Parallel Simulation Research Articles

Mitigating noise in digital and digital–analog quantum computation

Noisy Intermediate-Scale Quantum (NISQ) devices lack error correction, limiting scalability for quantum algorithms. In this context, digital-analog quantum computing (DAQC) offers a more resilient alternative quantum computing paradigm that outperforms digital quantum computation by combining the flexibility of single-qubit gates with the robustness of analog simulations. This work explores the impact of noise on both digital and DAQC paradigms and demonstrates DAQC’s effectiveness in error mitigation. We compare the quantum Fourier transform and quantum phase estimation algorithms under a wide range of single and two-qubit noise sources in superconducting processors. DAQC consistently surpasses digital approaches in fidelity, particularly as processor size increases. Moreover, zero-noise extrapolation further enhances DAQC by mitigating decoherence and intrinsic errors, achieving fidelities above 0.95 for 8 qubits, and reducing computation errors to the order of 10−3. These results establish DAQC as a viable alternative for quantum computing in the NISQ era.

Numerical simulation and experimental analysis of thermal conduction and stress in laser cladding repair of thin-walled parts

To minimize the deformation of thin-walled parts during the laser cladding process and enhance the quality of the repaired parts, the Finite Element Abaqus software was used to develop a thermal conduction and 3D stress model, the heat source used in the experiments was a double ellipsoid heat source model. This model simulates the thermal process and the deformation resulting from residual stresses in the laser cladding process on a titanium alloy substrate with a thickness of 2 mm. Through the simulation, the temperature field of the substrate at the end of each pass was obtained, and the cladding sequence with the smallest temperature gradient during the multi-pass laser melting process was determined. By combining simulation and practice, the multilayer stacking pattern was optimized to achieve deformation control. Analog simulation experiments indicate that the optimized single-layer multi-pass laser cladding method results in less heat accumulation and thermal stress compared to the unidirectional sequential scanning method. The flatness test results show that the specimens have less deformation with the optimized multilayer stacking method. In addition, the metallographic organisation of the two cladding specimens is analysed in this paper, and the results show that the optimised specimens exhibit excellent microstructures and properties.

A parallel compositional reservoir simulator for large-scale CO2 geological storage modeling and assessment

Storing CO2 in deep aquifers and depleted gas reservoirs is an effective way to achieve carbon neutrality. However, the numerical simulation of CO2 storage in these formations is challenging due to the complexity of gases-brine systems. The number of gas species included in the gases-brine fluid models of existing simulators cannot meet the rapidly evolving CO2 sequestration scenarios. To address this intricate issue, we developed a three-dimensional fully implicit parallel CO2 geological storage simulator (PRSI-CGCS) on distributed-memory computers based on our in-house parallel platform. This simulator uses a compositional fluid model with a diverse range of gas species, including CO2, C1 ~ C3, N2, H2S, as well as newly added gases H2 and O2, which may be encountered in geological CO2 storages. Besides, we provide more suitable scaling factors for different gases in the stability analysis bypassing (SAB) method to accelerate the gases-brine phase equilibrium calculations. PRSI-CGCS does not incorporate energy conservation equations, and salt precipitation or dissolution is also not considered. Numerical experiments show that our simulator is scalable, robust and validated to simulate large-scale CO2 storage problems with hundreds of millions of grid blocks on a parallel supercomputer cluster. Besides, after our modification on scaling factors, the SAB method can reduce the number of stability analyses by 61.39 % to 88.71 %, thereby reducing simulation time. Furthermore, case studies indicate that injecting O2 and H2 along with CO2 reduces the stability or capacity of CO2 storage and increases the pressure required for injection. However, this impact is not significant when the impurity content is less than 10 %.

