Precise Numerical Simulations Research Articles

Theory of Electron Spin Resonance Spectroscopy in Scanning Tunneling Microscopy.

The integration of scanning tunneling microscopy (STM) and electron spin resonance (ESR) spectroscopy has emerged as a powerful and innovative tool for discerning spin excitations and spin-spin interactions within atoms and molecules adsorbed on surfaces. However, the origin of the STM-ESR signal and the underlying mechanisms that govern the essential features of the measured spectra have remained elusive, thereby significantly impeding the future development of the STM-ESR approach. Here, we construct a model to carry out precise numerical simulations of STM-ESR spectra for a single hydrogenated Ti adatom and a hydrogenated Ti dimer, achieving excellent agreement with experimental observations. We further develop an analytic theory that elucidates the fundamental origin of the signal as well as the essential features in the measured spectra. These new theoretical developments establish a solid foundation for the on-demand detection and manipulation of atomic-scale spin states, with promising implications for cutting-edge applications in spin sensing, quantum information, and quantum computing.

Explosion risks: Variety of deflagration-to-detonation transition scenarios in smooth tubes

In the framework of comprehensive assessment of explosion risks on board of spacecrafts and on the facilities of launch places, the paper is focused on the detailed analysis of particular scenarios of deflagration-to-detonation transition taking place in smooth tubes filled with acetylene-oxygen mixtures of different compositions. By means of precise numerical simulation it is demonstrated that various scenarios of detonation onset can take place depending on the mixture composition and its initial thermodynamic state. It is demonstrated that independent on the particular scenario always the basic mechanism of detonation onset via the formation of strong enough shock wave takes place. In more reactive mixtures the strong shock originates from the self-sustained process of joint pressure build up and reaction intensification exactly at the flame front. In less reactive mixtures the transient flow behavior leads to the shock waves generation and interaction. As a result, a brand new reaction kernel could arise in the area of shock waves interaction. In number of cases, that leads to the coupling between the shock wave and the newborn reaction front and results in the strong shock formation and further detonation onset.

Modeling the Time Evolution of Compact Binary Systems with Machine Learning

This work introduces advanced computational techniques for modeling the time evolution of compact binary systems using machine learning. The dynamics of compact binary systems, such as black holes and neutron stars, present significant nonlinear challenges due to the strong gravitational interactions and the requirement for precise numerical simulations. Traditional methods, like the post-Newtonian approximation, often require significant computational resources and face challenges in accuracy and efficiency. Here, we employed machine learning algorithms, including deep learning models like long short-term memory (LSTM) and temporal convolutional network (TCN), to predict the future evolution of these systems based on extensive simulation data. Our results demonstrate that employing both LSTM and TCN even as black-box predictors for sequence prediction can also significantly improve the prediction accuracy without physics-informed neural networks (as partial differential equation solvers with prior knowledge or inductive bias. By employing LSTM and TCN, we obtained R 2 values of 99.74% and 99.19% for the evolutionary orbits of the compact binaries data set, respectively. Our models demonstrate the ability to effectively capture the dynamics of the binaries, achieving high prediction performance with significantly reduced computational overhead by a factor of 40, compared to conventional numerical methods. This study paves the way for more effective and computationally scalable approaches to the understanding of gravitational phenomena and predictive modeling in gravitational-wave astronomy.

Structural Response Analysis and Comfort Evaluation of Residential Buildings: A Combined Wind Tunnel and FEM Approach

With global urbanization accelerating, high-rise buildings have become a common feature in the urban landscape, especially in coastal cities, where they encounter unique wind-load challenges. This study aims to quantify the structural response and occupant comfort of a high-rise residential building under wind-induced accelerations by integrating wind tunnel testing with finite element analysis (FEA). The research focuses on critical response parameters, including displacement, acceleration, and stress, to evaluate the building’s performance. Wind tunnel tests provided detailed wind pressure distribution data across the building’s surface, while multi-degree-of-freedom and finite element models facilitated precise numerical simulations. The findings highlight a significant directional and temporal variability in wind-load responses, with the most pronounced effects observed at a wind-direction angle of 105° relative to the building’s front-facing axis (0°). The study confirms that the combined application of wind tunnel tests and FEA offers a comprehensive approach to understanding wind-induced responses, essential for the scientifically accurate and effective design of high-rise structures.

