Accurate Simulation Research Articles

Simulation and attribution analysis of runoff–sediment in the Upper Basin of Fenhe River, China

ABSTRACT High-precision simulation of runoff–sediment is a significant challenge due to the combined impacts of climate change and human activities. In this paper, runoff–sediment processes were simulated, and their impact attribution was analyzed using the Soil and Water Assessment Tool (SWAT) model in the upper Fenhe River basin of the Loess Plateau, China. A SWAT model was constructed to assess its applicability during the historical baseline period, which reflects low human activity. However, the simulation results for the comprehensive impact period, using the calibrated historical baseline model, were unsatisfactory. Consequently, a method was proposed to enhance the accuracy of simulation results by considering the presence of soil-retaining dams. This method incorporates large and small soil-retaining dams as reservoirs and ponds, respectively, into SWAT. The results indicate that the accuracy of runoff and sediment simulation reaches a satisfactory level. The attribution analysis results show that human activities have a greater impact on runoff and sediment than climate change, with land use change and soil-retaining dams being particularly significant. The construction of soil-retaining dams plays a more significant role in reducing runoff and sediment. These findings provide valuable insights into the management and utilization of runoff and sediment in river basins.

Numerical simulation and experimental study of three-phase distribution characteristics of leaked light non-aqueous phase liquid from buried pipelines in soils containing groundwater and gas.

Leakage accidents of buried pipelines have become increasingly common due to the prolonged service of some pipelines which have been in use for more than 150years. Therefore, there is an urgent need for accurate prediction of pollution scope to aid in the development of emergency remediation strategies. This study investigated the distribution of a light non-aqueous phase liquid in soils containing gas and water through numerical simulations and laboratory experiments. Firstly, a three-dimensional porous medium model was established using ANSYS FLUENT, and for the first time, the distribution of gas and groundwater in soil environments was simulated in the model. Subsequently, the distribution of the three phases of diesel, gas, and water in soil was studied with different leakage velocities and it was found that the leakage velocity played a significant role in the distribution. The areas of diesel in soils at 60min were 0.112m2, 0.194m2, 0.217m2, and 0.252m2, with corresponding volumes of 0.028m3, 0.070m3, 0.086m3, and 0.106m3, respectively, for leakage velocities of 1.3m/s, 3.4m/s, 4.6m/s, and 4.9m/s. Calculation formulas for distribution areas and volumes were also developed to aid in future prevention and control strategies under different leakage velocities. The study also compared the distribution areas and volumes of diesel in soils with and without groundwater, and it was found that distribution scopes were larger in soils containing groundwater due to capillary force. In order to validate the accuracy of the numerical simulation, laboratory experiments were conducted to study the diffusion of oil, gas, and water under different leakage velocities. The results showed good agreement between the experiments and the simulations. The research findings are of great significance for preventing soil pollution and provide a theoretical basis for developing scientifically sound soil remediation strategies.

Prediction of land use for the next 30 years using the PLUS model's multi-scenario simulation in Guizhou Province, China

Land use changes significantly impact the structure and functioning of ecosystems. The current research focus lies in how to utilize economic and policy instruments to regulate conflicts among stakeholders effectively. The objective is to facilitate rational planning and sustainable development of land utilization resources. The PLUS model integrates a rule-based mining method for land expansion analysis and a CA model based on multi-type stochastic seeding mechanism, which can be used to mine the driving factors of land expansion and predict the patch-level evolution of land use landscapes. Using the PLUS model, a simulation was conducted to study the future land use distribution in the research area over the next 30 years. Based on land use data from Guizhou Province in 2000, 2010, and 2020, a total of 16 driving factors were selected from three aspects: geographical environment, transportation network, and socio-economic conditions. Four scenarios, namely natural development, urban development, ecological conservation, and farmland rotection, were established. Comparative analysis of the simulated differences among the various scenarios was performed. (1) The overall accuracy of the land use simulation using the PLUS model in the study area was 0.983, with a Kappa coefficient of 0.972 and a FoM coefficient of 0.509. The research accuracy meets the simulation requirements. (2) Through the simulation of four different scenarios, the study investigated the land use changes in Guizhou Province over the next 30 years. Each scenario exhibited distinct impacts on land utilization. Comprehensive comparison of the different simulation results revealed that the farmland protection scenario aligns with the sustainable development goals of the research area. Currently, there is a relative scarcity of research on land use simulation, particularly in model application, for Guizhou Province. This study aims to provide a reference for the rational planning of land resources and high-quality urban construction in Guizhou, promoting the high-quality economic development in tandem with advanced ecological and environmental protection.

