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To limit climate change and promote energy security, there is widespread interest toward transitioning existing fossil fueled combustion systems to sustainable, alternative fuels such as hydrogen (H2) and ammonia (NH3) without negatively impacting air quality. However, quantifying the emission rate of air pollutants such as nitrogen oxides (NOx) is a nuanced process when comparing pollutant emissions across different fuels, as discussed in our paper GT2022-80971 presented last year. That study indicated that the standardized approach for measuring combustion emissions in terms of dry, oxygen-referenced volumetric concentrations (i.e., dry ppmv at the reference O2 concentration (ppmvdr)) inflates reported pollutant emissions by up to 40% for hydrogen combustion relative to natural gas. In this paper, we extend our prior analysis of these so-called "indirect effects" on emissions values to ammonia (NH3) and cracked ammonia (i.e., molecular hydrogen and nitrogen, 3H2 per N2) fuel blends. The results reveal that ppmvdr-based pollutant reporting approaches have a less prominent influence on emissions interpretations for molecular ammonia-methane blends than for hydrogen-methane blends. Nonetheless, we still find that ppmvdr reporting induces up to a 10% relative increase in apparent emissions when comparing 100% NH3 and 100% methane (CH4) fuels at an equal mass-per-work emission rate. Cracking the ammonia is shown to increase this relative bias up to 21% in comparison to a methane system. Further analysis shows how drying, dilution, thermodynamic, and performance effects each influence the relationship between ppmvdr and mass-per-work emissions across the spectrum of fuels and fuel blends. Following discussion of these findings, we conclude that quantifying combustion emissions using ppmvdr is generally inappropriate for emissions comparisons and advise the combustion community to shift toward robust mass-per-energy metrics when quantifying pollutant emissions. [ABSTRACT FROM AUTHOR]

Flow data are often decomposed using proper orthogonal decomposition (POD) of the space–time separated form, $\boldsymbol {q}'\left (\boldsymbol {x},t\right)=\sum _j a_j\left (t\right)\boldsymbol {\phi }_j\left (\boldsymbol {x}\right)$ , which targets spatially correlated flow structures in an optimal manner. This paper analyses permuted POD (PPOD), which decomposes data as $\boldsymbol {q}'\left (\boldsymbol {x},t\right)=\sum _j a_j\left (\boldsymbol {n}\right)\boldsymbol {\phi }_j\left (s,t\right)$ , where $\boldsymbol {x}=(s,\boldsymbol {n})$ is a general spatial coordinate system, $s$ is the coordinate along the bulk advection direction and $\boldsymbol {n}=(n_1,n_2)$ are along mutually orthogonal directions normal to the advection characteristic. This separation of variables is associated with a fundamentally different inner product space for which PPOD is optimal and targets correlations in $s,t$ space. This paper presents mathematical features of PPOD, followed by analysis of three experimental datasets from high-Reynolds-number, turbulent shear flows: a wake, a swirling annular jet and a jet in cross-flow. In the wake and swirling jet cases, the leading PPOD and space-only POD modes focus on similar features but differ in convergence rates and fidelity in capturing spatial and temporal information. In contrast, the leading PPOD and space-only POD modes for the jet in cross-flow capture completely different features – advecting shear layer structures and flapping of the jet column, respectively. This example demonstrates how the different inner product spaces, which order the PPOD and space-only POD modes according to different measures of variance, provide unique 'lenses' into features of advection-dominated flows, allowing complementary insights. [ABSTRACT FROM AUTHOR]