Numerical Simulations Research Articles

Plasmon-mediated dynamics and lasing of nanoemitters enhanced by dispersing nanorings.

We investigate the plasmon-mediated nonlinear dynamics and the optics of a laser emission of random nanoemitters (NEs) embedded in a two-dimensional (2D) lattice of conducting nanorings (NRs) enhanced by plasmon-polariton (PP) excitations. The interaction of quantum NEs with the PP field in the NRs perturbs the dynamics of the electronic populations in NEs, leading to a significant dependence of laser generation (dynamics) on the plasma frequency ωp of PP. This results in a strong coupling of NE field emission with the PP field and sharp variations of the average current in the NR lattice. The phase transition in the system was found when the macroscopic structures of PP fields are excited simultaneously in different regions of the system if ωp (control parameter) reaches critical value ωc. We have established the analytical dependence of the PP current I = I(ωp/ωc) on the plasma frequency, which is in excellent agreement with the results of numerical simulations. This effect may allow the design of new types of PP active devices with the use of conducting NRs in modern nanoelectronics.

In vivo mapping of cellular resolution neuropathology in brain ischemia with diffusion MRI.

Noninvasive mapping of cellular pathology can provide critical diagnostic and prognostic information. Recent advances in diffusion magnetic resonance imaging enabled in vivo examination of tissue microstructures well beyond the imaging resolution. Here, we proposed to use diffusion time-dependent diffusion kurtosis imaging (tDKI) to simultaneously assess cellular morphology and transmembrane permeability in hypoxic-ischemic (HI) brain injury. Through numerical simulations and organoid imaging, we demonstrated the feasibility of capturing effective size and permeability changes using tDKI. In vivo MRI of HI-injured mouse brains detected a shift of the tDKI peak to longer diffusion times, suggesting swelling of the cellular processes. Furthermore, we observed a faster decrease of the tDKI tail, reflecting increased transmembrane permeability associated with up-regulated water exchange or necrosis. Such information, unavailable from a single diffusion time, can predict salvageable tissues. Preliminary applications of tDKI in patients with ischemic stroke suggested increased transmembrane permeability in stroke regions, illustrating tDKI's potential for detecting pathological changes in the clinics.

Study on Variation Law of Hazardous Areas in Goaf Based on Coal Oxidation Kinetics Theory

ABSTRACT Regarding various problems on safety production in coal mines, residual coal in goaf’s spontaneous combustion is extensive, determining its hazardous areas is critical to extinguish or prevent fires in goaf. Taking production practices at the 010803-working face of Wang Wa Coal Mine as background, a combined approach involving on-site experiments, laboratory tests, and numerical simulations is adopted to investigate “three-zone” spontaneous combustion as well as the distribution patterns of temperature fields in shallowly buried coal seams. The mathematical and physical models of spontaneous combustion in goaf are constructed. Upon oxidation of coal at low temperatures, experimental data about the intensity of release of heat and the rate of consumption of oxygen inform the construction of a kinetic model for coal oxidation in goaf. Utilizing field-measured data on air leakage, a distribution model for permeability within the working face’s goaf is constructed. To study combustion “three-zone” and temperature field distribution patterns in goaf under various air supply volumes, as well as to quantitatively analyze the relationship between the width and area of oxidation zones, the area of high-temperature areas, and the distribution of high-temperature sites in the goaf, with relation to the working face’s supply volumes of air. The results show that the maximum simulated air leakage in the goaf of the working face is 148.03 m3/min, with a relative error of just 1.4% compared to the measured value. Furthermore, validation confirms that the simulated oxygen concentration variation pattern closely aligns with the measured oxygen concentration. As the airflow rate at the working face increases, the oxidation zone width at the intake side gradually grows. While on the return side, it gradually decreases, resulting in a maximum width difference of 22.6 m. The goaf area’s concentration of oxygen exhibits an “S” shape distribution pattern. The overall temperature change range within the high-temperature zone of the goaf is 4.5 K, gradually migrating toward the deeper areas. As the air supply rate rises, oxidation zone area gradually decreases, exhibiting an overall sinusoidal distribution. Concurrently, the high-temperature zone area progressively expands, conforming to a Boltzmann function distribution. When the Air supply rate is 800 m3/min to 1120 m3/min, the areas of the two are linearly correlated with the air supply volumes. Under varying air supply volumes, the position of the high-temperature point within the goaf changes in both strike and dip directions, and the relative distance with the intake air corner follows an exponential function in both strike and dip directions. Through comprehensive analysis, it is concluded that the actual air supply volume on-site should ideally range between 800 m3/min and 1120 m3/min. This range effectively prevents and controls the hazardous area of spontaneous combustion in the goaf and optimizes the ventilation scheme.

