arXiv subjects

Philipp Schlatter

Publications and source records attributed to Philipp Schlatter.

At least 19 recordsLinked to original sources

arXiv2026

Drag reduction and subcritical turbulence in controlled pipe flows

Pipe flow controlled by streamwise-travelling waves of azimuthal wall velocity is studied using direct numerical simulations at a bulk Reynolds number $Re_b=4900$. A comprehensive analysis of drag reduction shows that the flow response differs fundamentally from that of channel flow. Under suitable forcing, pipe flow relaminarizes, whereas channel flow does not. Depending on the control parameters, the flow exhibits the spatially localized turbulent state characteristic of transitional pipe flow, with turbulent puffs persisting at bulk Reynolds numbers up to three times higher than in the uncontrolled case. The bulk Reynolds number alone does not determine the onset of localization. Moreover, drag reduction, which alters the natural relation between bulk and friction velocities, is insufficient to identify a universal onset criterion. An intermittency indicator based on the spatial variance of the cross-sectional turbulent kinetic energy relates the emergence of localized turbulence to the low wall friction produced by the control, although the correspondence is not one-to-one. Despite their qualitative resemblance to canonical turbulent puffs, the controlled puffs exhibit distinct properties; for example, their fronts may propagate faster than the bulk flow. Overall, this work provides a comprehensive characterization of the subcritical turbulent state and its turbulent puffs in controlled pipe flow, and offers a new perspective on control strategies that aim at flow relaminarization.

physics.flu-dyn↗

arXiv2026

Disentangling coherent structures and the origin of swirl-switching

The physical origin of swirl-switching in turbulent bent-pipe flow remains the subject of ongoing research. We perform three direct numerical simulations (DNS) of flow through a $180^{\circ}$ bent pipe for curvature $\gamma=0.2$ at $Re_D=5,300$ and $Re_D=10,000$, and for curvature $\gamma=0.4$ at $Re_D=5,300$. Here, $Re_D$ is the Reynolds number based on the bulk velocity $U_b$ and pipe diameter $D$. The DNS data are subsequently used to conduct modal decompositions and local stability analysis (LSA). We discuss the limitations of common modal decomposition methods and introduce a new decomposition method, filtered Hilbert proper orthogonal decomposition (FHPOD), enabling us to isolate different instabilities as distinct FHPOD modes. FHPOD reveals seven modes belonging to four distinct families: a low-frequency axial mode, two swirl-switching modes, two swirl-breathing modes and a pair of downstream shear-layer modes. The swirl-switching and swirl-breathing modes are localised to the curved section of the bent-pipe flow and represent the sinuous and varicose instabilities of the Dean vortices, respectively. The DNS data are used to perform local stability analysis of the mean flow using the frozen eddy-viscosity method at the two streamwise wavenumbers associated with the two swirl-switching and swirl-breathing modes. The linear stability analysis reveals six unstable branches, with the swirl-switching mode being the most unstable branch. Another unstable branch represents the swirl-breathing mode. The modes obtained from the local stability analysis are in close agreement with their FHPOD counterparts in both spatial features and the range of Strouhal numbers. The results support the interpretation that swirl-switching arises as an intrinsic instability of the bent-pipe mean flow, which can be excited by incoming turbulent structures.

physics.flu-dyn↗

arXiv2026

Causally coherent structures in turbulent dynamical systems

The extraction of spatio-temporal coherence in high-dimensional, chaotic, non-linear dynamical systems, such as turbulent flows, remains a fundamental challenge in physics, mathematics and engineering. In this work, we employ Shannon transfer entropy (TE) to identify causally coherent motions in a zero-pressure-gradient turbulent boundary layer (TBL). This causality metric, rooted in information theory, enables the identification of sources and targets in dynamical systems using the corresponding time series. However, TE requires sophisticated tuning of various hyperparameters, such as the Markovian order of the source ($m$), which can spatially vary in wall-bounded turbulent flow. Here, we present an adaptive tuning and discuss the influence of $m$ across different TBLs. We introduce the concept of causally coherent structures (CCS), i.e. coherent structures interpreted as spatio-temporal patterns of causality. Moreover, the net transfer entropy flux is also utilised to identify boundary layer locations acting either as sources or targets. The standard viscous, logarithmic, and outer layers are characterised by information fluxes, highlighting, for example, dominant top-down interactions between the inner and outer layers, analogously to the classical energy cascade. This work extends techniques previously employed in the literature, such as correlation and spectral analysis, and presents an approach that is inherently general and applicable to a wide range of chaotic dynamical systems, with applications in cognitive sciences, systems biology and finance.

