The prediction of departure from nucleate boiling (DNB) has always been a crucial aspect of thermal-hydraulic codes for the analysis of Light Water reactors. In this paper, GeN-Foam, a multi-physics code developed bas...
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A deep learning based method with the convolutional neural network (CNN) algorithm for determining the impact parameters is developed by using constrained molecular-dynamics model simulations, focusing on the heavy-io...
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A deep learning based method with the convolutional neural network (CNN) algorithm for determining the impact parameters is developed by using constrained molecular-dynamics model simulations, focusing on the heavy-ion collisions at the low-intermediate incident energies from several tens to one hundred MeV/nucleon in which the emissions of heavy fragments with the charge numbers larger than three become crucial. To make the CNN applicable in the task of the impact-parameter determination at the present energy range, specific improvements are made in the input selection, the CNN construction, and the CNN training. It is demonstrated from the comparisons of the deep CNN method and the conventional methods with the impact-parameter-sensitive observables that the deep CNN method shows better performance for determining the impact parameters, especially leading to the capability of providing better recognition of the central collision events. With a proper consideration of the experimental filter effect in both training and testing processes to keep consistency with the actual experiments, the good performance of the deep CNN method holds and performs significantly better in terms of predicting the impact parameters and recognizing the central collision events compared with conventional methods, demonstrating the superiority of the present deep CNN method. The deep CNN method with the consideration of the filter effect is applied in the deduction of nuclear stopping power. Higher accuracy for the stopping power deduction is achieved benefitting from the better impact-parameter determination using the deep CNN method, compared with using conventional methods. This result reveals the importance of selecting a reliable impact-parameter-determination method in the experimental deduction of the nuclear stopping power as well as other observables.
The ASME code allows the designer to circumvent many of the problems arising in design by elastic analysis by performing plastic or limit analysis of the component. Unlike elastic analysis, these analyses take account...
The ASME code allows the designer to circumvent many of the problems arising in design by elastic analysis by performing plastic or limit analysis of the component. Unlike elastic analysis, these analyses take account of the redistribution of stress upon yield. In limit analysis, the calculation of the limit load for finite plate with different semi-elliptical defects is an important stage for mechanical safety evaluation. The elastic compensation technique proposed by Mackenzie can be used to provide the limit load of a structure with a finite element method and aims at producing a suitable stress filed for the lower bound theory. In the present study, it is assumed that the finite plate exists three kinds of semi-elliptical cracks with low aspect ratio a/c (a/c=0.1, 0.5 and 1.0), respectively. The improved elastic compensation technique realized by finite element method is applied in the limit load estimation. At last, the results show that the improved elastic compensation technique can be used to obtain the limit value for the finite plate with different semi-elliptical defects under tensile loading. Moreover, the improved elastic compensation technique can be easily implemented in any finite element method and enables the limit load for any complex structure to be obtained swiftly.
OpenFOAM is a free, open-source software package that can be used for the solutions of computational fluid dynamics and simulation of various fluid flow processes. Nevertheless, OpenFOAM still lacks default settings a...
OpenFOAM is a free, open-source software package that can be used for the solutions of computational fluid dynamics and simulation of various fluid flow processes. Nevertheless, OpenFOAM still lacks default settings and a large number of different numerical schemes and turbulent models should be validated. In this paper, the unsteady flow around a cylinder (Re=3900) is calculated by the large eddy simulation of OpenFOAM. The predictions include the drag and lift coefficient, the pressure distribution around the cylinder, the velocity distribution and Reynolds stress distribution in the wake region, as well as the prediction of the recirculation length and separation angle. Thanks to several simulations, these five subgrid-scale (SGS) models are compared and studied: The Smagorinsky SGS model, wall adaptive local eddy visibility SGS model, dynamic SGS model with Lagrangian averaging, dynamic one equation eddy visibility model, one equation eddy visibility model. The numerical results are verified with the published experimental data.
The conventional design of composite pipe fittings is highly dependent on experimental testing and finite element simulations, which are both costly and time-intensive. To address these challenges, this study introduc...
The conventional design of composite pipe fittings is highly dependent on experimental testing and finite element simulations, which are both costly and time-intensive. To address these challenges, this study introduces a deep learning-based inverse design approach to optimize the stiffness characteristics of carbon fiber reinforced polymer (CFRP) composite pipe fittings. Two predictive models were developed: a Long-Short-Term Memory-Based (LSTMB) Model and a Multi-Head Attention-Based (MHAB) Model. Comparative evaluations revealed that the MHAB model outperformed the LSTMB model in terms of predictive accuracy and generalization capability. Based on this, a population-based optimization algorithm was integrated to achieve the inverse design of the composite pipe fittings, ensuring efficient structural optimization while satisfying design constraints. The proposed method was validated through two optimization case studies, demonstrating its effectiveness in improving the efficiency and precision of composite pipe fitting design. This study highlights the potential of deep learning, particularly the Transformer framework, to accelerate the design and optimization of composite materials.
