将自动化仿真工作流与三维可视化集成，用于元宇宙虚拟实验
July 21st, 2026
本书为领域专家研究人员提供了一种通用且符合FAIR标准的方法，旨在将仿真和数据处理工具整合进3D虚拟实验的自动化工作流程中。中子学的一个例子展示了如何搭建本地Galaxy实例、包裹OpenMC和文件转换工具、从Omniverse启动工作流程，以及可视化转换后的3D输出。
将自动化模拟工作流程与3D可视化集成，用于元宇宙中的虚拟实验。工作流程是一系列工具，定义它们之间数据的流动。在许多虚拟实验中，研究人员手动连接多个仿真和处理工具，这导致扩展性差且阻碍了重复性。
工作流解决了这个问题，因为它们抽象、自动化、可扩展、可移动、可共享且可引用。它们允许将现有的仿真代码和处理步骤整合到易于使用界面的工具中。这些脚本可以相互关联并重新配置，无需手动创建定制自动化脚本。
工具也可以共享，减少学习新界面的需求，使研究人员能够专注于配置模拟和解读结果。该协议展示了本地容器化工作流管理器的部署。在这种配置中，终端用户以3D可视化平台作为前端交互。
与工作流程的链接以一个轻量级的Python扩展形式出现，该扩展通过API调用工作流管理器启动模拟并接收结果，所有这些都来自可视化平台内部。这里展示的系统本质上是通用的，几乎可以应用于任何进行仿真和处理步骤的应用中。本案例研究采用了聚变能源电厂背景下的简单中子模拟。
在聚变反应堆中，氘和氚融合产生能量和中子。氘是氢的天然同位素，但氚必须由反应堆自身利用聚变过程中产生的中子来制造。为了模拟产生的中子，使用了蒙特卡洛模拟代码，跟踪中子绕过反应堆，并在中子通过时与材料施加随机相互作用。
作为比较指标，计算了一个关键绩效指标氚繁殖比，该指标提供了中子相互作用产生的氚与反应中氚消耗量的比值。该模拟使用基于球形托卡马克形状的简单CAD几何，模拟包裹等离子体容器的氚量，并输出生成的中子轨迹的dat集。开始该协议时，假设容器引擎和三维可视化平台均已安装，并按照项目仓库迁移到本地机器。
使用提供的shell脚本启动工作流管理器服务器，该脚本使用容器引擎部署可移植实例。系统运行后，访问本地网页界面创建管理员用户账户并登录。进入用户偏好设置生成唯一的 API 密钥。
当3D可视化平台调用工作流程管理器时，该密钥用于验证。接下来，通过在扩展文件夹中导航到相关文件来配置3D可视化平台。在这个配置文件中添加API密钥。
确保该私钥保持安全，且不提交到公共仓库。接下来，通过进入扩展菜单，点击设置图标，并从克隆仓库添加扩展文件夹的本地路径来配置3D可视化平台。最后，启用扩展。
可以选择自动加载选项，使其在重启时自动启用。要运行任何单一工具，输入文件必须先上传到服务中。创造一个新的历史，并命名它。
历史记录作为每组工具或工作流的独立工作区。点击上传按钮。从仓库中的示例文件中选择所需的CAD几何和配置数据集，然后点击开始。
输入会显示在屏幕右侧的历史面板中。在左侧面板进入所需的蒙特卡洛模拟工具并点击它。从当前历史中选择相关的输入数据集，然后点击执行来运行工具。
输出会从橙色（表示正在处理）变为绿色（表示已完成）。灰色用于高亮待处理步骤，红色用于失败步骤。第一次运行可能需要更长时间，因为工具的容器映像正在下载。
后续的运行应该会更快。工具运行完成后，可以查看模拟结果。点击历史面板中的氚育种比数据集以展开。
该模型显示中子模拟计算出的比值及相关的统计误差。工具在工作流程中更为有用，允许在一次调用中调用多个工具，并自动控制工具间的数据流。要复制之前的分析流程，点击“工作流程”，然后创建并命名一个新的工作流程。
点击工具面板导入工具面板。然后添加两个数据集作为输入并命名它们。当所有工具和输入都集中在画布上时，通过点击和拖动将它们连接起来，将一个步骤的输出连接到下一步的输入，定义数据流。
用右上角的图标保存工作流程。工作流程的运行方式与单一工具差不多。进入顶部栏的“工作流程”标签，点击新工作流的运行图标。
选择对应的输入数据集，然后点击运行工作流程。这会执行之前手动执行的所有步骤，并将中间输出保存到历史中。通过检查输出文件或在界面中显示氚繁殖比率来验证运行。
要从3D可视化平台运行工作流程，必须先将输出文件转换为兼容的3D格式。创建一个与之前相同的工作流程，但包含更多步骤。通过中间步骤将轨道格式转换为美元，以及通过中间步骤将CAD格式转换为美元。
保存这个工作流程。打开3D元宇宙平台。启用的扩展应该可以在视口中使用。
把它停靠到一边。点击“获取工作流程”以从连接的实例中提取可用工作流列表。选择新创建的工作流程，并点击“获取输入”。
该工作流程定义的输入将显示出来。为每个节点选择标准的CAD几何体和配置文件。然后，点击运行工作流程。
一旦工作流程完成，扩展的信息窗口中会出现通知。点击文件管理器下的刷新，查看已完成的工作流程实例列表。选择当前实例。
再次刷新以获取文件列表。选择要查看的文件并点击“拉取文件”。3D文件加载到主视口，基于文本的文件会出现在信息窗口中。
成功的模拟运行将产生氚繁殖比的输出。由于这是一个随机过程，使用较小的样本量以求速度，使用库中标准配置的期望值应约为0.76，方差为正负0.01。在配置输入中增加批次数量和粒子数量可以减少这些散射，但代价是运行时间更长。
成功运行还会生成一组转换后的可视化文件、CAD几何体和中子轨迹，每个文件格式均兼容3D可视化平台。这些数据可以下载并在外部查看器中查看，或直接拉入3D可视化平台。该方法展示了领域专家研究人员如何将模拟工具集成到3D虚拟实验的自动化工作流程中。
该方法支持FAIR数据原则，运行历史捕获完整的Providence元数据，工具封装在版本控制的容器中，使分析流程既可复现又可共享。虽然工作流管理器本身可以在标准硬件上本地运行，但要充分利用3D虚拟化平台的完整光线追踪引擎，则需要兼容的高性能RTX显卡。
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Overview
This protocol demonstrates the integration of automated simulation workflows with 3D visualization platforms for virtual experiments, specifically within the context of fusion neutronics. By deploying a locally containerized workflow manager and connecting it to a 3D visualization front end, researchers can automate, scale, and share complex simulation pipelines, enhancing reproducibility and adherence to FAIR data principles.
