GridFire is a C++ library designed to perform general nuclear network evolution using the Reaclib library. It is part of the larger SERiF project within the 4D-STAR collaboration.
GridFire is architected to balance physical fidelity, computational efficiency, and extensibility when simulating complex nuclear reaction networks. Users begin by defining a composition, which is used to construct a full GraphEngine representation of the reaction network. To manage the inherent stiffness and multi-scale nature of these networks, GridFire employs a layered view strategy: partitioning algorithms isolate fast and slow processes, adaptive culling removes negligible reactions at runtime, and implicit solvers stably integrate the remaining stiff system. This modular pipeline allows researchers to tailor accuracy versus performance trade-offs, reuse common engine components, and extend screening or partitioning models without modifying core integration routines.
| AdaptiveEngineView | Dynamically culls low-flow species and reactions during runtime | Iterative flux thresholding to remove reactions below a flow threshold | Large networks to reduce computational cost |
| DefinedEngineView | Restricts the network to a user-specified subset of species and reactions | Static network masking based on user-provided species/reaction lists | Targeted pathway studies or code-to-code comparisons |
| MultiscalePartitioningEngineView | Partitions the network into fast and slow subsets based on reaction timescales | Network partitioning following Hix & Thielemann Silicon Burning I & II (DOI:10.1086/177016,10.1086/306692)| Stiff, multi-scale networks requiring tailored integration |
| NetworkPrimingEngineView | Primes the network with an initial species or set of species for ignition studies| Single-species injection with transient flow analysis | Investigations of ignition triggers or initial seed sensitivities|
These engine views implement the common Engine interface and may be composed in any order to build complex network pipelines. New view types can be added by deriving from the `EngineView` base class, and linked into the composition chain without modifying core engine code.
**Python Extensibility:**
Through the Python bindings, users can subclass engine view classes directly in Python, override methods like `evaluate` or `generateStoichiometryMatrix`, and pass instances back into C++ solvers. This enables rapid prototyping of custom view strategies without touching C++ sources.
## Numerical Solver Strategies
GridFire defines a flexible solver architecture through the `networkfire::solver::NetworkSolverStrategy` interface, enabling multiple ODE integration algorithms to be used interchangeably with any engine that implements the `Engine` or `DynamicEngine` contract.
- **NetworkSolverStrategy<EngineT>**: Abstract strategy templated on an engine type. Requires implementation of:
```cpp
NetOut evaluate(const NetIn& netIn);
```
which integrates the network over one timestep and returns updated abundances, temperature, density, and diagnostics.
- **Integrator:** Implicit Rosenbrock4 scheme (order 4) via Boost.Odeint’s `rosenbrock4<double>`, optimized for stiff reaction networks with adaptive step size control using configurable absolute and relative tolerances.
- **Jacobian Assembly:** Employs the `JacobianFunctor` to assemble the Jacobian matrix (∂f/∂Y) at each step, enabling stable implicit integration.
- **RHS Evaluation:** Continues to use the `RHSManager` to compute and cache derivative evaluations and specific energy rates, minimizing redundant computations.
- **Linear Algebra:** Utilizes Boost.uBLAS for state vectors and dense Jacobian matrices, with sparse access patterns supported via coordinate lists of nonzero entries.
- **Error Control and Logging:** Absolute and relative tolerance parameters (`absTol`, `relTol`) are read from configuration; Quill logger captures integration diagnostics and step statistics.
### Algorithmic Workflow in DirectNetworkSolver
1.**Initialization:** Convert input temperature to T9 units, retrieve tolerances, and initialize state vector `Y` from equilibrated composition.
2.**Integrator Setup:** Construct the controlled Rosenbrock4 stepper and bind `RHSManager` and `JacobianFunctor`.
3.**Adaptive Integration Loop:**
- Perform `integrate_adaptive` advancing until `tMax`, catching any `StaleEngineTrigger` to repartition the network and update composition.
- On each substep, observe states and log via `RHSManager::observe`.
4.**Finalization:** Assemble final mass fractions, compute accumulated energy, and populate `NetOut` with updated composition and diagnostics.
### Future Solver Implementations
- **Operator Splitting Solvers:** Strategies to decouple thermodynamics, screening, and reaction substeps for performance on stiff, multi-scale networks.
- **GPU-Accelerated Solvers:** Planned use of CUDA/OpenCL backends for large-scale network integration.
These strategies can be developed by inheriting from `NetworkSolverStrategy` and registering against the same engine types without modifying existing engine code.
A representative workflow often composes multiple engine views to balance accuracy, stability, and performance when integrating stiff nuclear networks:
- **GraphEngine** constructs the full reaction network, capturing all species and reactions.
- **MultiscalePartitioningEngineView** segregates reactions by characteristic timescales (Hix & Thielemann), reducing the effective stiffness by treating fast processes separately.
- **AdaptiveEngineView** prunes low-flux species/reactions at runtime, decreasing dimensionality and improving computational efficiency.
- **DirectNetworkSolver** employs an implicit Rosenbrock method to stably integrate the remaining stiff system with adaptive step control.
This layered approach enhances stability for stiff networks while maintaining accuracy and performance.
## Related Projects
GridFire integrates with and builds upon several key 4D-STAR libraries:
- [fourdst](https://github.com/4D-STAR/fourdst): hub module managing versioning of `libcomposition`, `libconfig`, `liblogging`, and `libconstants`
