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Two-dimensional direct numerical simulations of planar laminar premixed ammonia/hydrogen/air flames are conducted for a wide range of equivalence ratios, hydrogen ($\rm H_2$) fractions in the fuel blend, pressures, and unburned temperatures to study intrinsic flame instabilities (IFIs) in the linear regime. For stoichiometric and lean mixtures at ambient conditions, a non-monotonic behavior of thermo-diffusive instabilities with increasing ($\rm H_2$) fraction is observed. Strongest instabilities occur for molar ($\rm H_2$) fractions of 40%. The analysis shows that this behavior is linked to the joint effect of variations of the effective Lewis number and Zeldovich number. IFIs in ammonia/hydrogen blends further show a non-monotonic trend with respect to pressure, which is found to be linked to the chemistry of the hydroperoxyl radical $\rm HO_2$. The addition of $\rm NH_3$ opens new reaction pathways for the consumption of $\rm HO_2$ resulting in a chain carrying behavior in contrast to its chain terminating nature in pure $\rm H_2$/air flames. Theoretically derived dispersion relations can predict the non-monotonic behavior for lean conditions. However, these are found to be sensitive to the different methods for evaluating the Zeldovich number available in the literature.

We show that various methods for explicitly building resolutions of unbounded complexes in fact fail when applied to a rather simple and explicit complex. We show that one way to rescue these methods is to assume Roos (Ab.4$^*$)-$k$ axiom, which we adapt to encompass also resolutions in the framework of relative homological algebra. In the end we discuss the existence of model structures for relative homological algebra for unbounded complex under the relative (Ab.4$^*$)-$k$ condition, and present a variety of examples where our results apply., Comment: 34 pages

Single photon detection and timing gathered increasing interest in the last few years due to both its necessity in the field of quantum sensing and the advantages of single quanta detection in the field of low level light imaging. While simple bucket detectors are mature enough for commercial applications, more complex imaging detectors are still a field of research with mostly prototype level detectors. A major problem in these detectors is the implementation of in-pixel timing circuitry, especially for two-dimensional imagers. One of the most promising approaches is the use of voltage controlled ring resonators in every pixel. Each of those is running independently, based on a voltage supplied by a global reference. However, this yields the problem that across the chip the supply voltage can change, which in turn changes the period of the ring resonator. Due to additional parasitic effects, this problem can worsen with increasing measurement time, leading to a drift of the timing information. We present here a method to identify and correct such temporal drifts of single photon detectors, based on asynchronous quantum ghost imaging. We also show the effect of this correction on a recent QGI measurement from our group., Comment: Template of MDPI sensors used as publication is intended in said journal

Thermodiffusive instabilities can have a leading order effect on flame propagation for lean premixed hydrogen flames. Many simulation studies have been performed to study this effect, but almost exclusively in two-dimensional (2D) or domain sizes too small to support the characteristic large-scale features of the instability. The main purpose of this study is to quantify the differences of 3D and 2D flames using simulations on sufficiently large domains. To this end, direct numerical simulations (DNS) of thermodiffusively unstable laminar premixed hydrogen flames stabilized in 3D domains were performed. The effects of confinement on the flame dynamics are rigorously investigated by varying the domain size. The flame burning velocity shows a strong dependence on the domain size when the lateral domain width is less than 50 laminar flame thicknesses. The characteristic patterns of the thermodiffusively unstable flame are analyzed in detail, including the instantaneous flame structure, global burning velocity, flame surface area, stretch factor, curvature distribution, and the cell size. The effects of the computational setup (2D vs. 3D) on the local flame front curvature and the distributions of the thermo-chemical quantities are quantified through a conditional analysis. The formation and destruction mechanisms of the distinct cellular structures observed in the 3D domain are analyzed focusing on the interactions between the flame dynamics and the flow field, and the contributions of the production of flame surface density, kinematic restoration, and curvature dissipation are quantified. Compared with the corresponding 2D simulation, the flame surface area in the cut plane of the 3D configuration is similar, yet the burning velocity and the stretch factor are increased. The reason for this is the difference in curvature statistics in 2D and 3D.