physics.flu-dyn↗

arXiv2025

Generating wall-bounded turbulent inflows at high Reynolds numbers

One of the main challenges in simulating high Reynolds number ($Re$) turbulent boundary layers (TBLs) is the long streamwise distance required for large-scale outer-layer structures to develop, making such simulations prohibitively expensive. We propose an inflow generation method for high $Re$ wall turbulence that leverages the known structure and scaling laws of TBLs, enabling shorter development lengths by providing rich input information. As observed from the inner-scaled pre-multiplied spectra of streamwise velocity, with an increase in $Re$ the outer region grows and occupies more of the spanwise wavenumber space in proportion to the increase in $Re$; while the inner region remains approximately the same. Exploiting this behavior, we generate high-$Re$ inflow conditions for a $\textit{target}$ $Re$ by starting from cross-stream velocity slices at a lower $\textit{base}$ $Re$. In spectral space, we identify the inner and outer region wavenumbers, and shift the outer-region components proportionally to the desired $Re$ increase. We closely examine the capability of this method by scaling a set of velocity slices at $Re_\theta=2240$ and $4430$ to $Re_\theta=8000$, and using them as inflow conditions for direct numerical simulations (DNS) of spatially developing TBLs growing from $Re_\theta=8000-9000$. The skin friction coefficient and shape factor predicted by the new method, regardless of the $\textit{base}$ $Re$ tested, is within $\pm3.5\%$ and $\pm0.5\%$, respectively, of that of a precursor simulation right from the inlet. Reynolds stresses match very well after approximately $8~\delta_{99_0}$. This gives an order of magnitude reduction in development length compared to other methods proposed in the literature.

physics.flu-dyn↗

arXiv2025

Collapse of turbulence in optimised curved pipe flow

The increased friction caused by turbulence is a significant contributor to energy consumption in the fluid-transport and piping industries. Here we describe a passive approach to reduce friction: we show that a local increase in streamwise flow curvature, combined with changing the circular cross-section to an oval, relaminarizes turbulent flow in curved pipes. We exemplify this effect in a $180^\circ$ bend at $Re_D = 10\,000$ and $20\,000$, well above the linear-stability limit. Curvature inhibits streamwise Reynolds stresses, and cross-sectional modifications weaken the secondary flow, together disrupting the near-wall regeneration cycle and collapsing turbulence. Simulations and experiments confirm that these geometric modifications suppress turbulence and reduce pressure loss by 53% and 36% compared with the baseline $180^\circ$ bend and an equal-length fully developed straight pipe, respectively. The results establish a passive, mechanism-based route to relaminarization in curved pipes with implications for energy-efficient control in other wall-bounded flows with curvature.

physics.flu-dyn↗

arXiv2025

Assessment of the Gradient Jump Penalisation in Large-Eddy Simulations of Turbulence

