Name: Integrating Automated Simulation Workflows with 3D Visualization for Virtual Experiments in the Metaverse (Video) | JoVE Journal
Uploaded: 2026-07-21T14:30:00.000+00:00
Duration: 9 min 23 s
Description: Learn automated simulation workflows for fusion neutronics using containerized tools and 3D visualization. This method enables reproducible, scalable virtual experiments with FAIR-compliant data sharing.

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This project has been supported by the UK Atomic Energy Authority through the Fusion Industry Program. The Fusion Industry Program is stimulating the growth of the UK fusion ecosystem and preparing it for the future global fusion powerplant market. More information about the Fusion Industry Program can be found online: https://ccfe.ukaea.uk/programmes/fusion-industry-programme/
The example repository accompanying this protocol is available at https://github.com/williamjsmith15/galaxy-omniverse-example (a public fork of https://github.com/UoMResearchIT/omniverse-workflows-fusion).

The authors have no conflicts of interest to disclose.

There are a few critical steps within the protocol. Most concern the initial setup of the workflow engine instance such as: adding the admin email (protocol step 1.2.2) as this is required for admin access to the tools and jobs panels; generating the API key correctly for the metaverse platform extension (protocol step 1.5.3) and pasting this correctly into the default values file; and when adding tools, ensuring that these are added to the tool config file (protocol step 4.1.6) before restarting as these are loaded in a...

The industrial metaverse combines digital and physical worlds to support design, simulation, and collaborative 3D visualization of engineered systems. It would typically consist of many interconnected digital twins of components to give an overall system view. Large organizations such as Boeing, BMW, Amazon, and many more are taking multiple approaches to create metaverses1. Systems for allowing multiple chains of simulation and processing tools have been developed and are being used. However, these examples are typically either bespoke to the application area2 or are commercial options3,4 with some lock-in to proprietary systems. Some open-source alternatives for building digital twins have been used to create some systems, such as Python Flask, with built-in simulation capabilities. Still, these are configured as bespoke pieces of code to carry out specific tasks relating to the particular model5. In the context of this protocol, the metaverse platform (NVIDIA Omniverse) functions as a 3D visualization and workflow interaction front-end: simulation outputs are loaded into a shared scene after a workflow run completes, and new runs can be triggered from within the same environment. This differs from live digital-twin systems in which real-time sensor feeds update the model continuously; the method demonstrated here supports batch workflow execution and post-run exploration of results. However, this is done in such a way that would support future work to integrate more systems into the metaverse platform to enable the creation of digital twins with workflow engines as the computational backend.
Workflows can be defined as chains of software tools explicitly specifying the data flow between them. They allow wrapping of existing simulation codes, processing scripts, and other steps in a typical analysis pipeline, without altering the function of these, but instead allowing them to be configured and reconfigured with standardized inputs and outputs that are tool agnostic. Workflows allow easy replication of results via the sharing of tools, also providing metadata and provenance about what versions of tools were used, in what order, and with which inputs. Tools themselves can be reused in many simulation pipelines, allowing researchers to spend less time setting up simulations and more time designing experiments and exploring the results. Workflow systems are also scalable, with methods to connect to different local compute, cloud and HPC resources, enabling many large-scale workflows to be run on specific hardware in an automated manner6.
The typical manual approach is inherently slow, error-prone, and difficult to reproduce, in which a researcher runs each simulation or post-processing tool by hand, moves intermediate files between environments, and has to document individual run inputs and outputs. In contrast to this, a workflow manager formalizes the data flow once and re-runs it deterministically. This brings many benefits relative to manual pipelines: the same workflow can be executed identically on different inputs, supporting parameter studies without bespoke scripting; every run automatically captures full provenance metadata, addressing any reproducibility gap; and once a tool has been wrapped, its reuse cost in subsequent workflows drops to near zero, other than compute time. These benefits have been quantified for bioinformatics by Wratten et al.7 and for proteomics/metabolomics by Perez-Riverol and Moreno8 and Verhoeven et al.9.
Historically, workflows have been used primarily in the bioinformatics field8,9 to great success with large public instances such as the European Galaxy server10,11, which by 2022 hosted more than 50,000 users, 2500 tools, executed over 47 million jobs and 260,000 workflow runs. The same workflow engine stack supports scaling onto HPC and cloud resources via the Pulsar distributed job-execution system6,11, with operational deployments spanning 13 Pulsar endpoints across 10 European countries. Among the many available workflow managers, including Snakemake, Nextflow, Toil, and CWL-compatible engines, the Galaxy workflow engine11 was selected for a few reasons. One of the primary reasons is its mature browser-based interface which lowers the barrier to entry for domain experts who do not work primarily on the command line; it exposes a complete representational state transfer (REST) application programming interface (API) (used in the present work to bridge to the metaverse front-end); its history and jobs model captures provenance in a form that is straightforward to surface to non-specialist collaborators; and it supports transparent HPC offload via the aforementioned Pulsar system (although this is not discussed in the protocol section in this paper). The approach described in this paper, however, is workflow-engine-agnostic in principle: equivalent integrations could be built atop alternative engines. The contribution of this work is not the workflow manager itself but the translation of a general-purpose workflow manager originally developed for bioinformatics to other fields (with the specific example of fusion neutronics here), and its integration with an industrial-metaverse platform (NVIDIA Omniverse), inside a fully containerized, locally deployable stack, applied to 3D virtual experiments.
Finally, containerization enables the sharing of many pieces of software by packaging code with the operating system and all the dependencies it needs to run. These environments avoid the issues of missing dependencies and the hassle of installing some simulation codes. They are similar in purpose to virtual machines but much lighter and more portable. They drastically increase the shareability and reproducibility of software packages. In this method, the workflow manager and the individual tools run in Docker12 containers, increasing compatibility with different operating systems as long as the user can run containers.
This protocol is intended for domain-expert researchers, for example, fusion neutronics engineers, computational fluid-dynamics analysts, or finite-element practitioners, who are fluent in the simulation tools of their own field but have not previously used a workflow manager or container-based deployment. Familiarity with a single simulation code and basic command-line operation is assumed; familiarity with Galaxy or the Omniverse is not. Readers new to containerization should consult the official Docker documentation (<a href="https://docs.docker.com/" target="_blank">https://docs.docker.com/</a>) or introductory training available at: <a href="https://uomresearchit.github.io/docker-introduction/" target="_blank">https://uomresearchit.github.io/docker-introduction/</a> before following Section 1; the basic commands needed to run the software are all contained within the protocol.
The rest of this report will cover the setup and use of the locally deployable system. Then, it will follow steps for developing new tools for the system and a method for linking other external packages to the workflow engine, such as a metaverse platform. Throughout the report, a neutronics simulation using OpenMC13 serves as the case study. OpenMC was selected because it demonstrates the full CAD-to-simulation-to-output-visualization pipeline that motivates the workflow architecture. A geometry file and a configuration file serve as structured inputs; the Monte Carlo neutron transport simulation produces a scalar metric (the tritium breeding ratio, TBR) that can be compared to a known range of values, and a spatially resolved neutron-track dataset that can be processed and presented in a visualizable format for the 3D rendering in the metaverse application.

