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Figure 13 in Computational investigation of cicada aerodynamics in forward flight

Figure 13. Transverse plane cut at mid-downstroke. (a) Cut through wing and body and (b) cut through the near wake (no wings or body being cut). (i) Contour of the Q-criterion, velocity vector and two-dimensional streamline seen from the back view. (ii) Vortex structure (Q ¼ 20), and the velocity vector. The view angle is different from that in column (i), and is adjusted to give a better view of the vortex structure. The velocity vectors are three dimensional, and are drawn at every three grid points only. (Online version in colour.)

Figure 11 in Computational investigation of cicada aerodynamics in forward flight

Figure 11. Force generation in one stroke. (a) Total force (FXT, FYT, FZT) is composed of forces from wings in both sides and the cicada body. (b) Force generated by the right wings and the body. (Online version in colour.)

Figure 9 in Computational investigation of cicada aerodynamics in forward flight

Figure 9. Leading edge vortex at the mid-downstroke, coloured by the spanwise vorticity. (Online version in colour.)

Figure 12 in Computational investigation of cicada aerodynamics in forward flight

Figure 12. Instantaneous specific power in a stroke cycle of cicada flight. (Online version in colour.)

Figure 6. Wing motion during a in Computational investigation of cicada aerodynamics in forward flight

Figure 6. Wing motion during a stroke. (a) Start of downstroke; (b) middownstroke; (c) start of upstroke; and (d) mid-upstroke.

Figure 8 in Computational investigation of cicada aerodynamics in forward flight

Figure 8. Streamline around the leading edge at mid-downstroke. Only the right wing is presented for a clearer view.

Figure 10 in Computational investigation of cicada aerodynamics in forward flight

Figure 10. Surface pressure distribution projected on the X-direction (left column) and the Y-direction (right column). (a) Mid-downstroke (Y – Z plane); (b) midupstroke (Y – Z plane); and (c) before the end of the upstroke (X – Y plane).

Figure 5 in Computational investigation of cicada aerodynamics in forward flight

Figure 5. Pitch angle (a) and effective angle of attack (aeff) of the forewing during downstroke and upstroke (a); aeff of the forewing over a full stroke cycle. The leading edge is denoted by a dot. The dotted line indicates the direction of the mean stroke plane (b). The downstroke is shaded. (Online version in colour.)

Figure 4 in Computational investigation of cicada aerodynamics in forward flight

Figure 4. Stoke angle (0° lateral, downstroke positive), deviation angle (upward positive) and pitch angle (rotation around wing span, smaller than 90° when leading edge is forward) for the forewing (a) and the hindwing (b). (Online version in colour.)

Figure 1 in Computational investigation of cicada aerodynamics in forward flight

Figure 1. Real cicada and its reconstruction. (a) Raw picture and (b) comparison of real and reconstructed cicada. (Online version in colour.)

Figure 2 in Computational investigation of cicada aerodynamics in forward flight

Figure 2. Polar coordinates defined by three Euler angles; the wing position shown here is at the mid-downstroke. (Online version in colour.)

Figure 3 in Computational investigation of cicada aerodynamics in forward flight

Figure 3. Body displacement of the cicada during forward flight, surface reconstructions were done for three strokes. (Online version in colour.)

Figure 15 in Computational investigation of cicada aerodynamics in forward flight

Figure 15. Lift production from this study and previous works on a robotic fruit fly wing. (a) Advanced rotation. (b) Delayed rotation. (Online version in colour.)

Figure 7 in Computational investigation of cicada aerodynamics in forward flight

Figure 7. (a – f) Time course of vortex development, visualized by the Q-criterion. The left and middle columns are back view and top view, respectively, and the vortex structures are coloured by spanwise vorticity. The right column shows the structures in projection view, coloured by streamwise vorticity. Colour bars in the first row apply to figures in the same column.

A Computational Analysis of the Ideological Landscape of Turkey and Electoral Behavior

Open the record for dataset details and reuse information.

Towards experimental classical verification of quantum computation

Source data underlying the graphical representations used in the figures.

Demonstration of Portable Performance of Scientific Machine Learning on High Performance Computing Systems

With the largest datasets to date and a diverse set of discoveries to be made, the current generation of scientific analyses are well poised to utilize artificial intelligence (AI) and machine learning (ML) on high performance computing (HPC) resources. Like never before, these workflows can be written in one portable language, python, which thanks to highly-optimized ML libraries achieves excellent cross-platform performance with little to no intervention by the user. In this demonstration, we explore the performance of several scientific AI/ML applications across leading HPC resources and highlight best practices for portable performance.

Data underpinning "Transmon platform for quantum computing challenged by chaotic fluctuations"

Data set underlying the figures in the article "Transmon platform for quantum computing challenged by chaotic fluctuations" (https://doi.org/10.1038/s41467-022-29940-y)From the perspective of many-body physics, the transmon qubit architectures currently developed for quantum computing are systems of coupled nonlinear quantum resonators. A certain amount of intentional frequency detuning ('disorder') is crucially required to protect individual qubit states against the destabilizing effects of nonlinear resonator coupling. In our paper, we investigate the stability of this variant of a many-body localized phase for system parameters relevant to current quantum processors developed by the IBM, Delft, and Google consortia, considering the cases of natural or engineered disorder. Applying three independent diagnostics of localization theory — a Kullback–Leibler analysis of spectral statistics, statistics of many-body wave functions (inverse participation ratios), and a Walsh transform of the many-body spectrum — we find that some of these computing platforms are dangerously close to a phase of uncontrollable chaotic fluctuations.

Data for Unbiasing fermionic quantum Monte Carlo with a quantum computer

Wavefunctions and Hamiltonians from "Unbiasing fermionic quantum Monte Carlo with a quantum computer"

Comparison between Parma Polyhedra Library and ParetoLib for computing the validity domain of a parametric Signal Temporal Logic (STL) expression

Comparison between Parma Polyhedra Library and ParetoLib for computing the validity domain of a parametric Signal Temporal Logic (STL) expression. The code and dataset in this folder is related to the example 5 in section 2 of paper "Mining of Extended Signal Temporal Logic Specifications with ParetoLib 2.0" in the journal Formal Methods and System Design.