperature profiles from wavelength-modulated TDLAS measurements within the plume of an axisymmetric McKenna burner. Water vapor transitions at 1391.67 nm and 1442.67 nm are probed using calibration free wavelength modulation spectroscopy with second harmonic detection (WMS-2f). A single collimated laser beam is swept parallel to the burner surface, where scans yield pairs of line-of-sight (LOS) data at multiple radial locations. Radial profiles of absorption data are reconstructed using Tikhonov regularized Abel inversion, which suppresses the amplification of experimental noise that is typically observed for reconstructions with high spatial resolution. Based on spectral data, temperatures and concentrations are calculated point-by-point. Here, a least-squares approach addresses difficulties due to modulation depths that cannot be universally optimized due to a non-uniform domain. Experimental results show successful reconstructions of temperature and concentration profiles based on two-transition, non-optimally modulated WMS-2f and Tikhonov regularized Abel inversion, and thus validate the technique as a viable diagnostic tool for flame measurements.Comment: This paper was published in Applied Optics and is made available as an electronic reprint with the permission of OSA. The paper can be found at the following URL on the OSA website: http://dx.doi.org/10.1364/AO.53.008095. Systematic or multiple reproduction or distribution to multiple locations via electronic or other means is prohibited and is subject to penalties under la

Improvements of Tomographic Quantification Technique for Temperature and Concentration Fields using a Multiplicative Algebric Reconstruction Technique

Each gas has its own unique spectrum lines. The intensity of the light passing through gases decreases following the Beer-Lambert law. This enables us to predict the density of the gases as well as its temperature. Recent advent of the tunable lasers enabled us to measure simultaneous temperature and concentration fields of the gases. In this study, a numerical prediction method in which the temperature and concentration fields of the H2O vapor gas are calculated is proposed. ART (Algebraic Reconstruction Technique) method and MART (Multiplicative Algebraic Reconstruction Technique) method were tested for the prediction. The data of the Harvard HITRAN table in which the thermo-dynamical properties and the light spectrum of the H2O are listed were used for the numerical simulation. The reconstructed temperature and concentration fields were compared with the original HITRAN data, through which the constructed method is validated. It was verified that the results obtained by MART method showed relatively better agreements with those of original data.Abstract 제1장 서 론 1.1 연구배경 및 목적 1.2 연구의 구성 제2장 온도장, 농도장 측정법의 이론적 배경 2.1 온도장, 농도장 측정법의 원리 2.2 선 강도(Line-strengths) 2.3 선형함수(Lineshape function) 2.4 Tomographical Reconstrucion Method(ART & MART method) 제3장 H2O 수증기의 온도장, 농도장 가상 측정방법 구축 및 성능평가 3.1 H20 수증기의 온도장, 농도장 측정방법 3.2 온도장, 농도장 ART법 결과 3.3 온도장, 농도장 MART법 결과 제4장 결론 및 고찰 참고문

An Investigation of the Application of Phase Change Materials in Practical Thermal Management Systems

This work investigates the application of alternative cooling techniques to thermal management. In the first section, this work presents models and extensive simulation studies on an alternative cooling strategy based upon phase change materials (PCMs) for the thermal management system of a LED headlight assembly. These studies have shown that properly chosen PCMs, when suspended in metal foam matrices, increased the thermal conductivity of the PCM. The increased thermal conductivity can enhance the cooling characteristics of a practical thermal management system for a LED headlight system. To further enhance the advantages of using PCMs, nanoparticle enhanced fluids (nanofluids) are desirable as an additional source of cooling. The use of nanofluids motivates the development of a new diagnostic tool for multiphase flows and a minimization algorithm for analyzing the data. For this purpose, the second section of this work develops a new technique that is based on wavelength-multiplexed laser extinction (WMLE) to measure particle sizes in multiphase flows. In the final section of this work, the simulated algorithm (SA) is investigated for analyzing the data collected in this work. Specifically, the parallelization of the SA technique is investigated to reduce the high computational cost associated with the SA algorithm

Tomographic imaging of combustion zones using tunable diode laser absorption spectroscopy (TDLAS)

