Data produced by simulating traffic scenarios using the BlueSky Open Air Traffic Simulator. The dataset was generated by applying three ATM operational concepts to urban airspace traffic scenarios: decentralised, hybrid and centralised.

The dataset consists of logs of information gathered during the simulations and live demonstation, as well as results.

Simantha is a discrete event simulation package written in Python that is designed to model the behavior of discrete manufacturing systems. Specifically, it focuses on asynchronous production lines with finite buffers. It also provides functionality for modeling the degradation and maintenance of machines in these systems. Classes for five basic manufacturing objects are included: source, machine, buffer, sink, and maintainer. These objects can be defined by the user and configured in different ways to model various real-world manufacturing systems. The object classes are also designed to be extensible so that they can be used to model more complex processes.In addition to modeling the behavior of existing systems, Simantha is also intended for use with simulation-based optimization and planning applications. For instance, users may be interested in evaluating alternative maintenance policies for a particular system. Estimating the expected system performance under each candidate policy will require a large number of simulation replications when the system is subject to a high degree of stochasticity. Simantha therefore supports parallel simulation replications to make this procedure more efficient.Github repository: https://github.com/usnistgov/simantha

Social networks are tied to population dynamics; interactions are driven by population density and demographic structure, while social relationships can be key determinants of survival and reproductive success. However, difficulties integrating models used in demography and network analysis have limited research at this interface. We introduce the R package genNetDem for simulating integrated network-demographic datasets. It can be used to create longitudinal social networks and/or capture-recapture datasets with known properties. It incorporates the ability to generate populations and their social networks, generate grouping events using these networks, simulate social network effects on individual survival, and flexibly sample these longitudinal datasets of social associations. By generating co-capture data with known statistical relationships it provides functionality for methodological research. We demonstrate its use with case studies testing how imputation and sampling design influence the success of adding network traits to conventional Cormack-Jolly-Seber (CJS) models. We show that incorporating social network effects in CJS models generates qualitatively accurate results, but with downward-biased parameter estimates when network position influences survival. Biases are greater when fewer interactions are sampled or fewer individuals are observed in each interaction. While our results indicate the potential of incorporating social effects within demographic models, they show that imputing missing network measures alone is insufficient to accurately estimate social effects on survival, pointing to the importance of incorporating network imputation approaches. genNetDem provides a flexible tool to aid these methodological advancements and help researchers test other sampling considerations in social network studies. Methods The dataset and code stored here is for Case Studies 1 and 2 in the paper. Datsets were generated using simulations in R. Here we provide 1) the R code used for the simulations; 2) the simulation outputs (as .RDS files); and 3) the R code to analyse simulation outputs and generate the tables and figures in the paper.

The global Simulation and Test Data Management market is projected to grow significantly, from 905.2 million in 2025 to 3,236.0 million by 2035 an it is reflecting a strong CAGR of 12.1%.

Global Simulation and Test Data Management Market Assessment

Category-wise Insights

We introduce Optimal-Transport-based Unfolding and Simulation (OTUS), a novel, fast simulator based on unsupervised machine-learning that is capable of predicting experimental data from theoretical models. Simulations are crucial in science because they map from theoretical models to experimental data, allowing scientists to test predictions of theoretical models against the reality of experiments. Experimental data is often reconstructed from indirect measurements causing the aggregate transformation from theoretical models to experimental data to be poorly described by analytical methods. Scientists instead rely on ad-hoc, numerical simulations at great computational cost. Capable of learning directly from data, OTUS trains a probabilistic autoencoder to transform directly between theoretical models and experimental data. This is achieved by identifying the probabilistic autoencoder's latent space with the space of theoretical models, causing the decoder network to become a fast, predictive simulator with the potential to replace current, computationally costly simulators. Using particle physics as an illustrative example, we provide proof-of-principle results for Z-boson and top-quark decays, but stress that OTUS can be widely applied to other fields.