From efficiency to equity: A multi-user paradigm in mobile route optimization

This study addresses the challenge of optimizing vehicle mobility in urban environments, which is significant for the advancement of smart city initiatives and spatial data analysis. We introduce a novel mobile recommendation system designed for multi-user scenarios, aiming to achieve a balance between effectiveness and fairness. The system prioritizes maximizing the profitability of vehicle service providers while ensuring an equitable distribution of recommended routes among users. Our approach features a redefined objective function that integrates a fairness criterion alongside path quality optimization. We further propose PSA-DLMA (Parallel Simulated Annealing with Deep Learning-Guided Move Adaptation), a stochastic path search method that leverages deep learning to guide move and strategy selection, alongside a dynamic termination mechanism and a parallel processing strategy. We validate our methodology using recent yellow taxi data from New York City and its surroundings, conducting comprehensive experiments to assess the performance of the system. The results demonstrate the superiority of PSA-DLMA over existing state-of-the-art solutions, offering significant contributions to improving urban vehicle mobility within the smart city framework.

Conjugate Heat Transfer Validation of an Optimized Film Cooling Configuration for a Turbine Vane Endwall

Abstract In this study, to improve overall cooling performance for a turbine endwall that incorporated internal jet impingement and external purge flow and discrete injection cooling, the external film cooling was re-designed based on the knowledge of film coverage patterns from a baseline design. Experimental conjugate heat transfer validation of the newly-designed cooling geometry was conducted by measuring overall cooling effectiveness over the endwall through an Infrared thermography technique and detecting aero-thermal fields at the cascade exit with five-hole and thermocouple probes. For a given total coolant flow rate, the influence of coolant split among different cooling sources was examined. Parallel computational simulations were undertaken to elaborate the experimentally-observed results by offering in-passage flow physics. Comparisons with the baseline design proved that the newly-designed cooling scheme improved the endwall overall cooling performance in terms of both effectiveness levels and coverage area. In addition to optimizing the cooling geometry, more efficient usage of the coolant was found to be linked with the proper coolant split, which helped the re-designed cooling geometry to achieve an improvement of cooling effectiveness by approximately 20%. The computational simulations produced satisfactory overall cooling effectiveness, but failed to capture mixing of coolant with mainstream flow. The flow interactions visualized by the simulations provided evidence that the coolant jets from the optimized cooling scheme increased mixing flow loss but those from the pressure side suppressed the inherent vortex flow, resulting in no aerodynamic penalty as compared with the baseline cooling design.

Magnetized Accretion onto and Feedback from Supermassive Black Holes in Elliptical Galaxies

We present 3D magnetohydrodynamic simulations of the fueling of supermassive black holes in elliptical galaxies from a turbulent cooling medium on galactic scales, taking M87* as a typical case. We find that the mass accretion rate is increased by a factor of ∼10 compared with analogous hydrodynamic simulations. The scaling of Ṁ∼r1/2 roughly holds from ∼10 pc to ∼10−3 pc (∼10 r g) with the accretion rate through the event horizon being ∼10−2 M ⊙ yr−1. The accretion flow on scales ∼0.03–3 kpc takes the form of magnetized filaments. Within ∼30 pc, the cold gas circularizes, forming a highly magnetized (β ∼ 10−3) thick disk supported by a primarily toroidal magnetic field. The cold disk is truncated and transitions to a turbulent hot accretion flow at ∼0.3 pc (103 r g). There are strong outflows toward the poles driven by the magnetic field. The outflow energy flux increases with smaller accretor size, reaching ∼3 × 1043 erg s−1 for r in = 8 r g; this corresponds to a nearly constant energy feedback efficiency of η ∼ 0.05–0.1 independent of accretor size. The feedback energy is enough to balance the total cooling of the M87/Virgo hot halo out to ∼50 kpc. The accreted magnetic flux at small radii is similar to that in magnetically arrested disk models, consistent with the formation of a powerful jet on horizon scales in M87. Our results motivate a subgrid model for accretion in lower-resolution simulations in which the hot gas accretion rate is suppressed relative to the Bondi rate by ∼(10rg/rB)1/2 .

Modelling collective invasion with reaction–diffusion equations: When does domain curvature matter?