Black hole-neutron star mergers with massive neutron stars in numerical relativity

We study the merger of black hole-neutron star (BH-NS) binaries in numerical relativity, focusing on the properties of the remnant disk and the ejecta, varying the mass of compactness of the NS and the mass and spin of the BH. We find that within the precision of our numerical simulations, the remnant disk mass and ejecta mass normalized by the NS baryon mass (M^rem and M^eje, respectively), and the cutoff frequency fcut normalized by the initial total gravitational mass of the system at infinite separation approximately agree among the models with the same NS compactness CNS=MNS/RNS, mass ratio Q=MBH/MNS, and dimensionless BH spin χBH irrespective of the NS mass MNS in the range of 1.092−1.691M⊙. This result shows that the merger outcome depends sensitively on Q, χBH, and CNS but only weakly on MNS. This justifies the approach of studying the dependence of NS tidal disruptions on the NS compactness by fixing the NS mass but changing the EOS. We further perform simulations with massive NSs of MNS=1.8M⊙, and compare our results of M^rem and M^eje with those given by existing fitting formulas to test their robustness for more compact NSs. We find that the fitting formulas obtained in the previous studies are accurate within the numerical errors assumed, while our results also suggest that further improvement is possible by systematically performing more precise numerical simulations. Published by the American Physical Society 2024

Enhancing cerebral arteriovenous malformation analysis: Development and application of patient-specific lumped parameter models based on 3D imaging data

Objectives:Cerebral arteriovenous malformations (AVMs) present complex neurovascular challenges, characterized by direct arteriovenous connections that disrupt normal brain blood flow dynamics. Traditional lumped parameter models (LPMs) offer a simplified angioarchitectural representation of AVMs, yet often fail to capture the intricate structure within the AVM nidus. This research aims at refining our understanding of AVM hemodynamics through the development of patient-specific LPMs utilizing three-dimensional (3D) medical imaging data for enhanced structural fidelity. Methods:This study commenced with the meticulous delineation of AVM vascular architecture using threshold segmentation and skeletonization techniques. The AVM nidus’s core structure was outlined, facilitating the extraction of vessel connections and the formation of a detailed fistulous vascular tree model. Sampling points, spatially distributed and derived from the pixel intensity in imaging data, guided the construction of a complex plexiform tree within the nidus by generating smaller Y-shaped vascular formations. This model was then integrated with an electrical analog model to enable precise numerical simulations of cerebral hemodynamics with AVMs. Results:The study successfully generated two distinct patient-specific AVM networks, mirroring the unique structural and morphological characteristics of the AVMs as captured in medical imaging. The models effectively represented the intricate fistulous and plexiform vessel structures within the nidus. Numerical analysis of these models revealed that AVMs induce a blood shunt effect, thereby diminishing blood perfusion to adjacent brain tissues. Conclusion:This investigation enhances the theoretical framework for AVM research by constructing patient-specific LPMs that accurately reflect the true vascular structures of AVMs. These models offer profound insights into the hemodynamic behaviors of AVMs, including their impact on cerebral circulation and the blood steal phenomenon. Further incorporation of clinical data into these models holds the promise of deepening the theoretical comprehension of AVMs and fostering advancements in the diagnosis and treatment of AVMs.

Analysis of damage characteristics of the rectangular steel container under near-earth explosion loading

In this study, the dynamic response of a semi-buried steel vessel under a near-surface explosion shock load was investigated through full-scale model tests. A three-dimensional precise numerical simulation model of the semi-buried steel vessel structure was established, and the dynamic response and damage consequences under different load conditions were analyzed. The influence of the thickness of corrugated steel on the dynamic damage and antiknock properties of the structure was studied. The results show that the weak part of the rectangular steel vessel structure is mainly located at the connection between the blast-facing panel and the beam column under near-surface explosion conditions. The peak stress and displacement of the blasting surface decrease with the increase in the horizontal angle of the incident wave, whilst/while increase with the height of the explosion. Under the same explosive yield, the main failure mode of the structure is the tearing of the plate and beam under plastic deformation. Increasing the thickness of the corrugated steel can significantly restrain the plastic deformation and acceleration of the plate. When the thickness is increased from 2 mm to 6 mm, the antiknock performance is significantly improved. However, when the thickness is increased from 6 mm to 8 mm, the improvement in antiknock performance slows down. Increasing the height of the enclosing soil can also effectively improve the explosion-proof performance of the structure, with full burial providing the best effect. The research results provide a theoretical basis for the application of rectangular steel vessel structures in the field of protection engineering.

