Efficient Simulation Research Articles

Research on wind pressure characteristics of traditional timber buildings: a case study of the main hall of Shisi Temple

Traditional timber buildings are sensitive to wind action. Studying the wind pressure characteristics is the premise for the preventive conservation of traditional timber buildings. To investigate the computational fluid dynamics (CFD) numerical simulation method for wind pressure on traditional timber buildings, a typical traditional timber building, the main hall of Shisi Temple, is chosen as a case to carry out the study. A comparative analysis is conducted to examine the effects of curve simplification of the roof slope, as well as the Dougong (bracket sets) and roof tile components, on the numerical simulation results of wind pressure on the building surface. Additionally, simplification schemes of geometric modeling are provided for the efficient and accurate simulation. The results indicate that moderate simplification of the roof curve has a relatively minor impact on the overall calculation of wind pressure, and the difference between the drag coefficients of the simplified model and the accurate model is no more than 3%. However, excessive simplification can lead to distorted simulation results, and a three-segment curve simplification method is recommended for roof cornices. The influence of Dougong on the wind pressure calculation results is negligible (within 5%), whereas roof tiles significantly reduce the drag coefficient, with an impact of over 30% at various wind directions. The impact of roof tiles on wind pressure distribution in traditional timber buildings lies in their alteration of the building aerodynamic shape rather than an increase in roof thickness. The findings can provide a basis for assessing the wind resistance of traditional timber buildings and helpful insights for improving the efficiency of wind pressure analyses of traditional timber structures.

An efficient modeling workflow for high-performance nanowire single-photon avalanche detector

Single-photon detector (SPD), an essential building block of the quantum communication system, plays a fundamental role in developing next-generation quantum technologies. In this work, we propose an efficient modeling workflow of nanowire SPDs utilizing avalanche breakdown at reverse-biased conditions. The proposed workflow is explored to maximize computational efficiency and balance time-consuming drift-diffusion simulation with fast script-based post-processing. Without excessive computational effort, we could predict a suite of key device performance metrics, including breakdown voltage, dark/light avalanche built-up time, photon detection efficiency, dark count rate, and the deterministic part of timing jitter due to device structures. Implementing the proposed workflow onto a single InP nanowire and comparing it to the extensively studied planar devices and superconducting nanowire SPDs, we showed the great potential of nanowire avalanche SPD to outperform their planar counterparts and obtain as superior performance as superconducting nanowires, i.e. achieve a high photon detection efficiency of 70% with a dark count rate less than 20 Hz at non-cryogenic temperature. The proposed workflow is not limited to single-nanowire or nanowire-based device modeling and can be readily extended to more complicated two-/three dimensional structures.

Collective Variable-Based Enhanced Sampling: From Human Learning to Machine Learning.

Enhanced-sampling algorithms relying on collective variables (CVs) are extensively employed to study complex (bio)chemical processes that are not amenable to brute-force molecular simulations. The selection of appropriate CVs characterizing the slow movement modes is of paramount importance for reliable and efficient enhanced-sampling simulations. In this Perspective, we first review the application and limitations of CVs obtained from chemical and geometrical intuition. We also introduce path-sampling algorithms, which can identify path-like CVs in a high-dimensional free-energy space. Machine-learning algorithms offer a viable approach to finding suitable CVs by analyzing trajectories from preliminary simulations. We discuss both the performance of machine-learning-derived CVs in enhanced-sampling simulations of experimental models and the challenges involved in applying these CVs to realistic, complex molecular assemblies. Moreover, we provide a prospective view of the potential advancements of machine-learning algorithms for the development of CVs in the field of enhanced-sampling simulations.

Efficient classical simulation of cluster state quantum circuits with alternative inputs

We provide new examples of pure entangled systems related to cluster state quantum computation that can be efficiently simulated classically. In cluster state quantum computation input qubits are initialised in the `equator' of the Bloch sphere, CZ gates are applied, and finally the qubits are measured adaptively using Z measurements or measurements of cos⁡(θ)X+sin⁡(θ)Y operators. We consider what happens when the initialisation step is modified, and show that for lattices of finite degree D, there is a constant λ≈2.06 such that if the qubits are prepared in a state that is within λ−D in trace distance of a state that is diagonal in the computational basis, then the system can be efficiently simulated classically in the sense of sampling from the output distribution within a desired total variation distance. In the square lattice with D=4 for instance, λ−D≈0.056. We develop a coarse grained version of the argument which increases the size of the classically efficient region. In the case of the square lattice of qubits, the size of the classically simulatable region increases in size to at least around ≈0.070, and in fact probably increases to around ≈0.1. The results generalise to a broader family of systems, including qudit systems where the interaction is diagonal in the computational basis and the measurements are either in the computational basis or unbiased to it. Potential readers who only want the short version can get much of the intuition from figures 1 to 3.

