34,986 research outputs found

    Meso-scale FDM material layout design strategies under manufacturability constraints and fracture conditions

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    In the manufacturability-driven design (MDD) perspective, manufacturability of the product or system is the most important of the design requirements. In addition to being able to ensure that complex designs (e.g., topology optimization) are manufacturable with a given process or process family, MDD also helps mechanical designers to take advantage of unique process-material effects generated during manufacturing. One of the most recognizable examples of this comes from the scanning-type family of additive manufacturing (AM) processes; the most notable and familiar member of this family is the fused deposition modeling (FDM) or fused filament fabrication (FFF) process. This process works by selectively depositing uniform, approximately isotropic beads or elements of molten thermoplastic material (typically structural engineering plastics) in a series of pre-specified traces to build each layer of the part. There are many interesting 2-D and 3-D mechanical design problems that can be explored by designing the layout of these elements. The resulting structured, hierarchical material (which is both manufacturable and customized layer-by-layer within the limits of the process and material) can be defined as a manufacturing process-driven structured material (MPDSM). This dissertation explores several practical methods for designing these element layouts for 2-D and 3-D meso-scale mechanical problems, focusing ultimately on design-for-fracture. Three different fracture conditions are explored: (1) cases where a crack must be prevented or stopped, (2) cases where the crack must be encouraged or accelerated, and (3) cases where cracks must grow in a simple pre-determined pattern. Several new design tools, including a mapping method for the FDM manufacturability constraints, three major literature reviews, the collection, organization, and analysis of several large (qualitative and quantitative) multi-scale datasets on the fracture behavior of FDM-processed materials, some new experimental equipment, and the refinement of a fast and simple g-code generator based on commercially-available software, were developed and refined to support the design of MPDSMs under fracture conditions. The refined design method and rules were experimentally validated using a series of case studies (involving both design and physical testing of the designs) at the end of the dissertation. Finally, a simple design guide for practicing engineers who are not experts in advanced solid mechanics nor process-tailored materials was developed from the results of this project.U of I OnlyAuthor's request

    Classical model emerges in quantum entanglement: Quantum Monte Carlo study for an Ising-Heisenberg bilayer

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    By developing a cluster sampling of stochastic series expansion quantum Monte Carlo method, we investigate a spin-1/21/2 model on a bilayer square lattice with intra-layer ferromagnetic (FM) Ising coupling and inter-layer antiferromagnetic Heisenberg interaction. The continuous quantum phase transition which occurs at gc=3.045(2)g_c=3.045(2) between the FM Ising phase and the dimerized phase is studied via large scale simulations. From the analyzes of critical exponents we show that this phase transition belongs to the (2+1)-dimensional Ising universality class. Besides, the quantum entanglement is strong between the two layers, especially in dimerized phase. The effective Hamiltonian of single layer seems like a transverse field Ising model. However, we found the quantum entanglement Hamiltonian is a pure classical Ising model without any quantum fluctuations. Furthermore, we give a more general explanation about how a classical entanglement Hamiltonian emerges

    Thread-safe lattice Boltzmann for high-performance computing on GPUs

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    We present thread-safe, highly-optimized lattice Boltzmann implementations, specifically aimed at exploiting the high memory bandwidth of GPU-based architectures. At variance with standard approaches to LB coding, the proposed strategy, based on the reconstruction of the post-collision distribution via Hermite projection, enforces data locality and avoids the onset of memory dependencies, which may arise during the propagation step, with no need to resort to more complex streaming strategies. The thread-safe lattice Boltzmann achieves peak performances, both in two and three dimensions and it allows to sensibly reduce the allocated memory ( tens of GigaBytes for order billions lattice nodes simulations) by retaining the algorithmic simplicity of standard LB computing. Our findings open attractive prospects for high-performance simulations of complex flows on GPU-based architectures

    Fragmentation of the High-mass "Starless'' Core G10.21-0.31: a Coherent Evolutionary Picture for Star Formation