Predicting flashback represents a pivotal challenge in the development of innovative perforated burners for household appliances, especially for substituting natural gas with hydrogen as fuel. Most existing numerical studies have utilized two-dimensional (2D) simulations to investigate flashback in these burners, primarily to reduce computational costs. However, the inherent complexity of flashback phenomena suggests that 2D models may inadequately capture the flame dynamics, potentially leading to inaccurate estimations of flashback limits. In this study, three-dimensional (3D) simulations are employed to examine the impact of the actual slit shapes on the flashback velocities of hydrogen-premixed flames. Steady-state simulations are conducted to compute flashback velocities for three equivalence ratios ($\phi=0.6$, $0.8$, and $1.0$), investigating slits with fixed width and varying lengths. Additionally, transient simulations are performed to investigate the flashback dynamics. The results are compared with those from 2D configurations to assess the reliability of the infinite slit approximation. For stable flames, 2D simulations underpredict the burner plate temperature compared to slits with lengths typical of practical devices but match the 3D results as $L\to\infty$. Conversely, flashback velocities are consistently underpredicted in 2D simulations compared to 3D simulations, even as $L\to\infty$. This is due to the critical role of the slit ends in flashback dynamics, where preferential diffusion, the Soret effect, and higher preheating due to a higher surface-to-volume ratio trigger the initiation of flashback in those regions. These findings underscore the necessity of employing 3D simulations to accurately estimate the flashback velocities in domestic perforated burners.

We study the behaviour of the Casson invariant $\lambda$, its square, and Othsuki's second invariant $\lambda_2$ as functions on the Johnson subgroup of the mapping class group. We show that since $\lambda$ and $d_2 = \lambda_2 - 18 \lambda^2$ are invariants that are morphisms on respectively the second and the third level of the Johnson filtration they never vanish on any level of this filtration. In contrast we prove that the invariant $\lambda_2-18\lambda^2 +3\lambda$ vanishes on the fifth level of the Johnson filtration, $\mathcal{M}_{g,1}(5)$, and as a consequence we prove that, for instance, the Poincar\'e homology sphere does not admit any Heegaard splitting with gluing map an element in $\mathcal{M}_{g,1}(5)$. Finally we determine a surgery formula for Othsuki's second invariant $\lambda_2$., Comment: 32 pages. Corrected a proof that needed a boundary argument. Added a section on the surgery formula for the new invariant

Mixture fraction-based models are widely employed for predicting turbulent non-premixed combustion processes due to their cost-effectiveness and well-established subfilter closure. In these models, the transport of reactive scalars in physical space is decomposed into two components: scalar transport relative to mixture fraction and transport of mixture fraction in physical space. Conventional flamelet models do not consider that these two processes have to be formulated consistently, which can lead to scalar mass conservation errors. In the context of multiphase flows, scalar transport in mixture fraction space is governed by three conditional flow-dependent parameters: the conditional scalar dissipation rate, the conditional scalar diffusion rate, and the conditional spray source term. The evolution of mixture fraction in physical space is typically modeled using the presumed Filtered Density Function (FDF) approach. This paper introduces a novel formulation for the conditional flow parameters that aligns with the presumed FDF approach, thereby ensuring scalar mass conservation. The proposed model is applied to a Large-Eddy Simulation (LES) of the inert ECN Spray A case, with a comparison against a conventional flow parameter model that employs an inverse error function shape for the scalar dissipation rate. The results indicate that the conventional model produces similar conditional dissipation rates to the new model in regions where combustion takes place. However, significant discrepancies are observed in the conditional diffusion rate, highlighting the susceptibility of the conventional model to scalar mass conservation errors for non-unity Lewis number scalars.

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.