Formalizing Coarse-Grained Representations of Anisotropic Interactions at Multimeric Protein Interfaces Using Virtual Sites.

Molecular simulations of biomacromolecules that assemble into multimeric complexes remain a challenge due to computationally inaccessible length and time scales. Low-resolution and implicit-solvent coarse-grained modeling approaches using traditional nonbonded interactions (both pairwise and spherically isotropic) have been able to partially address this gap. However, these models may fail to capture the complex anisotropic interactions present at macromolecular interfaces unless higher-order interaction potentials are incorporated at the expense of the computational cost. In this work, we introduce an alternate and systematic approach to represent directional interactions at protein-protein interfaces by using virtual sites restricted to pairwise interactions. We show that virtual site interaction parameters can be optimized within a relative entropy minimization framework by using only information from known statistics between coarse-grained sites. We compare our virtual site models to traditional coarse-grained models using two case studies of multimeric protein assemblies and find that the virtual site models predict pairwise correlations with higher fidelity and, more importantly, assembly behavior that is morphologically consistent with experiments. Our study underscores the importance of anisotropic interaction representations and paves the way for more accurate yet computationally efficient coarse-grained simulations of macromolecular assembly in future research.

A Method for Projecting Cloud Shadows Onto a Central Receiver Field to Predict Receiver Damage

This work demonstrates methods of mapping high-spatial-resolution direct normal irradiance (DNI) data from satellites, Total Sky Imagers (TSIs), and analogous data sources onto a heliostat field for characterizing the spatial and temporal variation of the incident flux on a central receiver tower during cloud transient events. The mapping methods are incorporated into an optical software module that interfaces with CoPylot–SolarPILOT’s python API– to provide computationally efficient optical simulation of the heliostat field and the solar power tower. Eventually, this optical model will be incorporated into optimization models whereby a plant operator can understand the effects of cloud transient events on overall power production and receiver lifetime due to creep-fatigue damage and therefore make better informed decisions about receiver shutdown events. By more accurately modelling the effects of cloud events on receiver flux maps, this work may determine the magnitude and frequency of thermal cycling on receiver tubes and panels using actual or realistic cloud shapes instead of averaged DNI values–which may undercount the total cycle number. This work may also prevent unnecessary plant shutdowns due to overly precautionary control strategies and characterize the relative impact of various cloud types on receiver life. We plan to eventually integrate this methodology into the System Advisor Model (SAM) to improve performance model accuracy during periods of cloudiness. In this paper, we demonstrate generating DNI maps and mapping them to a solar field in CoPylot using 10 m resolution data from publicly available Sentinel-2 satellite data over the Crescent Dunes plant.

A FE Model for the Efficient Simulation of the Thermo-Elastic Machine Tool Behavior

In modern machine tools, the thermo-elastic positioning error has a large share on the overall machine tool error. A multitude of different approaches can be found in the literature to correct this error in order to improve the accuracy of the machine tool. However, an FE-based approach to model the thermo-elastic error of 5 axis machine tools is still an active field of research. Therefore, the goal of this work is the improvement of the machine tool accuracy by modelling the machine tool error using a FE model. The model uses real time measurement data of the thermo-elastic error using an R-Test, as well as real time temperature measurements of the machine structure.

A variant of improved discrete velocity method for efficient simulation of flows in entire Knudsen number regimes

In this paper, a variant of the improved discrete velocity method (VIDVM) is proposed for flows in the whole Knudsen number regimes. This method retains the advantage of the improved discrete velocity method (IDVM), which calculates numerical fluxes through a self-adaptive strategy by combining the microscopic reconstruction and the macroscopic reconstruction. Like the IDVM, the microscopic reconstruction for VIDVM is also based on the collisionless Boltzmann solver. However, different from IDVM, the macroscopic reconstruction for VIDVM is established on the Euler solver instead of the Navier–Stokes solver. Considering that the Euler solver merely computes the inviscid fluxes while the Navier–Stokes solver additionally calculates the viscous fluxes, the present method could be more efficient than IDVM. To validate the accuracy and efficiency of the present scheme, some benchmark cases from the continuum regime to the free molecular regime are conducted. Results reveal that the present scheme can predict the flow as well as IDVM, but the present solver is more efficient than IDVM.

