Efficient Simulation Research Articles

Numerical Simulation of CO Generation and Combustion Efficiency in Sintering Process: Effect of Solid Fuel Particle Size

For sintering pot productive process with various fuel particle size distributions, a transient numerical simulation sintering model based on the computational fluid dynamics approach is developed using Fluent 2021R1. The model combines chemical reaction, mass and heat transfer, Euler–Euler model, and fluid flow in porous media. In this study, CO is employed as the combustion's intermediate product, which is further oxidized by secondary combustion in the high‐temperature zone. Through calculations, the solid fuel combustion behavior of the sintering is explained collectively with the changing bed temperature, CO emission, and solid fuel combustion efficiency of the process under various fuel particle size distribution. In the sintering process, the fuel particle size distribution is crucial for lowering CO emissions and increasing combustion efficiency. The combustion efficiency shows a tendency of increasing initially before decreasing with the reduction of solid fuel particle size, while CO emissions show a trend of reducing first and then increasing. It is advantageous to lower the CO emission in the sintering process, and the combustion efficiency of the sintering process is greatly boosted by 5.13% when the proportion of solid fuel with 5 mm particle size decreases and the proportion of solid fuel with 3 mm particle size increases.

A quantum algorithm for the lattice-Boltzmann method advection-diffusion equation

We present a versatile and efficient quantum algorithm based on the Lattice Boltzmann method (LBM) approximate solution of the linear advection-diffusion equation (ADE). We emphasize that the LBM approximation modifies the diffusion term of the underlying exact ADE and leads to a modified equation (mADE). Due to its versatility in terms of operator splitting, the proposed quantum LBM algorithm for the mADE provides a building block for future quantum algorithms to solve the linearized Navier-Stokes equation on quantum computers. We split the algorithm into four operations: initialization, collision, streaming, and calculation of the macroscopic quantities. We propose general quantum building blocks for each operator, which adapt intrinsically from the general three-dimensional case to smaller dimensions and apply to arbitrary lattice-velocity sets. Based on (sub-linear) amplitude data encoding, we propose improved initialization and collision operations with reduced complexity and efficient sampling-based simulation. Quantum streaming algorithms are based on previous developments. The proposed quantum algorithm allows for the computation of successive time steps, requiring full state measurement and reinitialization after every time step. It is validated by comparison with a digital implementation and based on analytical solutions in one and two dimensions. Furthermore, we demonstrate the versatility of the quantum algorithm for two cases with non-uniform advection velocities in two and three dimensions. Various velocity sets are considered to further highlight the flexibility of the algorithm. We benchmark our optimized quantum algorithm against previous methods employed in sampling-based quantum simulators. We demonstrate sampling efficiency, with sampling accelerated convergence requiring fewer shots.

Data‐driven simulation of functional fatigue in shape memory alloy wires

AbstractData‐driven mechanics offers great potential in engineering applications, where the efficient simulation of complex and, in particular, path‐dependent material behavior is often challenging. Within this approach, conventional material models are replaced by data sets containing snapshots of stress, strain and the history of both assumed to be sufficiently accurate representations of the underlying material behavior. Based on these snapshots and on physical admissibility, a distance function is built which is minimized to yield the boundary value problems' solution. The aim of this paper is to apply this framework to accurately simulate the complex behavior of shape memory alloy wires under cyclic loading, under which these materials exhibit a degradation of their features, denoted functional fatigue. The constructed synthetic data sets are enriched by real experimental data, showcasing that the data‐driven method is capable of combining both approaches of classic material modeling and modern methods which directly incorporate experimental data.

A hybrid simulation method towards the gamma ray phase contrast imaging for metallic material

A high efficiency simulation method for propagation-based phase-contrast imaging, called directional macro-wavefront (DMWF), is developed with the aim of simulating high-energy phase-contrast imaging. This method takes both Monte Carlo and wave optical propagation into consideration. Traditional wave-optics-based simulation methods for phase-contrast imaging encounter unacceptable computational complexity when high-energy radiation is used. In contrast, this method effectively addresses this issue by using macro-wavefront integration. Several simulation examples using typical parameters of inverse Compton scattering sources are presented to illustrate the excellent energy adaptability and efficiency of the DMWF method. This method provides a more efficient approach for phase-contrast imaging simulations, which will drive the advancement of high-energy phase-contrast imaging.

