Wall-bounded turbulent flows as those occurring in transportation (e.g. aviation) or industrial applications (e.g turbomachinery), are usually subjected to pressure gradients (PGs). The presence of such PGs affects greatly the development and physics of the turbulent boundary layer (TBL), making it an open research area. An important phenomena associated with the presence of strong adverse PGs (APGs) as appearing in wings, is the separation of the boundary layer, which can lead to stall.

The transition from laminar to turbulent flow is a long-standing research subject in the field of fluid mechanics. A basic necessity for such studies is a distinction between laminar and turbulent flow. In particular, a binary segmentation is desired where laminar and turbulent regions behave consistently over time. Previous works in this regard yield inconsistent results, or are restricted to 2D manifolds thereby neglecting the three-dimensional nature of the problem. In this paper, we present a novel use of feature-based methods to segmenting a 3D time-dependent flow into regions of laminar and turbulent behavior. It is based on the extraction of local extrema from a scalar field such as spanwise velocity. It turns out that the existence of many extrema in a region is a good indicator for turbulence. We derive a density function from the extracted extrema using a Kernel Density Estimate (KDE) and threshold it to achieve a binary segmentation into laminar and turbulent regions. We use an in-situ processing approach for data analysis during the simulation run. The two core components of our method exhibit drastically different performance characteristics: the extraction of extrema is embarrassingly parallel, while the KDE is more time-consuming. Hence, we decouple our algorithmic components to achieve a better overall system performance. Our method shows consistent results and enables the domain scientists to study the three-dimensional aspects of the laminar-turbulent transition that were difficult to assess before.

In reinforcement learning, tuning reward weights in the reward function is necessary to align behavior with user preferences. However, current approaches, which use pairwise comparisons for preference elicitation, are inefficient, because they miss much of the human ability to explore and judge groups of candidate solutions. The paper presents a novel visualization-based approach that better exploits the user's ability to quickly recognize interesting directions for reward tuning. It breaks down the tuning problem by using the visual information-seeking principle: overview first, zoom and filter, then details-on-demand. Following this principle, we built a visualization system comprising two interactively linked views: 1) an embedding view showing a contextual overview of all sampled behaviors and 2) a sample view displaying selected behaviors and visualizations of the detailed time-series data. A user can efficiently explore large sets of samples by iterating between these two views. The paper demonstrates that the proposed approach is capable of tuning rewards for challenging behaviors. The simulation-based evaluation shows that the system can reach optimal solutions with fewer queries relative to baselines.

The Morse-Smale complex is a standard tool in visual data analysis. The classic definition is based on a continuous view of the gradient of a scalar function where its zeros are the critical points. These points are connected via gradient curves and surfaces emanating from saddle points, known as separatrices. In a discrete setting, the Morse-Smale complex is commonly extracted by constructing a combinatorial gradient assuming the steepest descent direction. Previous works have shown that this method results in a geometric embedding of the separatrices that can be fundamentally different from those in the continuous case. To achieve a similar embedding, different approaches for constructing a combinatorial gradient were proposed. In this paper, we show that these approaches generate a different topology, i.e., the connectivity between critical points changes. Additionally, we demonstrate that the steepest descent method can compute topologically and geometrically accurate Morse-Smale complexes when applied to certain types of grids. Based on these observations, we suggest a method to attain both geometric and topological accuracy for the Morse-Smale complex of data sampled on a uniform grid.

Scatterplots are commonly used in various contexts, from scientific publications to infographics for the general public. However, not everyone is able to read them, and even experts may struggle to notice some important information such as overlapping clusters or temporal changes. To address these issues, a computational approach for annotating scatterplots has been developed. This approach involves various forms of annotation, including drawing lines to show correlations, circling areas to show clusters, and indicating movement with arrows. The approach is based on a study that identified common annotation strategies used by people to annotate scatterplots. These strategies are distilled into an automated method for generating graphical annotations on scatterplots. The method involves a problem formulation using a Markov Decision Process and a model for making annotation decisions. The model generates step-by-step graphical annotations by analyzing data insights and observing the chart. The final result conveys a narrative that is easy to understand and allows for the conveyance of temporal changes in the data. The study results suggest that the method can generate understandable and functional annotations that are comparable to those created by human experts. This approach can potentially reduce the time and effort required to read scatterplots, making it a useful tool for data visualization novices.

