Induction heating
Relate excitation, coil arrangement and material properties to temperature evolution. The first research priority is to compare prediction error and computation time against reserved reference cases.
Explore the programme ↗Connect the model to the machine.
A useful digital representation starts with the system it describes and the decisions it needs to support. Our direction combines numerical modelling, physical relationships and data, with induction heating as the first selected application.
A model.
A connection.
A purpose.
A numerical model solves a specified physical problem; a learned surrogate approximates selected outputs from its inputs. A digital twin adds a connection to an identified physical system, with operating data and an update process that keep the representation relevant to that asset.
Choose the architecture around the decision: compare a design, estimate a state or follow changing operating conditions. Each use sets different requirements for data quality, model updates, response time and acceptable prediction error.
Start with a defined physical problem.
Gas turbine systems
Investigate component-level flow or thermal models for aero and industrial engines. Geometry, inlet conditions, rotational speed and available reference data would determine which outputs can be assessed.
Cooling systems
Compare cooling mechanisms and refrigeration cycles at a defined thermal duty. Thermodynamic modelling and optimisation would examine energy input, operating conditions and practical equipment constraints together.
Which decision must the model support?
Share the CAD or drawings, material specification and operating conditions. Identify the quantity you need to predict and the decision it will inform. Any existing simulations or measurements help establish the reference basis and the work needed for a first study.
Geometry and operating conditions
Include units, dimensions, material-property sources, initial and boundary conditions, and the range of settings you want to compare. Identify which inputs are measured and which are assumed.
Reference data and evaluation
State how the reference results were produced, their resolution or measurement uncertainty, and which cases can remain outside model fitting. Agree the error measures and acceptance criteria for the intended decision.
Deliverables and responsibilities
Agree the first-stage model, analysis files and technical report, together with review responsibilities. Identify any experimental access, specialist knowledge or additional software required before committing to the scope.
Build depth before extending scope.
Begin with one component, process and operating range. Compare a reference solver, a simple predictive baseline and the candidate learned method. Record where each approach is useful before adding more geometries or conditions.
Expansion to other machinery and energy systems depends on access to the relevant equations, material data and measurements. Reusing the workflow can shorten setup, but performance must be established for the new application.