The sims zip file contains R code and accompanying files needed to run the R code. Overall this code demonstrates the R code used in the study is fully functional, documented, and reproducible and that this code could reproduce the simulation results from the study with sufficient computing time. The code as presented is for a single simulated dataset and will produce estimates and confidence intervals produced by all the methods used within the study when run on that one dataset. This dataset is associated with the following publication: Nethery, R., F. Mealli, J. Sacks, and F. Dominici. Evaluation of the Health Impacts of the 1990 Clean Air Act Amendments Using Causal Inference and Machine Learning. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION. Taylor & Francis Group, London, UK, 1-12, (2020).

To develop a simulation that collects both visual information, as well as grasp information about different objects using a multi-fingered hand. These sources of data can be used in the future to learn integrated object-action grasp representations.

Click “Export” on the right to download the vehicle trajectory data. The associated metadata and additional data can be downloaded below under "Attachments".

Researchers for the Next Generation Simulation (NGSIM) program collected detailed vehicle trajectory data on southbound US 101 and Lankershim Boulevard in Los Angeles, CA, eastbound I-80 in Emeryville, CA and Peachtree Street in Atlanta, Georgia. Data was collected through a network of synchronized digital video cameras. NGVIDEO, a customized software application developed for the NGSIM program, transcribed the vehicle trajectory data from the video. This vehicle trajectory data provided the precise location of each vehicle within the study area every one-tenth of a second, resulting in detailed lane positions and locations relative to other vehicles. Click the "Show More" button below to find additional contextual data and metadata for this dataset.

For site-specific NGSIM video file datasets, please see the following: - NGSIM I-80 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-I-80-Vide/2577-gpny - NGSIM US-101 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-US-101-Vi/4qzi-thur - NGSIM Lankershim Boulevard Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Lankershi/uv3e-y54k - NGSIM Peachtree Street Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Peachtree/mupt-aksf

This is a simulated dataset used for test.

According to Cognitive Market Research, the global Simulator market size is USD 18745.2 million in 2024 and will expand at a compound annual growth rate (CAGR) of 7.50% from 2024 to 2031.

North America Simulator market held 40% of the global revenue with a market size of USD 7496.88 million in 2024 and will grow at a compound annual growth rate (CAGR) of 5.7% from 2024 to 2031.
Europe Simulator is projected to expand at a compound annual growth rate (CAGR) of 6.0% from 2024 to 2031. Europe accounted for a share of over 30% of the global market size of USD 5622.66 million.
Asia Pacific Simulator market held 23% of the global revenue with a market size of USD 4310.71 million in 2024 and will grow at a compound annual growth rate (CAGR) of 9.5% from 2024 to 2031.
Latin America Simulator market held 5% of the global revenue with a market size of USD 937.11 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.9% from 2024 to 2031.
Middle East and Africa Simulator market held 2% of the global revenue with a market size of USD 374.84 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.2% from 2024 to 2031.
An increasing number of sectors and training facilities are able to use simulator solutions because to their evolution into smaller, more cost-effective packages.
Advanced simulation technology is becoming more easily accessible and comes with lower upfront costs thanks to cloud-based simulation platforms and subscription arrangements.

Rising Demand for Training and Skill Development to Increase the Demand Globally

Effective and practical training approaches drive the rising need for training and skill development in various industries, such as engineering, aviation, healthcare, and transportation. Simulators are essential for minimizing hazards and cutting expenses related to traditional training methods by offering a secure and regulated environment for practicing complex skills and procedures. Additionally, the need for simulator-based training solutions is increased by the growing emphasis on worker safety and regulatory compliance. Simulators are being used increasingly as a more efficient and safe way to build skills in various professional fields as industries place a higher priority on labor competency and compliance with strict requirements.

Advancements in Technology to Propel Market Growth

Rapid advancements in haptics, augmented reality, and virtual reality technologies are transforming training effectiveness and user engagement through simulation experiences. These developments create a more genuine learning environment by submerging people in realistic settings. Furthermore, incorporating artificial intelligence (AI) enhances simulators by introducing dynamic scenarios, customized learning paths, and automated performance evaluations. AI-powered simulations adjust to each user's skill level, providing effective and customized training. Combining VR, AR, haptics, and AI makes training more realistic and creates an adaptable and versatile training environment. This represents a paradigm shift in how technology is used to support learning across various businesses and sectors.

