Engineering Digital Twin
Decision-ready digital twins for thermo-fluid and energy systems—combining credible models, physical measurements and operational data to support design, troubleshooting, monitoring, optimisation and scale-up.
Core capability: thermo-fluid engineering with fit-for-purpose modelling, targeted measurement and a clear pathway from evidence to action.
What we help you achieve
De-risk design decisions
Predict performance, identify sensitivities, and define safe operating windows before committing to fabrication or procurement.
Troubleshoot performance issues
Find root causes of hotspots, maldistribution, pressure-drop penalties, vibration drivers, or unexpected degradation.
Support scale-up & optimisation
Translate lab results to pilot/operation using models that capture the dominant physics and quantify uncertainty.
Typical use cases
Thermo-fluid systems
- Heat exchangers, heating/cooling layouts, thermal management
- Flow distribution, mixing, pressure drop, separation
- Boundary-layer driven performance limits and hotspots
Phase-change & energy systems
- Phase-change materials for storage/transport
- Boiling / condensation / interface-driven behaviour (as required)
- Hydrogenation / metal-hydride storage: thermal management and cycle optimisation
Aerodynamics & turbomachinery
- External/internal aerodynamics for performance and noise
- Turbulence modelling choices that match decision needs
- Turbomachinery: performance prediction and loss drivers (as scoped)
Measurement, monitoring & field tests
- Data acquisition strategy and instrument selection
- Calibration and post-processing workflows
- Advanced diagnostics (e.g., LIF, PIV) when appropriate
From physical system to living model
Measurement-informed digital twins
An effective digital twin is more than a simulation. It connects a physical asset or experiment to a computational representation, then uses measured evidence to calibrate, validate and update that representation as conditions change.
Observe
Measure the physical system at the spatial and temporal resolution needed for the decision.
Integrate
Synchronise, calibrate and quality-check measurement, operational and contextual data.
Model
Combine the data with CFD, reduced-order, analytical or data-driven models.
Act
Use the twin for monitoring, diagnosis, forecasting, optimisation and decision support.
Point and distributed sensors
Temperature, pressure, flow, vibration, acoustic, humidity, light and other sensors provide continuous operating data. Sensor selection, placement, calibration, uncertainty and sampling rate are designed around the quantities the twin must infer or predict.
PIV — Particle Image Velocimetry
PIV provides non-intrusive velocity fields for resolving flow structures, validating CFD and developing reduced-order representations. Time-resolved or phase-locked measurements can capture transient and periodic behaviour.
LIF — Laser-Induced Fluorescence
LIF maps scalar fields such as temperature or concentration. When combined with PIV, it links transport behaviour to the underlying flow and provides rich validation data for coupled thermo-fluid models.
Infrared thermography
Thermal imaging captures spatial surface-temperature distributions, hotspots and transient thermal response. Emissivity, reflections, viewing geometry and reference measurements are managed to obtain defensible quantitative data.
Integrated by design: these measurements can be combined with laboratory tests, field observations, existing control-system data, CFD, LBM, reduced-order models, system identification, uncertainty quantification and machine learning. The result is a fit-for-purpose twin with traceable assumptions and known confidence—not simply a dashboard or a static model.
Methods we use (fit-for-purpose)
Core modelling
- CFD workflows for flow and heat transfer (Eulerian methods)
- Verification-minded setup: assumptions, boundary conditions, sensitivity checks
- Validation planning using available measurements and operational data
Advanced techniques (when warranted)
- Lattice Boltzmann Method (LBM) for specialised flow/transport contexts
- Discrete Element Method (DEM) for particulate/granular interactions
- Smooth Particle Hydrodynamics (SPH) for free-surface and complex interface behaviour
Key principle: we choose the minimum fidelity that answers the decision safely—then increase fidelity only if it changes the decision.
Deliverables you can use
Decision brief
What we found, what it means, and what to do next—written for stakeholders.
Technical pack
Assumptions, inputs, model setup, sensitivity checks, and a clear audit trail.
Handover & uplift
Runbook + optional training so your team can extend the work internally.
What we need from you (to start)
Minimum inputs
- The decision you’re trying to make (A vs B / target / pass-fail)
- Geometry / drawings (even rough), operating ranges, and constraints
- Any available data (flows, temperatures, pressures, duty, material properties)
- Deadline and definition of “success”
If available (helps a lot)
- Known failure modes / observed issues (photos, thermal images, logs)
- Previous calculations/simulations and what you did/didn’t trust
- Site limitations (measurement access, sensor limits, safety constraints)
Not sure whether modelling is worth it? Start with the checklist insight and send us your answers.