Numerical study on hydrodynamic interaction between two parallel surge-released ships advancing in head regular waves based on the hybrid method

This study examines the hydrodynamic interaction between two parallel surge-released ships in head regular waves. A hybrid approach combining potential flow theory and functional-decomposition URANS is used for an efficient and accurate simulation. The methodology involves a surge-released module, 4DOF motion equations, multiple coordinate systems, and a dynamic structured grid. Two ship models are used for validation, comparing numerical and experimental results in ship motions and wave loads. The analysis shows good agreement, with differences of less than 5 % in heave and pitch motions, less than 8 % in roll motions, and less than 15 % in total resistance and sway interference forces. Present study also observes consistent trajectories of the wave system between the two ships. The influence of surge motion and wave height on wave-ship-ship interaction is investigated, emphasizing the importance of considering surge motion in seakeeping performance and longitudinal separation. The occurrence of parametric roll in Ship B is accurately resolved by the hybrid method and the parametric roll amplitude is smaller under ship-to-ship interaction conditions. Wave-induced moments play a limited role in this phenomenon, while the dominating roll restoring moment exhibits a hardening effect. Accordingly, roll motion and moments remain relatively unchanged with increasing wave height, indicating the strong nonlinear nature of parametric roll.

An Efficient and Expressive Fully Policy-Hidden Ciphertext-Policy Attribute-Based Encryption Scheme for Satellite Service Systems

Satellite service systems transfer data from satellite providers to the big data industry, which includes data traders and data analytics companies. This system needs to provide access to numerous users whose specific identities are unknown. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) allows unidentified users with the proper attributes to decrypt data, providing fine-grained access control of data. However, traditional CP-ABE does not protect access policies. Access policies are uploaded to the cloud, stored, and downloaded in plain text, making them vulnerable to privacy breaches. When the access policy is completely hidden, users need to use their own attributes to try matching one by one, which is an inefficient process. In order to efficiently hide the access policy fully, this paper introduces a new efficient and expressive Fully Policy-Hidden Ciphertext-Policy Attribute-Based Encryption scheme (CP-ABE-FPH), which integrates the 2-way handshake O-PSI method with the ROBDD method. The integration offers advantages: (1) High efficiency and high expressiveness. The access policy using ROBDD is highly expressive but computationally intensive due to its recursive nature. This shortcoming is overcome in CP-ABE-FPH using the proposed O-PSI method, and the access policy is matched quickly and secretly. (2) High flexibility. The decryption process does not require the owner or the Key Generation Center (KGC) to be online, and system attributes can be added at any time. Security analysis shows that the access policy is fully hidden. Efficiency analysis and simulation results show that the proposed scheme is highly efficient in decryption compared with existing schemes.

Research on efficiency simulation model of pumping stations based on data-driven methods

The operational efficiency error of a pumping station unit is affected by several factors, resulting in a large error between the theoretical and actual pumping efficiencies. To solve the problem of accurately simulating the operating efficiency of the pumping station unit, this study conducted research on efficiency simulation models of pumping based on data-driven from three aspects: ①model algorithm, ②feature input and ③response output. In this study, eight machine learning models were introduced to establish pumping unit efficiency simulation models and they were compared with the traditional polynomial regression models, and the models with better performance were selected. Then the experiment proposed using “upstream water level + downstream water level” (UWL + DWL) instead of the traditional “head” (H) as feature input to train the models, and discussed the effects on efficiency simulation accuracy with direct fitting efficiency and fitting power then calculate the efficiency through formula. An example analysis was carried out using the historical data of eight units at Pizhou Station and Suining 2nd Station on the East Line of the South-to-North Water Diversion Project. The experimental results show that ① among all methods, the models based on Gaussian Process Regression (GPR) has the most accurate in ERMS, EMA, R², and EMI indicators, R² is close to 0.92. ②After UWL + DWL was used to train the models, all the test set indicators of the GPR models improved, and the EMA indicator decreased from an average of 0.39–0.26 %. ③ The study found that the fitting efficiency directly is better than the calculation efficiency after fitting the power, but the R² of fitting the power is better than the efficiency, which may provide another idea for the optimal goal of economic operation of pumping stations. ④ On the whole, trainning the GPR models with UWL + DWL to simulate efficiency has the highest precision, for example the EMA and EMI indicators of No.4 unit in Pizhou station can be reduced from 16.49 % and 20.40–0.18 % and 1.55 % respectively. The research results have practical significance and can provide strong support for the economical operation of pump stations.