Market Restraints of the Simulator

Data Security and Privacy Concerns to Limit the Sales

Data security and privacy are legitimate concerns raised by the junction of technology in simulators that use AI capabilities and combine real-world data. Strict precautions must be taken to prevent any breaches due to including sensitive data. It is critical to solve these issues by putting strong security mechanisms in place and adhering to ethical data practices to build user trust and promote wider adoption. It is crucial to ensure encryption, access restrictions, and compliance with data protection laws to reduce the risks of unauthorized access to or misuse of private and sensitive data. In addition to protecting user privacy, a proactive approach to data security lays the groundwork for the responsible and secure development of simulator technologies across industries.

Impact of COVID-19 on the Simulator Market

The COVID-19 pandemic significantly affected the simulator market since lockdowns and travel restrictions interfered with conventional training techniques. Significant downturns were experienced by the aviation and automotive sectors, which are key users of simulators. But as remote and digital alternat...

Files descriptions:

All csv files refer to results from the different models (PAMM, AARs, Linear models, MRPPs) on each iteration of the simulation. One row being one iteration.
"results_perfect_detection.csv" refers to the results from the first simulation part with all the observations.
"results_imperfect_detection.csv" refers to the results from the first simulation part with randomly thinned observations to mimick imperfect detection.

ID_run: identified of the iteration (N: number of sites, D_AB: duration of the effect of A on B, D_BA: duration of the effect of B on A, AB: effect of A on B, BA: effect of B on A, Se: seed number of the iteration).
PAMM30: p-value of the PAMM running on the 30-days survey.
PAMM7: p-value of the PAMM running on the 7-days survey.
AAR1: ratio value for the Avoidance-Attraction-Ratio calculating AB/BA.
AAR2: ratio value for the Avoidance-Attraction-Ratio calculating BAB/BB.
Harmsen_P: p-value from the linear model with interaction Species1*Species2 from Harmsen et al. (2009).
Niedballa_P: p-value from the linear model comparing AB to BA (Niedballa et al. 2021).
Karanth_permA: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species A (Karanth et al. 2017).
MurphyAB_permA: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021).
MurphyBA_permA: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021).
Karanth_permB: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species B (Karanth et al. 2017).
MurphyAB_permB: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021).
MurphyBA_permB: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021).

"results_int_dir_perf_det.csv" refers to the results from the second simulation part, with all the observations.
"results_int_dir_imperf_det.csv" refers to the results from the second simulation part, with randomly thinned observations to mimick imperfect detection.
ID_run: identified of the iteration (N: number of sites, D_AB: duration of the effect of A on B, D_BA: duration of the effect of B on A, AB: effect of A on B, BA: effect of B on A, Se: seed number of the iteration).
p_pamm7_AB: p-value of the PAMM running on the 7-days survey testing for the effect of A on B.
p_pamm7_AB: p-value of the PAMM running on the 7-days survey testing for the effect of B on A.
AAR1: ratio value for the Avoidance-Attraction-Ratio calculating AB/BA.
AAR2_BAB: ratio value for the Avoidance-Attraction-Ratio calculating BAB/BB.
AAR2_ABA: ratio value for the Avoidance-Attraction-Ratio calculating ABA/AA.
Harmsen_P: p-value from the linear model with interaction Species1*Species2 from Harmsen et al. (2009).
Niedballa_P: p-value from the linear model comparing AB to BA (Niedballa et al. 2021).
Karanth_permA: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species A (Karanth et al. 2017).
MurphyAB_permA: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021).
MurphyBA_permA: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021).
Karanth_permB: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species B (Karanth et al. 2017).
MurphyAB_permB: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021).
MurphyBA_permB: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021).

