arXiv subjects

Temistocle Grenga

Publications and source records attributed to Temistocle Grenga.

10 recordsLinked to original sources

arXiv2026

Large-eddy simulations of a lean hydrogen premixed turbulent jet flame with tabulated chemistry

Large-eddy simulations (LES) of a planar turbulent lean hydrogen-air jet flame at Re = 11000 are performed using a tabulated flamelet model based on mixture-averaged diffusion that incorporates detailed transport, including differential and preferential diffusion, wall heat loss, and thermodiffusion. The approach is extended to turbulent combustion in LES using a presumed-shape probability density function formulation that accounts for sub-filter effects. The flame exhibits a highly corrugated front, driven by local variations of mixture fraction induced by strong thermodiffusive transport. These effects significantly alter both the flame structure and morphology. The LES results are systematically compared to a reference direct numerical simulation across varying LES filters through different mesh resolutions to evaluate the predictive capability of the model. The LES accurately reproduces instantaneous flow structures and thermodiffusive effects. Global flame characteristics including flame length, surface area, and consumption speed, are well captured and show limited sensitivity to mesh resolution. The role of thermodiffusion is also examined, showing that its incorporation leads to a more reactive flame and should not be neglected in the formulation. Heat losses are incorporated into the tabulated chemistry framework for completeness but are found to have a negligible impact, consistent with the walls weak influence in the present configuration. Overall, the results demonstrate that the proposed approach provides reliable predictions of the main flame characteristics, with remaining discrepancies primarily associated with unresolved sub-filter effects that deserve further investigation.

physics.flu-dyn↗

arXiv2025

Dynamic mixed turbulence modeling using a super-resolution generative adversarial approach

A dynamic mixed super-resolution model (DMSRM) for large-eddy simulations (LESs) is proposed, which combines the traditional dynamic mixed model (DMM) formulation with the generation of super-resolved velocity fields from which the subfilter-scale (SFS) stress tensor can be computed. A data-driven super-resolution generative adversarial network (SR-GAN) is employed to upsample the grid-filtered velocity fields by a factor of two, enabling the evaluation of both scale-similarity and the dynamic Smagorinsky contributions. A priori analyses of forced homogeneous isotropic turbulence show that the SR-GAN accurately reconstructs fine-scale flow features and generalizes well across different filter sizes and higher Reynolds number flow configurations, even for unseen input fields. The DMSRM reproduces SFS stresses and energy dissipation more accurately than the traditional DMM. A posteriori LES calculations further confirm that DMSRM predicts the energy spectrum and intermittency more accurately than DMM, even for different LES grid-scale resolutions and for higher Reynolds numbers than those used for training. Unlike DMM, DMSRM yields realistic backscatter and physically consistent SFS energy dissipation. These improvements arise from the physically accurate super-resolved fields generated by the SR-GAN, from which SFS stresses are directly computed. The result is a closure that accurately reproduces stress magnitudes and dissipation while reducing reliance on additional dissipation from the dynamic term. The DMSRM formulation achieves a balance of physical fidelity, robustness, and computational efficiency, offering a promising alternative to traditional DMMs for turbulence LES modeling.

physics.flu-dyn↗

arXiv2024

ZipGAN: Super-Resolution-based Generative Adversarial Network Framework for Data Compression of Direct Numerical Simulations

The advancement of high-performance computing has enabled the generation of large direct numerical simulation (DNS) datasets of turbulent flows, driving the need for efficient compression/decompression techniques that reduce storage demands while maintaining fidelity. Traditional methods, such as the discrete wavelet transform (DWT), cannot achieve compression ratios of 8 or higher for complex turbulent flows without introducing significant encoding/decoding errors. On the other hand, a super-resolution-based generative adversarial network (SR-GAN), called ZipGAN, can accurately reconstruct fine-scale features, preserving velocity gradients and structural details, even at a compression ratio of 512, thanks to the more efficient representation of the data in a compact latent space. Additional benefits are ascribed to adversarial training. The high GAN training time is significantly reduced with a progressive transfer learning approach and, once trained, they can be applied independently of the Reynolds number. It is demonstrated that ZipGAN can enhance dataset temporal resolution without additional simulation overhead by generating high-quality intermediate fields from compressed snapshots. The ZipGAN discriminator can reliably evaluate the quality of decoded fields, ensuring fidelity even in the absence of original DNS fields. Hence, ZipGAN compression/decompression method presents a highly efficient and scalable alternative for large-scale DNS storage and transfer, offering substantial advantages over the DWT methods in terms of compression efficiency, reconstruction fidelity, and temporal resolution enhancement.

physics.flu-dyn↗

arXiv2024

Analysis of thermodiffusive instabilities in hydrogen premixed flames using a tabulated flamelet model

Preferential diffusion plays a critical role in the evolution of lean premixed hydrogen flames, influencing flame surface corrugation and overall flame behavior. Simulating such flames with tabulated chemistry (TC) methods remains challenging due to the complexity of flame dynamics. A detailed assessment of flamelet-based manifolds for capturing these dynamics is still needed. This work incorporates preferential diffusion via mixture-averaged molecular diffusion within TC to study the propagation and structure of freely propagating hydrogen flames influenced by intrinsic instabilities. Model performance is evaluated against detailed chemistry (DC) calculations, focusing on linear and non-linear regimes and sensitivity to pressure and temperature variations. The impact of mesh resolution on flame response is also examined to assess the method's capabilities without subgrid models. The linear regime is analyzed through the dispersion relation, revealing that higher temperature or pressure extends the range of wave numbers accurately predicted by the model, although some overprediction of flame wrinkling in stable regions is observed. The nonlinear regime is assessed by comparing global flame parameters and flame structure to reference solutions, showing that the model captures key flame descriptors with relative errors under 20%. Overall, the model effectively reproduces key effects governing flames with thermodiffusive instabilities, offering a viable alternative to DC at a significantly reduced computational cost.

physics.flu-dyn↗

arXiv2024

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.

physics.flu-dyn↗

arXiv2023

Three-dimensional numerical investigation of flashback in premixed hydrogen flames within perforated burners

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 simulations 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 $W$ and varying length $L$. 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 favorable aerodynamics, 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.

physics.flu-dyn↗

arXiv2023

Physics-based reduced-order modeling of flash-boiling sprays in the context of internal combustion engines

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.

physics.flu-dyn↗

arXiv2023

Dimensional Analysis of Vapor Bubble Growth Considering Bubble-bubble Interactions in Flash Boiling Microdroplets of Highly Volatile Liquid Electrofuels

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.

physics.flu-dyn↗

arXiv2022

Gradient Information and Regularization for Gene Expression Programming to Develop Data-Driven Physics Closure Models

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.

physics.comp-ph↗

arXiv2022

Predictive data-driven model based on generative adversarial network for premixed turbulence-combustion regimes

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.

physics.flu-dyn↗