Simulation Output Data Research Articles

Cloudy with a chance of precision: satellite’s autoconversion rates forecasting powered by machine learning

Abstract Precipitation is one of the most relevant weather and climate processes. Its formation rate is sensitive to perturbations such as by the interactions between aerosols, clouds, and precipitation. These interactions constitute one of the biggest uncertainties in determining the radiative forcing of climate change. High-resolution simulations such as the ICOsahedral non-hydrostatic large-eddy model (ICON-LEM) offer valuable insights into these interactions. However, due to exceptionally high computation costs, it can only be employed for a limited period and area. We address this challenge by developing new models powered by emerging machine learning approaches capable of forecasting autoconversion rates—the rate at which small droplets collide and coalesce becoming larger droplets—from satellite observations providing long-term global spatial coverage for more than two decades. In particular, our approach involves two phases: (1) we develop machine learning models which are capable of predicting autoconversion rates by leveraging high-resolution climate model data, (2) we repurpose our best machine learning model to predict autoconversion rates directly from satellite observations. We compare the performance of our machine learning models against simulation data under several different conditions, showing from both visual and statistical inspections that our approaches are able to identify key features of the reference simulation data to a high degree. Additionally, the autoconversion rates obtained from the simulation output and satellite data (predicted) demonstrate statistical concordance. By efficiently predicting this, we advance our comprehension of one of the key processes in precipitation formation, crucial for understanding cloud responses to anthropogenic aerosols and, ultimately, climate change.

An Analytic Framework for Assessing Artificial Intelligence and Assistive Automation Enabled Command and Control Decision Aids for Mission Effectiveness

The U.S. Army has significant interest in operationalizing Artificial Intelligence and Assistive Automation (AI/AA) technologies on the battlefield to help collate, classify, and clarify multiple streams of situational and sensor data to provide a Commander with a clear, accurate operating picture to enable rapid and appropriate decision-making. This paper offers a methodology integrated with combat simulation output data into an analytic assessment framework. This framework helps assess AI/AA enabled Decision Aids for command and control with respect to mission effectiveness. Our methodology is demonstrated via a real-world operational vignette of an AI/AA-augmented Battalion assigned to clearing a sector of the battlefield. Results indicate that the simulated scenario with an AI/AA advantage modeled led to a higher expected mission effectiveness score.

Validating the credibility of solar simulation tools using a real-world case study

Solar energy is being recognized around the world for generating clean power using photovoltaic (PV) panels. Inadequate planning and sizing of PV panel systems leads to increased costs, emphasising the significance of reviewing solar energy simulationtools for accuracy and credibility. Even with the current literature, it could be challenging to select the software tools that are appropriate for the research. This paper identified, reviewed and validated four system-based (System Advisor Model (SAM), PVsyst, PV*Sol and Solarius PV) and three online software tools (Helioscope, PV-Watts and PV-GIS). It provides necessary information on the software tools which compiled in a features matrix under seven categories. The validation process involves using real-world data from an 84.5KW solar plant installed on a building in Nagpur, India, to assess the accuracy and performance of the software tools. The statistical metrics used for validation are Absolute Deviation (AD), Root Mean Square Error (RMSE) accompanied by Mean Bias Error (MBE) and Nash-Sutcliffe Efficiency (NSE) for a comprehensive understanding of the accuracy and reliability. The findings show a discrepancy in the simulated output data and the recorded data of the tools with PV*Sol and Solarius PV showcasing the least error. In conclusion, the findings of this research provide valuable insights and guidance for users seeking to make informed decisions in selecting the most appropriate Solar PV simulation tool tailored to their specific needs and requirements.

MODELLING AND SIMULATION OF DYNAMIC PROCESSES OF PNEUMATIC LINES

The paper presents the simulation of dynamic processes of pneumatic lines using the program "SIMULINK" from the product " MatLab". Simulation is a process of forming, preparing and entering a mathematical model in a computational environment and obtaining the results of the calculations performed in this environment. The mathematical model of the pneumatic line to be simulated is according to previously presented equations. For the simulation of the dynamic processes, various real parameters of the dimensions and lengths of the pneumatic lines with the corresponding coefficients are introduced into the model. The simulation of the transient responses was carried out with stepwise variation and sinusoidal input signal supplied by the set point device and pneumatic line lengths of 1 m, 5 m and 10 m. The simulation output data were output to the interface in the form of graphs showing the dependence of the observed quantities, the input and output pressure through the pneumatic line, and the input and output flow rates. From the mathematical model and the experiments performed, it can be concluded that the transient responses in the simulations correspond to the dynamic processes of pneumatic lines of different lengths.