Statistical Analysis of Bubble Parameters from a Model Bubble Column with and without Counter-Current Flow

Bubble columns are widely used in numerous industrial processes because of their advantages in operation, design, and maintenance compared to other multiphase reactor types. In contrast to their simple design, the generated flow conditions inside a bubble column reactor are quite complex, especially in continuous mode with counter-current liquid flow. For the design and optimization of such reactors, precise numerical simulations and modelling are needed. These simulations and models have to be validated with experimental data. For this reason, experiments were carried out in a laboratory-scale bubble column using shadow imaging and particle image velocimetry (PIV) techniques with and without counter-current liquid flow. In the experiments, two types of gases—relatively poorly soluble air and well-soluble CO2—were used and the bubbles were generated with three different capillary diameters. With changing gas and liquid flow rates, overall, 108 different flow conditions were investigated. In addition to the liquid flow fields captured by PIV, shadow imaging data were also statistically evaluated in the measurement volume and bubble parameters such as bubble diameter, velocity, aspect ratio, bubble motion direction, and inclination. The bubble slip velocity was calculated from the measured liquid and bubble velocities. The analysis of these parameters shows that the counter-current liquid flow has a noticeable influence on the bubble parameters, especially on the bubble velocity and motion direction. In the case of CO2 bubbles, remarkable bubble shrinkage was observed with counter-current liquid flow due to the enhanced mass transfer. The results obtained for bubble aspect ratio are compared to known correlations from the literature. The comprehensive and extensive bubble data obtained in this study will now be used as a source for the development of correlations needed in the validation of numerical simulations and models. The data are available from the authors on request.

Quantitative analysis of the numerical simulation uncertainties from geological models in CO2 geological storage: A case study of Shenhua CCS project

The intensifying global climate change has prompted the imperative implementation of CO2 capture and storage (CCS) projects as a mitigation strategy. Ensuring the safety and reliability of these projects requires meticulous validation, including the establishment of geological models and conducting numerical simulations. In CO2 geological storage initiatives, the limitation of well data during the initial stages leads to data deficiency. This scarcity compromises the precision of geological and numerical models, hindering their ability to accurately depict actual subsurface conditions. Meanwhile, parameters related to heterogeneity significantly also impact storage effectiveness and safety. This study addresses these challenges by utilizing the Shenhua CCS demonstration project as a case study. Various heterogeneous parameters are selected, and local and global sensitivity analysis methods are subsequently introduced to determine the ranges and sequences of these parameters in numerical simulations. The simulation results can aid in assessing the influence of various heterogeneous parameters on the CO2 plume and bottom hole pressure. The study establishes the importance ranking of various heterogeneous parameters under different temporal and spatial conditions through sensitivity analysis. The findings reveal the following key points:1. During the small-scale injection period, the CO2 plume is particularly sensitive to variations in net-to-gross and vertical permeable properties.2. During and after larger-scale injections, the net-to-gross significantly impacts plume evolution, while bottom hole pressure is predominantly influenced by variations in vertical permeable properties.3. Both the CO2 plume and well bottom pressure are primarily affected by changes in sand body morphologies, especially at low net-to-gross scenarios.These conclusions assist in prioritizing the collection of critical parameter data in CCS projects, facilitating the establishment of more precise and reliable geological and numerical simulation models. The heightened accuracy and reliability of these models contribute to improving their predictive capabilities, ultimately guiding engineering practices.

Spatio-temporal complexity in a prey-predator system with Holling type-IV response and Leslie-type numerical response: Turing and steady-state bifurcations

In this study, we unravel the intricate dynamics of a spatio-temporal prey-predator model featuring a Holling type-IV functional response and Leslie-type predator numerical response under Neumann boundary conditions. Our analysis encompasses the uniform persistence and global asymptotic stability of a positive equilibrium, validated by precise numerical simulations. Additionally, we explore Turing instability and spatial pattern emergence through linear stability analysis. Our primary emphasis lies in the realm of spatio-temporal bifurcation analysis, through which we establish criteria for the presence or absence of non-constant steady states within n-dimensional diffusion models. Moreover, we discern precise conditions governing Hopf bifurcation and steady-state bifurcation in 1-dimensional diffusion models. These findings offer theoretical insights that align with the intricate dynamic patterns observed in our numerical simulations.