<table><tbody><tr><td>Bioblend</td><td>Galaxy Project</td><td>v1.2+</td><td>Python library providing a high-level wrapper around the Galaxy REST API. This is used in the Omniverse extension helper scripts to list workflows, retrieve input definitions, and launch jobs. Installed automatically within the relevant Docker images; no host installation required.</td></tr><tr><td>Docker Containers</td><td>Docker</td><td>v24.0.5</td><td>Containerization runtime used to package each simulation and post-processing tool with all its dependencies, ensuring portability and reproducibility.</td></tr><tr><td>Galaxy</td><td>Galaxy Project</td><td>v22.05</td><td>Open-source workflow engine used to orchestrate the chained simulation and processing tools and to expose them via a REST API.</td></tr><tr><td>Git</td><td>Git SCM</td><td>v2+</td><td>Required to clone the repository to follow along with the protocol</td></tr><tr><td>NVIDIA RTX GPU</td><td>NVIDIA</td><td>-</td><td>Required for real-time ray-traced rendering in Omniverse (Section 3). Users without RTX hardware can complete all steps through Section 2 and use ParaView for visualisation (see Discussion).</td></tr><tr><td>Omniverse</td><td>NVIDIA</td><td>Code 2022.3.3</td><td>NVIDIA&#039;s collaborative 3D platform. This is used as the visualization and interaction front-end for workflow outputs via a custom Kit extension.</td></tr><tr><td>ParaView</td><td>Kitware</td><td>v5.11</td><td>Open-source scientific visualization application used as a non-RTX fallback for inspecting intermediate .vtk/.vtp outputs.</td></tr><tr><td>Protocol Repository</td><td>Custom</td><td>v1.0</td><td>Contains Galaxy configuration along with all tool XML wrappers, run scripts, Dockerfiles, test data, and the Omniverse extension. Cloned in Step 1.2.1. Key files are also provided as direct supplementary uploads (see I.2).</td></tr><tr><td>Python</td><td>Python</td><td>v3.10+</td><td>Runtime required for the OpenMC run script and the Omniverse extension helper scripts. Included within the relevant Docker images or with Omniverse download; no separate host installation is required.</td></tr><tr><td>The OpenMC Monte Carlo Code</td><td>OpenMC</td><td>v0.13.3</td><td>Open-source Monte Carlo particle-transport code used here for fusion neutronics simulation. This provides the tritium breeding ratio (TBR) and neutron-track outputs.</td></tr><tr><td>Windows Subsystem for Linux (WSL)</td><td>Microsoft</td><td>v2</td><td>Required to run Docker on Windows hosts (install via `wsl --install` in powershell). Linux and Mac users do not need this.</td></tr></tbody></table>