Flash-boiling injection is one of the most effective ways to accomplish improved atomization compared to the high-pressure injection strategy. The tiny droplets formed via flash-boiling lead to fast fuel-air mixing and can subsequently improve combustion performance in engines. Most of the previous studies related to the topic focused on modeling flash-boiling sprays using three-dimensional (3D) computational fluid dynamics (CFD) techniques such as direct numerical simulations (DNS), large-eddy simulations (LES), and Reynolds-averaged Navier-Stokes (RANS) simulations. However, reduced order models can have significant advantages for applications such as the design of experiments, screening novel fuel candidates, and creating digital twins, for instance, because of the lower computational cost. In this study, the previously developed cross-sectionally averaged spray (CAS) model is thus extended for use in simulations of flash-boiling sprays. The present CAS model incorporates several physical submodels in flash-boiling sprays such as those for air entrainment, drag, superheated droplet evaporation, flash-boiling induced breakup, and aerodynamic breakup models. The CAS model is then applied to different fuels to investigate macroscopic spray characteristics such as liquid and vapor penetration lengths under flash-boiling conditions. It is found that the newly developed CAS model captures the trends in global flash-boiling spray characteristics reasonably well for different operating conditions and fuels. Moreover, the CAS model is shown to be faster by up to four orders of magnitude compared with simulations of 3D flash-boiling sprays. The model can be useful for many practical applications as a reduced-order flash-boiling model to perform low-cost computational representations of higher-order complex phenomena., Comment: 36 pages, 8 figures

Premixed flames are susceptible to hydrodynamic and thermodiffusive instabilities that wrinkle the flame front and lead to complex multiscale patterns. They strongly impact the flame propagation and dynamics, increasing the speed of a laminar flame by several folds, easily as large as a factor of five for lean hydrogen flames at high pressure. The dispersion relation, which represents the growth rate of the different harmonic components of the perturbation of the flame front for different wavelengths, is useful to understand the dynamics during the linear phase of flame instabilities. In this work, an efficient and accurate approach based on a Fourier analysis of flame wrinkling is proposed to calculate the dispersion relation. Differently from the typical approach based on perturbing the flame with a single wavelength, the flame is perturbed with a spectrum of sine waves and their growth is followed with a spectral analysis. With the present method, the full dispersion relation is computed with a single simulation; this is significantly more efficient computationally than running a series of simulations with a single-wavelength perturbation for each point of the dispersion relation. It is shown that the presented approach is accurate and also solves an issue encountered when a single perturbation is imposed to compute the growth rate of large wavelengths. Several numerical and initialisation parameters, including resolution, domain size, and amplitude of the initial perturbation, are studied systematically and assessed.

Consider a finite connected $2$-complex $X$ endowed with a piecewise Riemannian metric and whose fundamental group is freely indecomposable, of rank at least $3$, and in which every $2$-generated subgroup is free. In this paper we show that we can always find a connected graph $\Gamma\subset X$ such that $\pi_1 \Gamma\simeq {\mathbb F}_2 \hookrightarrow\pi_1 X$ (in short, a $2$-incompressible graph) whose length satisfies the following curvature-free inequality: $\ell(\Gamma)\leq 4\sqrt{2\text{Area}(X)}$. This generalizes a previous inequality proved by Gromov for closed Riemannian surfaces with negative Euler characteristic. As a consequence we obtain that the volume entropy of such $2$-complexes with unit area is always bounded away from zero., Comment: Accepted version to be published in Revista Matem\'atica Iberoamericana

Electrofuels (e-fuels) produced from renewable electricity and carbon sources have gained significant attention in recent years as promising alternatives to fossil fuels for the transportation sector. However, the highly volatile e-fuels, such as short-chain oxymethylene ethers are prone to flash vaporization phenomena, which is associated with the formation and growth of vapor bubbles, followed by explosive bursting of the liquid jet. The simulation of a flash boiling spray of such highly volatile liquid fuels in the context of automotive or cryogenic engines is numerically challenging due to several reasons, including (1) the complexity of the bubble growth process in the presence of multiple vapor bubbles and (2) the need to use an extremely small time step size to accurately capture the underlying physics associated with the flash boiling process. In this paper, we first present a bubble growth model in flash boiling microdroplets considering bubble interactions along with the finite droplet size effects. Based on the dimensional analysis of the newly derived Rayleigh Plesset equation, a simplified semi-analytical solution for bubble growth, which also includes the bubble interactions, is then derived to estimate the bubble growth behavior with reasonable accuracy using the larger time step sizes for a wide range of operating conditions. The derived semi-analytical solution is shown to be a good approximation for describing the bubble growth rate over the whole lifetime of the bubble. The bubble interactions are found to delay the onset of droplet bursting due to the slower growth of the vapor bubble compared to the bubble growth without bubble interactions. Furthermore, in a comparison with DNS results, the proposed bubble growth model is shown to reasonably capture the impact of bubble interactions leading to smaller volumetric droplet expansion.