Predicting the Color Polymorphism of ROY from a Time-Dependent Optimally Tuned Screened Range-Separated Hybrid Functional.

Polymorphism is a well-known property of molecular crystals, which allows the same molecule to form solids with several crystalline structures that can differ significantly in physical properties. Polymorphs that possess different optical absorption properties in the visible range may exhibit different perceived colors, a phenomenon known as color polymorphism. One striking example of color polymorphism is given by 5-methyl-2-[(2-nitrophenyl)amino]-3-thiophenecarbonitrile, known as ROY for its red-orange-yellow colors. First-principles prediction of color polymorphism may help in polymorph assignment and design but has proven to be challenging. Here, we predict the absorption spectra and simulate the colors of 12 ROY polymorphs using the general, nonempirical method of time-dependent (TD) optimally tuned screened range-separated hybrid (OT-SRSH) functional. For 5 ROY polymorphs with known experimental absorption spectra, we show that the TD-OT-SRSH approach predicts absorption spectra in quantitative agreement with experiment. For all polymorphs, we show that an accurate simulation of the colors is obtained, paving the way to a fully predictive, low-cost calculation of color polymorphism.

Enhancing debris flow simulation accuracy through high-resolution terrain information: a case study utilizing LS-RAPID and UAV-derived models

Enhancing debris flow simulation accuracy through high-resolution terrain information: a case study utilizing LS-RAPID and UAV-derived models

Machine Learning-Driven Calibration of Traffic Models Based on a Real-Time Video Analysis

Accurate traffic simulation models play a crucial role in developing intelligent transport systems that offer timely traffic information to users and efficient traffic management. However, calibrating these models to represent real-world traffic conditions accurately poses a significant challenge due to the dynamic nature of traffic flow and the limitations of traditional calibration methods. This article introduces a machine learning-based approach to calibrate macroscopic traffic simulation models using real-time traffic video stream data. The proposed method for creating and calibrating a traffic simulation model has significantly improved the statistical correspondence between the generated vehicle characteristics and real data about cars on the simulated road section. The correspondence has increased from 37% to 73%. Machine learning models trained on generated data and tested on real data show improved accuracy rates. Mean absolute error, mean square error, and mean absolute percentage error decreased by more than two orders of magnitude. The coefficient of determination has also increased, approaching 1. This method eliminates the need to deploy wireless sensor networks, which can reduce the cost of implementing intelligent transport systems.

Impact of High-Resolution Land Cover on Simulation of a Warm-Sector Torrential Rainfall Event in Guangzhou

This study on the warm-sector heavy rainfall event in Guangzhou on 7 May 2017, examined the effects and mechanisms of incorporating 30 m high-resolution land surface data into its numerical simulation. The updated 1km numerical model, integrating 30 m high-resolution land surface data, successfully captured the initiation, back-building, and organized development of warm-sector convections in Huadu and Zengcheng District. The analysis revealed that the high spatial resolution of the surface data led to a reduced urban area footprint (urban −6.31%), increased vegetation cover (forest 11.63%, croplands 1%), and enhanced surface runoff (water 2.77%) compared with a model’s default land cover (900 m). These changes mitigated the urban heat island (UHI) effect within the metropolitan area and decreased the surface sensible heat flux. This reduction contributed to a pronounced temperature gradient between Huadu Mountain and the urban area. Additionally, a stronger high-pressure recirculation and sea–land breezes facilitated the transport of warm and moist air from the sea inland, creating a humid corridor along the sea–land interface. The consistent influx of warm and moist air near the mountain front, where strong temperature gradients were present, forcibly triggered warm-sector convection, intensifying its organization. This study highlighted the critical role of high-resolution land surface data in the accurate numerical simulation of warm-sector heavy rainfall.