Real-world cellular invasion processes often take place in curved geometries. Such problems are frequently simplified in models to neglect the curved geometry in favour of computational simplicity, yet doing so risks inaccuracies in any model-based predictions. To quantify the conditions under which neglecting a curved geometry is justifiable, we explore the dynamics of a system of reaction–diffusion equations (RDEs) on a two-dimensional annular geometry analytically. Defining ϵ as the ratio of the annulus thickness δ and radius r0 we derive, through an asymptotic expansion, the conditions under which it is appropriate to ignore the domain curvature for a general system of reaction–diffusion equations. To highlight the consequences of these results, we simulate solutions to the Fisher–Kolmogorov–Petrovsky–Piskunov (Fisher–KPP) model, a paradigm nonlinear RDE typically used to model spatial invasion, on an annular geometry. Thus, we quantify the size of the deviation from an analogous simulation on the rectangle, and how this deviation changes across the width of the annulus. We further characterise the nature of the solutions through numerical simulations for different values of r0 and δ. Our results provide insight into when it is appropriate to neglect the domain curvature in studying travelling wave behaviour in RDEs.

Study on the Irradiation Evolution and Radiation Resistance of PdTi Alloys.

Medium-entropy alloys (MEAs) exhibit exceptional mechanical properties, thermal properties, and irradiation resistance, making them promising candidates for aerospace and nuclear applications. This study utilized molecular dynamics simulations to examine the defect behavior in PdTi alloys under various irradiation conditions. Simulations were performed using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) with the modified embedded-atom method (MEAM) potential to describe interatomic interactions. Various temperatures, primary knock-on atom (PKA) energies, and elemental ratios were tested to understand the formation and evolution of defects. The results show that compared to pure Pd, PdTi alloys with increased entropy exhibit significantly enhanced irradiation resistance at higher temperatures and PKA energies. This study explored the impact of different elemental ratios, including Pd, PdTi1.5, PdTi, and Pd1.5Ti. Findings indicate that increasing the Pd concentration enhances the alloy's irradiation resistance, improving mobility and recombination rates of defect clusters. A one-to-one Pd-to-Ti ratio demonstrated optimal performance. Temperature analysis revealed that at 300 K and 600 K, PdTi alloys exhibit excellent irradiation resistance at a PKA energy of 30 keV. However, as the temperature rises to 900 K, the irradiation resistance decreases slightly, and at 1200 K, the performance is likely to decline further. This study offers some useful insights into the irradiation evolution and radiation resistance of PdTi medium-entropy alloys, which may help inform their potential applications in the nuclear field and contribute to the further development of MEAs in this area.

Parallelization of Three Dimensional Cardiac Simulation on GPU.

The simulation of electrophysiological cardiac models plays an important role in facilitating the investigation of cardiac behavior under various conditions. However, these simulations often require a lot of computational resources. To address this challenge, this study introduced a method for speeding up three-dimensional cardiac simulations using GPU parallelization. A series of optimizations was introduced, encompassing various aspects such as data storage, algorithmic enhancements, and data transfer. The experimental results reveal that the optimized GPU parallel simulations achieve an approximate 50-fold acceleration compared with their CPU serial program. This investigation substantiates the considerable potential of GPUs in advancing the field of cardiac electrophysiology simulations.

A Comprehensive Analog–Mixed Signal (AMS) Simulations Environment

The analog–mixed signal simulation environment, used for verifying non-volatile memory macrocells, is presented. It has been adopted over the last decade, providing excellent results in verification coverage, efficiency, and flexibility. This methodology ensures a smooth and effective transition from full transistor/fully analog simulations to fully digital simulations while maintaining most of the environment’s features. This allows verification designers to exchange data, stimuli, and results, thereby enhancing debugging capabilities and reducing simulation time.

Proving Correctness of Parallel Implementations of Transition System Models

This article addresses the long-standing problem of program correctness for programs that describe systems of parallel executing processes. We propose a new method for proving correctness of parallel implementations of high-level models expressed as transition systems. The implementation language underlying the method is based on the concurrency model of actors and active objects. The method defines program correctness in terms of a simulation relation between the transition system that specifies the program semantics of the parallel program and the transition system that is described by the correctness specification. The simulation relation itself abstracts from the fine-grained interleaving of parallel processes by exploiting a global confluence property of the concurrency model of the implementation language considered in this article. As a proof of concept, we apply our method to the correctness of a parallel simulator of multicore memory systems.