FloPy Workflows for Creating Structured and Unstructured MODFLOW Models.

FloPy is a Python package for creating, running, and post-processing MODFLOW-based groundwater flow and transport models. FloPy functionality has expanded to support the latest version of MODFLOW (MODFLOW 6) including support for unstructured grids. FloPy can simplify the process required to download MODFLOW-based and other executables for Linux, MacOS, and Windows operating systems. Expanded FloPy capabilities include (1) full support for structured and unstructured spatial discretizations; (2) geoprocessing of spatial features and raster data to develop model input for supported discretization types; (3) the addition of functionality to provide direct access to simulated output data; (4) extension of plotting capabilities to unstructured MODFLOW 6 discretization types; and (5) the ability to export model data to shapefiles, NetCDF, and VTK formats for processing, analysis, and visualization by other software products. Examples of using expanded FloPy capabilities are presented for a hypothetical watershed. An unstructured groundwater flow and transport model, with several advanced stress packages, is presented to demonstrate how FloPy can be used to develop complicated unstructured model datasets from original source data (shapefiles and rasters), post-process model results, and plot simulated results.

Remobilized dust dynamics and inventory evolution in ITER-like start-up plasmas

The dynamics of remobilized metallic dust are simulated with the MIGRAINe code in low-power ITER discharges meant to emulate reactor start-up environments. The particle size and speed distributions are varied along with plasma profiles to investigate the influence of initial and background parameters on high-Z impurity production by vaporization as well as long-term in-vessel dust inventory evolution over the course of consecutive discharges. Simulation output data regarding beryllium remobilization can be integrated into more global ITER models to guide the operation of dust diagnostics and facilitate the compliance with nuclear safety restrictions, while results on tungsten particles serve as a basis for first assessments of dust dynamics in DEMO.

Contested logistics simulation output analysis with approximate dynamic programming: a proposed methodology

PurposeRapid sensitivity analysis and near-optimal decision-making in contested environments are valuable requirements when providing military logistics support. Port of debarkation denial motivates maneuver from strategic operational locations, further complicating logistics support. Simulations enable rapid concept design, experiment and testing that meet these complicated logistic support demands. However, simulation model analyses are time consuming as output data complexity grows with simulation input. This paper proposes a methodology that leverages the benefits of simulation-based insight and the computational speed of approximate dynamic programming (ADP).Design/methodology/approachThis paper describes a simulated contested logistics environment and demonstrates how output data informs the parameters required for the ADP dialect of reinforcement learning (aka Q-learning). Q-learning output includes a near-optimal policy that prescribes decisions for each state modeled in the simulation. This paper's methods conform to DoD simulation modeling practices complemented with AI-enabled decision-making.FindingsThis study demonstrates simulation output data as a means of state–space reduction to mitigate the curse of dimensionality. Furthermore, massive amounts of simulation output data become unwieldy. This work demonstrates how Q-learning parameters reflect simulation inputs so that simulation model behavior can compare to near-optimal policies.Originality/valueFast computation is attractive for sensitivity analysis while divorcing evaluation from scenario-based limitations. The United States military is eager to embrace emerging AI analytic techniques to inform decision-making but is hesitant to abandon simulation modeling. This paper proposes Q-learning as an aid to overcome cognitive limitations in a way that satisfies the desire to wield AI-enabled decision-making combined with modeling and simulation.

Compact Modeling Technology for the Simulation of Integrated Circuits Based on Graphene Field-Effect Transistors.

The progress made toward the definition of a modular compact modeling technology for graphene field-effect transistors (GFETs) that enables the electrical analysis of arbitrary GFET-based integrated circuits is reported. A set of primary models embracing the main physical principles defines the ideal GFET response under DC, transient (time domain), AC (frequency domain), and noise (frequency domain) analysis. Another set of secondary models accounts for the GFET non-idealities, such as extrinsic-, short-channel-, trapping/detrapping-, self-heating-, and non-quasi static-effects, which can have a significant impact under static and/or dynamic operation. At both device and circuit levels, significant consistency is demonstrated between the simulation output and experimental data for relevant operating conditions. Additionally, a perspective of the challenges during the scale up of the GFET modeling technology toward higher technology readiness levels while drawing a collaborative scenario among fabrication technology groups, modeling groups, and circuit designers, is provided.