This research investigates the efficacy of the gradient jump penalisation (GJP) in large eddy simulations (LES) when coupled with active subgrid-scale (SGS) models. GJP is a stabilisation method tailored for the continuous Galerkin spectral element method, aiming at mitigating non-physical oscillations induced by discontinuous velocity gradients across element interfaces. We demonstrate that GJP effectively smoothens fields from LES without a salient impact on flow dynamics for the Taylor--Green vortex (TGV) at $Re=1600$, periodic hill flows at bulk Reynolds numbers $Re_b=10595$ and $37000$, as well as turbulent channel flow at $Re_τ \approx 550$. In the TGV case, the application of GJP results in decreased fluctuations at only high wavenumbers compared to simulations without GJP. The periodic hill flow simulations indicate the applicability of GJP in wall-resolved LES (WRLES) involving curved geometries, though it tends to dissipate some of the finer details in the solution. Finally, in the analysis of the canonical turbulent channel flow cases, GJP leads a higher resolved turbulent kinetic energy than simulations without GJP and direct numerical simulations. GJP's mechanism is identified as providing enhanced dissipation at high wavenumbers but accompanied with insufficient dissipation at low wavenumbers, leading to a pronounced spectral cut-off. Non-physical oscillations on element interfaces are reflected as spikes in the power spectral density. By evaluating the sharpness of the strongest spike, GJP is shown to smoothen the spectra, however without completely removing the gradient jumps at low computational resolution.

physics.flu-dyn↗

arXiv2025

Effects of lower floating-point precision on scale-resolving numerical simulations of turbulence

Modern computing clusters offer specialized hardware for reduced-precision arithmetic that can speed up the time to solution significantly. This is possible due to a decrease in data movement, as well as the ability to perform arithmetic operations at a faster rate. However, for high-fidelity simulations of turbulence, such as direct and large-eddy simulation, the impact of reduced precision on the computed solution and the resulting uncertainty across flow solvers and different flow cases have not been explored in detail and limits the optimal utilization of new high-performance computing systems. In this work, the effect of reduced precision is studied using four diverse computational fluid dynamics (CFD) solvers (two incompressible, Neko and Simson, and two compressible, PadeLibs and SSDC) using four test cases: turbulent channel flow at Retau = 550 and higher, forced transition in a channel, flow over a cylinder at ReD = 3900, and compressible flow over a wing section at Rec = 50000. We observe that the flow physics are remarkably robust with respect to reduction in lower floating-point precision, and that often other forms of uncertainty, due to for example time averaging, often have a much larger impact on the computed result. Our results indicate that different terms in the Navier-Stokes equations can be computed to a lower floating-point accuracy without affecting the results. In particular, standard IEEE single precision can be used effectively for the entirety of the simulation, showing no significant discrepancies from double-precision results across the solvers and cases considered. Potential pitfalls are also discussed.

physics.flu-dyn↗

arXiv2025

Robustness and uncertainty of direct numerical simulation under the influence of rounding and noise

Numerical precision in large-scale scientific computations has become an emerging topic due to recent developments in computer hardware. Lower floating point precision offers the potential for significant performance improvements, but the uncertainty added from reducing the numerical precision is a major obstacle for it to reach prevalence in high-fidelity simulations of turbulence. In the present work, the impact of reducing the numerical precision under different rounding schemes is investigated and compared to the presence of white noise in the simulation data to obtain statistical averages of different quantities in the flow. To investigate how this impacts the simulation, an experimental methodology to assess the impact of these sources of uncertainty is proposed, in which each realization $u^i$ at time $t_i$ is perturbed, either by constraining the flow to a coarser discretization of the phase space (corresponding to low precision formats rounded with deterministic and stochastic rounding) or by perturbing the flow with white noise with a uniform distribution. The purpose of this approach is to assess the limiting factors for precision, and how robust a direct numerical simulation (DNS) is to noise and numerical precision. Our results indicate that for low-Re turbulent channel flow, stochastic rounding and noise impacts the results significantly less than deterministic rounding, indicating potential benefits of stochastic rounding over conventional round-to-nearest. We find that to capture the probability density function of the velocity change in time, the floating point precision is especially important in regions with small relative velocity changes and low turbulence intensity, but less important in regions with large velocity gradients and variations such as in the near-wall region.

physics.flu-dyn↗

arXiv2025

PySEMTools: A library for post-processing hexahedral spectral element data

PySEMTools is a Python-based library for post-processing simulation data produced with high-order hexahedral elements in the context of the spectral element method in computational fluid dynamics. It aims to minimize intermediate steps typically needed when analyzing large files. Specifically, the need to use separate codebases (like the solvers themselves) at post-processing. For this effect, we leverage the use of message passing interface (MPI) for distributed computing to perform typical data processing tasks such as spectrally accurate differentiation, integration, interpolation, and reduced order modeling, among others, on a spectral element mesh. All the functionalities are provided in self-contained Python code and do not depend on the use of a particular solver. We believe that `PySEMTools` provides tools to researchers to accelerate scientific discovery and reduce the entry requirements for the use of advanced methods in computational fluid dynamics.

