Analysis of a constant-coefficient pressure equation method for fast computations of two-phase flows at high density ratios

An analysis of a modified pressure-correction formulation for fast simulations of fully resolved incompressible two-phase flows has been carried out. By splitting of the density weighted pressure gradient, the pressure equation is reduced to a constant-coefficient Poisson equation, for which efficient linear solvers can be used. While the gain in speed-up is well documented, the error introduced by the temporal extrapolation of the pressure gradient requires further investigations. In this paper it is shown that the modified pressure equation can lead to unphysical pressure oscillations and large errors. By appropriately combining the extrapolated pressure gradient with a matching volume fraction gradient grid convergence at high density ratios could be recovered. The cases of a one-dimensional front and a sphere translating at uniform velocity were first considered, allowing to decouple the pressure equation from the momentum equation. Subsequently, the case of a rising bubble in an upflow is analysed for which the full set of governing equations is solved. The pressure jump extrapolation error has been found dependent on the density ratio and the CFL number. Ultimately, the gain in the computational time, made possible by the use of fast Poisson solvers, should be weighted by the additional computational time the reduction of the aforementioned error may require

Building a map of the breast cancer proteome - Strategies to increase coverage

Amongst the various –omics sciences, proteomics has the highest potential for functional characterization and consequently can contribute significantly to the field of cancer research. In particular, the focus of this thesis is on breast cancer. Alas, since state-of-the-art technologies cannot meet the complexity of upper eukaryotic proteomes, a complete resolution of clinical samples is still unachievable. Comprehensive mapping of proteins involved in cancer and of their PTMs is proposed in this thesis as a general strategy to increase the output of mass-spectrometry based proteomics. Different approaches to improve the coverage of this map are proposed: optimization of sample fractionation, focusing on difficult sub-proteomes, targeting of specific biological processes and optimization of data analysis. A combination of these approaches will provide a growing collection of empirical MS-spectra, which will enhance the detection by shotgun proteomics and facilitate the transition towards the development of targeted assays

Data-driven spectral turbulence modeling for Rayleigh-B\'enard convection

A data-driven turbulence model for coarse-grained numerical simulations of two-dimensional Rayleigh-B\'enard convection is proposed. The model starts from high-fidelity data and is based on adjusting the Fourier coefficients of the numerical solution, with the aim of accurately reproducing the kinetic energy spectra as seen in the high-fidelity reference findings. No assumptions about the underlying PDE or numerical discretization are used in the formulation of the model. We also develop a constraint on the heat flux to guarantee accurate Nusselt number estimates on coarse computational grids and high Rayleigh numbers. Model performance is assessed in coarse numerical simulations at $Ra=10^{10}$. We focus on key features including kinetic energy spectra, wall-normal flow statistics, and global flow statistics. The method of data-driven modeling of flow dynamics is found to reproduce the reference kinetic energy spectra well across all scales and yields good results for flow statistics and average heat transfer, leading to computationally cheap surrogate models. Large-scale forcing extracted from the high-fidelity simulation leads to accurate Nusselt number predictions across two decades of Rayleigh numbers, centered around the targeted reference at $Ra=10^{10}$.Comment: 29 pages, 15 figure

Data-driven spectral turbulence modeling for Rayleigh-Bénard convection

A data-driven turbulence model for coarse-grained numerical simulations of two-dimensional Rayleigh-B\'enard convection is proposed. The model starts from high-fidelity data and is based on adjusting the Fourier coefficients of the numerical solution, with the aim of accurately reproducing the kinetic energy spectra as seen in the high-fidelity reference findings. No assumptions about the underlying PDE or numerical discretization are used in the formulation of the model. We also develop a constraint on the heat flux to guarantee accurate Nusselt number estimates on coarse computational grids and high Rayleigh numbers. Model performance is assessed in coarse numerical simulations at $Ra=10^{10}$. We focus on key features including kinetic energy spectra, wall-normal flow statistics, and global flow statistics. The method of data-driven modeling of flow dynamics is found to reproduce the reference kinetic energy spectra well across all scales and yields good results for flow statistics and average heat transfer, leading to computationally cheap surrogate models. Large-scale forcing extracted from the high-fidelity simulation leads to accurate Nusselt number predictions across two decades of Rayleigh numbers, centered around the targeted reference at $Ra=10^{10}$

An efficient geometric method for incompressible hydrodynamics on the sphere

We present an efficient and highly scalable geometric method for two-dimensional ideal fluid dynamics on the sphere. The starting point is Zeitlin's finite-dimensional model of hydrodynamics. The efficiency stems from exploiting a tridiagonal splitting of the discrete spherical Laplacian combined with highly optimized, scalable numerical algorithms. For time-stepping, we adopt a recently developed isospectral integrator able to preserve the geometric structure of Euler's equations, in particular conservation of the Casimir functions. To overcome previous computational bottlenecks, we formulate the matrix Lie algebra basis through a sequence of tridiagonal eigenvalue problems, efficiently solved by well-established linear algebra libraries. The same tridiagonal splitting allows for computation of the stream matrix, involving the inverse Laplacian, for which we design an efficient parallel implementation on distributed memory systems. The resulting overall computational complexity is $\mathcal{O}(N^3)$ per time-step for $N^2$ spatial degrees of freedom. The dominating computational cost is matrix-matrix multiplication, carried out via the parallel library ScaLAPACK. Scaling tests show approximately linear scaling up to around $2500$ cores for the matrix size $N=4096$ with a computational time per time-step of about $0.55$ seconds. These results allow for long-time simulations and the gathering of statistical quantities while simultaneously conserving the Casimir functions. We illustrate the developed algorithm for Euler's equations at the resolution $N=2048$

