Personal profile


Dr. Vivek T. Ramamoorthy is a Lecturer in Mechanical Engineering at the School of Physics, Engineering and Computer Science (SPECS), University of Hertfordshire. He received a master's degree in mechanical design from the Indian Institute of Technology Madras, where he specialised in Vibration and Acoustics, and worked on predicting noise in power transformers using statistical energy analysis. After this master's, he was selected as an AGFS fellow and worked as a scientific officer at the Atomic Energy Regulatory Board, where his research focus was on estimating the seismic margin available in existing piping design codes for class-I piping systems subject to large earthquake loads. In 2018, he was a Marie Sklodowska Curie early-stage researcher fellowship in the No2Noise EU project and received a PhD in Computer Science from the University of Nottingham for his work on heuristic and metaheuristic methods for topology optimisation of acoustic porous materials. Dr. Ramamoorthy's research interests lie at the intersection of mechanical engineering and computer science with a focus on structural topology optimisation, artificial intelligence, and acoustics of porous materials. 

Research interests


Metaheuristics, Acoustics, Topology optimisation, Structural Dynamics, Artificial intelligence


Structural Topology Optimisation methods constitute the current state-of-the-art for designing shapes of lightweight and performant mechanical parts and structures. Given a fixed amount of material, these methods can identify near-optimal shapes or layouts that maximise the structural strength. The resulting intricate optimal shapes can be fabricated using additive manufacturing techniques. As an example, using topology-optimised lightweight parts in Airbus A380 has resulted in a 1000 kg weight reduction per aircraft. With an enormous potential weight and cost savings, there is an immense benefit to improving these techniques and their applicability to various domains. Dr. Ramamoorthy's interests are focused on making topology optimisation methods more efficient in terms of computational resource requirements using artificial intelligence methods and bridging the gaps in terms of the manufacturability of optimised shapes.

Metaheuristic techniques such as genetic algorithms and covariance matrix adaptation evolution strategy have contributed to tackling challenging optimisation problems that cannot be solved in practicable times using exact methods. In the case study of optimising acoustic porous material shapes, Ramamoorthy et al. 2021 ( have shown that using a combination of gradient-free heuristic and conventional gradient methods like solid isotropic material with penalisation (SIMP) results in better Pareto-optimal shapes by avoiding getting stuck at local optimal solutions within the same computational budget. Future interests include better understanding the fitness landscapes in different problem domains and devising domain-specific strategies.

A particular application area of interest is the use of topology optimisation in passive noise control. By introducing cavities of air pockets within sound-absorbing foams, one can create favourable resonances that improve sound absorption in low frequencies, which are particularly hard to attenuate.


Teaching specialisms

Module support

Dr. Ramamoorthy teaches the following modules:

Semester A (Oct-Dec)

4ENT1163 Applied Design (CDIO) (Auto & Mech)

7ENT1145 Data Analytics and Artificial Intelligence 

Semester B (Jan-April)

4ENT1165 Programming for Aircraft Engineers 

5ENT1125 Industrial Mechanics (CDIO)

6ENT1159 Acoustics

Teaching Philosophy: Nurture > Nature

Given enough time and proper support, anyone can become a genius at any topic. The Polgar sisters' experiment is a testament to this. 

Game perspective

Conventional learning methods such as books, lectures and tutorials cannot be replaced in providing formal learning. However, with the ever-growing forest of human knowledge, these learning paradigms would limit one's ability to master a wide range of topics in a given lifetime. In this regard, unconventional learning methods such as physical demonstrations, interactive demos or games can reduce the learning time and impart a more profound intuition.

One of my specialisms is developing interactive web demos to help students understand concepts quickly and deeply. Examples of my work include:

1. Genetic Algorithm tutorial: A simple interactive web app where one can understand the concept behind genetic algorithms in 5 minutes or less.

2. Damping ratio demo: An interactive web app that helps gain an intuition for the concept of damping ratios in vibrating spring-mass systems. 

3. Co-variance Matrix Adaptation Evolution Strategy: A demo of the CMA-ES optimisation strategy.



Education/Academic qualification

Computer Science, Doctor of Philosophy, Heuristics and metaheuristics in the design of sound-absorbing porous materials, University of Nottingham


Mechanical Engineering Design, Master of Technology, Indian Institute of Technology Madras (IITM)



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