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🔬 Introducing BioShockNet — a new postdoctoral research programme at IIT Roorkee
Primary blast injury to the abdominal viscera remains a leading contributor to fatal trauma in military explosions, IED incidents, and industrial blast accidents. Yet existing computational models for predicting these injuries are fragmented — scattered tissue data, unverified couplings, and unidentifiable parameters.
BioShockNet (Blast-Induced Organ Shock Homogenisation and Kinetic Network) is a 24-month, purely computational framework that tackles this gap through five staged pillars:
▸ Pillar I — Reduced shock EOS for auxetic metamaterial inserts via two-scale homogenisation (re-entrant hexagonal closed-form; semi-analytical closures for chiral, star-polygon, and hierarchical families)
▸ Pillar II — High-rate biphasic continuum damage model for liver, spleen, small intestine, and lung — with full parameter identifiability assessment (profile-likelihood + Bayesian posterior)
▸ Pillar III — A time-dependent auxetic–tissue transmission kernel for rapid screening — verified against analytical limiting cases, never replacing the full solver
▸ Pillar IV — Three-scale ALE–FSI framework in LS-DYNA with HDMR/PCFE sensitivity decomposition and a rigorous verification suite (mesh, timestep, artificial viscosity, contact, damage regularisation)
▸ Pillar V — Calibrated conditional diffusion surrogate for probabilistic injury prediction and simulator-verified reverse design of auxetic armour geometries
What makes this different:
✦ Physics-first: every stage is grounded in Rankine–Hugoniot, Mie–Grüneisen, and thermodynamically consistent CDM — not black-box ML
✦ Verification-before-acceleration: surrogates are introduced only after the full solver is verified
✦ Identifiability-audited: no undiagnosable parameter cocktails
✦ Open science: tissue material library, EOS atlas, LS-DYNA model decks, and PyTorch surrogate — all on Zenodo and GitHub
✦ Defence-aligned: design charts for STANAG 4569 blast threats; input to BIS IS 17051 body armour standard
This builds on our group's existing strengths in HDMR/PCFE surrogate modelling (50+ publications), auxetic composite mechanics (DMSRDE, ISRO), blast FE (ARMREB, DRDL), and deep learning for structural analysis — applied to a new domain where the stakes are lives.
Looking forward to sharing progress as this unfolds.
Rajib Chowdhury
Professor, Department of Civil Engineering
Computational Mechanics Lab, IIT Roorkee
#BlastBiomechanics #AuxeticMetamaterials #ComputationalMechanics #ALEFSI #SurrogateModelling #HDMR #ContinuumDamageMechanics #DefenceResearch #IITRoorkee #LSDYNA #OpenScience #BodyArmour #MakeInIndia #PostdocResearch #BlastInjury #MultiscaleModelling
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