Numerical modeling: the effect of land subsidence on hydrodynamic conditions and coastal morphology in Pekalongan

This study examines the effect of land subsidence on hydrodynamic conditions and coastal morphology through numerical modeling using MIKE21 with 30-day simulation time. Land subsidence is assumed to be 10 cm/year, which affects bathymetry. There are 2 modeling scenarios, scenario 1 before (2016) and scenario 2 after land subsidence (2021). River discharge is assumed to be constant at 10 m3/s. The model calibration result on water level elevation and current velocity of the model simulation output and field data comparison based on the variation of Chezy Number, the smallest error was obtained at Chezy Number 50 m0.5/s. The sediment transport model sensitivity test was conducted on parameters of model type, sediment grain size, and river discharge. The result of sensitivity test demonstrated that the sediment grain size is the most sensitive parameter. By comparing the two model scenario results show that the current velocity in scenario 1 is greater than scenario 2 by 20%, significant wave height in scenario 1 is smaller than scenario 2 up to 0.1 m, and sedimentation in Mangrove Park increases up to ± 0.01m. Land subsidence reduces the current velocity, increases the significant wave height and sedimentation.

Narrative In Situ Visual Analysis for Large-Scale Ocean Eddy Evolution.

The rapid development of high-performance computing systems has led to a rapid increase in the speed of flow field simulation calculations. However, large-scale simulation output data lead to storage bottlenecks and inefficient data analysis. In this work, we used in situ visualization to process the simulation analysis of large-scale flow fields. Combined with narrative visual analysis, we designed a large-scale ocean flow field eddy evolution analysis system based on in situ visualization. Our system can generate high-precision eddy streamline structures in real time and supports eddy statistical analysis and tracking analysis at different ocean regional scales. Through the case data analysis of ocean simulation, we demonstrated the efficiency and effectiveness of the system.

Modeling the efficacy of different anti-angiogenic drugs on treatment of solid tumors using 3D computational modeling and machine learning

Accurate simulation of tumor growth during chemotherapy has significant potential to alleviate the risk of unknown side effects and optimize clinical trials. In this study, a 3D simulation model encompassing angiogenesis and tumor growth was developed to identify the vascular endothelial growth factor (VEGF) concentration and visualize the formation of a microvascular network. Accordingly, three anti-angiogenic drugs (Bevacizumab, Ranibizumab, and Brolucizumab) at different concentrations were evaluated in terms of their efficacy. Moreover, comprehensive mechanisms of tumor cell proliferation and endothelial cell angiogenesis are proposed to provide accurate predictions for optimizing drug treatments. The evaluation of simulation output data can extract additional features such as tumor volume, tumor cell number, and the length of new vessels using machine learning (ML) techniques. These were investigated to examine the different stages of tumor growth and the efficacy of different drugs. The results indicate that brolucizuman has the best efficacy by decreasing the length of sprouting new vessels by up to 16%. The optimal concentration was obtained at 10 mol m−3 with an effectiveness percentage of 42% at 20 days post-treatment. Furthermore, by performing comparative analysis, the best ML method (matching the performance of the reference simulations) was identified as reinforcement learning with a 3.3% mean absolute error (MAE) and an average accuracy of 94.3%.

In Vivo Validation of a Cardiovascular Simulation Model in Pigs

Many computer simulation models of the cardiovascular system, of varying complexity and objectives, have been proposed in physiological science. Every model needs to be parameterized and evaluated individually. We conducted a porcine animal model to parameterize and evaluate a computer simulation model, recently proposed by our group. The results of an animal model, on thirteen healthy pigs, were used to generate consistent parameterization data for the full heart computer simulation model. To evaluate the simulation model, differences between the resulting simulation output and original animal data were analysed. The input parameters of the animal model, used to individualize the computer simulation, showed high interindividual variability (range of coefficient of variation: 10.1–84.5%), which was well-reflected by the resulting haemodynamic output parameters of the simulation (range of coefficient of variation: 12.6–45.7%). The overall bias between the animal and simulation model was low (mean: −3.24%, range: from −26.5 to 20.1%). The simulation model used in this study was able to adapt to the high physiological variability in the animal model. Possible reasons for the remaining differences between the animal and simulation model might be a static measurement error, unconsidered inaccuracies within the model, or unconsidered physiological interactions.