The grain boundary (GB) microstructure influences and is influenced by the development of residual stresses during synthesis of polycrystalline thin films. Recent studies have shown that the frustration between the pr...
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The supercooled water droplets in the air will form a water film on the aircraft during flight, which will then form icing, which will have an impact on the aerodynamic performance of the aircraft and even endanger th...
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(数字)9781837242108
The supercooled water droplets in the air will form a water film on the aircraft during flight, which will then form icing, which will have an impact on the aerodynamic performance of the aircraft and even endanger the flight safety. In this paper, based on the Eulerian Wall Films model, the partial wetting effect is introduced, and the water film flow on flat plates driven by airflow shear force is investigated. The flow behaviours of water film with different contact angles under different flow rates were explored through water film test. The effectiveness of the calculation method for water film flow characteristics in this paper was verified by comparison between numerical simulation and test records, which laid a foundation for the anti-icing calculation in subsequent studies.
Recent experiments have unveiled a fascinating phenomenon: excitable cells exhibit mechanical memory, whereby their present excitation behaviours strongly depend on their past mechanical experiences. However, the unde...
Recent experiments have unveiled a fascinating phenomenon: excitable cells exhibit mechanical memory, whereby their present excitation behaviours strongly depend on their past mechanical experiences. However, the underlying mechanism of this phenomenon remains elusive. Here, we introduce an electromechanical framework that integrates mechanical cell deformation, state transformations of mechanosensitive (MS) channels (such as Piezo channels), and transmembrane ion fluxes. We reveal that MS channel inactivation yields a history-dependent excitation dynamics, characterized by a progressive decline in subsequent activated currents with increasing amplitude, speed, and duration of prior mechanical stimuli. Moreover, MS channel inactivation in preceding stimulation results in a refractory period during which cells cannot elicit new action potentials upon subsequent mechanical stimuli. Finally, we show that cells can adapt to preceding mechanical stimulation due to inactivation of MS channels, resulting in a higher activated threshold stimulation. Thus, MS channel inactivation favors the reduction of firing activities in response to prolonged and repeated mechanical stimuli ("neural adaptation"), which may protect neurons against over-activation and damage. We then conduct two virtual experiments to predict how changes in mechanical properties of neurons modify their excitation behaviours. These findings together emphasize a critical role of MS channel inactivation in governing the mechanical memory and neural adaptation of excitable cells, shedding new light on the intricate interplay between mechanical forces and cellular electrical responses.
ObjectiveThis study aimed to verify the biomechanical efficacy of a novel internal fixation system for repairing lumbar spondylolysis (LS). Additionally, the changes in the mechanical performance of the novel internal...
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ObjectiveThis study aimed to verify the biomechanical efficacy of a novel internal fixation system for repairing lumbar spondylolysis (LS). Additionally, the changes in the mechanical performance of the novel internal fixation device during compression were *** healthy 25-year-old male volunteer was recruited for lumbar spine CT data acquisition to construct and validate a nonlinear finite element model of the L4-S1 spinal segment (A). Based on this, models were established for L5 spondylolysis (B), traditional internal fixation (C), and the pressurization process of the novel LS repair device fixation (D→E→F). For these six models, we constrained the lower surface of the S1 vertebral body while applying an axial compression force of 500 N and a moment load of 7.5 N·m on the upper surface of the L4 vertebral body to simulate six motions of the lumbar spine. The performance of each finite element model was evaluated by comparing the range of motion (ROM), maximum displacement, and maximum pressure experienced by the lumbar spine under different motion *** with Model C, Models D, E, and F exhibited a reduced ROM and maximum displacement (1%-38.5%, 12.8%-63.6%, and 32.8%-80.2%) compared with Model C during six different motions. Notably, compared with Model C, the novel internal fixation models consistently demonstrated a decreasing trend in the maximum stress on the intervertebral discs (IVD) and an increasing trend in the maximum stress on the articular cartilage and maximum stress and displacement of the bone graft. Moreover, the novel internal fixation model displayed larger ranges of evenly distributed stresses on the isthmus and reduced maximum stress on the internal fixation device across all six motions, with improved stiffness effects during the pressurization process of the novel internal fixation system (D→E→F).ConclusionsAccording to several mechanical comparisons, the novel internal fixation system had better biomechan
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