Improve Simulation Efficiency Research Articles

Data simulations for advancing psychological research: Insights, preparations and investigations

Simulation has become an essential tool in psychological research, offering unique insights into statistical concepts, optimising research project planning and modelling human behaviour and cognition. This tutorial navigates simulations' diverse applications across various research stages. This tutorial begins by demonstrating simulations' impact on understanding statistical concepts. Amidst growing demands for effect size reporting and interpretation, simulations can help clarify the relationship between data and effect sizes, benefiting both educational purposes and advanced research. Simulations play a crucial role in planning psychological research. They provide flexible solutions for power, helping to determine necessary sample sizes and aiding in robust research planning, providing evidence of planned procedures. Simulations are pivotal in exploring human behaviour and cognition, as well as statistical method properties. By forcing researchers to translate verbal theory into formal models, simulation can help specify the assumptions. The paper also discusses improving simulation efficiency, such as using calculation clusters, optimising simulation practices and recognises simulations' limitations. This paper emphasises simulations' versatile role in psychological research. They enable deep insights into complex datasets, streamline project preparation and uncover subtleties in human behaviour, cognition and statistical methodologies.

Dissipative Particle Dynamics Simulation of Protein Folding in Explicit and Implicit Solvents: Coarse-Grained Model for Atomic Resolution.

Advancements have been made to dissipative particle dynamics (DPD), a robust coarse-grained (CG) simulation method, to study the folded structures of four miniproteins (1L2Y, 1WN8, 1YRF, and 2I9M) in explicit and implicit solvents. In this endeavor, we aim to establish model parametrization and enhance computational efficiency. Unlike traditional CG models that use empirical force parameters, ex-force parameters (r0(ex), , δd, δp) of DPD particles constructed for specific research purposes can be obtained from atomistic molecular dynamics simulations. On the other hand, im-force parameters (r0(im), c, σ) can be derived from ex-DPD simulations, according to the underlying thermodynamic theory. Based on a mapping scheme proposed for the modeling of amino acids, all-atom proteins can be converted into a CG model. Both ex-/im-DPDs are then carried out to investigate the folding pathways of the four mini-proteins. Structural analysis of the RMSDs shows that the im-simulated proteins have greater structural similarity to native proteins than the ex-simulated ones. The constructed CG models achieve a resolution of Angstrom (Å), a level normally associated with atomic models. Additionally, speed tests reveal that im-DPD accelerates the simulation process and significantly improves simulation efficiency.

High-fidelity simulations of boundary layer transition on an underwater axisymmetric body with stability theory

During the cruise status of an underwater high-speed vehicle, the flow around the vehicle's head typically transitions from a laminar to a turbulent state, triggering flow noise that can interfere with the normal operation of sonar. In order to accurately investigate this flow noise through numerical simulation, a high-fidelity turbulent flow field solution is essential. Common traditional turbulence numerical simulation methods, such as unsteady Reynolds-averaged simulation and large eddy simulation (LES), struggle to capture high-frequency turbulent fluctuations accurately due to their inability to directly resolve small-scale eddy structures, which results in compromising the simulation accuracy of high-frequency flow noise. To address this issue, this paper employs direct numerical simulation (DNS) to achieve high-fidelity resolution of the turbulent flow field, thereby enabling a more accurate assessment of flow noise distribution on the vehicle's surface. Meanwhile, considering significant computational resources required to solve the entire flow field in an underwater high Reynolds number environment, this study also incorporates the fixed transition modeling method and stability theory to confine the DNS computational domain to the vicinity of the transitional zone to improve simulation efficiency. Comparative analysis of flow noise monitoring results in the laminar, transitional, and turbulent zones revealed that the flow noise source in the laminar zone exhibits the lowest amplitude across all frequencies, while the flow noise source in the transitional zone features the highest amplitude, approximately 10 dB higher than that in the turbulent zone. Moreover, significant amplitudes in high-frequency components (above 30 kHz) are detected in both the transition and turbulent zones. Additionally, this study employs LES with the Smagorinsky model to simulate the flow field within the same computational domain as DNS, demonstrating the limitations of the Smagorinsky model-based LES in capturing high-frequency flow noise.