Electrophysiology lab efficiency using cryoballoon for pulmonary vein isolation in Latin America: A sub-analysis of the Cryo Global Registry Study.

Cryoballoon ablation for pulmonary vein isolation is a time-efficient procedure that can alleviate stress on electrophysiology lab resources. This analysis modeled the impact of cryoballoon ablation on electrophysiology lab operation using data from Latin America. Data from centers in Argentina, Mexico, Colombia, and Chile of the were used as inputs for an electrophysiology lab efficiency simulation model. The model used the assumption that either two (today's electrophysiology lab operations) or three (including electrophysiology lab operational changes) cryoballoon ablation procedures could be performed per day. The endpoints were the percentage of days that resulted in 1) overtime and 2) time left for an extra non-ablation electrophysiology procedure. Data from a total of 232 procedures from six Latin American centers were included in the analysis. The average electrophysiology lab occupancy time for all procedures in Latin America was 132 ± 62 minutes. In the Current Scenario (two procedures per day), 7.4% of simulated days resulted in overtime, and 81.4% had enough time for an extra electrophysiology procedure. In the Enhanced Productivity Scenario (three procedures per day), 16.4% of days used overtime, while 67.4% allowed time for an extra non-ablation electrophysiology procedure. Using real-world, Latin American-specific data, we found that with operational changes, three ablation procedures could feasibly be performed daily, leaving time for an extra electrophysiology procedure on more than half of days. Thus, use of cryoballoon ablation is an effective tool to enhance electrophysiology lab efficiency in resource-constrained regions such as Latin America.

Efficient numerical simulation method for nonlinear guided wave in frequency domain

Abstract Nonlinear ultrasonic guided wave has attracted increasing attention for its ultra-sensitive to detect the incipient damages in service material by analyzing the amplitude of second harmonic wave. However, the mainstream method of simulating the second harmonic generation (SHG) in nonlinear material is using finite element method with time-domain solver, which is extremely time-consuming and can hardly simulate the huge and complex structures. In this paper, we propose a flexible finite element method based on frequency solver to simulate the SHG. The primary wave field, the second harmonic wave field and the static component field are separately set. This method is realized in Comsol Multiphysics software, the case of the SHG in plate indicates that the calculation efficiency has been significantly improved in compare with the time domain solver, whereas the results of time-domain solver and frequency domain solver have good agreement with each other. This method can be easily applied in the SHG analysis in huge and complex structures. Moreover, the primary wave field, the second harmonic wave field and the static component field can be respectively presented in the post-processing to help provide more information about physical insight of SHG.

Efficient option pricing in the rough Heston model using weak simulation schemes

We provide an efficient and accurate simulation scheme for the rough Heston model in the standard (H>0) as well as the hyper-rough regime ( -1/2 $ ]]> H > − 1 / 2 ). The scheme is based on low-dimensional Markovian approximations of the rough Heston process derived in [Bayer and Breneis, arXiv:2309.07023], and provides a weak approximation to the rough Heston process. Numerical experiments show that the new scheme exhibits second-order weak convergence, while the computational cost increases linearly with respect to the number of time steps. In comparison, existing schemes based on discretization of the underlying stochastic Volterra integrals such as Gatheral's HQE scheme show a quadratic dependence of the computational cost. Extensive numerical tests for standard and path-dependent European options and Bermudan options show the method's accuracy and efficiency.

