Dynamics Algorithm Research Articles

Confined Dynamics in Spherical Polymer Brushes.

We investigate the dynamics of polymers grafted to spherical nanoparticles in solution using hybrid molecular dynamics simulations with a coarse-grained solvent modeled via the multiparticle collision dynamics algorithm. The mean-square displacements of monomers near the surface of the nanoparticle exhibit a plateau on intermediate time scales, indicating confined dynamics reminiscent of those reported in neutron spin-echo experiments. The confined dynamics vanish beyond a specific radial distance from the nanoparticle surface that depends on the polymer grafting density. We show that this dynamical confinement transition follows theoretical predictions for the critical distance associated with the structural transition from confined to semidilute brush regimes. These findings suggest the existence of a hitherto unreported dynamic length scale connected with theoretically predicted static fluctuations in spherical polymer brushes and provide new insights into recent experimental observations.

Distilling identifiable and interpretable dynamic models from biological data.

Mechanistic dynamical models allow us to study the behavior of complex biological systems. They can provide an objective and quantitative understanding that would be difficult to achieve through other means. However, the systematic development of these models is a non-trivial exercise and an open problem in computational biology. Currently, many research efforts are focused on model discovery, i.e. automating the development of interpretable models from data. One of the main frameworks is sparse regression, where the sparse identification of nonlinear dynamics (SINDy) algorithm and its variants have enjoyed great success. SINDy-PI is an extension which allows the discovery of rational nonlinear terms, thus enabling the identification of kinetic functions common in biochemical networks, such as Michaelis-Menten. SINDy-PI also pays special attention to the recovery of parsimonious models (Occam's razor). Here we focus on biological models composed of sets of deterministic nonlinear ordinary differential equations. We present a methodology that, combined with SINDy-PI, allows the automatic discovery of structurally identifiable and observable models which are also mechanistically interpretable. The lack of structural identifiability and observability makes it impossible to uniquely infer parameter and state variables, which can compromise the usefulness of a model by distorting its mechanistic significance and hampering its ability to produce biological insights. We illustrate the performance of our method with six case studies. We find that, despite enforcing sparsity, SINDy-PI sometimes yields models that are unidentifiable. In these cases we show how our method transforms their equations in order to obtain a structurally identifiable and observable model which is also interpretable.

3D computational fluid and particle dynamics simulations: metrics of aerosol capture by impaction filters * *Contribution of NIST; not subject to U.S. copyright. Certain commercial equipment, instruments, software, or materials are identified in this paper in order to specify the experimental procedure and virtual experimentation adequately. Such identification is not intended to imply recommendation or endorsement by NIST, nor is

Human studies provide valuable information on components or analytes recovered from exhaled breath, but there are limitations due to inter-individual and intra-individual variation. Future development and implementation of breath tests based on aerosol analysis require a clear understanding of how human factors interact with device geometry to influence particle transport and deposition. The computational fluid and particle dynamics (CFPD) algorithm combines (i) the Eulerian approach to fluid dynamics and (ii) the Lagrangian approach to single particle transport and deposition to predict how particles are carried in fluids and deposited on surfaces. In this work, we developed a 3D multiscale CFPD model to provide insight into human factors that could be important to control or measure during sampling. We designed the model to characterize the local transport, spatial distribution, and deposition of polydisperse particles in a single impaction filter of a commercial aerosol collection device. We highlight the use of decoupling numerical strategies to simultaneously quantify the influence of filter geometry, fluid flowrate, and particle size. Our numerical models showed the remarkable effect of flowrate on aerosol dynamics. Specifically, aerosol mass deposition, spatial distribution, and deposition mechanisms inside the filter. This work as well as future studies on the effect of filter geometry and human factors on aerosol collection will guide the development, standardization, and validation of breath sampling protocols for current and emerging breath tests for forensic and clinical applications.

