arXiv subjects

Jörg Schumacher

Publications and source records attributed to Jörg Schumacher.

At least 37 records · Page 2Linked to original sources

arXiv2025

Scale-resolved turbulent Prandtl number for Rayleigh-Bénard convection at $\boldsymbol{Pr =10^{-3}}$

We present a framework to calculate the scale-resolved turbulent Prandtl number $Pr_t$ for the well-mixed and highly inertial bulk of a turbulent Rayleigh-Bénard mesoscale convection layer at a molecular Prandtl number $Pr=10^{-3}$. It builds on Kolmogorov's refined similarity hypothesis of homogeneous isotropic fluid and passive scalar turbulence, based on log-normally distributed amplitudes of kinetic energy and scalar dissipation rates that are coarse-grained over variable scales $r$ in the inertial subrange. Our definition of turbulent (or eddy) viscosity and diffusivity does not rely on mean gradient-based Boussinesq closures of Reynolds stresses and convective heat fluxes. Such gradients are practically absent or indefinite in the bulk. The present study is based on direct numerical simulation of plane-layer convection at an aspect ratio of $Γ=25$ for Rayleigh numbers $10^5\leq Ra \leq 10^7$. We find that the turbulent Prandtl number is effectively up to 4 orders of magnitude larger than the molecular one, $\Pr_t\sim 10$. This holds particularly for the upper end of the inertial subrange, where the eddy diffusivity exceeds the molecular value, $κ_e(r)>κ$. Highly inertial low-Prandtl-number convection behaves effectively as a high-Prandtl number flow, which also supports previous models for the prominent application case of solar convection.

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arXiv2025

Quantum-assisted tracer dispersion in turbulent shear flow

We present a quantum-assisted generative algorithm for synthetic tracks of Lagrangian tracer particles in a turbulent shear flow. The parallelism and sampling properties of quantum algorithms are used to build and optimize a parametric quantum circuit, which generates a quantum state that corresponds to the joint probability density function of the classical turbulent velocity components, p(u_1^{\prime}, u_2^{\prime}, u_3^{\prime}). Velocity samples are drawn by one-shot measurements on the quantum circuit. The hybrid quantum-classical algorithm is validated with two classical methods, a standard stochastic Lagrangian model and a classical sampling scheme in the form of a Markov-chain Monte Carlo approach. We consider a homogeneous turbulent shear flow with a constant shear rate S as a proof of concept for which the velocity fluctuations are Gaussian. The generation of the joint probability density function is also tested on a real quantum device, the 20-qubit IQM Resonance quantum computing platform for cases of up to 10 qubits. Our study paves the way to applications of Lagrangian small-scale parameterizations of turbulent transport in complex turbulent flows by quantum computers.

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arXiv2025

Three-dimensional velocity gradient statistics in a mesoscale convection laboratory experiment

We present three-dimensional velocity gradient statistics from Rayleigh--Bénard convection experiments in a horizontally extended cell of aspect ratio 25, a paradigm for mesoscale convection. The Rayleigh number $Ra$ ranges from $3.7 \times 10^5$ to $4.8 \times 10^6$, and the Prandtl number $Pr$ from 5 to 7.1. Spatio-temporally resolved volumetric data are reconstructed from moderately dense Lagrangian particle tracking measurements. All nine components of the velocity gradient tensor from the experiments show good agreement with those from direct numerical simulations, both conducted at $Ra = 1 \times 10^6$ and $Pr = 6.6$. The focus of our analysis is on non-Gaussian velocity gradient statistics. Specifically, we examine the probability density functions (PDFs) of components of the velocity gradient tensor, vorticity components, kinetic energy dissipation, and local enstrophy at different heights in the bottom half of the cell. The probability of high-amplitude derivatives increases from the bulk to the bottom plate. A similar trend is observed with increasing $Ra$ at fixed height. Both indicate enhanced small-scale intermittency of the velocity field. Furthermore, doubly-logarithmic plots of the PDFs of normalized energy dissipation and local enstrophy at all heights show that the left tails follow slopes of $3/2$ and $1/2$, respectively, in agreement with numerical results. In general, the left tails of the dissipation and local enstrophy distributions show higher probability values with increasing proximity towards the plate, compared to those in the bulk.

