National laboratories conduct complex research requiring coordination of multiple simulation codes, data sources, and computational resources. Orchestrating these distributed components while maintaining reproducibility and data provenance is a fundamental challenge in computational science.
The Challenge
Research teams at national laboratories face unique orchestration challenges:
- Heterogeneous Components: Simulations combine physics codes written in different languages, using different frameworks
- Computational Scale: Research workloads span from single workstations to supercomputer clusters
- Data Provenance: Scientific reproducibility requires complete tracking of simulation parameters and results
- Component Isolation: Simulation failures should not cascade or corrupt shared resources
- Configuration Complexity: Multi-physics scenarios involve hundreds of parameters across multiple codes
- Collaboration: Research teams need to share scenarios and results across organizational boundaries
How NeuroSim Solves It
NeuroSim provides a distributed orchestration platform designed for research environments where component diversity and isolation are paramount. The plugin architecture allows integration of existing simulation codes without modification, while Kafka-based messaging provides the scale needed for large-parameter-space studies.
Schema-driven configuration enables researchers to define complex multi-physics scenarios declaratively, ensuring reproducibility. Binary-level plugin isolation protects against component failures, and the scenario lifecycle system provides complete audit trails for data provenance.
Key Capabilities
- Language-Agnostic Plugins: Integrate simulation codes written in Python, C++, Fortran, Julia, or any compiled language
- Distributed Orchestration: Coordinate components across workstations, clusters, and cloud resources
- Parameter Sweep Support: Run hundreds of simulation variants concurrently for sensitivity analysis
- Schema-Driven Configuration: Define scenarios using JSON Schema for validation and reproducibility
- Event-Driven Architecture: Apache Kafka backbone enables high-throughput data exchange between components
- Binary Isolation: Plugin failures are contained and don't affect other simulation components
- Audit Logging: Complete event history for data provenance and scientific reproducibility
Example Scenario
A research team is studying coupled thermal-hydraulic-structural behavior in advanced reactor designs. Their workflow combines three independent simulation codes: a thermal analysis tool, a fluid dynamics solver, and a structural mechanics code.
Using NeuroSim, they define a scenario where the thermal code outputs temperature distributions, which drive the fluid solver, which in turn provides pressure loads to the structural code. Each code runs as an isolated plugin, communicating via Kafka topics.
The team configures a parameter sweep varying inlet temperatures, flow rates, and material properties across 200 combinations. NeuroSim orchestrates the execution, manages data flow between components, and captures all results with full provenance tracking.
When one structural analysis run encounters a convergence issue, the plugin isolation prevents the failure from affecting other runs. The team identifies the problematic parameter combination, refines the mesh, and reruns only that case.
Getting Started
Ready to orchestrate your research simulations? Visit our Getting Started guide to learn how to deploy NeuroSim and create your first multi-physics scenario.