On the self-similarity of line segments in decaying homogeneous isotropic turbulence

The self-similarity of a passive scalar in homogeneous isotropic decaying turbulence is investigated by the method of line segments (M. Gauding et al., Physics of Fluids 27.9 (2015): 095102). The analysis is based on a highly resolved direct numerical simulation of decaying turbulence. The method of line segments is used to perform a decomposition of the scalar field into smaller sub-units based on the extremal points of the scalar along a straight line. These sub-units (the so-called line segments) are parameterized by their length $\ell$ and the difference $\Delta\phi$ of the scalar field between the ending points. Line segments can be understood as thin local convective-diffusive structures in which diffusive processes are enhanced by compressive strain. From DNS, it is shown that the marginal distribution function of the length~$\ell$ assumes complete self-similarity when re-scaled by the mean length $\ell_m$. The joint statistics of $\Delta\phi$ and $\ell$, from which the local gradient $g=\Delta\phi/\ell$ can be defined, play an important role in understanding the turbulence mixing and flow structure. Large values of $g$ occur at a small but finite length scale. Statistics of $g$ are characterized by rare but strong deviations that exceed the standard deviation by more than one order of magnitude. It is shown that these events break complete self-similarity of line segments, which confirms the standard paradigm of turbulence that intense events (which are known as internal intermittency) are not self-similar

The Role of Differential Diffusion during Early Flame Kernel Development under Engine Conditions -- Part II: Effect of Flame Structure and Geometry

From experimental spark ignition (SI) engine studies, it is known that the slow-down of early flame kernel development caused by the ($\mathrm{Le}>1$)-property of common transportation-fuel/air mixtures tends to increase cycle-to-cycle variations (CCV). To improve the fundamental understanding of the complex phenomena inside the flame structure of developing flame kernels, an engine-relevant DNS database is investigated in this work. Conclusive analyses are enabled by considering equivalent flame kernels and turbulent planar flames computed with $\mathrm{Le}>1$ and $\mathrm{Le}=1$. In Part I of the present study (Falkenstein et al., Combust. Flame, 2019), a reduced representation of the local mixture state based on the parameters local enthalpy, local equivalence ratio, and H-radical mass fraction was proposed for the purpose of this analysis. Here, a coupling relation for the diffusion-controlled mixture parameter local enthalpy with local flame geometry and structure is derived, characterized by the key parameters $\kappa$ and ${\ |\nabla c \ t|/\ |\nabla c \ |_{\mathrm{lam}}}$. The analysis shows that the large positive global mean curvature intrinsic to the flame kernel configuration may detrimentally affect the local mixture state inside the reaction zone, particularly during the initial kernel development phase. External energy supply by spark ignition may effectively bridge over this critical stage, which causes the impact of global mean flame kernel curvature to be small under the present conditions compared to the overall effect of$\mathrm{Le}\neq1$. Once ignition effects have decayed, the mixture state inside the reaction zone locally exhibits an identical dependence on$\ |\nabla c \ |$ as in a strained laminar flame. This implies that differential diffusion effects under engine-typical Karlovitz numbers are not weakened by small-scale turbulent mixing

The Role of Differential Diffusion during Early Flame Kernel Development under Engine Conditions -- Part I: Analysis of the Heat-Release-Rate Response

Although experimental evidence for the correlation between early flame kernel development and cycle-to-cycle variations (CCV) in spark ignition (SI) engines was provided long ago, there is still a lack of fundamental understanding of early flame/turbulence interactions, and accurate models for full engine simulations do not exist. Since the flame kernel is initiated with small size, i.e. with large positive curvature, differential diffusion is expected to severely alter early flame growth in non-unity-Lewis-number (${\mathrm{Le}\neq1}$) mixtures as typically used in engines. In this work, a DNS database of developing iso-octane/air flame kernels and planar flames has been established with flame conditions representative for stoichiometric engine part-load operation. Differential diffusion effects on the global heat release rate are analyzed by relating the present findings to equivalent flames computed in the ${\mathrm{Le}=1}$ limit. It is shown that in the early kernel development phase, the normal propagation velocity is significantly reduced with detrimental consequences on the global burning rate of the flame kernel. Besides this impact on the overall mass burning rate, the initial production of flame surface area by the normal propagation term in the flame area balance equation is noticeably reduced. By using the optimal estimator concept, it is shown that strong fluctuations in local heat release rate inherent to ${\mathrm{Le}\neq1}$ flames in the thin reaction zones regime are mainly contained in the parameters local equivalence ratio, enthalpy, and H-radical mass fraction. Differential diffusion couples the evolution of these parameters to the unsteady flame geometry and structure, which is analyzed in Part II of the present study (Falkenstein et al., Combust. Flame, 2019)

