Simulation & Modeling
Digital Twin Engine for industrial operations
A first-principles simulation framework that connects asset-level physics models to process-level digital twins. Wire equipment models into a directed graph, test what-if scenarios, and turn process impact into maintenance work, field action, APIs, reports, and capital planning decisions.
Digital twin proof
Digital twins are useful when they feed decisions, work, and evidence.
TwinEdge connects process models to DataOps, edge runtime, governed agents, AssetOps EAM, Field, API/MCP products, and capital planning so simulations do not stay trapped in engineering software.
9
Built-in models
Hydraulic, pneumatic, thermal, electrochemical, and process models.
DAG
Process graph
Equipment models compose into full process and facility simulations.
Action
Connected output
Twin results flow into agents, EAM, Field, API/MCP, and planning.
How the Engine Works
Four stages from raw sensor data to asset physics, process simulation, and operational action.
Sensor Ingestion
Raw telemetry from supported source families is normalized into a unified tag namespace. Each sensor maps to a model input port.
Model Composition
Individual physics models are wired into a directed acyclic graph (DAG). Outputs of one model become inputs to the next — just like real process flow.
State Propagation
On every tick, sensor values flow through the graph. Each model evaluates its equations and passes calculated states downstream. Performance is scoped by model complexity and deployment.
Scenario Execution
Fork the live graph state, inject a perturbation (pump trip, setpoint change, load spike), and propagate forward to see system-wide impact — without touching real equipment.
Built-In Model Library
Nine first-principles equipment models ready to compose into any process.
| Equipment Model | Physics Domain | Inputs | Outputs | Edge Latency |
|---|---|---|---|---|
| Centrifugal Pump | Hydraulics | P_in, P_out, Flow, Power, Speed | η, BEP%, NPSH_m, SE | <8ms |
| Positive Displacement Blower | Pneumatics | P_in, P_out, Airflow, Power | η, Surge_margin, T_out | <6ms |
| Centrifugal Chiller | Thermodynamics | T_chws, T_chwr, T_cws, Power | COP, kW/ton, Capacity% | <10ms |
| Diesel Generator | Electromechanical | Fuel_rate, Power_out, Freq, T_exhaust | η_thermal, kWh/gal, Load% | <5ms |
| Battery Bank | Electrochemical | V_bus, I_charge, I_discharge, T_cell | SOC, SOH, C-rate, Capacity | <4ms |
| Cooling Tower | Psychrometrics | T_hw, T_cw, T_wb, Airflow | Approach, Range, η_evap | <7ms |
| Gravity Clarifier | Sedimentation | Flow, TSS_in, Blanket_depth | SOR, TSS_out, Sludge_rate | <5ms |
| Media Filter | Filtration | Flow, dP, TSS_in, Run_time | TSS_out, Backwash_ETA, Capacity% | <4ms |
| UV Disinfection | Photochemistry | Flow, UVT, Lamp_hrs, Power | Dose_mJ, Log_inact, Lamp_life% | <3ms |
Engine Capabilities
The simulation framework that powers every TwinEdge digital twin.
Process-Level Composition
Wire individual equipment models into full process trains. A pump connects to a pipe connects to a clarifier — just like the real system. Change one and see the cascade.
What-If Scenario API
Fork the live process state, inject a perturbation, and simulate forward. Test pump trips, setpoint changes, storm events, and equipment failures without touching real equipment.
Directed Acyclic Graph (DAG)
Models are nodes. Data flows are edges. The engine resolves execution order, handles parallel branches, and propagates state changes in topological order.
Scoped Propagation
Graph evaluation is sized by sensor volume, model complexity, edge host capacity, and deployment requirements.
Edge + Cloud Split Execution
Time-critical models run on the edge for real-time response. Fleet aggregation, historical trending, and model retraining run in the cloud. Each layer does what it does best.
Model Lifecycle Management
Create models from the built-in library or custom equations. Validate against historical data. Deploy to edge. Monitor drift. Retrain when conditions change.
Physics Analytics vs Digital Twin Engine
Physics Analytics monitors individual equipment. The Digital Twin Engine simulates entire systems.
| Physics Analytics | Digital Twin Engine | |
|---|---|---|
| Scope | Single equipment asset | Entire process train or facility |
| Example | Pump curve + efficiency at one operating point | What happens to the whole plant when that pump trips |
| Output | Real-time KPIs (η, NPSH, BEP%) | System state propagation + scenario comparison |
| Input | 3-5 sensors per asset | All sensors across the process, composed through a model graph |
| Use case | Monitor and trend individual equipment health | Simulate operational decisions before executing them |
Technical Specifications
Performance is sized around model complexity, source cadence, and the edge or cloud capacity selected for your deployment.
Graph evaluation latency
Depends on graph size and edge host capacity
Max models per edge host
Depends on model complexity and deployment profile
Scenario throughput
Parallel what-if evaluations sized during activation
Supported model types
Plus custom equation builder
State snapshot interval
Configurable per process
Scenario horizon
Forward simulation window
Graph serialization
Version-controlled, portable
Model hot-swap
Replace models without restarting the graph
Simulate Before You Operate
Test operational decisions on the digital twin before executing them on real equipment. See the system-wide impact of every change — in under 100 milliseconds.