Dr Saju Thomas Abraham
University of Bristol, Indira Gandhi Centre for Atomic Research, Homi Bhabha National Institute, Mahatma Gandhi University
About
With a Masters in Physics, I stepped into the world of non-destructive evaluation (NDE) through the Junior Research Fellowship scheme of the Department of Atomic Energy (DAE), Government of India, in 2006. Subsequently, I joined as a Scientific Officer in the Quality Assurance Division (QAD) of Indira Gandhi Centre for Atomic Research (IGCAR), the second largest nuclear establishment in India. This centre is dedicated to the Fast Breeder Reactor (FBR) programs.
My research primarily focuses on assessing the structural integrity of critical components used in the nuclear energy sector. To this end, I extend my research into numerical and experimental investigations of ultrasonic wave interactions with microstructures and defects in metallic materials, thereby bridging the interdisciplinary domains of physics and engineering. I have cultivated multidisciplinary approaches to address challenges in characterising material degradation, identifying defects, and ensuring the structural integrity of the safety-critical components.
My research interests include FEM-assisted ultrasonic diffraction experiments to monitor fatigue crack growth, image crack morphology in non-planar materials, defect detection in welds at elevated temperature, and assess second-phase precipitates in structural materials. To analyse and interpret experimental ultrasonic data effectively, I use signal processing, correlation techniques, machine learning, and finite element methods coupled with electron microscopy and x-ray diffraction. I have worked with a wide range of materials, including austenitic stainless steel, carbon steel, ferritic and martensitic steel, nickel-based alloys, and titanium, along with their welded and fabricated components whose thicknesses vary from 3 mm to 50 mm. I have also contributed to developing NDE methodologies to characterise nickel-based alloy coatings, dissimilar material welds, diffusion-bonded titanium-stainless steel joints, and fatigue crack nucleation using advanced ultrasonic techniques coupled with adaptive signal processing, numerical modelling, and machine learning.
In recognition of my contributions to the projects critical to national interests, I have received four Group Achievement Awards from the DAE. I am also a Level III certificate holder in ultrasonic testing, as per IS 13805.
Currently, I am a 'Royal Society Newton International Fellow' at the University of Bristol, working towards the detection and characterisation of hydrogen-induced damage in structural materials through unique destructive and non-destructive methodologies.
Employment
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University of Bristol Royal Society Newton International Fellow2026 - Present
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Indira Gandhi Centre for Atomic Research Scientific Officer2008 - 2026
Education
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Homi Bhabha National Institute PhD2013 - 2021
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Homi Bhabha National Institute MSc2011 - 2013
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Mahatma Gandhi University MSc2001 - 2003
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Mahatma Gandhi University BSc1998 - 2001
Projects & Funding
Projects & funding information is unavailable.
Publications (21)
- Diffusion bonding of PZT disc to nickel wear plate for fabrication of high temperature under-sodium ultrasonic transducers Save
- Diffusion bonding of piezoelectric crystal to metal wear plate Save
- Quality assurance challenges during manufacturing of taper roller bearings for high temperature applications Save
- Diffusion bonding of piezoelectric crystal to metal wear plate Save
- Qualification of Carbon steel end caps for inlet water sub header of the steam generator of fast breeder test reactor Save
- Crystal plasticity finite element modelling on the influence of grain size and shape parameters on the tensile stiffness and yield strength Save
- Diffusion bonding of piezoelectric crystal to nickel wear plate Save
- A novel ultrasonic non-destructive testing methodology to monitor fatigue crack growth in compact tension specimens Save
- An experimental investigation on the combined effect of plastic deformation and grain size variation on the acoustic nonlinearity parameter Save
- A machine learning approach to nonlinear ultrasonics for classifying annealing conditions in austenitic stainless steel Save