Reduced-order modeling of two-dimensional turbulent Rayleigh-Bénard flow by hybrid quantum-classical reservoir computing. - In: Physical review research, ISSN 2643-1564, Bd. 5 (2023), 4, 043242, S. 043242-1-043242-13
Two hybrid quantum-classical reservoir computing models are presented to reproduce the low-order statistical properties of a two-dimensional turbulent Rayleigh-Bénard convection flow at a Rayleigh number Ra=105 and 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. The latter is composed of vertical velocity and temperature and measures the global turbulent heat transfer across the convection layer; it manifests locally in coherent hot and cold thermal plumes that rise from the bottom and fall from the top boundaries. 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 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 or open-loop mode, i.e., they receive three POD modes as an input at each step and reconstruct the 13 missing modes. We analyze 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 with the classical reservoir network. Most importantly, the quantum reservoirs are by a factor of four to eight smaller in comparison with the classical case.
https://doi.org/10.1103/PhysRevResearch.5.043242
Data-driven estimation of scalar quantities from planar velocity measurements by deep learning applied to temperature in thermal convection. - In: Experiments in fluids, ISSN 1432-1114, Bd. 64 (2023), 12, 191, S. 1-18
The measurement of the transport of scalar quantities within flows is oftentimes laborious, difficult or even unfeasible. On the other hand, velocity measurement techniques are very advanced and give high-resolution, high-fidelity experimental data. Hence, we explore the capabilities of a deep learning model to predict the scalar quantity, in our case temperature, from measured velocity data. Our method is purely data-driven and based on the u-net architecture and, therefore, well-suited for planar experimental data. We demonstrate the applicability of the u-net on experimental temperature and velocity data, measured in large aspect ratio Rayleigh-Bénard convection at Pr = 7.1 and Ra = 2 x 10^5, 4 x 10^5, 7 x 10^5. We conduct a hyper-parameter optimization and ablation study to ensure appropriate training convergence and test different architectural variations for the u-net. We test two application scenarios that are of interest to experimentalists. One, in which the u-net is trained with data of the same experimental run and one in which the u-net is trained on data of different Ra. Our analysis shows that the u-net can predict temperature fields similar to the measurement data and preserves typical spatial structure sizes. Moreover, the analysis of the heat transfer associated with the temperature showed good agreement when the u-net is trained with data of the same experimental run. The relative difference between measured and reconstructed local heat transfer of the system characterized by the Nusselt number Nu is between 0.3 and 14.1% depending on Ra. We conclude that deep learning has the potential to supplement measurements and can partially alleviate the expense of additional measurement of the scalar quantity.
https://doi.org/10.1007/s00348-023-03736-2
Dual-band nested circularly polarized antenna array for 5G automotive satellite communications. - In: Applied Sciences, ISSN 2076-3417, Bd. 13 (2023), 21, 11915, S. 1-15
Currently, 5G low-earth orbit satellite communications offer enhanced wireless coverage beyond the reach of 5G terrestrial networks, with important implications, particularly for automated and connected vehicles. Such wireless automotive mass-market applications demand well-designed compact user equipment antenna terminals offering non-terrestrial jointly with terrestrial communications. The antenna should be low-profile, conformal, and meet specific parameter values for gain and operational frequency bandwidth, tailored to the intended applications, in line with the aesthetic design requirements of passenger cars. This work presents an original concept for a dual-band nested circularly polarized automotive user terminal that operates at the S-band frequencies around 3.5 GHz and Ka-band frequencies around 28 GHz, namely within the 5G new-radio bands n78 and n257, respectively. The proposed terminal is designed to be integrated into the plastic components of a passenger vehicle. The arrays consist of 2 × 2 aperture-coupled corner-truncated microstrip slot patch antenna elements for the n78 band and of 4 × 4 single-layer edge-truncated microstrip circular slot patch antenna elements for the n257 band. The embedded arrays offer, across the two bands, respectively, 9.9 and 13.7 dBi measured realized gain and 3-dB axial ratio bandwidths of 100 and 1500 MHz for the n78 and n257 bands along the broadside direction. Detailed link budget calculations anticipate uplink data rates of 21 and 6 Mbit/s, respectively, deeming it suitable for various automotive mobility and Internet-of-Things applications.