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arXiv2025

Diffuselet method for three-dimensional turbulent mixing of a cloudy air filament

The mixing properties of vapor content, temperature and particle fields are of paramount importance in cloud turbulence as they pertain to essential processes, such as cloud water droplet evaporation and entrainment. Our study examines the mixing of a single cloudy air (which implies droplet-laden) filament with its clear air environment, a characteristic process at the cloud edge, in two ways. The first consists of three-dimensional combined Euler-Lagrangian direct numerical simulations which describe the scalar supersaturation as an Eulerian field and the individual cloud water droplets as an ensemble of Lagrangian tracers. The second way builds on the recently developed diffuselet method, a kinematic Lagrangian framework that decomposes a scalar filament into a collection of small sections subject to deformation by local stirring and cross-sheet diffusion. The Schmidt number is Sc=0.7. The entrainment process causes a deformation of the supersaturated cloud filament in combination with diffusion until the system reaches a well-mixed equilibrium state, which implies for the present configuration that all droplets are evaporated. We compare the time dependence of the mean square and probability density function of the supersaturation field. For the initial period of the mixing process they agree very well; at later stages deviations caused by non-zero mean of the conserved scalar are observed. For the cases including cloud water droplets, we investigate also the impact of droplet number density and condensation growth response. Turbulence causes deviations from the d^2-law similar to recent experiments in sprays. A simulation at a Schmidt number that is by a factor of 100 larger than in clouds improves the agreement between simulation and diffuselet method significantly. The latter result promotes the diffuselet framework as an efficient parametrization for turbulent high-Sc...

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arXiv2025

Effects of conjugate heat transfer on large-scale flow structures in convection

The constant temperature and constant heat flux thermal boundary conditions, both developing distinct flow patterns, represent limiting cases of ideally conducting and insulating plates in Rayleigh-Bénard convection (RBC) flows, respectively. This study bridges the gap in between, using a conjugate heat transfer (CHT) set-up and studying finite thermal diffusivity ratios $\kapparatioIL$ to better represent real-life conditions in experiments. A three-dimensional RBC configuration including two fluid-confining plates is studied via direct numerical simulations given a Prandtl number $\Pr=1$. The fluid layer of height $H$ and horizontal extension $L$ obeys no-slip boundary conditions at the two solid-fluid interfaces and an aspect ratio of $Γ=L/H=30$ while the relative thickness of each plate is $\Gs=H_s/H=15$. The entire domain is laterally periodic. Here, different $\kapparatioIL$ are investigated for moderate Rayleigh numbers $\Ra=\left\{ 10^4, 10^5 \right\}$. We observe a gradual shift of the size of the characteristic flow patterns and their induced heat and mass transfer as $\kapparatioIL$ is varied, suggesting a relation between the recently studied turbulent superstructures and supergranules for constant temperature and constant heat flux boundary conditions, respectively. Performing a linear stability analysis for this CHT configuration confirms these observations theoretically while extending previous studies by investigating the impact of a varying solid plate thickness $\Gs$. Moreover, we study the impact of $\kapparatioIL$ on both the thermal and viscous boundary layers. Given the prevalence of finite $\kapparatioIL$ in nature, this work is a starting point to extend our understanding of pattern formation in geo- and astrophysical convection flows.