Scripts files description:
1_Functions: R script containing the functions:
- MRPP from Karanth et al. (2017) adapted here for time efficiency.
- MRPP from Murphy et al. (2021) adapted here for time efficiency.
- Version of the ct_to_recurrent() function from the recurrent package adapted to process parallized on the simulation datasets.
- The simulation() function used to simulate two species observations with reciprocal effect on each other.
2_Simulations: R script containing the parameters definitions for all iterations (for the two parts of the simulations), the simulation paralellization and the random thinning mimicking imperfect detection.
3_Approaches comparison: R script containing the fit of the different models tested on the simulated data.
3_1_Real data comparison: R script containing the fit of the different models tested on the real data example from Murphy et al. 2021.
4_Graphs: R script containing the code for plotting results from the simulation part and appendices.
5_1_Appendix - Check for similarity between codes for Karanth et al 2017 method: R script containing Karanth et al. (2017) and Murphy et al. (2021) codes lines and the adapted version for time-efficiency matter and a comparison to verify similarity of results.
5_2_Appendix - Multi-response procedure permutation difference: R script containing R code to test for difference of the MRPPs approaches according to the species on which permutation are done.

The Water Quality analysis simulation Program, an enhancement of the original WASP. This model helps users interpret and predict water quality responses to natural phenomena and man-made pollution for variious pollution management decisions.

This data set consists of both measured and simulated optical intensities scattered off periodic line arrays, with simulations based upon an average geometric model for these lines. These data were generated in order to determine the average feature sizes based on optical scattering, which is an inverse problem for which solutions to the forward problem are calculated using electromagnetic simulations after a parameterization of the feature geometry. Here, the array of features measured and modeled is periodic in one-dimension (i.e., a line grating) with a nominal line width of 100 nm placed at 300 nm intervals, or pitch = 300 nm; the short-hand label for the features is "L100P300." The entirety of the modeled data is included, over two thousand simulations that are indexed using a top, middle, and bottom linewidth as floating parameters. Two subsets of these data, featuring differing sampling strategies, are also provided. This data set also contains angle-resolved optical measurements with uncertainties for nine arrays which differ in their dimensions due to lithographic variations using a focus/exposure matrix, as identified in a previous publication (https://doi.org/10.1117/12.777131). We have previously reported line widths determined from these measurements based upon non-linear regression to compare theory to experiment. Machine learning approaches are to be fostered for solving such inverse problems. Data are formatted for direct use in "Model-Based Optical Metrology in R: MoR" software which is also available from data.nist.gov. (https://doi.org/10.18434/T4/1426859). Note: Certain commercial materials are identified in this dataset in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the materials are necessarily the best available for the purpose.

