Application note · Specialised

Application Note: Hybrid CFD/FEA Campaigns and Surrogate Models

Run validated physics at scale, then train bounded surrogate models to screen designs and shorten engineering iteration.

This workflow uses high-fidelity CFD or FEA to generate a design space, then trains a surrogate to make rapid estimates inside a documented validity envelope. The surrogate accelerates screening; it does not replace engineering sign-off.

Physics baseline

Choose reference cases with measured or accepted outputs. Validate mesh, material/boundary conditions, solver tolerances and convergence on AxiKeystone. Record numerical results before parallel optimisation so faster runs cannot hide changed physics.

Scale-out decision

Test domain decompositions on AxiVector and measure compute, MPI and I/O time. Use AxiLattice when design-of-experiments throughput and several users justify a scheduler. Many independent one-node cases may outperform one poorly scaling three-node case.

Dataset design

Sample the parameter space deliberately, include boundaries and known difficult regimes, and preserve failed/non-converged cases as information. Store geometry/version, mesh, solver configuration, residuals and outputs with each row. Split validation by geometry family or time when appropriate to prevent leakage.

Surrogate deployment

Expose prediction, uncertainty and validity-domain checks together. Route out-of-domain or high-consequence candidates back to the solver. Version the model with the simulation dataset and retrain only through controlled workflows. Keep plots and reports labelled as surrogate or full-order results.

Acceptance

Measure full-order numerical agreement, multi-node efficiency, campaign throughput, surrogate error by operating regime and false confidence outside the training domain. Demonstrate restart, dataset lineage and the ability to reproduce a selected design from source parameters.

Primary technical references

References are provided for software architecture and implementation planning. Validate the versions, licences, support matrix and regulated-use requirements applicable to the final deployment.

Relevant Hyperion platforms

Hyperion X1140-C

AxiKeystone

A CPU- and memory-led HPC platform for simulation, genomics, analytics, compilation and workloads with large in-memory working sets.

One 4U node; 128 AMD EPYC cores; 1.15TB ECC DDR5; one NVIDIA RTX PRO 4000 24GB; enterprise U.2 NVMe.

Hyperion X2160

AxiVector

A tightly coupled two-node simulation and mathematical-computing platform for MPI, CFD, FEA and optimisation.

Two 4U nodes; 256 AMD EPYC cores; 2.3TB aggregate ECC DDR5; two 96GB GPUs; direct 100GbE RoCEv2 RDMA between nodes.

Hyperion X3140

AxiLattice

A shared departmental CPU-compute facility for research pipelines, simulation, genomics and queued multi-user work.

Three 4U nodes; 384 AMD EPYC cores; 3.4TB aggregate ECC DDR5; three professional GPUs; switched 100GbE RDMA, 25GbE storage and Slurm.

Configuration and quotation

Validate this workload on Hyperion

Final architecture and price depend on representative code and data, concurrency, storage, networking, site infrastructure, component availability and export compliance.

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