physics.comp-ph↗

arXiv2024

In-Situ Techniques on GPU-Accelerated Data-Intensive Applications

The computational power of High-Performance Computing (HPC) systems is constantly increasing, however, their input/output (IO) performance grows relatively slowly, and their storage capacity is also limited. This unbalance presents significant challenges for applications such as Molecular Dynamics (MD) and Computational Fluid Dynamics (CFD), which generate massive amounts of data for further visualization or analysis. At the same time, checkpointing is crucial for long runs on HPC clusters, due to limited walltimes and/or failures of system components, and typically requires the storage of large amount of data. Thus, restricted IO performance and storage capacity can lead to bottlenecks for the performance of full application workflows (as compared to computational kernels without IO). In-situ techniques, where data is further processed while still in memory rather to write it out over the I/O subsystem, can help to tackle these problems. In contrast to traditional post-processing methods, in-situ techniques can reduce or avoid the need to write or read data via the IO subsystem. They offer a promising approach for applications aiming to leverage the full power of large scale HPC systems. In-situ techniques can also be applied to hybrid computational nodes on HPC systems consisting of graphics processing units (GPUs) and central processing units (CPUs). On one node, the GPUs would have significant performance advantages over the CPUs. Therefore, current approaches for GPU-accelerated applications often focus on maximizing GPU usage, leaving CPUs underutilized. In-situ tasks using CPUs to perform data analysis or preprocess data concurrently to the running simulation, offer a possibility to improve this underutilization.

cs.PF↗

arXiv2024

Understanding the Impact of Synchronous, Asynchronous, and Hybrid In-Situ Techniques in Computational Fluid Dynamics Applications

High-Performance Computing (HPC) systems provide input/output (IO) performance growing relatively slowly compared to peak computational performance and have limited storage capacity. Computational Fluid Dynamics (CFD) applications aiming to leverage the full power of Exascale HPC systems, such as the solver Nek5000, will generate massive data for further processing. These data need to be efficiently stored via the IO subsystem. However, limited IO performance and storage capacity may result in performance, and thus scientific discovery, bottlenecks. In comparison to traditional post-processing methods, in-situ techniques can reduce or avoid writing and reading the data through the IO subsystem, promising to be a solution to these problems. In this paper, we study the performance and resource usage of three in-situ use cases: data compression, image generation, and uncertainty quantification. We furthermore analyze three approaches when these in-situ tasks and the simulation are executed synchronously, asynchronously, or in a hybrid manner. In-situ compression can be used to reduce the IO time and storage requirements while maintaining data accuracy. Furthermore, in-situ visualization and analysis can save Terabytes of data from being routed through the IO subsystem to storage. However, the overall efficiency is crucially dependent on the characteristics of both, the in-situ task and the simulation. In some cases, the overhead introduced by the in-situ tasks can be substantial. Therefore, it is essential to choose the proper in-situ approach, synchronous, asynchronous, or hybrid, to minimize overhead and maximize the benefits of concurrent execution.