Friction modelling and the use of a physics-informed neural network for estimating frictional torque characteristics

This paper presents an exploration of friction modeling encompassing theoretical and practical aspects, utilizing a planar or 2D contact system. Various white-box friction models, including static and dynamic variants, are introduced, highlighting the superior capability of dynamic models in comprehensively capturing friction effects, substantiated through numerical simulation. Practical aspects of friction measurement and data-driven friction modeling are elucidated. The discourse extends to the development of grey-box and black-box friction models. A significant contribution lies in the proposition of a physics-informed neural network-based friction modeling approach, presenting it as an advanced and preferable alternative for friction estimation. To exemplify its efficacy, a case study of a torsion-based frictional contact scenario, employing Physics-Informed Neural Network (PINN) and the Nelder–Mead (N–M) algorithm for concurrent dynamics and friction model identification, was examined. Experimental data from a double torsion pendulum system, characterized by discontinuous dynamics, is employed for training. Results demonstrate the PINN’s superiority, providing more accurate representation of stick–slip phases at the contact zone and exhibiting faster performance compared to the N–M algorithm. The paper concludes by deliberating on challenges, prospects, and future directions in friction modeling.

Atomic motion behavior calculation and bonding mechanism analysis of explosive welding of high-strength and high-hardness titanium alloy Ti6Al4V/aluminum alloy 7075

The explosive welding technology of titanium alloy Ti6Al4V/aluminum alloy 7075 is the most valuable research problem in the field of Ti/Al explosive welding. In this paper, the smooth particle hydrodynamics and molecular dynamics algorithms are used to conduct multi-scale numerical calculations on the explosive welding of Ti6Al4V and 7075. The interface morphology, interface atomic movement, lattice changes and crystal defects are comprehensively analyzed. With the calculation as a reference, the Ti6Al4V/7075 composite with a flat interface is prepared. The calculation results indicate that with the transformation of titanium aluminum crystal structure, a large number of various dislocations, mainly 1/6〈110〉dislocations, are generated on the titanium side; During explosive welding, uniform and stable element diffusion occurred. Element diffusion mainly occurred in the unloading stage when the pressure was 0. The melting zone and ablation zone were composed of TiAl and TiAl3, respectively; The fundamental reason for the combination of Ti6Al4V/7075 is the diffusion of elements and the solid-phase binding of atoms.

Nonadiabatic Coupling in Trajectory Surface Hopping: Accurate Time Derivative Couplings by the Curvature-Driven Approximation.

Trajectory surface hopping (TSH) is a widely used mixed quantum-classical dynamics method that is used to simulate molecular dynamics with multiple electronic states. In TSH, time-derivative coupling is employed to propagate the electronic coefficients and in that way to determine when the electronic state on which the nuclear trajectory is propagated switches. In this work, we discuss nonadiabatic TSH dynamics algorithms employing the curvature-driven approximation and overlap-based time derivative couplings, and we report test calculations on six photochemical reactions where we compare the results to one another and to calculations employing analytic nonadiabatic coupling vectors. We correct previous published results thanks to a bug found in the software. We also provide additional, more detailed studies of the time-derivative couplings. Our results show good agreement between curvature-driven algorithms and overlap-based algorithms.

ESBD: Exponential Strain-based Dynamics using XPBD algorithm

Strain-based Dynamics (SBD) (Müller et al., 2014) is a well-known method to simulate the deformable models by using Green’s strain tensor within the Position-based Dynamics (PBD) algorithm. Similar to the other position-based constraints, SBD performs stable and robust. However, when we transform the base algorithm from PBD to Extended Position-based Dynamics (XPBD), SBD suffers from the cumbersome parameter adjustments and uncontrollable numerical dissipation, which generates highly stiff results. To overcome these limitations, we propose Exponential Strain-based Dynamics (ESBD) which reformulates the Green’s strain tensor with an exponential treatment. Our proposed method is less sensitive to the increased numerical dissipation, so it provides vivid simulation of deformable objects with fewer parameter tunings. In order to present the advantages, we compare our method with SBD by using the XPBD method and other well-known techniques. We observe that ESBD improves the hyperelastic behavior of the Green’s strain tensor as a projected constraint without altering the underlying XPBD algorithm. Therefore, it is possible to adapt ESBD to the other existing numerical integration and iteration methods. Our proposed method is stable, straightforward to follow and produces high quality dynamic simulations.

Lane formation of colloidal particles driven in parallel by gravity.