Continuous Time Simulation and System-Level Model of a MVDC Distribution Grid Including SST and MMC-Based AFE

Medium-voltage DC (MVDC) technology has gained increasing attention in recent years. Power electronics devices dominate these grids. Accurate simulation of such a grid, with detailed models of switching semiconductors, can quickly became very time-consuming, according to the number of connected devices to be simulated. A simulation approach based on interactions on a continuous time model can be very interesting, especially for developing a system-level control model of such a modern MVDC distribution grid. The aim of this paper is to present all the steps required for obtaining a continuous time modelling of a +/−10 kV MVDC grid case study, including a solid-state transformer (SST)- and modular multilevel converter (MMC)-based active front end (AFE). An additional aim of this paper is to supply educational content about the use of the continuous time simulation approach, thanks to a detailed description of the various devices modelled into the presented MVDC grid. The results of a certain number of simulation scenarios are eventually presented.

Information Search Within a Web Page: Modeling the Full Sequence of Eye Movement Decisions, Subjective Value Updating, and First Clicks

Online retail settings often present shoppers with large, complex choice sets where they need to quickly and dynamically weigh the benefits and costs of search within each web page. We build a model of information search within a web page using eye-tracking data collected during two incentive-compatible online shopping experiments, in which participants browsed the websites of two different clothing retailers (Experiments 1 and 2), as well as previously reported data from a laboratory experiment involving choices among snack food assortments (Experiment 3). Our model incorporates features that build upon recent advances in descriptive and normative models of information sampling and search in psychology and economics. First, our model captures how people decide where to look by treating eye fixations on clickable options as a series of “split-second” decisions that depend on estimates of option attractiveness and navigation effort. Second, our model assumes that the value of each option is learned via Bayesian updating. Third, the choice to end search on the web page depends on a dynamic decision threshold. Our model outperforms benchmarks that assume random search, instant learning, fixed thresholds, nonheterogeneous thresholds, and stochastic accumulator stopping rules. Explicitly modeling the sequence of eye fixation decisions results in accurate counterfactual simulations of the effects of hypothetical product orderings on search duration and quality as verified using experimental manipulation, and it can be applied flexibly to a wide range of web-page layouts. Systematic differences across experiments highlight the importance of accounting for product familiarity, choice-set size, and the role of category outside options. This paper was accepted by Ilia Tsetlin, behavioral economics and decision analysis. Funding: This work was supported by the Jay H. Baker Retailing Center and the Wharton Behavioral Lab at the University of Pennsylvania. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02983 .

Optimization of Execution Microscopic Extrusion Parameter Characterizations for Color Polycarbonate Grading: General Trend and Box–Behnken Designs

This research article concentrates on process conditions in addition to improving color selections in polymer compounders and developing more accurate simulation models. The feed rate (FR), temperature (T) and screw speed (SS) are three processing variables that the research investigates using general trends (GTs) and Box–Behnken design (BBD) response surface methodology. The identical set of processing settings was tweaked at three separate phases independently of one another. This study uses the experimental design to investigate process parameters’ optimization while holding all other parameters constant. This design was given the name GT. To develop this design and its statistical optimization, this study used the software of the design expert method. A regression model was run in this design, which displayed collective as well as individual effects of the parameters on color images. The values of tri-stimulus color with the best optimization had the smallest proper color variance (dE*). To obtain information on pigment characteristics, an SEM image analysis was conducted, which aids in improving future designs and overcoming manufacturing issues that affect color fluctuation properties and waste reduction for various chemical grades, both of which enhance environmentally friendly processes.

Fast prediction of concrete equivalent modulus based on the random aggregate model and image quadtree SBFEM

Abstract To evaluate the mechanical property of concrete materials rapidly, a fast prediction model of the concrete equivalent modulus is proposed based on the random aggregate model and scaled boundary finite element method (SBFEM). First, a random aggregate model of meso-concrete is employed to construct the representative volume element (RVE) according to the aggregate content, gradation, shape, etc. Second, the RVE model is transformed to be a grayscale image and stored as a digital matrix. The quadtree mesh is partitioned automatically for simulation by SBFEM. There are only six types of unique subdomains, and the hanging node does not affect the simulation accuracy. The global stiffness matrix can be assembled directly according to the six subdomain stiffness matrices. Finally, the equivalent modulus is predicted by using the numerical homogenization method. Several numerical examples are employed to verify this model, and the results are compared with that of other methods. The result indicates that the proposed model can efficiently determine the equivalent modulus. Furthermore, the effect of the aggregate gradation, shape, porosity, and pore water are studied and analysed in this work. The proposed model is potential and helpful in predicting the mechanical properties of concrete or other composite materials.