https://doi.org/10.3390/app132111915
Stories of astrobiology, SETI, and UAPs: science and the search for extraterrestrial life in German news media from 2009 to 2022. - In: Science communication, ISSN 1552-8545, Bd. 45 (2023), 6, S. 788-823
The search for extraterrestrial intelligent (SETI) and non-intelligent extraterrestrial life has recently received considerable attention in academia and international news media. Since media frames of scientific space exploration potentially influence public support and perceptions of science, the German news media’s coverage of extraterrestrial life was analyzed. The three dominant frames from 2009 to 2022 were beneficial space exploration, unidentified aerial phenomena (UAP)/extraterrestrial intelligence (ETI), and SETI risk. Two frames relied primarily on scientific sources, mainly universities/research centers, NASA, the SETI Institute, and Stephen Hawking. The European Space Agency (ESA), the German Aerospace Center (DLR), and astrobiology as a discipline were rarely cited. Implications for science and risk communication are discussed.
https://doi.org/10.1177/10755470231206797
Investigation of the fiber length and the mechanical properties of waste recycled from continuous glass fiber-reinforced polypropylene. - In: Recycling, ISSN 2313-4321, Bd. 8 (2023), 6, 82, S. 1-20
This paper explores the mechanical recycling of continuous fiber-reinforced thermoplastics (CFRTPs) waste into injection molded products, focusing on the influence of recycling parameters on fiber length and mechanical properties. CFRTPs are gaining attention for their promising attributes, including weight-specific mechanical properties, short cycle times, storability, and recyclability, making them suitable for diverse applications. However, as CFRTP production rates rise, recycling strategies become crucial for sustainability. This study investigates the processability of CFRTP waste, defines size reduction conditions, and evaluates the impact of various compounding parameters such as temperature, screw speed, and fiber volume content during extrusion. The research findings indicate that higher screw speeds lead to fiber length reduction, whereas elevated temperatures result in longer fibers. Increased fiber volume intensifies interactions, resulting in shorter lengths. Additionally, the study examines the influence of injection molding parameters such as back pressure, screw speed, and initial fiber length on the resulting fiber length and mechanical properties of injection molded specimens, emphasizing the need for precise parameter control to optimize performance in recycled CFRTPs. Key findings are that increasing the initial fiber length from 260 μm to 455 μm results in an average fiber length after injection molding of 225 μm and 341 μm, respectively. This implies that longer initial fibers are more prone to breakage. Regarding the mechanical properties, increasing back pressure from 20 bar to 60 bar results in a reduction in Young’s modulus of approximately 40 MPa. Higher screw speed also reduces modulus by approximately 70 MPa due to intensified fiber-screw interactions. However, back pressure and screw speed have neutral effects on the tensile strength and the elongation at break.
https://doi.org/10.3390/recycling8060082
Data augmentation for design of concentric tube continuum robots by generative adversarial networks. - In: Proceedings in applied mathematics and mechanics, ISSN 1617-7061, Bd. 23 (2023), 4, e202300278, S. 1-7
Concentric tube continuum robots are a promising type of robot for various medical applications. Their application in neurosurgery poses challenging requirements for design and control that can be addressed by physics-informed data-based approaches. A prerequisite to data-based modeling is an informative, rich data set. However, limited access to experimental data raises interest in partially or entirely synthetic data sets. In this contribution, we study the application of generative adversarial networks (GANs) for data augmentation in a data-based design process of such robots. We propose a GAN framework suitable for curve-fitting to generate synthetic trajectories of robots along with their corresponding control parameters. Our evaluation shows that the GANs can efficiently produce meaningful synthetic trajectories and control parameter pairs that show a good agreement with simulated trajectories.
https://doi.org/10.1002/pamm.202300278
Stage-cost design for optimal and model predictive control of linear port-Hamiltonian systems: energy efficiency and robustness. - In: Proceedings in applied mathematics and mechanics, ISSN 1617-7061, Bd. 23 (2023), 4, e202300296, S. 1-9
We consider singular optimal control of port-Hamiltonian systems with minimal energy supply. We investigate the robustness of different stage-cost designs w.r.t. time discretization and show that alternative formulations that are equivalent in continuous time, differ strongly in view of discretization. Furthermore, we consider the impact of additional quadratic control regularization and demonstrate that this leads to a considerable increase in energy consumption. Then, we extend our results to the tracking problem within model predictive control and show that the intrinsic but singular choice of the cost functional as the supplied energy leads to a substantial improvement of the closed-loop performance.