This work concentrates on enabling the usage of a specific variant of tunable diode laser absorption spectroscopy (abbr. TDLAS) for tomogaphically reconstructing spatially varying temperature and concentrations of gases with as few reconstruction artifacts as possible. The specific variant of TDLAS used here is known as wavelength modulation with second harmonic detection (abbr. WMS-2f) which uses the wavelength dependent absorbance information of two different spectroscopic transitions to determine temperature and concentration values. Traditionally, WMS-2f has generally been applied to domains where temperature although unknown, was spatially largely invariant while concentration was constant and known to a reasonable approximation (_x0006_+/- 10% ). In case of unknown temperatures and concentrations with large variations in space such techniques do not hold good since TDLAS is a “line-of-sight” (LOS) technique. To alleviate this problem, computer tomographic methods were developed and used to convert LOS projection data measured using WMS-2f TDLAS into spatially resolved local measurements. These locally reconstructed measurements have been used to determine temperature and concentration of points inside the flame following a new temperature and concentration determination strategy for WMS-2f that was also developed for this work. Specifically, the vibrational transitions (in the 1.39 microns to 1.44 microns range) of water vapor (H2O) in an axi-symmetric laminar flame issuing from a standard flat flame burner (McKenna burner) was probed using telecom grade diode lasers. The temperature and concentration of water vapor inside this flame was reconstructed using axi-symmetric Abel de-convolution method. The two different sources of errors in Abel’s deconvolution - regularization errors and perturbation errors, were analyzed and strategies for their mitigation were discussed. Numerical studies also revealed the existence of a third kind of error - tomographic TDLAS artifact. For 2D tomography, studies showing the required number of views, number of rays per view, orientation of the view and the best possible algorithm were conducted. Finally, data from 1D tomography was extrapolated to 2D and reconstructions were benchmarked with the results of 1D tomography

Laser Absorption Chemical Species Tomography

This thesis outlines two advancements in the field of limited data absorption tomography. First, a novel reconstruction algorithm integrating the use of level set methods is presented that incorporates the additional a priori knowledge of a distinct interface between the species of interest and co-flow. The added a priori further reduces the ill-posedness of the system to produce a final concentration distribution that explains the laser absorption measurements and is qualitatively consistent with advection/diffusion transport. The algorithm is demonstrated by solving a simulated laser tomography experiment on a turbulent methane plume, and is compared with the current state-of-the-art reconstruction algorithm. Given the limited number of attenuated measurements, accurate reconstructions are also highly dependent on the locations sampled by the measurement array. This thesis displays how the mathematical properties of the coefficient matrix, A, formed by the locations of the lasers, are related to the information content of the attenuation data using a Tikhonov reconstruction framework. This formulation, in turn, becomes a basis for a beam arrangement design algorithm that minimizes the reliance on additional assumed information about the concentration distribution. Using genetic algorithms, beam arrangements can be optimized for a given application by incorporating physical constraints of the beam locations. The algorithm is demonstrated by optimizing unconstrained and constrained arrangements of light sources and detectors. Simulated experiments are performed to validate the optimality of the arrangements

Tomographic Laser Absorption Imaging of Combustion Gases in the Mid-wave Infrared

This dissertation describes advancements in mid-infrared laser absorption tomography for spatio-temporal measurements of thermochemistry in reacting flows relevant to combustion systems. Tunable laser absorption spectroscopy is combined with tomographic reconstruction techniques to resolve small diameter ( < 1 cm) non-uniform flow fields with steep spatial gradients, leveraging emerging mid-wave infrared photonics. Multiple novel measurement methods, hardware configurations, and image processing techniques were investigated. Initially, a mid-infrared laser absorption tomography sensing method was developed for quantitative measurement of CO and CO2 concentrations and temperature distributions in turbulent premixed jet flames using a translation-stage-mounted optical system. This sensing approach was used to examine effects of varying fuel structure on carbon oxidation over a range of Reynolds number regimes. It was found that spatial and temporal resolution is limited in this method due to the finite laser beam size (~ 1 mm) and the slow mechanical translation of the optical system. To address these limitations, a novel laser absorption imaging (LAI) technique, that expands a single laser beam and replaces the detector with a high-speed infrared camera, was introduced to achieve enhanced spatial and temporal resolution for thermo-chemical imaging. As a demonstration of this new technique, distributions of combustion species were imaged in both axisymmetric and non-axisymmetric flow fields using linear tomography algorithms. For non-axisymetric flows, the limited view tomography problem often results in a blurring effect and artifacts in the reconstructed flow-field. In an effort to address these issues, state-of-the-art deep learning neural networks were developed and applied to solve the limited angle inversion. Initial results suggest that deep neural networks have potential to more accurately predict flame structures with fewer projection angles than linear tomography. This work provides a foundation for a new approach to quantitative time-resolved 3D thermo-chemical imaging in high-temperature reacting flows

Optimisation de l'implantation des pratiques de gestion optimales (PGO) dans les réseaux de drainage urbain

