In recent years, the civil aviation industry has encountered growing pressure from the public and regulators to curb air pollutants and climate emissions. Sustainable aviation fuels (SAFs) produced through the Hydroprocessed Esters and Fatty Acids (HEFA) method have been approved as blends with conventional Jet A-1 under ASTM D7566. However, comparative burner-scale evidence linking composition, gaseous emissions, and emission indices (EI) across lean to rich combustion remains limited, particularly for HEFA-rich blends and SO2 behaviour. This study employs the FAA-certified Next Generation (NexGen) burner-for the first time applied to HEFA-SAF and HEFA-rich blends to measure O2, CO2, CO, NOx and SO2 for Jet A-1 (B0), HEFA-SAF (B100) and their blends (B50J50, B70J30, B90J10) across twelve equivalence ratios (phi = 0.50-1.44), compute fuelnormalised EI-CO, EI-CO2, EI-NOx and EI-SO2, none of which have been reported from this platform for SAF or blends and pair emissions with GC-MS end-member fingerprints. NexGen burner provides a realistic fire conditions, like those encountered in real-life accidents, particularly in areas near aircraft engines. HEFA blending improved key properties (lower density and viscosity, lower freezing point, higher flash point and gravimetric energy density). B100 showed the highest residual O2 and the lowest CO, NOx and SO2; EI-CO peaked at 0.811 g/kg fuel (B0) but plunged to 0.010 g/kg fuel for B100, while EI-NOx decreased from 0.198 g/kg fuel (B0) to 0.014 g/kg fuel for B100. HEFA-rich blends EI-CO decreased and their EI-CO2 increased. However, EI-NOx increased non-linearly with HEFA fraction under lean-to-stoichiometric conditions (phi = 0.50-1.0). Attributed to the higher gravimetric energy density of HEFA-rich blends increasing local flame temperature and amplifying the thermal Zeldovich NOx pathway; an equivalence-ratio-dependent trade-off that reverses at rich conditions where B100 retains its 80.1% NOx advantage over B0. Non-monotonic EI-SO2 across the blend series suggested intra-batch sulphur variability in the HEFA-SAF supply. These results provides a single-platform, composition-aware dataset that supports emissions benchmarking and blend optimization for certification. Future work should evaluate non-volatile particulate matter and complement the results with FTIR or GCxGC analysis to reduce co-elution ambiguity. Also, combine emissions with heat release, and fuel-specific energy conversion metrics.
Doppler broadening positron annihilation spectroscopy is a powerful technique for investigation of neutral and negatively-charged atomic-scale defects in materials. However, it has one important drawback, which is that studies are typically designed to only illuminate relative differences in positron annihilation characteristics in a specific suite of samples. It is difficult and considered ill-advised to compare the data that different laboratories report for apparently similar samples, which limits the scope of applications of the technique. The root of the problem is that the resolution and calibration of the detector used to collect the energy spectra of the positron-electron annihilation radiation affect their shape and the S and W parameters that are used for its characterisation. The present study explores whether this obstacle can be overcome by modelling the differences between the data sets that were obtained via different detectors or the same detector at different times. We were able to reproduce the observed differences between the analyses of UO2 that were acquired in our laboratory over the past ∼25 years using the same detector that has changed its properties. However, our attempt to reproduce the differences between our and literature data did not work out as well, which can be pinned in part to a real difference between the samples and in part to the incompleteness of the published descriptions of the methodology. We conclude with recommendations to improve current practices of data acquisition and reporting.
Particle deposition and clogging in porous media control the performance of many natural and engineered applications, including groundwater filtration, geothermal injection, and subsurface fluid management. Once deposits form, permeability progressively declines and flow pathways become blocked, making mitigation strategies essential for maintaining long-term operation. Oscillatory injection has been proposed as a potential strategy to delay clogging, yet how its effectiveness depends on physicochemical conditions and complex pore geometries remains poorly understood. Here, we use microfluidic experiments with colloidal suspensions to investigate how oscillatory flow modulates particle transport and clogging in tortuous porous domains. Controlled oscillatory forcing is applied over a range of frequencies while monitoring permeability evolution and deposition dynamics. Under saline conditions, oscillatory flow delays clogging and increases the injected volume before hydraulic failure with increasing frequency, consistent with electrostatic screening that promotes particle aggregation and allows cyclic pressure fluctuations to destabilize growing deposits intermittently. However, the frequency response changes with physicochemical conditions and can even reverse. As ionic strength decreases and electrostatic interactions become stronger, aggregation is suppressed, and clogging becomes increasingly governed by particle crowding and pore bridging, where higher-frequency oscillations can instead accelerate blockage. These findings demonstrate that oscillatory injection does not have a universal effect on clogging, but instead depends on the balance between hydrodynamic forcing and particle interactions. This regime-dependent behavior provides a basis for designing more effective injection strategies in porous media.
Understanding the mechanical behavior of quasi-parallel fiber networks is essential for improving the manufacturing processes of fiber-reinforced composites. Mesoscale models of dry yarns and reinforcements require constitutive laws that accurately reflect the heterogeneous microstructure of fiber bundles. This study aims to develop a numerical generator of random fiber bundles for microscopic parametric studies of compaction behavior. A real fiber bundle was first reconstructed from X-ray microtomography data, and the numerical strategy was validated by tracking fiber cross-sections along the bundle length, with a fiber-position error of 5.2
This paper deals with the synthesis of an observer for nonlinear Andronov-Hopf oscillators exhibiting state observability singularities. These singularities are addressed by considering fictitious outputs to access unobservable states. The resulting global immersion is extended into a diffeomorphism through appropriate dynamic extension for proposing singularity-free multi-output standard and low-power High Gain Observers (HGO) in the original coordinates. Both observers are applied to a nonlinear oscillator to estimate variables in a wake flow behind a bluff body for fluid mechanics purposes. Simulation results prove the efficacy and robustness of the proposed observer compared to a standard HGO.