Introduction Preservation and management of semi-arid ecosystems requires understanding of the processes involved in soil erosion and their interaction with plant community. Rainfall simulations on natural plots provide an effective way of obtaining a large amount of erosion data under controlled conditions in a short period of time. This dataset contains hydrological (rainfall, runoff, flow velocity), erosion (sediment concentration and rate), vegetation (plant cover), and other supplementary information from 272 rainfall simulation experiments conducted on 23 rangeland locations in Arizona and Nevada between 2002 and 2013. The dataset advances our understanding of basic hydrological and biological processes that drive soil erosion on arid rangelands. It can be used to quantify runoff, infiltration, and erosion rates on a variety of ecological sites in the Southwestern USA. Inclusion of wildfire and brush treatment locations combined with long term observations makes it important for studying vegetation recovery, ecological transitions, and effect of management. It is also a valuable resource for erosion model parameterization and validation. Instrumentation Rainfall was generated by a portable, computer-controlled, variable intensity simulator (Walnut Gulch Rainfall Simulator). The WGRS can deliver rainfall rates ranging between 13 and 178 mm/h with variability coefficient of 11% across 2 by 6.1 m area. Estimated kinetic energy of simulated rainfall was 204 kJ/ha/mm and drop size ranged from 0.288 to 7.2 mm. Detailed description and design of the simulator is available in Stone and Paige (2003). Prior to each field season the simulator was calibrated over a range of intensities using a set of 56 rain gages. During the experiments windbreaks were setup around the simulator to minimize the effect of wind on rain distribution. On some of the plots, in addition to rainfall only treatment, run-on flow was applied at the top edge of the plot. The purpose of run-on water application was to simulate hydrological processes that occur on longer slopes (>6 m) where upper portion of the slope contributes runoff onto the lower portion. Runoff rate from the plot was measured using a calibrated V-shaped supercritical flume equipped with depth gage. Overland flow velocity on the plots was measured using electrolyte and fluorescent dye solution. Dye moving from the application point at 3.2 m distance to the outlet was timed with stopwatch. Electrolyte transport in the flow was measured by resistivity sensors imbedded in edge of the outlet flume. Maximum flow velocity was defined as velocity of the leading edge of the solution and was determined from beginning of the electrolyte breakthrough curve and verified by visual observation (dye). Mean flow velocity was calculated using mean travel time obtained from the electrolyte solution breakthrough curve using moment equation. Soil loss from the plots was determined from runoff samples collected during each run. Sampling interval was variable and aimed to represent rising and falling limbs of the hydrograph, any changes in runoff rate, and steady state conditions. This resulted in approximately 30 to 50 samples per simulation. Shortly before every simulation plot surface and vegetative cover was measured at 400 point grid using a laser and line-point intercept procedure (Herrick et al., 2005). Vegetative cover was classified as forbs, grass, and shrub. Surface cover was characterized as rock, litter, plant basal area, and bare soil. These 4 metrics were further classified as protected (located under plant canopy) and unprotected (not covered by the canopy). In addition, plant canopy and basal area gaps were measured on the plots over three lengthwise and six crosswise transects. Experimental procedure Four to eight 6.1 m by 2 m replicated rainfall simulation plots were established on each site. The plots were bound by sheet metal borders hammered into the ground on three sides. On the down slope side a collection trough was installed to channel runoff into the measuring flume. If a site was revisited, repeat simulations were always conducted on the same long term plots. The experimental procedure was as follows. First, the plot was subjected to 45 min, 65 mm/h intensity simulated rainfall (dry run) intended to create initial saturated condition that could be replicated across all sites. This was followed by a 45 minute pause and a second simulation with varying intensity (wet run). During wet runs two modes of water application were used as: rainfall or run-on. Rainfall wet runs typically consisted of series of application rates (65, 100, 125, 150, and 180 mm/h) that were increased after runoff had reached steady state for at least five minutes. Runoff samples were collected on the rising and falling limb of the hydrograph and during each steady state (a minimum of 3 samples). Overland flow velocities were measured during each steady state as previously described. When used, run-on wet runs followed the same procedure as rainfall runs, except water application rates varied between 100 and 300 mm/h. In approximately 20% of simulation experiments the wet run was followed by another simulation (wet2 run) after a 45 min pause. Wet2 runs were similar to wet runs and also consisted of series of varying intensity rainfalls and/or run-on inputs. Resulting Data The dataset contains hydrological, erosion, vegetation, and ecological data from 272 rainfall simulation experiments conducted on 12 sq. m plots at 23 rangeland locations in Arizona and Nevada. The experiments were conducted between 2002 and 2013, with some locations being revisited multiple times. Resources in this dataset:Resource Title: Appendix B. Lists of sites and general information. File Name: Rainfall Simulation Sites Summary.xlsxResource Description: The table contains list or rainfall simulation sites and individual plots, their coordinates, topographic, soil, ecological and vegetation characteristics, and dates of simulation experiments. The sites grouped by common geographic area.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Appendix F. Site pictures. File Name: Site photos.zipResource Description: Pictures of rainfall simulation sites and plots.Resource Title: Appendix C. Rainfall simulations. File Name: Rainfall simulation.csvResource Description: Please see Appendix C. Rainfall simulations (revised) for data with errors corrected (11/27/2017). The table contains rainfall, runoff, sediment, and flow velocity data from rainfall simulation experimentsResource Software Recommended: MS Access,url: https://products.office.com/en-us/access Resource Title: Appendix C. Rainfall simulations. File Name: Rainfall simulation.csvResource Description: Please see Appendix C. Rainfall simulations (revised) for data with errors corrected (11/27/2017). The table contains rainfall, runoff, sediment, and flow velocity data from rainfall simulation experimentsResource Software Recommended: MS Excel,url: https://products.office.com/en-us/excel Resource Title: Appendix E. Simulation sites map. File Name: Rainfall Simulator Sites Map.zipResource Description: Map of rainfall simulation sites with embedded images in Google Earth.Resource Software Recommended: Google Earth,url: https://www.google.com/earth/ Resource Title: Appendix D. Ground and vegetation cover. File Name: Plot Ground and Vegetation Cover.csvResource Description: The table contains ground (rock, litter, basal, bare soil) cover, foliar cover, and basal gap on plots immediately prior to simulation experiments. Resource Software Recommended: Microsoft Access,url: https://products.office.com/en-us/access Resource Title: Appendix D. Ground and vegetation cover. File Name: Plot Ground and Vegetation Cover.csvResource Description: The table contains ground (rock, litter, basal, bare soil) cover, foliar cover, and basal gap on plots immediately prior to simulation experiments. Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Appendix A. Data dictionary. File Name: Data dictionary.csvResource Description: Explanation of terms and unitsResource Software Recommended: MS Excel,url: https://products.office.com/en-us/excel Resource Title: Appendix A. Data dictionary. File Name: Data dictionary.csvResource Description: Explanation of terms and unitsResource Software Recommended: MS Access,url: https://products.office.com/en-us/access Resource Title: Appendix C. Rainfall simulations (revised). File Name: Rainfall simulation (R11272017).csvResource Description: The table contains rainfall, runoff, sediment, and flow velocity data from rainfall simulation experiments (updated 11/27/2017)Resource Software Recommended: Microsoft Access,url: https://products.office.com/en-us/access