We investigate the lane formation in nonequilibrium systems of colloidal particles moving in parallel that are driven by the force of gravity. For this setup, an experimental implementation of a channel on a slope can be conceptualized. We employ the Brownian dynamics algorithm and confine the repulsive particles with hard walls based on the solution of the Smoluchowski equationin the half space. A difference of the driving force acting on the colloids could be achieved by using two spherical particle types with differing diameters but equal mass density. First, we investigate how a difference in the channel slope affects the lane formation of the systems, after which we analyze the lanes that formed. We find that the large particles push the small particles to the walls, resulting in exclusively small particle lanes at the walls. This contrasts the equilibrium state, where depletion forces push the larger particles to the walls. Additionally, we have a closer look at the mechanisms by which the lanes form. Finally, we find system parameter values that foster lane formation to lay the foundation for an experimental realization of our proposed setup. To round this off, we give an exemplary calculation of the slope angle needed to get the experimental system into a state of lane order. With the examination of lane order in systems that are driven in parallel, we hope to deepen our understanding of nonequilibrium order phenomena.

Governing Equation Identification of Nonlinear Single Degree-of-Freedom Oscillators With Coulomb Friction Using Explicit Stick and Slip Temporal Constraints

Abstract The friction force at joints of engineering structures is usually unknown and not directly identifiable. This contribution explores a procedure for obtaining the governing equation of motion and correctly identifying the unknown Coulomb friction force of a mass-spring-dashpot system. In particular, a single degree-of-freedom system is investigated both numerically and experimentally. The proposed procedure extends the state-of-the-art data-driven sparse identification of nonlinear dynamics (SINDy) algorithm by developing a methodology that explicitly imposes constraints encoding knowledge of the nonsmooth dynamics experienced during stick-slip phenomena. The proposed algorithm consists of three steps: (i) data segregation of mass-motion from mass-sticking during stick-slip response; (ii) application of SINDy on the mass-motion dataset to obtain the functional form of the governing equation; and (iii) applying sticking and slipping conditions to identify the unknown system parameters. It is shown that the proposed approach yields an improved estimate of the uncertain system parameters such as stiffness, viscous damping, and magnitude of friction force (all mass normalized) for various signal-to-noise ratios compared to SINDy.

Optimization for Pipeline Corrosion Sensor Placement in Oil-Water Two-Phase Flow Using CFD Simulations and Genetic Algorithm.

Internal corrosion is a major concern in ensuring the safety of transmission and gathering pipelines in Structural Health Monitoring (SHM). It usually requires numerous sensors deployed inside the piping system to comprehensively cover the locations with high corrosion rates. This study presents a hybrid modeling strategy using Computational Fluid Dynamics (CFD) and Genetic Algorithm (GA) to improve the sensor placement scheme for corrosion detection and monitoring. The essence of the proposed strategy harnesses the well-validated physical modeling capability of the CFD to simulate the oil-water two-phase flow and the stochastic searching ability of the GA to explore better solutions on a global level. The CFD-based corrosion rate prediction was validated through experimental results and further used to form the initial population for GA optimization. Importantly, fitness was defined by considering both sensing effectiveness and cost of sensor coverage. The hybrid modeling strategy was implemented through case studies, where three typical pipe fittings were used to demonstrate the applicability of the sensor layout design for corrosion detection in pipelines. The GA optimization results show high accuracy for sensor placement inside the pipelines. The best fitness of the U-shaped, upward-inclined, and downward-inclined pipes were 0.9415, 0.9064, and 0.9183, respectively. Upon this, the hybrid modeling strategy can provide a promising tool for the pipeline industry to design the practical placement.

A hybrid off-lattice kinetic Monte Carlo/molecular dynamics method for amorphous thin film growth

The ability to understand and model the growth of amorphous thin films on solid surfaces is essential to a wide range of industrial applications, from the deposition of wear-resistant coatings to the production of solar cells. Here, a three-dimensional (3D) hybrid off-lattice kinetic Monte Carlo/molecular dynamics (kMC/MD) algorithm is developed to study the growth of thin amorphous films on solid substrates with atomistic resolution over timescales of tens of seconds. We use this method to study the growth of polyphosphate films from tricresyl phosphate (TCP) molecules on an iron substrate. Molecular adsorption/desorption, bond breaking/formation processes, and diffusion of iron ions through the film are simulated in the kMC stage and the film is relaxed during the MD stage. The kMC/MD method is approximately eleven orders of magnitude faster than equivalent reactive force field (ReaxFF) MD simulations. The simulated film growth rate and topology agree well with experimental results and the chemical structure of the film is consistent with previous molecular dynamics simulations of iron polyphosphates. The newly-developed hybrid kMC/MD methodology can be adapted to yield important insights into thin film growth for several other potential applications.

Over- and Undercoordinated Atoms as a Source of Electron and Hole Traps in Amorphous Silicon Nitride (a-Si3N4).

