AI/ML-based Trimmed Body NTF & Global Modes Prediction and Optimization using ODYSSEE CAE
A Hexagon MI / MSC Software case study describing how Satven — a leading Indian automotive engineering bureau — used ODYSSEE CAE (LUNAR) machine learning to cut trimmed body NVH simulation and optimisation time from multiple days to a few minutes, while maintaining 91% correlation with conventional CAE results.
Headlines at a glance
The headline numbers and findings as they appear in the report — designed for fast scanning by leadership teams.
NTF prediction runtime: regular CAE vs LUNAR after ML training
Run time with LUNAR for NTF prediction after ML training is 10 seconds, versus 3
Run time with LUNAR for optimisation (after ML training) is 2-5 minutes, compare
Correlation between LUNAR prediction and conventional CAE results is 91%, valida
Hexagon MI / MSC Software — Satven case study
Key Findings
Major discoveries and conclusions from AI/ML-based Trimmed Body NTF & Global Modes Prediction and Optimization using ODYSSEE CAE.
NTF Prediction: 3 Hours to 10 Seconds
Run time with LUNAR for NTF prediction after ML training is 10 seconds, versus 3 hours with regular CAE.
Optimisation: 72 Hours to Minutes
Run time with LUNAR for optimisation (after ML training) is 2-5 minutes, compared to 72 hours with regular CAE.
91% CAE Correlation
Correlation between LUNAR prediction and conventional CAE results is 91%, validating ML as a reliable surrogate for NVH simulation.
Satven Competitive Advantage
Satven, a leading Indian automotive engineering bureau with operations in Munich, Hyderabad and Chennai, deployed the solution for competitive advantage.
Executive Summary
A concise overview of the report's scope, methodology and conclusions.
Satven, an Indian automotive engineering bureau, adopted Hexagon's ODYSSEE CAE (LUNAR) to cut trimmed body NVH simulation and optimisation from multiple days to a few minutes — without losing engineering fidelity.
LUNAR predicts the NTF (Noise Transfer Function — excitation at TBA, response at driver ear sound pressure level) in 10 seconds after training, compared with 3 hours for the regular CAE process, while maintaining a 91% correlation with conventional CAE results.
For optimisation, LUNAR cuts runtime from 72 hours with regular CAE to 2–5 minutes — enabling design exploration cycles that were previously impractical and delivering a definite competitive advantage to Satven's automotive customers.
Topics & sections covered
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Satven company background
Trimmed body NTF & global modes — challenge
ODYSSEE CAE (LUNAR) AI/ML approach
Validation: LUNAR vs CAE results
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What You'll Learn
- NTF prediction accelerated from 3 hours to 10 seconds using LUNAR ML
- Optimisation compressed from 72 hours to 2-5 minutes
- 91% correlation validates ML as a reliable NVH simulation surrogate
- Practical deployment within an automotive engineering bureau workflow
- Competitive advantage through dramatically reduced design cycle timelines
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