Evaluation of Heat Transfer and Fluid Dynamics across a Backward Facing Step for Mobile Cooling Applications Utilizing CNT Nanofluid in Laminar Conditions

In a variety of engineering applications, the efficacy of heat dissipation in mobile cooling systems is greatly influenced by the Backward Facing Step. Its significance in optimizing cooling solutions for mobile devices is highlighted by the fact that its design and fluid dynamics are crucial in minimizing skin friction and improving passive heat transfer. In this paper, we present a verification of an advanced numerical model for heat transfer and fluid flow through a Backward Facing Step, used in mobile cooling. The objective of this study is to explore fluid separation, a method enhancing passive heat transfer and reducing skin friction. ANSYS/FLUENT software has been used to solve the backward facing step in a horizontal duct filled with pure water. Carbon nanotube (CNT) dispresed into the base fluid at different volume fractions of 0.2%, 0.65%, and 1%. This study focused on laminar flow conditions ranging from Reynolds numbers 200 to 900. In order to reduce the computation time and ensuring the accuracy and reliability of numerical simulations, a grid independence study has been conducted. The findings revealed a substantial rise in the average Nusselt number and heat transfer coefficient with increased Reynolds number and volume fraction of nanoparticles. Specifically, the nanofluid (CNT/water) exhibited the highest average Nusselt number and heat transfer coefficient with volume fractions 1%. Furthermore, the research showed a decrease in the skin friction factor as both Reynolds number increased and nanoparticles’ volume fraction decreased. The increments of nanoparticles' concentrations lead to increase viscosity, promotes agglomeration, alters flow behaviour by inducing turbulence, and enhances heat transfer. These factors collectively contribute to higher skin friction due to increased resistance to fluid flow and disrupted streamline patterns

An international benchmark for wind plant wakes from the American WAKE ExperimeNt (AWAKEN)

This article introduces the first benchmark study within the International Energy Agency Wind Task 57 framework, focusing on wind plant wakes. Leveraging data from the American WAKE ExperimeNt (AWAKEN), the benchmark aims to assess the accuracy of simulation tools in modeling wind plant wakes and their impact on the downstream flow under diverse inflow conditions. The AWAKEN field campaign, conducted in Oklahoma from 2022 to 2024, provides unprecedented observations of wind plant-atmosphere interactions, thus offering a large dataset to validate numerical models of different complexity. The benchmark will include three phases—code calibration, blind comparison, and iteration—allowing participants to refine their numerical models based on the feedback from the benchmark team. This article describes the benchmark case study selected from observations providing details on atmospheric conditions, wake evidence, and wind turbine operation. The benchmark’s structure and timeline, along with the expected publication of results, are discussed as well. This collaborative effort aims to enhance the accuracy of wind plant wake simulations, thus contributing to the improvement of wind energy production estimates.

Viscoelastic properties of the equine hoof wall

The equine hoof wall has outstanding impact resistance, which enables high-velocity gallop over hard terrain with minimum damage. To better understand its viscoelastic behavior, complex moduli were determined using two complementary techniques: conventional (∼5 mm length scale) and nano (∼1 µm length scale) dynamic mechanical analysis (DMA). The evolution of their magnitudes was measured for two hydration conditions: fully hydrated and ambient. The storage modulus of the ambient hoof wall was approximately 400 MPa in macro-scale experiments, decreasing to ∼250 MPa with hydration. In contrast, the loss tangent decreased for both hydrated (∼0.1–0.07) and ambient (∼0.04–0.01) conditions, over the frequency range of 1–10 Hz. Nano-DMA indentation tests conducted up to 200 Hz showed little frequency dependence beyond 10 Hz. The loss tangent of tubular regions showed more hydration sensitivity than in intertubular regions, but no significant difference in storage modulus was observed. Loss tangent and effective stiffness were higher in indentations for both hydration levels. This behavior is attributed to the hoof wall's hierarchical structure, which has porosity, functionally graded aspects, and material interfaces that are not captured at the scale of indentation. The hoof wall's viscoelasticity characterized in this work has implications for the design of bioinspired impact-resistant materials and structures. Statement of significanceThe outer wall of horse hooves evolved to withstand heavy impacts during gallop. While previous studies have measured the properties of the hoof wall in slowly changing conditions, we wanted to quantify its behavior using experiments that replicate the quickly changing forces of impact. Since the hoof wall's structure is complex and contributes to its overall performance, smaller scale experiments were also performed. The behavior of the hoof wall was within the range of other biological materials and polymers. When hydrated, it becomes softer and can dissipate more energy. This work improves our understanding of the hoof's function and allows for more accurate simulations that can account for different impact speeds.