Investigation of torsion in asymmetric mid-rise structures on the pile-raft system considering soil-structure interaction

The adverse conditions of the soil such as soft soil can have seismic consequences that may either amplify seismic response or render the structure vulnerable. On one hand, the design and construction of asymmetric structures have become prevalent due to aesthetic considerations and structural space limitations. Studies indicate that structures with geometric irregularities are susceptible to seismic forces, leading to non-uniform distribution of floor drifts, excessive torsional behavior, and more, depending on the degree of irregularity. In this research, the main goal was to investigate the effect of soil and structure interaction on the torsion of asymmetric Mid-Rise Structures. To achieve this goal two asymmetric Steel moment frame system 15-story structures aim to address the effect of interaction between soil and structure on the seismic performance, one with a rigid base and the other with a flexible base, one with a rigid base and the other with a flexible base. To mitigate the settlement of soil-associated structures, a pile-raft system is employed, considering the softness of the soil. Additionally, to study the torsional behavior of these structures, their planes are asymmetrically designed in L-shape and C-shape configurations, subjected to four ground motion excitations in both far and near-field scenarios. Three-dimensional finite element numerical simulations are performed using Abaqus software. Various configurations of the pile-raft system for the asymmetric C-shape structure are examined to assess the effects of pile diameter and length on the torsional irregularity coefficient. The results show that the soil-structure interaction increases the maximum floor rotation angle, placing these structures in critical conditions concerning torsional irregularity under near-field and far-field seismic excitations.

Research on the Influencing Factors of Pulsed Hydraulic Fracturing Effects in Hot Dry Rock Based on Numerical Simulation

Research on the Influencing Factors of Pulsed Hydraulic Fracturing Effects in Hot Dry Rock Based on Numerical Simulation

2D and 3D numerical modelling for preliminary assessment of long-term deterioration in Irish glacial till geotechnical slopes

ABSTRACT Long-term geotechnical slope deterioration, influenced by weathering and meteorological factors, presents stability challenges to infrastructure. Wetting and drying cycles lead to pore water pressure variations, causing deformations and slope failure. Studies on glacial tills which investigate deterioration in geotechnical slopes focus on variables like pore water pressure, soil water retention, compaction, freeze–thaw cycles and cracking. This research conducts a preliminary assessment of an Irish glacial till-cut slope, establishing a data-driven foundation for long-term slope behaviour studies. Data analysis, geospatial modelling and numerical simulations were performed on a cut slope in Castleblayney (Ireland), considering short-term/undrained and long-term/drained conditions. FLAC/Slope and Scoops3D were used for 2D and 3D slope stability analyses, applying the First-Order Second Moment (FOSM) probabilistic approach to assess how minor soil property changes affect slope stability, including long-term deterioration scenarios. The study underscores the importance of precise instrument placement within Irish glacial till geotechnical cut slopes, particularly at the uppermost part where shallow and deep failures coincide under long-term and short-term scenarios. This informs strategic instrument positioning for accurate slope deterioration investigations. This research lays the groundwork for understanding mechanisms driving geotechnical slope deterioration and provides insights for future studies on geotechnical asset deterioration models in Irish glacial tills.

Numerical study and dynamics analysis of diabetes mellitus with co-infection of COVID-19 virus by using fractal fractional operator

COVID-19 is linked to diabetes, increasing the likelihood and severity of outcomes due to hyperglycemia, immune system impairment, vascular problems, and comorbidities like hypertension, obesity, and cardiovascular disease, which can lead to catastrophic outcomes. The study presents a novel COVID-19 management approach for diabetic patients using a fractal fractional operator and Mittag-Leffler kernel. It uses the Lipschitz criterion and linear growth to identify the solution singularity and analyzes the global derivative impact, confirming unique solutions and demonstrating the bounded nature of the proposed system. The study examines the impact of COVID-19 on individuals with diabetes, using global stability analysis and quantitative examination of equilibrium states. Sensitivity analysis is conducted using reproductive numbers to determine the disease’s status in society and the impact of control strategies, highlighting the importance of understanding epidemic problems and their properties. This study uses two-step Lagrange polynomial to analyze the impact of the fractional operator on a proposed model. Numerical simulations using MATLAB validate the effects of COVID-19 on diabetic patients and allow predictions based on the established theoretical framework, supporting the theoretical findings. This study will help to observe and understand how COVID-19 affects people with diabetes. This will help with control plans in the future to lessen the effects of COVID-19.