Benchmark simulations of radiative transfer in participating binary stochastic mixtures in two dimensions

We study radiative transfer in participating binary stochastic mixtures in two dimensions (2D) by developing an accurate and efficient simulation tool. For two different sets of physical parameters, 2D benchmark results are presented, and it is found that the influence of the stochastic mixture on radiative transfer is clearly parameter-dependent. Our results confirm that previous multidimensional results obtained in different studies are basically consistent, which is interpreted in terms of the relationship between the photon mean free path lp and the system size L. Nonlinear effects, including those due to scattering and radiation–material coupling, are also discussed. To further understand the particle size effect, we employ a dimensionless parameter lp/L, from which a critical particle size can be derived. On the basis of further 2D simulations, we find that an inhomogeneous mix is obtained for lp/L > 0.1. Furthermore, 2D material temperature distributions reveal that self-shielding and particle–particle shielding of radiation occur, and are enhanced when lp/L is increased. Our work is expected to provide benchmark results to verify proposed homogenized models and/or other codes for stochastic radiative transfer in realistic physical scenarios.

An efficient multi-fidelity simulation approach for performance prediction of adaptive cycle engines

Adaptive cycle engine (ACE) with multi-stream, modulated bypass ratio, and high-thrust/high-efficiency mode provides the capability of multiple mission adaptation for the next-generation aviation propulsion system. The low-fidelity zero-dimensional (0D) performance simulation method is commonly adopted in conventional engines such as turbofan and turbojet. However, it is hard to accommodate well to ACE with complex variable geometry schemes and strong interaction between components. This paper presents an efficient multi-fidelity simulation approach, often referred to as component zooming, for predicting the performance of ACE with the three-stream configuration. The one-dimensional (1D) mean-line models of the adaptive fan and low-pressure turbine (LPT) are integrated into the 0D ACE model by the iteratively-coupled method. The performance predicted by the multi-fidelity ACE models with different component zooming strategies is compared. The maximum deviations of thrust, specific fuel consumption, turbine inlet temperature, and bypass ratio between the 0D/1D Adaptive Fan/1D LPT coupled model and 0D ACE model are −10.8%, −4.4%, −5.4% (−75 K), and 1.6%, respectively, which are considerable and non-negligible for the application in the design stage of ACE. The computing time of all the multi-fidelity models is less than 2 minutes. The effects of the key aerodynamic parameters of the adaptive fan and LPT on the engine performance are also evaluated. The proposed approach provides a generic and efficient solution for multi-fidelity ACE performance prediction with acceptable computing resource requirements and time cost, which is applicable in the engine conceptual and preliminary design stage.

Dynamic Simulation and Collision Detection for Flexible Mechanical Systems with Contact Using the Floating Frame of Reference Formulation

Abstract Contact simulation is essential in modelling mechanical systems. The contact models require accurate geometric information, which is determined through collision detection methods. When the mechanical system includes flexible bodies such as structural components, the dynamic formulation and collision detection can be more challenging, as the geometric boundaries of such components keep changing during the simulation. The floating frame of reference (FFR) formulation is suitable for flexible systems with small deformation. In this work, a stable and efficient dynamic simulation method is introduced for flexible systems with contact based on the FFR formulation. In addition, a curve-based collision detection method is proposed, which is more consistent with the dynamic formulation and more efficient than common existing collision detection methods. Case studies of flexible beams and multibody systems are employed to demonstrate the performance of the proposed dynamic simulation and collision detection methods.