Key Study Components
Area of Science
- Computational physics
- Fusion energy research
- Scientific workflow automation
- 3D visualization
Background
- Virtual experiments often require the integration of multiple simulation, processing, and visualization tools.
- Manual integration is common but does not scale well and hinders reproducibility.
- Automated workflows can abstract and automate these processes, making them more portable and shareable.
- Adhering to FAIR data principles is increasingly important for scientific research.
Purpose of Study
- To demonstrate a reproducible, containerized workflow system for running and visualizing simulation experiments.
- To show how simulation tools can be integrated and automated within a workflow manager.
- To enable seamless data flow from simulation to 3D visualization platforms.
Methods Used
- Deployment of a local workflow manager using containerization (e.g., Docker).
- Integration of simulation tools (OpenMC for neutronics) and format conversion utilities.
- Connection of the workflow manager to a 3D visualization platform via a Python extension and API.
- Execution of simulation workflows, including input upload, tool chaining, and output visualization in platforms like NVIDIA Omniverse.
Main Results
- Automated workflows can be created, saved, and re-run with new inputs without manual reconfiguration.
- Simulation outputs, such as tritium breeding ratio and neutron track datasets, are generated and converted for 3D visualization.
- Workflows and tools are portable, shareable, and version-controlled, supporting reproducibility.
- The system supports full provenance tracking and can be extended to HPC or cloud resources.
Conclusions
- Containerized workflow systems streamline the integration of simulation and visualization tools for virtual experiments.
- The approach enhances reproducibility, scalability, and data sharing in computational research.
- Researchers can focus on scientific analysis rather than bespoke automation, benefiting from FAIR-compliant workflows.
Frequently Asked Questions
Executive Industry Relevance
Automated simulation workflows integrated with 3D visualization platforms enable scalable, reproducible virtual experiments, directly addressing bottlenecks in manual data integration and analysis. This approach enhances predictive confidence and operational efficiency in computational modeling, supporting robust decision-making at key discovery and preclinical inflection points. By adhering to FAIR data principles and enabling seamless tool reuse, the system strengthens enterprise-wide R&D continuity and data integrity.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Automated workflows facilitate rapid hypothesis testing and mechanistic de-risking in complex biological simulations.
- Containerized tools ensure reproducibility and traceability of simulation results for target validation.
- Abstracted workflow management supports scalable evaluation of multiple biological scenarios.
Screening & Assay Development
- Standardized data pipelines prepare simulation outputs for downstream quantitative analysis and visualization.
- Reusable workflow components enable consistent assay development and cross-study comparability.
- Automated format conversion ensures compatibility with advanced 3D visualization platforms for screening readiness.
Translational & Preclinical Research
- Provenance-tracked simulation histories support translational continuity and regulatory documentation.
- Quantitative outputs, such as performance ratios, inform preclinical modeling and risk assessment.
- Workflow portability enables adaptation across disease-relevant systems and preclinical models.
Pipeline & Workflow Integration
This containerized workflow system bridges early discovery, screening, and preclinical modeling by automating simulation, data conversion, and visualization steps within a unified platform.
- Discovery Biology: Enables robust hypothesis testing and pathway analysis through automated, reproducible simulations.
- Screening: Delivers standardized, quantitative outputs ready for high-throughput evaluation and visualization.
- Analytics: Provides statistical readouts, such as tritium breeding ratios, supporting comparative analysis across conditions.
- Translational Research: Maintains full provenance and data integrity for downstream translational studies.
- Enterprise Reuse: Supports tool and workflow sharing across teams, enhancing R&D scalability and efficiency.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence and reduces mechanistic ambiguity in simulation-driven research.
- Operational Value: Promotes standardization, reproducibility, and rapid reconfiguration of simulation pipelines.
- Strategic Value: Enables informed go/no-go decisions and capital-efficient portfolio management.
- Portfolio Impact: Facilitates risk-adjusted prioritization and advancement of discovery and preclinical programs.
Implementation Considerations
- Requires expertise in simulation modeling, workflow management, and 3D visualization platforms.
- Needs containerization infrastructure and compatible high-performance graphics hardware for full visualization capabilities.
- Demands cross-team standardization of data formats and workflow components.
- Supports adaptation to diverse simulation codes and biological models with modest integration effort.
- Dependent on secure management of API keys and provenance metadata for reproducibility.