Implementation and validation of an extended Schnerr-Sauer cavitation model for non-isothermal flows in OpenFOAM

Abstract In the present work cavitation in liquid hydrogen and nitrogen was investigated by using the open source toolbox OpenFOAM. Simulations were performed by means of a mass transfer model, based on the homogeneous mixture approach in combination with the Volume of Fluid (VOF) method for the reconstruction the liquid-vapor interface. Two additional transport equations were considered, i.e. the liquid volume fraction advection and the temperature equation. The implementation of an extended Schnerr- Sauer model allowed for the introduction of the thermal effects in terms of latent heat release/absorption and convective heat transfer inside the liquid-vapor interface. A set of Antoine-like equations relate the saturation conditions to the local conditions

a diagnostics tool for aero engines health monitoring using machine learning technique

Abstract In this work an integrated heath monitoring platform is proposed and developed for performance analysis and degradation diagnostics of gas turbine engines. The aim is to link engine measurable data to its health status. A numerical tool has been implemented in order to calculate engine performance in design condition and to create a database of expected vales. Then different degradation levels have been introduced in the two main components, compressor and turbine of a single spool turbojet and the diagnostics instruments have been trained to detect the component fault. In order to evaluate the performance prediction two different machine learning based techniques, namely, artificial neural network (ANN) and support vector machine (SVM) have been compared. Synthetic data generation has been carried out to show how the degradation effects can affect the engine performance. The two main degradation causes considered are the compressor fouling and turbine erosion. The machine learning techniques were applied with two aims: aero-engine performance prediction and health diagnostics. The study was carried out based on three samples flights, whose data were used for the training and testing process of the prediction and diagnostics tools. The knowledge and the continuous monitoring of the engine health status can be crucial for maintenance and fleet management operations

Mode decomposition methods for the analysis of cavitating flows in turbomachinery

Abstract The present work is aimed at the characterization of the cavitating flow regimes by applying the coupled POD/DMD technique to the vapor volume fraction field. The proposed approach provided an improved spatio-temporal-frequency description of the flow, based on the detection of the most energetic flow structures with information about their shape and size, and their decomposition into wave patterns oscillating with specific frequency and decay rate. The novel technique was applied to numerical results concerning the bubble cavitation and the supercavitation regimes of 2D water flows around a NACA hydrofoil at ambient temperature. Numerical simulations were performed by using a homogenous mixture model equipped with an extended Schnerr-Sauer cavitation model, in combination with a Volume of Fluid (VOF) interface tracking method. The proposed approached provided a better characterization of the unsteady cavitating flow, and allowed for a deeper insight about the dynamics of the vapor cavity, especially in cases involving the more chaotic regime of supercavitation. In particular, POD results figured out the most energetic coherent vapor structures associated to each cavitation regime: the first mode highlighted the main sheet cavity which grew on the hydrofoil up to detached, the second mode pointed out the cavitating/condensating doublet structures and the third mode figured out the smaller structures owning less energy but a higher frequency content. DMD modes performed a decomposition of the coherent structures detected by means of the POD analysis, into a subset of vapor pattern periodically evolving with a single frequency and a characteristic decay rate. Furthermore, results showed that the supercavitating flow structures owned characteristic frequencies which ranged from 5 to 26 Hz, while the less intensive bubble cavitation regime was characterized by frequencies ranging from 15 to 42 Hz

Modeling viscous effects on boundary layer of rarefied gas flows inside micronozzles in the slip regime condition

Abstract The present work provided a numerical investigation of the supersonic flow of rarefied gas into a planar micronozzle characterized by small depth and long divergent section. 2D and 3D computational fluid dynamics (CFD) computations were performed using the continuum Navier-Stokes equations in combination with partial slip conditions at walls, based on a the establishment of the slip regime related to a Knudsen number ranging between 1 x 10-3 and 1 x 10-1. Different partial slip conditions were considered, i.e. the ideal case of pure slip conditions and the full viscous case with Maxwellian slip conditions on sidewalls and planar walls, as well as the case of Maxwellian slip just on sidewalls. The Maxwell slip model was set with a tangential accommodation coefficient equal (TMAC) to 0.8. Comparisons were based on the estimation of the global performance of the micronozzle in terms of thrust force, specific impulse, discharge coefficient and Isp-efficiency. It resulted that when the nozzle depth was neglected, 3D simulations led to the same solution obtained by means of 2D computations inside the micronozzle. The boundary layer thicknesses experienced a linear growth on the sidewalls, and the viscous losses produced a reduction of the performance of about the 95%. Significant differences were found in the prediction of the jet plume, which took the typical bell-shape form in cases involving 2D computations, yet 3D simulations estimated a plume characterized by the succession of oblique shock waves and expansion fan waves. Instead, when the nozzle depth was considered, 3D simulations underlined a completely different behavior of the flow because of the establishment of the nozzle blockage and a viscous heating. The performance suffered an intense degradation of about the 47%, and the analysis of the jet plume highlighted the formation of the Mach disk followed by the typical diamond-shaped subsonic recirculation region