cs.PF↗

arXiv2024

The impact of finite span and wing-tip vortices on a turbulent NACA0012 wing

High-fidelity simulations are conducted to investigate the turbulent boundary layers around a finite-span NACA0012 wing with a rounded wing-tip geometry at a chord-based Reynolds number of $Re_c=200\,000$ and at various angles of attack up to $10^\circ$. The study aims to discern the differences between the boundary layers on the finite-span wing and those on infinite-span wings at equivalent angles of attack. The finite-span boundary layers exhibit: (i) an altered streamwise and a non-zero spanwise pressure gradient as a result of the variable downwash induced by the wing-tip vortices (an inviscid effect typical of finite-span wings); (ii) differences in the flow history at different wall-normal distances, caused by the variable flow angle in the wall-normal direction (due to constant pressure gradients and variable momentum normal to the wall); (iii) laminar flow entrainment into the turbulent boundary layers near the wing tip (due to a laminar/turbulent interface); and (iv) variations in boundary layer thickness across the span, attributed to the variable wall-normal velocity in that direction (a primarily inviscid effect). These physical effects are then used to explain the differences in the Reynolds stress profiles and other boundary layer quantities, including the reduced near-wall peak of the streamwise Reynolds stress and the elevated Reynolds stress levels near the boundary layer edge, both observed in the finite-span wings. Other aspects of the flow, such as the downstream development of wing-tip vortices and their interactions with the surrounding flow, are reserved for future investigations.

physics.flu-dyn↗

arXiv2024

Supercomputers as a Continous Medium

As supercomputers' complexity has grown, the traditional boundaries between processor, memory, network, and accelerators have blurred, making a homogeneous computer model, in which the overall computer system is modeled as a continuous medium with homogeneously distributed computational power, memory, and data movement transfer capabilities, an intriguing and powerful abstraction. By applying a homogeneous computer model to algorithms with a given I/O complexity, we recover from first principles, other discrete computer models, such as the roofline model, parallel computing laws, such as Amdahl's and Gustafson's laws, and phenomenological observations, such as super-linear speedup. One of the homogeneous computer model's distinctive advantages is the capability of directly linking the performance limits of an application to the physical properties of a classical computer system. Applying the homogeneous computer model to supercomputers, such as Frontier, Fugaku, and the Nvidia DGX GH200, shows that applications, such as Conjugate Gradient (CG) and Fast Fourier Transforms (FFT), are rapidly approaching the fundamental classical computational limits, where the performance of even denser systems in terms of compute and memory are fundamentally limited by the speed of light.

cs.DC↗

arXiv2024

Experience and Analysis of Scalable High-Fidelity Computational Fluid Dynamics on Modular Supercomputing Architectures

The never-ending computational demand from simulations of turbulence makes computational fluid dynamics (CFD) a prime application use case for current and future exascale systems. High-order finite element methods, such as the spectral element method, have been gaining traction as they offer high performance on both multicore CPUs and modern GPU-based accelerators. In this work, we assess how high-fidelity CFD using the spectral element method can exploit the modular supercomputing architecture at scale through domain partitioning, where the computational domain is split between a Booster module powered by GPUs and a Cluster module with conventional CPU nodes. We investigate several different flow cases and computer systems based on the modular supercomputing architecture (MSA). We observe that for our simulations, the communication overhead and load balancing issues incurred by incorporating different computing architectures are seldom worthwhile, especially when I/O is also considered, but when the simulation at hand requires more than the combined global memory on the GPUs, utilizing additional CPUs to increase the available memory can be fruitful. We support our results with a simple performance model to assess when running across modules might be beneficial. As MSA is becoming more widespread and efforts to increase system utilization are growing more important our results give insight into when and how a monolithic application can utilize and spread out to more than one module and obtain a faster time to solution.

cs.DC↗

arXiv2024

Wall-modeled large-eddy simulation based on spectral-element discretization

This article analyses the simulation methodology for wall-modeled large-eddy simulations using solvers based on the spectral-element method (SEM). To that end, algebraic wall modeling is implemented in the popular SEM solver Nek5000. It is combined with explicit subgrid-scale (SGS) modeling, which is shown to perform better than the high-frequency filtering traditionally used with the SEM. In particular, the Vreman model exhibits a good balance in terms stabilizing the simulations, yet retaining good resolution of the turbulent scales. Some difficulties associated with SEM simulations on relatively coarse grids are also revealed: jumps in derivatives across element boundaries, lack of convergence for weakly formulated boundary conditions, and the necessity for the SGS model as a damper for high-frequency modes. In spite of these, state-of-the-art accuracy is achieved for turbulent channel flow and flat-plate turbulent boundary layer flow cases, proving the SEM to be a an excellent numerical framework for massively-parallel high-order WMLES.