Evaluation of the simulation performance of WRF-Solar for a summer month in China using ground observation network data

Solar energy is a pivotal clean energy source in the transition to carbon neutrality from fossil fuels. However, the intermittent and stochastic characteristics of solar radiation pose challenges for accurate simulation and prediction. Accurately simulating and predicting solar radiation and its variability are crucial for optimizing solar energy utilization. This study conducted simulation experiments using the WRF-Solar model from 25 June to 25 July 2022, to evaluate the accuracy and performance of the simulated solar radiation across China. The simulations covered the whole country with a grid spacing of 27 km and were compared with ground observation network data from the Chinese Ecosystem Research Network. The results indicated that WRF-Solar can accurately capture the spatiotemporal patterns of global horizontal irradiance over China, but there is still an overestimation of solar radiation, and the model underestimates the total cloud cover. The root-mean-square error ranged from 92.83 to 188.13 Wm−2 and the mean bias (MB) ranged from 21.05 to 56.22 Wm−2. The simulation showed the smallest MB at Lhasa on the Qinghai–Tibet Plateau, while the largest MB was observed in Southeast China. To enhance the accuracy of solar radiation simulation, the authors compared the Fast All-sky Radiation Model for Solar with the Rapid Radiative Transfer Model for General Circulation Models and found that the former provides better simulation.摘要准确模拟和预测太阳辐射及其变化对于优化太阳能利用至关重要. 本研究使用WRF-Solar模式对中国2022 年 6 月 25 日至 7 月 25 日的太阳辐射情况进行了深入模拟, 模式网格水平分辨率为27 km, 通过与中国生态系统研究网络 (CERN) 的地面观测网络数据的37个观测站点进行对比, 以评估模式性能. 结果表明: WRF-Solar模式能较好地捕捉到中国上空的辐照度GHI (Global Horizontal Irradiance) 的时空分布特征, 但存在高估太阳辐射量, 以及低估总云量的情况.全国的总辐射模拟均方根误差范围为92.83–188.13 W m−2, 平均偏差范围为21.05–56.22 W m−2. 青藏高原拉萨站的偏差最小, 在中国东南部的偏差最大. 为了进一步提升太阳辐射模拟的精确度, 本研究还对比了FARMS与RRTMG辐射方案, 发现FARMS方案的模拟精度更高.

On the role of surface morphology in impacting-freezing dynamics of supercooled droplets

A thorough understanding of droplet impact and freezing is vital in preventing ice accretion on many outdoor devices. This simulation-based study investigated the effect of surface morphology on the impacting-freezing process of a supercooled droplet. Also, the variations of Weber number and supercooling temperature were studied numerically. The droplet impact and freezing process were simulated with the volume of fluid method and freezing model. A more accurate simulation was achieved by modeling the supercooled droplet and the dynamic contact angle. At the given ranges of the input parameters, the main factors that guaranteed droplet rebounding after collision were determined. The supercooling temperature and the groove width should be above 266 K and less than 0.21 mm, respectively. The droplet should also maintain its cohesion and integrity during impact. Creating grooves on a surface is novel and paves a new way to understand the impact and solidification of water droplets in supercooled conditions.