Enhancing Mechanical Properties of Graphene/Aluminum Nanocomposites via Microstructure Design Using Molecular Dynamics Simulations.

This study explores the mechanical properties of graphene/aluminum (Gr/Al) nanocomposites through nanoindentation testing performed via molecular dynamics simulations in a large-scale atomic/molecular massively parallel simulator (LAMMPS). The simulation model was initially subjected to energy minimization at 300 K, followed by relaxation for 50 ps under the NPT ensemble, wherein the number of atoms (N), simulation temperature (T), and pressure (P) were conserved. After the model was fully relaxed, loading and unloading simulations were performed. This study focused on the effects of the Gr arrangement with a brick-and-mortar structure and incorporation of high-entropy alloy (HEA) coatings on mechanical properties. The findings revealed that Gr sheets (GSs) significantly impeded dislocation propagation, preventing the dislocation network from penetrating the Gr layer within the plastic zone. However, interactions between dislocations and GSs in the Gr/Al nanocomposites resulted in reduced hardness compared with that of pure aluminum. After modifying the arrangement of GSs and introducing HEA (FeNiCrCoAl) coatings, the elastic modulus and hardness of the Gr/Al nanocomposites were 83 and 9.5 GPa, respectively, representing increases of 21.5% and 17.3% compared with those of pure aluminum. This study demonstrates that vertically oriented GSs in combination with HEA coatings at a mass fraction of 3.4% significantly enhance the mechanical properties of the Gr/Al nanocomposites.

Towards Double-Layer Dynamic Heterogeneous Redundancy Architecture for Reliable Railway Passenger Service System

Researchers have proposed the dynamic heterogeneous redundancy (DHR) architecture, which integrates dynamic, heterogeneous, redundant, and closed-loop feedback elements into the system, to fortify the reliability of the railway passenger service system (RPSS). However, there are at least two weaknesses with the common DHR architectures: (1) they need system nodes with enough computing and storage resources; (2) they have hardly considered the reliability of DHR architecture. To this end, this paper proposes a double-layer DHR (DDHR) architecture to ensure the reliability of RPSS. This architecture introduces a set of algorithms, which are optimized co-computation and ruling weight optimization algorithms for the data processing flow of the DDHR architecture. This set improves the reliability of the DDHR architecture. For the evaluation of the reliability of DDHR architecture, this paper also proposes two metrics: (1) Dynamic available similarity metric. This metric does not rely on the overall similarity of the double-layer redundant executor sets but evaluates the similarity of their performance under the specified interaction paths within a single scheduling cycle. The smaller its similarity, the higher its reliability. (2) Scheduling cycle under dual-layer similarity threshold. This metric evaluates the reliability of the RPSS under actual conditions by setting the schedulable similarity thresholds between the same and different layers of the dual-layer redundant executives in the scheduling process. Finally, analog simulation experiments and prototype system building experiments are carried out, whose numerical experimental results show that the DDHR architecture outperforms the traditional DHR architecture in terms of reliability and performance under different redundancy and dynamically available similarity thresholds, while the algorithmic complexity and multi-tasking concurrency performance are slightly weaker than that of the DHR architecture, but can be applied to the main operations of the RPSS in general.

Large scale massively parallel numerical simulation for blast effects on structures

An efficient large-scale parallel computing program for blast effects on structures is developed based on software platform mode, which adopts component-based three-layer software architecture to achieve disciplinary hierarchy. The parallel layer encapsulates high-performance data structures and shields large-scale parallel computing technology, enabling efficient mesh adaptivity. The common numerical algorithm layer encapsulates fluid, structural, and coupling algorithm processes providing standardized interfaces for extending numerical schemes. The physical model layer provides extension interfaces for state equations, source terms, initial boundary values, etc, to facilitate quick customization of large-scale parallel software for blast effects on structures. Typical examples verify the correctness, effectiveness and high precision of the proposed program in fluid-structure coupling problems. The parallel efficiency of 300,000 processor cores with hundreds of millions of grids reach 60.84% for the shock wave transmission. Finally, the dynamic behavior of multi-storey buildings under blast waves is simulated to reveal the damage model of the overall collapse. These results show that the software has a broad application prospect in the field of explosion. The results suggest that the software holds significant potential for application in the field of explosion, particularly in intricate and large-scale scenarios.