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    G10.21-0.31 is a 70 μ\mum-dark high-mass starless core (M>300M>300 M⊙\mathrm{M_{\odot}} within r<0.15r<0.15 pc) identified in SpitzerSpitzer, HerschelHerschel, and APEX continuum surveys, and is believed to harbor the initial stages of high-mass star formation. We present ALMA and SMA observations to resolve the internal structure of this promising high-mass starless core. Sensitive high-resolution ALMA 1.3 mm dust continuum emission reveals three cores of mass ranging 11-18 M⊙\mathrm{M_{\odot}}, characterized by a turbulent fragmentation. Core 1, 2, and 3 represent a coherent evolution at three different evolutionary stages, characterized by outflows (CO, SiO), gas temperature (H2CO\mathrm{H_2CO}), and deuteration (N2D+/N2H+\mathrm{N_2D^+/N_2H^+}). We confirm the potential to form high-mass stars in G10.21 and explore the evolution path of high-mass star formation. Yet, no high-mass prestellar core is present in G10.21. This suggests a dynamical star formation where cores grow in mass over time.Comment: 30 pages, 13 figures; accepted for publication in Ap

    Length-dry mass regressions for Leptonema (Trichoptera, Hydropsychidae) larvae in a Neotropical headwater stream

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    Abstract: Aim The objectives of this study were to evaluate which allometric measurements of Leptonema larvae are most suitable in order to develop mathematical equations to describe biomass relationships for the population of this taxon in a reference condition headwater stream. Methods We measured four body dimensions (body length, interocular distance, horizontal width of cephalic capsule and vertical width of the cephalic capsule) of 65 Leptonema larvae, which were collected in February 2022, in the Taboões spring, Serra do Rola Moça State Park, Minas Gerais, using a Surber sampler. For the determination of allometric measurements, each individual was photographed under a dissecting stereomicroscope (Leica M80) equipped with a digital camera. Each photographed specimen's length was measured using the Motic Image Plus 2.0 software. After measuring the linear body dimension and direct measurement of the biomass, we used these values to calculate the length-mass mathematical equations. To the equations use power models: DM = a Lb, where a/b are constants, DM is the dry mass, L is the linear body dimension. Results Among body dimensions of Leptonema larvae, body length showed the greatest range of variation, with values ranging from 4.03 to 25.28 mm, followed by head capsule vertical width (0.51 - 2.69 mm), head capsule horizontal width (0.55 - 2.22 mm) and interocular distance (0.24 - 1.88 mm). Our results show that body length provided the best-fitting equation for estimating biomass (R2 = 0.90). However, we observed a close fit between the other allometric measures, including high coefficients of determination, head capsule horizontal width (R2 = 0.85), interocular distance (R2 = 0.82), head capsule vertical width (R2 = 0.78). Conclusions These results will be useful in providing the best allometric measurement and equations to estimate the biomass of Leptonema larvae from the tropics

    Differentiable programming tensor networks for Kitaev magnets

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    We present a general computational framework to investigate ground state properties of quantum spin models on infinite two-dimensional lattices using automatic differentiation-based gradient optimization of infinite projected entangled-pair states. The approach exploits the variational uniform matrix product states to contract infinite tensor networks with unit-cell structure and incorporates automatic differentiation to optimize the local tensors. We applied this framework to the Kitaev-type model, which involves complex interactions and competing ground states. To evaluate the accuracy of this method, we compared the results with exact solutions for the Kitaev model and found that it has a better agreement for various observables compared to previous tensor network calculations based on imaginary-time projection. Additionally, by finding out the ground state with lower variational energy compared to previous studies, we provided convincing evidence for the existence of nematic paramagnetic phases and 18-site configuration in the phase diagram of the KK-Γ\Gamma model. Furthermore, in the case of the realistic KK-JJ-Γ\Gamma-Γ′\Gamma' model for the Kitaev material α\alpha-RuCl3_3, we discovered a non-colinear zigzag ground state. Lastly, we also find that the strength of the critical out-of-plane magnetic field that suppresses such a zigzag state has a lower transition field value than the previous finite-cylinder calculations. The framework is versatile and will be useful for a quick scan of phase diagrams for a broad class of quantum spin models