Reactive Transport Process of Earthquake‐induced Hydro‐chemical Changes in Guanding Thermal Spring, Western Sichuan, China

AbstractEarthquake‐related hydrochemical changes in thermal springs have been widely observed; however, quantitative modeling of the reactive transport process is absent. In the present study, we apply reactive transport simulation to capture the hydrochemical responses in a thermal spring following the Wenchuan Ms 8.0 and Lushan Ms 7.0 earthquakes. We first constrain deep reservoir geothermal fluid compositions and temperature by multicomponent geothermometry, and then a reactive geochemical transport model is constructed to reproduce the hydrochemical evolution process. The results show that the recharge from the shallow aquifer increases gradually until it reaches a peak because of the permeability enhancement caused by the Lushan earthquake, which may be the mechanism to explain the earthquake‐related hydrochemical responses. In contrast to the postseismic effect of the Wenchuan earthquake, the chemical evolution can be considered as hydrochemical anomalies related to the Lushan earthquake. This study proves that the efficient simulation of reactive transport processes is useful for investigating earthquake‐related signals in hydrochemical time series.

A Review of Contact Models’ Properties for Discrete Element Simulation in Agricultural Engineering

In agricultural engineering, the discrete element simulation of the operational structure, object of movement, and force has become a standard method of modern agricultural equipment design. The selection and development of an appropriate contact model are critical factors affecting the accuracy of the process of the simulation calculation of the movement and force. Understanding how to choose or establish suitable contact models according to different research fields, objects, and purposes has become the focus of present research. This paper gives an overview of contact models for discrete element simulation, summarizes and analyzes the simulation calculation basis of different contact models, and focuses on the application status and scenarios of different models at this stage. It analyzes and summarizes the selection basis and application fields of contact models. The next direction in the development of discrete element simulation contact models should be the hybrid application of multicontact models and the precise development of specialized contact models. It is necessary to establish a standardized parameter-calibration process for different contact models to guarantee the accuracy of the models, to improve the application of computer arithmetic, and to establish an efficient and accurate simulation contact model selection and application in the field of agricultural engineering. Efficient and accurate simulation contact model selection, design theory, and calculation processes will improve the efficiency of modern agricultural machinery design.

A Quantum Algorithm from Response Theory: Digital Quantum Simulation of Two-Dimensional Electronic Spectroscopy.

Multidimensional optical spectroscopies are powerful techniques to investigate energy transfer pathways in natural and artificial systems. Because of the high information content of the spectra, numerical simulations of the optical response are of primary importance to assist the interpretation of spectral features. However, the increasing complexity of the investigated systems and their quantum dynamics call for the development of novel simulation strategies. In this work, we consider using digital quantum computers. By combining quantum dynamical simulation and nonlinear response theory, we present a quantum algorithm for computing the optical response of molecular systems. The quantum advantage stems from the efficient quantum simulation of the dynamics governed by the molecular Hamiltonian, and it is demonstrated by explicitly considering exciton-vibrational coupling. The protocol is tested on a near-term quantum device, providing the digital quantum simulation of the linear and nonlinear response of simple molecular models.

Efficient quantum simulation of nonlinear interactions using SNAP and Rabi gates

Quantum simulations provide means to probe challenging problems within controllable quantum systems. However, implementing or simulating deep-strong nonlinear couplings between bosonic oscillators on physical platforms remains a challenge. We present a deterministic simulation technique that efficiently and accurately models nonlinear bosonic dynamics. This technique alternates between tunable Rabi and SNAP gates, both of which are available on experimental platforms such as trapped ions and superconducting circuits. Our proposed simulation method facilitates high-fidelity modeling of phenomena that emerge from higher-order bosonic interactions, with an exponential reduction in resource usage compared to other techniques. We demonstrate the potential of our technique by accurately reproducing key phenomena and other distinctive characteristics of ideal nonlinear optomechanical systems. Our technique serves as a valuable tool for simulating complex quantum interactions, simultaneously paving the way for new capabilities in quantum computing through the use of hybrid qubit-oscillator systems.