Influence of adversarial training on super-resolution turbulence reconstruction

Supervised super-resolution deep convolutional neural networks (CNNs) have gained significant attention for their potential in reconstructing velocity and scalar fields in turbulent flows. Despite their popularity, CNNs currently lack the ability to accurately produce high-frequency and small-scale features, and tests of their generalizability to out-of-sample flows are not widespread. Generative adversarial networks (GANs), which consist of two distinct neural networks (NNs), a generator and discriminator, are a promising alternative, allowing for both semi-supervised and unsupervised training. The difference in the flow fields produced by these two NN architectures has not been thoroughly investigated, and a comprehensive understanding of the discriminator's role has yet to be developed. This study assesses the effectiveness of the unsupervised adversarial training in GANs for turbulence reconstruction in forced homogeneous isotropic turbulence. GAN-based architectures are found to outperform supervised CNNs for turbulent flow reconstruction for in-sample cases. The reconstruction accuracy of both architectures diminishes for out-of-sample cases, though the GAN's discriminator network significantly improves the generator's out-of-sample robustness using either an additional unsupervised training step with large eddy simulation input fields and a dynamic selection of the most suitable upsampling factor. These enhance the generator's ability to reconstruct small-scale gradients, turbulence intermittency, and velocity-gradient probability density functions. The extrapolation capability of the GAN-based model is demonstrated for out-of-sample flows at higher Reynolds numbers. Based on these findings, incorporating discriminator-based training is recommended to enhance the reconstruction capability of super-resolution CNNs

Necessidades de cuidados do utente

As instituições de saúde devem ser vistas como respostas completas, multidisciplinares e multifacetadas. A Rede Nacional de Cuidados Continuados Integrados integra o conceito de atenção integral ao doente/cuidador biopsicossocial, pelo que surge a expectativa da existência de objetivos atingíveis e mensuráveis, se na realidade o profissional integra a globalidade do ser hwnano mesmo quando os objetivos não atingidos são de áreas que não lhe dizem respeito, sem ignorá-lo e tomá-lo importante aos olhos de toda a equipa. Neste estudo, pretendemos identificar as necessidades de cuidados no utente, percecionadas pelo próprio, pelo profissional de enfermagem que o cuida e pelo seu cuidador informal, bem como encaixar o papel da comWlÍcação doente/profissional de saúde/cuidador na avaliação biopsicossocial do doente. Num estudo descritivo, simples de plano transversal, com uma vertente exploratória, foram aplicados 102 questionários, cada qual com 3 secções de preenchimento autónomo pelo utente, pelo profissional de saúde e pelo cuidador, nas Unidades de Cuidados Continuados de Longa Duração e Manutenção selecionadas. Verificou-se que as necessidades biológicas são as mais referidas pelos utentes e pelos enfermeiros, sendo que o cuidador privilegia todas as necessidades (biológicas, psicológicas e sociais)