A simulation-based optimization approach for the calibration of a discrete event simulation model of an emergency department

Accurate modeling of the patient flow within an Emergency Department (ED) is required by all studies dealing with the increasing and well-known problem of overcrowding. Since Discrete Event Simulation (DES) models are often adopted with the aim of assessing solutions for reducing the impact of this worldwide phenomenon, an accurate estimation of the service time of the ED processes is necessary to guarantee the reliability of the results. However, simulation models concerning EDs are frequently affected by missing data, thus requiring a proper estimation of some unknown parameters. In this paper, a simulation-based optimization approach is used to estimate the incomplete data in the patient flow within an ED by adopting a model calibration procedure. The objective function of the resulting minimization problem represents the deviation between simulation output and real data, while the constraints ensure that the response of the simulation is sufficiently accurate according to the precision required. Data from a real case study related to a big ED in Italy is used to test the effectiveness of the proposed approach. The experimental results show that the model calibration allows recovering the missing parameters, thus leading to an accurate DES model.

Logistics Engineering Simulation Using Computer 3D Modeling Technology

3D modeling technology is an important branch of interdisciplinary fields such as computer graphics, intelligent information processing, computer vision, and artificial intelligence. Through computer digitization, collecting three-dimensional data information of the target object, and then processing and simulation reproduction through computer technology, plays an important role in logistics engineering (LE). The purpose of this paper is the simulation research of LE based on computer three-dimensional modeling technology. This paper takes LE as the research object, firstly elaborates the functional and non-functional requirements of the system separately, and establishes an intelligent logistics system. This paper uses Flexsim simulation software to establish a logistics distribution simulation model. Based on the data collected in the survey, the model is parameterized. Through the data output from the simulation, the simulation data of the original logistics system and the logistics system designed in this paper are compared and analyzed. The simulation output data shows that the total number of products transported in and out of the warehouse of the original system’s 6 transport planes is 15,559, and the total number of products transported in and out of the warehouse of the 6 transport planes in the logistics system proposed in this paper is 17,144 pieces. It can be seen that this system has strong transportation efficiency in LE.

Buildingenergy.ninja: A web-based surrogate model for instant building energy time series for any climate.

Machine learning-based surrogate models are trained on building energy simulation input and output data. Their key advantage is their computational speed allowing them to produce building performance estimates in fractions of a second. In this work we showcase the use of deep convolutional neural network surrogate models embedded into a web application, allowing users to rapidly explore building performance at high spatio-temporal resolution. Users can pick any climate on an interactive map, customize a building design with thirteen decisive design parameters, and the surrogate model allows them to retrieve hourly heating and cooling load time series data in fractions of a second.In this work, we further show that the surrogate model reaches an accuracy of R 2 > 0.93 (MAE < 0.27 kWh) for unseen design specifications and climates. These results motivate the use of computationally cheap surrogate models to replace building energy simulation for a wide variety of tasks in the future.

On Preserving Scientific Integrity for Climate Model Data in the HPC Era

Over the last 30 years, the Computational Science Graduate Fellowship (CSGF) program has played an integral role in preparing a large and diverse community of computational scientists to push the limits of high-performance computing (HPC). To celebrate the CSGF program’s enduring influence, I share my perspective from the climate modeling community, which has used HPC to better understand the earth’s climate system. While the benefits of HPC in climate science have been enormous, rapid computing advances have brought new challenges. One difficulty is quality assurance—ensuring that large and complex codes running on multiple platforms are correct. A second is mitigating the increasingly large data volumes. Here, I describe our software quality assurance framework and strategies to reduce climate data volumes. In these complementary efforts, our focus has been preserving the scientific integrity of the simulation output data, a priority shared across HPC model simulation disciplines.