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arXiv2025

Second quantization for classical nonlinear dynamics

Using techniques from many-body quantum theory, we propose a framework for representing the evolution of observables of measure-preserving ergodic flows through infinite-dimensional rotation systems on tori. This approach is based on a class of weighted Fock spaces $F_w(\mathcal H_τ)$ generated by a 1-parameter family of reproducing kernel Hilbert spaces $\mathcal H_τ$, and endowed with commutative Banach algebra structure under the symmetric tensor product using a subconvolutive weight $w$. We describe the construction of the spaces $F_w(\mathcal H_τ)$ and show that their Banach algebra spectra, $σ(F_w(\mathcal H_τ))$, decompose into a family of tori of potentially infinite dimension. Spectrally consistent unitary approximations $U^t_τ$ of the Koopman operator acting on $\mathcal H_τ$ are then lifted to rotation systems on these tori akin to the topological models of ergodic systems with pure point spectra in the Halmos--von Neumann theorem. Our scheme also employs a procedure for representing observables of the original system by polynomial functions on finite-dimensional tori in $σ(F_w(\mathcal H_τ))$ of arbitrarily large degree, with coefficients determined from pointwise products of eigenfunctions of $U^t_τ$. This leads to models for the Koopman evolution of observables on $L^2$ built from tensor products of finite collections of approximate Koopman eigenfunctions. Numerically, the scheme is amenable to consistent data-driven implementation using kernel methods. We illustrate it with applications to Stepanoff flows on the 2-torus and the Lorenz 63 system. Connections with quantum computing are also discussed.

math.DS↗

arXiv2025

Deciphering boundary layer dynamics in high-Rayleigh-number convection using 3360 GPUs and a high-scaling in-situ workflow

Turbulent heat and momentum transfer processes due to thermal convection cover many scales and are of great importance for several natural and technical flows. One consequence is that a fully resolved three-dimensional analysis of these turbulent transfers at high Rayleigh numbers, which includes the boundary layers, is possible only using supercomputers. The visualization of these dynamics poses an additional hurdle since the thermal and viscous boundary layers in thermal convection fluctuate strongly. In order to track these fluctuations continuously, data must be tapped at high frequency for visualization, which is difficult to achieve using conventional methods. This paper makes two main contributions in this context. First, it discusses the simulations of turbulent Rayleigh-Bénard convection up to Rayleigh numbers of $Ra=10^{12}$ computed with NekRS on GPUs. The largest simulation was run on 840 nodes with 3360 GPU on the JUWELS Booster supercomputer. Secondly, an in-situ workflow using ASCENT is presented, which was successfully used to visualize the high-frequency turbulent fluctuations.

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arXiv2025

Turbulent mesoscale convection in the Boussinesq limit and beyond

Mesoscale convection covers an intermediate scale range between small-scale turbulence and the global organization of the convection flow. It is often characterized by an order of the convection patterns despite very high Rayleigh numbers and strong turbulent fluctuations. In this review, we discuss several aspects of mesoscale convection, which have been investigated by three-dimensional direct numerical simulations. The numerical studies are performed in a characteristic configuration of a plane layer that is heated from below and cooled from above or subject to constant heat flux at the top and bottom boundaries. We discuss the role of the thermal and mechanical boundary conditions for structure formation and study the impact of the domain shape as well as the Prandtl number. With respect to the latter, we focus on low values that arise in astrophysical convection and are partly not anymore accessible in laboratory experiments with liquid metals. Beside these experiments in the Boussinesq approximation, we report studies of non-Boussinesq mesoscale convection. This is done by investigating effects of compressibility and temperature dependence of material properties. The kinetic energy dissipation rate turns out to remain a central quantity for the turbulent mixing in compressible convection. Their different components, statistics, relation to the turbulent viscosity, and the multifractal properties are discussed.