physics.flu-dyn↗

arXiv2024

Direct numerical simulation of transition under free-stream turbulence and the influence of large integral length scales

Under action of free-stream turbulence (FST), elongated streamwise streaky structures are generated inside the boundary layer, and their amplitude and wavelength are crucial for the transition onset. While turbulence intensity is strongly correlated with the transitional Reynolds number, characteristic length scales of the FST are often considered to have a slight impact on the transition location. However, a recent experiment by Fransson & Shahinfar (2020} shows significant effects of FST scales. They found that, for higher free-stream turbulence levels and larger integral length scales, an increase in the length scale postpones transition, contrary to established literature. Here, we aim at understanding these results by performing a series of high-fidelity simulations. These results provide understanding why the FST integral length scale affects the transition location differently. These integral length scales are so large that the wide streaks introduced in the boundary layer have substantially lower growth in the laminar region upstream of the transition to turbulence, than streaks induced by smaller integral length scales. The energy in the boundary layer subsequently propagate to smaller spanwise scales as a result of the non-linear interaction. When the energy has reached smaller spanwise scales larger amplitude streaks results in regions where the streak growth are larger. It takes longer for the energy from the wider streaks to propagate to the spanwise scales associated with the breakdown to turbulence, than for the those with smaller spanwise scales. Thus there is a faster transition for FST with lower integral length scales in this case.

physics.flu-dyn↗

arXiv2023

Characterisation of the laminar pulsatile flow in toroidal pipes

This study analyses the main characteristics of the fully developed laminar pulsatile flow in a toroidal pipe as the governing parameters vary. A novel computational technique is developed to obtain time-periodic solutions of the Navier$\unicode{x2013}$Stokes equations. They are computed as fixed points of the system in the frequency domain via a Newton$\unicode{x2013}$Raphson method. Advantages and drawbacks of the adopted methodology with respect to a time-stepping technique are discussed. The unsteady component of the driving pressure gradient is found to change linearly with the pulsation amplitude, with a proportionality coefficient dependent on the pulsation frequency. Although the time-averaged streamwise wall shear stress is very close to the value in the steady case, significant fluctuations are observed within the period. Flow reversal occurs during certain time intervals in the period for high pulsation amplitudes. The analysis of the spatial structure of the unsteady component of the velocity field shows that three different flow regimes can be identified, depending on the pulsation frequency, termed quasi-steady, intermediate and plug-flow regimes.

physics.flu-dyn↗

arXiv2023

In-situ Estimation of Time-averaging Uncertainties in Turbulent Flow Simulations

The statistics obtained from turbulent flow simulations are generally uncertain due to finite time averaging. The techniques available in the literature to accurately estimate these uncertainties typically only work in an offline mode, that is, they require access to all available samples of a time series at once. In addition to the impossibility of online monitoring of uncertainties during the course of simulations, such an offline approach can lead to input/output (I/O) deficiencies and large storage/memory requirements, which can be problematic for large-scale simulations of turbulent flows. Here, we designed, implemented and tested a framework for estimating time-averaging uncertainties in turbulence statistics in an in-situ (online/streaming/updating) manner. The proposed algorithm relies on a novel low-memory update formula for computing the sample-estimated autocorrelation functions (ACFs). Based on this, smooth modeled ACFs of turbulence quantities can be generated to accurately estimate the time-averaging uncertainties in the corresponding sample mean estimators. The resulting uncertainty estimates are highly robust, accurate, and quantitatively the same as those obtained by standard offline estimators. Moreover, the computational overhead added by the in-situ algorithm is found to be negligible. The framework is completely general and can be used with any flow solver and also integrated into the simulations over conformal and complex meshes created by adopting adaptive mesh refinement techniques. The results of the study are encouraging for the further development of the in-situ framework for other uncertainty quantification and data-driven analyses relevant not only to large-scale turbulent flow simulations, but also to the simulation of other dynamical systems leading to time-varying quantities with autocorrelated samples.

physics.flu-dyn↗