Probing Interstellar Dust with Infrared Echoes from the Cas A Supernova

We present the analysis of an IRS 5-38 {\mu}m spectrum and MIPS photometric measurements of an infrared echo near the Cassiopeia A supernova remnant observed with the Spitzer Space Telescope. We have modeled the recorded echo accounting for PAHs, quantum-heated carbon and silicate grains, as well as thermal carbon and silicate particles. Using the fact that optical light echo spectroscopy has established that Cas A originated from a type IIb supernova explosion showing an optical spectrum remarkably similar to the prototypical type IIb SN 1993J, we use the latter to construct template data input for our simulations. We are then able to reproduce the recorded infrared echo spectrum by combining the emission of dust heated by the UV burst produced at the shock breakout after the core-collapse and dust heated by optical light emitted near the visual maximum of the supernova light curve, where the UV burst and optical light curve characteristics are based on SN 1993J. We find a mean density of \sim680 H cm^{-3} for the echo region, with a size of a few light years across. We also find evidence of dust processing in the form of a lack of small PAHs with less than \sim300 carbon atoms, consistent with a scenario of PAHs destruction by the UV burst via photodissociation at the estimated distance of the echo region from Cas A. Furthermore, our simulations suggest that the weak 11 {\mu}m features of our recorded infrared echo spectrum are consistent with a strong dehydrogenated state of the PAHs. This exploratory study highlights the potential of investigating dust processing in the interstellar medium through infrared echoes.Comment: 16 pages, 14 figures, accepted for publication in the Astrophysical Journa

Scaling Computational Fluid Dynamics: In Situ Visualization of NekRS using SENSEI

In the realm of Computational Fluid Dynamics (CFD), the demand for memory and computation resources is extreme, necessitating the use of leadership-scale computing platforms for practical domain sizes. This intensive requirement renders traditional checkpointing methods ineffective due to the significant slowdown in simulations while saving state data to disk. As we progress towards exascale and GPU-driven High-Performance Computing (HPC) and confront larger problem sizes, the choice becomes increasingly stark: to compromise data fidelity or to reduce resolution. To navigate this challenge, this study advocates for the use of in situ analysis and visualization techniques. These allow more frequent data "snapshots" to be taken directly from memory, thus avoiding the need for disruptive checkpointing. We detail our approach of instrumenting NekRS, a GPU-focused thermal-fluid simulation code employing the spectral element method (SEM), and describe varied in situ and in transit strategies for data rendering. Additionally, we provide concrete scientific use-cases and report on runs performed on Polaris, Argonne Leadership Computing Facility's (ALCF) 44 Petaflop supercomputer and J\"ulich Wizard for European Leadership Science (JUWELS) Booster, J\"ulich Supercomputing Centre's (JSC) 71 Petaflop High Performance Computing (HPC) system, offering practical insight into the implications of our methodology

The destruction and survival of dust in the shell around SN 2008S

SN 2008S erupted in early 2008 in the grand design spiral galaxy NGC 6946. The progenitor was detected by Prieto et al. in Spitzer Space Telescope images taken over the four years prior to the explosion, but was not detected in deep optical images, from which they inferred a self-obscured object with a mass of about 10 Msun. We obtained Spitzer observations of SN 2008S five days after its discovery, as well as coordinated Gemini and Spitzer optical and infrared observations six months after its outburst. We have constructed radiative transfer dust models for the object before and after the outburst, using the same r^-2 density distribution of pre-existing amorphous carbon grains for all epochs and taking light-travel time effects into account for the early post-outburst epoch. We rule out silicate grains as a significant component of the dust around SN 2008S. The inner radius of the dust shell moved outwards from its pre-outburst value of 85 AU to a post-outburst value of 1250 AU, attributable to grain vaporisation by the light flash from SN 2008S. Although this caused the circumstellar extinction to decrease from Av = 15 before the outburst to 0.8 after the outburst, we estimate that less than 2% of the overall circumstellar dust mass was destroyed. The total mass-loss rate from the progenitor star is estimated to have been (0.5-1.0)x10^-4 Msun yr^-1. The derived dust mass-loss rate of 5x10^-7 Msun yr^-1 implies a total dust injection into the ISM of up to 0.01 Msun over the suggested duration of the self-obscured phase. We consider the potential contribution of objects like SN 2008S to the dust enrichment of galaxies.Comment: 9 pages, 7 figures, 3 tables. rv2. To appear in MNRA