A velocity evaluation method for in-pipe metallic flow through inversion of magnetic field perturbation measured surrounding the pipe

It is important to measure the global and/or local velocity of an in-pipe metallic flow to control its running state in applications such as a Tokamak fusion reactor. The magnetic field outside the pipe wall will be perturbed by the motion induced eddy current when the liquid metal flows across an applied static magnetic field. This phenomenon gives a possibility to evaluate the in-pipe velocity from the measured magnetic field perturbation signals. In this paper, a non-intrusive velocity evaluation method is proposed accordingly for measuring the velocity of liquid metal through measurement and inversion of the magnetic field surrounding the pipe. An efficient forward simulation method to calculate the magnetic field near a metallic flow in a static environmental magnetic field is developed at first. An inversion scheme based on the singular value decomposition and the L-curve method is then proposed to reconstruct the velocity distribution at a pipe cross-section with the linear equations correlating the flow velocity and the magnetic field regulated using the Tikhonov method. The reconstruction results of pipe flows of different velocity modes verified the feasibility and efficiency of the proposed velocity measurement method for in-pipe metallic flows.

An efficient parameterized simulation framework for 3D scarf-repaired composite laminates

This paper proposes a parameterized simulation framework for predicting the load-bearing capacity of 3D scarf-repaired composite laminates which remarkably reduces the modeling time from several hours to just a few seconds. The specific implementation is demonstrated through a detailed step-by-step explanation. The main contribution lies in: (1) By introducing a reusable geometric partitioning method for composite layers and implementing a partitioning method-based numbering scheme for element and node, the effective management of element and node IDs of elements and nodes in such complex 3D models is greatly facilitated; (2) By ingeniously combining open-source meshing tools and text rewriting techniques, a highly efficient approach has been developed to construct complete models for such structures, showcasing the remarkable potential of these existing techniques in this field. The reliability of the efficient parameterized simulation framework proposed in this study is verified through the comparison between strength values acquired from progressive damage analysis (PDA) and experiment data reported in the existing literature.

High efficiency integrated urban flood inundation simulation based on the urban hydrologic unit

As the impact of flooding intensifies globally in metropolitan areas, there is an urgent need for high-resolution flood forecasting models to facilitate preparedness and mitigate flood impacts. In response, we have proposed a high efficiency algorithm (HEA) designed to simulate urban flood inundation, leveraging the urban hydrologic unit (UHU) concept from an urban hydrological cycle perspective. The HEA significantly reduces the computational demands associated with high-resolution modeling by using a hydrologic unit derived from the digital elevation model (DEM). This approach effectively shifts the overwhelming burden of computational load to the hydrodynamic algorithm (HDA), which is grounded in the Shallow Water Equations (SWEs), thus enabling efficient flood inundation simulation crucial for time-critical decision making during extreme events. Our research also incorporated the HEA and HDA methods into a self-developed, distributed-framework basin modeling system, named the Taihu Basin Model (TBM). Both the HEA and HDA models were calibrated and validated with observed data on pipe level, flow rate, and inundation depth from our Shuangqiaobang experimental fields. Comparative analysis, focusing on simulated urban flood inundation distribution characteristics, simulation accuracy and timeliness, demonstrated the superior effieicney of the HEA method by three orders of magnitude. The consistency between observed data and the simulated results of both HEA and HDA models underscores the accuracy and reliablility of these models. The HEA model’s simulated urban flood inundation distribution appears more patchy and localized compared to the HDA model, due to the generalization of urban UHUs in the TBM.

Qutrit circuits and algebraic relations: A pathway to efficient spin-1 Hamiltonian simulation

Qutrit circuits and algebraic relations: A pathway to efficient spin-1 Hamiltonian simulation

OpenFOAM mechanical simulations for confronting structural challenges and attaining high-fidelity results

Multi-dimensional models are now a unified collection of tools for simulating the combustion process in internal combustion engines. To address key challenges in combustion technology and to advance our ability to predict, control, and optimize combustion processes through a novel and integrated approach. In this direction, the present study emphasizes the integration of advanced computational fluid dynamics (CFD) techniques with OpenFOAM to conduct a comprehensive analysis of laminar combustion inside the intake manifold. This integrated approach offers a unique and holistic perspective on combustion processes, a perspective that has not been extensively explored in prior research. Furthermore, FVM is introduced as a novel methodology for the efficient and accurate simulation of these multi-physical phenomena, thereby contributing to the advancement of combustion modeling. The analysis focuses on the flammable energy, energy deposition, and combustion inside the intake manifold of an engine. The goal is to create a comprehensive model that accounts for the intricate interactions between thermal energy, velocity, and species concentration, ultimately leading to a deeper insight into combustion phenomena. The maximum flammable energy is observed near the outlet of the considered intake manifold.