Assessment of machine learning models trained by molecular dynamics simulations results for inferring ethanol adsorption on an aluminium surface

Molecular dynamics (MD) simulations can reduce our need for experimental tests and provide detailed insight into the chemical reactions and binding kinetics. There are two challenges while dealing with MD simulations: one is the time and length scale limitations, and the latter is efficiently processing the massive amount of data resulting from the MD simulations and generating the proper reaction rates. In this work, we evaluated the use of regression machine learning (ML) methods to solve these two challenges by developing a framework for ethanol adsorption on an Aluminium (Al) slab. This framework comprises three main stages: first, an all-atom molecular dynamics model; second, ML regression models; and third, validation and testing. In stage one, the adsorption of ethanol molecules on the Al surface for various temperatures, velocities and concentrations is simulated using the large-scale atomic/molecular massively parallel simulator (LAMMPS) and ReaxFF. The outcome of stage one is utilised for training, testing, and validating the predictive models in stages two and three. We developed and evaluated 28 different ML models for predicting the number of adsorbed molecules over time, including linear regression, support vector machine (SVM), decision trees, ensemble, Gaussian process regression (GPR), neural network (NN) and Bayesian hyper-parameter optimisation models. Based on the results, the Bayesian-based GPR showed the highest accuracy and the lowest training time. The developed model can predict the number of adsorbed molecules for new cases within seconds, while MD simulations take a few weeks. This adsorption rate can then be used in macroscale simulations to tackle the time and length scale limitations. The proposed numerical framework has the potential to be generalised and, therefore, contribute to future low-cost binding reaction estimations, providing a valuable tool for industry and experimentalists.

Adsorption of cytochrome c on different self-assembled monolayers: The role of surface chemistry and charge density.

In this work, the adsorption behavior of cytochrome c (Cyt-c) on five different self-assembled monolayers (SAMs) (i.e., CH3-SAM, OH-SAM, NH2-SAM, COOH-SAM, and OSO3--SAM) was studied by combined parallel tempering Monte Carlo and molecular dynamics simulations. The results show that Cyt-c binds to the CH3-SAM through a hydrophobic patch (especially Ile81) and undergoes a slight reorientation, while the adsorption on the OH-SAM is relatively weak. Cyt-c cannot stably bind to the lower surface charge density (SCD, 7% protonation) NH2-SAM even under a relatively high ionic strength condition, while a higher SCD of 25% protonation promotes Cyt-c adsorption on the NH2-SAM. The preferred adsorption orientations of Cyt-c on the negatively-charged surfaces are very similar, regardless of the surface chemistry and the SCD. As the SCD increases, more counterions are attracted to the charged surfaces, forming distinct counterion layers. The secondary structure of Cyt-c is well kept when adsorbed on these SAMs except the OSO3--SAM surface. The deactivation of redox properties for Cyt-c adsorbed on the highly negatively-charged surface is due to the confinement of heme reorientation and the farther position of the central iron to the surfaces, as well as the relatively larger conformation change of Cyt-c adsorbed on the OSO3--SAM surface. This work may provide insightful guidance for the design of Cyt-c-based bioelectronic devices and controlled enzyme immobilization.

Drag and Thermal Reduction of Spiked Hypersonic Vehicle in Thermochemical Nonequilibrium

