CAE [computer aided engineering] analysis of NVH of ICE & Electric vehicles | EveryEng

Skip to main content

Self-paced Intermediate

CAE [computer aided engineering] analysis of NVH of ICE & Electric vehicles

4(115)

1 enrolled

1983 views

₹ 999

318 min

Anytime

English

1983 views

MILIND AMBARDEKARConsultant

Rail & Transport

7-day money-back guarantee
Lifetime access
Certificate of completion

Volume pricing for groups of 5+

Why enroll

With automotive NVH becoming more complex, CAE has become indispensable in predicting, diagnosing, and solving issues before a physical prototype is built.

This course is ideal for NVH engineers, CAE analysts, and vehicle development professionals looking to master industry-standard simulation techniques for refining ICE and EV designs.

Whether optimizing body stiffness, acoustic insulation, or powertrain structural modal performance, this training offers cutting-edge methodologies to achieve the world-class refinement in modern vehicles.

Your instructor

MILIND AMBARDEKAR

Self employed

Consultant

34+ yrs experience·India

Is this course for you?

You should take this if

You work in Automotive or Rail & Transport
You're a Mechanical Engineering / Noise & Vibration Engineering professional
You have some foundational knowledge in the subject
You prefer self-paced learning you can revisit

You should skip if

You're looking for an introductory overview course
You need a different specialisation outside Mechanical Engineering
You need live interaction with an instructor

Course details

To provide a comprehensive understanding of CAE techniques for NVH analysis, covering structural, acoustic, and multi-physics simulations to optimize both ICE and EV vehicle performance.

This course explores how CAE is revolutionizing NVH refinement, from modelling millions of Degrees of Freedom (DOFs) in Finite Element Analysis (FEA) to advanced acoustic and vibration simulations.

It covers panel contribution analysis, structure-fluid coupling, and Boundary Element Method (BEM) for mid-frequency NVH issues. Participants will gain expertise in Pass-By Noise simulations, high-frequency SEA (Statistical Energy Analysis) methods, and fatigue durability assessments for high cycle loads

Additionally, CFD (computational fluid dynamics) -based flow-induced acoustics and MBD (Multi-Body Dynamics) for Vehicle NVH will be introduced. The course will conclude with optimization strategies to balance NVH performance with other critical vehicle attributes like cost, weight, durability and handling stability.

Course suitable for

Key topics covered

Fundamentals and Strategic Role
Structural and Dynamic Analysis
Acoustic Simulation Strategies
Durability and Performance Optimization

Course content

The course is readily available, allowing learners to start and complete it at their own pace.

6 lectures5 hr 18 min

NVH Part 1
53 min
Single to Multi-DOF System
57 min
Panel Contribution Analysis
61 min
Rigid Body Dyanamics Analysis
50 min
Application of SEA
57 min
Variability in Physical Proto Type
40 min

Opportunities that await you!

Career opportunities

✎ Where this fits — what comes before, what comes next

₹999

Access anytime

Questions and Answers

Q: You're reviewing a pass-by noise CAE report after a homologation fail and searching "pass-by noise CAE trend drifting but within limit"; the last three runs show +1.2 dB(A) per build while the current result is 3% outside the acceptance limit at 74.3 dB(A) vs a 72 dB(A) target—what response best protects programme sign-off?

A: A. Freezing the model hides a systematic drift and risks a non-repeatable homologation outcome. B. Forcing a correction masks the physical cause and will break correlation at the next build. C. Local mesh refinement ignores the system-level contributors driving the trend. D. Updating DFMEA and linking the trend to panel contribution shifts addresses root cause before another expensive test.

Q: During a pre-homologation NVH audit you're googling "vehicle NVH CAE correlation checklist before test"; what sequence should you verify on-site to validate CAE-to-test correlation for an EV whine issue?

A: A. Starting with subjective data biases the correlation and hides setup errors. B. Post-test calibration can’t recover invalid data from a bad setup. C. Verifying boundary conditions and sensor placement up front aligns test physics with the model. D. Running first risks burning test time on data you can’t legally use.

Q: While checking "panel contribution analysis dB summation error" you need to combine three uncorrelated panel contributions of 68 dB, 70 dB, and 71 dB to compare against a 72 dB(A) limit—what total level should CAE report?

A: A. Linear addition ignores the logarithmic nature of acoustic energy. B. Averaging under-predicts combined energy and gives false comfort. C. Dominance assumptions drop real contributors and break traceability. D. Logarithmic summation correctly reflects uncorrelated source energy.

Q: You're searching "FEA NVH model drawing missing constraint symbol" while reviewing a GA extract showing a powertrain mount with one translational DOF released—what inconsistency should raise a red flag?

A: A. Unit scaling is traceable and usually caught by solver checks. B. A DOF mismatch breaks physical equivalence between model and test. C. Origin placement doesn’t change constraints if transforms are consistent. D. Matching revisions don’t guarantee the constraints are right.