This is the simulation dataset used in the following article:
Cats, P., Sitlapersad, R.S.*, den Otter, W.K., Thornton, A.R., van Roij, R.: Capacitance and Structure of Electric Double Layers: Comparing Brownian Dynamics and Classical Density Functional Theory. J Solution Chem (2021). https://doi.org/10.1007/s10953-021-01090-7
The dataset includes the input and output data of the simulations (ASCII files), which were performed in LAMMPS, and the codes that were used to analyse the raw simulation data (Matlab script files (.m) and bash script files). Also files with the results reported in the article are shared (Matlab data files (.mat) and text files). In addition, the dataset also contains binary files such as GROMACS trajectory files (.xtc) and LAMMPS restart files.

Aqua planet simulations using HiRAM GFDL model to study rapid adjustment of clouds. Data used in the paper "On the Causal Relationship between the Moist Diabatic Circulation and Cloud Rapid Adjustment to Increasing CO2", Journal of Advances in Modeling Earth System, 2019. There are data for three different cases, which are labelled cre1-uw-a17.3-S1365-oh1e8-diur ("CtrL" in the paper), cre0-uw-a30.7-S1365-oh1e8-diur ("Cre0" in the paper), and cre1-uwe0-a02.5-S1365-oh1e8-diur ("Ent0" in the paper). For each of the three cases, data is available over an 11 year period, and with 100, 200, 400, and 800 ppmv atmospheric CO2.

WEC-Sim (Wave Energy Converter SIMulator) is an open-source wave energy converter (WEC) simulation tool. The code is developed in MATLAB/SIMULINK using the multi-body dynamics solver SimMechanics. WEC-Sim has the ability to model devices that are comprised of rigid bodies, power-take-off systems, and mooring systems. Simulations are performed in the time-_domain by solving the governing WEC equations of motion in 6 degrees-of-freedom. The WEC-Sim project is funded by the U.S. Department of Energy's Wind and Water Power Technologies Office and the code development effort is a collaboration between the National Renewable Energy Laboratory (NREL) and Sandia National Laboratories (SNL).