Silicon nitride films are widely used as the charge storage layer of charge trap flash (CTF) devices due to their high charge trap densities. The nature of the charge trapping sites in these materials responsible for the memory effect in CTF devices is still unclear. Most prominently, the Si dangling bond or K-center has been identified as an amphoteric trap center. Nevertheless, experiments have shown that these dangling bonds only make up a small portion of the total density of electrical active defects, motivating the search for other charge trapping sites. Here, we use a machine-learned force field to create model structures of amorphous Si3N4 by simulating a melt-and-quench procedure with a molecular dynamics algorithm. Subsequently, we employ density functional theory in conjunction with a hybrid functional to investigate the structural properties and electronic states of our model structures. We show that electrons and holes can localize near over- and under-coordinated atoms, thereby introducing defect states in the band gap after structural relaxation. We analyze these trapping sites within a nonradiative multi-phonon model by calculating relaxation energies and thermodynamic charge transition levels. The resulting defect parameters are used to model the potential energy curves of the defect systems in different charge states and to extract the classical energy barrier for charge transfer. The high energy barriers for charge emission compared to the vanishing barriers for charge capture at the defect sites show that intrinsic electron traps can contribute to the memory effect in charge trap flash devices.

Load–displacement relation and gap distribution between rough surfaces: Partial differential equations approach

We develop a theoretical model to predict the load–displacement relation and probability density function for gaps between contacting rough surfaces. We derive partial differential equations from the previous model (Joe et al., 2018), and extend them to the non-adhesive contact problem. The predictions of the present theory are compared with numerical results using the Green’s function molecular dynamics algorithm, and a good agreement is obtained.

Research on Electromagnetic Dynamics Algorithm of Reluctance Launcher during Acceleration Process

The structure of the reluctance electromagnetic launcher (REML) is equivalent to an inductor with a movable iron core. The dynamic current in the launching process is easy to be measured by instruments, but the data such as the position, velocity of the projectile and the equivalent inductance are not only difficult to solve by analytical methods, but also difficult to measure by instruments, which seriously hinders the development of the control technology. To solve this problem, referring to the method of introducing the inductance matrix into the motor algorithm model to realize semi‐analytic calculation, a special algorithm model is proposed to calculate the launch process data of REML based on the current data. According to the working characteristics of the reluctance coil transmitter, it is pointed out that its inductance matrix should be specifically expressed as a three‐dimensional data table rather than a traditional two‐dimensional table. The current can be obtained by closed‐loop calculation according to the proposed algorithm block diagram when the circuit parameters are known, or measured by instruments when the circuit parameters are unknown, and then the launch process data is obtained indirectly through calculation. In this study, an experimental platform was built, and the algorithm model was established using MATLAB/Simulink. The comparison of the dynamic launch process data of the finite element method (FEM) showed that the method was feasible. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

An Improved Marine Predators Algorithm-Tuned Fractional-Order PID Controller for Automatic Voltage Regulator System

One of the most popular controllers for the automatic voltage regulator (AVR) in maintaining the voltage level of a synchronous generator is the fractional-order proportional–integral-derivative (FOPID) controller. Unfortunately, tuning the FOPID controller is challenging since there are five gains compared to the three gains of a conventional proportional–integral–derivative (PID) controller. Therefore, this research work presents a variant of the marine predators algorithm (MPA) for tuning the FOPID controller of the AVR system. Here, two modifications are applied to the existing MPA: the hybridization between MPA and the safe experimentation dynamics algorithm (SEDA) in the updating mechanism to solve the local optima issue, and the introduction of a tunable step size adaptive coefficient (CF) to improve the searching capability. The effectiveness of the proposed method in tuning the FOPID controller of the AVR system was assessed in terms of the convergence curve of the objective function, the statistical analysis of the objective function, Wilcoxon’s rank test, the step response analysis, stability analyses, and robustness analyses where the AVR system was subjected to noise, disturbance, and parameter uncertainties. We have shown that our proposed controller has improved the AVR system’s transient response and also produced about two times better results for objective function compared with other recent metaheuristic optimization-tuned FOPID controllers.

Translational and rotational non-Gaussianities in homogeneous freely evolving granular gases.