A hardness-based constitutive model for numerical simulation of blind rivet with gradient hardness distribution

A286 superalloy blind rivets find extensive applications in the structural connections of aerospace equipment owing to their exceptional strength and heat resistance. However, the gradient hardness distribution within the rivet sleeves complicates achieving precise numerical simulation results with traditional constitutive models. This study aims to establish a constitutive model suitable for materials exhibiting gradient hardness to support the development of A286 superalloy blind rivets. Initially, quasi-static compression experiments were conducted on A286 superalloy samples at different hardness levels (160–324 HV), strain rates (0.01–10 s⁻¹), and temperatures (25–600°C). Subsequently, the influence of material microstructural evolution on flow stress during compression was investigated using electron backscatter diffraction technique. Furthermore, a hardness-based Johnson-Cook (J-C) constitutive model was developed by introducing hardness and offset compensation onto the original J-C model and compared with its counterpart. Finally, the hardness-based model was employed to simulate the bulge compression forming of A286 blind rivets and validated through experimental trials. The findings reveal that dislocation strengthening predominantly contributes to the increased material flow stress with escalating strain rates and sample hardness. Compared to the original J-C model, the stress prediction error of the hardness-based model reduced from 26.8 % to 2.9 %, effectively depicting the variation in material flow stress under different hardness values. The simulated values of bulge morphology, dimensions, and load closely align with experimental data, affirming the efficacy of the hardness-based model in numerically simulating blind rivets with gradient hardness distribution.

Uranus Orbiter and Probe: A Radio Science Investigation to Determine the Planet’s Gravity Field, Depth of the Winds, and Tidal Deformations

The most recent Planetary Science and Astrobiology Decadal Survey has proposed Uranus as the target for NASA’s next large-scale mission. The interior structure and atmosphere of the planet are currently poorly understood, and objectives for investigating Uranus’s deeper regions and composition are highly ranked. Traditionally, gravity science has served as one of the primary means for probing the depths of planetary bodies and inferring their internal density distributions. In this work, we present precise numerical simulations of an onboard radio science experiment designed to determine Uranus’s gravity field and tidal deformations, which would offer a rare view into the planet’s interior. We focus on the mission’s orbital planning, discussing crucial parameters such as the number of pericenter passes, orbital inclination, and periapsis altitude necessary to meet the gravity measurement requirements for a Uranus orbiter. Our findings suggest that eight close encounters may be sufficient to determine the zonal gravity field up to J 8 with a relative accuracy of 10%, if the trajectory is optimized. This would allow for the decoupling of the gravity field components due to interior structure and zonal winds. Additionally, we find that the expected end-of-mission uncertainty on Uranus’s Love number k 22 is of order ∼0.01 (3σ). This level of accuracy may offer crucial information about Uranus’s inner state and allow for discriminating between a liquid and solid core, thus shedding light on crucial aspects of the planet’s formation and evolution.

Study of fractional variable-order lymphatic filariasis infection model

Abstract Variable-order derivatives are the natural extension of ordinary as well as of fractional-order differentiations and integration, respectively. Numerous suggestions for fractional variable-order operators have been made in the literature over time. Therefore, this is the moment to shine a light on the variable-order fractional calculus, due to the fact that it accurately describes the mathematical underpinnings and emphasizing the modeling utility via using contemporary numerical techniques. This study focuses on investigating a fractional variable-order model of lymphatic filariasis infection using with Atangana–Beleanue–Caputo derivative. Our investigations have led to the development of newly refined results, focusing on both qualitative and numerical aspects of analysis. To achieve our research objectives, we employ the fixed point theorems of Banach and Krasnoselskii. These theorems serve as powerful tools, allowing us to establish results regarding the existence of solutions to the model. Additionally, for precise numerical simulations, we employ the fractional Euler’s method, a sophisticated computational technique that allows us to effectively simulate and interpret the results both numerically and graphically. These graphs illustrate distinct variable-orders, providing a comprehensive understanding of the model’s behavior under different conditions. Here, it should be kept in mind that we have select various continuous functions for variable to present our graphical illustration.