    Thermodynamic Assessment and Optimisation of Supercritical and Transcritical Power Cycles Operating on CO2 Mixtures by Means of Artificial Neural Networks

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    Feb 21, 2022 to Feb 24, 2022, San Antonio, TX, United StatesClosed supercritical and transcritical power cycles operating on Carbon Dioxide have proven to be a promising technology for power generation and, as such, they are being researched by numerous international projects today. Despite the advantageous features of these cycles enabling very high efficiencies in intermediate temperature applications, the major shortcoming of the technology is a strong dependence on ambient temperature; in order to perform compression near the CO2 critical point (31ºC), low ambient temperatures are needed. This is particularly challenging in Concentrated Solar Power applications, typically found in hot, semi-arid locations. To overcome this limitation, the SCARABEUS project explores the idea of blending raw carbon dioxide with small amounts of certain dopants in order to shift the critical temperature of the resulting working fluid to higher values, hence enabling gaseous compression near the critical point or even liquid compression regardless of a high ambient temperature. Different dopants have been studied within the project so far (i.e. C6F6, TiCl4 and SO2) but the final selection will have to account for trade-offs between thermodynamic performance, economic metrics and system reliability. Bearing all this in mind, the present paper deals with the development of a non-physics-based model using Artificial Neural Networks (ANN), developed using Matlab’s Deep Learning Toolbox, to enable SCARABEUS system optimisation without running the detailed – and extremely time consuming – thermal models, developed with Thermoflex and Matlab software. In the first part of the paper, the candidate dopants and cycle layouts are presented and discussed, and a thorough description of the ANN training methodology is provided, along with all the main assumptions and hypothesis made. In the second part of the manuscript, results confirms that the ANN is a reliable tool capable of successfully reproducing the detailed Thermoflex model, estimating the cycle thermal efficiency with a Root Mean Square Error lower than 0.2 percentage points. Furthermore, the great advantage of using the Artificial Neural Network proposed is demonstrated by the huge reduction in the computational time needed, up to 99% lower than the one consumed by the detailed model. Finally, the high flexibility and versatility of the ANN is shown, applying this tool in different scenarios and estimating different cycle thermal efficiency for a great variety of boundary conditions.Unión Europea H2020-81498

    Calibration of the convective parameters in stellar pulsation hydrocodes

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    Despite the appearance of two- and three-dimensional models thanks to the rapid growth of computing performance, numerical hydrocodes used to model radial stellar pulsations still apply a one-dimensional stellar envelope model without any realistic atmosphere, in which a significant improvement was the inclusion of turbulent convection. However, turbulent convection is an inherently multi-dimensional physical process in the vicinity of the ionization zones that generate pulsation. The description of these processes in one dimension can only be approximated based on simplified theoretical considerations involving several undetermined dimensionless parameters. In this work, we confront two one-dimensional numerical codes, namely the Budapest-Florida code (BpF) and the MESA Radial Stellar Pulsations module (RSP), with radial velocity observations of several non-modulated RRab stars of the M3 globular cluster and specified the undetermined convective parameters by the measured data for both codes independently. Our determination shows that some of the parameters depend on the effective temperature, which dependence is established for the first time in this work, and we also found some degeneracy between the parameters. This procedure gives as by-product suggestions for parameters of the publicly available RSP code extensively used recently by researchers through the MESA package. This work is part of the preparatory work to establish a theoretical framework required to make progress based on the results of one-dimensional models to couple them with multi-dimensional ones for further detailed analysis of physical processes.Comment: Accepted in MNRAS, 19 pages, 14 figures, for associated mp4 video see https://konkoly.hu/KIK/data_en.html#P
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