Data-driven stochastic Lie transport modelling of the 2D Euler equations

Stochastic modelling of coarse-grid SPDEs of the two-dimensional Euler equations, in the framework of Stochastic Advection by Lie Transport (SALT) [Cotter et al, 2019], is considered. We propose and assess a number of models as stochastic forcing. The latter is decomposed in terms of a deterministic basis (empirical orthogonal functions) multiplied by temporal traces, here regarded as stochastic processes. In particular, we construct the stochastic forcing from the probability density functions (pdfs) and the correlation times obtained from a fine-grid data set. We perform uncertainty quantification tests to compare the different stochastic models. In particular, comparison to Gaussian noise, in terms of ensemble mean and ensemble spread, is conducted. Reduced uncertainty is observed for the developed models. On short timescales, such as those used for data assimilation [Cotter et al, 2020a], the former models show a reduced ensemble mean error and a reduced spread. Estimating the pdfs yielded stochastic ensembles which rarely failed to capture the reference solution on short timescales, as is demonstrated by rank histograms. Overall, introducing correlation into the stochastic models improves the quality of the coarse-grid predictions with respect to white noise.Comment: 14 pages, 10 figure

Data-driven stochastic spectral modeling for coarsening of the two-dimensional Euler equations on the sphere

A resolution-independent data-driven stochastic parametrization method for subgrid-scale processes in coarsened fluid descriptions is proposed. The method enables the inclusion of high-fidelity data into the coarsened flow model, thereby enabling accurate simulations also with the coarser representation. The small-scale parametrization is introduced at the level of the Fourier coefficients of the coarsened numerical solution. It is designed to reproduce the kinetic energy spectra observed in high-fidelity data of the same system. The approach is based on a control feedback term reminiscent of continuous data assimilation. The method relies solely on the availability of high-fidelity data from a statistically steady state. No assumptions are made regarding the adopted discretization method or the selected coarser resolution. The performance of the method is assessed for the two-dimensional Euler equations on the sphere. Applying the method at two significantly coarser resolutions yields good results for the mean and variance of the Fourier coefficients. Stable and accurate large-scale dynamics can be simulated over long integration times

Zeitlin truncation of a Shallow Water Quasi-Geostrophic model for planetary flow

In this work, we consider a Shallow-Water Quasi Geostrophic equation on the sphere, as a model for global large-scale atmospheric dynamics. This equation, previously studied by Verkley (2009) and Schubert et al. (2009), possesses a rich geometric structure, called Lie-Poisson, and admits an infinite number of conserved quantities, called Casimirs. In this paper, we develop a Casimir preserving numerical method for long-time simulations of this equation. The method develops in two steps: firstly, we construct an N-dimensional Lie-Poisson system that converges to the continuous one in the limit $N \to \infty$; secondly, we integrate in time the finite-dimensional system using an isospectral time integrator, developed by Modin and Viviani (2020). We demonstrate the efficacy of this computational method by simulating a flow on the entire sphere for different values of the Lamb parameter. We particularly focus on rotation-induced effects, such as the formation of jets. In agreement with shallow water models of the atmosphere, we observe the formation of robust latitudinal jets and a decrease in the zonal wind amplitude with latitude. Furthermore, spectra of the kinetic energy are computed as a point of reference for future studies

Data-driven spectral turbulence modelling for Rayleigh–Bénard convection

A data-driven turbulence model for coarse-grained numerical simulations of two-dimensional Rayleigh–Bénard convection is proposed. The model starts from high-fidelity data and is based on adjusting the Fourier coefficients of the numerical solution, with the aim of accurately reproducing the kinetic energy spectra as seen in the high-fidelity reference findings. No assumptions about the underlying partial differential equation or numerical discretization are used in the formulation of the model. We also develop a constraint on the heat flux to guarantee accurate Nusselt number estimates on coarse computational grids and high Rayleigh numbers. Model performance is assessed in coarse numerical simulations at Ra=1010 . We focus on key features including kinetic energy spectra, wall-normal flow statistics and global flow statistics. The method of data-driven modelling of flow dynamics is found to reproduce the reference kinetic energy spectra well across all scales and yields good results for flow statistics and average heat transfer, leading to computationally cheap surrogate models. Large-scale forcing extracted from the high-fidelity simulation leads to accurate Nusselt number predictions across two decades of Rayleigh numbers, centred around the targeted reference at Ra=1010

Casimir preserving stochastic Lie-Poisson integrators

Casimir preserving integrators for stochastic Lie-Poisson equations with Stratonovich noise are developed extending Runge-Kutta Munthe-Kaas methods. The underlying Lie-Poisson structure is preserved along stochastic trajectories. A related stochastic differential equation on the Lie algebra is derived. The solution of this differential equation updates the evolution of the Lie-Poisson dynamics by means of the exponential map. The constructed numerical method conserves Casimir-invariants exactly, which is important for long time integration. This is illustrated numerically for the case of the stochastic heavy top and the stochastic sine-Euler equations