Effect of Cross-Well Natural Fractures and Fracture Network on Production History Match and Well Location Optimization in an Ultra-Deep Gas Reservoir

Understanding subsurface natural fracture systems is crucial to characterize well production dynamics and long-term productivity potential. In addition, the placement of future wells can benefit from in-depth fracture network connectivity investigations, vastly improving new wells’ profitability and life cycles if they are placed in dense, well-connected natural fracture zones. In this study, a novel natural fracture calibration workflow is proposed. This workflow starts with the extraction of sector geology and a natural fracture model from the pre-built full-field model. Then, a cross wellbore discrete fracture network (CW-DFN) is created using a novel CW-DFN generation tool, based on image log data. An innovative fracture network identification tool is developed to detect the interconnected regional fracture network (IcFN) with CW-DFN. The non-intrusive embedded discrete fracture model (EDFM) is utilized to numerically incorporate the complex IcFN and CW-DFN in a reservoir simulation, and it is history-matched by tuning their conductivities. This workflow is applied to a single vertical well within a natural fracture carbonate reservoir in Northwest China. The study results show that the number of CW-DFNs is 11, and the number of IcFNs is 72. The non-intersected natural fractures only account for 5.5% of the production, and thus can be removed to improve simulation efficiency. The history-matching absolute average relative deviation (AARD) is 15.16%. The calibrated effective fracture permeability is 280 millidarcy, with an aperture of 0.001 m, equating to a conductivity of 0.28 millidarcy-meter. The 30-year gas production forecast is estimated to be 1.66 billion cubic meters based on a history-matched model. Finally, if the well is drilled to the east of the sector, 30-year production declines to 1.33 billion cubic meters (a reduction of 20%). However, if the well is drilled to the west of the sector, 30-year production increases to 2 billion cubic meters (an improvement of 20.5%).

Optimization of gamified Learning Education Model based on XR + 5G technology

The new intelligent technology represented by 5G and XR has become the core driving force of the fourth industrial revolution, and the integrated application of the two in the field of education is also promoting the continuous transformation of teaching to gamification. Based on XR + 5G fusion technology, this paper proposes the optimization design of a gamified education model. Firstly, the problem of how to improve the network performance through D2D communication in a full-load 5G network is studied, and a D2D communication resource allocation framework based on cross-layer optimization design is proposed. In D2D user access control, a D2D user access criterion based on the distance between the CU user and D2D user receiver is designed according to the QoS requirement, maximum transmit power limit, and user channel information. XR simulation platform was designed based on a 5G network with optimized communication resource allocation. UMa and InH simulation scenarios were set according to XR business model working scenarios, and Wrap Around algorithm was used to model two circles of interference for users, ensuring the reliability of results while improving simulation efficiency. Finally, the platform channel was modeled strictly according to the channel modeling method in technical document 38.901, and the channel was calibrated with the channel provided by 3GPP. Based on the simulation platform, gamified education design is optimized. Including class platform and class platform gamification design, system demand analysis, and database design. In the gamification design, a relatively novel classroom interaction mode is introduced to increase students’ enthusiasm in class. In the demand analysis of the system, the main problems in class are listed, based on which the business and functional requirements of the gamification platform are determined. Experiments show that in the IID scenario, the proposed XR + 5G fusion technology can achieve 1.09 times the training speed and obtain 0.83 % learning accuracy gain compared with the individual learning algorithm. In the Non-IID scenario, the training speed of the proposed fusion technique is 1.03 times that of the individual learning algorithm, and there is a learning accuracy gain of 2.27 %.