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arXiv2025

Slim multi-scale convolutional autoencoder-based reduced-order models for interpretable features of a complex dynamical system

In recent years, data-driven deep learning models have gained significant interest in the analysis of turbulent dynamical systems. Within the context of reduced-order models (ROMs), convolutional autoencoders (CAEs) pose a universally applicable alternative to conventional approaches. They can learn nonlinear transformations directly from data, without prior knowledge of the system. However, the features generated by such models lack interpretability. Thus, the resulting model is a black-box which effectively reduces the complexity of the system, but does not provide insights into the meaning of the latent features. To address this critical issue, we introduce a novel interpretable CAE approach for high-dimensional fluid flow data that maintains the reconstruction quality of conventional CAEs and allows for feature interpretation. Our method can be easily integrated into any existing CAE architecture with minor modifications of the training process. We compare our approach to Proper Orthogonal Decomposition (POD) and two existing methods for interpretable CAEs. We apply all methods to three different experimental turbulent Rayleigh-Bénard convection datasets with varying complexity. Our results show that the proposed method is lightweight, easy to train, and achieves relative reconstruction performance improvements of up to 6.4% over POD for 64 modes. The relative improvement increases to up to 229.8% as the number of modes decreases. Additionally, our method delivers interpretable features similar to those of POD and is significantly less resource-intensive than existing CAE approaches, using less than 2% of the parameters. These approaches either trade interpretability for reconstruction performance or only provide interpretability to a limited extend.

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arXiv2024

Digital twin of a large-aspect-ratio Rayleigh-Bénard experiment: Role of thermal boundary conditions, measurement errors and uncertainties

Albeit laboratory experiments and numerical simulations have proven themselves successful in enhancing our understanding of long-living large-scale flow structures in horizontally extended Rayleigh-Bénard convection, some discrepancies with respect to their size and induced heat transfer remain. This study traces these discrepancies back to their origins. We start by generating a digital twin of one standard experimental set-up. This twin is subsequently simplified in steps to understand the effect of non-ideal thermal boundary conditions, and the experimental measurement procedure is mimicked using numerical data. Although this allows explaining the increased observed size of the flow structures in the experiment relative to past numerical simulations, our data suggests that the vertical velocity magnitude has been underestimated in the experiments. A subsequent re-assessment of the latter's original data reveals an incorrect calibration model. The re-processed data show a relative increase in $u_{z}$ of roughly $24 \%$, resolving the previously observed discrepancies. This digital twin of a laboratory experiment for thermal convection at Rayleigh numbers $\Ra = \left\{ 2, 4, 7 \right\} \times 10^{5}$, a Prandtl number $\Pr = 7.1$, and an aspect ratio $Γ= 25$ highlights the role of different thermal boundary conditions as well as a reliable calibration and measurement procedure.

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arXiv2024

Asymmetrically connected reservoir networks learn better

We show that connectivity within the high-dimensional recurrent layer of a reservoir network is crucial for its performance. To this end, we systematically investigate the impact of network connectivity on its performance, i.e., we examine the symmetry and structure of the reservoir in relation to its computational power. Reservoirs with random and asymmetric connections are found to perform better for an exemplary Mackey-Glass time series than all structured reservoirs, including biologically inspired connectivities, such as small-world topologies. This result is quantified by the information processing capacity of the different network topologies which becomes highest for asymmetric and randomly connected networks.

cs.NE↗

arXiv2024

No sustained mean velocity in the boundary region of plane thermal convection

We study the dynamics of thermal and momentum boundary regions in three-dimensional direct numerical simulations of Rayleigh-Bénard convection for the Rayleigh number range $10^5 \le Ra \le 10^{11}$ and $Pr=0.7$. Using a Cartesian slab with horizontal periodic boundary conditions and an aspect ratio of 4, we obtain statistical homogeneity in the horizontal $x$- and $y$-directions, thus approximating best an extended convection layer relevant for most geo- and astrophysical flow applications. We observe upon canonical use of combined long-time and area averages, with averaging periods of at least 100 free-fall times, that a global coherent mean flow is practically absent and that the magnitude of the velocity fluctuations is larger than the mean by up to 2 orders of magnitude. The velocity field close to the wall is a collection of differently oriented local shear-dominated flow patches interspersed by extensive shear-free incoherent regions which can be as large as the whole cross section, unlike for a closed cylindrical convection cell of aspect ratio of the order 1. The incoherent regions occupy a $60\%$ area fraction for all Rayleigh numbers investigated here. Rather than resulting in a pronounced mean with small fluctuations about such a mean, as found in small-aspect-ratio convection, the velocity field is dominated by strong fluctuations of all three components around a non-existent or weak mean. We discuss the consequences of these observations for convection layers with larger aspect ratios, including boundary layer instabilities and the resulting turbulent heat transport.