Learning accurate numerical constants when developing algebraic models is a known challenge for evolutionary algorithms, such as Gene Expression Programming (GEP). This paper introduces the concept of adaptive symbols to the GEP framework by Weatheritt and Sandberg (2016) to develop advanced physics closure models. Adaptive symbols utilize gradient information to learn locally optimal numerical constants during model training, for which we investigate two types of nonlinear optimization algorithms. The second contribution of this work is implementing two regularization techniques to incentivize the development of implementable and interpretable closure models. We apply $L_2$ regularization to ensure small magnitude numerical constants and devise a novel complexity metric that supports the development of low complexity models via custom symbol complexities and multi-objective optimization. This extended framework is employed to four use cases, namely rediscovering Sutherland's viscosity law, developing laminar flame speed combustion models and training two types of fluid dynamics turbulence models. The model prediction accuracy and the convergence speed of training are improved significantly across all of the more and less complex use cases, respectively. The two regularization methods are essential for developing implementable closure models and we demonstrate that the developed turbulence models substantially improve simulations over state-of-the-art models.

Several decision points exist in business processes (e.g., whether a purchase order needs a manager's approval or not), and different decisions are made for different process instances based on their characteristics (e.g., a purchase order higher than $500 needs a manager approval). Decision mining in process mining aims to describe/predict the routing of a process instance at a decision point of the process. By predicting the decision, one can take proactive actions to improve the process. For instance, when a bottleneck is developing in one of the possible decisions, one can predict the decision and bypass the bottleneck. However, despite its huge potential for such operational support, existing techniques for decision mining have focused largely on describing decisions but not on predicting them, deploying decision trees to produce logical expressions to explain the decision. In this work, we aim to enhance the predictive capability of decision mining to enable proactive operational support by deploying more advanced machine learning algorithms. Our proposed approach provides explanations of the predicted decisions using SHAP values to support the elicitation of proactive actions. We have implemented a Web application to support the proposed approach and evaluated the approach using the implementation.

Models for finite-rate-chemistry in underresolved flows still pose one of the main challenges for predictive simulations of complex configurations. The problem gets even more challenging if turbulence is involved. This work advances the recently developed PIESRGAN modeling approach to turbulent premixed combustion. For that, the physical information processed by the network and considered in the loss function are adjusted, the training process is smoothed, and especially effects from density changes are considered. The resulting model provides good results for a priori and a posteriori tests on direct numerical simulation data of a fully turbulent premixed flame kernel. The limits of the modeling approach are discussed. Finally, the model is employed to compute further realizations of the premixed flame kernel, which are analyzed with a scale-sensitive framework regarding their cycle-to-cycle variations. The work shows that the data-driven PIESRGAN subfilter model can very accurately reproduce direct numerical simulation data on much coarser meshes, which is hardly possible with classical subfilter models, and enables studying statistical processes more efficiently due to the smaller computing cost.

In this paper, deep learning (DL) methods are evaluated in the context of turbulent flows. Various generative adversarial networks (GANs) are discussed with respect to their suitability for understanding and modeling turbulence. Wasserstein GANs (WGANs) are then chosen to generate small-scale turbulence. Highly resolved direct numerical simulation (DNS) turbulent data is used for training the WGANs and the effect of network parameters, such as learning rate and loss function, is studied. Qualitatively good agreement between DNS input data and generated turbulent structures is shown. A quantitative statistical assessment of the predicted turbulent fields is performed.