This dataset consists of computational fluid dynamics (CFD) output for various spacer configurations in a feed-water channel in reverse osmosis (RO) applications. Feed-water channels transport brine solution to the RO membrane surfaces. The spacers embedded in the channels help improve membrane performance by disrupting the concentration boundary layer growth on membrane surfaces. Refer to the "Related Work" resource below for more details. This dataset considers a feed-water channel of length 150mm. The inlet brine velocity and concentration are fixed at 0.1m/s and 100kg/m3 respectively. The diameter of the cylindrical spacers is fixed as 0.3mm and six varying inter-spacer distances of 0.75mm, 1mm, 1.5mm, 2mm, 2.5mm, and 3mm are simulated. The dataset comprising the steady, spatial fields of solute concentration, velocity, and density near each spacer is placed in the folder corresponding to the spacer configuration considered. We run two sets of CFD simulations and include the outputs from both sets for each configuration: (1) with a coarser mesh, producing low-resolution (LR) data of spatial resolution 20x20, and (2) with a finer mesh, producing high-resolution (HR) data of spatial resolution 100x100. These data points can be treated as images with the quantities of interest as their channels and can be used to train machine learning models to learn a mapping from the LR images as inputs to the HR images as outputs.

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Gaming Simulators Market Size 2025-2029

The gaming simulators market size is forecast to increase by USD 16.51 billion at a CAGR of 17.5% between 2024 and 2029.

The market is experiencing significant growth, driven by the integration of Virtual Reality (VR) headsets and the acceptance of 360-degree cameras as next-generation technology. These advancements provide enriching gaming experiences, replicating real-world environments with unprecedented accuracy. However, the high cost of gaming simulators remains a significant challenge for market expansion. Despite this, opportunities abound for companies that can offer affordable solutions or cater to niche markets, such as military training, aviation simulation, and professional sports training. As the technology matures and becomes more accessible, the gaming simulator market is poised to disrupt traditional industries and create new revenue streams. Companies seeking to capitalize on these opportunities must stay abreast of emerging trends and navigate the evolving regulatory landscape to ensure long-term success.

What will be the Size of the Gaming Simulators Market during the forecast period?

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The market encompasses a range of enriching experiences, including virtual reality (VR) and augmented reality (AR) simulations, next-level racing, and high-fidelity simulations. These innovative technologies offer users the opportunity to engage with various game genres, such as simulation games, in a more interactive and realistic manner. The market's growth is driven by the increasing popularity of VR and high-speed internet networks, enabling seamless gameplay and enhanced user experiences. Amusement and theme parks have also adopted simulators as a new attraction, catering to the demand for stress busters and interactive entertainment. Touch devices and popular games further expand the market's reach, making simulation games accessible to a wider audience.
The perception of simulation games as enriching training experiences, the industry adheres to stringent safety regulations to ensure authentic and safe environments for users. The future of the simulator industry lies in continued innovation, delivering increasingly realistic and engaging experiences for users across various industries and applications.

How is this Gaming Simulators Industry segmented?

The gaming simulators industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Hardware
  Software


End-user

  Commercial
  Residential


Type

  Racing
  Shooting
  Flight


Variant

  3-DOF
  6-DOF
  2-DOF
  1-DOF


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    The Netherlands
    UK


  APAC

    China
    India
    Japan


  South America



  Middle East and Africa

By Component Insights

The hardware segment is estimated to witness significant growth during the forecast period. The market has witnessed significant hardware advancements since 2010, with companies like Vertuix and Elsaco investing heavily to improve the gaming interface. Hardware investments include VR headsets, gaming cockpits, motion detectors, and haptic feedback systems. While hardware sales contribute to market revenue, their potential for enhancing the gaming experience is limited. The gaming landscape has evolved from PC gaming to mobile gaming, VR headsets, and now simulators. Virtual reality, cloud computing, and augmented reality are key trends driving market growth. Simulation games, including life sims, city-builder sims, survival simulations, agricultural sims, sports sims, and flight simulation, are popular genres.

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The hardware segment was valued at USD 5.28 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 48% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The North American market holds the largest share in the gaming simulators industry due to the region's high average disposable income. This economic factor, coupled with the increasing popularity of e-sports, has made gaming simulators more accessible to consumers. The proliferation of streaming platforms like Twitch and YouTube, which offer free telecasts of e-sports events, has further fueled the growth of the gaming market in North America. Additionally, the US government's issuance of visas to e-sports gamers, simil

Example computer code (R script) and associated data to run the Greater Glider simulation example in the manuscript.