RÉSUMÉ Le principe du tout-à-l’égout, au plus vite et le plus loin possible, est de plus en plus remplacé par le principe de la rétention le plus en amont et le plus longtemps possible. Les études se sont multipliées et les villes se sont dirigées vers l'utilisation des modèles qui se basent sur les Pratiques de Gestion Optimales (PGO). Ces pratiques sont principalement basées sur l’infiltration et la rétention des eaux le plus amont possible à l'endroit où elles ont été générées. Elles sont en mesure de résoudre les problèmes de mise en charge et de refoulement dans les réseaux et de déversement des eaux sans traitement dans les milieux récepteurs. Le couplage d’un modèle de simulation hydraulique et hydrologique avec des algorithmes d’optimisation métaheuristique est souvent utilisé pour le choix et l’emplacement des PGO dans un réseau de drainage. Le recours à cette procédure est dicté par la non-linéarité et la complexité des équations à résoudre. Dès lors, un temps considérable de calcul est nécessaire et l’obtention d’un optimum global n’est pas garantie.----------ABSTRACT The principle of “all to the sewer as quickly and as far as possible,” was superceded in America and Europe by the principle of “retention and infiltration as early and as long as possible”. This is in line with the approach to use Best Management Practices (BMPs) in watersheds to mitigate some of effects in the hydrological cycle caused by land-use modifications and climate change. These practices are mainly based on the infiltration and retention of water at the place where it is generated. They can provide both quantitative and qualitative control for runoff, reducing peak flows, runoff volumes and pollutant concentrations before discharging into the natural environment. Coupling hydraulic and hydrological simulation models with meta-heuristic optimization algorithms is often used for the selection and location of BMPs in a drainage network. The use of this procedure is dictated by the nonlinearity and complexity of equations to be solved. Therefore, considerable computational time is required and obtaining a global optimum is not guaranteed

Bayesian Methods for Gas-Phase Tomography

Gas-phase tomography refers to a set of techniques that determine the 2D or 3D distribution of a target species in a jet, plume, or flame using measurements of light, made around the boundary of a flow area. Reconstructed quantities may include the concentration of one or more species, temperature, pressure, and optical density, among others. Tomography is increasingly used to study fundamental aspects of turbulent combustion and monitor emissions for regulatory compliance. This thesis develops statistical methods to improve gas-phase tomography and reports two novel experimental applications. Tomography is an inverse problem, meaning that a forward model (calculating measurements of light for a known distribution of gas) is inverted to estimate the model parameters (transforming experimental data into a gas distribution). The measurement modality varies with the problem geometry and objective of the experiment. For instance, transmittance data from an array of laser beams that transect a jet may be inverted to recover 2D fields of concentration and temperature; and multiple high-resolution images of a flame, captured from different angles, are used to reconstruct wrinkling of the 3D reacting zone. Forward models for gas-phase tomography modalities share a common mathematical form, that of a Fredholm integral equation of the first-kind (IFK). The inversion of coupled IFKs is necessarily ill-posed, however, meaning that solutions are either unstable or non-unique. Measurements are thus insufficient in themselves to generate a realistic image of the gas and additional information must be incorporated into the reconstruction procedure. Statistical inversion is an approach to inverse problems in which the measurements, experimental parameters, and quantities of interest are treated as random variables, characterized by a probability distribution. These distributions reflect uncertainty about the target due to fluctuations in the flow field, noise in the data, errors in the forward model, and the ill-posed nature of reconstruction. The Bayesian framework for tomography features a likelihood probability density function (pdf), which describes the chance of observing a measurement for a given distribution of gas, and prior pdf, which assigns a relative plausibility to candidate distributions based on assumptions about the flow physics. Bayes’ equation updates information about the target in response to measurement data, combining the likelihood and prior functions to form a posterior pdf. The posterior is usually summarized by the maximum a posteriori (MAP) estimate, which is the most likely distribution of gas for a set of data, subject to the effects of noise, model errors, and prior information. The framework can be used to estimate credibility intervals for a reconstruction and the form of Bayes’ equation suggests procedures for improving gas tomography. The accuracy of reconstructions depends on the information content of the data, which is a function of the experimental design, as well as the specificity and validity of the prior. This thesis employs theoretical arguments and experimental measurements of scalar fluctuations to justify joint-normal likelihood and prior pdfs for gas-phase tomography. Three methods are introduced to improve each stage of the inverse problem: to develop priors, design optimal experiments, and select a discretization scheme. First, a self-similarity analysis of turbulent jets—common targets in gas tomography—is used to construct an advanced prior, informed by an estimate of the jet’s spatial covariance. Next, a Bayesian objective function is proposed to optimize beam positions in limited-data arrays, which are necessary in scenarios where optical access to the flow area is restricted. Finally, a Bayesian expression for model selection is derived from the joint-normal pdfs and employed to select a mathematical basis to reconstruct a flow. Extensive numerical evidence is presented to validate these methods. The dissertation continues with two novel experiments, conducted in a Bayesian way. Broadband absorption tomography is a new technique intended for quantitative emissions detection from spectrally-convolved absorption signals. Theoretical foundations for the diagnostic are developed and the results of a proof-of-concept emissions detection experiment are reported. Lastly, background-oriented schlieren (BOS) tomography is applied to combustion for the first time. BOS tomography employs measurements of beam steering to reconstruct a fluid’s optical density field, which can be used to infer temperature and density. The application of BOS tomography to flame imaging sets the stage for instantaneous 3D combustion thermometry. Numerical and experimental results reported in this thesis support a Bayesian approach to gas-phase tomography. Bayesian tomography makes the role of prior information explicit, which can be leveraged to optimize reconstructions and design better imaging systems in support of research on fluid flow and combustion dynamics