integrating automated simulation workflows with 3d visualization for virtual experiments in the metaverse

Integrating Automated Simulation Workflows with 3D Visualization for Virtual Experiments in the Metaverse

In many virtual experiments, multiple software packages are used with varied simulation types, pre- and post-processing tools, and tools to visualize the experiments&#39; results&#8212;often a combination of all. The typical method of integrating these is a manual one, with bespoke solutions created for each application area, which scales poorly and impedes sharing and reproducibility.
This protocol demonstrates the deployment and use of a locally containerized workflow system. Following it, users will launch a local Galaxy instance using Docker, create and run an OpenMC neutronics simulation workflow, pass the outputs through a chain of format-conversion tools, and load the results into both ParaView and NVIDIA Omniverse for visualization. The containerized deployment promotes reproducibility and portability on any machine meeting the hardware requirements described in Section 1.
Once the system is running, workflows can be re-run against new inputs without manual reconfiguration, additional simulation codes can be wrapped as new tools with modest effort, and tools can be used in multiple workflows and application areas. The approach supports the findable, accessible, interoperable, reusable (FAIR) data principles: run histories capture full provenance metadata, workflows are exportable as portable files and can be shared directly between Galaxy instances, and tools are packaged in version-controlled containers that can be published in a public repository. Scalability to high-performance computing (HPC) or cloud resources via Galaxy&#39;s Pulsar system is a natural extension of the architecture described here.
The method is demonstrated through a fusion neutronics case study. OpenMC is used to simulate neutron transport in a Direct Accelerated Geometry Monte Carlo (DAGMC) computer-aided design (CAD) geometry, producing a tritium breeding ratio (TBR) result and a neutron-track dataset. The simulation workflows are then connected to the NVIDIA Omniverse as the metaverse platform for invocation and visualization.

If the simulations are run with the provided inputs in the git repository, the following results should be obtained:
On successful completion of Step 2.1.3, both the TBR and Tracks output datasets will appear in the History panel in green, indicating a successful run. A representative TBR value using the supplied configuration file (5 batches of 1,000 particles) is approximately 0.76, with variations between runs of approximately ±0.01. Theref...

A generalized, FAIR-compliant method is presented for domain-expert researchers seeking to integrate simulation and data-processing tools into automated workflows for 3D virtual experiments. A neutronics example demonstrates setting up a local Galaxy instance, wrapping OpenMC and file-conversion tools, launching workflows from Omniverse, and visualizing the converted 3D outputs.

In many virtual experiments, multiple software packages are used with varied simulation types, pre- and post-processing tools, and tools to visualize the ...