Validation of mSOUND using a fully heterogeneous skull model

Transcranial ultrasound has found an increasing number of applications in recent years, including the treatment of neurological conditions through thermal ablation and neuromodulation. Ensuring the success and safety of such treatments necessitates precise numerical simulations of transcranial ultrasound, a pivotal aspect of treatment planning involving phase correction. Addressing this demand, an open-source wave solver named mSOUND (https://m-sound.github.io/mSOUND/home) was developed specifically for modeling focused ultrasound in heterogeneous media. A recent intercomparison study (J. Acoust. Soc. Am. 152, 1003–1019, 2022) scrutinized mSOUND alongside other wave solvers like k-Wave, demonstrating its accuracy in modeling wave propagation through a homogeneous skull. This study extends the assessment to evaluate mSOUND's accuracy in modeling wave propagation through a fully heterogeneous skull, utilizing CT images of an ex vivo human skull. The obtained results are systematically compared with those from k-Wave, revealing a high level of agreement.

Research on precise and standardized numerical simulation strategy for vehicle aerodynamics

PurposeThe purpose of this study is to propose a precise and standardized strategy for numerically simulating vehicle aerodynamics.Design/methodology/approachError sources in computational fluid dynamics were analyzed. Additionally, controllable experiential and discretization errors, which significantly influence the calculated results, are expounded upon. Considering the airflow mechanism around a vehicle, the computational efficiency and accuracy of each solution strategy were compared and analyzed through numerous computational cases. Finally, the most suitable numerical strategy, including the turbulence model, simplified vehicle model, calculation domain, boundary conditions, grids and discretization scheme, was identified. Two simplified vehicle models were introduced, and relevant wind tunnel tests were performed to validate the selected strategy.FindingsErrors in vehicle computational aerodynamics mainly stem from the unreasonable simplification of the vehicle model, calculation domain, definite solution conditions, grid strategy and discretization schemes. Using the proposed standardized numerical strategy, the simulated steady and transient aerodynamic characteristics agreed well with the experimental results.Originality/valueBuilding upon the modified Low-Reynolds Number k-e model and Scale Adaptive Simulation model, to the best of the authors’ knowledge, a precise and standardized numerical simulation strategy for vehicle aerodynamics is proposed for the first time, which can be integrated into vehicle research and design.

Nonlinear flow of couple stress fluid layer over an inclined plate

The issue of stability of a thin couple-stress liquid layer flows on an inclined plane was inspected. The thin-film approximation was employed to obtain a Benney-like differential equation, that described the time record of the interface profile The linear transition state and reduction ratio of maximum classical (Newtonian) growth-rate were discussed. The complete evolution equation was solved numerically using the method of lines in order to support the novelty of the work. The linear stability could be enhanced by increasing the couple-stress coefficient and surface tension as well as reducing the inclination. However, the ordinary viscosity played an irregular role. The linear results predicted conditions (windows) in which the non-Newtonian film was more stable than its Newtonian counterpart. The nonlinear stimulation anticipated the existence of sock waves in certain situations. The appearance of instability through the linear subcritical region as well as irregular influences with respect to surface tension and couple-stress property was revealed. The nonlinear approach was more accurate in describing the stability issue than the linear one. Such results could be employed to attain the optimum statuses with regard to the film stability, and control the shock waves. They would not only enable accurate practical implementation in the design of inertial confinement fusion capsules and supernova explosions and implosions modeling, but also would allow for precise numerical simulation.