Integrating Machine Learning and the Finite Element Method for Assessing Stiffness Degradation in Photovoltaic Modules.

This study introduces a novel machine learning (ML) method utilizing a stacked auto-encoder network to predict stiffness degradation in photovoltaic (PV) modules with pre-existing cracks. The input data for the training process was derived from numerical simulations, ensuring a comprehensive representation of module behavior under various conditions. The findings highlight the robust predictive capability of the model, as evidenced by its impressive R2 value of 0.961 and notably low root mean square error (RMSE) of 4.02%. These metrics significantly outperform those of other conventional methods, including the convolutional neural network (CNN) with R2 of 0.905 and RMSE of 9.43%, the space vector machine (SVM) with R2 of 0.827 and RMSE of 17.93%, and the random forest (RF) with R2 of 0.899 and RMSE of 11.02%. Moreover, the findings suggest that the predictive dynamics of degradation are affected by the varying weight functions of different input parameters, such as climate temperature, grain size, material effort, and pre-crack size, as the degradation level changes. Furthermore, a geometric analysis reveals model deficiencies where significant overestimations correlate with thicker glass components, while pronounced underestimations are predominantly associated with thinner layers of polycrystalline silicon wafer and Ethylene Vinyl Acetate in the module. As a case study, it demonstrated that to maintain a constant degradation level between 1.30 and 1.32 in a PV module with components featuring consistent geometric attributes, the input parameters must be kept within specific ranges: climate temperature ranging from 33 to 57°C, grain size ranging from 36 to 81 μm, material effort ranging from 0.74 to 0.81, and pre-crack size ranging from 24 to 32 μm. Therefore, this underscores that the ML model not only predicts degradation but also delineates the parameter space required to achieve a consistent output value.

Weak time-scale separation at the onset of oscillatory magnetoconvection in rapidly rotating fluids

Convective instabilities are one of the integral parts of the dynamics of flows driven by thermal buoyancy. Naturally, physical phenomena exhibit a wide disparity in the length and timescales of the field variables in numerical simulations and experimental observations. Such variations are not represented in the traditional normal mode stability analysis attempting to understand the onset of convection. This study attempts to incorporate different time constants for different perturbation variables in the linear stability analysis with the help of a Taylor series expansion. The infinite horizontal layer model is chosen for simplicity. Apart from the classical Rayleigh-Bénard system, additional physical effects such as background rotation and magnetic field have been considered with plausible implications for geophysical flow applications. The time scale separation is implemented by considering a slight change in the frequency of temperature perturbation compared to that for other physical quantities. Both analytical and numerical methods have been utilised for the investigation. The threshold buoyancy force is reduced when the temperature perturbation has a smaller frequency than the frequencies of other variables. Besides that, the onset wavenumber and frequency of the oscillatory modes are modified due to weak scale separation from the onset characteristics of the reference case. In particular, enhanced frequency of temperature perturbations leads to smaller-scaled magnetically controlled convective rolls and larger-scaled viscously controlled instabilities at the onset. A robust dependence of the onset characteristics with the parameter quantifying the timescale separation is obtained. Additionally, two transitions in the convective onset modes with scale separation have been identified.

Verification and validation of a coupled CFD–FEM approach with overset structured grid through free decay and regular wave tests

This paper presents a comprehensive verification and validation study applied to the hydrodynamic analysis of a semi-submersible Floating Offshore Wind Turbine (FOWT) platform. The numerical simulation is conducted using a coupled Computational Fluid Dynamics-Finite Element Method (CFD–FEM) approach. The CFD program incorporates an overset grid interpolation method, facilitating a more systematic grid refinement in the estimation of discretization uncertainties. A mooring analysis program, based on the FEM scheme, is integrated with the CFD program to simulate the dynamic responses of mooring system. The necessity of the nonlinear mooring model is demonstrated by comparing solutions with a comparative test using a linear mooring model. The verification study, based on a least-squares-based extrapolation method, quantifies the spatial and temporal discretization errors of the numerical model. The numerical solutions based on the baseline model are further validated against the model test measurements. A pitch free decay model test and a regular wave model test conducted within the OC5 project are utilized for the verification and validation study. Results from both tests demonstrate the reliability and accuracy of the coupled method.