Sourcing Hydrogen for the Production of Sustainable Aviation Fuels

Sustainable aviation fuels (SAFs) are the near-term technological solution to decarbonize the aviation industry sector. There are several pathways to obtain biojet fuels, which can be classified into four main categories, namely oil-to-jet, alcohol-to-jet, gas-to-jet, and sugar-to-jet. All of them share the need for hydrogen to obtain a drop-in fuel that can be blended with petroleum-based jet fuel. The hydrogen input requirements affect the life cycle greenhouse gas emissions, increase the biojet fuel cost and hinder the construction of distributed processing plants. This study addresses the problem of hydrogen sourcing in the production of SAFs through a systematic literature review. Techno-economic studies of biojet fuel production using different feedstocks and conversion pathways are analyzed focusing on the methods of hydrogen provision. The technological options used to generate the required hydrogen within the conversion process itself as well as externally, along with the main strategies to reduce the hydrogen demand are identified. The production yields and the hydrogen consumption of several SAF production pathways are compared. The jet fuel yields reach values as high as 0.66 for hydroprocessing of vegetable oils with external hydrogen provision, while they drop to 0.10 for production from lignocellulosic biomass with internal hydrogen sourcing. The results of the analysis highlight the real potential of four among the most promising routes for the production of biojet fuels when the burden related to hydrogen demand is properly taken into account

Experimental and Numerical Investigations on the Effect of Different Air-Fuel Mixing Strategies on the Performance of a Lean Liquid Fueled Swirled Combustor☆

Abstract In the present work the performance of a multipoint lean direct injection strategy for low emission aero-propulsion systems has been experimentally and numerically investigated, and compared with the single point injection strategy. A swirler liquid fueled combustor was designed and used in experiments to investigate the flame behavior in lean and ultra-lean conditions for both the single-point and the multi-points injection strategies. Multipoint injection has been realized injecting an amount of fuel upstream the swirler inlet and using also the central injector as a "pilot" injection. As regarding the experimental facilities, the combustor is equipped with four optical accesses for high speed flame imaging and with pressure and temperature sensors. Experimental data on flame characteristics and pollutant emissions are obtained. The characterization of the flame was realized using intensified high rate CCD camera for the acquisition in the ultraviolet spectral range. In front of the camera various combinations of optical filters were installed to selectively record the respective chemiluminescent species (OH* and CH*). Computational fluid dynamic (CFD) simulations were also performed for a deeper understanding of the flame characteristics under the two injection strategies. The typical combustor operations were reproduced to more deeply understand the differences between the injection modes and the related flame patterns. The numerical results show different temperature and species fields predicted for the non-premixed and the partially premixed cases and furnish relevant information about the fluid dynamics in the combustion chamber in both the injection conditions

Optimization of micro single dielectric barrier discharge plasma actuator models based on experimental velocity and body force fields

Recently, the Micro Single Dielectric Barrier Discharge Plasma Actuator has become attractive for application in aeronautics and micropopulsion thrusters. The present work carried out a preliminary characterization of such device, acting on initially quiescent air by experimental and numerical approaches. Sinusoidal voltage excitation with amplitude up to 7 kV and frequency up to 2.5 kHz was applied. The induced flow was investigated by particle image velocimetry and the measured velocity fields were used to estimate experimentally the time-averaged induced body force distributions by a differential method. Plasma induced forces were modeled by following three different approaches, later implemented as a source term in the Navier-Stokes equations for the fluid flow simulations. Potentialities, advantages and disadvantages of the considered force modeling methods were investigated. Quantitative comparison of the experimental and numerical induced force, as well as of the velocity fields, allowed establishing which model best predicted the actuator effects. The algebraic Dual Potential Model provided a good agreement between experimental and simulated results, in terms of flow velocities and thickness of the induced wall-jet. The downstream decay of the wall-jet velocity, experimentally observed, was also successfully predicted. A maximum induced velocity of ≈2 m/s was obtained and a jet thickness of ≈3 mm

