Understanding N2O formation and consumption in ammonia combustion is crucial in realizing the impact of ammonia as an alternative fuel to mitigate the impact of climate change. This study demonstrates the feasibility of using Raman spectroscopy for in-situ N2O measurements in ammonia flames. Raman spectra were acquired along a NH3/H2/N2-air flame in a laminar opposed jet burner, using a pulsed laser combined with a three-disk rotating shutter system to suppress the luminous flame background. This setup enabled the clear detection of the N2O Raman spectrum. Raman libraries of N2, O2, H2, NO, and N2O were fitted to the spectra using a newly developed fitting routine. This yielded qualitative N2O mole fractions along the flame that align closely with numerical simulations based on recently published chemical reaction models for ammonia oxidation, paving the way for future quantitative N2O measurements in ammonia flames. Since no prior libraries for temperature-dependent N2O Raman spectra were available, a methodology for its simulation is introduced. High-resolution N2O spectra were acquired between 295 and 1091 K as validation data for the simulation. Despite minor deviations, the simulation effectively captures the spectral shape and temperature dependence of the Raman cross sections, enabling its use in the spectral fitting routine towards quantitative in-situ concentration measurements.
Magnetic Particle Imaging is an imaging modality that exploits the non-linear magnetization response of superparamagnetic nanoparticles to a dynamic magnetic field. In the multivariate case, measurement-based reconstruction approaches are common and involve a system matrix whose acquisition is time consuming and needs to be repeated whenever the scanning setup changes. Our approach relies on reconstruction formulae derived from a mathematical model of the MPI signal encoding. A particular feature of the reconstruction formulae and the corresponding algorithms is that these are independent of the particular scanning trajectories. In this paper, we present basic ways of leveraging this independence property to enhance the quality of the reconstruction by merging data from different scans. In particular, we show how to combine scans of the same specimen under different rotation angles. We demonstrate the potential of the proposed techniques with numerical experiments.
We consider geometric Hermite subdivision for planar curves, i.e., iteratively refining an input polygon with additional tangent or normal vector information sitting in the vertices. The building block for the (nonlinear) subdivision schemes we propose is based on clothoidal averaging, i.e., averaging w.r.t. locally interpolating clothoids, which are curves of linear curvature. To this end, we derive a new strategy to approximate Hermite interpolating clothoids. We employ the proposed approach to define the geometric Hermite analogues of the well-known Lane-Riesenfeld and four-point schemes. We present numerical results produced by the proposed schemes and discuss their features.
Signals and images with discontinuities appear in many problems in such diverse areas as biology, medicine, mechanics and electrical engineering. The concrete data are often discrete, indirect and noisy measurements of some quantities describing the signal under consideration. A frequent task is to find the segments of the signal or image which corresponds to finding the discontinuities or jumps in the data. Methods based on minimizing the piecewise constant Mumford–Shah functional—whose discretized version is known as Potts energy—are advantageous in this scenario, in particular, in connection with segmentation. However, due to their non-convexity, minimization of such energies is challenging. In this paper, we propose a new iterative minimization strategy for the multivariate Potts energy dealing with indirect, noisy measurements. We provide a convergence analysis and underpin our findings with numerical experiments.