Population genetic simulation: Benchmarking frameworks for non-standard models of natural selection.

Population genetic simulation has emerged as a common tool for investigating increasingly complex evolutionary and demographic models. Software capable of handling high-level model complexity has recently been developed, and the advancement of tree sequence recording now allows simulations to merge the efficiency and genealogical insight of coalescent simulations with the flexibility of forward simulations. However, frameworks utilizing these features have not yet been compared and benchmarked. Here, we evaluate various simulation workflows using the coalescent simulator msprime and the forward simulator SLiM, to assess resource efficiency and determine an optimal simulation framework. Three aspects were evaluated: (1) the burn-in, to establish an equilibrium level of neutral diversity in the population; (2) the forward simulation, in which temporally fluctuating selection is acting; and (3) the final computation of summary statistics. We provide typical memory and computation time requirements for each step. We find that the fastest framework, a combination of coalescent and forward simulation with tree sequence recording, increases simulation speed by over twenty times compared to classical forward simulations without tree sequence recording, although it does require six times more memory. Overall, using efficient simulation workflows can lead to a substantial improvement when modelling complex evolutionary scenarios-although the optimal framework ultimately depends on the available computational resources.

Three-dimensional modeling of impact fractures in brittle materials via peridynamics

Accurate prediction of the impact fracture behavior of brittle materials is crucial in various industries. The paper employs the peridynamic method to model the penetration and damage in brittle materials during impact. The proposed three-dimensional peridynamic impact fracture model (3D-PDIFM) overpasses other numerical models by adopting a new computational technique to couple the impact collisions of different types of projectiles with the peridynamic method. Moreover, the 3D-PDIFM is highly efficient in impact fracture simulations by focusing on the effects of the projectile without simulating its particles, thereby saving more computational efforts. Validation through comparison with experimental measurements and observations ensures that the 3D-PDIFM accurately represents the real-world impact fracture behavior of brittle materials. The paper also highlights the efficiency of the 3D-PDIFM in comparison to traditional mesh-based numerical simulations. Therefore, the 3D-PDIFM can be significant for practical engineering applications as efficient simulations can save time and computational resources. Using the validated model, the impact fracture behavior of PMMA under various conditions, such as changes in panel thickness, impact area, and projectile velocity, is investigated. This analysis gains more insights into how these factors affect the brittle material's response to impact. Moreover, the impact fracture behaviors of tempered glass and float glass panels are examined under different conditions of impact loadings. This study also explains the penetration and impact fracture phenomena in brittle materials. Overall, using the 3D-PDIFM as a modeling and simulation tool can help engineers and researchers analyze the behavior of glass panels under various impact scenarios, thus allowing for optimization of design parameters to improve impact resistance.

Study on the Concentration of Top Air Pollutants in Xuzhou City in Winter 2020 Based on the WRF-Chem and ADMS-Urban Models

In recent years, the issue of air pollution has garnered significant public attention globally, with a particular emphasis on the challenge of atmospheric fine particulate matter (PM2.5) pollution. The efficient and precise simulation of changes in pollutant concentrations, as well as their spatial and temporal distribution, is essential for effectively addressing the air pollution issue. In this paper, the WRF-Chem model is used to simulate the meteorological elements including temperature (T), relative humidity (RH), wind speed (WS), and pressure (P), and the concentrations of PM2.5 and PM10 atmospheric pollutants in December 2020 in Xuzhou City. Simultaneously, the ADMS-Urban model was employed to conduct a higher spatial resolution study of PM2.5 concentrations during the heavy pollution days of 11–12 December 2020 in Xuzhou City. The study shows that the WRF-Chem model can simulate the meteorological conditions of the study time period better, and the correlation coefficients (R) of pressure, temperature, wind speed, and relative humidity are 0.99, 0.87, 0.75, and 0.70, respectively. The WRF-Chem model can accurately simulate the PM2.5 concentration on clean days (R of 0.66), but the simulation of polluted days is not satisfactory. Therefore, the ADMS-Urban model was chosen to simulate the PM2.5 concentration on polluted days in the center of Xuzhou City. The ADMS-Urban model can simulate the distribution characteristics and concentration changes of PM2.5 around roads and buildings in the center of Xuzhou City. Comparing the simulation results of the two models, it was found that the two models have their own advantages in PM2.5 concentration simulation, and how to better couple the two models is the next research direction.