A conservative discontinuous-Galerkin-in-time (DGiT) multirate time integration framework for interface-coupled problems with applications to solid–solid interaction and air–sea models

In this paper we extend the DGiT multirate framework, developed in Connors and Sockwell (2022) for scalar transmission problems, to a solid–solid interaction (SSI) problem involving two coupled elastic solids and a coupled air–sea model with the rotating, thermal shallow water equations. In so doing we aim to demonstrate the broad applicability of the mathematical theory and governing principles established in Connors and Sockwell (2022) to coupled problems characterized by subproblems evolving at different temporal scales. Multirate time integration algorithms employing different time steps, optimized for the dynamics of each subproblem, can significantly improve simulation efficiency for such coupled problems. However, development of multirate algorithms is a highly non-trivial task due to the coupling, which can impact accuracy, stability or other desired properties such as preservation of system invariants. DGiT provides a general template for multirate time integration that can achieve these properties. To elucidate the manner in which DGiT accomplishes this task, we fully detail each step in the application of the framework to the SSI and air–sea coupled problems. Numerical examples illustrate key properties of the resulting multirate schemes for both problems.

The Efficiency of Surface Impedance Technique in the Transverse Wave Approach for the EM-Modeling of Fractal-Like Tree Structure used in 5G Applications

Fractal antenna technology is a promising approach for 5G applications because its complex nature offers optimization potential in terms of time and space trade-offs. However, the computational effort required to analyze such antennas is significant. This paper investigates the Advanced Transverse Wave Approach (ATWA), which utilizes the surface impedance technique to improve simulation efficiency. This study introduces and analyzes a fractal-like 5G tree structure, displaying improved computational accuracy and efficacy, as well as peak memory utilization compared to current works. The proposed approach demonstrates significant effectiveness in enhancing the performance of complex fractal antennas for 5G technology and shows promise for integration with cloud, fog, and edge computing environments. This integration could potentially optimize data processing and network efficiency in these advanced computing landscapes.

Channel Measurement and Modeling for Millimeter-Wave Automotive Radar

Millimeter-wave (mmWave) automotive radar can detect the surrounding environment using highly directional, high-frequency electromagnetic waves. With these advantages, mmWave radar has become an important component of autonomous driving and integrated sensing systems. Designing systems and sensing algorithms requires the application of realistic channel models and simulation. In this paper, the propagation channel is measured, characterized, and modeled for mmWave automotive radar with typical configurations in scenarios. Based on the channel measurements in urban street and expressway environments, ray-tracing (RT) technology is verified for modeling important objects regarding radar cross-section and echo power. A hybrid channel model is proposed by integrating RT with the stochastic modeling of targets and surrounding environments, which significantly improves simulation efficiency and provides more flexibility for virtual tests in various complex environments.

Studying on the upper limit of the coarse-grained DEM model for simulating mixing processes in a rolling drum with complex wall boundary

Compared with the original Discrete Element Method, the coarse-grained DEM significantly improves simulation efficiency. In CG DEM, the coarse-grained ratio is defined as the diameter of the coarse-grained particle divided by the diameter of the original particle. When the upper limit of coarse-grained ratio is used, the simulation is the most efficient. The upper value for simulating mixing process of spherical fuel particles was determined using a multi-level methodology. The adequacy of the upper value of the coarse-grained ratio was assessed by comparing the mixing characteristics and kinetic properties of the coarse-grained particle systems with those of the original particle systems in a rotating drum with blades. The impact of rotation speed and particle number on the upper value was examined and discussed. It was observed that the kinetic energy increases with an increase in the coarse-grained ratio, limiting the efficiency improvement. To ensure kinetic energy conservation, a scaling law for the restitution coefficient was proposed. Combined with the Reduced Particle Stiffness method, the simulation time of the coarse-grained case is only 1/3030 that of the original case (with a particle number of 2.3 million) when the upper limit of the coarse-grained ratio was used (12.0).

