Optimise the operating point before changing the physical system.
Connect digital twins, calibrated simulation, surrogate models, uncertainty budgets and reliability criteria so design and operating changes can be evaluated before physical execution.
Start with the bottleneck that consumes expensive evidence.
Industrial optimisation is rarely a single-objective search. Throughput, quality, energy, cost and equipment limits interact, models can be imperfect, and the best predicted point may sit too close to a hard constraint. A useful optimisation system must therefore combine performance with calibration, uncertainty and explicit acceptance criteria.
Create a quantitative decision layer between engineering evidence and a proposed physical change, so candidate designs or operating points can be screened, simulated, calibrated and reliability-checked before progression.
See how an operating state becomes a reliability-aware quantitative decision.
A digital twin can expose the asset and its current environment while LargeQuant evaluates constraints, uncertainty and reliability around the operating choice.
Open Stratomission →From objective to evidence and next action.
- 01Define the engineering objective, controllable variables, operating envelope and deterministic constraints.
- 02Register or build the digital-twin representation and connect the observations used for calibration.
- 03Calibrate model parameters against accepted trajectories or measured observations and retain residual evidence.
- 04Use trusted CFD, FEA or other engineering solvers for high-fidelity evaluation where configured.
- 05Fit surrogate or multi-fidelity models to reduce the cost of repeated high-fidelity evaluations.
- 06Propose candidate operating/design points and reject deterministic constraint violations before execution.
- 07Build an uncertainty budget across surrogate prediction, measurement, observation noise, discrepancy and operational variability.
- 08Apply an explicit reliability threshold, record the decision evidence and use the accepted result to choose the next design point.
The programme combines modelling, execution and evidence.
Digital twins
Physics-linked engineering twins with observation-based calibration.
CFD / FEA execution
Trusted OpenFOAM and CalculiX execution boundaries when the runtimes are available.
HPC orchestration
Controlled Slurm submission and reconciliation for approved engineering cases.
Multi-fidelity optimisation
Use lower- and higher-cost evidence in a single campaign.
Reliability analysis
Probability-based acceptance against recorded quantitative criteria.
Decision evidence
Signed records linking the candidate, uncertainty, constraints, execution and decision.
What connects into the programme.
- Plant or design variables and operating limits
- Historical operating or test data
- Digital-twin/calibration observations
- CFD/FEA case templates and solver results
- Quality, throughput, cost or energy objectives
What the team gets back.
- Calibrated twin states and residual diagnostics
- Candidate design/operating points
- Solver and HPC execution evidence
- Uncertainty budgets and acceptance probabilities
- Reliability-gated decision records and assurance packs
Why the operating loop matters.
- Evaluate more design alternatives before physical modification.
- Reserve expensive solver or test capacity for the most informative candidates.
- Make model-versus-measurement uncertainty explicit.
- Prevent autonomous progression when recorded reliability criteria are not met.
- Give engineering teams an inspectable record of why a recommendation was made.