Theoretical and experimental investigations on a data-driven trajectory planning scheme for dynamic shape control of piezo-actuated compliant morphing structures

Piezo-actuated compliant morphing structures can transmit motion and force via elastic deformation with lower weight, simplified design, and higher accuracy, a typical example is the variable camber wing. However, the rapid shape change of compliant structures is a challenging control problem because it often results in a high level of vibration due to the structural flexibility necessary to achieve the morphing requirement. This study presents a model-free data-driven trajectory planning approach based on spline curves and surrogate-based optimization (SBO) to obtain smooth “rest-to-rest” morphing trajectories. The macro-fiber composite (MFC) patches are used for piezoelectric actuators to generate elastic deformation. The flexible beam and variable camber wing are used for both linear numerical simulation and experimental tests with various nonlinearities and uncertainties. An optimization criterion is proposed to minimize both transient and residual vibrations during the “rest-to-rest” motion. An extraordinarily terminal displacement error function is added to eliminate the effects of the hysteresis and creep of the actuator. The control inputs, i.e., voltage profiles for the MFCs, are defined using cubic spline curves via only a few control points. Then, the optimization problem is solved by employing a Kriging surrogate model-based algorithm integrated with the expected improvement (EI) infilling-sampling criterion, which constructs an efficient algorithm similar to reinforcement learning. The optimal morphing trajectories can be highly efficiently obtained by using only 4 control points and less than 150 samplings. The experimental results of both the plate model and the variable camber wing model show that the proposed method can not only achieve a smooth dynamic morphing trajectory with minimized vibration but also automatically compensate for hysteresis and creep. The proposed method provides an effective means for the rapid realization of an optimized morphing trajectory for compliant morphing structures.

Distributed reinforcement learning-based optimization of resource scheduling for telematics

As a reliable computational model in telematics, Mobile Edge Computing (MEC) is utilized to lighten the load on individual autonomous vehicles. It enables computationally expensive and latency-sensitive jobs to be offloaded from cars with limited processing power to servers at edge nodes. This paper presents the joint optimization problem as a mixed nonlinear integer programming problem. The scheduling of vehicle tasks and the distribution of communication resources are then rationalized using a resource allocation algorithm based on Deep Deterministic Policy Gradient (DDPG) reinforcement learning to arrive at a suboptimal solution based on the resource allocation policies of single-intelligent DDPG and multi-intelligent DDPG. The numerical simulation demonstrates the superior service experience of the proposed joint optimization strategy of distributed task offloading and resource allocation based on multi-intelligent DDPG over other association and channel selection strategies and joint resource allocation based on single-intelligent DDPG.

Simulation and verification of discrete element parameter calibration of pulverized coal particles

ABSTRACT To enhance the simulation efficiency of the EDEM discrete element numerical simulation software for coal powder mineral particles, experimental data on the rest angle of coal powder particles were collected as the response variable. Five parameters related to coal powder particles within the Hertz-Mindlin no-slip contact model were selected for investigation. Initially, the Plackett-Burman experiment identified three parameters: collision restitution coefficient, static friction coefficient, and rolling friction coefficient, significantly influencing the resting angle. Subsequently, the steepest ascent experiment determined the significant parameter ranges as follows: the collision restitution coefficient ranged from 0.20 to 0.40, the particle-particle static friction coefficient ranged from 0.30 to 0.50, and the particle-particle dynamic friction coefficient ranged from 0.06 to 0.10. Finally, the second-order regression model for the angle of repose and the saliency parameters was developed and optimized using the Box-Behnken experimental design. The following parameter combination was obtained: the collision recovery coefficient was 0.30, the particle-particle static friction coefficient was 0.49, and the particle-particle dynamic friction coefficient was 0.09. This study aimed to compare the angle of repose between the simulation experiment and the actual experiment. The relative error was only 1.02%, indicating that the simulation experiment achieved the optimal parameter combination.

A Qualitative Analysis of Leukemia Fractional Order SICW Model

Using a series of fundamental differential equations, including the Caputo derivative, which makes it easier to specify the initial conditions of the differential equations, we present a fractional order concept of leukemia in this study. The universality, positivity, and boundedness of solutions are first established. The local stability properties of the equilibrium are studied using the fractional Routh-Hurwitz stability criteria. The differential equation system has been solved using unconventional finite difference techniques. The Leukemia Fractional Order SICW model introduces several innovative elements compared to traditional epidemiological and disease models. This stands out due to its integration of fractional-order differential equations, inclusion of leukemic cells and immune cells compartments, simulation of treatment strategies, consideration of waning immunity, and its application to leukemia-specific scenarios. These elements collectively make it a valuable tool for studying leukemia dynamics, exploring treatment options, and improving our understanding of how the immune system interacts with cancer cells in leukemia patients. Numerical simulations of the model are shown at the conclusion to interpret our theoretical outcomes in support of various fractional orders of derivative options. From there, we can observe how the evolution of the system components is impacted by the fractional derivative .