Two-stage assessment: Towards a novel and holistic evaluation of urban geographically isolated wetland sustainability under global warming-induced dryness and loss

Global wetlands are vital for carbon sequestration and providing ecosystem services in sustainable cities. However, urban wetlands, particularly geographically isolated wetlands (GIWs), face threats from climate change and rapid urbanization. The vulnerability of GIWs, considering the critical water flux exchanges with their surroundings, remains unclear. This study proposes a groundbreaking two-stage assessment framework for evaluating GIW sustainability. The first stage quantifies dryness risk by assessing the absence of surface water storage. The second stage evaluates wetland loss risk by examining unsustainable conditions of lacking saturated soil. The framework utilizes a hydrological model incorporated with 20 Global Climate Models (GCMs) under multiple greenhouse gas emission scenarios. Moreover, Taguchi optimization is firstly introduced the hydro-climate field to improve simulation efficiency, with a new modification by a modified robust design. Results indicate an average 26 cm drop in the GIW water table by 2099 under moderate GCM scenarios, with an 86% risk of drying and a 64% risk of transitioning to loss. Notably, GIWs face over twice the risk of loss compared to upland areas, but demonstrate an advantage in water reservation under climate change. By proposing and employing this innovative approach, this research provides valuable insights into the interconnectedness of GIWs, offering a more comprehensive understanding of long-term urban ecological integrity. The findings also contribute to the long-term sustainability of urban wetlands and enhance the resilience of urban ecosystems.

Experimental study and numerical simulation of the influence of ball milling on mechanical and physical properties of matcha powder

Ball milling is a crucial step in matcha powder processing. In this study, experimental and numerical simulation investigations on the mechanical and physical properties of matcha powder during processing were conducted. Based on the discrete element method (DEM), a scaling model was established to improve simulation efficiency. Furthermore, three typical models of matcha particles were established and the coefficients of rolling friction and static friction were calibrated which were 0.064 and 0.50 respectively. Above this, the correlation between the mechanical and physical properties of matcha powder was also investigated. The results suggest that the morphological characteristics of matcha powders showed a highly significant correlation with the collision velocity; the flowability of matcha particles exhibited a significant correlation with both the collision velocity and the number of collisions. Predictably, these findings may provide a reference for the selection of processing parameters during matcha processing and the optimal design of production equipment.

Fast and precise DEM parameter calibration for Cucurbita ficifolia seeds

The lack of discrete element method (DEM) models and calibration parameters for Cucurbita ficifolia seeds, as well as low accuracy and efficiency of common parameters calibration methods, hinder the application of DEM for computer simulation in air-suction directional seeding equipment. In this study, the DEM parameters of the seeds were calibrated. The angle of repose (AOR), intrinsic parameters, and partial contact parameters of the seeds were experimentally measured. The seed 3D models were reconstructed based on the three-view profile information. The parameters and their value ranges were filtered through the Plackett–Burman design and steepest ascent test. The response surface method (RSM) and machine learning were utilised for optimisation inversion of the parameters. The experiments showed that the geometric relative error of the seed model was 0.69–6.54%, which meets the modelling requirements for DEM. The seed–seed static friction coefficient, the seed–seed and the seed–PVC rolling friction coefficient were 0.341, 0.026, and 0.059, respectively, which were obtained by inverting the GA-BP regression model via the Genetic Algorithm. The simulated AOR was 26.64°, with a relative error compared to the actual AOR of 1.64%, which was better than the simulated AOR obtained by RSM optimisation. The greater the smoothing value setting in EDEM software, the less the particle filling, resulting in improved simulation efficiency but reduced model accuracy. The CPU + GPU(CUDA) solver showed high DEM solution efficiency. The results reveal that the method can be used to quickly and accurately construct a 3D model of the seed, and the parameter optimisation accuracy of GA-BP-GA is better than that of RSM.

Two-dimensional viscoelastic beam element for ultra-large space structures based on absolute nodal coordinate formulation

Internal damping is an important factor in the dynamic behaviors of ultra-large space structures in zero damping space environment. To precisely describe the damping, this paper proposes a two-dimensional viscoelastic Euler-Bernoulli beam element based on absolute nodal coordinate formulation and Kelvin-Voigt damping model, where the damping stress is linearly dependent on the stress rate. Firstly, a precise generalized damping force model is developed using the principle of virtual work. A simplified generalized damping force model is then proposed to improve simulation efficiency by neglecting the axial damping force and a part of the transverse damping force. Numerical simulations show that the proposed damping force models will not affect the rigid-body motion of the beam. Then, a simple formula is presented to estimate the damping ratio of the beam for different parameters, which shows good agreement with the logarithmic decrement method in simulations. Finally, the effects of viscoelastic damping on the dynamic behaviors of a Sun-facing beam in space are investigated. Simulation results reveal that the viscoelastic damping can greatly reduce the vibration amplitude of the beam near the resonance frequencies.

