FT Wind SensorMultiphysics inference · design to manufacturing

From simulation
to decision.

Collapse a slow, expert-heavy engineering loop into rapid design exploration and tolerance-linked manufacturing decisions.

Aero aerodynamic behaviourThermal temperature effectsAcoustic resonance responseInstant surrogate inference
THE BOTTLENECK

A decision should not take another simulation cycle.

Today, computational physicists, mechanical engineers and market leaders must align before a design can move. For an FT Technologies wind sensor, that decision sits across aerodynamic, thermal and acoustic behaviour—nonlinear simulations that can take days before the team gets the evidence needed for its next discussion.

01

Discuss

Specialists translate market needs into geometry and simulation questions.

→
02

Wait for physics

Coupled simulations take time, delaying the evidence needed for a decision.

→
03

Build again

Every new manufactured design adds cost before the next learning cycle begins.

From days to interactionExplore geometry instantly→Measure the response→Compare with real tolerances→Decide pass / fail
Sensor geometry diagram showing the five dimensions d1, d2, d3, d4 and d5 in millimetres.
Design reference

Five geometry parameters connect design intent, predicted response and manufacturing inspection.

01

Explore before you simulate again.

Forward LightGBM surrogate — geometry → acoustic response Instead of waiting for another full physics run, the surrogate gives the team an immediate view of how a proposed geometry shifts resonance position, amplitude and width.

After extensive feature studies and evaluation, the response was represented as a baseline plus up to three fitted Gaussian resonances. The model predicts those physically meaningful shape parameters—not 360 unrelated pressure values—and reconstructs the complete curve.

  1. Change a design. Adjust an exact dimension inside the learned design space.
  2. See the consequence. The acoustic curve, maximum pressure and resonance peaks update immediately.
  3. Discuss with evidence. Physics, mechanical and commercial teams can compare options before committing simulation or manufacturing resources.
Geometry
✦

Stay inside the learned design space.
Controls are limited to the geometry ranges used to train the model.

Predicted pressure responseReady
Maximum pressure—
At frequency—
Frequency (kHz)
02

Turn a measurement into a decision.

Inverse 1D CNN inspector — measured response → inferred geometry The convolutional model reads the complete 61-point sweep, learning resonance shapes and spacing across frequency to infer the five dimensions that most likely produced it.

This closes the loop between modelling and manufacturing. A measured part can be compared with its known reference geometry and actual engineering tolerance, producing an immediate dimension-by-dimension pass/fail view.

  1. Load the measured response. Use a test example, upload a frequency-pressure CSV or paste rows directly.
  2. Infer the manufactured geometry. The CNN returns all five dimensions in millimetres from the acoustic signature.
  3. Apply the real tolerance. Select 0, ±1, ±2 or ±5%; every dimension is marked inside or outside the permitted range.
Pressure measurementCSV · Hz + Pa
Applied to each dimension relative to its reference value.
or paste rows
Inferred configurationWaiting for measurement
02

No sweep loaded yet.

Choose a held-out test example to compare the CNN estimate with its known geometry.