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My research and R&D activities span fundamental acoustics, computational modelling, experimental characterization, and engineering applications. My work has evolved from acoustic metamaterials and intelligent acoustic design during my doctoral research to industrial R&D in building and architectural acoustics.
My research and R&D activities span fundamental acoustics, computational modelling, experimental characterization, and engineering applications. My work has evolved from acoustic metamaterials and intelligent acoustic design during my doctoral research to industrial R&D in building and architectural acoustics.
Current Research & R&D
Current Research & R&D
April 2024- Ongoing
April 2024- Ongoing
Building & Architectural Acoustics
Research and development focused on the acoustic performance of building systems, including partitions, ceilings, glazing, acoustic materials and room environments.
Computational Acoustics
Numerical modelling and simulation for acoustic prediction, system optimization and performance assessment.
Experimental Acoustics
Experimental characterization and validation of acoustic materials and building systems, including sound transmission and room-acoustic measurements.
Acoustic Product & System Development
Application-oriented R&D connecting acoustic simulation and experimental validation with product and system development.
Acoustic Signal Processing & Imaging
Development of signal-processing and microphone-array approaches for acoustic characterization and sound-source localization.
Collaborated with the Indian Navy and Larsen & Toubro to develop phased array ultrasonic inspection procedures for austenite weld samples.
Acoustic performance of Helmholtz resonator integrated microperforated panel absorber
Acoustic performance of Helmholtz resonator integrated microperforated panel absorber
Designed a low-mid frequency sound absorber consisting of the parallel combination of the microperforated panel (MPP) and multiple Helmholtz resonators with the inserted neck (HRINs).
The designed acoustic absorber exhibited quasi-perfect absorption in the frequency range of 347–630 Hz.
Inverse design of acoustic absorbers using the deep neural network
Inverse design of acoustic absorbers using the deep neural network
Initially, a deep neural network-based inverse design (IDDN) methodology is developed to predict the geometric parameters of acoustic absorbers from their sound absorption characteristics.
Then the accuracy of the IDDN method is improved by proposing a convolutional autoencoder-based inverse design (ICAN) scheme.
Development of MPP and HRs with inserted curved neck (MHRICN) absorber design and ultra-low-frequency acoustic absorbers employing pie-sliced resonators.
Development of MPP and HRs with inserted curved neck (MHRICN) absorber design and ultra-low-frequency acoustic absorbers employing pie-sliced resonators.
Designed an acoustic absorber consisting of the parallel conjunction of MPP and HRs with an inserted curvy neck using autoencoder-based inverse design methodology.
An ultra-low-frequency absorber was developed, and it showed nearly perfect absorption in the frequency range of 66–100 Hz.
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