A Fast Simulation Method for Evaluating the Single-Event Effect in Aerospace Integrated Circuits.

With the continuous progress in integrated circuit technology, single-event effect (SEE) has become a key factor affecting the reliability of aerospace integrated circuits. Simulating fault injection using the computer simulation technique effectively reflects the SEE in aerospace integrated circuits. Due to various masking effects, only a small number of faults will result in errors; the traditional method of injecting one fault in one workload execution is inefficient. The method of injecting multiple faults in one workload execution will make it impossible to judge which fault results in errors because the propagation characteristic of SEE and faults may affect each other. This paper proposes an improved multi-point fault injection method to improve simulation efficiency and solve the problems of the general multi-point fault injection method. If one workload execution does not result in errors, multiple faults can be verified by one workload execution. If one workload execution results in errors, a specific grouping method can be used to determine which faults result in errors. The experimental results show that the proposed method achieves a good acceleration effect and significantly improves the simulation efficiency.

High-efficiency Monte Carlo simulation based on CADIS method for Gamma Density Measurement

Formation density measurement is one of the most important part in reservoir hydrocarbon evaluation, which relies on accurate acquisition of the detector energy deposition spectra. Monte Carlo method is usually used to solve such problems, while huge computational time will be consumed due to the accuracy requirements of simulations. Therefore, this work aims to improve simulation efficiency based on the CADIS methods in Geant4 and achieve accurate density calculations. To obtain energy deposition spectra, this manuscript introduces a scoring method to assign weight windows according to energy group in Geant4 and to collect energy deposition spectra according to particle weights. This method is verified in simulated spectra using a dual-detector gamma density tool. Five test pits with measurement data are selected to demonstrate the accuracy of the calculated densities. The results show that the energy deposition spectra obtained by CADIS method can effectively improve the accuracy of density calculation.

Equivalent of distribution network with distributed photovoltaics for electromechanical transient study based on user-defined modeling

As more and more distributed photovoltaics (PVs) are installed in distribution networks, the dynamic characteristics of PVs affect the operation of the bulk power system. Accurate simulation of PV characteristics is essential for the needs of power system transient security and stability analysis. However, because of the large number of distributed PVs, detailed modeling of each PV significantly increases the electromechanical transient simulation time. The equivalent study of distribution networks with distributed PVs is needed to improve simulation efficiency. However, existing equivalent methods of PVs mainly focus on centralized PVs but are unsuitable for distributed PVs with significantly different dynamic characteristics. To improve simulation efficiency, this paper proposes an equivalent method of the distribution network with distributed PVs for electromechanical transient study based on user-defined (UD) modeling. Firstly, the equivalent structure of the distribution network is established. The static equivalent of the transmission lines and the dynamic equivalent model of loads are carried out. Then, a UD model based on PSASP is established for the dynamic equivalent of the distributed PVs. Compared with the current PV models in simulation software, the UD model has multiple sets of parameters for the equivalent of distributed PVs with different dynamic characteristics. Finally, the particle swarm optimization (PSO) algorithm is used to obtain the parameters of the equivalent PV. The effectiveness of the proposed method is verified under an example of a distribution network with PVs in PSASP.