The importance of roughness in the modeling of granular gases has been increasingly considered in recent years. In this paper, a freely evolving homogeneous granular gas of inelastic and rough hard disks or spheres is studied under the assumptions of the Boltzmann kinetic equation. The homogeneous cooling state is studied from a theoretical point of view using a Sonine approximation, in contrast to a previous Maxwellian approach. A general theoretical description is done in terms of d_{t} translational and d_{r} rotational degrees of freedom, which accounts for the cases of spheres (d_{t}=d_{r}=3) and disks (d_{t}=2,d_{r}=1) within a unified framework. The non-Gaussianities of the velocity distribution function of this state are determined by means of the first nontrivial cumulants and by the derivation of non-Maxwellian high-velocity tails. The results are validated by computer simulations using direct simulation Monte Carlo and event-driven molecular dynamics algorithms.

Residual strength prediction and structural response of a multi-chamber cabin to a fire

In this study, the fire safety of a ship structure is evaluated using a fire dynamics algorithm for large eddy simulations and a structural-thermal coupling method. Taking a two-story cabin as an example, a typical I-channel is adopted and the fire-thermal-structure coupling calculation of the cabin is realised through a thermal-mechanical coupling data interface. On this basis, the failure characteristics, stress distribution, and deformation modes of the cabin under different heat release rate (0–8 MW) and fire locations (side and middle chambers) are studied. The results show that an increase in the heat release rate in the side chamber causes the failure path of the cabin to gradually expand from the fire-source side to the sagging plane. This differs from the failure mode of the cabin in the sagging plane at ambient temperature. When a high-power fire occurs in the middle chamber of the cabin, its spatial layout significantly influences the damage location. Moreover, the residual strength of the cabin at different fire positions in a heat release rate range of 0–8 MW is predicted.

Electrostatic hindrance to diffusion in flexible crosslinked gels: A coarse-grained simulation study.

In this work, we study how electrostatic forces slow down the diffusion of solute in flexible gels through coarse-grained simulations. The model used explicitly considers the movement of solute particles and polyelectrolyte chains. These movements are performed by following a Brownian dynamics algorithm. The effect of three electrostatic parameters characterizing the system (solute charge, polyelectrolyte chain charge, and ionic strength) is analyzed. Our results show that the behavior of both the diffusion coefficient and the anomalous diffusion exponent changes upon the reversal of the electric charge of one of the species. In addition, the diffusion coefficient in flexible gels differs significantly from that in rigid gels if the ionic strength is low enough. However, the effect of chain flexibility on the exponent of anomalous diffusion is significant even at high ionic strength (100 mM). Our simulations also prove that varying the polyelectrolyte chain charge does not have exactly the same effect as varying the solute particle charge.

MD-Bench: A performance-focused prototyping harness for state-of-the-art short-range molecular dynamics algorithms

Molecular dynamics (MD) simulations provide considerable benefits for the investigation and experimentation of systems at atomic level. Their usage is widespread into several research fields, but their system size and timescale are crucially limited by the available computing power. Performance engineering of MD kernels is therefore critical to understand their bottlenecks and investigate possible improvements. For that reason, we developed MD-Bench, a performance-focused prototyping harness for short-range MD kernels that implements state-of-the-art algorithms from multiple production applications such as LAMMPS and GROMACS. The MD-Bench source code is simple, understandable, and extensible, and therefore well suited for benchmarking, teaching, and researching MD algorithms. In this paper we introduce MD-Bench, describe its design, structure, and implemented algorithms. Finally, we show five use-cases of MD-Bench and describe how these are useful to gain a deeper understanding of the performance of MD kernels.

TenCirChem: An Efficient Quantum Computational Chemistry Package for the NISQ Era.

TenCirChem is an open-source Python library for simulating variational quantum algorithms for quantum computational chemistry. TenCirChem shows high-performance in the simulation of unitary coupled-cluster circuits, using compact representations of quantum states and excitation operators. Additionally, TenCirChem supports noisy circuit simulation and provides algorithms for variational quantum dynamics. TenCirChem's capabilities are demonstrated through various examples, such as the calculation of the potential energy curve of H2O with a 6-31G(d) basis set using a 34-qubit quantum circuit, the examination of the impact of quantum gate errors on the variational energy of the H2 molecule, and the exploration of the Marcus inverted region for charge transfer rate based on variational quantum dynamics. Furthermore, TenCirChem is capable of running real quantum hardware experiments, making it a versatile tool for both simulation and experimentation in the field of quantum computational chemistry.