Neural networks for emergent behavior in biological microstructures

PurposeThis paper develops a neural network surrogate model based on a discrete lattice approach to investigate the influence of complex microstructures on the emergent behavior of biological networks.Design/methodology/approachThe adaptability of network-forming organisms, such as, slime molds, relies on fluid-to-solid state transitions and dynamic behaviors at the level of the discrete microstructure, which continuum modeling methods struggle to capture effectively. To address this challenge, we present an optimized approach that combines lattice spring modeling with machine learning to capture dynamic behavior and develop nonlinear constitutive relationships.FindingsThis integrated approach allows us to predict the dynamic response of biological materials with heterogeneous microstructures, overcoming the limitations of conventional trial-and-error lattice design. The study investigates the microstructural behavior of biological materials using a neural network-based surrogate model. The results indicate that our surrogate model is effective in capturing the behavior of discrete lattice microstructures in biological materials.Research limitations/implicationsThe combination of numerical simulations and machine learning endows simulations of the slime mold Physarum polycephalum with a more accurate description of its emergent behavior and offers a pathway for the development of more effective lattice structures across a wide range of applications.Originality/valueThe novelty of this research lies in integrating lattice spring modeling and machine learning to explore the dynamic behavior of biological materials. This combined approach surpasses conventional methods, providing a more holistic and accurate representation of emergent behaviors in organisms.

Crystal channeling investigations at medical synchrotron regimes

An experimental setup to investigate ion channeling in the hundreds MeV/u energy range is described. A short bent crystal, aligned to the incoming beam by an angular actuator, should deflect ions into a single plane pixel detector used to identify channeled and unchanneled particles. In order to enhance the footprint of channeling on the recorded signals, the incoming particle emittance is tailored on the crystalline plane acceptance by means of massive collimators. Numerical simulations of low energy carbon ions are performed with FLUKA to fully understand the measurement conditions demonstrate the feasibility of the test, to be performed in the experimental room of the National Center for Oncological Hadrontherapy accelerator complex in Pavia, Italy.

Numerical Simulation of Fishtail Biomimetic Groove for Dry Gas Seals

Numerical Simulation of Fishtail Biomimetic Groove for Dry Gas Seals

Lift-connected surface codes

We use the recently introduced lifted product to construct a family of quantum low density parity check codes (QLDPC codes). The codes we obtain can be viewed as stacks of surface codes that are interconnected, leading to the name lift-connected surface (LCS) codes. LCS codes offer a wide range of parameters—a particularly striking feature is that they show interesting properties that are favorable compared to the standard surface code. For example, already at moderate numbers of physical qubits in the order of tens, LCS codes of equal size have lower logical error rate or similarly, require fewer qubits for a fixed target logical error rate. We present and analyze the construction and provide numerical simulation results for the logical error rate under code capacity and phenomenological noise. These results show that LCS codes attain thresholds that are comparable to corresponding (non-connected) copies of surface codes, while the logical error rate can be orders of magnitude lower, even for representatives with the same parameters. This provides a code family showing the potential of modern product constructions at already small qubit numbers. Their amenability to 3D-local connectivity renders them particularly relevant for near-term implementations.

Resilient adaptive event-triggered containment control of nonlinear multi-agent system under concurrent DoS attacks and disturbances

This paper presents a secure containment control problem of nonlinear Multi-Agent Systems (MASs) under aperiodic Denial of Service (DoS) attacks and external disturbances simultaneously. A novel adaptive neural network (NN)-based event-triggered control is considered that uses the nonlinear estimator to predict the state of other agents. Since data access is denied during DoS attacks, the overall system switches between two modes of stable and unstable containment behaviours. Therefore, the maximum of attack duration and frequency is determined such that the overall system evolution leads to containment convergence in the presence of DoS attacks. We proposed an adaptive NN-based distributed disturbance observer to estimate external disturbances in a nonlinear system's dynamics. The state estimator predicts neighbouring agents' states, and each agent's input and event times are determined without monitoring other agents. The directed graph topology is used to determine data exchange among agents instead of an undirected graph that reduces implementation conditions. Zeno-free behaviour is also proved by analysis of the system. Eventually, the numerical simulation of the proposed approach is shown. Abbreviations: DoS attacks, Containment control of multi-agent system

Numerical Simulation of Vertical Well Depressurization-Assisted In Situ Heating Mining in a Class 1-Type Hydrate Reservoir

Numerical Simulation of Vertical Well Depressurization-Assisted In Situ Heating Mining in a Class 1-Type Hydrate Reservoir