Integrating automated simulation workflows with 3D visualization for virtual experiments in the Metaverse. A workflow is a chain of tools defining the flow of data between them. In many virtual experiments, researchers manually connect multiple simulation and processing tools, which scales poorly and impedes reproducibility.Workflows solve this because they are abstract, automated, scalable, portable, shareable, and citable. They allow existing simulation codes and processing steps to be wrapped into tools that are available in an easy-to-use interface. These can then be linked together and reconfigured without manually creating bespoke automation scripts.The tools can also be shared, reducing the requirement to learn new interfaces and allowing researchers to instead focus on configuring simulations and interpreting results. This protocol demonstrates the deployment of a locally containerized workflow manager. In this configuration, the end user interacts with a 3D visualization platform as the front end.The link to the workflows comes in the form of a lightweight Python extension that makes API calls to the workflow manager to launch simulations and receive the results, all from inside the visualization platform. The system presented here is general by nature and can be applied in almost any application where simulation and processing steps are performed. For this case study, a simple neutronic simulation in the context of a fusion energy power plant is used.In a fusion reactor, deuterium and tritium are fused together to produce energy and neutrons. Deuterium is a natural isotope of hydrogen, but tritium must be produced by the reactor itself using neutrons generated in the fusion events. To simulate the neutrons produced, a Monte Carlo simulation code is used, which follows the neutrons around the reactor, applying random interactions with the material as they pass through.As a comparative measure, a key performance indicator called the tritium breeding ratio is calculated, which provides the ratio of tritium produced by neutron interactions to the amount of tritium consumed in the reaction. The simulation uses a simple CAD geometry based on the spherical tokamak shape to model the amount of tritium produced by the blanket wrapping around the plasma vessel, and outputs a dat set of the generated neutron tracks. To begin the protocol, assume the container engine and 3D visualization platform have both been installed, following the project repository to the local machine.Launch the workflow manager server using the provided shell script, which uses the container engine to deploy a portable instance. Once the system is running, access the local web interface to create an administrative user account and log in. Navigate to the user preferences to generate a unique API key.This key authenticates the 3D visualization platform when it calls the workflow manager. Next, configure the 3D visualization platform by navigating to the relevant file in the extensions folder. Add the API key to this configuration file.Ensure this private key remains secure and is not committed to public repositories. Next, configure the 3D visualization platform by navigating to the extensions menu, clicking the settings icon, and adding the local path of the extension folder from the cloned repository. Finally, enable the extension.The auto-load option could be selected so that it is automatically enabled on relaunch. To run any single tool, input files must first be uploaded to the service. Create a new history and name it.Histories act as separate workspaces for each set of tools or workflows run. Click the Upload button. Select the required CAD geometry and configuration datasets from the example files in the repository and click Start.The inputs will appear in the History panel on the right-hand side of the screen. Navigate to the required Monte Carlo simulation tool on the left-hand panel and click on it. Select the relevant input data sets from the current history and run the tool by clicking execute.The outputs will change from orange, indicating that they are processing, to green, indicating that they have finished. Gray is used to highlight pending steps and red for failed steps. The first run may take longer as the tool's container image is downloaded.Subsequent runs should be faster. Once the tool has finished running, the results of the simulation can be viewed. Click on the tritium breeding ratio dataset in the history panel to expand it.This displays the ratio calculated by the neutronic simulation and associated statistical error. Tools are more useful when used in workflows, which allow multiple tools to be called in a single invocation and control the flow of data between tools automatically. To replicate the previous analysis pipeline, click on Workflows, then create and name a new workflow.Import the tools for the Tool panel by clicking on them. And then add two datasets as inputs and name them. When all the tools and inputs are on the canvas, join them together by clicking and dragging to connect the output of one step to the input of the next, defining the flow of data.Save the workflow using the icon in the top right. The workflow is run in much the same way as a single tool. Navigate to the Workflows tab in the top bar and click the run icon on the new workflow.Select the corresponding input datasets, then click Run Workflow. This executes all the steps that were previously run manually, saving intermediate outputs to the history. The run can be verified by inspecting the output files or by displaying the tritium breeding ratio in the interface.To run workflows from the 3D visualization platform, the output files must first be converted to a compatible 3D format. Create a workflow that mirrors the previous one but with additional steps. A track format conversion to USD and a CAD format conversion to USD via intermediate steps.Save this workflow. Open the 3D Metaverse platform. The enabled extension should be available in the viewport.Dock it to one side. Click Get Workflows to pull the list of available workflows from the connected instance. Select the newly created workflow with conversion steps to USD and click Get Inputs.The inputs defined for this workflow will appear. Select the standard CAD geometry and configuration files for each. Then, click Run Workflow.Once the workflow is completed, a notification appears in the information window of the extension. Click Refresh under the file manager to view the list of completed workflow instances. Select the current instance.Refresh again to retrieve the file list. Choose the files to view and click Pull File. 3D files load to the main viewport and text-based files appear in the information window.A successful simulation run will generate a tritium breeding ratio output. Because this is a stochastic process using a small sample size for speed, the expected value using the standard configuration provided in the repository should be approximately 0.76, with a variance of plus or minus 0.01. Increasing the batch count and particle count in the config input reduces this scatter at the cost of longer run times.A successful run also produces a set of converted visualization files, the CAD geometry, and the neutron tracks, each in a format compatible with the 3D visualization platform. These can be downloaded and inspected in an external viewer or pulled directly into the 3D visualization platform. This method demonstrates how domain expert researchers can integrate simulation tools into automated workflows for 3D virtual experiments.The approach supports the FAIR data principles, run histories capture full providence metadata, and tools are packaged in version-controlled containers, making the analysis pipeline both reproducible and shareable. While the workflow manager itself can be run locally on standard hardware, utilization of the 3D virtualization platform's full ray tracing engine requires a compatible high-performance RTX graphics card.

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