Numerical investigation of the oxy-fuel combustion in the fluidized bed using macroscopic model supported by CFD-DDPM

Oxy-fuel combustion (OFC) presents a promising strategy for reducing carbon emissions from coal combustion. Traditional multiphase flow models (MFM) used for OFC simulations face challenges in integration and optimization with systems like carbon capture and electrolysis. Constructing a cost-effective and versatile macroscopic model simulation is critical for the industrial application of OFC. This study employs CFD-DDPM to obtain fluid dynamics parameters and couples heat and mass transfer models to construct macroscopic model of OFC based on a fluidized bed. Through calculations of OFC using both the macroscopic model and CFD-DDPM, molar fractions of O2, CO2, NO, and SO2 under various combustion conditions were found consistent with experimental measurements. Predictions under Recirculated Flue Gas (RFG) conditions showed comparable O2 and CO2 molar fractions at 30 % O2 & RFG, with the macroscopic model achieving 10.3 % and 69.3 %, versus 6.9 % and 72 % in CFD-DDPM. This demonstrates the ability of a macroscopic model to predict the design of the OFC in the fluidized bed. Importantly, the computational time of the macroscopic model is over 99 % shorter than that of CFD-DDPM. Consequently, employing a rapid simulation based on the macroscopic model method enables efficient simulation of OFC systems and facilitates the integration of oxygen combustion systems with other technologies like electrolysis. This approach provides valuable guidance for the development and application of OFC technology.

Ray Tracing of the New Multi-Modal X-ray Imaging Beamline PolyX at SOLARIS National Synchrotron Radiation Centre

The aim of the presented research is to evaluate the potential performance of a new bending magnet X-ray beamline—PolyX, designed for microimaging and microspectroscopy at the National Synchrotron Radiation Centre SOLARIS in Krakow. Due to the short beamline length (<15 m), PolyX uses compact polycapillary and single-bounce monocapillary optics for X-ray focusing in the 4–15 keV energy range. Polycapillary optics require a dedicated approach for an efficient simulation of X-ray propagation in multiple capillary channels. Therefore, the PolyX beamline was ray traced by combining XRT (XRayTracer) and polycap libraries. In addition, to estimate the X-ray fluorescence spectra excited by focused beams, Monte Carlo simulations were conducted using XMI-MSIM. All simulations were aimed to estimate the crucial X-ray beam properties, i.e., the flux, the spot size, and the energy spectrum, for monochromatic and polychromatic X-ray beams.

Advancing Climate Modeling through High-Performance Computing: Towards More Accurate and Efficient Simulations

A crucial branch of science called climate modeling uses mathematical equations and computer simulations to study and forecast the Earth's climate sys- tem. The main elements of climate modeling, such as general circulation models (GCMs), data assimilation methods, and numerical formulations, are outlined in this paper. GCMs, which include grid point and spectral models, are effective instruments for examining the behavior of the climate. Four-Dimensional Data Assimilation (4D-Var) is one example of a data assimilation technique that in- corporates observational data into models to improve their correctness. Numeri cal methods, ocean dynamics, heat transport, radiative transfer, and atmospheric dynamics are all included in numerical formulations. The simulation of different climate processes is possible because to these mathematical representations. Fur thermore, the detection of precipitation patterns within climate modeling is using machine learning techniques like Random Forest more frequently. This paper highlights the importance of high-performance computing (HPC) in climate modeling, boosting efficiency and simulations, in the context of research technique. Advanced data assimilation and validation techniques are also examined, as well as the influence of high-resolution modeling on small-scale climatic processes. On HPC platforms, accessibility to climate modeling is addressed. It is shown how climate modeling crosses physics, mathematics, computer science, and engineering to be interdisciplinary. A comprehensive understanding of the Earth's intricate climate system gains from the integration of all its parts, from atmospheric dynamics to data assimilation. We explore the consequences of these research approaches, their contribution to enhancing climate prediction models, and the influence of various factors on climatic variables in the debate. Climate modeling becomes an essential tool for studying precipitation patterns and climate change, ultimately improving our comprehension of the complex cli- mate system on Earth.