Experimental and two-scale numerical studies on the behavior of prestressed concrete-steel hybrid wind turbine tower models

Prestressed concrete-steel hybrid (PCSH) wind turbine tower has been recognized as an efficient alternative to traditional steel tube supporting towers for wind farms in harsh sites such as mountain regions. To investigate the global and local mechanical behavior including failure patterns of a full scale PCSH wind turbine towers, an experimental study on a substructure of a scaled PCSH tower is carried out firstly. Then, a material subroutine for three-dimensional (3D) fiber beam elements suitable for implicit analysis on the mechanical behavior of the substructure of the scaled PCSH tower is developed. A two-scale numerical model of the substructure of the scaled PCSH wind turbine tower employing the developed 3D fiber beam elements and solid elements is developed. The accuracy and efficiency of the developed two-scale numerical model for the behavior simulation of the substructure of the scaled PCSH wind turbine tower is illustrated by comparing the numerical results with the experimental measurements. Additional comparison of the simulation results using the developed two-scale numerical model is made with them of a model using sole 3D fiber beam elements and a model using sole solid elements, respectively. Results show that the force–displacement curve determined with the developed two-scale model is close to the test result and coincident well with that of the two models using 3D fiber beam elements and the solid elements. The developed two-scale model can describe the global behavior, the local failure patterns and the stress distribution of the tested specimen accurately with improved simulation efficiency. Finally, the validated two-scale modelling approach is employed to analyze the static behavior, modal parameter, and fatigue of the optimized full-scale PCSH wind turbine tower previously developed by the authors. The simulation results show that the two-scale model can reflect the stress of the PCSH wind turbine tower at the critical position as well as the global mechanical behavior with higher computational efficiency. The fiber beam model can be used for predicting only global, but not local, behavior.

The occurrence probability prediction model of 2D and 3D freak waves generated by wave superposition

The wave superposition is a common method to generate freak waves by assigning energy of wave spectrum to a focusing wave train and a random wave train. Traditionally, the freak wave generation by wave superposition requires repeatedly try for many times before obtaining satisfactory results. In this process, freak wave may not occur when wave component number or energy portion of focusing wave train is small and freak wave may break up when that is too large. It may also contribute to the discrepancy of significant wave height between target value and generated result when energy portion of focusing wave train is large. To improve simulation efficiency, the paper proposes a model to quantitatively predict the occurrence probability of freak waves in superposition cases of two and three wave trains. The error of significant wave height could be artificially controlled. The occurrence probability prediction model of freak wave is also extended to three-dimensional wave fields. The accuracy is well validated by statistical data. According to prediction results, two-dimensional and three-dimensional freak waves are successfully generated.

Effectiveness of Monte Carlo Simulations of the S&P 500 Index before and after the Outbreak of the SARS-COV-2 Pandemic

Risk analysis is an integral part of studying the behavior of financial markets. Crises and emergencies challenge analysts trying to predict the value of stock indices by questioning their assumptions. One such event was the coronavirus pandemic, which undoubtedly affected our economy. The purpose of this study was to examine the impact of the COVID-19 pandemic on the US market and to assess the change in effectiveness of the Monte Carlo method due to the pandemic. The study was realized with 12 MC simulations of daily S&P 500 index prices using historical data from 11.03.2015 to 11.03.2021. The negative impact of the pandemic on the accuracy of MC simulations was observed, lowering the confidence of the results. Changes in sensitivity depending on the chosen time period were also detected. The results may prompt consideration of modifying MC simulations during instability and provide information indicating the use of shorter time series to improve simulation efficiency during crises.

A Comparison of Weighted Stochastic Simulation Methods for the Analysis of Genetic Circuits.

Rare events are of particular interest in synthetic biology because rare biochemical events may be catastrophic to a biological system by, for example, triggering irreversible events such as off-target drug delivery. To estimate the probability of rare events efficiently, several weighted stochastic simulation methods have been developed. Under optimal parameters and model conditions, these methods can greatly improve simulation efficiency in comparison to traditional stochastic simulation. Unfortunately, the optimal parameters and conditions cannot be deduced a priori. This paper presents a critical survey of weighted stochastic simulation methods. It shows that the methods considered here cannot consistently, efficiently, and exactly accomplish the task of rare event simulation without resorting to a computationally expensive calibration procedure, which undermines their overall efficiency. The results suggest that further development is needed before these methods can be deployed for general use in biological simulations.