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arXiv2024

Compressible turbulent convection: The role of temperature-dependent thermal conductivity and dynamic viscosity

The impact of variable material properties, such as temperature-dependent thermal conductivity and dynamical viscosity, on the dynamics of a fully compressible turbulent convection flow beyond the anelastic limit are studied in the present work by two series of three-dimensional direct numerical simulations in a layer of aspect ratio 4 with periodic boundary conditions in both horizontal directions. One simulation series is for a weakly stratified adiabatic background, one for a strongly stratified one. The Rayleigh number is $10^5$ and the Prandtl number is 0.7 throughout this study. The temperature dependence of material parameters is imposed as a power law with an exponent $β$. It generates a superadiabaticity $\varepsilon(z)$ that varies across the convection layer. Central statistical quantities of the flow, such as the mean superadiabatic temperature, temperature and density fluctuations, or turbulent Mach numbers are compared in the form of horizontal plane-time averaged profiles. It is found that the additional material parameter dependence causes systematic quantitative changes of all these quantities, but no qualitative ones. A growing temperature power law exponent $β$ also enhances the turbulent momentum transfer in the weak stratification case by 40\%, it reduces the turbulent heat transfer by up to 50\% in the strong stratification case.

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arXiv2024

Connecting finite-time Lyapunov exponents with supersaturation and droplet dynamics in the bulk of a turbulent cloud

The impact of turbulent mixing on the droplet size distribution is studied deep inside a warm ice-free cloud. A simplified cloud mixing model was implemented therefore which summarizes the balance equations of water vapor mixing ratio and temperature to an effective advection-diffusion equation for the supersaturation field $s(\textbf{x},t)$. Our three-dimensional direct numerical simulations connect the scalar supersaturation field to the cloud droplet dynamics, in particular to the droplet size distribution for different box sizes. In addition, finite-time Lyapunov exponents are monitored such that we can relate regions of higher compressive strain to those of high local supersaturation amplitudes. We find that the mixing process in terms of the droplet evaporation is always homogeneous in the bulk of the cloud, while being inhomogeneous in view to the relaxation of the supersaturation field. The probability density function of $λ_3$ is related to the one of $s$ by a simple one-dimensional aggregation model. The distributions of the compressive finite-time Lyapunov exponents $λ_3$, the supersaturation field, and the droplet size are found to be Gaussian.

physics.flu-dyn↗

arXiv2023

Two quantum algorithms for solving the one-dimensional advection-diffusion equation

Two quantum algorithms are presented for the numerical solution of a linear one-dimensional advection-diffusion equation with periodic boundary conditions. Their accuracy and performance with increasing qubit number are compared point-by-point with each other. Specifically, we solve the linear partial differential equation with a Quantum Linear Systems Algorithms (QLSA) based on the Harrow--Hassidim--Lloyd method and a Variational Quantum Algorithm (VQA), for resolutions that can be encoded using up to 6 qubits, which corresponds to $N=64$ grid points on the unit interval. Both algorithms are of hybrid nature, i.e., they involve a combination of classical and quantum computing building blocks. The QLSA and VQA are solved as ideal statevector simulations using the in-house solver QFlowS and open-access Qiskit software, respectively. We discuss several aspects of both algorithms which are crucial for a successful performance in both cases. These are the sizes of an additional quantum register for the quantum phase estimation for the QLSA and the choice of the algorithm of the minimization of the cost function for the VQA. The latter algorithm is also implemented in the noisy Qiskit framework including measurement and decoherence circuit noise. We reflect the current limitations and suggest some possible routes of future research for the numerical simulation of classical fluid flows on a quantum computer.