SMED Simulation in Optimising the Operating Output of Tandem Press Line in the Automotive Industry using WITNESS Software

The automotive industry is one of the most advanced industries in the world. The demand for vehicles keeps increasing over time, mainly influenced by technology’s sophistication and impressive designs. This research is focusing on the production performance of the stamping process in Malaysia’s automotive industry. The main issue observed and evidenced in data collected from industry players is that an unacceptably long time is taken in the die/tooling change process, which grossly affects the planned production output. The most significant direct impact on this situation is the increase in operating costs due to additional time taken to meet the planned production output. Therefore, this research aims to establish a new framework that can optimise the efficiency of the dies changeover process, which could lower the overall production cost, which could partially transfer the saving to the consumers. The study was conducted in two stages; the first is on data collection correlates with the automotive stamping premise to create a simulation procedure, and the second is to analyse the current production output using WITNESS simulation. These simulated output data have later been compared to the actual industrial data. Using a systematic dies changeover process, a new alternative framework was proposed. The framework combined single minute exchange of die (SMED), lean tools and simulation to become an integrated single minute exchange of die (ISMED). It shows that the proposed method decreased the changeovers processes from the initial of 1509.5 seconds to 750.75 seconds, then further reduced to 569.75 seconds, with the nett time reduction of 62.2%. The result shows that the daily production output has increased gradually from 1100 pieces to 1500 pieces, further increasing to 2145 pieces. As evidenced in analytical data, it is believed that this new framework can strengthen the total output in the manufacturing process, especially in the manufacturing of automotive stamping parts.

Climate change-induced economic impact assessment by parameterizing spatially heterogeneous CO2 distribution

The spatially heterogeneous distribution of CO2 concentrations is an uncertain factor in the assessment of climate change impact. We developed and calibrated a simple climate module to project regional temperature increases using atmospheric simulation output data from the Coupled Model Inter-comparison Project Phase 6 (CMIP6). We compared the differences in regional atmospheric temperature changes arising from spatially homogeneous and heterogeneous distributions of CO2 and assessed economic loss. The results indicated that spatially heterogeneous distribution of CO2 affected the projection of regional temperature increases, with faster temperature rises in industrially advanced regions. Compared with a homogeneous distribution, economic damage was aggravated in the Northern Hemisphere using a heterogeneous distribution, especially in middle-latitude regions. China saw increases in economic damage of 0.030%, 0.007%, and 0.002% in 2100 under SSP5-RCP8.5, SSP2-RCP4.5, and SSP1-RCP2.6, respectively. This suggests that impact assessments that ignore spatially heterogeneous CO2 concentrations could lead to inaccurate estimates of climate damage. These findings indicate that it is necessary to parameterize the spatial distribution of CO2 in traditional climate change assessments. The results provide a more accurate estimation of regional temperature increases and climate change-induced economic damage to support policymaking for mitigation and adaptation.

Numerical Study of Bed Topography Variations Owing to Placement of Triad Series of Vertical Bridge Piers Installed in a 180-degree Bend with Different Relative Radii Using SSIIM Numerical Model

In this paper, the scouring around a triad series of vertical bridge piers located in a bend is investigated using SSIIM software. To this end, some of the most essential parameters such as the relative radius and the bridge pier position in both transverse and longitudinal directions to the flow have been examined. Then the bed topography variations have been detected within a flume. It is worth mentioning that the experiments have been conducted in a 180-degree sharp bend in the presence of the bridge piers. The available experimental results were used to ensure correctness of the simulated output data. Accordingly, it is evident that the simulated model not only is capable of estimating the amount of the maximum scour and sedimentation but also can effectively predict their position using an experimental model. Based on the results, it is indicated that the maximum scour depth is reported in the case of the bridge pier transverse to the flow installed at the 60-degree position of the bend with a relative radius of 5. Under these circumstances, the maximum depth of scour is evaluated to be 1.14 times the depth of flow at the beginning of the bend. Also, the maximum volume of scour around bridge piers has been detected in this case.

Knowledge Discovery in Simulation Data

This article provides a comprehensive and in-depth overview of our work on knowledge discovery in simulations. Application-wise, we focus on manufacturing simulations. Specifically, we propose and discuss a methodology for designing, executing, and analyzing large-scale simulation experiments with a broad coverage of possible system behavior targeted at generating knowledge about the system. Based on the concept of data farming, we suggest a two-phase process which starts with a data generation phase, in which a smart experiment design is used to set up and efficiently execute a large number of simulation experiments. In the second phase, the knowledge discovery phase, data mining and visually aided analysis methods are applied on the gathered simulation input and output data. This article gives insights into this knowledge discovery phase by discussing different machine learning approaches and their suitability for different manufacturing simulation problems. With this, we provide guidelines on how to conduct knowledge discovery studies within the manufacturing simulation context. We also introduce different case studies, both academic and applied, and use them to validate our methodology.