This study utilizes the in-house parallel direct simulation Monte Carlo–quantum dynamics method to model the aerodynamic flowfield of a spiked reentry vehicle within a rarefied high-altitude atmosphere. Investigating the flight of spiked aircraft under various flight conditions and spike sizes and considering both the real gas effect and the rarefied gas effect, the aerodynamic characteristic of the aircraft is evaluated. Performance evaluation for effectiveness in drag reduction and thermal protection is based on drag and the maximum stagnation heat flux coefficient at the vehicle’s shoulder. Findings indicate that, in the rarefied atmosphere, the distinction between the characteristic shock intensities of spiked and unspiked vehicles is not pronounced, which results in a drag reduction of approximately 5%, accompanied by a more than 100% increase in thermal effect on the vehicle’s shoulder, obviously poor in effectiveness. Subsequently, this study examines the spiked vehicle equipped with a lateral jet, and this augmented approach achieves a marked reduction in the intensity of the shock wave impacting the vehicle’s shoulder with a drag reduction exceeding 10% and attenuates the thermal impact by over 50% in comparison to the implementation of spiking alone, thereby demonstrating superior performance in terms of effectiveness.

Stochastic symplectic reduced-order modeling for model-form uncertainty quantification in molecular dynamics simulations in various statistical ensembles

This work focuses on the representation of model-form uncertainties in molecular dynamics simulations in various statistical ensembles. In prior contributions, the modeling of such uncertainties was formalized and applied to quantify the impact of, and the error generated by, pair-potential selection in the microcanonical ensemble (NVE). In this work, we extend this formulation and present a linear-subspace reduced-order model for the canonical (NVT) and isobaric (NPT) ensembles. The symplectic reduced-order basis is randomized on the tangent space of the Stiefel manifold to provide topological relationships and capture model-form uncertainty. Using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS), we assess the relevance of these stochastic reduced-order atomistic models on canonical problems involving a Lennard-Jones fluid and an argon crystal melt.

Computational insights into the structural, thermodynamic and transport properties of CaF2-MgF2 binary fluoride system at high temperatures

The structural, thermodynamic and transport properties of the CaF2-MgF2 molten salt system were investigated with ab initio molecular dynamics (AIMD), system-specific neural network interatomic potentials (NNIPs) and universal PreFerred Potentials (PFP). We trained an NNIP model using AIMD data as input and used this potential to efficiently simulate the interactions within a large supercell in a temperature range of 1273–1773 K. The Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) code was employed to validate our trained NNIP model. The Matlantis software with universal PFP is also presented to prove its feasibility for MD calculations and can be considered as a useful alternative simulation tool for higher-order systems where existing potentials are not readily available. We calculated structural and thermodynamic properties including radial distribution function (RDF), angular distribution function (ADF), specific heat capacity, ionic self-diffusivity, and viscosity. Our results indicate that the system exhibited a high degree of structural disorder, with the Ca, Mg, and F ions forming a liquid solution. Using PFP, the positions of the first peak in RDFs for Ca-F and Mg-F pairs are only slightly left-shifted (<0.05 Å), and the estimated viscosity of the melt decreases from 4.613 mPa·s to 1.846 mPa·s with an increase in temperature from 1273 K to 1773 K, in agreement with the NNIP trained specifically for CaF2-MgF2. Our results provide valuable insights into the properties of the CaF2-MgF2 system at high temperatures and serve as predictive models for the development of new electrolytes that could be used for silicon epitaxy by adding silica.

A High–Throughput Molecular Dynamics Study for the Modeling of Cryogenic Solid Formation

To predict the favorable thermodynamical conditions and characterize cryogenic pellet formations for applications in nuclear fusion reactors, a high–throughput molecular dynamics study based on a unified framework to simulate the growth process of cryogenic solids (molecular deuterium, neon, argon) under gas pressure have been designed. These elements are used in fusion nuclear plants as fuel materials and to reduce the damage risks for the plasma-facing components in case of a plasma disruption. The unified framework is based on the use of workflows that permit management in HPC facilities, the submission of a massive number of molecular dynamics simulations, and handle huge amounts of data. This simplifies a variety of operations for the user, allowing for significant time savings and efficient organization of the generated data. This approach permits the use of large-scale parallel simulations on supercomputers to reproduce the solid–gas equilibrium curves of cryogenic solids like molecular deuterium, neon, and argon, and to analyze and characterize the reconstructed solid phase in terms of the separation between initial and reconstructed solid slabs, the smoothness of the free surfaces and type of the crystal structure. These properties represent good indicators for the quality of the final materials and provide effective indications regarding the optimal thermodynamical conditions of the growing process.