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arXiv2023

Reduced-order modeling of two-dimensional turbulent Rayleigh-Bénard flow by hybrid quantum-classical reservoir computing

Two hybrid quantum-classical reservoir computing models are presented to reproduce low-order statistical properties of a two-dimensional turbulent Rayleigh-Bénard convection flow at a Rayleigh number Ra=1e+5 and a Prandtl number Pr=10. These properties comprise the mean vertical profiles of the root mean square velocity and temperature and the turbulent convective heat flux. Both quantum algorithms differ by the arrangement of the circuit layers of the quantum reservoir, in particular the entanglement layers. The second of the two quantum circuit architectures, denoted as H2, enables a complete execution of the reservoir update inside the quantum circuit without the usage of external memory. Their performance is compared with that of a classical reservoir computing model. Therefore, all three models have to learn the nonlinear and chaotic dynamics of the turbulent flow at hand in a lower-dimensional latent data space which is spanned by the time-dependent expansion coefficients of the 16 most energetic Proper Orthogonal Decomposition (POD) modes. These training data are generated by a POD snapshot analysis from direct numerical simulations of the original turbulent flow. All reservoir computing models are operated in the reconstruction mode. We analyse different measures of the reconstruction error in dependence on the hyperparameters which are specific for the quantum cases or shared with the classical counterpart, such as the reservoir size and the leaking rate. We show that both quantum algorithms are able to reconstruct the essential statistical properties of the turbulent convection flow successfully with similar performance compared to the classical reservoir network. Most importantly, the quantum reservoirs are by a factor of 4 to 8 smaller in comparison to the classical case.

physics.flu-dyn↗

arXiv2023

Strongly superadiabatic and stratified limits of compressible convection

Fully compressible turbulent convection beyond the Oberbeck-Boussinesq limit and anelastic regime is studied in three-dimensional numerical simulations. Superadiabaticity $ε$ and dissipation number $D$, which measures the strength of stratification of adiabatic equilibria, cause two limits of compressible convection -- nearly top-down-symmetric, strongly superadiabatic and highly top-down-asymmetric, strongly stratified convection. Highest turbulent Mach numbers $M_t$ follow for a symmetric blend of these two limits which we term the fully compressible case. Particularly, the strongly stratified convection case leads to a fluctuation-reduced top layer in the convection zone, a strongly reduced global heat transfer, and differing boundary layer dynamics between top and bottom. We detect this asymmetry for growing dissipation number $D$ also in the phase plane which is spanned by the turbulent Mach number $M_t$ and the dilatation parameter $δ$ which relates the dilatational velocity fluctuations to the solenoidal ones. A detailed analysis of the different transport currents in the fully compressible energy budget relates the low-$D$ convection cases to the standard definition of the dimensionless Nusselt number in the Oberbeck-Boussinesq limit.

physics.flu-dyn↗

arXiv2023

Lagrangian studies of coherent sets and heat transport in constant heat flux-driven turbulent Rayleigh-Bénard convection

We explore the mechanisms of heat transfer in a turbulent constant heat flux-driven Rayleigh-Bénard convection flow, which exhibits a hierarchy of flow structures from granules to supergranules. Our computational framework makes use of time-dependent flow networks. These are based on trajectories of Lagrangian tracer particles that are advected in the flow. We identify coherent sets in the Lagrangian frame of reference as those sets of trajectories that stay closely together for an extended time span under the action of the turbulent flow. Depending on the choice of the measure of coherence, sets with different characteristics are detected. First, the application of a recently proposed evolutionary spectral clustering scheme allows us to extract granular coherent features that are shown to contribute significantly less to the global heat transfer than their spatial complements. Moreover, splits and mergers of these (leaking) coherent sets leave spectral footprints. Secondly, trajectories which exhibit a small node degree in the corresponding network represent objectively highly coherent flow structures and can be related to supergranules as the other stage of the present flow hierarchy. We demonstrate that the supergranular flow structures play a key role in the vertical heat transport and that they exhibit a greater spatial extension than the granular structures obtained from spectral clustering.

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