Role of the H-bond between L53 and T56 for Aquaporin-4 epitope in Neuromyelitis Optica

Aquaporin-4 (AQP4) is the CNS water channel organized into well-ordered protein aggregates called Orthogonal Arrays of Particles (OAPs). Neuromyelitis Optica (NMO) is an autoimmune disease caused by anti-OAP autoantibodies (AQP4-IgG). Molecular Dynamics (MD) simulations have identified an H-bond between L53 and T56 as the key for AQP4 epitope and therefore of potential interest for drug design in NMO field. In the present study, we have experimentally tested this MD-prediction using the classic mutagenesis approach. We substituted T56 with V56 and tested this mutant for AQP4 aggregates and AQP4-IgG binding. gSTED super-resolution microscopy showed that the mutation does not affect AQP4 aggregate dimension; immunofluorescence and cytofluorimetric analysis demonstrated its unaltered AQP4-IgG binding, therefore invalidating the MD-prediction. We later investigated whether AQP4, expressed in Sf9 insect and HEK-293F cells, is able to correctly aggregate before and after the purification steps usually applied to obtain AQP4 crystal. The results demonstrated that AQP4-IgG recognizes AQP4 expressed in Sf9 and HEK-293F cells by immunofluorescence even though BN-PAGE analysis showed that AQP4 forms smaller aggregates when expressed in insect cells compared to mammalian cell lines. Notably, after AQP4 purification, from both insect and HEK-293F cells, no aggregates are detectable by BN-PAGE and AQP4-IgG binding is impaired in sandwich ELISA assays. All together these results indicate that 1) the MD prediction under analysis is not supported by experimental data and 2) the procedure to obtain AQP4 crystals might affect its native architecture and, as a consequence, MD simulations. In conclusion, given the complex nature of the AQP4 epitope, MD might not be the suitable for molecular medicine advances in NMO

Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine

Micro turbojets are used for propelling radio-controlled aircraft, aerial targets, and personal air vehicles. When compared to full-scale engines, they are characterized by relatively low efficiency and durability. In this context, the degraded performance of gas path components could lead to an unacceptable reduction in the overall engine performance. In this work, a data-driven model based on a conventional artificial neural network (ANN) and an extreme learning machine (ELM) was used for estimating the performance degradation of the micro turbojet. The training datasets containing the performance data of the engine with degraded components were generated using the validated GSP model and the Monte Carlo approach. In particular, compressor and turbine performance degradation were simulated for three different flight regimes. It was confirmed that component degradation had a similar impact in flight than at sea level. Finally, the datasets were used in the training and testing process of the ELM algorithm with four different input vectors. Two vectors had an extensive number of virtual sensors, and the other two were reduced to just fuel flow and exhaust gas temperature. Even with the small number of sensors, the high prediction accuracy of ELM was maintained for takeoff and cruise but was slightly worse for variable flight conditions

Increased levels of interleukin-6 exacerbate the dystrophic phenotype in mdx mice

Duchenne muscular dystrophy (DMD) is characterized by progressive lethal muscle degeneration and chronic inflammatory response. The mdx mouse strain has served as the animal model for human DMD. However, while DMD patients undergo extensive necrosis, the affected muscles of adult mdx mice rapidly regenerates and regains structural and functional integrity. The basis for the mild effects observed in mice compared with the lethal consequences in humans remains unknown. In this study, we provide evidence that interleukin-6 (IL-6) is causally linked to the pathogenesis of muscular dystrophy. We report that forced expression of IL-6, in the adult mdx mice, recapitulates the severe phenotypic characteristics of DMD in humans. Increased levels of IL-6 exacerbate the dystrophic muscle phenotype, sustaining inflammatory response and repeated cycles of muscle degeneration and regeneration, leading to exhaustion of satellite cells. The mdx/IL6 mouse closely approximates the human disease and more faithfully recapitulates the disease progression in humans. This study promises to significantly advance our understanding of the pathogenic mechanisms that lead to DMD