Model order reduction of time-domain acoustic finite element simulations with perfectly matched layers

This paper presents a stability-preserving model reduction method for an acoustic finite element model with perfectly matched layers (PMLs). PMLs are often introduced into an unbounded domain to simulate the Sommerfeld radiation condition. These layers act as anisotropic damping materials to absorb the scattered field, of which the material properties are frequency- and coordinate-dependent. The corresponding time-domain model size is often very large due to this frequency-dependent property and the number of elements needed per wavelength. Therefore, to enable efficient transient simulations, this paper proposes a two-step method to generate stable reduced order models (ROMs) of such systems. Firstly, the modified and stable version of PMLs is projected by a one-sided split basis, which gives a stable intermediate ROM. Secondly, the intermediate ROM is modified to satisfy the stability-preserving condition by applying the modal transformation. Applying any one-sided model order reduction method on this modified model leads to a stable and small ROM. This two-step method is further extended to account for the locally-conformal PML model by reformulating it in curvilinear coordinates, which works for arbitrary convex truncated domains. The proposed method is successfully verified by several numerical simulations.

Individualized dose calculation for internal exposure on radionuclide intake: GPU acceleration approach

Objective. The rapid and accurate assessment of internal exposure dose is a crucial safeguard for personnel health and safety. This study aims to investigate a precise and efficient GPU Monte Carlo simulation approach for internal exposure dose calculation. It directly calculates doses from common radioactive nuclides intake, like 60Co for occupational exposure, allowing personalized assessments. Approach. This study developed a GPU-accelerated Monte Carlo program for internal exposure on radionuclide intake, successfully realizing photoelectronic coupled transport, nuclide simulation, and optimized acceleration. The generation of internal irradiation sources and sampling methods were achieved, along with the establishment of a personalized phantom construction process. Three irradiation scenarios were simulated to assess computational accuracy and efficiency, and to investigate the influence of posture variations on internal dose estimations. Main results. Using the International Commission on Radiological Protection (ICRP) voxel-type phantom, the internal dose of radionuclides in individual organs was calculated, exhibiting relative deviation of less than 3% in comparison to organ dose results interpolated by Specific Absorbed Fractions in ICRP Publication 133. Employing the Chinese reference phantom for calculating internal irradiation dose from the intake of various radionuclides, the use of GPU Monte Carlo program significantly shortened the simulation time compared to using CPU programs, by a factor of 150–500. Internal dose estimation utilizing a seated Chinese phantom revealed up to a 75% maximum difference in organ dose compared to the same phantom in a standing posture. Significance. This study presents a rapid GPU-based simulation method for internal irradiation doses, capable of directly simulating dose outcomes from nuclide intake and accommodating individualized phantoms for more realistic and expeditious calculations tailored to specific internal irradiation scenarios. It provides an effective and feasible tool for precisely calculating internal irradiation doses in real-world scenarios.

(Invited) Real-Time Quantum–Classical Simulation as an Observation Tool for Dynamic Exciton Phenomena

Real-time computational simulations are promising tools for investigating ultrafast and nanoscale dynamical phenomena because of the absence of limitations in spatial and temporal resolutions. In this talk, we will introduce our efficient computational simulation technique for coupled electronic–nuclear dynamics and show how it can provide the microscopic insights into dynamic exciton phenomena such as exciton dissociation at donor–acceptor interfaces.

Deep learning tight-binding approach for large-scale electronic simulations at finite temperatures with ab initio accuracy

Simulating electronic behavior in materials and devices with realistic large system sizes remains a formidable task within the ab initio framework due to its computational intensity. Here we show DeePTB, an efficient deep learning-based tight-binding approach with ab initio accuracy to address this issue. By training on structural data and corresponding ab initio eigenvalues, the DeePTB model can efficiently predict tight-binding Hamiltonians for unseen structures, enabling efficient simulations of large-size systems under external perturbations such as finite temperatures and strain. This capability is vital for semiconductor band gap engineering and materials design. When combined with molecular dynamics, DeePTB facilitates efficient and accurate finite-temperature simulations of both atomic and electronic behavior simultaneously. This is demonstrated by computing the temperature-dependent electronic properties of a gallium phosphide system with 106 atoms. The availability of DeePTB bridges the gap between accuracy and scalability in electronic simulations, potentially advancing materials science and related fields by enabling large-scale electronic structure calculations.