The multi-objective optimisation design of outlet guide vanes of diagonal flow fan based on sobol sensitivity analysis

Diagonal flow fans offer substantial energy-saving potential and find broad application across various sectors. Their performance relies heavily on factors like outlet guide vanes and spacing relative to moving blades. However, research into enhancing fan performance through optimized guide vanes and spacing remains limited. In this study, we focus on improving the accuracy of predicting the internal flow field of diagonal flow fans. This paper incorporate rotation and curvature effects using the Large Eddy Simulation (LES) model and introduce stress terms with helicity constraints to create a non-linear subgrid-scale model. This refined model enables more precise numerical simulations. By employing accurate simulations, we optimize the outlet guide vane configuration and conduct sensitivity analysis. We utilize a Radial Basis Function (RBF) model coupled with the Sobol method for this purpose. The optimized guide vane design exhibits enhanced resistance to airflow separation compared to the original, resulting in notable reductions in flow losses within the grille channel. Experimental tests are performed on the diagonal flow fan both before and after optimization. At the specified operating point, the second guide vane optimization leads to a 1.28 m3/min increase in fan flow, a 4.33% rise in total pressure efficiency, and a 2.2 dB noise reduction. These findings underscore the accuracy of the helicity correction model in predicting diagonal flow fan behavior. The multi-objective optimization approach, combining the RBF proxy model with the Sobol method, proves highly reliable. It offers valuable design insights for similar fans and establishes a credible design methodology.

Radio Telescope Surface Measurement via Deep Learning

This paper proposes a new method for accurately measuring the surface deformation of radio telescope antennas based on deep learning. A deep convolutional neural network is used to predict surface deformations by mapping the near-field intensity of the antenna, instead of relying entirely on a physical model. The proposed method could offer precise measurement of surface deformations in real time with only a single image of near-field intensity pattern. To optimize the deep learning model, a preliminary U-net based deep convolutional neural network (DCNN) model was developed based on a large data set generated by an approximate physical model, a partial differential equation (PDE). The network parameters were then fine-tuned using transfer learning with a small data set obtained by high precision numerical simulation. During this process, the fine-tuning layers that achieved optimal performance for the U-net network was studied. The final results show that the proposed method significantly improves the accuracy of antenna surface deformation recovery. Additionally, singular value decomposition (SVD) technology is employed to denoise the intensity image, which facilitates the application of the proposed method to actual deformation measurement.

Flow field reconstruction in inlet of scramjet at Mach 10 based on physical information neural network

This paper proposed the physical information residual spatial pyramid pooling (PIResSpp) convolutional neural network that is highly robust and introduces a residual neural network architecture that can satisfactorily fit high-dimensional functions by using jumping connections to reduce the risk of overfitting. Key features of the flow field were extracted by using pooling kernels of different sizes and were then stitched together to fuse its local and global features. The axisymmetric inlet of the scramjet generated by the Bezier curve was established through highly precise numerical simulations, and datasets of flow fields under different geometric configurations were constructed according to the parametric design. The PIResSpp model was trained on a sample dataset, and mapping relationships were established between the parameters of incoming flow/those of the geometry of the inlet, and the velocity, pressure, and density fields in it. Finally, the results of reconstruction of the flow field at the inlet with different design parameters were tested and compared with the outcomes of various deep learning models. The results show that the average peak signal-to-noise ratio of the flow field reconstructed by the proposed model was 36.427, with a correlation coefficient higher than 97%.

Calculation method of HJC constitutive model parameters of natural joint angle slate

In the course of underground engineering, layered slate is often encountered. Understanding the mechanical characteristics of layered slate is a prerequisite for engineering construction and disaster prevention and mitigation. As a result, at the beginning of a project, a large number of indoor tests are required, which are time-consuming and laborious. In addition, the natural joint angle of the layered slate is random, so it is extremely difficult to establish a database of the mechanical characteristics of layered slate. Hence, it is necessary to find a simple, feasible, and high-precision method to determine the Holmquist–Johnson–Cook (HJC) constitutive model parameters for naturally jointed layered slate with different dip angles. This study first determines the HJC constitutive model parameters of layered slate with five specific joint dip angles (0°, 30°, 45°, 60°, and 90°) through static tests and the split Hopkinson pressure bar (SHPB) test. Furthermore, by employing sensitivity analysis methods, the influence of key parameters of the HJC constitutive model on the dynamic peak stress of slate is determined. Among them, parameters A and B have the most significant impact on stress, exceeding 50%. Thirdly, a nonlinear fitting regression method is used to determine the HJC constitutive model parameters of naturally jointed angular slate. The relationship between the HJC model parameters and the inclination angle of slate joints is derived, and the accuracy of these parameters is verified through numerical simulation methods. The error between the numerical simulation and indoor experiments is within 10%, indicating a high level of simulation accuracy. The research findings provide a highly precise numerical simulation method for similar projects.