Prediction and study of impact performance of mining plastic shell based on PSO-BP neural network

Abstract In order to accurately and quickly predict the anti-impact performance of the plastic shell of a mining sensor, taking the plastic shell of a mine dust concentration sensor as the research object, finite element simulation software is used to simulate and predict its performance in the impact test. Compared with the experimental results, the accuracy of finite element simulation is verified. The batch simulation is carried out by using the finite element method, and the BP neural network and the PSO (particle swarm optimization)-BP neural network are established. The comparison results show that the PSO (particle swarm optimization)-BP neural network has higher accuracy than the BP neural network, and can more accurately predict the mechanical properties of the mine sensor shell under impact conditions. At the same time, compared with the finite element method, it has the advantage of efficiency. The prediction time can be improved to 5-10 milliseconds by using PSO-BP neural network mode, compared to hours of the finite element simulation. This method provides a research method for efficiently and accurately obtaining the impact mechanical performance of mine plastic shells and optimizing structural parameters.

Applicability research and experimental verification based on the coupling of turbulence model and mesh types to capture jet characteristics

Through the experimental verification, the accuracy of the cavitation jet feature captured by the coupling numerical calculation with different mesh types and different turbulence models is evaluated. Firstly, we built a high-speed jet experimental platform, and a high-speed camera and a paperless recorder are used to record the pressure, flow rate, and jet cloud oscillation of the jet pump in different states. The oscillation period and length of the jet cloud under different cavitation numbers are analyzed by the gray value method. The numerical results are compared with the experimental results. It is found that after the numerical calculation converges, the states of the jet cloud calculated by K-O and LES coupled with different meshes are periodically oscillating, while K-E does not. However, the maximum jet cloud lengths calculated by K-E and K-O turbulence models under different mesh types are nearly the same. Overall, the oscillation period calculated by the LES model is more in line with the experimental results, but its amplitude and maximum length are more different from the experimental results. In terms of capturing the jet cloud, critical pressure ratio, turbulent state and vapor volume fraction, the accuracy order of numerical calculation results are P-H (polyhedron-hexahedron mesh)≈P (polyhedron mesh) >H-T (hexahedral-tetrahedral mesh) >T (tetrahedral mesh), P-H≈P>T>H-T, P-H≈P>H-T>T and P-H>P>T>H-T, respectively. This research can provide more appropriate solutions for different concerns, as well as guidance for improving the accuracy and efficiency of simulations, and provides a basis for optimizing and developing specialized mesh types and turbulence models.

A 10T SRAM architecture with 40 % enhanced throughput for IMC applications benchmarked with CIFAR-10 dataset

This research paper introduces a memory architecture that handles standard memory storage operations and enables in-memory computations, surpassing the capabilities of conventional SRAM bit-cells. The proposed architecture in this work effectively eliminates read-disturb issues and facilitates bit-wise operations like NAND, NOR, and XNOR, all without requiring intricate analog peripheral circuits. The suggested bit-cell architecture offers enhanced throughput compared to existing In-Memory Computing (IMC) bit-cell architectures, making it a more suitable design for IMC applications. Parallelism offers enhanced throughput due to the unique bit-cell architecture, which allows all the bit-wise operations to be achieved simultaneously in a single cycle. The validity of the suggested architecture has been confirmed through Monte-Carlo variation analysis, utilizing UMC 28 nm PDK transistor models to ensure its robustness. Furthermore, architecture is benchmarked using the CIFAR-10 dataset, which entails assessing its performance across various machine learning models via the NeuroSim Simulator. The proposed architecture offers a substantial increase of up to 40 % in throughput (TOPS/W) compared to the existing architectures. Utilizing accurate Monte-Carlo simulations with 1000 samples, the stability of the proposed 10T bit-cell is validated at worst-case PVT corners, up to 6σ variations.

PREDICTING DIABETES USING DEEP LEARNING TECHNIQUES: A STUDY ON THE PIMA DATASET

The design and assessment of closed loop control systems must include accurate and stable accuracy analysis and simulation. An overview of the significance of closed loop control systems and its uses in several engineering disciplines is given in this paper. The paper then explores the different techniques used for evaluating the accuracy and stability of closed loop control systems, including steady-state error analysis, stability analysis, and simulation techniques. It also discusses the design and tuning of control algorithms to optimize the performance of the system, and identifies the different factors that can affect the accuracy and stability of closed loop control systems, such as sensor noise, model inaccuracies, and environmental disturbances.