Spontaneous Raman spectroscopy (SRS) is a conventional in-situ laser diagnostic method that has been widely used for measurements of temperature and major species. However, SRS in sooting flames suffers from strong interference including laser-induced fluorescence, laser-induced incandescence, and flame luminosity, which is a long-lasting challenge. This work introduces a low-cost, easy-to-implement, and calibration-free SRS thermometry in sooting flames based on a 355-nm nanosecond laser beam. Several strategies were utilized to increase the signal-to-noise ratio and suppress the interference: (1) nanosecond ICCD gate width; (2) optimized ICCD gate delay; (3) specially designed focusing shape of the laser beam; (4) ultraviolet polarizer filter. The temperature was obtained by fitting the contour of Stokes-Raman spectra of N2 molecules, which does not require additional calibration. Based on the measured temperature, the mole fraction of major species can be obtained with calibration. This method was used in the temperature and major species measurements of an ethylene-based counterflow diffusion flame. The experimental results show an excellent agreement with the simulation results, demonstrating the feasibility of performing non-intrusive laser diagnostics of sooting and other particle-laden flames accurately.

Premixed flames exhibit different asymptotic regimes of interaction between heat release and turbulence depending on their respective length scales. At high Karlovitz number, the dilatation caused by heat release does not have any relevant effect on turbulent kinetic energy with respect to non-reacting flow, while at low Karlovitz number, the mean shear is a sink of turbulent kinetic energy, and counter-gradient transport is observed. This latter phenomenon is not well captured by closure models commonly used in Large Eddy Simulations that are based on gradient diffusion. The massive amount of data available from Direct Numerical Simulation (DNS) opens the possibility to develop data-driven models able to represent physical mechanisms and non-linear features present in both these regimes. In this work, the databases are formed by DNSs of two planar hydrogen/air flames at different Karlovitz numbers corresponding to the two asymptotic regimes. In this context, the Generative Adversarial Network (GAN) gives the possibility to successfully recognize and reconstruct both gradient and counter-gradient phenomena if trained with databases where both regimes are included. Two GAN models were first trained each for a specific Karlovitz number and tested using the same dataset in order to verify the capability of the models to learn the features of a single asymptotic regime and assess its accuracy. In both cases, the GAN models were able to reconstruct the Reynolds stress subfilter scales accurately. Later, the GAN was trained with a mixture of both datasets to create a model containing physical knowledge of both combustion regimes. This model was able to reconstruct the subfilter scales for both cases capturing the interaction between heat release and turbulence closely to the DNS as shown from the turbulent kinetic budget and barycentric maps., Comment: 24 pages, 11 figures. This article has been submitted for publication in Combustion Science and Technology, published by Taylor & Francis

In global efforts to reduce harmful greenhouse gas emissions from the transport sector, novel bio-hybrid liquid fuels from renewable energy and carbon sources can be a major form of energy for future propulsion systems due to their high energy density. A fundamental understanding of the spray and mixing performance of the new fuel candidates in combustion systems is necessary to design and develop the fuels for advanced combustion concepts. In the fuel design process, a large number of candidates is required to be screened to arrive at potential fuels for further detailed investigations. For such a screening process, three-dimensional (3D) simulation models are computationally too expensive and hence unfeasible. Therefore, in this paper, we present a fast, reduced-order model (ROM) for inert sprays. The model is based on the cross-sectionally averaged spray (CAS) model derived by Wan (1997) from 3D multiphase equations. The original model was first tested against a wide range of conditions and different fuels. The discrepancies between the CAS model and experimental data are addressed by integrating state-of-the-art breakup and evaporation models. A transport equation for vapor mass fraction is proposed, which is important for evaporation modeling. Furthermore, the model is extended to consider polydisperse droplets by modeling the droplet size distribution by commonly used presumed probability density functions, such as Rosin-Rammler, lognormal, and gamma distributions. The improved CAS model is capable of predicting trends in the macroscopic spray characteristics for a wide range of conditions and fuels. The computational cost of the CAS model is lower than the 3D simulation methods by up to 6 orders of magnitude depending on the method. This enables the model to be used not only for the rapid screening of novel fuel candidates, but also for other applications, where ROMs are useful., Comment: 44 pages, 14 figures