We detail our developments in the high-fidelity spectral-element code Neko that are essential for unprecedented large-scale direct numerical simulations of fully developed turbulence. Major inno- vations are modular multi-backend design enabling performance portability across a wide range of GPUs and CPUs, a GPU-optimized preconditioner with task overlapping for the pressure-Poisson equation and in-situ data compression. We carry out initial runs of Rayleigh–Bénard Convection (RBC) at extreme scale on the LUMI and Leonardo supercomputers. We show how Neko is able to strongly scale to 16,384 GPUs and obtain results that are not pos- sible without careful consideration and optimization of the entire simulation workflow. These developments in Neko will help resolv- ing the long-standing question regarding the ultimate regime in RBC. 

Dendritic spines are submicron, subcellular compartments whose shape is defined by actin filaments and associated proteins. Accurately mapping the cytoskeleton is a challenge, given the small size of its components. It remains unclear whether the actin-associated structures analyzed in dendritic spines of neurons in vitro apply to dendritic spines of intact, mature neurons in situ. Here, we combined advanced preparative methods with multitilt serial section electron microscopy (EM) tomography and computational analysis to reveal the full three-dimensional (3D) internal architecture of spines in the intact brains of male mice at nanometer resolution. We compared hippocampal (CA1) pyramidal cells and cerebellar Purkinje cells in terms of the length distribution and connectivity of filaments, their branching-angles and absolute orientations, and the elementary loops formed by the network. Despite differences in shape and size across spines and between spine heads and necks, the internal organization was remarkably similar in both neuron types and largely homogeneous throughout the spine volume. In the tortuous mesh of highly branched and interconnected filaments, branches exhibited no preferred orientation except in the immediate vicinity of the cell membrane. We found that new filaments preferentially split off from the convex side of a bending filament, consistent with the behavior of Arp2/3-mediated branching of actin under mechanical deformation. Based on the quantitative analysis, the spine cytoskeleton is likely subject to considerable mechanical force in situ. 

In situ visualization on high-performance computing systems allows us to analyze simulation results that would otherwise be impossible, given the size of the simulation data sets and offline post-processing execution time. We develop an in situ adaptor for Paraview Catalyst and Nek5000, a massively parallel Fortran and C code for computational fluid dynamics. We perform a strong scalability test up to 2048 cores on KTH’s Beskow Cray XC40 supercomputer and assess in situ visualization’s impact on the Nek5000 performance. In our study case, a high-fidelity simulation of turbulent flow, we observe that in situ operations significantly limit the strong scalability of the code, reducing the relative parallel efficiency to only ≈ 21 % on 2048 cores (the relative efficiency of Nek5000 without in situ operations is ≈ 99 %). Through profiling with Arm MAP, we identified a bottleneck in the image composition step (that uses the Radix-kr algorithm) where a majority of the time is spent on MPI communication. We also identified an imbalance of in situ processing time between rank 0 and all other ranks. In our case, better scaling and load-balancing in the parallel image composition would considerably improve the performance of Nek5000 with in situ capabilities. In general, the result of this study highlights the technical challenges posed by the integration of high-performance simulation codes and data-analysis libraries and their practical use in complex cases, even when efficient algorithms already exist for a certain application scenario.

Creating a static visualization for a time-dependent scalar field is a non-trivial task, yet very insightful as it shows the dynamics in one picture. Existing approaches are based on a linearization of the domain or on feature tracking. Domain linearizations use space-filling curves to place all sample points into a 1D domain, thereby breaking up individual features. Feature tracking methods explicitly respect feature continuity in space and time, but generally neglect the data context in which those features live. We present a feature-based linearization of the spatial domain that keeps features together and preserves their context by involving all data samples. We use augmented merge trees to linearize the domain and show that our linearized function has the same merge tree as the original data. A greedy optimization scheme aligns the trees over time providing temporal continuity. This leads to a static 2D visualization with one temporal dimension, and all spatial dimensions compressed into one. We compare our method against other domain linearizations as well as feature-tracking approaches, and apply it to several real-world data sets.

Dense flow visualization is a popular visualization paradigm. Traditionally, the various models and methods in this area use a continuous formulation, resting upon the solid foundation of functional analysis. In this work, we examine a discrete formulation of dense flow visualization. From probability theory, we derive a similarity matrix that measures the similarity between different points in the flow domain, leading to the discovery of a whole new class of visualization models. Using this matrix, we propose a novel visualization approach consisting of the computation of spectral embeddings, i.e., characteristic domain maps, defined by particle mixture probabilities. These embeddings are scalar fields that give insight into the mixing processes of the flow on different scales. The approach of spectral embeddings is already well studied in image segmentation, and we see that spectral embeddings are connected to Fourier expansions and frequencies. We showcase the utility of our method using different 2D and 3D flows.