https://doi.org/10.1002/pamm.202300296
On the behavior of prolate spheroids in a standing surface acoustic wave field. - In: Microfluidics and nanofluidics, ISSN 1613-4990, Bd. 27 (2023), 12, 81, S. 1-19
The active manipulation of particle and cell trajectories in fluids by high-frequency standing surface acoustic waves (sSAW) allows to separate particles and cells systematically depending on their size and acoustic contrast. However, process technologies and biomedical applications usually operate with non-spherical particles, for which the prediction of acoustic forces is highly challenging and remains a subject of ongoing research. In this study, the dynamical behavior of prolate spheroids exposed to a three-dimensional acoustic field with multiple pressure nodes along the channel width is examined. Optical measurements reveal an alignment of the particles orthogonal to the pressure nodes of the sSAW, which has not been reported in literature so far. The dynamical behavior of the particles is analyzed under controlled initial conditions for various motion patterns by imposing a phase shift on the sSAW. To gain detailed understanding of the particle dynamics, a three-dimensional numerical model is developed to predict the acoustic force and torque acting on a prolate spheroid. Considering the acoustically induced streaming around the particle, the numerical results are in excellent agreement with experimental findings. Using the proposed numerical model, a dependence of the acoustic force on the particle shape is found in relation to the acoustic impedance of the channel ceiling. Hence, the numerical model presented herein promises high progress for the design of separation devices utilizing sSAW, exploiting an additional separation criterion based on the particle shape.
https://doi.org/10.1007/s10404-023-02690-z
Accelerating large table scan using Processing-In-Memory technology. - In: Datenbank-Spektrum, ISSN 1610-1995, Bd. 23 (2023), 3, S. 199-209
Today’s systems are capable of storing large amounts of data in main memory. Particularly, in-memory DBMSs benefit from this development. However, the processing of data from the main memory necessarily has to run via the CPU. This creates a bottleneck, which affects the possible performance of the DBMS. Processing-In-Memory (PIM) is a paradigm to overcome this problem, which was not available in commercial systems for a long time. With the availability of UPMEM, a commercial product is finally available that provides PIM technology in hardware. In this work, we focus on the acceleration of the table scan, a fundamental database query operation. We show and investigate an approach that can be used to optimize this operation by using PIM. We evaluate the PIM scan in terms of parallelism and execution time in benchmarks with different table sizes and compare it to a traditional CPU-based table scan. The result is a PIM table scan that outperforms the CPU-based scan significantly.
https://doi.org/10.1007/s13222-023-00456-z
Flower electrodes for comfortable dry electroencephalography. - In: Scientific reports, ISSN 2045-2322, Bd. 13 (2023), 16589, S. 1-15
Dry electroencephalography (EEG) electrodes provide rapid, gel-free, and easy EEG preparation, but with limited wearing comfort. We propose a novel dry electrode comprising multiple tilted pins in a flower-like arrangement. The novel Flower electrode increases wearing comfort and contact area while maintaining ease of use. In a study with 20 volunteers, we compare the performance of a novel 64-channel dry Flower electrode cap to a commercial dry Multipin electrode cap in sitting and supine positions. The wearing comfort of the Flower cap was rated as significantly improved both in sitting and supine positions. The channel reliability and average impedances of both electrode systems were comparable. Averaged VEP components showed no considerable differences in global field power amplitude and latency, as well as in signal-to-noise ratio and topography. No considerable differences were found in the power spectral density of the resting state EEGs between 1 and 40 Hz. Overall, our findings provide evidence for equivalent channel reliability and signal characteristics of the compared cap systems in the sitting and supine positions. The reliability, signal quality, and significantly improved wearing comfort of the Flower electrode allow new fields of applications for dry EEG in long-term monitoring, sensitive populations, and recording in supine position.
https://doi.org/10.1038/s41598-023-42732-8